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ACCESS Journal:

Access to science, business, innovation in the digital economy

ISSN 2683-1007 (online)

http://journal.access-bg.org

2025, Volume 6, Issue 3, September

http://doi.org/10.46656/access.2025.6.3

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ISSN: 2683-1007

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Access to science, business, innovation in digital economy, 2025, 6(3)

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SCIENTIFIC COUNCIL

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T.F. Gorbachev Kuzbass State Technical University, Kemerovo

EDITORS-IN-CHIEF:

EDITORIAL BOARD MEMBERS:

Giga ABUSERIDZE, Caucasus University

Tbilisi

Ludmila ALEKSEJEVA, Daugavpils University

Daugavpils

Alina BADULESCU, University of Oradea

Oradea

Iurie CARAUS, University of Montreal

Montreal

Manoj Kumar CHAUDHARY, Tribhuvan University

Kathmandu

Sreten CUZOVIC, University of Nis

Nis

Francesca DI VIRGILIO, University of Molise

Campobasso

Deniss DYAKONS, ISMA University

Riga

Badri GECHBAIA, Batumi Shota Rustaveli State University

Batumi

Saidakhror GULYAMOV, Institute of Advanced Training and Statistical Research

Tashkent

Zhibek KHUSSAINOVA, S.Seifullin Kazakh Agro Technical Research University

Astana

Viktor KOVAL, Izmail State University of Humanities

Izmail

Nayden NENKOV, Konstantin Preslavsky University of Shumen

Shumen

Yashar SALAMZADEH, Sunderland

Sunderland

Jan-Urban SANDAL, Fil. Dr. Jan-U. Sandal Institute

Oslo

Mykola SIDAK, Bratislava University of Economics and Mamagement

Bratislava

Sanjay K. SINGH, Indian Institute of Management Lucknow

Lucknow

Ludmila STEMPLEWSKA, Vistula Academy of Finance and Business in Warsaw

Warsaw

Olena SUSHCHENKO, Simon Kuznets Kharkiv National University of Economics

Kharkiv

Gonca TELLI, Dogus University

Istanbul

Nguyen Van TRU, Information business software consultancy (IBC)

Singapore

Ho Chi Minh

Vladas TUMALAVIČIUS, General Jonas Žemaitis Military Academy of Lithuania

Vilnius

Kadri UKRAINSKI , University of Tartu

Tartu

Talgat UTEUBAYEV, L.N. Gumilyov Eurasian National University

Astana

Radostin VAZOV, Sofia University St. Kliment Ohridski

Sofia

Ludmila ZAKONNOVA, T.F. Gorbachev Kuzbass State Technical University

Kemerovo

ACCESS: Access to science, business, innovation in digital economy, ISSN 2683-1007 (online)

Volume 6, Issue 3, September 2025

CONTENTS

RESEARCH ARTICLES

Lyazzat AKILZHANOVA, Dinara JAKUPOVA, Zhadyra ARKENOVA, Zhansaya ZHUNISSOVA,

Ainur MAZINA

BRIDGING SECTORS: HOW GOVERNMENT SPENDING ENHANCES THE EMBEDDEDNESS OF

INFORMATION PROCESSES IN KAZAKHSTAN’S ECONOMY

483-497

Eiji HAYASHISHITA, Tetsuaki ODA

INDUSTRY DIFFERENCES IN INTELLECTUAL PROPERTY DISCLOSURE AND CORPORATE

VALUE: EVIDENCE FROM JAPAN

498-512

Liudmyla DOROKHOVA, Silvia BELOEVA, Nataliya VENELINOVA, Dzhavid MIRZOIEV

VIRTUAL TOURISM: DEVELOPMENT, TYPES AND FORMS, OPPORTUNITIES AND

DISADVANTAGES

513-531

Saule ISKENDIROVA, Aigerim AMIROVA, Aliya DAUESHOVA, Azamat ZHANSEITOV, Rymkul

ISMAILOVA

ADAPTING TO THE AI REVOLUTION: COMPARATIVE ANALYSIS OF NATIONAL WORKFORCE

STRATEGIES

532-545

Francis Okechukwu CHIKELEZE, Anatolijs KRIVINS, Valters KAZE, Thomas ETALONG

RISK MANAGEMENT PRACTICES AND ORGANIZATIONAL PERFORMANCE IN

TRANSPORTATION COMPANIES

546-566

Ifeanyi MBUKANMA, Sunday Olabisi ADEWARA

EXPLORING THE RELATIONSHIP BETWEEN SHARE PRICES, EARNINGS, AND DIVIDENDS IN

HIGH-DIVIDEND MINING FIRMS LISTED ON THE JOHANNESBURG STOCK EXCHANGE

567-582

Svitlana LABUNSKA, Mykola SIDAK, Andriy PYLYPENKO, Marharyta SOBAKAR

ASSESSMENT OF AGRICULTURAL EXPORT STRUCTURE’S IMPACT ON NATIONAL

ECONOMIC DEVELOPMENT: UKRAINIAN CASE

583-598

Besma HKIRI, Chaker ALOUI

CORRELATING INVESTOR SENTIMENTS AND SAUDI STOCK MARKET BEHAVIOUR: A

WAVELET-BASED APPROACH

599-614

Sefer AYDOGAN

IMPACT OF AI-AUGMENTED DIGITAL LEADERSHIP ON REMOTE TEAM PERFORMANCE:

AN EXPLORATORY STUDY OF TURKISH SMES

615-633

Eva CIPI, Eljona ZANAJ, Marsia CIPI, Betim CICO

DESIGN INSIGHTS FROM THE LANDSCAPE OF SUSTAINABILITY APPS

634-667

Guidelines for authors

Advertisements

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

483

BRIDGING SECTORS: HOW GOVERNMENT SPENDING ENHANCES

THE EMBEDDEDNESS OF INFORMATION PROCESSES IN

KAZAKHSTAN’S ECONOMY

Lyazzat Akilzhanova1, Dinara Jakupova2, Zhadyra Arkenova3, Zhansaya Zhunissova4

Ainur Mazina5

1), 3) Karaganda University of Kazpotrebsoyuz, Karaganda, Kazakhstan

2) NCJSC “Karaganda Medical University”, Karaganda, Kazakhstan

4) Karaganda Higher Polytechnic College, Karaganda, Kazakhstan

5) Karaganda State University named after E. A. Buketov, Karaganda, Kazakhstan

e-mails: 1 Akilzhanova.l@keu.kz, 2 dzhakupova@qmu.kz, 3Arkenova.zh@keu.kz,4 krg-koll-6839@bilim09.kz,

5 mazina_a@buketov.edu.kz

Received: 11 May 2025 Accepted: 20 July 2025 Online Published: 31 July 2025

ABSTRACT

Objectives: The integration of digital technologies and artificial intelligence into national economies is increasingly

recognized as a driver of economic modernization and cross-sectoral connectivity. In Kazakhstan, government

expenditure has played a central role in building digital infrastructure and supporting the digital transformation of key

sectors. Objectives: This paper aims to assess the impact of government expenditure on the intersectoral embeddedness

of information processes in Kazakhstan’s economy from 2001 to 2023, with a focus on how targeted public investment

fosters digital integration across industries. Methods/Approach The analysis employs annual Input-Output Table data

and utilizes the dependency coefficient as a measure of information process integration, applying the Autoregressive

Distributed Lag (ARDL) model to estimate short- and long-term effects while controlling for macroeconomic variables

such as inflation and unemployment. Results: The results show that increased government spending significantly

enhances the integration of information processes with other sectors of the economy, confirming the positive impact of

public investment in digital infrastructure, digitalization of public services, and support for AI adoption. Conclusions:

These findings underscore the importance of sustained, well-targeted government expenditure in accelerating digital

transformation. Policy recommendations include prioritizing the expansion of digital infrastructure, promoting

interoperability, investing in digital skills, and ensuring that the integration of AI technologies is ethical and inclusive

across all sectors of the economy.

Keywords: government expenditure, information processes, sectoral embeddedness, dependency coefficient, digital

transformation, artificial intelligence, policy recommendations

JEL classification: J24, O33, O15

Paper type: Research article

Citation: Akilzhanova, L., Jakupova, D., Arkenova, Zh., Zhunissova, Zh., Mazina, A. (2025). Bridging sectors: how

government spending enhances the embeddedness of information processes in Kazakhstan’s economy. Access to science,

business, innovation in digital economy, ACCESS Press, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

INTRODUCTION

The accelerating evolution of digital technologies is redefining the landscape of national economies and

altering established relationships among sectors. Today, information processes—encompassing

telecommunications, data analytics, and artificial intelligence—serve as the backbone for economic

development, innovation, and societal advancement. For emerging economies such as Kazakhstan, advancing

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

484

digital transformation is regarded as a strategic priority to boost productivity, elevate the quality of public

services, and enhance national competitiveness in an increasingly digitalized world.

Within this context, the seamless integration of information processes across diverse sectors is now

recognized as a cornerstone of sustainable and inclusive development. Effective digital integration not only

promotes cross-sectoral innovation and operational efficiency, but also enables new forms of collaboration

and business models that drive modernization. Public policy, especially government expenditure on digital

infrastructure and information technologies, plays a pivotal role in enabling these transformations by providing

essential investments, regulatory support, and institutional capacity.

However, the pathways through which government spending translates into deeper integration of

information processes with other industries remain insufficiently explored, particularly in the context of

emerging markets. Gaining a clearer understanding of these mechanisms holds both practical and scholarly

significance, offering guidance for the design of effective fiscal and industrial policies that address the unique

challenges and opportunities associated with digitalization.

LITERATURE REVIEW

The scholarly literature examining the intersection of government expenditure, digital infrastructure, and

economic development has expanded markedly over the past two decades (Georgiev et al., 2022; Tireuov et

al., 2018; Tireuov et al., 2019. This growing body of research reflects a global recognition of the pivotal role

that information and communication technologies (ICT) now play in driving national productivity, fostering

innovation, and enabling cross-sectoral connectivity.

Foundational studies such as Madden and Savage (1997) established that public investment in

telecommunications infrastructure yields substantial spillover benefits for the broader economy, beyond the

immediate ICT sector. Their findings highlighted that well-targeted government expenditure supports

digitalization in related industries and facilitates the modernization of economic systems. Building on this,

Czernich, Falck, Kretschmer, and Woessmann (2011) demonstrated a strong correlation between the

expansion of broadband networks—often enabled by public policy—and GDP growth, as well as productivity

gains and sectoral spillovers, particularly in advanced economies. These studies collectively underscore the

view that robust digital infrastructure serves as a catalyst for wider economic transformation.

Subsequent research has emphasized the enabling role of government in fostering digital innovation and

facilitating the diffusion of advanced information processes. For example, Vu (2011) highlighted how

government-led digital initiatives, such as investments in digital platforms and e-government services, create

an enabling environment for both public and private sector innovation. Bertschek, Briglauer, Hüschelrath,

Kauf, and Niebel (2015) also point out that policy-driven broadband rollouts have accelerated digital adoption

and delivered notable productivity improvements, even in sectors that were traditionally less reliant on digital

technologies.

The rise of artificial intelligence (AI) has added further complexity and opportunity to the digitalization

agenda. Recent policy analyses by organizations such as the OECD (2020) and the World Bank (2016)

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

485

emphasize the importance of coordinated public investment in AI infrastructure, open data ecosystems, and

digital skills development. These reports argue that strategic government spending in these areas can accelerate

sectoral convergence, foster innovation across the economy, and support the digital transformation of public

services.

Recent global research highlights the growing importance of digital transformation and government

investment in driving sustainable economic growth and social inclusion. For example, Cardona, Kretschmer,

and Strobel (2013) demonstrate that digital infrastructure and ICT adoption play a critical role in fostering

innovation and productivity across sectors, especially when supported by strategic public policy. In parallel,

Banga and te Velde (2018) emphasize that well-designed digital strategies are essential for addressing digital

divides and promoting inclusive economic participation in developing countries.

While the bulk of this literature has historically centered on high-income countries, there is a growing—

though still limited—evidence base from emerging markets. Studies such as Kim, Kelly, and Raja (2010) have

pointed to the vital role of government interventions in overcoming digital divides and market failures,

particularly where private investment alone cannot achieve widespread digital adoption. Targeted policy

measures supporting digital literacy, as well as the digitalization of traditional sectors like agriculture, health,

and education, have been shown to foster more resilient, inclusive, and interconnected economic systems (ITU,

2024). Despite these advances, significant gaps remain. Much of the extant research is concerned primarily

with direct economic effects—such as sectoral output, employment, or productivity—while subtler metrics

that capture the depth and quality of sectoral integration are less frequently analyzed. In particular, the concept

of "embeddedness"—the extent to which ICT and digital solutions are woven into the operational fabric of

various industries—remains underexplored, especially via empirical frameworks leveraging Input-Output

Table data.

Furthermore, as public digital investment becomes an increasingly acknowledged driver of macroeconomic

benefit, there is still limited understanding of the mechanisms through which government spending enhances

the structural role of information processes, especially amid rapid AI development. Scholars increasingly

recognize the need to move beyond aggregate economic indicators and investigate how fiscal policy shapes

cross-sectoral digital linkages, innovation spillovers, and the transition to knowledge-based economies

(Espolov et al., 2023; Khussainova et al., 2023; Tireuov et al., 2023; Khussainova et al., 2024; Tireuov et al.,

2020).

In summary, prior research provides robust evidence for the positive impact of public investment on digital

transformation and economic growth. However, the mechanisms by which government expenditure promotes

the intersectoral embeddedness of information processes—especially in emerging markets such as

Kazakhstan—remain insufficiently understood. Addressing these research gaps is essential for the formulation

of effective, evidence-based policy recommendations in an era characterized by accelerating artificial

intelligence adoption and pervasive digitalization

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

486

METHODOLOGY

This section details the methodological approach used to analyze the impact of government expenditure on

the integration of information processes within Kazakhstan’s economy. The empirical analysis relies on annual

Input-Output Table data for the period 2001–2023. The dependency coefficient is used as the primary indicator

of sectoral embeddedness, while government expenditure on information processes serves as the main

explanatory variable. Inflation and unemployment rates are included as macroeconomic control variables.

Given the different integration orders of the variables and the limited sample size, the Autoregressive

Distributed Lag (ARDL) model is adopted. All variables are tested for stationarity using Augmented Dickey-

Fuller (ADF) tests. Cointegration analysis is conducted using the Johansen procedure, which provides

evidence of a long-term equilibrium relationship among the variables. The ARDL model enables the

estimation of both short-run and long-run effects, and model diagnostics are performed to ensure the validity

and robustness of the results.

This methodological design directly addresses the core research hypothesis (H1): Increased government

expenditure on information processes is positively associated with the embeddedness (integration) of

information processes across other sectors in Kazakhstan’s economy.

Dependent Variable: Dependency Coefficient

The dependency coefficient serves as the principal dependent variable in this study. It quantifies the extent

to which outputs from the information processes sector (including communication, IT, broadcasting, and

information services) are utilized by other industries within the economy. Calculated using Input-Output Table

(IOT) data, this coefficient reflects the embeddedness or integration of information processes, highlighting

their role as a connective infrastructure across sectors such as finance, healthcare, logistics, and education. A

higher dependency coefficient indicates stronger inter-sectoral linkages and greater digital integration, making

it a robust indicator of the digital economy’s reach and influence.

Key Independent Variable: Government Expenditure

Government expenditure captures the aggregate amount of public funds allocated to ICT infrastructure,

digitalization programs, and related capital and current consumption in the information processes sector. The

variable incorporates both capital investments (e.g., broadband networks, data centers, public IT procurement)

and current government consumption (e.g., operational ICT spending, digital services for public

administration). Data are drawn from the Input-Output Table’s rows for government final consumption and

gross fixed capital formation in relevant ICT industries. This variable reflects the government’s role as a

catalyst for digital transformation and sectoral connectivity, with direct implications for the integration of

information processes across the economy.

Control Variables

Inflation:

Measured as the annual percentage change in the consumer price index, inflation is included as a control

variable to account for macroeconomic instability that could affect the real value of both government

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

487

expenditure and sectoral outputs. High inflation may dampen the effectiveness of public investment and create

volatility in the ICT sector. Moreover, maintaining low and stable inflation is crucial for preserving the real

value of public investments and ensuring predictable conditions for sustained growth in the ICT sector

(Abuseridze, 2020).

Unemployment Rate:

The unemployment rate, expressed as a percentage of the labor force, serves as a control for overall

economic activity and labor market dynamics. Fluctuations in unemployment can influence both demand for

digital services and the availability of skilled ICT professionals, indirectly affecting the sector’s integration

with other industries.

Variables excluded due to multicollinearity:

GDP and Urbanization were excluded from the model because of high correlation with government

expenditure and information processes output, as demonstrated in the correlation matrix analysis.

Additional Notes (for Methods/Data section):

Sector Selection:

The information processes sector is defined in accordance with the General Classification of Economic

Activities (GCEA), encompassing telecommunications, IT services, publishing, broadcasting, and information

services (codes 58, 59, 60, 61, 62, 63).

Calculation Basis:

All Input-Output Table values are used at basic prices, excluding taxes and subsidies, to ensure

comparability and avoid distortions.

Justification for Using the ARDL Model

The Autoregressive Distributed Lag (ARDL) modeling approach is particularly well-suited to the structure

of the dataset employed in this study for several reasons:

1. Different Orders of Integration: The ARDL technique can accommodate variables that are integrated

of different orders, specifically I(0) and I(1), provided none are I(2) or higher. As shown in the stationarity

tests, the key dependent variable—Dependency Coefficient—is stationary at first difference (I(1)), while

government expenditure and the gross output of information processes become stationary only at the third

difference (I(3)). However, the ARDL framework is still applicable as long as the final model does not include

I(2) or higher-order variables. Other regressors, such as inflation and unemployment, are stationary at levels

(I(0)).

2. Cointegration Evidence: The Johansen cointegration test suggests the presence of a cointegrating

relationship between the variables, indicating a potential long-run equilibrium linkage. This further justifies

the use of the ARDL approach, which is capable of detecting both short-term and long-term relationships when

cointegration tests yield conclusive results.

3. Suitability for Small Samples: The ARDL method is recognized for its robustness in studies with small

to moderate sample sizes, which is the case here with annual data for 23 years (2001–2023).

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

488

4. Capturing Both Short- and Long-Term Dynamics: The ARDL model enables the estimation of both

short-run and long-run relationships among variables, which is essential for understanding the immediate and

persistent effects of government spending on the embeddedness of information processes.

5. Flexibility in Lag Structure: ARDL models allow for different lag lengths for each variable, which is

advantageous when economic variables may respond to shocks or policy changes at different speeds.

RESULTS

This section presents the empirical results of the econometric analysis examining the impact of government

expenditure on the embeddedness of information processes within Kazakhstan’s economy. The analysis is

based on annual Input-Output Table data covering the period from 2001 to 2023 and employs the

Autoregressive Distributed Lag (ARDL) modeling approach, as outlined in the methodology section.

Descriptive statistics for all variables are provided, followed by the results of stationarity and cointegration

tests. The main findings from the ARDL model estimation are then discussed, with a particular focus on the

short-term and long-term effects of government expenditure on the dependency coefficient—a proxy for the

integration of information processes across economic sectors. Model diagnostics are also reported to assess

the validity and robustness of the regression results.

The subsequent subsections interpret the estimated coefficients and compare the empirical findings with

prior studies in the field, highlighting the implications for economic policy and digital transformation strategies

in Kazakhstan.

Table 1. ARDL Model Results for the Dependency Coefficient

Variable

Coefficient

Std.

Error

Statistic

P-value

95% Confidence Interval

const

-0.2948

0.103

-2.861

0.013

[-0.516, -0.074]

L1_dependency

-0.3128

0.346

-0.904

0.381

[-1.055, 0.429]

L1_gov_costs

3.48e-10

1.2e-10

2.900

0.012

[9.06e-11, 6.05e-10]

Unemployment

0.1261

0.035

3.652

0.003

[0.052, 0.200]

Inflation

0.0010

0.003

0.314

0.758

[-0.006, 0.008]

L1_D_dependency

0.0343

0.220

0.156

0.878

[-0.438, 0.506]

L1_D_gov_costs

-1.35e-10

4.77e-10

-0.283

0.781

[-1.16e-09, 8.87e-10]

Source: Own calculations

Table 2. Model Summary and Diagnostics

Indicator

Value

R-squared

0.861

Adjusted R-squared

0.801

F-statistic

14.44

Prob (F-statistic)

2.80e-05

Durbin-Watson

2.275

AIC

-59.16

BIC

-51.85

Source: Own calculations

Table 3. Model Diagnostics

Test

Statistic

p-value

Breusch-Pagan (heteroskedasticity)

5.4753

0.4845

Ljung-Box (serial correlation)

5.3582

0.2525

Jarque-Bera (normality)

5.6556

0.0591

ADF (residuals stationarity)

-2.846

0.052

Source: Own calculations

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 483-497, https://doi.org/10.46656/access.2025.6.3(1)

https://journal.access-bg.org/

489

The ARDL regression results in Table 1 provide insight into the relationship between government

expenditure and the embeddedness of information processes within Kazakhstan’s economy. The model

demonstrates a high explanatory power, with an adjusted R-squared of 0.801, indicating that approximately

80% of the variation in the dependency coefficient is explained by the included variables.

A key finding is the positive and statistically significant effect of the lagged value of government

expenditure (L1_gov_costs) on the dependency coefficient (coefficient = 3.48e-10, p = 0.012). This suggests

that increases in government spending are associated with a greater integration of information processes into

other sectors in subsequent periods, supporting the view that targeted public investment promotes digital

interconnectedness across the economy.

Unemployment also exhibits a significant positive association (coefficient = 0.1261, p = 0.003), potentially

reflecting the structural adjustments and labor market changes accompanying the digital transformation of the

economy.

Other variables, including inflation and lagged values of the dependency coefficient and government

spending changes, do not show statistically significant effects in this specification.

Model diagnostics – Table 2,3 indicate that the regression is robust, with no evidence of serial correlation

or heteroskedasticity, and residuals are approximately normally distributed. However, the large condition

number points to possible multicollinearity or numerical issues, which should be considered when interpreting

the results.

Overall, the findings suggest that government expenditure plays a critical role in fostering the integration

of information processes with other industries, thereby supporting Kazakhstan’s ongoing digital

transformation.

DISCUSSION

The empirical findings of this study confirm a statistically significant positive association between lagged

government expenditure and the embeddedness of information processes within Kazakhstan’s economy.

Specifically, the ARDL model results indicate that increases in government spending on information processes

contribute to a greater integration of these processes with other sectors in subsequent periods. This supports

the central hypothesis (H1) and highlights the important role of fiscal policy in fostering the digital

transformation of the national economy.

One practical example of how increased government expenditure can strengthen the linkages between

information processes and other sectors is through large-scale investment in digital infrastructure and public

digitalization programs. For instance, when the government allocates funding to develop nationwide high-

speed broadband networks, upgrade data centers, and support e-government platforms, this creates a

foundation that allows industries such as healthcare, education, finance, and logistics to integrate advanced

information systems into their operations.

For example:

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Healthcare: Government investments in digital health infrastructure enable hospitals and clinics to adopt

electronic health records, telemedicine services, and AI-powered diagnostics. These systems require robust

information processing and create real-time connections between the health sector, insurance companies, and

regulatory authorities.

Agriculture: Subsidizing smart farming initiatives and digital platforms allows farmers to use AI-driven

data analytics for crop management, which connects agriculture with information technology providers,

logistics companies, and the food processing sector.

Education: Funding for digital classrooms, learning management systems, and AI tutoring platforms

integrates education more closely with the ICT sector, content creators, and assessment bodies.

In all these cases, targeted government spending not only upgrades the information sector itself but also

multiplies its connections to other industries, fostering cross-sectoral digital ecosystems.

These results align with a growing body of international literature that underscores the catalytic role of

public investment in ICT infrastructure in strengthening sectoral connectivity and driving economic

modernization. For instance, studies by Madden & Savage (1997) and Czernich et al. (2011) have consistently

found that increased government spending on digital infrastructure is associated with substantial productivity

spillovers and a deeper integration of information and communication technologies into traditional industries.

This process not only elevates the performance of the ICT sector itself but also stimulates innovation and

efficiency gains throughout the broader economy. Such dynamics highlight the strategic importance of

sustained public investment as a lever for inclusive and innovation-driven growth in the digital age

(Abuseridze et al., 2022a).

Furthermore, Vu (2011) emphasizes that public investment in ICT can act as a powerful accelerator for

cross-sectoral innovation, fostering the development of digital ecosystems and supporting structural

transformation, particularly in emerging markets. The positive relationship identified in the case of Kazakhstan

thus echoes similar patterns reported in the OECD and several Asia-Pacific economies, where strategic

government spending has played a central role in the digitalization of public services, manufacturing, and

service industries (see, for example, OECD, 2020; World Bank, 2016).

A particularly interesting dimension of the present analysis is the observed significant effect of

unemployment on the dependency coefficient. While a positive association between unemployment and the

integration of information processes may initially appear counterintuitive, recent research suggests that labor

market disruptions can serve as a catalyst for structural realignments and increased adoption of digital

solutions. Brynjolfsson & McAfee (2014) argue that periods of higher unemployment may incentivize firms

and public institutions to implement more efficient information technologies and automation tools, thereby

accelerating the integration of digital processes across the economy. In the Kazakhstani context, this dynamic

could reflect adaptive strategies among businesses and public agencies seeking to enhance resilience and

maintain competitiveness during periods of economic stress. This interpretation resonates with broader digital

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transformation patterns observed in emerging economies, where labor market volatility often drives

accelerated technological uptake and institutional innovation (Abuseridze et al., 2022b).

It is also notable that inflation and short-term changes in government spending were not found to have

significant effects on the integration of information processes in the model. This finding is consistent with

prior studies (Czernich, Falck, Kretschmer, & Woessmann, 2011), which indicate that the transformative

impacts of digital investment are primarily realized over the long term, rather than as immediate responses to

short-term macroeconomic fluctuations. These results further support the argument that sustained, well-

targeted public investment is essential for achieving meaningful digital integration and unlocking broader

economic benefits.

Despite the robustness of the results, as indicated by model diagnostics, the large condition number suggests

potential multicollinearity or numerical challenges, which should be considered when interpreting the

magnitude of the estimated coefficients. Additionally, the analysis is limited by the availability of annual data

and the scope of Input-Output Table classifications, which may not capture all dimensions of digital

integration.

In summary, the findings reinforce the idea that sustained and targeted government expenditure is a key

driver of the digital transformation and sectoral integration of information processes in Kazakhstan. These

results echo international experience, demonstrating the effectiveness of fiscal policy as a lever for building a

knowledge-based and interconnected economy.

Policy Recommendations in the Age of Artificial Intelligence

Given the accelerating development and adoption of artificial intelligence, several policy directions become

especially relevant:

1. Prioritize AI-Ready Digital Infrastructure: Allocate resources for next-generation digital networks

(e.g., 5G, cloud computing, high-performance computing clusters) to ensure that all sectors can access

and leverage AI-driven information processes.

2. Promote Interoperability Standards: Develop and enforce standards that facilitate seamless data

sharing and integration between sectors (e.g., open APIs, unified data governance), which is critical

for AI applications that depend on cross-sectoral data flows.

3. Support Human Capital Development: Invest in digital skills, AI literacy, and continuous upskilling

of the workforce to enable public and private organizations in all sectors to adopt and adapt AI

technologies effectively.

4. Foster Public-Private Partnerships: Encourage collaborations between government, academia, and

industry to accelerate AI adoption in traditional sectors, leveraging state investment as a catalyst for

innovation.

5. Ensure Ethical and Inclusive AI Adoption: Design policies that address the ethical, legal, and social

implications of AI—ensuring that digital transformation and inter-sectoral integration benefit all

groups and minimize potential risks (e.g., privacy, algorithmic bias).

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6. Monitor and Evaluate Integration Outcomes: Establish regular assessment mechanisms to track how

government spending on AI and digital infrastructure is influencing the integration of information

processes across the economy, allowing for evidence-based policy adjustments.

7. Expand digital education: Continue investing in e-learning infrastructure and digital curricula to ensure

all students, regardless of location, can access high-quality education and develop essential digital

skills (Kadirbayeva et al., 2021).

8. Promote university–industry collaboration: Encourage the creation of endowment funds and similar

mechanisms to strengthen partnerships between higher education and business, improving the

relevance and impact of training programs in sectors such as tourism (Khusainova et al., 2021).

9. Support vulnerable workers: Implement targeted policies to support agricultural workers and other

vulnerable groups, combining digital innovation with robust social protection systems to ensure an

inclusive digital transition (Kussainova et al., 2024).

10. Promote sector-specific digital strategies: Develop and implement targeted digitalization initiatives for

agriculture, food production, and healthcare to address their unique challenges and leverage best

practices (Okutayeva et al., 2023; Smailov et al., 2022).

11. Support integrated cluster development: Foster the creation of agricultural and industrial clusters that

bring together businesses, government, and research institutions, enabling effective digital innovation

and sustainable growth (Temirgaliyeva et al., 2019).

12. Enhance cross-sectoral learning: Facilitate the transfer of digital and environmental innovations

between sectors to maximize overall economic resilience and modernization (Okutayeva et al., 2023;

Smailov et al., 2022).

In practical terms, the Kazakhstani experience demonstrates that the success of digital transformation

policies depends on coordinated action across multiple domains—including digital infrastructure, human

capital management, educational reform, and sustainability initiatives. Policy makers should draw on

successful cases such as the digitalization of public enterprises (Adilova et al., 2025), the management of

intellectual capital (Aimukhanbetova et al., 2019), the modernization of higher education (Akhmedyarov et

al., 2025), and the integration of sustainability practices in economic planning (Belgibayeva et al., 2024). These

examples collectively highlight the need for an ecosystem approach—one that links fiscal investments to

organizational development, workforce preparation, and responsible innovation for long-term digital

competitiveness. Such an integrated framework not only enhances policy coherence but also ensures that

digital transformation efforts translate into inclusive, resilient, and context-sensitive development outcomes

(Abuseridze et al., 2022c).

Policy makers should also consider targeted support for female entrepreneurship, as it has been shown to

accelerate economic growth and enhance the benefits of digitalization and innovation in various contexts,

including Kazakhstan (Akybayeva et al., 2024). Facilitating access to finance, mentorship, and digital skills

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training for women entrepreneurs can help unlock untapped potential in the workforce and contribute to more

balanced, sustainable economic development.

Strategic and well-targeted government expenditure in the digital and AI domains can create the necessary

infrastructure, human capital, and regulatory environment to ensure that information processes become deeply

embedded across all economic sectors—thus maximizing the benefits of Kazakhstan’s digital transformation

in the age of artificial intelligence.

CONCLUSION

This study has provided empirical evidence on the relationship between government expenditure and the

integration of information processes with other sectors in Kazakhstan’s economy. By employing the

dependency coefficient as a novel indicator of digital embeddedness and utilizing the ARDL modeling

approach, the analysis demonstrates that targeted government spending plays a critical role in strengthening

intersectoral digital linkages. The results underscore the importance of sustained public investment in digital

infrastructure and information technologies, particularly in the context of rapid technological change and

artificial intelligence adoption.

The findings suggest that policy measures aimed at expanding digital infrastructure, fostering

interoperability, and supporting human capital development are essential for maximizing the economic

benefits of digital transformation. The positive association between government expenditure and information

process integration highlights the potential for fiscal policy to act as a catalyst for cross-sectoral innovation

and modernization in emerging markets like Kazakhstan.

Despite these contributions, the research is subject to certain limitations. The analysis relies on annual data,

which may mask short-term fluctuations and dynamic adjustments. Additionally, the study focuses on a single

country context, limiting the generalizability of the results. Future research could address these limitations by

employing higher-frequency data, expanding the analysis to include cross-country comparisons, or

investigating the specific mechanisms through which public investment influences digital integration—such

as the role of public-private partnerships, regulatory frameworks, or sector-specific digital initiatives.

Further studies could also explore the impacts of government spending on advanced digital technologies,

including artificial intelligence, big data analytics, and cloud computing, and examine how these investments

interact with labor market outcomes, innovation capacity, and social inclusion. By deepening our

understanding of these processes, future research can provide valuable insights for policymakers seeking to

design effective digital transformation strategies in the era of artificial intelligence.

Conflict of interest:

The authors declare no conflict of interest.

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Author Contributions:

Conceptualization, L.A. and Z.Z.; methodology, D.J.; software, Z.Z.; validation, Z.A., A.M. and D.J.; formal

analysis, L.A.; investigation, Z.A.; resources, D.J.; data curation, L.A.; writing—original draft preparation,

L.A.; writing—review and editing, A.M.; visualization, Z.Z.; supervision, Z.A.; project administration, L.A.;

funding acquisition, L.A. All authors have read and agreed to the published version of the manuscript.

Acknowledgement

We greatly appreciate the valuable contributions of the Research Development Institute and every team

member who took the time to support in this study.

Institutional Review Board Statement: not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

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About the authors

Lyazzat AKILZHANOVA,

PhD student at Karaganda University of Kazpotrebsoyuz. Former Deputy Akim

(Mayor) of the city of Karaganda. Holds degrees in Law and Economics, as well

as a Master’s degree in Public and Local Administration. She has extensive

professional experience, including positions in the Parliament of the Republic of

Kazakhstan and in local executive bodies.

Research interests: local governance, administrative law, public policy, and legal

regulation of regional development.

ORCID ID: 0000-0002-6326-6565

Dinara JAKUPOVA,

Assistant Professor at the Department of History of Kazakhstan and Socio-Political

Disciplines, Karaganda Medical University. From 2015 to 2017, she served as

Director of the Research Institute for Patriotic Education and Assistant to the Vice-

Rector for Educational Work at Karaganda State Technical University.

Research interests: socio-political education, economic and legal literacy,

entrepreneurship, and academic thinking.

ORCID ID: 0000-0001-9150-5351

Zhadyra ARKENOVA,

holds the degree of Doctor of Philosophy (PhD) in State and Local Government

(specialty 6D051000). The degree was awarded by the Orders of the Chairman of

the Committee for Quality Assurance in the Sphere of Science and Higher

Education of the Ministry of Education and Science of the Republic of Kazakhstan:

No. 384 (August 10, 2023), No. 484 and 488 (September 12, 2023).

Research interests: public administration, local governance, and institutional

development.

ORCID ID: 0000-0002-3523-5489

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Zhansaya ZHUNISSOVA,

Lecturer at Karaganda Higher Polytechnic College. She holds degrees in Software

Engineering from Karaganda Polytechnic College (2009–2013) and in Finance

(2013–2016) & Computer Science (2016–2018) from Buketov Karaganda State

University.

Research interests: digital technologies in education, applied informatics, and IT

in vocational training.

ORCID ID: 0009-0004-6281-447X

Ainur MAZINA,

PhD, Senior Lecturer at the Department of Accounting and Auditing, Karaganda

University named after Academician E.A. Buketov. She holds a Master of

Science degree and a Doctor of Philosophy (PhD) degree.

Research interests: accounting, auditing, financial reporting, and higher

education in economics.

ORCID ID: 0000-0001-7788-7357

This work is licensed under the Creative Commons Attribution International License (CC BY)

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INDUSTRY DIFFERENCES IN INTELLECTUAL PROPERTY

DISCLOSURE AND CORPORATE VALUE: EVIDENCE FROM JAPAN

Eiji Hayashishita

*, Tetsuaki Oda2

1),2) Graduate School of Technology Management (MOT), Ritsumeikan University, Osaka, Japan

e-mails: 1) eiji.h1018@gmail.com, 2) odatetsuaki@gmail.com

Received: 15 June 2025 Accepted: 16 July 2025 Online Published: 31 July 2025

ABSTRACT

Objective: This study examines how intellectual property (IP) and intangible asset (IA) disclosure under Japan’s

Corporate Governance Code (CGC)—specifically Supplementary Principles 3.1.3 and 4.2.2—affects corporate valuation.

It focuses on disclosure behaviour across the Tokyo Stock Exchange (TSE) 33 industry sectors and its impact on financial

performance and investor evaluation. Method: Companies listed on the TSE Prime and Standard Markets were classified

into Comply and Explain groups based on their 2023 Corporate Governance Reports. Disclosure trends were analysed

across the TSE33 industry sectors. Chi-square tests assessed the independence between industry classification and

disclosure behaviour. Financial indicators such as R&D ratio, ROE, market capitalization, and PBR were compared

using the Mann–Whitney U test. A regression analysis examined the link between the comply rate and patent application

intensity using Japan Patent Office data. Results: The Comply group exhibited higher market capitalization, suggesting

a positive association between IP disclosure and investor valuation. Industry-level differences were significant, especially

in Chemicals and Other Products. A moderate positive correlation (R = 0.644, p = 0.017) was observed between patent

activity and the comply rate, indicating that innovation-active sectors tend to disclose more. Conclusion: IP/IA disclosure

correlates with higher corporate valuation and mirrors underlying innovation, with notable variation across TSE33

industries. While disclosure fosters investor confidence, risks by symbolic comply persist in highly conforming sectors.

These findings support aligning governance with intangible-driven value creation.

Keywords: Corporate Value, Intellectual Property, Information Disclosure, Corporate Governance Code, Japan

JEL classification: E22, G34, O32, O34, O53

Paper type: Research article

Citation: Hayashishita, E., Oda, T. (2025). Industry differences in intellectual property disclosure and corporate value: evidence

from Japan. Access to science, business, innovation in digital economy, ACCESS Press, 6(3), 498-512,

https://doi.org/10.46656/access.2025.6.3(2)

INTRODUCTION

In today’s knowledge-based economy, the foundation of company value and sustainable competitive

advantage has increasingly shifted from tangible assets to intangible assets (IA), such as intellectual property

(IP), human capital, brand equity, and proprietary technologies. As companies move from manufacturing-

centric toward innovation-driven business models, their ability to manage, invest in, and transparently disclose

IA has become a critical determinant of long-term success.

This structural transformation is clearly reflected in global capital markets. Tomo (2022) reports that, as of

2020, IA accounted for approximately 90% of the total corporate value of U.S. companies listed on the S&P

500, and about 75% for European companies on the S&P 350. Similarly, Brand Finance (2019) estimated the

total value of IA assets held by S&P 500 companies at a record USD 21 trillion in 2018—an increase of 128%

Corresponding author, Eiji Hayashishita – eiji.h1018@gmail.com

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from 2005. Notably, in the same year, leading technology companies such as Microsoft and Facebook

disclosed that over 95% of their market capitalization was derived from IA. These trends underscore a growing

consensus among investors that IP and other IA serve as primary drivers of corporate value.

Although corporate value can be defined from various perspectives, market capitalization remains one of

the most widely used metrics, as it reflects investor expectations and perceived company worth. The increasing

influence of IP on market capitalization suggests that investors are integrating IA elements—often

underrepresented in conventional financial statements—into their valuation models (Choi et al., 2000).

Moreover, the materiality and visibility of IA vary significantly across industries (Qureshi, 2017). For

instance, sectors such as pharmaceuticals are highly dependent on IA resources, while traditional

manufacturing industries rely more heavily on physical assets. These sectoral differences imply that investor

perceptions and market valuations of intangibles are likely to differ according to industry-specific

characteristics.

JAPAN’S CORPORATE GOVERNANCE CODE

Japan’s Corporate Governance Code (CGC) was introduced by the Tokyo Stock Exchange (TSE) in June

2015 as a set of guidelines for listed companies, outlining core principles of corporate governance while

allowing flexibility in implementation based on each company’s specific circumstances. The overarching

objective of the CGC is to enhance economic stability and promote the sustainable growth of companies that

adopt its principles. The phrase “to achieve sustainable growth and increase corporate value over the medium

to long term” (TSE, 2021a) is frequently cited in the Code and often referenced in corporate governance

disclosures.

The CGC comprises five fundamental principles: (1) securing the rights and equal treatment of

shareholders; (2) appropriate cooperation with stakeholders other than shareholders; (3) ensuring appropriate

information disclosure and transparency; (4) responsibilities of the board; and (5) dialogue with shareholders.

These are further specified into 31 principles and 47 supplementary principles. The Code adopts a “comply or

explain” framework, permitting companies to deviate from specific principles provided that a reasonable

explanation is offered. It is subject to periodic revision to reflect evolving market dynamics and societal

expectations, with major updates in 2018 and, most recently, in June 2021.

While the 2018 revision encouraged proactive governance initiatives, the 2021 revision introduced more

prescriptive requirements, aligning closely with the structural reforms accompanying the launch of the TSE

Prime Market in April 2022. As stated in TSE (2021b):

“The Prime Market is expected to be a market attractive to both domestic and global investors... Therefore,

it is important for companies listed on the Prime Market to advance efforts toward a higher level of corporate

governance.”

This highlights the CGC’s dual role as both a governance framework and a strategic mechanism for

enhancing investor confidence in Japanese capital markets.

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The 2021 revision also marked the first explicit inclusion of IA, including IP, in the CGC. Supplementary

Principle 4.2.2 requires boards of directors to oversee the allocation of management resources and the

formulation of business portfolio strategies with consideration for IA investments to support sustainable

growth. In parallel, Supplementary Principle 3.1.3 calls for the disclosure of clear, specific information

regarding IP and IA investments in alignment with each company’s business strategy and key management

challenges.

These developments reflect a growing emphasis on “sustainability” and “IP/IA strategies” as central themes

in corporate governance, especially in the wake of the COVID-19 pandemic. Enhanced transparency in these

areas is expected to reduce information asymmetry between companies and investors, thereby improving

corporate valuation.

Recent research by Hayashishita et al. (2025) indicates that effective IP/IA disclosure reduces information

asymmetry, enhances market valuation, and ultimately contributes to long-term corporate value in Japan.

However, the degree to which such disclosure influences valuation remains insufficiently understood—

particularly considering industry-specific differences. Variations in business models, asset structures, and

innovation intensity suggest that the impact of IP/IA disclosure may differ substantially across sectors.

Given this context, the present study aims to clarify how the relationship between IP/IA disclosure and

corporate valuation varies by industry. By classifying companies according to their disclosure status under

Supplementary Principles 3.1.3 and 4.2.2 and analysing sectoral differences across the 33 industry sector

classifications defined by the TSE, this study seeks to provide a more nuanced understanding of how IP/IA

contribute to corporate value under varying industrial conditions.

LITERATURE REVIEW

Dancaková (2022) conducted a comparative analysis of the impact of intangible assets on company market

value across different countries and industries. The study examined 250 listed companies in France, Germany,

and Switzerland, focusing on four major sectors: manufacturing, information and communications,

professional and technical services, and finance and insurance. The analysis considered various intangible

components disclosed through financial statements, including R&D expenditures, patents, and trademarks.

The findings revealed significant cross-country and cross-industry differences in how intangible assets and

innovation activities influence company valuation.

Qureshi (2017) analysed companies in the United Kingdom between 1998 and 2003 to investigate how the

market values different types of intangible investments—such as goodwill, R&D, and advertising. The study

found that market valuation varied significantly depending on company-specific characteristics, including

industry classification (manufacturing vs. non-manufacturing), company size (large vs. small), and

profitability (profitable vs. unprofitable). These results underscore the context-dependent nature of intangible

asset valuation, which is shaped by both internal and external structural factors.

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King et al. (2024) explored the use of the market-to-book ratio (MBR) as a proxy for intangible value

among U.S. listed companies. Their findings showed that industries heavily reliant on intangible assets—

particularly the pharmaceutical sector—tend to exhibit substantially higher MBRs than industries grounded in

tangible assets, such as the materials sector. The study concluded that the influence of intangible assets on

company valuation varies considerably across sectors, reflecting differences in innovation intensity and

dependence on non-physical capital.

Taken together, prior research consistently demonstrates that the contribution of intangible assets to

corporate valuation is neither uniform nor universal, but rather highly contingent on industry structure,

innovation strategy, and asset composition. Building on this foundation, the present study aims to examine the

industry-specific impact of IP and IA disclosure on company valuation in the Japanese context. By comparing

financial characteristics between companies that disclose such information and those that do not, this study

seeks to clarify how disclosure practices influence corporate value across industrial sectors.

METHODOLOGY

The subjects of this study are companies listed on the Tokyo Stock Exchange (TSE) Prime and Standard

Markets. This is because the TSE requires companies listed on the Prime and Standard Markets to apply all

principles of the CGC. Corporate information was extracted and collected using the comprehensive corporate

information database EOL by I-N Information Systems, Ltd. In a text search of the Corporate Governance

Report 2023, companies that had "intellectual property" written in the disclosure items based on each principle

of the code were classified into the comply group, and companies that had "intellectual property" written in

the item for the reason for NOT disclosing each principle of the code were classified into the explain group.

The number of companies analysed is shown in Table 1. Additionally, financial data from the 2023 securities

reports of each selected company was extracted and combined.

To assess industry-level differences, companies were classified into 33 industry sectors as defined by the

TSE. This classification enabled a cross-industry analysis of IP/IA-related disclosure behaviour.

For all 33 industry sectors, financial indicators were compared between the Comply and Explain groups to

examine the relationship between disclosure behaviour and industry sector level characteristics relevant to IP

/IA disclosure. Based on the results, four industries—Construction, Chemicals, Other Products, and Retail

Trade—demonstrated were selected for further in-depth analysis.

The financial indicators analysed included the following:

 Market capitalization (Market Cap)

 R&D ratio (R&D cost-to-sales ratio),

 Return on equity (ROE)

 Price-to-book ratio (PBR)

Market capitalization was log-transformed to normalize its distribution and reduce the influence of extreme

values. R&D rate was included to capture technological innovation and new product development. Prior

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research has shown a positive relationship between R&D activity and the extent of IP disclosure (Lin et al.,

2024). ROE, calculated as net income divided by shareholders’ equity, was used as a measure of profitability

and capital efficiency. It is widely recognized as a key indicator in investor evaluations (Edori et al., 2024;

Adelicia et al., 2023).

Market capitalization and PBR were adopted as proxies for investor valuation. PBR, calculated by dividing

market capitalization by net assets, reflects the company’s market value relative to its book value. A PBR

above 1 is often interpreted as an indication of intangible value or anticipated future profitability stemming

from non-financial capital, such as intellectual property (Yanagi et al., 2017).

Furthermore, to assess the actual state of IP-related activities, this study drew on data from the Survey on

Patent Application Trends by Industry published by the Japan Patent Office (JPO). Based on this dataset, we

analysed the number of patent applications by industry in 2022 and examined their correlation with patterns

of disclosure behaviour.

Table 1. The number of companies analysed

Market

Comply group

Explain group

Total

TSE Prime

925

161

1086

TSE Standard

529

304

833

Total

1454

465

1919

Source: Compiled by the authors

ANALYTICAL METHOD

To determine whether disclosure behaviour is independent of industry classification, a chi-square test of

independence was conducted using data from the 33 industry sectors defined by TSE.

To further examine industry-specific disclosure trends, the Comply Rate—defined as the proportion of

companies within each industry that belong to the Comply group—was calculated. This enabled cross-industry

comparisons to identify notable patterns and deviations in disclosure behaviour.

To compare financial indicators between the Comply and Explain groups, the normality of each variable

was tested using the Shapiro–Wilk test. As most variables were not normally distributed, the Mann–Whitney

U test was employed as a non-parametric test. it is robust to deviations from normality and less sensitive to

outliers, making it suitable for the analytical objectives of this study.

To account for differences in industry size, the average number of patent applications per company was

calculated by dividing the total number of applications in each industry by the number of companies in that

industry. This standardization allowed for meaningful comparisons of IP activity across industries of varying

scale.

Finally, a simple linear regression analysis was conducted to assess the relationship between the average

number of patent applications per company and the comply rate across industries.

All statistical analyses were performed using IBM SPSS Statistics, Version 29.0.0.0.

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RESULTS

Table 2 presents the distribution of Comply and Explain groups across the 33 industries classified by the Tokyo

Stock Exchange (TSE), along with the standardized residuals derived from the chi-square test. The comply

rate—defined as the percentage of companies in each industry classified into the Comply group—is also

reported.

(1) High comply Group: Comply Rate ≥ 85%

Industries such as Marine Transportation, Air Transportation, Mining, Electric Power and Gas, and

Insurance exhibited full comply, with 100% of companies choosing to comply.

Other industries with comply rates of 85% or higher included Banks, Pharmaceuticals, Nonferrous Metals,

Iron and Steel, Construction, Pulp and Paper, and Other Financing Business.

(2) Moderate comply Group: Comply Rate 70–84%

Industries falling within this range included Transportation Equipment, Chemicals, Machinery, Textiles

and Apparels, Electric Appliances, Glass and Ceramic Products, Rubber Products, Precision Instruments,

Metal Products, Wholesale Trade, Warehousing and Harbor Transportation Services, Other Products, Foods,

Fisheries, Agriculture and Forestry, Oil and Coal Products, Information and Communication, and Land

Transportation.

(3) Low comply Group: Comply Rate < 70%

Industries with relatively low comply rates included Services, Retail Trade, Securities and Commodities

Futures, and Real Estate.

A chi-square test of independence was conducted to examine the association between industry classification

and IP disclosure behaviour across the 33 TSE industry sectors. The result indicated a statistically significant

association between industry sector and disclosure status: χ²(21, N = 1798) = 66.916, p < .001.

In accordance with the assumptions of the chi-square test, 11 industry sectors with expected cell frequencies

below 5 were excluded from the analysis. These included: Marine Transportation, Air Transportation, Mining,

Electric Power and Gas, Insurance, Pulp and Paper, Other Financing Business, Rubber Products, Warehousing

and Harbor Transportation Services, Fisheries, Agriculture and Forestry, and Oil and Coal Products. This

exclusion was necessary to satisfy the test assumptions and ensure statistical reliability.

The table presents the standardized residuals from the chi-square test. Industries with statistically significant

differences, defined as those with standardized residuals exceeding ±1.96, included Banks, Pharmaceuticals,

Construction, Retail Trade, Securities and Commodities Futures, and Real Estate. Positive values mean that

the observed values were more than the expected values, and negative values mean that the observed values

were less than the expected values.

In industry sectors with a 100% compliance rate, no companies were classified as "Explain," and thus,

financial data comparisons could not be conducted. For the remaining sectors, the non-parametric Mann–

Whitney U test was employed to examine differences in the median financial indicators between the Comply

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and Explain groups. Results are presented for the Construction, Chemicals, Other Products, and Retail Trade

sectors, where notable differences were observed.

Table 2. Distribution of Comply and Explain Groups across TSE 33 Industries

TSE 33 Industry Sectors

Comply group

Explain group

Total

Comply

Rate [%]

Observed

Frequency

Standardized

Residual

Observed

Frequency

Standardized

Residual

(1)

Marine Transportation

N/A

100

Air Transportation

N/A

100

Mining

N/A

100

Electric Power and Gas

N/A

100

Insurance

N/A

100

Banks

3.5

-3.5

97.7

Pharmaceuticals

2.7

-2.7

96.6

Nonferrous Metals

1.6

-1.6

90.5

Iron and Steel

1.3

-1.3

87.0

Construction

2.3

-2.3

86.1

Pulp and Paper

N/A

85.7

Other Financing Business

N/A

85.0

(2)

Transportation Equipment

1.3

-1.3

82.0

Chemicals

112

1.4

-1.4

140

80.0

Machinery

119

1.4

-1.4

149

79.9

Textiles and Apparels

0.6

-0.6

79.4

Electric Appliances

120

1.2

-1.2

152

78.9

Glass and Ceramics Products

0.5

-0.5

78.8

Rubber Products

N/A

78.6

Precision Instruments

0.0

75.0

Metal Products

0.0

75.0

Wholesale Trade

124

-0.4

0.4

168

73.8

Warehousing and Harbor Transportation

N/A

73.7

Other Products

-0.4

0.4

72.9

Foods

-0.6

0.6

72.2

Fishery, Agriculture and Forestry

N/A

71.4

Oil and Coal Products

N/A

71.4

Information and Communication

140

-1.5

1.5

198

70.7

Land Transportation

-0.8

0.8

70.0

(3)

Services

123

-1.9

1.9

178

69.1

Retail Trade

-3.0

3.0

142

64.8

Securities and Commodities

Futures

-1.2

1.2

63.6

Real Estate

-3.9

3.9

54.0

Total

1454

465

1919

75.8

Source: Author's analysis

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The results for the Construction sector are presented in Table 3. Market capitalization was significantly

higher in the Comply group (median = 24.20) than in the Explain group (median = 23.07), Z = –2.019, p = .044.

No significant differences were found between the two groups in the research and development cost-to-sales

ratio (R&D ratio) (Z = –0.381, p = .703), return on equity (ROE) (Z = –0.691, p = .489), or price-to-book ratio

(PBR) (Z = –1.188, p = .235).

Table 3. Mann-Whitney U Test Result (Construction)

Comply group

Explain group

Indicator

N=68

N=11

Median

(Q1‐Q3)

Median

(Q1-Q3)

Market Cap

24.20

23.08-25.54

23.07

22.35-23.80

-2.019

.044

R&D ratio

0.28

0.13-0.61

0.32

0.08-0.63

-0.381

.703

ROE

6.90

4.17-8.81

5.89

1.55-8.08

-0.691

.489

PBR

0.86

0.55-1.16

0.68

0.41-0.95

-1.188

.235

Source: Authors’ own analysis

The results for the Chemicals sector are presented in Table 4. Market Cap was significantly higher in the

Comply group (median = 24.65) than in the Explain group (median = 22.95), Z = –4.058, p < .001. The R&D

ratio was also significantly higher in the Comply group (median = 2.89) than in the Explain group (median =

1.94), Z = –3.222, p = .001. No significant differences were observed between the two groups in ROE (Z = –

0.151, p = .880) or PBR (Z = –1.545, p = .122).

Table 4. Mann-Whitney U Test Result (Chemicals)

Comply group

Explain group

Indicator

N=112

N=28

Median

(Q1‐Q3)

Median

(Q1-Q3)

Market Cap

24.65

23.61-26.09

22.95

22.42-24.43

-4.058

<.001

R&D ratio

2.89

1.65-3.99

1.94

0.32-2.80

-3.222

.001

ROE

6.63

3.39-10.11

6.41

4.05-10.79

-0.151

.880

PBR

1.01

0.69-1.72

0.70

0.55-1.40

-1.545

.122

Source: Authors’ own analysis

The results for the Other Products sector are presented in Table 5. Market Cap was significantly higher in

the Comply group (median = 24.81) than in the Explain group (median = 22.60), Z = –3.120, p = .002. The

R&D ratio was also significantly higher in the Comply group (median = 1.50) than in the Explain group

(median = 0.67), Z = –2.262, p = .024. No significant difference was observed in ROE between the two groups

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(Z = –1.802, p = .071). In contrast, PBR was significantly higher in the Comply group (median = 1.27) than in

the Explain group (median = 0.60), Z = –2.694, p = .007.

Table 5. Mann-Whitney U Test Result (Other Products)

Comply group

Explain group

Indicator

N=43

N=16

Median

(Q1‐Q3)

Median

(Q1-Q3)

Market Cap

24.81

22.57-25.67

22.60

21.97-23.25

-3.120

.002

R&D ratio

1.50

0.53-3.07

0.67

0.29-1.25

-2.262

.024

ROE

7.78

2.72-11.06

3.97

-0.24-7.34

-1.802

.071

PBR

1.27

0.71-2.75

0.60

0.40-1.00

-2.694

.007

Source: Authors’ own analysis

The results for the Retail Trade sector are presented in Table 6. Market Cap was significantly higher in the

Comply group (median = 24.58) than in the Explain group (median = 22.91), Z = –4.813, p < .001.

No significant difference was found in the R&D ratio between the two groups (Z = –1.519, p = .129). ROE

was also not significantly different (Z = –0.078, p = .938), nor was PBR (Z = –0.593, p = .553).

Table 6. Mann-Whitney U Test Result (Retail Trade)

Comply group

Explain group

Indicator

N=92

N=50

Median

(Q1‐Q3)

Median

(Q1-Q3)

Market Cap

24.58

23.20-25.72

22.91

21.76-24.12

-4.813

<.001

R&D ratio

0.05

0.01-0.22

0.15

0.10-0.26

-1.519

.129

ROE

6.74

2.91-11.59

6.69

0.37-13.27

-0.078

.938

PBR

1.45

0.84-2.80

1.60

0.98-3.66

-0.593

.553

Source: Authors’ own analysis

Table 7 presents the distribution of annual patent applications by industry for the year 2022. To account for

differences in industry size, the number of applications was standardized by dividing by the number of

companies in each industry, resulting in the number of applications per company, which is shown in

descending order. It should be noted that the industry classifications used by the Japan Patent Office (JPO)

differ from the 33-industry classification employed by the Tokyo Stock Exchange (TSE).

Industries were categorized into three groups based on the average number of patent applications per

company. Patent application intensity varied considerably across industries, as shown in the following

classification based on the average number of patent applications per company:

(A) High-intensity group (100 or more applications per company)

Transportation Equipment, Electric Appliances, Iron, Steel and Nonferrous Metals, Precision Instruments.

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(B) Medium-intensity group (50–99 applications per company)

Chemicals, Other Products, Machinery.

Textiles and Apparels, Pulp and Paper, Glass and Ceramics Products, Non-Manufacturing industry,

Construction, Public Research Institutions, Metal Products, Foods, Information and Communications,

Pharmaceuticals, Individuals, Wholesale Trade and Retail Trade.

These results highlight substantial variation in patenting activity across industry sectors.

Table 7. Distribution of Annual Patent Applications by Industry Sectors

To further examine the relationship between comply behaviour and innovation activity, Figure 1 was

created using data from Table 2 (Comply Rate) and Table 7 (Patent Applications per Company). The figure

includes only industries with comply rates of 84% or lower, excluding those in Group (1), which showed

uniformly high comply rate and were therefore interpreted separately in the analysis. Figure 1 demonstrates a

moderate positive correlation between the average number of patent applications per company and the comply

rate. This correlation was statistically significant (R = 0.644, R² = 0.415, p = 0.017).

Industry Sectors

Number

of Companies

Patent

Application

Patent

Applications

per Company

(A)

Transportation Equipment

125

21813

174.5

Electric Appliances

276

43235

156.6

Iron, Steel and Nonferrous Metals

6467

106.0

Precision Instruments

9718

104.5

(B)

Chemicals

202

15512

76.8

Other Products

189

10284

54.4

Machinery

186

9752

52.4

(C)

Textiles and Apparels, Pulp and Paper

2505

40.4

Glass and Ceramics Products

191

7514

39.3

Non-Manufacturing industry

321

12437

38.7

Construction

135

4057

30.1

Public Research Institutions

276

8010

29.0

Metal Products

106

2576

24.3

Foods

139

1729

12.4

Information & Communication

209

2548

12.2

Pharmaceuticals

785

10.2

Individuals

217

2.6

Wholesale Trade, Retail Trade

387

427

1.1

Total

3118

159588

51.2

Source: JPO (2024) and author’s calculation

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Figure 1. Relationship between Comply Rate and Patent Application per Company

Source: Authors’ own analysis

DISCUSSION

(1) High Comply Rate Group (Comply Rate: ≥ 85%)

A high comply rate—indicating that most companies within an industry adopt each principle of the

Corporate Governance Code (CGC)—aligns with the underlying objectives of the CGC and can be viewed as

desirable from a governance perspective. The supplementary principles analysed in this study specifically

concern the disclosure of intellectual property (IP) and other intangible assets (IA). For companies in the

Comply group, appropriate disclosure may reduce information asymmetry with investors, thereby contributing

to more accurate corporate valuation.

Although this group contains few Explain group companies, limiting the scope for statistical comparison,

the Construction displayed a significant difference in market capitalization between Comply and Explain

groups. This suggests that comply is positively associated with investor perception. In Construction, patents

protect innovations in building materials and construction methods, supporting companies' technological

superiority (Goy, 2024).

Similarly, the Pharmaceuticals is fundamentally dependent on patent protection, making IP and IA

particularly critical for company competitiveness (Saha et al., 2011).

However, instances of superficial or symbolic comply have been noted. Furuta (2024) argues that

companies may choose to formally comply—rather than provide explanations—out of concern for potential

negative investor reactions. All 100 companies in the TOPIX 100 index opted for "Comply" with

Supplementary Principle 3.1.3, suggesting the possibility of formal comply without substantive disclosure.

(2) Moderate Comply Rate Group (Comply Rate: 70–84%)

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This group mainly comprises manufacturing industries characterized by intense competition and many

companies, providing a robust basis for statistical comparisons.

In the Chemicals, significant differences were observed between the Comply and Explain groups in both

market capitalization and R&D ratio. The companies in the Comply group tend to invest more heavily in R&D,

a behaviour that appears to be rewarded by investors through higher market valuations. However, ROE and

PBR did not differ significantly. This may be due to the high proportion of tangible assets typical of Japanese

manufacturing sectors such as chemicals, which depresses the total asset turnover ratio for Comply companies

(Hayashishita et al., 2025).

The Other Products exhibited significant differences not only in market capitalization and R&D investment

but also in PBR. Notably, the Comply group includes globally prominent companies such as Nintendo (3.72,

PBR as of 2023)—recognized for its proprietary game IP and hardware platforms—Yamaha (2.85) and Roland

(4.60) in the musical instrument sector, and ASICS (13.87) and YONEX (3.02) in the sports equipment market.

These companies have high PBRs, reflecting strong investor confidence. Their brand power and technological

capabilities illustrate how IP and IA drive global competitiveness.

(3) Low Comply Rate Group (Comply Rate: < 70%)

In the Retail Trade, market capitalization differed significantly between Comply and Explain groups,

indicating relatively stronger investor confidence in Comply groups. However, retail businesses typically offer

similar products, making product-level patent protection difficult. Competitive differentiation in this sector

often depends on price, service, or location, rather than technological innovation. Moreover, IA such as store

layout or customer service methods are easily imitated and offer limited sustainable advantage. Industries with

comply rates below 70%—such as Services, Retail Trade, Securities and Commodities Futures, and Real

Estate—exhibit relatively low levels of IP activity, which likely contributes to the higher prevalence of Explain

responses.

Across all industries, the consistent finding of significant differences in market capitalization between

Comply and Explain groups suggests that comply with disclosure-related principles is positively evaluated by

investors. This reinforces the importance of IP in enhancing company competitiveness.

However, within the high comply group, Group (1), including Pharmaceuticals and Construction, the

average number of patent applications per company are low (fewer than 50 annually). This suggests a strategic

emphasis on patent quality rather than quantity, reflecting a "selective" IP approach.

By contrast, the moderate comply group, Group (2), includes sectors in which companies file more than 50

or even 100 patent applications per year. As articulated in Hitachi’s Five Fighting Patents (5FP) strategy

(Hitachi, 2009), competitive manufacturing environments may demand not only strong patent content but also

large patent portfolios as a defensive mechanism.

Figure 1 demonstrates a statistically significant moderate positive correlation between Comply Rate and

the average number of patent applications per company (R = 0.644, R² = 0.415, p = 0.017). A higher number

of patent applications typically reflects a larger IP portfolio, which enables companies to differentiate

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themselves and more confidently choose to comply with disclosure-related principles. These findings provide

empirical support for the link between IP activity and disclosure behaviour.

CONCLUSION

This study investigated the relationship between the disclosure of intellectual property (IP) and intangible

assets (IA) and corporate valuation across 33 industries listed on the Tokyo Stock Exchange (TSE), with a

particular emphasis on comply with Japan’s Corporate Governance Code (CGC), specifically Supplementary

Principles 3.1.3 and 4.2.2. companies were categorized into Comply and Explain groups based on their

disclosure behaviour, and statistical analyses were conducted to examine the association between disclosure

practices and financial performance indicators.

The findings indicate that comply with IP/IA-related disclosure principles is generally associated with

higher market capitalization, suggesting that investors respond positively to companies that actively disclose

IA. However, the strength of this association varies considerably across industries. Manufacturing sectors—

such as Chemicals and Other Products—exhibited significant differences not only in market capitalization but

also in R&D ratio and price-to-book ratios (PBR), highlighting the strategic value of IP in technology-driven

and highly competitive sectors.

In contrast, industries such as Retail Trade and Services, where opportunities for patentable innovation are

limited and competition is often based on pricing or customer experience, displayed lower comply rates and

weaker links between disclosure and financial outcomes. These sectoral variations underscore the context-

dependent materiality of IP/IA disclosure and its differentiated impact across business models.

Importantly, the analysis revealed a moderate, statistically significant positive correlation between the

average number of patent applications per company and the comply rate. This suggests that companies with

active IP strategies are also more inclined to comply with disclosure-related CGC principles, reinforcing the

idea that disclosure behaviour is closely aligned with underlying innovation activities.

While the CGC aims to enhance corporate transparency and long-term value creation, this study also

acknowledges the possibility of symbolic or formalistic comply, particularly in sectors with near universal

comply.

In sum, this study provides empirical evidence that IP/IA disclosure practices are not only influenced by

industry characteristics but also have meaningful implications for how companies are perceived and valued in

capital markets. These insights offer practical implications for corporate managers, investors, and

policymakers seeking to improve disclosure quality and leverage IP/IA as a foundation for sustainable growth

and competitive advantage.

Conflict of interests: The authors declare no conflict of interest.

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Author Contributions: Conceptualization, E.H. and T.O.; methodology, E.H. and T.O.; investigation, E.H.;

project administration, E.H. and T.O.; data curation, E.H.; resources, E.H. and T.O.; supervision, T.O.;

validation, T.O.; writing—original draft preparation, E.H.; writing—review and editing, E.H. and T.O. All

authors have read and agreed to the published version of the manuscript.

Funding /Acknowledgement

This research was supported by a grant-in-aid from Zengin Foundation for Studies on Economics and Finance.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The data presented in this study are available on request from the corresponding

author. The data are not publicly available due to privacy issues.

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About the authors

Eiji HAYASHISHITA,

Doctoral Student, Graduate School of Technology Management (MOT),

Ritsumeikan University, Osaka, Japan.

Research interests: intellectual property strategy, technology management,

business strategy.

ORCID ID: 0009-0006-3101-516X

Tetsuaki ODA,

Professor, Graduate School of Technology Management (MOT),

Ritsumeikan University, Osaka, Japan.

Research interests: intellectual property strategy, intellectual property

management, healthcare and welfare.

ORCID ID: 0000-0002-6484-8460

This work is licensed under the Creative Commons Attribution International License (CC BY)

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VIRTUAL TOURISM: DEVELOPMENT, TYPES AND FORMS,

OPPORTUNITIES AND DISADVANTAGES

Liudmyla Dorokhova1, Silvia Beloeva2, Nataliya Venelinova3, Dzhavid Mirzoiev4

1 University of Tartu, Tartu, Estonia

2, 3 “Angel Kanchev” University of Ruse, Ruse, Bulgaria

4 Simon Kuznets Kharkiv National University of Economics, Kharkiv, Ukraine

e-mails: 1liudmyla.dorokhova@ut.ee, 2sbeloeva@uni-ruse.bg, 3nvenelinova@uni-ruse.bg, 4javid20082@gmail.com

Received: 13 February 2025 Accepted: 13 May 2025 Online Published: 31 July 2025

ABSTRACT

Objectives: In the context of the computerization of society as a whole and the economy of the service sector in particular,

the study of virtual tourism is essential for evaluating its impact on traveler behavior and the transformation of the

traditional tourism industry in the conditions of digitalization. Analyzing virtual tourism allows us to identify its potential

for sustainable development, expanding access to cultural heritage, and reducing the burden on popular tourist

destinations. Methods/Approaches: A literature-based approach was used to analyze and summarize scientific literature

to identify key theoretical concepts and practical aspects of virtual tourism, as well as its development, features,

advantages, and limitations, compared with traditional forms of tourism. The specifics of user behavior in the case of

virtual tourism, its demand, and its impact on the tourism industry were examined. Results: A classification of virtual

tours is presented according to types, objectives, and functions, as well as the level of implementation and technologies

used. The main advantages and disadvantages of virtual tours are highlighted, along with target customer groups and

their characteristics. Conclusions: Virtual reality tourism is actively developing and successfully utilizing the latest

computer information technologies and gadgets. Virtual tourism activities represent a promising direction and a

significant part of the travel industry, its elements, and components.

Keywords: virtual tourism, virtual experience, tourism innovations, virtual reality, augmented reality.

JEL classification: L83, L86, Z32

Paper type: Research Paper

Citation: Dorokhova, L., Beloeva, S., Venelinova, N., Mirzoiev, D. (2025). Virtual tourism: development, types and forms,

opportunities and disadvantages. Access to science, business, innovation in digital economy, ACCESS Press, 6(3), 513-

531, https://doi.org/10.46656/access.2025.6.3(3)

INTRODUCTION

Virtual tourism is rapidly becoming one of the most promising and technologically advanced areas in the

modern tourism industry. In recent years, its demand has increased significantly, due to both general factors

(COVID-19, conflicts in various parts of the world) and the intensive development of virtual and augmented

reality technologies, as well as expanded access to high-speed Internet networks. (Chen et al., 2023;

Yordanova, 2023; Petrova & Tairov, 2022; Phoong et al., 2024; Petrova et al., 2025; Tairov et al., 2024). The

study by Li et al. (2022) revealed a significant positive relationship between heightened COVID-19 risk

perception and user preference for virtual tourism. Factors such as ease of use, perceived usefulness, autonomy,

and enjoyment shaped attitudes toward virtual tourism, with perceived risk directly affecting these attitudes.

Experts recognized this type of tourism as a safe alternative during crises. Some studies in one of the most

affected countries during the pandemic (China) show that the use of VR notably reduced the travel anxiety -

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the respondents reported lower anxiety levels after VR exposure (Wang Q et al., 2024). The findings of Chang

& Yang (2024) indicate that virtual reality tourism significantly increases hedonic and eudaemonic well-being

in long-term care residents, with presence and flow serving as mediating factors. On the other hand, the

expanding offers of virtual tourism propose environmentally sustainable tourism, mitigating physical travel’s

ecological footprint, and enlarging access to those with economic or physical barriers. In this case,

digitalization fosters better life stability and reduces the spread of risks.

Now, anyone, regardless of their location, financial situation, or physical limitations, can embark on a

journey to the most amazing corners of the planet without leaving home. Today, the study of the features of

virtual tourism is becoming especially relevant for the reasons presented in Figure 1. Below, we will take a

closer look at and analyze some of these reasons.’

Figure 1. Reasons for studying virtual tourism

Source: Author's elaboration based on literature review

The COVID-19 pandemic led to border closures and numerous travel restrictions, making traditional travel

inaccessible for many potential clients and consumers (Zeqiri, 2024; Kumar et al., 2024; Liu & Tian, 2024).

Despite the world gradually returning to its normal rhythm, interest in virtual travel remains strong, as it allows

people to explore the world safely without risking their health (Blaer, 2023; Esteban et al., 2023).

Environmental issues and the pursuit of sustainable development are driving people to seek alternative ways

to travel. Virtual tourism offers the opportunity to explore nature and cultural attractions without harming the

environment, reducing carbon emissions associated with flights and mass tourism.

The digitalization of life and technological advancements are making virtual travel more accessible and

realistic. Modern VR headsets, 3D tourism, interactive maps, and panoramic filming allow users to enjoy a

high-quality experience that closely resembles real travel (Varol & Öksüz, 2025; Hoang et al., 2023). More

and more companies and travel agencies are offering virtual tours, making them an integral part of the modern

travel industry.

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Virtual tourism is becoming an important tool for people with disabilities who may not always have the

opportunity to take traditional trips (Xu et al., 2023). It provides a chance to experience the joy of travel,

discover new cultures, and expand one's horizons without leaving home.

The increasing congestion of tourist destinations and overcrowding of popular attractions make finding new

ways to explore the world necessary. Virtual tours help reduce the burden on popular locations, preserving

their cultural and natural value for future generations (Nam et al., 2024).

Virtual tourism is becoming an essential part of marketing strategies for the hospitality and tourism industry

(Luo & Xia, 2024). Companies use virtual tours to attract customers, allowing them to explore hotels, resorts,

and excursions in advance (Petr & Caudan, 2024). This enhances trust and improves the customer experience

(Lin & Yeh, 2022).

Virtual tourism is becoming a powerful tool in educational technology. Virtual tours enable deeper

immersion into history, culture, and geography, making learning more engaging and interactive. They allow

students and enthusiasts to explore historical landmarks, museums, and natural wonders in a realistic and

immersive way. This approach enhances knowledge retention, fosters curiosity, and makes education more

accessible to people of all ages and backgrounds (Baker et al., 2023; Nam et al., 2023).

Students and schoolchildren can use virtual tours for educational purposes, gaining valuable knowledge in

an interactive and engaging format. These tours allow them to visit historical landmarks, museums, and cultural

sites without leaving the classroom, making learning more dynamic and immersive. Virtual tourism also

supports experiential learning by providing 360-degree views, guided narrations, and interactive elements that

enhance understanding. This technology helps bridge geographical barriers, offering access to global

educational experiences regardless of location or financial constraints (Widawski & Oleśniewicz, 2023). This

is especially relevant for educational institutions where organizing real excursions is impossible.

Virtual tourism contributes to developing small tourist destinations often overshadowed by large cities and

popular resorts. Through virtual tours, people can discover unique natural and cultural sites, attracting more

attention and tourist interest to these locations (Li et al., 2023).

Traditional tourism requires significant time, money, and careful planning. Fulfilling a travel dream is not

always possible due to budget constraints, visa issues, or even global events like pandemics. Virtual tourism

offers an alternative, allowing people to explore the world without these obstacles (Bilynets et al., 2023).

With virtual tourism, one can "visit" museums, ancient cities, mountain peaks, and even the depths of the

ocean, using only a computer, smartphone, or VR headset.

One of the main advantages of virtual tourism is its accessibility. Anyone can discover the landmarks of

different countries, stroll through the streets of Paris, explore the Egyptian pyramids, or enjoy panoramic 360-

degree views of the Grand Canyon. Many museums, such as the Louvre or the British Museum, offer virtual

tours, where users can study exhibits in great detail without spending money on tickets or travel. In this sense,

virtual tourism has immense educational value. It allows for a deeper immersion into various places' history,

culture, and geography, helping young people expand their horizons.

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Students and schoolchildren can use virtual tours for educational purposes, gaining valuable knowledge in

an interactive format (Zhang et al., 2023).

Despite its many advantages, virtual tourism is not without its drawbacks. It cannot fully replace real trips,

as it does not convey the sensations of immersion, smells, tastes, and physical presence. However, technology

continues to evolve, and in the future, virtual travel may become even more realistic and interactive.

Thus, virtual tourism is a promising direction that opens new opportunities for travelers, researchers, and

adventure enthusiasts. It not only makes the world more accessible but also changes the perception of

traditional ways of discovering new places. Virtual travel is becoming an integral part of modern life, and its

potential is only beginning to unfold.

MATERIALS AND METHODS

Virtual tourism as a phenomenon and concept.

Firstly, it is important to look closer at the definition and understanding of virtual tourism. Virtual tourism is

a combination of the concepts of virtual reality and tourism. Essentially, virtual tourism allows individuals to

gain some tourist experience without the need to travel or engage in specific physical actions.

Virtual tourism has various forms and levels of technological sophistication. In its simplest form, it is

represented by videos of tourist destinations. The potential tourist watches and perceives the video using their

auditory and visual senses. More complex forms of virtual tourism involve immersion in a computer-generated

artificial environment using headsets or simulators. In such cases, various technical props (gloves, shoes,

clothing elements, helmets) are used to provide additional sensations of movement (roller coasters, rally racing,

space flights), tactile experiences (vibration, touch, water splashes), smells, and sounds.

Virtual tourism uses and is technologically based on various forms of digital realities, the main ones being

virtual, augmented, and mixed reality. (Liu & Tian, 2024; Zhang et al., 2025).

Now, virtualization has gone far beyond the tourism sector and has entered everyday life (buying houses,

clothing, furniture, and interior items based on virtual experiences; virtually visiting museums and landmarks;

using virtual reality in medicine, healthcare, education, leisure, sports, and physical culture). Virtual reality

technologies are already widely and successfully used in the tourism industry as an effective marketing tool.

Travel companies and operators, as well as representatives of tourist attractions, use virtual reality to promote

their services, hoping that the immersive experience gained by potential clients through this technology will

help increase the number of real visitors and consumers of tourism products (Geng et al., 2023).

Virtual reality is also used to complement the physical tourism experience (Yoon & Zou, 2023). For

example, a theme park may feature a mix of real and virtual attractions. Museums enhance their exhibits with

virtual displays, presentations, and events. Modern tourism trends show a dynamic growth in the popularity of

virtual reality as a complete alternative to physical experiences. This is due to both new objective challenges

for real tourism and the greater accessibility, development, and market availability of the necessary

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technological tools, such as mobile devices, user devices, equipment for creating and using virtual reality, and

specialized software.

It is important to note that "virtual experience" and "virtual reality" are not the same concepts. Virtual

reality is the simulation or representation of a specific environment using media, computers, and technical

tools.

Virtual experience, on the other hand, is a human experience that utilizes technology rather than an

experience related to the technical equipment itself.

It refers to the extent to which consumers feel their presence in a virtual space, the experience of interacting

within a virtual environment through a computer-mediated setting, based on the concept of telepresence; the

illusion of "being present" in a mediated environment, experiencing presence within a setting through a

communication medium (Nam et al., 2023).

At the same time, the nature and components of the virtual experience are constantly evolving alongside

the progress of information and computer technologies.

Safe virtual tourism

The projection of the VR through the virtual experience of the “travelers” is still contradictory. While

validated screening tools, for example, the CSQ-VR, effectively quantify symptom manifestation and

performance degradation (Koonchanok et al., 2023), prolonged engagement with virtual reality technologies

carries a risk of negative side effects, including motion sickness, eyestrain, headaches, seizures, and accidents

stemming from diminished environmental awareness.

However, a potential downside of VR environments is their capacity to serve as unintentional tools for the

surreptitious accumulation of user information, thereby posing a threat to user privacy. For instance,

adversarial VR applications demonstrate the capacity to extract sensitive personal data, including precise

anthropometric measurements, from the subtle nuances and characteristic patterns present in a user's

movements within the virtual environment. Using biometric modalities for authentication within metaverse

applications raises significant issues about the potential for the centralization of users' personal data, leading

to privacy risks (Nair et al., 2022).

The discrepancy between users' perceived safety and the actual safety levels within social VR platforms

like Horizon Worlds has been highlighted by several incidents compromising virtual safety and security

(Zheng et al., 2022). The disturbingly high rate of harassment reported by close to fifty percent of women

using VR technology demonstrates the critical requirement for stronger safety protocols and protective

measures to be integrated into the design and functionality of virtual reality systems, especially with the rise

of virtual tourism popularity. Table 1 offers a comparative perspective of key aspects of secure virtual tourism,

considering the ramifications of inadequate safety measures.

Despite virtual tourism's creation of unprecedentedly safe access to travel, therapeutic, and educational

opportunities, a crucial consideration remains that its underlying technological and social infrastructure lacks

inherent safety features, raising significant concerns that require attention.

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The emergence of challenges such as cyber-sickness, coupled with the risks of data misuse and the

amplification of social vulnerabilities, complicates the perception of virtual technologies as purely positive

advancements.

Virtual tourism stands at an inflection: it is an opportunity to democratize, sustain, and energize cultural

engagement. To fully realize its potential, virtual tourism must transcend novelty to embed safety and ethical

awareness in its core framework.

Table 1. Safe virtual tourism vs. the safety of virtual tourism – comparative plan.

Dimension

Safe Virtual Tourism (SVT)

Lack of safety in virtual tourism

Health risks

Reduces the potential transmission of

infections and travel anxiety

Induces cybersickness, seizures

Well-being

Enhances mental and emotional states

Potential stress and disorientation after overuse of

VR/AR technologies

Inclusivity

Opens access to remote and physically

inaccessible sites

May exclude the access of people who are sensitive

to VR

Privacy

No physical data collected

Highly personal behavioral data at risk

Social interaction

Guided experiences, self-paced

Threats of harassment in VR-based social networks

Environmental

Lowers carbon footprint

No direct risk

Source: authors

Consequently, virtual tourism provides an effective, enjoyable, and ecologically responsible collection of

diverse virtual experiences in the tourism sector; these experiences vary from the simple act of viewing

promotional videos to the immersive engagement of interactive museum tours and the comprehensive

simulation of a complete trip using specialized software.

By leveraging information and computer technologies, it has become possible to generate, develop, and

refine the tourist experience entirely within a virtual online environment, removing the requirement for

physical movement or participation in typical forms of tourist activity.

The time dynamics of virtual tourism development.

Figure 2. Trends of virtual tourism total financial volume (left) and state of virtual tourism market-2023 (right)

Source: https://www.researchandmarkets.com/reports/6024485/virtual-reality-in-tourism-market-report#product--summary

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Figures 2 - 4 present the development trends of virtual tourism in terms of the total financial volume of the

market, distribution by regions, market share for different types of technologies used, and the state of the

virtual tourism market as of 2023.

Figure 3. Virtual Reality in Tourism Market Share (%) by Region (2019-2031)

Source: https://www.cognitivemarketresearch.com/virtual-reality-in-tourism-market-report?srsltid=AfmBOorC093HNe-

GXcYF9yscUDwJZ-iuSiPDzSjMcvCNJuthBbek1GFx

Figure 4. Virtual Reality in Tourism Market Share (%) by Type in 2019-2031

Source: https://www.cognitivemarketresearch.com/virtual-reality-in-tourism-market-report?srsltid=AfmBOorC093HNe-

GXcYF9yscUDwJZ-iuSiPDzSjMcvCNJuthBbek1GFx

Figure 5. Global virtual reality in the tourism market forecast until 2033.

Source: https://market.us/report/virtual-reality-in-tourism-market/

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As for the longer term, the global virtual reality in tourism market is projected to reach approximately USD

59.9 billion by 2033, up from USD 8.9 billion in 2023, reflecting a compound annual growth rate (CAGR) of

21% over the forecast period from 2024 to 2033. In 2023, North America led the market with a dominant share

of over 37%, generating around USD 3.3 billion in revenue (Figure 5).

Stages of virtual tourism development and evolution of virtual reality in tourism.

The standard classification of the virtual tourism stages and their time evolution for marketing purposes is

shown in Figure 6.

Figure 6. Time evolution of virtual reality in tourism in 2010-2023

Source: Andziak, 2024.

At the initial stage of the introduction of virtualization technologies into the tourism sector, their main

purpose was primarily to provide marketing support and promote tourism products and services.

Representatives of the industry — organizations managing tourist destinations, tour operators, travel agencies,

and other subjects of the tourism market — initially considered virtual tourism as an auxiliary means of

marketing influence on the target audience. (Kucharska & Malinowska, 2024; Chakraborty et al., 2024).

Marketing and promotion laid the foundation for developing the virtual tourism industry. A virtual

demonstration of how unforgettable a vacation or travel experience will be has a more substantial impact on

potential customers than traditional methods such as travel brochures, guidebooks, and websites.

Next, opportunities were developed to enhance the real tourism experience for customers. Virtual

technologies were introduced as a means of improving, complementing, and enriching real-world travel

experiences.

The use of virtual attractions in theme parks, sensory exhibits in museums, interactive displays and

augmented reality at exhibitions, and additional virtual features when visiting tourist sites and landmarks, all

contribute to increased visitor satisfaction and a more immersive tourism experience.

The next stage was the emergence and development of virtual tourism experiences.

This involved the creation and market introduction of fundamentally new virtual travel opportunities and

experiences. Varying in themes and technologies, these experiences provide users with an artificial yet

immersive tourism experience.

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Virtual tourism often compresses real-world events, highlighting only key moments or the most exciting

parts. For example, a multi-hour safari is condensed into just a few minutes, eliminating the time spent

searching for animals and focusing solely on actual sightings (Barker & Rodway-Dyer, 2022).

This type of virtual tourism saw significant growth after the outbreak of COVID-19 in 2020 due to

lockdowns, travel restrictions, mobility challenges, and isolation (Zajadacz & Halik, 2024).

Next, a partial replacement of real tourism with virtual tourism began to emerge. For various reasons, virtual

tourism becomes a valuable alternative when real travel is impossible.

Collaboration between high-tech IT companies and key stakeholders in the tourism industry has led to the

development of innovative tourism approaches, new destinations, technologies, and virtualization methods.

Despite the recovery of traditional tourism, the demand for virtual tourism continues to grow. A new,

objective need has emerged, along with more excellent receptiveness and willingness to engage in virtual

tourism in ways that are physically unattainable.

Finally, virtual technologies have made it possible to experience otherwise impossible adventures. The

virtual tourism industry allows people to embark on extraordinary journeys through digital means.

For instance, someone can visit a destination virtually if they lack the financial resources or physical ability

to do so in real life.

Thanks to the possibilities of virtual tourism, a person who does not have swimming skills can take part in

computer modeling of the behavior and visual sensations of a diver during deep-sea diving, virtually explore

hard-to-reach or fundamentally inaccessible to mass visits territories, survey the urban space from a bird's eye

view or virtually visit the surface of the Moon.

It should be noted that at all stages of the formation and development of virtual tourism, there is a close

dependence on the level of technological progress.

There is an obvious, direct correlation between the growth and scaling of the virtual tourism industry and

achievements in the field of digital and information technologies (Jiang et al., 2023).

Types of virtual tourism as aspects of its development.

Virtual tourism comes in various forms and scales. The simplest forms of virtual tourism require only a

computer or mobile device, while more complex formats involve the use of high-tech solutions and specialized

equipment. Virtual tourism is now generally classified into several main types, as shown in Figure 7.

Figure 7. Primary forms of virtual tourism.

Source: Author's elaboration based on literature sources

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Study and trial before purchase. As noted earlier, virtual tourism initially focused on marketing aspects.

Virtual reality technologies and specialized software for creating virtual experiences provide potential

customers with the opportunity to “try the product before buying.” This form of marketing has demonstrated

high efficiency, which has led to the active implementation and development of marketing strategies using

virtual reality in the tourism industry (Ghali et al., 2024).

The use of virtual tourism as a marketing tool is particularly useful when the product or service being

offered is of high value. One example is the initiative of British Airways, which has developed a virtual tour

of the business class cabin on the route between London City Airport and New York.

This technology gives potential passengers the opportunity to preview the service conditions and the interior

of the aircraft before deciding whether to purchase an air ticket.

Visiting real places without physically traveling. The most famous example is Google Earth, which

revolutionized virtual tourism by allowing users to view and explore locations worldwide with a click of a

button.

Nowadays, almost all regions of the world are digitized and recorded using visual documentation

technologies, in particular the tools provided by Google services such as Google Street View.

The tourism industry actively utilizes these emerging information technology opportunities to create

various virtual tours, tailored to the target audience's requests, capabilities, and characteristics (Kowalczyk et

al., 2023).

Visiting places and events of the past. One of the significant technological advances in virtual tourism is

the ability to reconstruct historical sites and tourist attractions that have lost their authentic form. By integrating

archival images, computer modeling, and graphic technologies, it is possible to create virtual reproductions of

objects and events of the past.

This provides users with the opportunity to undertake immersive journeys in time and space, allowing them

to explore lost cultural heritage and interact with historical contexts that are not available in reality.

Visiting hard-to-reach places. Many places in the world are closed to the public. Limited access to certain

geographic objects may be due to a number of factors, including the physical inability of a particular person

to travel, their location in remote regions, and the closure of the territory to the general public.

However, with the development of virtual tourism, the concept of inaccessibility is gradually losing its

relevance: numerous virtual excursions provide users with the opportunity to visit almost any point on the

globe in digital format.

Visiting places that do not exist. One of the innovative directions in the development of virtual tourism is

the possibility of visiting spaces that have no real physical analogues.

An example of such a format of computer-simulated tourism is the Second Life platform - a virtual

environment that is a digital world created entirely using graphics and information technologies.

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Within this platform, users have the opportunity to create personalized virtual images - avatars (the so-

called digital twins), with the help of which they move around various locations and interact with other

participants, while in a completely virtualized space.

Technologies used in virtual tourism.

The leading technologies used in virtual tourism (Figure 8) are divided into three main categories: virtual

reality, augmented reality, and 360-degree video.

Figure 8. Technologies used in virtual tourism

Source: https://c8.alamy.com/comp/2ATDYAC/reality-virtuality-continuum-infographic-with-examples-real-environment-

augmented-reality-augmented-virtuality-and-virtual-reality-2ATDYAC.jpg

360-degree video. It is a type of digital media content that allows viewers to see a panoramic view in all

directions. Such videos are often captured with special cameras, creating an immersive visual experience (Liu

et al., 2024). 360-degree videos allow viewers to explore a place remotely while feeling like they are almost

physically present at the site, which makes them accessible to a wide range of users.

Augmented reality. For example, you can point your smartphone camera at a building (or use WiFi) and

learn more about its history, architecture, and significance.

Virtual reality. This technology allows users to immerse themselves in a computer environment. Using

headsets and motion sensors, tourists can experience a digital simulation of a real place and activities in it,

which is especially useful for people who, due to physical or financial limitations, cannot travel.

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New developments in the field of technology.

Haptic technologies incorporate haptic feedback into virtual experiences, and tactile devices allow users to

sense textures, surfaces of the environments they are exploring, and the virtual effects of weather and smells.

Guides based on artificial intelligence and natural language processing allow you to personalize and

individualize the possibilities for obtaining a virtual tourist experience.

Considering the technologies used to provide opportunities for virtual tourism, it is advisable to clarify once

again their features and differences that are important for understanding.

In particular, augmented reality is about adding digital content to the real environment. In other words,

augmented reality (AR) is a technology in which additional sensory data (visual, auditory, and others) are

integrated into the user's field of perception, which helps to expand the information context about the

environment and modify its perception.

Augmented virtuality, on the other hand, consists of adding physical content to a virtual environment using

computer technology, headsets, smartphones, special glasses, helmets, gloves, and other devices (Maziriri et

al., 2023).

That is, augmented virtuality (AV) is a virtual reality in which objects from the real world are present. It is

a transition stage from reality to virtuality and is a composition of real and virtual Objects.

Such a virtual space combines physical elements, objects, and people with the virtual world, with the

possibility of interaction in real-time. Cameras provide this interaction with a motion sensor, computer vision

technologies, tactile sensors, and other equipment with appropriate specialized software.

RESULTS AND DISCUSSION

Virtual reality offers immersive experiences, facilitates travel planning, improves marketing, promotes

sustainable tourism, and affects both consumers and businesses. Thus, it is possible to identify the main

advantages of using virtual reality in tourism, presented in Table 2.

Table 2. The main advantages of using virtual reality in tourism

What is improving

Description

Examples

Advanced

marketing.

Engages users with compelling visual content,

making marketing campaigns more effective.

360-degree videos of the best moments of travel

destinations on social media.

Innovative

customer service

Provide unique customer service experiences, such

as virtual concierge services or interactive tour

guides.

Registration and concierge services using virtual

reality provide insight into local features.

Sustainability

Promotes sustainable tourism by offering virtual

experiences that reduce the need for physical travel

Virtual visits to environmentally sensitive areas

that reduce the impact of pedestrian traffic.

Product

development

Helps companies develop or improve products and

services by simulating traveler experiences and

feedback.

Testing new travel packages or tours in a virtual

environment before launch.

Increasing

bookings

Entertaining previews increase the conversion rate of

viewers into real travelers.

Virtual previews of luxury accommodations

lead to direct booking.

Available tourism

opportunities

It allows those who cannot travel due to budget or

mobility constraints to explore the world virtually.

Virtual travel to remote or hard-to-reach locations.

Source: Composed by authors based on literature.

The most substantial positive aspects of virtual tourism.

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Accessibility. Various accessibility issues are addressed, allowing people with physical, financial, legal, or

time constraints to enjoy travel. People with disabilities or mobility issues can explore destinations without

physical obstacles. Individuals with limited time or resources can visit multiple locations without incurring

significant travel costs or time. Those who are afraid to travel can gain the experience of being in a new

environment, gaining confidence for potential future trips.

Environmental protection (sustainability). It minimizes the environmental impact of traditional travel,

contributing to global conservation efforts. Reduces carbon emissions by reducing air and land transport.

Reduces adverse impacts on local ecosystems and wildlife. Reduces waste and tourist traffic to popular holiday

destinations

Education and training. Game-changing opportunities for education and learning, and immersion in

different cultures or historical contexts. Virtual recreation of historical events allows users to better understand

and empathize with past events and situations. Three-D models and interactive tours of museums or cultural

heritage sites increase the engagement and educational level of visitors.

Professional training in the tourism and hospitality industry using virtual simulations to teach practical

skills and customer service.

Separately, the consumer's most attractive aspects can be emphasized again.

Resources. Virtual tours are characterized by minimal expenditure of personal resources, including time

and financial resources. Most of these formats are provided free of charge and do not require preliminary

preparation related to physical movement. The user can pause the virtual visit at any time and resume it at a

convenient time, which ensures a high degree of flexibility and accessibility of interaction with tourist content.

Also, virtual tourism is very often a preliminary step and preparation for a real trip.

Remote interactions. Virtual tourism provides the opportunity for remote interaction with geographic

regions and objects, access to which is difficult or completely impossible in the context of traditional (offline)

tourism. This is important not only for people with disabilities. For example, unmarried European women are

not allowed to enter many Muslim countries; Minors are restricted in their ability to cross state borders without

being accompanied by legal representatives, and a significant portion of the adult population faces family

obligations that limit their mobility. These categories include, in particular, mothers with infants, as well as

parents raising several children.

Low risks. Virtual travel involves significantly less risk than traditional travel, which involves physical

movement. Under no circumstances does a virtual tour endanger the life and health of the traveler.

Contactless. This is especially convenient for tourists who do not speak foreign (local) languages, as it

does not require direct verbal interaction with the local population and allows avoiding problems associated

with the language barrier and lack of awareness of the region of stay or destination.

From another point of view, there are also attractive aspects for the manufacturing companies in the

tourism industry, service business, cultural institutions, sports, and education. This is a good tool for promoting

a destination, a tourist destination. At the same time, the advertising of offline services when selling is

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improved, customer loyalty is increased, and the image of a modern, innovation-oriented organization is

created, including the organization of training programs and the popularization of cultural heritage.

In several situations, virtual tourism is especially useful and necessary. For example, for people with

disabilities, the difficulties of real travel are overcome due to various factors such as limited accessibility or

accommodation of tourist services. You can travel from the comfort of your home, get acquainted with tourist

attractions without physical barriers, and participate in tourist activities without the need for additional help or

support.

In the case of older people, age restrictions, reduced mobility, the need for medical support and monitoring,

and chronic diseases prevent them from traveling. Virtual tourism, on the other hand, allows older people to

travel without physical exertion, get mental stimulation through interactive experiences, and remember their

previous real travels through digital activities and entertainment (Geng, 2022).

From an educational point of view, for students, parents, and teachers, virtual tourism provides an

accessible way to interact with different cultures and places, the opportunity to develop teamwork skills

through interactive group tasks, improves memorization, and makes learning more engaging, informative, and

effective.

The connection between virtual tourism and wellbeing.

An artificial environment can improve people's health and psychological well-being, and virtual visits to

historical sites can contribute to recovery, improve memory and attention, and reduce stress (Deng et al., 2024).

Virtual visitors to historical sites report better subjective well-being, greater satisfaction with life, health, and

the amount of free time.

Virtual visits to historical sites are of strong importance for the awareness of their cultural value, their

intellectual, moral, and artistic aspects, and the effect of a sense of well-being resulting from their beauty or

historical importance. There is a personal connection with people and the spirit of past times, reverence, and

positive emotions of surprise and respect. At the same time, cultural and historical places are often more

attractive than places of recreation, and the beauty found in fine arts, architecture, and music enhances and

develops positive emotions.

At the same time, virtual, digital travel has a tangible impact on the individual. Virtual observation of walks

in a historical setting and visiting natural and historical attractions improves well-being, increases the level of

self-perception of happiness, relaxation, and perceived recovery, and is comparable to the effect of real

tourism. A change in the sensory or emotional state can also be obtained through virtual tourism. Positive

emotions can be associated with repeated virtual visits to places that have emotional significance, for example,

a virtual return to places that you liked during real trips earlier.

Virtual tour’s classification criteria.

According to the type of primary satisfied need - entertainment, hobby classes, ethnic, household, historic,

sports, cognitive, business, Congress, cult (religious), Event, nostalgic.

The technological complexity of presenting information, a set of video materials, can be considered an

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elementary, low-tech example of a virtual tour. At the same time, there is the possibility of creating high-tech,

highly realistic imitations of reality that require the use of specialized equipment, such as chairs for transmitting

sensory sensations or glasses for creating a 360-degree view effect, and other devices.

The purpose of creating a tour - informational and familiarization (this type of virtual tour allows you to

demonstrate not only publicly available, but also, for example, museum collections stored in funds and

storerooms); advertising and demonstration (help attract interest to tourist destinations or hotel

establishments); educational and cultural (provide the opportunity for in-depth visual acquaintance with the

objects of the exhibition, expanding cognitive horizons); social and rehabilitation (help improve the

socialization of people with disabilities). For a correct understanding of the phenomenon of virtual tourism, it

is also important to highlight a number of specific, but fundamentally significant characteristics. Virtual

tourism is a form of remote "movement" to any, however remote, but really existing geographic space (for

example, an online visit to the Louvre, a computer simulation of the moon landing built on documentary

material). However, a trip to the fantastic planet Pandora, realistically created and depicted by James Cameron

in the film Avatar, cannot be considered virtual tourism.

In offline tourist travel, the purpose of the visit is a country, region, city, and, less often, a particular

institution. Even if the final destination of the trip is a specific object - be it a museum, a high-class restaurant,

or a hotel complex (with no plans to leave its territory) - in the process of travel, the tourist inevitably forms a

certain perception of the surrounding region. In the context of virtual tourism, the scope of the "visited" space

is usually limited and more fragmented (for example, viewing only the Mona Lisa painting in the Louvre,

without "entering" other rooms of the museum). Thus, within the framework of a virtual tour, the user does

not need to become familiar with intermediate objects or visit them on the way to the target location.

CONCLUSIONS

Thus, we can highlight the main advantages and disadvantages of virtual tours. Among the first is convenience,

as people can visit their desired destinations and experience the sights while physically being anywhere and

without moving; accessibility, consumers with disabilities or health concerns can have experiences that would

be inaccessible to them or available with high risks and efforts in a real tour; flexibility and freedom, it is

possible to repeatedly pause/resume most virtual tours, continuing them from where the stop was the next time;

cost-effectiveness, in comparison with real tourist tours, virtual tours are much cheaper; low environmental

impact, from an ecological point of view, virtual tours have a much less negative impact on the environment

and nature.

At the same time, virtual tourism has some disadvantages: it is the lack of physical interaction, tourists

cannot really interact with various aspects and objects of the destination; still, existing technological and

financial limitations, which can potentially spoil the experience; lack and short lifespan of relevant

information, multimedia information may become outdated, give fewer details or be inaccurate, no longer

relevant; lack or inadequacy of social interaction, many multimedia formats limit social interaction, which is

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an integral part of travel for some people; monetization restrictions, monetization of virtual tours only,

excluding the economy at the destination; For some people, virtual environments have a disorienting,

nauseating effect, and other side effects, so in this case, caution and following the recommendations of doctors

are necessary.

Author Contributions: All authors have contributed equally to the paper. All authors have read and agreed

to the published version of the manuscript.

Funding /Acknowledgement: This study is financed by the European Union - Next Generation EU, through

the National Recovery and Resilience Plan of the Republic of Bulgaria, project № BG-RRP-2.013-0001.

Institutional Review Board Statement: not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Conflict of interest: The authors declare no conflict of interest.

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About the authors

Liudmyla DOROKHOVA

PhD, Visiting Prof., Department of Marketing, School of Economics and Business

Administration, University of Tartu, Estonia

Research interests: marketing, consumer behavior, service quality

ORCID ID: 0000-0002-3859-628X

Silvia BELOEVA,

PhD, Assist. Prof., Department of Management and Social Activities, University of Ruse

“Angel Kanchev”, Ruse, Bulgaria

Research interests: social work, creativity and anxiety management, management and

administration.

ORCID ID: 0009-0008-6847-9923

Web of Science Researcher ID: JKI-0733-2023

Nataliya VENELINOVA,

PhD, Assoc. Prof., Department of Management and Social Activities, University of Ruse

“Angel Kanchev”, Ruse, Bulgaria

Research interests: strategic management, communication management, crisis

management, emergency management and security, social affairs

ORCID ID: 0000-0002-7580-5263

Web of Science Researcher ID: IQX-0177-2023

Dzhavid MIRZOIEV,

Doctor of Philosophy, Assoc. Prof., Department of Marketing, Simon Kuznets Kharkiv

National University of Economics, Kharkiv, Ukraine

Research interests: marketing, high education, international economic relations.

ORCID ID: 0000-0002-3555-7672

This work is licensed under the Creative Commons Attribution International License (CC BY)

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ADAPTING TO THE AI REVOLUTION: COMPARATIVE ANALYSIS OF

NATIONAL WORKFORCE STRATEGIES

Saule Iskendirova1, Aigerim Amirova2, Aliya Daueshova3, Azamat Zhanseitov4*, Rymkul

Ismailova5

1), 2), 3), 4) Academy of Public Administration under the President of the Republic of Kazakhstan, Astana,

Kazakhstan

5) Astana IT University, Astana, Kazakhstan

e-mails: 1s.iskendirova@apa.kz, 2a.amirova@apa.kz, 3Aliya.Daueshova@apa.kz, 4a.zhanseitov.apa.kz,

5r.ismailova@astanait.edu.kz

Received: 30 April 2025 Accepted: 11 July 2025 Online Published: 08 August 2025

ABSTRACT

This paper assesses national workforce strategies for adapting to artificial intelligence (AI) across 55 countries. The

background of the research is defined by the rapid diffusion of AI technologies, which creates new challenges and

opportunities for labour markets and policy-makers worldwide. Objectives: The primary objective is to identify the key

determinants that drive the adoption of both skills-oriented and transformation-oriented strategies in national AI

workforce development. Methods/Approach: The study constructs two composite indices—AI Skills Focus and AI

Transformation Focus—based on employer survey data. These indices capture, respectively, the prioritization of hiring

and training for AI-related skills, and the implementation of broader organizational strategies such as business model

reorientation and workforce transitions driven by AI. Five explanatory variables are analyzed: funding for reskilling and

upskilling, skills gaps in the labour market, the proportion of tasks performed by technology (human-machine frontier),

labour-market churn, and the degree of organizational AI exposure. Results: The findings demonstrate that

organizational AI exposure is the strongest and most consistent predictor of both indices, indicating that widespread AI

adoption at the firm level is closely linked to national workforce adaptation. In contrast, larger skills gaps are associated

with lower adoption of AI workforce strategies, suggesting that talent shortages impede rather than accelerate

adaptation. Conclusions: The results highlight the importance of ecosystem-based approaches in public governance,

where collaboration among government, employers, and education providers fosters readiness and adaptability. The

study recommends policy measures focused on building supportive institutional environments and addressing skills

shortages through integrated, long-term reforms.

Keywords: human resource management, artificial intelligence, labour market, AI exposure, human-centricity,

ecosystem, digitalization

JEL classification: J24; O33; O57; I28

Paper type: Research article

Citation: Iskendirova, S., Amirova, A., Daueshova, A., Zhanseitov, A., Ismailova, R. (2025). Adapting to the AI revolution:

comparative analysis of national workforce strategies. Access to science, business, innovation in digital economy, ACCESS

Press, 6(3), 532-545, https://doi.org/10.46656/access.2025.6.3(4)

INTRODUCTION

The rapid proliferation of artificial intelligence (AI) technologies is fundamentally reshaping the nature of

work and skill requirements across global labour markets. AI-driven automation and augmentation are

expected to alter job roles, create new occupations, and render certain tasks obsolete, challenging both

employers and governments to respond with effective workforce adaptation strategies (Tairov et al., 2024;

Antonova, Beloeva, & Todorova, 2024). In this context, countries worldwide face an urgent imperative to

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anticipate and manage the workforce implications of technological change, ensuring that both individuals and

organizations are equipped to thrive in the AI era.

Despite growing consensus about the transformative impact of AI, there remains significant variation in

how countries prepare their workforces for this new landscape. Some nations have launched large-scale

initiatives to upskill and reskill employees, promote lifelong learning, and stimulate public-private

collaboration in digital education. Others are only beginning to recognize the challenges posed by AI or

struggle to address persistent gaps in skills and institutional capacity. While existing research often highlights

the need for reskilling and training, less is known about the determinants that drive countries to adopt particular

workforce strategies in response to AI. In particular, the extent to which national adaptation efforts are shaped

by factors such as funding, labour-market structure, the prevalence of technology, and the diffusion of AI at

the organizational level remains underexplored. (Benhmama & Bennani, 2024; Chornous et al., 2025; Le, Le

& Le, 2025).

This study addresses these gaps by conducting a cross-country analysis of workforce strategies for AI

adaptation using recent employer survey data from 55 countries. The research introduces two composite

indices — AI Skills Focus and AI Transformation Focus—to capture both the emphasis on skill development

and the breadth of organizational adaptation strategies at the national level. By modelling these indices as

functions of key explanatory variables—including funding for reskilling, skills gaps, labour-market churn, the

share of tasks performed by technology, and organizational AI exposure—the study aims to uncover the

primary drivers of national workforce strategies in the context of AI transformation.

The findings contribute to the international literature by clarifying which factors most strongly predict

workforce adaptation to AI and by highlighting the importance of ecosystem-based approaches in public

governance. The study further provides evidence-based policy recommendations to guide governments,

employers, and educators in building resilient, future-ready workforces.

LITERATURE REVIEW

The advent of artificial intelligence (AI) is widely recognized as a transformative force in contemporary

economies, with significant implications for labour markets, education systems, and public policy. Early

studies have documented the potential for AI and automation to both displace and augment jobs, leading to

profound shifts in skill requirements and organizational structures (Arntz, Gregory, & Zierahn, 2016; Bessen,

2018). The literature identifies two primary pathways for workforce adaptation: skill development (upskilling

and reskilling) and broader organizational transformation, including business model innovation and internal

labour reallocation (Brynjolfsson & McAfee, 2014; World Economic Forum, 2020; OECD, 2023).

Recent international surveys and policy analyses underscore the urgency of investing in human capital to

address the challenges and opportunities posed by AI. The World Economic Forum’s series of Future of Jobs

Reports highlights that most employers anticipate significant skill disruption, with demand rising for complex

cognitive, digital, and social-emotional competencies (World Economic Forum, 2020; World Economic

Forum, 2025). National governments and supranational bodies such as the European Commission and OECD

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have responded by prioritizing reskilling initiatives, promoting lifelong learning, and facilitating industry-

academic partnerships to align educational curricula with labour market needs (OECD, 2023; European

Commission, 2023; UNESCO, 2024).

However, scholars note considerable variation in the effectiveness of these strategies across countries.

Factors such as institutional capacity, funding mechanisms, and collaboration between stakeholders play

crucial roles in determining the impact of workforce policies (McKinsey Global Institute, 2023; ILO, 2024;

OECD, 2024). For instance, OECD research indicates that countries with well-coordinated ecosystem

environments—where public governance actively supports cooperation among government, employers, and

education providers—tend to achieve better outcomes in AI workforce adaptation (OECD, 2024; World Bank,

2022).

Recent research also demonstrates the effectiveness of digital management platforms in the public sector,

with evidence from Kazakhstan showing that digital solutions for remote work can significantly enhance

operational efficiency and oversight in large national organizations such as the railway sector (Adilova et al.,

2025). The role of digital and technological transformation in related fields, such as agriculture, is also well

documented in the literature. For instance, studies from Kazakhstan demonstrate how the integration of

renewable energy technologies contributes to greater efficiency and sustainability in agricultural production

systems (Bolyssov et al., 2019). Recent studies have also explored the role of human capital and

entrepreneurship in driving economic development. For example, analysis of cross-country data reveals that

female entrepreneurship has a significant positive impact on economic growth in both developing and

developed economies, highlighting the importance of inclusive workforce strategies (Akybayeva et al, 2024).

Such evidence reinforces the argument that integrated strategies combining digital innovation, sustainable

practices, and inclusive entrepreneurship are essential for long-term socioeconomic resilience (Abuseridze,

2021).

A growing body of research also highlights the role of organizational AI exposure as a catalyst for

workforce transformation. Firms already implementing AI are more likely to invest in employee reskilling and

to adopt innovative approaches to talent management (World Economic Forum, 2023; McKinsey Global

Institute, 2023). Conversely, persistent skills gaps and limited access to training infrastructure are cited as

major barriers to effective adaptation, particularly in emerging economies (ILO, 2024; World Bank, 2022).

The broader context of economic modernization in Kazakhstan includes not only workforce and education

policies, but also the management of natural resources and the acceleration of digital transformation in

business processes. Judicial practices regarding foreign investment in the natural resources sector play a

significant role in shaping the country’s investment climate and sustainable development strategies

(Yessengeldin et al., 2019). At the same time, recent empirical evidence demonstrates that digital

transformation is having a profound effect on business process management, increasing efficiency and

innovation in Kazakhstan’s private sector (Zhumanova et al., 2023). In this regard, the judiciary's capacity to

ensure transparent, consistent, and investor-friendly rulings is essential for fostering legal certainty and

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reinforcing the institutional foundations of Kazakhstan’s modernization agenda. For example, Georgia’s

judicial reforms in the early 2000s, which enhanced transparency and investor confidence, are often cited as a

key factor in the country’s improved investment climate highlighting the critical role of the judiciary in

supporting economic transformation (Abuseridze & Kikilashvili, 2025).

Despite the proliferation of policy reports and empirical studies, gaps remain regarding the comparative

analysis of national determinants that drive AI workforce strategies. There is a need for more systematic

research examining how factors such as funding, skills shortages, labour-market churn, technological intensity,

and ecosystem coordination jointly influence the adoption of both skill-focused and transformation-oriented

policies. This study aims to address this gap by providing new cross-country evidence on the key drivers of

national AI workforce strategies.

METHODOLOGY

Each country in the sample has determined its own AI workforce strategy based on responses from national

employer surveys. Data for the AI Skills Focus index are available for all 55 countries included in the analysis,

capturing efforts related to hiring and training for AI-related skills. In contrast, not all countries report

strategies related to broader organizational or business transformation in response to AI (such as workforce

transitions or business model adaptation). If a country’s survey responses indicate no implementation of any

AI Transformation Focus strategy, the value for this index is recorded as zero. As a result, some countries are

represented only by their focus on AI skills development, while others combine both skills and transformation

strategies in their approach. This procedure allows for the inclusion of all countries in the analysis and ensures

that the absence of transformation strategies is clearly reflected in the data.

1. AI Skills Focus Index

The AI Skills Focus index measures the extent to which organizations prioritize developing and acquiring

AI-related skills. It is calculated as the arithmetic mean of the following three items (all of which are available

for every country in the sample):

The share of employers hiring new people with skills to design AI tools and enhancements appropriate for

the organization’s needs,

The share hiring new people with skills to better work alongside AI,

The share reskilling and upskilling their existing workforce to better work alongside AI.

2. AI Transformation Focus Index

The AI Transformation Focus index captures broader organizational strategies related to business model

adaptation and workforce transition in response to AI. It is calculated as the arithmetic mean of the following

items:

The share of employers re-orienting their organization to target new business opportunities created by AI,

The share transitioning employees from jobs that AI will cause to decline, to other roles within the

organization,

The share downsizing the workforce where AI can replicate people’s work (included only where available).

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The AI Skills Focus index is available for all 55 countries in the analysis. In contrast, the AI Transformation

Focus index—which captures broader organizational transformation strategies in response to AI—is available

for a subset of 30 countries. For the remaining countries, no relevant transformation strategies were reported

in the survey, and thus the value for this index is recorded as zero.

Explanatory Variables

The explanatory variables used in this study are drawn from the World Economic Forum’s Future of Jobs

Report 2025 ([World Economic Forum, 2025]). These include:

Funding for reskilling and upskilling (the share of employers identifying public or private investment in

training as a priority),

Skills gaps in the labour market (the proportion of organizations reporting shortages of relevant skills as a

key barrier to transformation),

Human-machine frontier—Technology (the share of tasks primarily performed by technology, such as

robots and automation, in each country),

AI exposure (the proportion of organizations already implementing AI programmes),

Labour-market churn (the degree of turnover or structural change in the national labour market, reflecting

both job creation and job loss associated with technological transformation).

All variables are harmonized and normalized for comparability across countries.

Based on the literature review and the research questions, the following hypotheses are tested:

H1: Greater funding for reskilling and upskilling is positively associated with the adoption of both AI Skills

Focus and AI Transformation Focus strategies at the national level.

H2: Larger skills gaps in the labour market are associated with greater adoption of AI workforce strategies.

H3: A higher share of tasks performed by technology (Human-machine frontier) predicts increased

adoption of AI workforce strategies.

H4: Broader organizational AI exposure is positively associated with both AI Skills Focus and AI

Transformation Focus.

H5: Greater labour-market churn is positively associated with the adoption of both AI Skills Focus and AI

Transformation Focus strategies, as more dynamic labour markets may accelerate adaptation to technological

change.

To test the research hypotheses, we estimate separate ordinary least squares (OLS) regression models for

the two dependent variables—AI Skills Focus and AI Transformation Focus. Each model includes the five

explanatory variables described above. Robust standard errors are used to account for possible

heteroskedasticity. Model fit and diagnostic tests (including tests for multicollinearity, normality of residuals,

and heteroskedasticity) are reported and discussed in the Results section.

RESULTS

This section presents the empirical findings from the cross-country analysis of national workforce strategies

for artificial intelligence (AI) adaptation. The results are organized around the two composite indices

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constructed in this study: AI Skills Focus and AI Transformation Focus. Separate ordinary least squares (OLS)

regression models were estimated for each index, incorporating the five explanatory variables: funding for

reskilling and upskilling, skills gaps in the labour market, human-machine frontier, labour-market churn, and

AI exposure. Model diagnostics were performed to ensure the validity and robustness of the results Table 2,4.

The following subsections report the main regression outcomes for both indices, highlight statistically

significant predictors, and discuss the explanatory power of each model. These results provide insights into

the factors that most strongly influence national approaches to AI workforce adaptation.

Table 1. Regression Results: AI Skills Focus

Variable

Coefficient

Std. Error

t-value

p-value

Constant

0.428

0.173

2.469

0.016

Funding for reskilling and upskilling

–0.0006

0.119

–0.005

0.996

Skills gaps

–0.413

0.099

–4.191

<0.001

Human-machine frontier

0.206

0.175

1.174

0.245

AI exposure

0.579

0.117

4.959

<0.001

Source: Own calculations

Table 2. AI Skills Focus — Diagnostic Tests

Test

Value

p-value

Note

R-squared

0.523

—

Adj. R-squared

0.485

—

F-statistic

13.70

1.30e-07

Model is statistically significant

RMSE

0.0555

—

Shapiro-Wilk (normality)

0.9849

0.7157

Residuals are normally distributed

Breusch-Pagan (heteroskedasticity)

1.9404

0.7467

No evidence of heteroskedasticity

Durbin-Watson (autocorrelation)

1.845

—

No autocorrelation in residuals

Omnibus (normality of residuals)

0.247

0.884

Residuals are normally distributed

Jarque-Bera (normality of residuals)

0.092

0.955

Residuals are normally distributed

Condition number (VIF)

46.4

—

No multicollinearity (all VIF < 1.2)

Source: Own calculations

The regression results in Table 1 indicate that AI exposure (the share of organizations already implementing

AI programmes) is a strong and statistically significant predictor of national focus on AI-related skills

strategies (β = 0.58, p < 0.001). This suggests that in countries where a greater proportion of organizations are

adopting AI, there is a higher likelihood of widespread reskilling, upskilling, and hiring for AI competencies.

Conversely, skills gaps in the labour market are negatively associated with AI Skills Focus (β = –0.41, p <

0.001). This finding implies that countries facing significant talent shortages are less likely to engage

proactively in AI-related workforce development, potentially due to systemic barriers or insufficient capacity

to deliver training at scale.

Table 3. Regression Results: AI Transformation Focus

Variable

Coefficient

Std. Error

t-value

p-value

Constant

0.301

0.172

1.747

0.099

Skills gaps

–0.216

0.115

–1.876

0.075

Labour-market churn

–0.148

0.198

–0.749

0.458

AI exposure

0.540

0.147

3.670

0.001

Source: Own calculations

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Funding for reskilling and upskilling, as well as the share of tasks performed by technology (human-

machine frontier), were not statistically significant in this model.

Table 4. AI Transformation Focus — Diagnostic Tests

Test

Value

p-value

Note

R-squared

0.470

—

Adj. R-squared

0.408

—

F-statistic

7.673

0.00078

Model is statistically significant

RMSE

0.0572

—

Shapiro-Wilk (normality)

0.9873

0.9699

Residuals are normally distributed

Breusch-Pagan (heteroskedasticity)

4.7948

0.1875

No evidence of heteroskedasticity

Durbin-Watson (autocorrelation)

1.550

—

No autocorrelation in residuals

Omnibus (normality of residuals)

0.013

0.993

Residuals are normally distributed

Jarque-Bera (normality of residuals)

0.138

0.933

Residuals are normally distributed

Condition number (VIF)

31.8

—

No multicollinearity (all VIF < 1.1)

Source: Own calculations

For the AI Transformation Focus index – Table 3, AI exposure again emerges as the main significant

predictor (β = 0.54, p = 0.001), reinforcing the idea that widespread organizational adoption of AI is closely

linked to broader business model transformation and internal workforce reorganization. Skills gaps also have

a negative association with AI Transformation Focus (β = –0.22, p = 0.075), although this effect is only

marginally significant. Other predictors (e.g., labour-market churn) do not reach statistical significance.

The results in table 4 provide robust support for H4, demonstrating that higher levels of organizational AI

exposure are consistently and positively related to the adoption of both skills-oriented and transformation-

oriented AI workforce strategies at the national level. H2 is only partially supported, as skills gaps appear to

be a significant—but negative—predictor, suggesting that rather than driving innovation, talent shortages may

inhibit a country’s ability to implement AI workforce strategies effectively. H1 and H3 are not supported, as

funding for reskilling and the prevalence of technology-performed tasks were not significant in either

regression.

Overall, the findings highlight the importance of widespread AI adoption at the organizational level as the

primary driver of national workforce adaptation strategies, while also underscoring the need to address talent

shortages that may otherwise impede progress.

DISCUSSION

The present study offers new cross-country evidence on the determinants of national AI workforce strategies

by leveraging employer survey data from 55 countries. Our analysis demonstrates that the breadth of

organizational AI adoption (“AI exposure”) emerges as the most robust predictor of both skill-oriented and

transformation-oriented strategies. In contrast, persistent skills gaps in the labour market are negatively

associated with the adoption of these strategies, and variables such as funding for reskilling or the share of

technology-driven tasks do not significantly explain variation across countries.

These findings are in line with recent research that positions AI adoption and diffusion at the organizational

level as a key enabler of broader workforce transformation (ILO, 2024; McKinsey Global Institute, 2023;

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OECD, 2023; World Economic Forum, 2023; World Economic Forum, 2025). The World Economic Forum

reports that organizations already implementing AI are more likely to invest in reskilling and upskilling and

to restructure their business models and internal roles in response to technological change (World Economic

Forum, 2023; World Economic Forum, 2025). Our results extend these insights by confirming this relationship

at the national level across diverse economies, showing that AI exposure remains a critical catalyst for

workforce adaptation strategies regardless of a country’s economic development status (OECD, 2023; ILO,

2024).

The negative association between skills gaps and AI strategy adoption suggests that, contrary to

expectations in some of the literature, talent shortages do not stimulate greater training and transformation

efforts, but rather constrain them (OECD, 2023; World Bank, 2022). This is consistent with evidence from the

OECD and McKinsey Global Institute, which highlight that skills shortages represent a major bottleneck for

digital and AI transition, especially in emerging and mid-income economies where education and training

infrastructure is less developed (McKinsey Global Institute, 2023; OECD, 2023). Our findings thus underscore

the need for targeted policy interventions to expand both the quantity and quality of AI-related talent, rather

than relying on market forces alone to close the gap (European Commission, 2023; UNESCO, 2024).

Interestingly, funding for reskilling and upskilling was not a significant predictor in our models. This result

diverges from policy discourse emphasizing the importance of increased investment in training as a

precondition for successful digital transformation (European Commission, 2023; OECD, 2024). A possible

explanation is that funding alone may not guarantee the effectiveness of workforce strategies unless

accompanied by institutional capacity, industry demand, and access to relevant curricula (OECD, 2024; World

Bank, 2022). Recent case studies demonstrate that countries with robust institutional frameworks for lifelong

learning and active collaboration between government, employers, and education providers see stronger

returns on training investment (OECD, 2024; UNESCO, 2024).

Another novel finding is the lack of significant effect for the human-machine frontier—the share of tasks

already performed by technology—on AI workforce strategies. While one might expect that greater

automation would compel more rapid adaptation, our results suggest that actual organizational readiness and

willingness to engage in AI-driven transformation are more important than structural technological exposure

per se (ILO, 2024; World Bank, 2022). This observation aligns with recent reports cautioning that

technological potential does not automatically translate into broad-based workforce change without

complementary changes in organizational culture and management practices (ILO, 2024; World Economic

Forum, 2025).

In sum, our results highlight that AI workforce transformation is driven less by generic “push” factors such

as funding or automation levels, and more by the “pull” of widespread organizational AI adoption (World

Economic Forum, 2023; World Economic Forum, 2025). At the same time, severe talent shortages act as a

brake, not a catalyst, on strategic adaptation (McKinsey Global Institute, 2023; OECD, 2023). These

conclusions reinforce calls in the latest global reports for integrated approaches that combine national

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investment in AI diffusion with strategic initiatives to build and attract AI talent, improve educational quality,

and foster industry-academic partnerships (European Commission, 2023; OECD, 2024; UNESCO, 2024;

World Economic Forum, 2025).

Policy Recommendations

The findings of this study indicate that effective national adaptation to AI in the workforce depends on

more than individual training initiatives or funding alone; it requires the presence of a supportive ecosystem

environment, especially in public governance.

First, governments should foster an ecosystem approach by actively coordinating and incentivizing

collaboration among key stakeholders—public agencies, private employers, educational institutions,

technology providers, and civil society. This means building platforms for dialogue, co-creation of training

programs, and transparent policy frameworks supporting lifelong learning and digital skills. Countries with

robust, well-coordinated governance ecosystems achieve greater progress in both the diffusion of AI and

workforce transformation (OECD, 2024; UNESCO, 2024; World Economic Forum, 2025).

Second, public administration should move beyond siloed, top-down programs to build adaptive and

integrated systems that support AI readiness at all levels. This includes:

Incentivizing public-private partnerships and cross-sectoral initiatives;

Embedding digital and AI literacy across all stages of education, from schools to higher education and adult

learning;

Ensuring that regulatory and funding mechanisms are flexible, inclusive, and responsive to rapid

technological and labour market change.

Third, policy should prioritize the adoption of AI not only in large corporations, but also among SMEs and

the public sector to broaden exposure and readiness (OECD, 2024).

Fourth, investments should target not only training budgets but also the development of institutional

capacity and incentives for collaboration between employers, education systems, and governments (UNESCO,

2024; European Commission, 2023). This will help ensure that upskilling and reskilling efforts are sustainable

and aligned with actual labour market needs.

Fifth, addressing skills gaps requires both short-term retraining initiatives and long-term reforms in STEM

and digital education (World Bank, 2022; OECD, 2023). Building strong educational foundations for future

generations is as important as responding to current talent shortages.

Additionally, data-driven governance and monitoring should be enhanced to track workforce trends, skills

gaps, and the impact of AI-related policies. Open data sharing among ecosystem actors can further accelerate

best practice adoption and training innovation.

Policy makers should also encourage innovative partnerships between the private sector and higher

education institutions to align training programs with evolving labour market needs. The experience of

endowment funds in Kazakhstan demonstrates that such collaborations can effectively support the

development of industry-relevant skills and contribute to workforce readiness, particularly in specialized fields

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like tourism (Khusainova et al., 2021). This underscores the broader potential of public-private-academic

synergies to drive human capital development and sustain the momentum of digital and sectoral transformation

(Abuseridze et al., 2024).

Efforts to improve workforce development and sectoral performance should be supported by

comprehensive reforms and effective leadership across key public sectors. For example, evidence from

Kazakhstan’s healthcare system demonstrates that policy measures aimed at enhancing efficiency and

sustainability can yield significant socio-economic benefits (Smailov et al., 2022). At the same time, research

shows that leadership quality and targeted expertise development within the education sector are critical for

improving teacher performance and adapting to evolving educational needs (Sayabek et al., 2018). As noted

by recent studies, effective leadership combined with targeted reforms is key to achieving sustainable progress

across sectors (Abuseridze et al., 2022).

Finally, the public sector should lead by example—investing in AI upskilling and transformation of its own

workforce, modernizing HR practices, and piloting innovative approaches to talent development.

These recommendations align with the direction of the OECD AI Policy Observatory (OECD, 2024),

UNESCO (UNESCO, 2024), and the World Economic Forum’s “Reskilling Revolution” initiative (World

Economic Forum, 2023; World Economic Forum, 2025), which collectively emphasize ecosystem-level

responses to the challenges and opportunities posed by AI for the global workforce.

CONCLUSION

This research advances the international understanding of how countries are responding to the rapid

proliferation of artificial intelligence (AI) by empirically analyzing the determinants of national workforce

strategies. Through the development of two original composite indices—AI Skills Focus and AI

Transformation Focus—this study systematically captures both the emphasis on workforce training and the

broader organizational adaptation strategies adopted by countries worldwide. By leveraging recent employer

survey data across 55 nations, the research offers robust comparative insights that were previously lacking in

the literature.

The findings consistently demonstrate that organizational AI exposure, measured as the proportion of firms

already implementing AI technologies, is the strongest and most reliable predictor of a nation’s readiness to

adopt both skills-oriented and transformation-oriented strategies. This underscores the central role that

widespread AI adoption at the enterprise level plays in driving national workforce transformation. Notably,

the analysis also reveals that persistent skills gaps in the labour market present a significant obstacle to

effective AI adaptation. Rather than spurring more aggressive upskilling or transformation efforts, a shortage

of digital and technical talent appears to constrain countries’ ability to respond proactively to the demands of

the AI era.

Other examined factors - including funding for reskilling and upskilling, the extent of automation (human-

machine frontier), and labour-market churn - were not found to be significant predictors in most models. This

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finding suggests that simply increasing training budgets or exposure to automation technologies, in isolation,

may be insufficient to ensure national preparedness for AI-driven change.

The results point to the importance of adopting a holistic, ecosystem-based approach to workforce policy.

This approach goes beyond individual training initiatives and places a premium on active coordination among

government agencies, employers, and educational institutions. It also highlights the value of developing

supportive institutional environments that enable continuous learning, encourage public-private collaboration,

and facilitate rapid adaptation to emerging technologies.

Policy recommendations arising from this study emphasize the need for governments to foster AI adoption

across all sectors—including small and medium-sized enterprises and the public sector - while simultaneously

investing in institutional capacity, strengthening digital and STEM education, and addressing skills shortages

through both short-term interventions and long-term reform.

Ultimately, preparing the workforce for the opportunities and challenges presented by AI requires more

than reactive training programs. It demands resilient institutions, flexible governance systems, and a culture

of lifelong learning that together can sustain national competitiveness and social inclusion in the face of rapid

technological change.

Author Contributions:

Conceptualization, A.A. and A.D.; methodology, S.I.; software, A.Z.; validation, S.I., A.Z. and A.D.; formal

analysis, A.A.; investigation, A.Z.; resources, A.D.; data curation, S.I.; writing—original draft preparation,

S.I.; writing—review and editing, R.I.; visualization, A.Z.; supervision, A.D.; project administration, A.A.;

funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.

Funding /Acknowledgement:

This research is funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan

(BR24993258).

Institutional Review Board Statement: not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Conflict of interest: The authors declare no conflict of interest.

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About the authors

Saule ISKENDIROVA,

Candidate of Economic Sciences, associated professor, Professor of The Branch of

Akmola Region, The Academy of Public Administration under the President of the

Republic of Kazakhstan: Kokshetau, Kazakhstan. Awarded the national title “The

Best University Lecturer” in 2014.

Research interests: strategic planning, higher education system, strategic human

resource management, talent management, public personnel policy, ecosystem and

human-centric approach in HRM, change management in the public sector,

performance assessment and digital transformation in public administration,

management and decision making.

ORCID: 0000-0003-3596-8831

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Aigerim AMIROVA,

Doctor in Public Administration, research project leader. She has over 15 years of

experience in public service and around 8 years in academic research. Certified in

HR management (SPHRi), project management (PRINCE2 Foundation), and data

analytics. Recipient of the state award “For Distinguished Labor” (2015) and other

departmental honors. Active participant in interagency working groups on public

administration reform, including civil service, public services, decentralization, and

transformation of the state planning system.

Research interests: transformational project management, project governance,

strategic HR management, and innovation in the public sector.

ORCID: 0000-0003-1250-0777

Aliya DAUESHOVA,

PhD, Head of the Competency Assessment and Talent Management Sector at the

Institute of Human Resource Management, and Associate Professor at the Institute

of Management, Academy of Public Administration under the President of the

Republic of Kazakhstan.

Research interests: HR management, talent management, competency assessment,

regional governance, urban management, and metropolitan area governance.

ORCID: 0000-0002-9872-6111

Azamat ZHANSEITOV,

Associate Professor at the Karaganda Regional Branch of the Academy of Public

Administration under the President of the Republic of Kazakhstan, Karaganda,

Kazakhstan.

Research interests: finance, economics, higher education system, strategic human

resource management, public personnel policy, performance assessment, digital

transformation in public administration, management, and decision-making.

ORCID: 0000-0001-9495-0530

Rymkul ISMAILOVA,

Doctor of Economic Sciences, Professor at the School of Creative Industries, Astana

IT University, Astana, Kazakhstan. Recipient of the “For Merit in the Development

of Science of the Republic of Kazakhstan” badge (2011), the “Best University

Lecturer” grant (2007), and the international “Bolashak” scholarship (2013).

Author of over 170 scientific publications and supervisor of six PhD graduates.

Research interests: project management, agile and hybrid project methodologies,

project cost management, investment activity, and innovation.

ORCID: 0000-0002-8934-5181

This work is licensed under the Creative Commons Attribution International License (CC BY)

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RISK MANAGEMENT PRACTICES AND ORGANIZATIONAL

PERFORMANCE IN TRANSPORTATION COMPANIES

Francis Okechukwu Chikeleze1, Anatolijs Krivins2, Valters Kaze3, Thomas Etalong4

1) Enugu State University of Science and Technology, Enugu, Nigeria

2) Daugavpils University, Daugavpils, Latvia

3) RISEBA University of Applied Sciences, Riga, Latvia

4) ACE Intercontinental Research Institute, Enugu, Nigeria

1 Okeychikeleze@gmail.com; 2anatolijs777@gmail.com; 3valters.kaze@riseba.lv; 4thomasalama@gmail.com

Received: 20 May 2025 Accepted: 13 July 2025 Online Published: 08 August 2025

ABSTRACT

This article explores how the transport companies operating in Enugu manage their risk exposures to improve their

overall performance. The problem of the study is premised on the fact that loss exposures, if not correctly managed,

could result in high losses for business entities both in terms of financial and market performance, effectiveness,

efficiency and other performance areas. The objective of the study was therefore to assess the level of compliance with

standard risk management adopted by these transport companies to ascertain how their management of loss exposures

can be enhanced to reduce the occurrence and impact of these potential losses and mitigate their impact if they

eventually occur. The independent variable in the study was the risk management practices of these companies. In

contrast, the dependent variable was their performance, broken down into effectiveness, efficiency in service delivery,

profitability, customer satisfaction and retention, growth, long-term survival, and their competitive advantage. The

study adopted the survey method to generate qualitative data from respondents. The population of transport companies

operating in Enugu is not quantifiable; therefore, the researcher selected ten companies, comprising both government-

sponsored and private companies. All the selected companies constituted the sample for the study. The study used

primary data generated through the questionnaire instrument, and percentages were used in analysing the generated

data. The study revealed that the risk management practices adopted by the selected companies enhanced their

effectiveness and efficiency in service delivery by 90%; increased their profitability by 70%; increased their customer

satisfaction and retention by 80%; encouraged their growth and long-term survival by 40%; and enhanced their

company's competitive advantage by 70%. The challenges facing the selected companies in managing their risks

include general ignorance of the importance of planned risk management practice (70%); inadequate support by the

executive and management cadre (40%); poor funding of risk management activities (50%); and paucity of deliberate

formulation and implementation of risk management policies (30%). The study concluded that risk management

practice has significant implications for organisational performance and consequently recommends intensive

enlightenment on the risk management function, executive support, and adequate funding for planned risk management

practices, and deliberate policies on risk management. The implication of the study is to highlight that unless

organisations plug the drain of their resources through effective risk management practices, revenues generated will

still be drained through poorly-managed losses, ultimately giving rise to poor performance.

Keywords: risk, management, performance, companies, organization, effectiveness, efficiency

JEL classification: G32, L91, O18, R41, R42

Paper type: Case Study

Citation: Chikeleze, F.O., Krivins, A., Kaze V., Etalong, T. (2025). Risk management practices and organizational

performance in transportation companies. Access to science, business, innovation in the digital economy, ACCESS

Press, 6(3), 546-566, https://doi.org/10.46656/access.2025.6.3(5)

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INTRODUCTION

The ultimate performance of organisations is traditionally judged by their net profit, which is calculated as

income minus expenditure. Much as some of their losses are captured under accounting disclosures, many

losses are not reflected in the organisation's accounting records. The usual absence of some of the salient

losses in published organisations' records usually results in their not being given adequate attention in

organisational management, even though they may significantly determine their overall progress, growth and

performance in the long run. Suppose these losses are given the prime place they occupy in organisational

performance. In that case, the loss exposures that give rise to them are bound to be given prime attention in

organisations' risk management practice, thereby minimising such incidence of losses.

Organisational loss exposures often emanate from both tangible and intangible areas such as assets,

operations, human capital, environment, financial, and legal issues. Even where organisations earn high

profit, the net profit is often depleted by the losses incurred in some of those areas mentioned above if they

are not well managed. In other words, organisational performance is not only measured by the amount of

their gross profit minus tangible expenses, but also by how well they have been able to manage leakages

arising from different loss exposures. All organisational performance indicators, such as effectiveness in

service delivery, financial performance, profitability, efficiency in resource deployment, stakeholders'

satisfaction, market performance, relevancy, and shareholders’ value, are attended by different risks (Krivins

et al., 2021) arising from associated loss exposures.

The achievement of good performance in all the above-mentioned areas is laden with challenges that

usually expose the organisations to various lost opportunities. For instance, the loss of critical marketing

personnel may affect an organisation's market share. Again, the loss of a critical software developer by an IT

organisation may seriously affect the performance of such an organisation. Litigation bordering on bad

product quality may tarnish the image of an organisation and erode its goodwill hitherto enjoyed in the

marketplace; such legal challenges not only lead to financial losses through penalties and compensation but

also harm customer loyalty, negatively affecting long-term business sustainability and competitive advantage

(Abuseridze, 2025). The financial cost of prosecuting such litigations may be reflected in the books of

accounts. However, the lost goodwill is hardly captured in tangible terms as part of the loss to the

organisation. Again, the loss of business opportunities is not usually captured in organisations' balance

sheets, but they are critical losses to organisational performance. These and many others are some of the loss

exposures that may not easily be reflected in an organisation's accounting books, but which have a significant

impact on their overall performance.

Effective risk management practices play a crucial role not only in direct risk mitigation but also in

supporting the broader organisational goals of sustainable business development and adaptability in rapidly

changing environments. For instance, Ščeulovs, Gaile-Sarkane, and Kaže (2011) highlighted the importance

of leveraging electronic environments and e-marketing tools as part of strategic initiatives to promote

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sustainable business growth among Latvian enterprises. However, their study revealed significant gaps in

risk awareness and a limited utilisation of digital tools among small and medium-sized businesses. Such

deficiencies impede companies’ ability to respond proactively to market shifts and consumer trends,

subsequently elevating the risks associated with missed opportunities and reduced competitiveness. A

healthy business environment would contribute to a more sustainable societal development in many areas,

even up to spatial development, which is quite often influenced by transportation and logistics businesses

from planning to managing environmental effects (Miszczak et al., 2024).

Similar observations regarding the importance of adaptive strategies and risk management can be drawn

from the Latvian banking (Durguti et al., 2024; Obeid, 2024) industry. It has been found that the evolution of

e-banking services was driven primarily by strategic adaptation to technological advances and shifts in

consumer preferences within a knowledge-based economy (Kaže et al., 2007). Organisations slow to adopt

digital transformation initiatives faced heightened strategic risks, including loss of market share, weakened

competitive positioning, and decreased consumer trust (Ščeulovs et al., 2011). These insights underscore that

robust risk management practices, encompassing technological adaptation and responsiveness to evolving

market conditions, are fundamental to maintaining organisational resilience and long-term sustainability.

By including all areas of leakages, organisations can comprehensively analyse and appreciate their total

loss in any business year, whether they are tangible or intangible. It may not be possible to measure the

monetary value of intangible losses. However, a good knowledge of their existence and proper management

will help to reduce their negative impact on organisational performance. Nevertheless, where they are not

taken cognisance of, the tendency is for organisations to continue to carry those loss exposures along with

them year after year without even knowing the limitations they impose on their performance.

Again, organisations can only manage their risk portfolios if they understand their loss exposures. Without a

credible way of identifying, analysing and estimating organisations' loss exposures, risk management may

not meet organisational needs because risk management is fundamentally based on a good understanding and

appreciation of the organisation's aggregate loss exposures. As pointed out by Rejda (2011), Rejda et al.

(2014), effective appreciation of the level and direction of organisations' loss exposure, as well as their

frequency and severity, goes a long way in prescribing their risk management strategies, preferences and

procedures. Therefore, where these loss exposures are substantially identified and given good attention, their

negative impact on organisational performance will be highly reduced, thereby giving the organisation

greater opportunity for better performance.

However, the common practice is that organisations often focus attention on what they have gained or lost

in the marketplace in terms of net profit or loss without much attention to the higher gain that would have

accrued to them if more attention were paid to managing their tangible and intangible loss exposures that

deplete their efforts. This apparent lack of due attention to effective management of organisations' loss

exposure has been predicated on their poor risk identification, assessment, analysis and deliberate efforts at

effective risk management practices.

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An essential, though frequently overlooked, aspect of risk management is the human factor related to

decision-making profiles of the personnel in charge of the business, particularly operations. This is an

important consideration for hiring, placing and training the personnel. An important driver that influences

managerial decisions regarding risk is the underlying system of human values. Recent empirical studies

highlight that personal values significantly shape individual risk perceptions and consequent behaviours.

Kaže, Škapars, and Bolinskis (2011) demonstrated through their analysis of consumer participation in the

shadow economy that individuals with values centred around self-interest and immediate gratification tend to

exhibit greater risk tolerance, reflected in their readiness to engage in informal economic activities, including

tax evasion. In contrast, individuals whose values emphasise tradition, stability, and societal welfare

generally display higher risk aversion and adherence to formal, compliant behaviours. Values might even

serve as a base for making decisions about markets by matching the target markets with their potential

customers, as shown in a study by Kaže, Škapars, and Ščeulovs (2011).

Moreover, innovative methodologies for measuring personal values using image-based techniques, as

developed by Kaže, Bolinskis, and Kurovs (2022), further confirm that understanding these motivational

underpinnings is crucial for effective managerial decision-making. Their research indicates that visual

approaches in measuring values not only bypass the social desirability biases often encountered in traditional

text-based surveys but also enhance respondent engagement and yield more precise insights into an

individual’s motivational profile. By integrating these image-based tools into organisational risk assessment

frameworks, managers in transportation companies could achieve a more nuanced understanding of their

employees’ risk attitudes, thereby refining their strategies for risk mitigation and enhancing overall

organisational performance.

Experience has also revealed that a good number of organisations go about their business without giving

due attention to possible loss exposures facing them. Some are aware that those loss exposures exist, while

some others are entirely ignorant of them. Again, some of those who are aware of such loss exposures make

feeble efforts to pre-empt them, while many others hope the losses will not occur. Consequent upon not

being aware of such loss exposures or not making adequate provisions to contain them should they occur,

exposes organisations to losses that would otherwise have been avoided both in frequency and magnitude. It

is common knowledge that risk management lapses have contributed substantially to avoidable losses by

organisations and have negatively affected their overall performance. On the other hand, if organisations can

do detailed risk analysis to identify their loss exposures and put standard risk management programmes in

place and strategies to avert or mitigate them, their performance outcome is bound to be higher in terms of

profitability, market share, efficiency, effectiveness and returns on investment.

This study is therefore focused on exploring ways of substantially reducing avoidable losses that impact

negatively on organisational performance by examining the extent to which organisations identify the

various loss exposures that attend their operations, their level of adherence to standard risk management

processes, and the strategies they adopt in addressing loss exposures. It also explores the challenges facing

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organisations in effectively managing their loss exposures and offers possible solutions to identified

challenges.

LITERATURE REVIEW

In modern conditions of globalisation, digitalisation and increasing external threats, effective risk

management becomes a key element of the organisational efficiency of transport companies. A variety of

risks that transport systems are subjected to - from natural disasters to technological failures and cyber

threats - requires an integrated approach that includes stability, flexibility, and innovative solutions. We have

identified five current areas in which researchers are focused.

Global calls of transport systems and supply chains can indicate the first area of research. The Covid-19

pandemic demonstrated the scale of the vulnerabilities of the global supply chains and set the task of

ensuring their stability in conditions of uncertainty for transport companies (Beloev, & Grozev, 2025;

Bulgakov et al., 2023). The pandemic exposed the fragility of just-in-time logistics models and underscored

the urgency of increasing resilience through diversified routing strategies and digital tracking systems

(Abuseridze, 2021). One of the practical tools for mitigating such risks is modal flexibility. In particular, the

analysis of the production sector of the United States showed that the use of air transport allows companies

to maintain the stability of stocks during periods of global failures (Ke et al., 2025). In turn, the global

container shipping network (GCSN) also showed high sensitivity to cascading malfunctions due to the high

degree of interconnectedness of ports. A model for assessing the stability of the network has been developed,

based on the analysis of the significance of ports and the redistribution of the load. Analysis of 686 ports

around the world revealed the need for accurate determination of throughput thresholds to prevent large-

scale cascading failures. Also, it proposed recommendations for port authorities to increase the stability of

the infrastructure (Cao et al., 2025).

The second block of research is innovative approaches to transport stability. Modern risks require multi-

level assessment and intervention systems (Pencheva et al., 2019). For example, an approach to assessing the

risk of road safety based on the VIKOR model, taking into account the regional features of the infrastructure,

road conditions and drivers' behaviour, which allows the development of differentiated security management

strategies depending on the level of socio-economic development of the region (Wei et al., 2025). In the

context of the growing role of transport hubs, the importance of developing effective evacuation strategies in

emergencies increases. For this, a mixed integer model is proposed, combining pedestrian flows and

multimodal vehicles. The created algorithm for Variable Neighbourhood Search (VNS) showed high

efficiency and a reduction of evacuation time by 17.2% (Tang et al., 2025). Particular attention is paid to

ensuring fire safety in extensive transport facilities. A methodology has been developed for assessing the

stability of fire systems in smart airports, including structural, functional, organisational and adaptive

components (Zhu et al., 2025). Modern approaches to increasing road safety actively use the explainable

artificial intelligence (XAI). The structure of decision -based support based on machine learning models

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(ANN, SVM, RF, XGBoost) has been developed, where methods for assessing the importance of features

(PFI (Permutation Feature Importance) and FIRM (Feature Importance Ranking Measures)) are used, which

increases transparency and validity of the conclusions for the transport policy (Abdulrashid et al., 2025).

The third direction is risk management and adaptation to extreme conditions. In conditions of adverse

weather, passenger behaviour changes significantly. The study of the behaviour of Shanghai passengers

during periods of storm warnings showed the influence of gender, educational and profitable factors on the

adaptation of routes (Zha & Li, 2025). For interregional transportation, it was established that passengers

more often adjust the dates of trips, which involves a change in the type of transport. The main determining

factors were the availability of transport, income, education and geographical position of the departure, while

snow and windy weather more often provoke changes in the plans (Yuan et al., 2025). Risk management is

important not only in passenger transportation but also in the management of water transport routes. The

study of the regulation of the Hui River (China) using the MIKE21 hydrodynamic model showed a

contradiction between the improvement of shipping and the risks of coastal erosion and sedimentation. In

response, adaptive strategies of dredging and restoration of coastal zones are proposed (Quan et al., 2025).

Long-term precipitation in the Great Bačka Canal near Vrbas revealed a stable tendency to reduce pollution

of some heavy metals, but the risks of copper and nickel are maintained. The study emphasises the

importance of transition to integrated precipitation management with elements of the circular economy

(Pilipović et al., 2025).

Fourth, the actual direction can be considered technological calls of digitalisation and cybersecurity. As

digital systems become central to public and private operations, the need for both technological innovation

and robust cybersecurity measures has grown. While digitalization enhances efficiency and accessibility, it

also increases exposure to cyber threats, making cybersecurity a core component of sustainable digital

transformation (Abuseridze et al., 2022). The development of digital technologies in the transport industry

creates new risks associated with cybersecurity and data management. The introduction of connected and

automated vehicles requires reliable security mechanisms when updating the air software over-the-air

(OTA). An adaptable OTA security approach has been developed, including threat analysis and risk

assessment according to the ISO/SAE 21434 standard, which can minimise system vulnerability (Iyieke et

al., 2025). Particular attention is also paid to safety in the construction of transport infrastructure facilities.

For the design of readiness for emergencies in the construction of the Chinese city rail transport, an

ontological method of searching for similar cases is proposed, based on the analysis of risk factors. This

allows you to increase the efficiency of using accumulated experience in risk management (Xu et al., 2025).

Mixed movement involving cars with adaptive cruise control (ACC) requires the development of new

management solutions. Two new road policies are proposed: limiting the use of ACC in high-density zones

and regulating time intervals. The combination of these measures with traditional approaches allows you to

reduce trolleys without increasing the accident rate (Yu & Yeo, 2025). Logistic companies switching to the

use of electric trucks (H-BEV) are faced with new types of failures, especially related to the charging

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infrastructure. The main problems have been identified and softening strategies have been developed,

including information support systems and additional resources, which is an important step towards a stable

zero emission of cargo transportation (Björklund et al., 2025).

Finally, fifthly, researchers pay increased attention to the role of smart transport systems and innovations.

The development of intellectual tools for evaluating and optimising transport systems plays an increasingly

important role in ensuring their stability and organisational efficiency. A method of assessing the reliability

of transport infrastructure using artificial intelligence and machine learning is proposed, which allows for

taking into account the complexity and uncertainty of modern systems. To optimise the train schedule, a

temporary window is proposed, which allows minimising conflicts with a limited throughput of railway

infrastructure (Paul et al., 2025; Chen et al., 2025). An intelligent model for assessing the state of railway

infrastructure has been developed, which increases the accuracy of monitoring and maintenance efficiency

(Bi et al., 2025). In the conditions of urban mobility, the integration of bus routes and bicycle rental systems

is proposed, which makes it possible to increase the availability of transport and the stability of the transport

system as a whole (Sadeghi et al., 2025). To assess the stability of transport systems in the conditions of

natural disasters, a hybrid simulation-electrical method has been developed, which helps to produce recovery

strategies with limited resources (Ma and Lee, 2025). Finally, the use of digital doubles to integrate logistics

flows and transport systems can significantly increase the accuracy of planning, the flexibility of operations

and the overall stability of logistics networks (see, for example, Dong et al., 2025).

A generalised analysis of modern studies demonstrates that effective risk management in transport

companies requires a systemic and multi-level approach. The integration of innovative technologies, digital

tools, flexible organisational strategies and adaptive management models allows transport companies not

only to minimise the impact of various threats, but also to increase general organisational efficiency in the

context of global challenges.

Regarding the role of members of the boards of enterprises in reducing these risks, the following can be

noted. Liang (2025) carried out a study on the role of board members in mitigating risk in an organisation

using content analysis. The tool of analysis came from a review of analytical research from journal articles,

books, and internet resources. The findings of the study indicated that corporate boards play a critical role in

framing modalities to prevent any form of risk within the organisation to optimise their profitability. Yahaya

(2024) studied the impact of the risk management committee (RMC) on firms as a mediation practice. The

study used a panel data collection method in collecting data from publicly traded companies covering a

period of ten years from 2013 to 2024. The collected data were tested using the multiple regression method.

The results indicate that although the existence of a Risk Management Committee (RMC) correlates with

reduced business risk, complete risk mitigation is attained only when risk management practices—such as

risk assessment, monitoring, and compliance verification—are actively executed. Wei (2025) carried out a

study titled "Optimising cash flow management for ship financing leasing: Risk identification, risk

assessment, and management strategies" using content analysis by sourcing data from public databases and

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internet records. They found out that as technology advances and market conditions shift, the risks and

challenges encountered by the ship financing leasing sector are ever evolving. The available literature

reviewed on the subject matter of the study centred more on the financial and other sectors (Muhamud et

al., 2024) without due attention to the transport sector. It also did not show any discussion that addressed

the transport companies in Enugu State, Nigeria. This was the gap the study filled.

METHODOLOGY

A telephone survey was conducted in the Enugu region of Nigeria between 4 and 18 May 2025. Ten

transport companies operating in Enugu participated in the study, including public and private sector

companies. Four employees were interviewed in each company: Management/top management (1 person) -

the CEO, COO or Chief Risk Officer (their opinion is important for understanding the strategic approach to

risks); Middle managers (1-2 people) - the head of the logistics department, IT manager or chief accountant

(they reflect the implementation of strategies in practice); Operational employees (1-2 people) - those who

face risks on a daily basis (e.g. dispatchers, maintenance specialists (their answers provide information

“from below”). Four questionnaires were received from each company, respectively, 40 questionnaires were

received from all companies.

The data generated in the study were analysed using simple percentages and aggregate scores for each

response option. The positive responses, such as very good and good, were grouped, while the negative

responses, such as bad and very bad, were also grouped. This was adopted rather than individual

presentations because of space constraints. The responses under "do not know" were eliminated, even though

they were elicited from respondents, because they made no meaningful contributions to the study.

Consequently, only relevant responses were ultimately presented.

This section dealt with how the data generated during the study was generated and analysed. The data

were presented and analysed in line with the objectives of the study.

Data Presentation and Analysis

The study used ten transport companies operating in Enugu for the survey on how the risk management

practices affect organisational performance. The transport companies used cut across the public and private

sectors since the same conditions virtually affect both sectors. Those used were Auto Star, Imo State

Transport Corporation (ITC), Abia Line, Abia Link, Libra Motors, Rivers Transport Company, GUO

Motors, Faith Motors, The Lord's Express, and Peace Mass Transit.

In presenting the data and accompanying discussions, the responses of those who have no opinion on the

questions asked were deliberately omitted because they do not contribute to the issue under discussion.

Again, the responses were grouped and summarised under the positive and negative dispositions rather than

discussing them separately due to space constraints. For example, very often and often were grouped as

positive, while hardly and never were grouped as non-positive responses. This was done for clarity and in

consideration of the maximum allowed page constraint for seminars.

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Objective no. 1

Objective 1 was aimed at identifying and examining the Risk management practices adopted by selected

organisations. Areas examined include the existence of the risk management function, adherence to standard

risk management process, use of critical risk management aids, risk management options and approaches

used by the companies. These are presented and discussed under their various figures and tables presented

below.

Yes: Independent unit Yes: Sub-unit of

another department No

Figure 1. Existence of risk management function in surveyed companies

Source: created by the authors based on research data

Identification of

loss exposures Risk assessment

and analysis Risk treatment Risk treatment

monitoring and

evaluation

Positive

Negative

Figure 2. Adherence to the risk management process

Source: created by the authors based on research data

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The study started with finding out whether the companies have risk management units, and the status of

those units, where they exist, in terms of being a fully independent unit or a sub-unit of a bigger unit. The

above indicates that 50 percent of the ten companies sampled have people performing the risk management

function, while 40 percent have people combining that function with other functions. Only 10 percent have

no staff designated to handle risk management functions.

In terms of how strictly the surveyed companies adhere to standard risk management practices in

managing their risks, it was revealed that 80 percent of the companies surveyed generally identify their loss

exposures; 60 percent conduct one form of risk assessment and analysis or the other; 80 percent engage in

risk treatment; and 70 percent monitor and evaluate their risk treatment. Conversely, 10 percent of the

companies do not identify their loss exposures; 30 percent do not carry out risk assessment and analysis; 20

percent do not engage in risk treatment; and 30 percent do not monitor and evaluate their risk management

activities.

Figure 3. Possession of risk management aids

Source: created by the authors based on research data

Risk retention

Non-insurance risk transfer

Insurance

Figure 4. Use of Risk retention, non-insurance transfer, and Insurance options

Source: created by the authors based on research data

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The aggregate response on whether or not the surveyed companies have vital risk management aids such

as Risk management policy, Risk appetite statement and exposure limits, Risk registers, Risk reports,

Internal risk audit reports and action plans, Risk management committee, Risk specialists, IT systems for

collecting, analysing and reporting risk information, and Activities to influence attitudes, perceptions and

behaviours towards risks positively, 60 percent assented to having such aids while 40 percent do not.

On the risk management options usually adopted, 40 percent of the surveyed companies retain their risks;

25 percent transfer their risks to non-insurers, while 35 percent transfer their risks to insurance companies.

27,5

32,5

Loss prevention

Loss reduction

Risk avoidance

Figure 5. Risk management approach used by the companies

Source: created by the authors based on research data

The companies were found to adopt various combinations of the risk management approaches of Loss

prevention, Loss reduction and Risk avoidance. However, while they gave 27,5 percent of their attention to

loss prevention, they gave 40 percent of their efforts to loss reduction and 32,5 percent to risk avoidance.

They all make use of the three approaches but give different levels of preference to each depending on the

need of the time.

Objective no. 2

Figure 6. Risk management practices and loss prevention

Source: created by the authors based on research data

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The second objective of the study was to examine how the risk management practices adopted by selected

organisations enhanced the management of their loss exposures and consequently, their overall performance.

The areas covered include loss prevention and reduction, as well as enhanced performance arising from

sound risk management practice.

The study evaluated the extent to which the risk management practices of surveyed companies enhanced

the prevention of losses in the areas of assets, operations, human resources, and finance. The aggregate

response from the ten companies studied claims that their risk management practices enhanced their capacity

to reduce their asset losses by 60 percent, and losses emanating from organisational operations, human, and

financial resources by 50 percent, respectively.

Figure 7. Effect of risk management practice on loss reduction

Source: created by the authors based on research data

How the risk management practice of the selected companies assisted in loss reduction to enhance their

overall performance was evaluated. Data obtained showed that their risk management practices encourage

holistic treatment of risks by 80 percent. It also enhanced the capacity of the selected companies to prepare in

advance for potential losses in the most economical way by 60 percent.

Figure 8. Risk management practices and overall performance

Source: created by the authors based on research data

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Again, following from the immediate advantage above, it reduced the anxiety for those companies for

unexpected and costly losses by 70 percent. It also enhanced more effective resource allocation by 50

percent.

Finally, their risk management practices reduce their losses by 90 percent by providing a summary of

threats that aid management decision-making. The risk management practices adopted by the selected

companies enhanced their overall performance in the various areas. Responses obtained indicate that it

enhanced their effectiveness and efficiency in service delivery by 90 percent; increased their profitability by

70 percent; increased their customer satisfaction and retention by 80 percent; encouraged their growth and

long-term survival by 40 percent; and enhanced their company's competitive advantage by 70 percent.

Objective no. 3

The third objective of the study sought to identify the significant challenges facing the risk management

practices of the selected transport companies in managing their risks. The findings on this objective are

discussed below.

Figure 9. Challenges facing the selected companies in their risk management practice

Source: created by the authors based on research data

On the challenges facing the selected companies in managing their risks, 70 percent of their challenges

were attributed to the general ignorance of the importance of planned risk management practice. Again,

inadequate support by the executive and management cadre of the companies to invest in formal risk

management practices scored 40 percent as a challenging factor. Poor funding of risk management activities

scored 50 percent as a limiting factor in their risk management efforts. Finally, the paucity of deliberate

formulation and implementation of risk management policies constitutes 30 percent of their limiting factors

in their risk management efforts.

Data analysis

The data was analysed using simple percentages.

Test of Hypotheses

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The hypotheses for the study were tested using the One-Sample Test on the t and p values. Where the p

value is less than 0.05, the null hypothesis is rejected, but where it is greater than 0.05, the null hypothesis is

accepted.

Table 1. Result of the test of hypotheses using the t-test

Source: Authors' Computation

Table 1 demonstrates hypothesis testing (Student's t-test) - these are the results of a one-sample t-Test,

which compares the sample mean with a given test value. In this case: Test Value = 3 - the hypothetical

value with which the mean in each category is compared. The table contains statistical indicators for various

variables (designated, for example, RA/SD, RA/P, etc.). For each variable (RA/SD, RR/CR, RT/SS, etc.), the

mean value is statistically significantly different from 3 (almost all p < 0.05). For example, for RA/SD

t=30.946, p=0.000, the average difference is 1.857 - this means that the average value in the sample is

approximately 1.857 higher than 3, which is very significant. For some variables (e.g. RA/SS, p=0.067) the

difference does not reach statistical significance (p>0.05).

In the context of the study, this means that the hypothesis that the average values for various risk

management aspects are 3 is rejected for most indicators, since the actual average values differ significantly

from 3. This confirms that risk management practices in companies differ from some baseline (3, possibly

neutral or average on the scale). The significant influence of the selected strategies and practices (e.g.

avoidance, retention, risk transfer) on various aspects of the companies' work is confirmed.

Risk management has a positive effect on service quality, profitability, customer retention and

competitive advantage (p<0.05); The effect on growth and long-term survival is not statistically confirmed

One-Sample Test

30.946

.000

1.85714

1.7352

1.9791

3.221

.003

.85714

.3163

1.3980

4.917

.000

1.14286

.6705

1.6152

1.892

.067

.57143

-.0424

1.1853

6.842

.000

.88571

.6226

1.1488

9.083

.000

1.22857

.9537

1.5035

9.255

.000

1.22857

.9588

1.4984

2.736

.010

.77143

.1985

1.3443

5.966

.000

1.02857

.6782

1.3789

7.072

.000

1.11429

.7941

1.4345

2.965

.005

.74286

.2337

1.2520

2.758

.009

.82857

.2181

1.4390

RA / SD

RA / P

RA / CR

RA / SS

RR / SD

RR / P

RR / CR

RR / SS

RT / SD

RT / P

RT / CR

RT / SS

Sig. (2-tailed)

Mean

Difference

Lower

Upper

95% Confidence

Interval of the

Difference

Test Value = 3

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(p>0.05). The statistical analysis supports the conclusions about the benefits of risk management, but notes

that the effect on the growth and survival of companies is less obvious.

Risk management practices adopted by the surveyed companies have a significant positive effect on the

service delivery of transport companies in Enugu state. The findings of the study indicate that the effect of

risk avoidance, risk retention and risk transfer on service delivery is all significant at .000. Therefore, the

hypothesis is accepted. Thus, the decision is that the above risk management strategies of selected

organisations have a significant effect on their service delivery.

Risk management practices adopted by the surveyed companies have a significant positive effect on the

profitability of transport companies in Enugu state. The findings of the study indicate that risk avoidance,

risk retention, and risk transfer are significant for the profitability of selected companies at 0.003, 0.000, and

0.000, respectively. Therefore, the hypothesis is accepted. Thus, the decision is that the above risk

management strategies of selected organisations have a significant effect on their profitability.

Risk management practices adopted by the selected companies have a significant positive effect on

customer retention of transport companies in Enugu state. The findings of the study indicate that risk

avoidance, risk retention and risk transfer are significant at 0.000, 0.000, and 0.005 for customer retention in

the surveyed companies. Therefore, the null hypothesis is rejected and the alternative hypothesis accepted.

Thus, the decision is that the risk management strategies of selected companies have a significant effect on

their customer retention.

Risk management practices adopted by the selected companies have a significant positive effect on the

growth and long-term survival of transport companies in Enugu state. The findings of the study indicate that

the impact of risk avoidance, risk retention, and risk transfer is not significant, 0.067, 0.010, and 0.009,

respectively, for growth and long-term survival. Therefore, the null hypothesis was not rejected. Thus, the

decision is that the risk management strategies of selected organisations have no significant effect on the

growth and long-term survival of the companies.

Risk management practices adopted by the selected companies have a significant positive effect on the

competitive advantage of transport companies in Enugu state. The findings of the study indicate that risk

avoidance, risk retention and risk transfer are significant at 0.000, 0.000, and 0.003 for customer retention in

the surveyed companies. Therefore, the null hypothesis is rejected and the alternative hypothesis accepted.

Thus, the decision is that the risk management strategies of selected companies have a significant effect on

their customer retention.

Discussion of findings

The first objective of the study focused on examining the risk management practices adopted by the

selected organisations. It was found out that the companies use a combination of risk retention, risk transfer

to non-insurance organisations, and risk transfer to insurance companies. The use of non-insurance transfer

by the companies is essentially when they operate on some Hired Vehicles (HV) contracting, where the

liabilities on those vehicles do not rest on the transport companies but on those who partner with the

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companies by providing and running their vehicles in the company's fleet. The second objective of the study

centred on examining how the risk management practices adopted by the selected companies enhanced the

management of their loss exposures and overall performance. It was gathered from the study that these risk

management practices significantly enhanced the proficiency of the selected companies in managing their

loss exposure and risks, leading to loss reduction and profitability of their businesses, ultimately resulting in

higher performance. Finally, the third objective of the study examined the possible challenges faced by the

companies in their risk management efforts. It was found out that general ignorance of the importance of

planned risk management practice, inadequate support by the executive and management cadre of the

companies to invest in formal risk management practices, poor funding of risk management activities, and

paucity of deliberate formulation and implementation of risk management policies constituted significant

challenges in their risk management efforts.

CONCLUSIONS

Studied companies have personnel performing risk management at different levels and extents and used a

combination of risk management practices that included risk retention, risk transfer to non-insurance

organisations, and insurance.

The combination of the risk management practices deployed by the companies resulted in a significant

reduction in losses and enhanced their profitability and overall performance.

The challenges facing the companies in their risk management efforts include a lack of understanding of

the importance of the risk management function, inadequate support by top management for the risk

management function, and poor funding of the risk management function.

The study determined that the examined organisations made adequate steps to implement standard risk

management strategies to mitigate their loss exposures. Nevertheless, the risk management function has not

yet received adequate recognition and priority within the framework of managing those organisations. It can

be concluded that greatly enhancing the current risk management efforts of these organisations will likely

raise their capacity to minimise losses, hence improving their profitability and overall performance.

Author Contributions:

All authors have contributed to the paper equally.

All authors have read and agreed to the published version of the manuscript.

Acknowledgement

not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Institutional Review Board Statement: not applicable

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Conflict of interests

The authors declare no conflict of interest.

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About the authors

Francis Okechukwu CHIKELEZE

Professor, Department of Public Administration, Faculty of Management Sciences, Enugu

State University of Science and Technology, Enugu, Nigeria

Research interests: Public Administration, Human Resource Management

ORCID ID: 0009-0004-6812-3944

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565

Anatolijs KRIVINS

Associate Professor, Dr.iur., Faculty of Humanities and Social Science, Daugavpils

University, Daugavpils, Latvia.

Research interests: business law, corruption, criminology, criminal law, public service,

municipalities.

ORCID ID: 0000-0003-1764-4091

Valters KAZE

Associate Professor, Dr.oec., RISEBA University of Applied Sciences, Riga, Latvia.

Research interests: digital transformation, artificial intelligence, consumer behaviour

and motivation.

ORCID ID: 0000-0003-1502-6994

Thomas ETALONG

PhD, Directorate of Research and Development, ACE Intercontinental Research Institute,

Enugu, Nigeria.

Research interests: Workforce diversity, Human Resource Management, Public Policy,

Security Studies, Change Management, Community Development, Sustainable

Development, Project Management, and Gender Studies

ORCID ID: 0000-0003-2472-4364

This work is licensed under the Creative Commons Attribution International License (CC BY)

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EXPLORING THE RELATIONSHIP BETWEEN SHARE PRICES,

EARNINGS, AND DIVIDENDS IN HIGH-DIVIDEND MINING FIRMS

LISTED ON THE JOHANNESBURG STOCK EXCHANGE

Ifeanyi Mbukanma

, Sunday Olabisi Adewara

1, 2 Walter Sisulu University, Mthatha, South Africa

e-mails: 1imbukanma@wsu.ac.za, 2 sadewara@wsu.ac.za

Received: 18 May 2025 Accepted: 20 August 2025 Online Published:21 August 2025

ABSTRACT

Background: The relationship between share prices, earnings, and dividends is critically important in financial research,

especially in volatile mining sectors. In South Africa, mining plays a crucial role in the economy, making understanding

these relationships on the Johannesburg Stock Exchange vital for investors and policymakers. Objectives: This study

investigates the correlation between the share prices of Anglo-American Platinum, Exxaro Resources, Kumba Resources

Ltd., and Impala Platinum Holdings and their earnings and dividends. With investors' growing interest in seeking stable

returns amidst market volatility, this research provides empirical evidence on how earnings and dividends affect share

prices in high-dividend-paying companies, a key economic sector in South Africa. Methods/Approach: The study uses a

quantitative, non-experimental correlational research design, analysing historical data from selected mining companies

listed on the JSE. Statistical techniques, including Pearson correlation analysis and log transformation to manage

outliers, were employed to explore the relationships between dependent and independent variables. Results: The analysis

shows significant positive correlations, evidenced by a correlation coefficient of 0.96 between earnings per share (EPS)

and dividends per share (DPS), 0.75 between price per share (PPS) and DPS, and 0.82 between EPS and PPS. The results

underscore the importance of profit and dividend stability as indicators of financial health for mining investors and

management. Conclusions: This study supports the Dividend Signalling Theory and Residual Theory of Dividends,

contributing to the financial theory literature in the context of high-dividend-paying mining companies. It was also

recommended that investors should pay attention to shares with increasing earnings, which, in turn, increase dividends

and expansion opportunities, justifying a greater stock value.

Keywords: share prices, earnings, dividends, mining firms, Johannesburg stock exchange

JEL classification: G12, G35, L72, G15

Paper type: Research article

Citation: Mbukanma, I; Adewara, S.O. (2025). Exploring the Relationship Between Share Prices, Earnings, and Dividends in

High-Dividend Mining Firms Listed on the Johannesburg Stock Exchange. Access to science, business, innovation in digital

economy, ACCESS Press, 6(3), 567-582, https://doi.org/10.46656/access.2025.6.3(6)

INTRODUCTION

Investigating the relationship among share prices, earnings, and dividends is crucial in finance, especially in

sectors characterised by significant volatility, such as mining. Juwawo (2022) and Kildaire (2020) indicate that

the relationship among share prices, earnings, and dividends has garnered attention from researchers, investors,

and financial professionals. Considering the unique characteristics and inherent volatility of the mining sector,

a crucial element of the South African economy, it is essential to comprehend this relationship within the

Corresponding author, Ifeanyi Mbukanma – imbukanma@wsu.ac.za

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framework of the Johannesburg Stock Exchange (JSE). According to Arsal (2021) and Nharo (2021), the

mining industry significantly contributes to the nation's gross domestic product (GDP). It is also distinguished

by enterprises that often undertake substantial capital expenditures, exhibit fluctuating share prices, and

maintain diverse dividend policies within the sector. Consequently, investigating the correlation among share

prices, profitability, and dividends is crucial for acquiring key insights into the operational efficiency and

financial sustainability of mining enterprises that distribute substantial dividends.

Earnings are a crucial determinant of share prices, since they often serve as an indicator of a company's

profitability. Gharaibeh, Saleh, Jawabreh, and Ali (2022), with Arsal (2021), indicate the company's capacity

to generate revenue and influence the perceptions and behaviours of investors and market players. The

anticipated correlation is based on the premise that increased earnings often lead to elevated share prices.

Higher share prices are driven by the anticipation of sustained profitability and expansion within the

organisation.

In the mining sector, where operational outcomes can be significantly influenced by external factors like

regulatory changes, commodity price fluctuations, and environmental issues, stakeholders must proceed with

caution and possess a comprehensive understanding of this relationship to recognise potential risks.

Dividends provide a concrete return on investment for shareholders and often reflect a company's financial

stability, as observed by Gormsen and Koijen (2020). Sundoro, Anggraini, and Pradiptya (2023) assert that

investors seeking steady earnings amid market fluctuations are often drawn to mining enterprises, particularly

those with a track record of substantial dividend payouts. The anticipation of sustained or augmented dividends

amidst increasing earnings often has a favourable influence on share prices. An investigation of the correlation

between dividend earnings and share prices uncovers essential elements that may guide investment strategies

and financial decision-making. This research examines the correlations among share prices, earnings, and

dividends of a group of high-dividend-paying mining companies listed on the Johannesburg Stock Exchange

(JSE).

The companies include Anglo American Platinum, Exxaro Resources, Kumba Resources Ltd., and Impala

Platinum Holdings Limited. This study seeks to identify patterns and provide empirical evidence on the

interactions of numerous financial metrics via the analysis of historical data and the use of rigorous statistical

methods. This study will enhance the current research on dividend policy and its effects on stock price

valuation in the mining sector. It would also provide investors and policymakers with actionable knowledge.

Therefore, examining these financial connections within the mining sector provides insights about the

operational efficacy of the involved companies. This result enhances the understanding of market behaviours

influenced by earnings and dividend strategies. This introductory discussion establishes the foundation for a

comprehensive analysis of the correlation between share prices, earnings, and dividends in mining companies

that provide substantial dividends and are listed on the Johannesburg Stock Exchange (JSE).

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RESEARCH RATIONALE AND PROBLEM STATEMENT

The relationship between share prices, earnings, and dividends is a critical area of study in finance, particularly

in industries characterised by high volatility, such as mining. This sector is vital to the South African economy,

with mining companies contributing significantly to the country’s gross domestic product (GDP) (Arsal, 2021;

Nharo, 2021). Considerable capital expenditure, volatile share prices, and diverse dividend policies often mark

such firms. Understanding the dynamics linking earnings, dividends, and share prices is essential for assessing

these high-dividend-paying mining entities' operational efficiency and financial sustainability.

Despite the acknowledged importance of these financial variables, existing research lacks clarity regarding

their interplay, particularly in the context of the Johannesburg Stock Exchange (JSE). The expectation that

rising earnings will lead to increased share prices is widely accepted; however, in the mining sector, external

influences such as regulatory changes, fluctuating commodity prices, and environmental concerns complicate

this relationship (Gharaibeh et al., 2022; Gormsen & Koijen, 2020; Mbukanma, Ani & Rena, 2019). Moreover,

dividends serve as a tangible return on investment, often influencing investor perceptions of a company’s

financial health and stability. Investors prioritise firms with a consistent dividend history, especially amid

market volatility; however, the extent to which dividends correlate with share prices and earnings in mining

companies remains underexplored.

This study investigates the interactions between share prices, earnings, and dividends among notable high-

dividend-paying mining firms, including Anglo American Platinum, Exxaro Resources, Kumba Resources

Ltd., and Impala Platinum Holdings Limited. This research will identify trends and provide empirical insights

into these financial linkages through comprehensive data analysis and rigorous statistical techniques.

Enhancing the understanding of how earnings and dividend strategies impact share prices in the mining sector

will equip investors and policymakers with valuable knowledge for strategic decision-making, ultimately

contributing to the existing body of research on dividend policy and its effects on stock valuation in this critical

industry.

RESEARCH QUESTION

Is there a positive, negative, or no correlation between the share price, earnings and dividends of selected high

dividend-paying mining companies and their earnings and dividends?

HYPOTHESES

The hypotheses of this study are the following:

H0: There is no correlation between the share price of selected high dividend-paying mining companies and

their earnings and dividends (at a significance level of 5%).

H1: There is a positive correlation between the share price of selected high dividend-paying mining companies

and their earnings and dividends (at a significance level of 5%).

H2: There is a negative correlation between the share price of selected high dividend-paying mining companies

and their earnings and dividends (at a significance level of 5%).

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LITERATURE REVIEW

Scholars have shown a growing interest in the correlation between share prices, earnings, and dividends of

high-dividend-paying mining companies listed on the Johannesburg Stock Exchange (JSE). Considering the

emerging nature and significance of ascertaining the correlation between share prices, earnings, and dividends

to the growth of listed institutions and, most significantly, mining companies, stakeholders, and shareholders

within the economic, financial, and corporate governance institutions have consistently emphasised the need

to have a scientific finding that could support investors and institutional decision-making (Mithila &

Kengatharan, 2025; Arsal, 2021). This literature review is conducted to reveal previous scholars' conceptual

understanding and contributions. It will also assist in identifying empirical findings and frameworks that could

provide a pathway to ascertain the correlation between share prices, earnings, and dividends of selected high-

dividend-paying mining companies listed on the JSE, as supported by Gharaibeh, Saleh, Jawabreh and Ali

(2022). Some of the considerable mining companies within this investigating category include Anglo

American Platinum, Exxaro Resources, Kumba Resources Ltd, and Impala Platinum Holdings Limited.

Theoretical Background to the Study

The theory is a detailed logical framework for explaining specific occurrences, which involves predictions

about how things interact (Oberauer & Lewandowsky, 2019). A theoretical framework is a foundational review

of current theories that guide the construction of the arguments in the researcher’s work (George, 2023).

Researchers use theories to explain facts, establish links, and make predictions. This study employed the

Dividend Signalling Theory and the Residual Theory of Dividends.

The Dividend Signalling Theory was proposed by Michael Spence in 1973. The theory suggested that once

a company announces a higher dividend than market expectations, investors would see this as an indication

that the company's future financial performance is more promising than anticipated. Given such a positive

indication, investors will increase the purchase of the company's stock, thereby raising the stock price. In

contrast, a lower-than-expected dividend announcement from a company would be seen as a warning indicator.

As such, a decline in dividends may indicate dismal results for the business, indicating that the firm's stock

price will drop (Gitman & Zutter, 2014).

On the other hand, the Residual Theory of Dividends, as proposed by Myron J Gordon and John Lintner in

1950, suggested that if a company meets the requirements for funding a successful investment for the business,

a dividend will be given if there is still net income available (Gitman & Zutter, 2014). According to this notion,

if a company has leftover cash, paying dividends should be the least important thing it does. The company will

not pay dividends if its income for that year is insufficient to cover its financial needs. Put another way, this

theory assumes that retained earnings provide most of the company's finances (Dahmash, Alshurafat, Hendawi,

Alzoubi & Al Amosh, 2023). Hence, when there is an increase in share prices beyond market expectations, the

company’s earnings will increase, automatically increasing the share of dividends to the shareholders.

Share Prices

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Investors globally sought to invest in high-dividend-paying companies for capital appreciation and

sustained return on investment. Indeed, South Africa, having several high-dividend-paying companies listed

on the JSE, has continuously attracted investors worldwide. However, share prices and the dynamics of mining

industries are essential factors to consider when investigating the growth of shares of high dividend-paying

mining companies listed on the JSE (Andleeb, 2024; Kildaire, 2020).

According to Lu, Li, Li, Sun and Wang (2020), the share price is the amount an investor pays to acquire a

company share. Share prices are influenced by several market conditions, which have continually caused price

fluctuation (Lu et al., 2020). Some of these market factors include variations in commodity prices. In addition,

the prices of mining commodities such as metals and minerals significantly influence the profitability and

revenue inflow of the mining companies (Gormsen & Koijen, 2020; Lu et al., 2020). As such, obtaining proper

analysis and evaluation of commodities in the market becomes essential.

Secondly, the share prices of listed companies, which are also informed by profitability margins, are largely

influenced by the production and operational costs of listed companies (Andleeb, 2024; Gormsen & Koijen,

2020). Consequently, mining companies' sustained operation depends on the efficiency of their labour

resources, equipment, and energy sources, which come at a high cost. Similarly, investors' sentiment towards

mining industries, geopolitical stability, and economic conditions greatly influences share prices. Other factors

influencing share prices of high-dividend-paying mining companies include, but are not limited to, variations

in environmental regulations, company financial performance, and developmental projects of listed mining

companies (Vijh, Chandola, Tikkiwal & Kumar, 2020).

Shares Earnings

Mining companies' financial performance and efficient operations provide information on the trends in the

earnings of high-dividend-paying mining companies. Earnings per share (EPS) is a fundamental financial

metric for assessing a company's profitability (Dua & Sharma, 2024; Arsal, 2021). It is calculated by deducting

preferred dividends from the net income and dividing this figure by the total number of common shares the

company has issued (Dahmash et al., 2023). As such, factors like the revenue generation that results from

extractions and sales of metals and minerals inform the trajectory of the company’s share earnings.

Additionally, operational costs, capital expenditures, and cost of depreciation, to mention but a few,

significantly influence earnings (Jian, He, Liu, & Sun, 2024; Dahmash et al., 2023). Over the years, financial

analysts have employed financial matrices such as company net income, rate of depreciation, profit margin,

and cash flow to balance earnings against holistic costs. However, some external and internal factors, such as

market demand and supply dynamics, operational efficiency, taxation, currency fluctuation, and market

sentiment, also impact the earnings rate of high-dividend mining companies (Anton, Lorensa, Purnama, Eddy,

& Andi, 2023). Thus, a sound company’s financial abilities and performance sustainable earnings of the

company shares.

Dividends of Shares

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Over the years, investors have continually emphasised the role that dividend dynamics and company

practices play in informing investment decision-making. Thus, a dividend is distributed to their shareholders

as a portion of the company’s profits (Mazur, Dang & Vo, 2023). The ability to pay dividends to shareholders

reflects the financial health and stability of the company. Similarly, capital appreciation, which results from

tangible returns from investment to investors, offers sustained income stability during market volatility

(Sundoro, Anggraini, & Pradiptya, 2023). However, dividend policies of high-dividend-paying mining

companies significantly emphasise growth opportunities, profitability, stability, cash flows, and shareholders'

preferences (Alrobai, & Albaz, 2024; Sundoro et al., 2023). Considerably, some internal and external factors

impact dividend payment. These factors include cash flows and profitability, commodity prices, market

demand and supply, management cost and operational efficiency, shareholders' preferences and tax

considerations, to mention but a few.

Conceptual Relationship Between Share Prices, Earnings and Dividends

Share prices, earnings, and dividends have been extensively examined, considering their role in ascertaining

a company’s financial health and operational efficiency. Share prices capture market sentiments and valuation

of investors of a company’s stock, which considers market conditions, the outlook of the industry and the

company's performance (Lu et al., 2020). On the other hand, earnings represent the company’s profitability,

which is informed by the ability to earn revenue and effectively manage expenditures (Dahmash et al., 2023).

Accordingly, dividends represent that portion of the company’s profit that is shared as a return on

investment to the shareholders. In other words, the Dividend per Share (DPS) represents the earnings per share,

and the former is a profit that a company distributes to its shareholders with the number of shares owned

(Alrobai & Albaz, 2024; Mazur, Dang & Vo, 2023). These dividends can be paid out as cash or as shares.

However, each of these associated features of share prices, earnings and dividends assists in shaping market

dynamics and investors' perceptions (Mahirun, Jannati, Kushermanto & Prasetiani, 2023). Thus, the

relationship is evident as the variation in earnings influences share prices and vice versa, and at the same time,

impacts both the growth and profitability of the company (Sixpence, Adeyeye & Rajaram, 2024). Thus, mining

companies offer a competitive dividend policy reflecting the company’s financial health and the capability to

continually generate returns for the shareholders.

However, several macro factors, such as prices of commodities, changes in government policies,

regulations, and geopolitical variables, play a subjective role in the relationship between share prices, earnings,

and dividends of mining companies (Mahirun et al., 2023; Mazur et al., 2023). Practically, a sharp increase in

commodity prices could positively increase the company's earnings, which could also prompt an increase in

dividend payouts to the company’s shareholders. On the other hand, the backdrop of commodity prices or

challenges associated with the company’s operations may also result in decreased earnings and, by extension,

cause modification in the company’s dividend policies and share prices.

Thus, the literature review section's focus was the correlation between share prices, earnings, and dividends

of high-dividend-paying mining companies listed on the JSE. Thus, this section has revealed the conceptual

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dimension of share prices, earnings, and dividends related to high-dividend-paying mining companies.

Accordingly, commodity prices, external market forces and internal operational dynamics were identified as

significant role players in the correlation between share prices, earnings, and dividends of high dividend-

paying mining companies. This study also employed the Dividend Signalling Theory and the Residual Theory

of Dividends, which were expected to provide a pathway to ascertain the correlation between share prices,

earnings, and dividends of selected high dividend-paying mining companies listed on the JSE.

METHODOLOGY

This study employs a positivist research paradigm, emphasising empirical observation and a quantitative

research method was used to investigate the correlation between share prices, earnings, and dividends of

selected high-dividend-paying mining companies listed on the Johannesburg Stock Exchange (JSE). Drawing

from the philosophical foundation established by August Comte, the positivist approach underscores the

importance of objective and measurable phenomena (Saunders, Lewis & Thornhill, 2009). This paradigm

aligns with the study's objective to establish objective scientific correlations among the identified variables.

The research adopts a non-experimental correlational approach. This approach allows for examining

relationships between variables that measure key domains (Roberts, 2021). Data for the analysis were sourced

from iRess, encompassing historical secondary data from 2008 to 2022, including the selected mining firms'

annual closing share prices, earnings, and dividends. Data analysis used Pearson correlation analysis with

EViews software to explore the relationships between the studied variables. This process involves thoroughly

examining the dataset structure, identifying trends and anomalies, and applying statistical techniques to

provide meaningful insights (Peck, Short & Olsen, 2020). Visual representations, such as graphs and summary

statistics, facilitated a clearer understanding of the findings, linking to the research questions and hypotheses.

Ethical considerations were prioritised throughout the research process. The study ensured data integrity

by avoiding falsification, providing accurate citations to all sources to prevent plagiarism, and safeguarding

the privacy and dignity of involved parties (Laas et al., 2022). Results were presented responsibly, reflecting

the study's commitment to professional research practices to benefit the sector's stakeholders under

investigation and society.

PRESENTATION OF RESULTS

This study aimed to ascertain the correlation between the share prices of selected high dividend-paying mining

companies and their earnings and dividends. As such, this section presents the result of the study with a

significant focus on attending to the research question and hypothesis. The data required was generated using

the iRess database. Accordingly, this section explains how the data exploration and correlation analysis were

performed and how the results are interpreted. Results such as correlation analysis, series graph, Boxplot

graph, and Dot Plot of the series were presented using tables and figures.

Data Exploration

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Data exploration in this study followed scientific rigour and steps, where after obtaining data from iRess,

the data was assessed and arranged to ensure that the dataset was used for analysis. Accordingly, analysis was

performed using Eviews, and results were generated visually. However, some irregularities were identified in

the dataset while performing the analysis, which resulted in some outliers. Thus, the outliers were treated by

transforming the data series using logarithms, and the results were also presented.

Correlation Analysis

A correlation analysis was performed to ascertain whether there is a positive, negative, or no correlation

between the share price of selected high dividend-paying mining companies and their earnings and dividends

(see Table 1).

Table 1. Correlation Analysis

Dividends Per Share

Earnings Per Share

Price Per Share

Dividends Per Share

Earnings Per Share

0.96

Price Per Share

0.75

0.82

Source: Author’s computation using EViews 12

Table 1 presents the correlation analysis of the series. From the table, the coefficients between the variables

DPS (Dividends Per Share), EPS (Earnings Per Share), and PPS (Price Per Share) can be depicted. The table

indicates a strong positive correlation between EPS and DPS (0.96), a moderately strong positive correlation

between PPS and DPS (0.75), and a very strong positive correlation between PPS and EPS (0.82). The

correlation analysis in Table 1 indicates significant correlations among DPS, EPS, and PPS. The strong positive

correlations, especially between EPS and DPS and between PPS and EPS, suggest potential multicollinearity

issues if these variables are used together as predictors in a regression model. As presented below, an additional

analysis was performed to understand the relationship pattern between the variables further.

Figure 1. Graph of the Series

Source: Author’s computation using EViews 12

Figure 1 presents the graph of the series. The graph visually represents how these variables change over

time, with evidence of outliers since some points that deviate from the overall pattern of the data. For example,

-40,000

40,000

80,000

120,000

160,000

200,000

1 - 08

1 - 11

1 - 14

1 - 17

1 - 20

2 - 08

2 - 11

2 - 14

2 - 17

2 - 20

3 - 08

3 - 11

3 - 14

3 - 17

3 - 20

4 - 08

4 - 11

4 - 14

4 - 17

4 - 20

DPS EPS PPS

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at points 1 – 08, 2 -08, 4 – 11, and 4 -20, further tests were required, which are presented below to confirm the

outliers.

-40,000

40,000

80,000

120,000

160,000

200,000

DPS EPS PPS

Figure 2. Boxplot Graph of the Series

Source: Author’s computation using EViews 12

Figure 2 presents the boxplot graph of the series. The figure confirms the presence of outliers, some of the

data points that fall above or below the typical range of values; these outliers in a boxplot may indicate unusual

or extreme values that could impact the overall data analysis.

-40,000

40,000

80,000

120,000

160,000

200,000

1 - 08

1 - 10

1 - 12

1 - 14

1 - 16

1 - 18

1 - 20

1 - 22

2 - 09

2 - 11

2 - 13

2 - 15

2 - 17

2 - 19

2 - 21

3 - 08

3 - 10

3 - 12

3 - 14

3 - 16

3 - 18

3 - 20

3 - 22

4 - 09

4 - 11

4 - 13

4 - 15

4 - 17

4 - 19

4 - 21

DPS EPS PPS

Figure 3. Figure of Dot Plot of the Series

Source: Author’s computation using EViews 12

Figure 3 presents the Dot Plot of the Series; this also helps to identify the outliers by displaying individual

data points for each series. By examining the dot plot closely, it is discovered that some data points deviate

from most of the data.

Resolving the Outliers

From the analysis, the presence of outliers was evident; thus, one way to treat these outliers is to transform

the series using logarithms; this was done on the series, and the outcomes are presented below:

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Table 2. Correlation Analysis (After Log Transformation of the Series)

Dividends Per Share

Earnings Per Share

Price Per Share

Dividends Per Share

Earnings Per Share

0.79

Price Per Share

0.50

0.58

Source: Author’s computation using EViews 12

Table 2 presents the correlation analysis after the series has been logged and transformed. The pairwise

correlation analysis reduced in values compared to the original analysis before the transformation of data. For

example, the correlation between DPS and EPS is 0.79, between DPS and PPS is 0.50, and between EPS and

PPS is 0.58. The suspicion of the presence of multicollinearity is reduced.

1 - 08

1 - 10

1 - 12

1 - 14

1 - 16

1 - 18

1 - 20

1 - 22

2 - 09

2 - 11

2 - 13

2 - 15

2 - 17

2 - 19

2 - 21

3 - 08

3 - 10

3 - 12

3 - 14

3 - 16

3 - 18

3 - 20

3 - 22

4 - 09

4 - 11

4 - 13

4 - 15

4 - 17

4 - 19

4 - 21

DPS EPS PPS

Figure 4. Graph of the Series (After Log Transformation of the Series)

Source: Author’s computation using EViews 12

Figure 4 presents the graph of the series (after the log transformation of the series); by doing so, the values

become close to each other over time. However, due to the zero value (or missing data), there is still the

presence of an outlier, but logging the series has removed the potential outlier without the zero values.

DPS EPS PPS

Figure 5. Boxplot Graph of the Series (After Log Transformation of the Series)

Source: Author’s computation using EViews 12

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Figure 5 presents the boxplot graph of the series (after the log transformation). The presence of the outlier

has been removed without the zero values. However, due to the zero value (or missing data), there is still the

presence of an outlier, but logging the series has removed the potential outlier without the zero values.

1 - 08

1 - 10

1 - 12

1 - 14

1 - 16

1 - 18

1 - 20

1 - 22

2 - 09

2 - 11

2 - 13

2 - 15

2 - 17

2 - 19

2 - 21

3 - 08

3 - 10

3 - 12

3 - 14

3 - 16

3 - 18

3 - 20

3 - 22

4 - 09

4 - 11

4 - 13

4 - 15

4 - 17

4 - 19

4 - 21

DPS EPS PPS

Figure 6. Figure of Dot Plot of the Series (After Log Transformation of the Series)

Source: Author’s computation using EViews 12

Figure 6 presents the Dot Plot of the Series (After the Log Transformation of the Series). The outliers,

excluding zero values, have been removed. However, due to the presence of zero values (or missing data), an

outlier remains. Despite this, the log transformation mitigates the potential outliers when zero values are not

considered.

INTERPRETATION AND DISCUSSION OF THE RESULTS

The purpose of this study was to reveal the correlation between the share prices of selected high dividend-

paying mining companies and their earnings and dividends. To support this objective, three hypotheses were

developed and tested. Thus, the correlation analysis result in Table 1 indicates a correlation between EPS and

DPS (0.96), between PPS and DPS (0.75), and a correlation between PPS and EPS (0.82). As such, H0, as

presented below, is rejected and not supported.

H0: There is no correlation between the share price of selected high dividend-paying mining companies

and their earnings and dividends (at a significance level of 5%).

Secondly, Table 1 indicates a strong positive correlation between EPS and DPS (0.96), a moderately strong

positive correlation between PPS and DPS (0.75), and a strong positive correlation between PPS and EPS

(0.82). Accordingly, it was established from the correlation analysis result that H1, as presented below, is

accepted and supported.

H1: There is a positive correlation between the share price of selected high dividend-paying mining

companies and their earnings and dividends (at a significance level of 5%).

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Accordingly, Table 1 shows a positive correlation between EPS and DPS (0.96), a positive correlation

between PPS and DPS (0.75), and a positive correlation between PPS and EPS (0.82). Based on this result, it

is established that H2, as presented below, is rejected and not supported.

H2: There is a negative correlation between the share price of selected high dividend-paying mining

companies and their earnings and dividends (at a significance level of 5%).

Furthermore, the positive correlation between EPS and DPS (0.96) indicates that firms with higher earnings

per share are very likely to pay higher dividends per share, and as DPS tends to rise significantly, EPS also

increases vice versa. Higher earnings enable companies to pay out profits as dividends (Rena & Mbukanma,

2022; Gormsen & Koijen, 2020; Lu et al., 2020). Strongly profitable companies (high EPS) may thus often

provide more wealth to shareholders via dividends (high DPS). Similarly, as PPS tends to increase as DPS

increases, although not as significantly as the EPS-DPS relationship. This implies that the market generally

assigns a greater value to companies that pay larger dividends to their shareholders (Sundoro et al., 2023).

Investors frequently perceive dividends as an indicator of a company's financial stability and future prospects.

Companies that consistently pay and potentially increase dividends may be perceived as stable and profitable,

resulting in a higher demand for their stock and, as a result, a higher price per share.

The correlation value of 0.82 indicates a very robust positive relationship between Price Per Share (PPS)

and Earnings Per Share (EPS). This indicates that as earnings per share (EPS) increases, the price per share

(PPS) rises significantly. This indicates that the market values firms with larger profits per share (Dahmash et

al., 2023). Earnings per share (EPS) is a crucial metric that measures a company's profitability and is frequently

monitored by investors. A high or rising EPS might indicate robust company performance, prompting investors

to purchase more shares and causing an increase in the share price. This statement illustrates that increased

profits would increase dividends and expansion opportunities, justifying a greater stock value.

Report on Missing Data or Zero Values in the Series

There were zero values for DPS from 2009, 2012 to 2016 for AMS, 2014 to 2019 for IMP. Finally, 2015

and 2016 for KIO. However, these periods could not confirm that there is missing data, a case which cannot

be ruled out as well. For instance, time series data showing a zero value can indicate different issues or

phenomena, which further specific analysis may be required. In this case, the series measures prices, which

may indicate the likelihood that no transaction occurred during that period. Since it measures prices, a zero

might be doubtful. Thus, if it is missing values, one way to overcome it is to use linear interpolation of the

data, which has scientific procedures for dealing with missing values in empirical studies.

CONTRIBUTIONS AND IMPLICATIONS TO BUSINESS PRACTICES

This study provides empirical evidence on how earnings and dividends affect share prices in high-dividend-

paying mining companies in South Africa with the support of the Dividend Signalling Theory and Residual

Theory of Dividends within the context of high-dividend-paying mining companies. Significant positive

correlations were identified between Earnings Per Share (EPS) and Dividends Per Share (DPS), Price Per

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Share (PPS), and DPS and EPS and PPS. The study also highlighted the role of external factors like commodity

prices and market conditions in influencing the operational landscape of mining companies and their financial

performance. The researchers also acknowledged the presence of outliers and missing data, emphasizing the

need for careful data handling in financial analysis. Thus, the following implications for business practices

were provided:

a. Importance of Profit and Dividend Stability: Underscores the importance of profit and dividend

stability as indicators of financial health for mining investors and management.

b. Informed Investment Strategies: Provides insights that can be used to inform investment strategies and

financial decision-making, especially in volatile sectors like mining.

c. Strategic Decision-Making: Equips investors and policymakers with valuable knowledge for strategic

decision-making regarding dividend policy and stock valuation.

d. Investor Perceptions: Reinforces that investors often perceive dividends as an indicator of a company's

financial stability and future prospects, influencing stock demand and price.

e. Market Valuation: Indicates that the market values firms with larger profits per share, as reflected in

the positive relationship between EPS and PPS.

CONCLUSION

The research study investigates the correlation between share prices, earnings per share (EPS), and dividends

per share (DPS) among high-dividend-paying mining companies on the Johannesburg Stock Exchange (JSE),

including Anglo American Platinum, Exxaro Resources, Kumba Resources Ltd., and Impala Platinum

Holdings Limited. This analysis is significant for investors and financial experts due to the mining industry's

volatility and importance to the South African economy.

Using Pearson correlation analysis on historical data from 2008-2022 with Eviews version 12, the study

reveals strong positive correlations among EPS, DPS, and share price (PPS). A notable correlation coefficient

0.96 between EPS and DPS indicates that higher earnings lead to increased dividends. This supports the

Dividend Signalling Theory, which suggests dividend announcements can enhance investor confidence and

share prices. Additionally, the correlation of 0.75 between PPS and DPS highlights that consistent dividend

payments are seen as a sign of financial stability. In contrast, the 0.82 correlation between EPS and PPS shows

that higher profitability drives stock prices.

The study also notes that external factors like commodity prices and market conditions influence earnings

and dividend policies. It addresses challenges such as outliers and missing data, employing log transformation

to ensure accurate findings. The results provide essential insights for investors, corporate managers, and

policymakers regarding the financial dynamics affecting decision-making in the mining sector. Future research

may explore the impact of macroeconomic factors on these relationships or analyse other sectors within the

JSE for a more comprehensive overview of market behaviours.

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Author Contributions: Conceptualisation, I.M. and S.O.A; methodology, I.M; software, I.M. and S.O.A;

validation, I.M. and S.O.A; formal analysis, I.M; investigation, S.O.A; resources, I.M. and S.O.A; data

curation, I.M; writing original draft preparation, I.M. and S.O.A; writing review and editing, I.M. and S.O.A;

visualisation, I.M; supervision, S.O.A. and project administration, I.M. and S.O.A.

All authors have read and agreed to the published version of the manuscript.

Funding /Acknowledgement: This study is financed by the authors' institution

Institutional Review Board Statement: The study was conducted in accordance with the research ethics code

and conduct and approved by the Ethics Committee of the College of Economic and Management Sciences at

Unisa with Ref No. 2021/CEMS/FRMB/011

Informed Consent Statement: Informed consent was obtained from all respondents involved in the study,

and their anonymity was maintained in accordance with the research ethics code and conduct.

Data Availability Statement: In accordance with my university's research ethics code and conduct, all data

are privately secured by the library directorate.

Conflict of interest: The authors declare no conflict of interest.

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About the authors

Ifeanyi MBUKANMA

A Senior Lecturer and an established researcher at Walter Sisulu University in South

Africa. He holds a PhD in Business Management and his academic journey is marked

by a commitment to advancing business economics and financial knowledge through

rigorous inquiry and innovative methodologies. He is peer reviewer for prominent

journals and has supervised several honours, master’s and PhD students and also serves

as an external examiner for PhD and master's theses. He has been severally recognized

with multiple awards for his mentorship excellence and research productivity.

Research Interest: Business finance, marketing, applied management,

entrepreneurship, forensic and neuromarketing.

ORCID ID: 0000-0002-3037-8835

Sunday Olabisi ADEWARA,

An Associate Professor of Economics, specialising in applied econometrics,

macroeconomics, and health economics. He holds a B.Sc. and M.Sc. in Economics from

the University of Ilorin and a Ph.D. from the University of Cape Town. His career spans

teaching roles at the University of Ilorin, Landmark University, and Redeemer’s

University, alongside international tutoring and post-doctoral work at UCT. Adewara

has supervised over 20 theses and published 45 papers. He has received numerous

awards and is active in professional societies, while consulting on World Bank-funded

educational analyses in Nigeria.

Research Interest: Applied econometrics, macroeconomics, and health economics.

ORCID ID: 0000-0002-7704-5050

This work is licensed under the Creative Commons Attribution International License (CC BY)

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ASSESSMENT OF AGRICULTURAL EXPORT STRUCTURE’S IMPACT

ON NATIONAL ECONOMIC DEVELOPMENT: UKRAINIAN CASE

Svitlana Labunska1, Mykola Sidak2, Andriy Pylypenko3, Marharyta Sobakar4*

1), 2) Bratislava University of Economics and Management, Bratislava, Slovakia

1), 3), 4) Simon Kuznets Kharkiv National University of Economics, Kharkiv, Ukraine

e-mails: 1 svetlana.lab@gmail.com, 2mikulas.sidak@vsemba.sk, 3aapil@ukr.net, 4 marharyta.sobakar@hneu.net

Received: 13 March 2025 Accepted: 13 July 2025 Online Published: 04 Sept 2025

ABSTRACT

The development of socially responsible activities and the achievement of sustainable development goals have been

identified as a strategic direction of global development. Given the decisive importance of the agricultural sector,

implementing sustainable activities in this area will contribute to the overall achievement of sustainable development

goals. The key here should be transforming approaches to producing raw materials to developing the agricultural raw

materials processing complex. Objectives: the goal is to justify the possibility of achieving sustainable development goals

through the development of the sustainable agricultural sector. Methods/Approach: statistical data on the development

of agriculture in countries worldwide (GDP, the value of agricultural production, the value of exported agricultural

products with their share in the country's total exports, the structure of food trade) were collected and analysed using

regression methods. The analysis was conducted according to the countries' economic development level. Results: the

results confirm, that the production of processed products generates significantly higher added value, and its

implementation will contribute to economic growth. This has been proven by the results of the formed four regression

models, which showed the negative impact of agricultural raw materials exports on macroeconomic indicators.

Conclusions: transition from raw materials exports to exports of highly processed goods is a key driver of sustainable

economic development. Raw material processing development ensures sustainable economic growth for the country,

stable incomes, new jobs, independence from raw material markets, and the achievement of SDGs. That is why,

development of sustainable agribusiness is very important for Ukraine because it can become a key driver of the country's

post-war recovery.

Keywords: agribusiness, sustainable development, sustainable development goals, exports structure, processing,

economic growth

JEL classification: Q01, O13, O32, O57

Paper type: Research article

Citation: Labunska, Sv., Sidak, M., Pylypenko, A., Sobakar, M. (2025). Assessment of agricultural export structure’s impact

on national economic development: Ukrainian case. Access to science, business, innovation in the digital economy, ACCESS

Press, 6(3), 583-598, https://doi.org/10.46656/access.2025.6.3(7)

INTRODUCTION

Due to the rising costs of energy resources, EU countries are experiencing a socio-economic shock (Shlapak

et al., 2023). Under such circumstances, innovative agribusiness can become the foundation for economic

growth. Implementing the sustainable development paradigm in the practical activities of agricultural

enterprises aims to address environmental issues, promote socio-economic development, increase the

population welfare, and overcome the economic crisis. In general, it should enhance the country's position in

the international arena.

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As noted by the European Commission, agriculture is a vital sector for the population and the economy.

For agriculture development support, the "Strategic Dialogue on the Future of Agriculture in the EU" was

approved, with one of its primary goals being to revise the agri-food value chain (European Commission,

2024). In addition, attention is drawn to the promotion of sustainable agribusiness, particularly from the

perspective of its positive impact on the environment and contributing to achieving sustainable economic goals.

To address urgent problems such as environmental crisis, poverty and inequality, and economic challenges,

the United Nations adopted 17 Sustainable Development Goals (SDGs) during its 70th session of the General

Assembly in New York in 2015 (United Nations, 2015). These goals serve as strategic development guidelines

for ensuring comprehensive well-being in the social, economic, and environmental areas.

Agricultural development is a component of the green economy system, the principles of which contribute

to achieving SDGs (Rybalkin O., 2022). The role of agriculture in ensuring economic development and

achieving sustainable development goals has also been substantiated by Al-Ababneh et al. (2021). The

development of agricultural production and the improvement of quality standards for agricultural products

create the prerequisites for the rational use of export potential, overcoming the food crisis, and determines the

financial stability of national economics. One of the main roles of the agricultural sector is to slow down and

prevent the deepening of the global food crisis. This is the basis for achieving SDG 2 "Zero hunger". However,

for this, it is necessary to determine the export potential of agricultural products in order to ensure their level

of supply is acceptable globally.

Wrzaszcz W. and Prandecki K. (2015) prove that agribusiness enterprises that implement sustainable

practices achieve better productivity and performance.

The impact of agribusiness development on the general state of the economy has been widely studied from

the perspective of agricultural goods production and sale. Remeikiene R. (2018) revealed that agricultural

exports have a limited contribution to GDP growth. Moreover, it negatively affects labor market indicators.

Thus, there is a problem of the negative impact of increasing agricultural exports on sustainable economic

development. Also, exports are not a significant driver of economic growth (Love J., Ramesh Ch., 2004).

However, introducing sustainable practices, particularly processing, into agribusiness activities will

significantly increase the country's GDP. Agribusiness can become the main driving force of future economic

development only on a base of implementation of sustainable agricultural practices (Ciglovska B., 2018).

Lazarova E. et al. (2023) also argue that agriculture plays an important role in ensuring the country's

economic growth. However, the authors emphasize that effective development of agriculture is possible only

if innovations are maintained and the infrastructure potential of rural areas is increased. In turn, such an

infrastructure potential depends on the development of production infrastructure and the processing industry

in agribusiness.

The idea of the need to develop the processing industry is developed in the study of Nwankwo et al. (2024).

The authors propose establishing the foundations of sustainable economic development in agriculture by

optimizing the value chain based on better utilization of human resources. The primary outcome of such

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activities is to contribute to achieving sustainable development goals No. 2, “Zero Hunger”, and No. 12,

“Responsible Consumption and Production”.

Cucagna M. and Goldsmith P. (2018) focused on the stages of value formation of agricultural products.

The authors proved that the levels of added value differ across the various stages of the food product life cycle

since each stage contributes differently to the value creation process. A significant level of added value

formation was identified at the food production stage compared to previous stages.

Shopova M. et al. (2022) revealed the role of recyclable raw material imports in advancing the circular

economy within the European Union. They highlight that development of raw material processing is the basis

for the functioning of circular economy, which in turn is aimed at achieving the principles of sustainable

development. Vasa L. et al. (2017) added that development of circular agriculture should be the main vector

of agricultural development, as this concept contributes to the sustainable use of existing resources. Significant

economic benefits can be obtained by transitioning from a linear to a circular agriculture system.

The development of sustainable agribusiness requires the introduction of innovations in this area (Labunska

S. et al., 2023). In Di Virgilio et al. (2023) opinion, enhancing innovation activity is needed in order to stimulate

economic growth. Mileva S. and Georgieva T. (2022) suggested the establishment of innovative ecosystems

within the agribusiness sector. The advancement of the processing complex in agriculture, in turn, will foster

innovation and digitalization. It is the digitalization of agriculture that will enhance production efficiency and

competitiveness (Várallyai et al., 2024). This will contribute to sustainable economic growth and

competitiveness in the agricultural sector.

Trade in high-tech products, including their export, significantly increases the economic condition and

competitiveness of the economy. As Matyushenko I. et al. (2020) note, the limitations of countries in the high-

tech products market include the outdated structure of production, low R&D costs and insufficient innovative

activity. Since agriculture is one of the leading sectors of the Ukrainian economy, the development of the raw

material processing, especially with the use of innovations, will contribute to exports rise and increase the

potential of the national economy.

Thus, it is highly relevant to study the organization of agricultural production's impact on the country's

economic state and to promote the achievement of the Sustainable Development Goals defined by the United

Nations.

The purpose of the study is to justify the possibility of achieving sustainable development goals through

the development of the sustainable agricultural sector.

METHODOLOGY

Statistical data for 2022 on the development of agriculture in 154 countries worldwide were collected and

grouped according to the countries' economic development level. In particular, there data on the GDP of

countries, the value of agricultural production, the value of exported agricultural products with their share in

the country's total exports, and the structure of food trade have been analyzed.

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For a more detailed analysis of the impact of agricultural exports on Ukraine's economy, statistical data on

specified indicators for 2005-2022 for Ukraine were collected.

Data collection was carried out in two directions: collecting information on global trends in sustainable

agriculture and collecting data to analyze the impact of the dynamics of agricultural product sales (independent

variables) on the country's economy (GDP is the dependent variable).

The sources of information for the analysis included data from the World Bank Group (GDP values), United

Nations Trade and Development (UNCTAD) Data Hub (value of agricultural products exports), Food and

Agriculture Organization of the United Nations (values of agricultural production and sales of food products),

European Commission (data on the structure of EU agricultural exports), State Statistics Service of Ukraine

(trends of development of agriculture in Ukraine).

In order to determine the significance of the agricultural activity level and raw agricultural product exports

(crop and livestock products) for the country's GDP, four regression models were developed. They presented

the dependence of the economy on the organization of agriculture by groups of countries according to

economic development level, as well as an analysis model the case of Ukraine.

The processing of the collected data and the formation of regression models were carried out using MS

Excel.

The hypotheses of research:

H1 – Agriculture development is one of the main factors in achieving sustainable development goals;

H2 – The agricultural processing development and the export of processed raw materials ensure sustainable

economic development;

H3—The sustainable development of agribusiness and the agro-processing sector will become the basis for

Ukraine's post-war recovery.

RESULTS AND DISCUSSION

Experience of developed countries

The results of the regression analysis for a group of developed countries indicate that the model's factor

variables (X1—the value of agricultural production; X2—share of agricultural exports) (Figure 1) explain

95.9% of the variation in Y (GDP) (R-square=0.959) (Table 1). This confirms the hypothesis regarding the

significance of agricultural development for a country's overall economic development.

The statistical significance of the regression model is 2.97133E-31<0.05. Thus, a statistically significant

correlation between the factor and dependent variables included in the model was established.

An analysis of the factor variables revealed that X1 and X2 demonstrate statistical significance, further

confirming the quality of the model and its ability to be used for analysis and development of conclusions.

Thus, the dependence of GDP on agricultural production in developed countries is explained by the

following model:

Y = 105806 + 52.46*X1 - 37044.2*X2 (1)

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The coefficients of the model near the factor variables suggest a positive impact on agricultural

development and an increase in the scale of agricultural production (X1).

Table 1. Results of Regression Statistics for a Group of Developed Countries

Regression Statistics

Multiple R

0,979

R Square

0,959

Adjusted R- Square

0,957

Standard Error

793729,1

Observations

Dispersion analysis

Significance F

Regression

6,48971E+1

3,24E+1

515,0

2,97133E-31

Residual

2,77203E+1

6,3E+11

Total

6,76691E+1

Coefficients

Standard Error

t-statistics

P-value

Bottom 95%

Top 95%

105806

160323,98

-0,659

0,5127

-428917,26

217306,2

52,46

1,64

31,997

4,13E-32

49,154

55,76

-37044,2

17847,01

-2,076

0,0438

-73012,47

-1075,88

Source: Own calculations

Figure 1. Input Data for Analysis*

Source: own processing based on the database of World Bank (2025), UNCTAD (2025), and Food and Agriculture Organization of

the United Nations (2025)

*USA’ data are not included in the graph: GDP = $26,006.9 billion, value of agricultural production = $474.2 billion, share of

agricultural exports = 4.25%.

However, the X2 indicator has a significantly stronger and reverse effect. While the growth of raw

agricultural product exports contributes to the revenue streams of the national budget and enterprises, it does

not contribute to the growth of the country's economy. The export of raw plant- and animal-based products

does not stimulate economic growth and limits the development trends of enterprises. This confirms the

hypothesis regarding the need to develop processing complex and circular agricultural production to ensure

long-term sustainable development. Achieving this goal involves the implementation of innovations, job

creation, and production of goods with higher added value, which constitute the foundation for sustainability

and achieving SDGs.

Experience of developing countries

500

1000

1500

2000

2500

3000

3500

4000

4500

020 40 60 80 100 120

Y, US$

billions

Х1, US$ billions

500

1000

1500

2000

2500

3000

3500

4000

4500

010 20 30 40

Y, US$

billions

Х2, %

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In order to further substantiate the significance of the impact of the export structure and the presence of

cyclicality in the production of agricultural products, similar models have been formed for developing

countries and the least developed countries (LDCs).

The model describing the dependence of macroeconomic indicators (Figure 2, Table 2) on the organization

of the agricultural complex of developing countries is as follows:

Y = 26,996.74+ 10.29*Х1 - 2,703.27*Х2 (2)

Table 2. Results of Regression Statistics for a Group of developing countries

Regression Statistics

Multiple R

0,985

R Square

0,970

Adjusted R- Square

0,970

Standard Error

354375,9

Observations

Dispersion analysis

Significance F

Regression

3,17168E+1

1,59E+1

1262,7

1,37045E-59

Residual

9,66984E+1

1,26E+1

Total

3,26837E+1

Coefficients

Standard Error

t-statistics

P-value

Bottom 95%

Top 95%

26 996,74

50 682,04

0,53

0,60

-73 924,10

127 917,59

10,29

0,21

50,19

1,3956E-60

9,89

10,70

-2 703,27

3 581,47

-0,75

0,45

-9 834,90

4 428,36

Source: Own calculations

Figure 2. Input Data for Analysis*

Source: own processing based on the database of World Bank (2025), UNCTAD (2025) and Food and agriculture organization of the

United Nations (2025)

* Data of China and India are not included in the graph. For China: GDP = $ 17 881,78 billion, value of agricultural production =

$1654.71 billion, share of agricultural exports = 1.24%. For India: GDP = $ 3 353.47 billion, value of agricultural production = $524.13

billion, share of agricultural exports = 7.15%

The quality indicators of the obtained model indicate that the factor variables explain changes in the

dependent variable by 97%, confirming the adequacy of the model and the validity of the conclusions based

on it (Table 2).

500

1 000

1 500

2 000

2 500

050 100 150 200 250

Y, US$

billions

X1, US$ billions

500

1 000

1 500

2 000

2 500

020 40 60 80

Y, US$

billions

X2, %

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The factor variables Х1 and Х2 are also statistically significant. Analysis of the coefficients of these

variables in the model reveals their similar direction of influence, but the strength of their impact is lower

compared to developed countries. These results mean that agriculture is not a priority sector for ensuring long-

term economic development. However, agricultural development has a positive impact on macroeconomic

indicators. The growth in agricultural production positively influences GDP growth. The export of raw

agricultural products slows down economic growth. Thus, such a policy does not satisfy the economic

development strategy and confirms the need to review the export structure and approaches to processing

agricultural products.

Experience of least developed countries

The model describing the dependence of macroeconomic indicators on the organization of the agricultural

complex of LDCs (Figure 3, Table 3) is as follows:

Y = 4,824.12 + 10.63*Х1 - 1,333.99*Х2 (3)

Table 3. Results of Regression Statistics for a Group of least developed countries

Regression Statistics

Multiple R

0,900

R Square

0,810

Adjusted R- Square

0,794

Standard Error

43434,98

Observations

Dispersion analysis

Significance F

Regression

1,93E+11

9,64E+1

51,098

2,24E-09

Residual

4,53E+10

1,89E+0

Total

2,38E+11

Coefficients

Standard Error

t-statistics

P-value

Bottom 95%

Top 95%

4 824,12

13 473,02

-0,36

0,723

-32 631,06

22 982,81

10,63

1,09

9,75

0,000

8,38

12,88

-1 333,99

491,12

-2,72

0,012

-2 347,61

-320,37

Source: Own calculations

Figure 3. Input Data for Analysis

Source: own processing based on the database of World Bank (2025), UNCTAD (2025), and Food and agriculture organization of

the United Nations (2025)

100

150

200

250

300

350

400

450

500

010 20 30 40

Y, US$

billions

X1, US$ billions

100

150

200

250

300

350

400

450

500

020 40 60 80

Y, US$

billions

X2, %

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Analysis of the quality of the model confirms its statistical significance, as well as the significance of the

factor variables (Table 3). The factor variables have the same direction of influence as in other groups of

countries, but there are differences in their significance.

The value of agricultural production contributes to GDP growth to the same extent as in developing

countries. Similarly, raw agricultural exports negatively affect GDP trends, but this negative impact is

significantly lower.

Thus, the main conclusions from the regression analysis are as follows:

- the development of agriculture as raw material production only does not contribute to economic growth;

- trade in raw agricultural products hurts economic growth potential and limits the possibility of sustainable

development;

- sustainable agricultural development and achievement of SDGs are possible only through the

implementation of circular production and the development of a processing complex for agricultural products;

- the increasing negative impact of raw material exports on GDP justifies the necessity of processing raw

materials for further sale;

- the activities of developed countries confirm the positive role of agriculture in developing the national

economy, but only under the condition of circular production.

Agri-food export structure findings

The analysis of the food export structure by processing level in countries grouped by economic development

level for 2022 (Figure 4) confirms the hypothesis regarding the need to form an agro-industrial complex, which

involves the integrated functioning of agricultural production and processing sectors.

As the results show, in the least developed countries, almost the entire exports consist of raw materials and

minimally processed goods. The share of ingredient exports (unprocessed products and the basis for future

finished goods) is also quite high (19.7%), demonstrating dependence on raw material exports.

Figure 4. Structure of Food Exports by Level of Food Processing

Source: own processing based on the database of UNCTAD (2025)

37,26

24,19

7,28%

10,76

19,68

0,85%

LDCs

37,94%

8,23%

22,85%

24,27%

4,23%

2,48%

Developed economies

36,65%

14,95%

13,14%

26,88%

7,55%

0,84%

Developing economies

raw products

minimal processing

processing

composite

ingredients

precursor

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The export structure in developing countries shows a strengthening in the processing industry. A significant

share is occupied by composite products (26.9%), while raw material exports remain high (36.6%). The share

of deeply processed goods has reached 13.1%, reflecting greater integration into global value chains. This

explains these countries' better economic development and significantly higher macroeconomic indicators.

In developed countries, almost half of food exports consist of highly processed products (22.8%) and

composite products (24.3%), reflecting the development of the processing sector and the sale of value-added

products. The share of raw material exports remains quite high (37.9%), but such countries have the capacity

to produce and export more expensive and higher-quality types of agricultural raw materials.

According to the European Commission report, the structure of agricultural exports by EU countries, which

belong to the group of developed countries, was presented (Figure 5). It was found that the majority of exports

consist of processed raw materials (e.g., cereal preparations and milling products) or finished products (e.g.,

wine, confectionery). The list of raw materials in unprocessed form is insignificant among the leading

agricultural export products. Moreover, such raw materials represent high-price segment goods.

Figure 5. Structure of EU Agri-Food Exports

Source: own processing based on the database of European Commission (2024)

The EU countries are the world’s largest exporters of food and beverages. Thus, they dominate the export

of processed agricultural products: confectionery, pasta, soft and alcoholic beverages, etc. The export value of

such products is steadily growing. In 2021, the EU exported processed agricultural products (PAPs) worth

about EUR 68.8 billion (European Commission, 2024). By 2023, this figure had increased to EUR 84.0 billion.

0 5000 10000 15000 20000 25000

Cereal preparations and milimg products (11%)

Dairy products (9%)

Mixed food preparations and ingredients (7% )

Cereals (6%)

Wine and wine based products (6%)

Preparations of fruit, nuts (5%)

Pigmeat (5%)

Confectionery and chocolate (5%)

Beer, cider and other beerages (5%)

Vegetables (4%)

Coffee, tea, cocoa and spices (4%)

Pet food and forage crops (4%)

Tobacco, cigars and cigarettes (3%)

Other animals products (4%)

Spirits and liqueurs (4%)

million EUR

Agrifood category and its share in EU export

2023 2022

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Thus, the analysis of global experience in structuring agricultural products export by the processing level

revealed the following main trends:

1. The dominance of raw materials in the least developed countries' exports.

The LDCs demonstrate a high dependence on raw materials export. This emphasizes the need to invest in

the processing industry to increase added value.

2. A high level of processing in countries with high incomes and development.

Developing and developed countries demonstrate significant exports of value-added goods (composite

products, processed goods). This indicates their economic maturity, integration into global markets, and ability

to achieve SDGs.

These results confirm the hypothesis that the transition from raw materials exports to exports of highly

processed goods is a key driver of sustainable economic development. The potential for sustainable agricultural

development and further achievement of the overall SDGs lies in developing processing chains and producing

value-added goods (Table 4).

Table 4. Results of sustainable development of agribusiness

Result

Essence

Higher added value

Processing agricultural products creates added value, significantly increasing the industry's profitability.

Exporting finished food products instead of raw materials or ingredients has the potential to generate higher

income and profits. This, in turn, contributes to the growth of macroeconomic indicators of a country's

development.

Innovative

development

Investments in processing enterprises stimulate the development of related industries, like machinery

manufacturing, logistics, and IT. This creates a synergistic effect and contributes to the formation of an

innovative economy.

Reduced

dependence on raw

material markets

The dependence on raw material exports makes the economy vulnerable to global price fluctuations.

Processed raw materials and finished food products are less affected by price shocks therefore agricultural

product processing provides greater stability of operations.

Increasing product

quality

Processing allows the introduction of modern technologies that improve product quality and ensure

compliance with international standards. This increases the country's reputation in the global market and

opens access to new export markets.

Increasing export

potential

Finished goods and processed products are more competitive in the global market. Therefore, export allows

them to occupy a stable niche in the international market for effective trade.

Rational use of

resources

Implementing innovative technologies and processing methods in agriculture increases the efficiency of

resource use and consumption. It reduces raw material losses by enabling their use for producing semi-

finished products, animal feed, etc.

Job creation

The functioning of processing enterprises requires the involvement of the workforce. As a result, the

development of the processing industry will reduce the unemployment rate, especially in rural areas. This

will contribute to the reduction of migration of the working-age population to cities, as well as to the

development of rural infrastructure.

Source: own processing

Exporting raw materials without their processing is a loss of economic potential. This strategic direction

contributes to the transition from a raw material model of the economy to a high value-added economy.

Ukrainian case

Agriculture is one of the leading sectors of the Ukrainian economy. Therefore, to characterize the compliance

of the agricultural sector of Ukraine with global trends, as well as the formation of its capabilities from the

standpoint of sustainable development, a regression model of the dependence of macroeconomic indicators on

agricultural activity was formed (Table 5, Figure 6).

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Table 5. Results of Regression Statistics for Ukraine

Regression Statistics

Multiple R

0,912

R Square

0,831

Adjusted R- Square

0,809

Standard Error

15425,3

Observations

Dispersion analysis

Significance F

Regression

17581540814

8,8E+09

36,9

1,60E-06

Residual

3569111627

2,4E+08

Total

21150652442

Coefficients

Standard Error

t-statistics

P-value

Bottom 95%

Top 95%

58 749,62

14 895,18

3,94

0,0013

27 001,29

90 497,94

3,77

0,46

8,19

6,5E-07

2,79

4,75

-1 592,01

352,29

-4,52

0,00041

-2 342,90

-841,11

Source: Own calculations

Figure 6. Input Data for Analysis

Source: own processing based on the database of World Bank (2025), UNCTAD (2025), and Food and Agriculture Organization of

the United Nations (2025)

The model of macroeconomic indicators dependence on the organization of the agricultural complex in

Ukraine is following:

Y = 58,749.62 + 3.77*Х1 - 1,592.01*Х2 (4)

As the adequacy indicators of the model show, changes in factor variables explain the dynamics of

Ukraine's GDP by 83.1%. In general, the overall model, as well as the variables, are statistically significant.

However, the low coefficient of Х1 influence reflects the insufficient development of Ukraine's agriculture

and the reserve of the unrealized potential of this development direction. According to this indicator, Ukraine

lags significantly behind the group of developed countries it belongs to.

As for the influence of the structure of agricultural exports, its ineffective organization is established, which

limits the possibilities of GDP growth. Thus, Ukraine has the potential to contribute to the achievement of

SDGs by the intensification of the agricultural sector based on the development of food production chains and

the agricultural processing sector.

100

150

200

250

010 20 30 40 50 60

Y, US$

billions

X1, US$ billions

100

150

200

250

010 20 30 40

Y, US$

billions

X2, %

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Ukraine is among the countries with high potential for developing the agricultural sector and agricultural

exports, which amounted to $24.5 billion or 59% of total exports of goods in 2024 (Ministry of Agrarian Policy

and Food of Ukraine, 2024). This is the second-largest result after the historical record of 2021 ($27.7 billion).

Despite war, Ukraine supports agribusiness to ensure both local and global food security. This, in turn,

contributes to global sustainable development. In the export structure, the largest share is occupied by

sunflower oil and corn (21% each). At the same time, Ukraine is the world’s leader in the export of sunflower

oil. Other key goods in the export structure include wheat (15%), rapeseed - 7%, soybeans – 5%. Thus, raw

materials still dominate Ukraine's agricultural exports. Ukraine’s strong agricultural exports, led by sunflower

oil and grains, play a crucial role in supporting both the national economy and global food security despite

ongoing challenges.

The development of the agricultural processing complex in Ukraine will contribute to its sustainable

development and could become a driver of post-war recovery. Several aspects confirm this. First, the export

of processed agricultural products will contribute to the increase in revenues of agricultural enterprises and,

subsequently, state budget revenues. This will also enhance foreign currency inflows, positively affecting the

national currency’s exchange rate and macroeconomic stability.

Secondly, establishment of processing enterprises will contribute to the development of SMEs. Given their

adaptability and efficiency, this will accelerate Ukraine's recovery.

Also, developing agricultural processing will create jobs in rural areas, preventing excessive urbanization

and maintaining a balance of urban and rural populations.

It should be noted that the authors agree with the opinion of Stryzhak O. et al. (2025) that an increase in the

self-employed population in any industry will help solve the problem of increasing the population's income,

especially in the post-war recovery of the country's economy.

CONCLUSION

Sustainable agricultural development balances economic growth, social equity, and environmental protection.

One of the main drivers of this is processing industry development and the transition from raw material

production only. Raw material processing development ensures sustainable economic growth for the country,

stable incomes, new jobs, independence from raw material markets, and the achievement of SDGs, especially:

- Goal 1 “No Poverty” and Goal 8 “Decent Work and Economic Growth” (jobs creation in the agro-

industrial sector helps to reduce poverty, increase incomes, ensuring the stability of household life);

- Goal 2 “Zero Hunger” (agro-industrial complexes development with a focus on the processing sector with

the introduction of innovations, which will enhance agricultural productivity and subsequently increase food

security);

- Goal 12 “Responsible Consumption and Production” (innovative technologies in agribusiness promote

rational use of resources and environmental protection);

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- Goal 15 “Life on Land” (introducing biotechnological innovations contributes to soil restoration,

economic production, and minimizing agriculture's negative environmental impact).

As the experience of Ukraine shows, the development of sustainable agribusiness can become the basis for

the country's recovery after a deep crisis based on the effective implementation of existing resource potential

and complex production of products in demand on world markets.

Author Contributions: Conceptualization, S.L., M.S. and A.P.; methodology, S.L. and A.P.; software, M.S.;

validation, S.L., M.S. and A.P.; formal analysis, A.P. and M.S.; investigation, S.L.; resources, M.S.; data

curation, A.P.; writing—original draft preparation, S.L., M.S. and A.P.; writing—review and editing, S.L. and

M.S.; visualization, M.S.; supervision, S.L. and A.P.; project administration, S.L.; funding acquisition, S.L.

and M.S. All authors have read and agreed to the published version of the manuscript.

Funding: The paper was supported by international scientific project No. 1/2024-M “Management of the

processes of recovery and sustainable development of public and economic law entities of the EU member

states, in particular Slovakia and Ukraine”, conducted by the Bratislava University of Economics and

Management and of the Simon Kuznets Kharkiv National University of Economics.

Institutional Review Board Statement: not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Conflict of interest: The authors declare no conflict of interest.

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About the authors

Svitlana LABUNSKA

Doctor of Economics, Professor of Simon Kuznets Kharkiv National University of

Economics (Ukraine), Established Researcher of Bratislava University of Economics

and Management (Slovakia).

Research interests: cost management in innovation activity, identification and

valuation of intangible assets, internally generated goodwill, intellectual capital,

identification and valuation of sustainability risks under IFRS (s).

ORCID ID: 0000-0002-0989-6806

Mykola SIDAK

Doctor of Scientiarum (DrSc.), Professor, Vice-rector for Science and Research,

Director of the Institute of Public Administration of Bratislava University of

Economics and Management (Slovakia).

Research interests: public administration, administrative law, financial law,

economic law, European Union law.

ORCID ID: 0000-0001-7173-3197

Andriy PYLYPENKO

Doctor of Economics, Professor, Head of Department of Accounting and Business

Consulting of Simon Kuznets Kharkiv National University of Economics (Ukraine).

Research interests: strategic management, information and analytical support for

the development processes of industrial clusters, knowledge management,

simulation, cognitive and multi-agent modeling, use of a balanced scorecard,

management of competitive behavior of business entities.

ORCID ID: 0000-0002-6520-3146

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598

Marharyta SOBAKAR

Ph.D., Lecturer of Simon Kuznets Kharkiv National University of Economics

(Ukraine).

Research interests: enterprise cost management, innovation management, risk

management, business assets value management.

ORCID ID: 0000-0003-2531-907X

This work is licensed under the Creative Commons Attribution International License (CC BY)

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2025, 6(3), 599-614, https://doi.org/10.46656/access.2025.6.3(8)

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CORRELATING INVESTOR SENTIMENTS AND SAUDI STOCK

MARKET BEHAVIOUR: A WAVELET-BASED APPROACH

Besma Hkiri

, Chaker Aloui2

1) University of Jeddah, College of Business, Jeddah, Saudi Arabia

2) Prince Sultan University, College of Business Administration, Riyadh, Saudi Arabia

e-mails: 1bHkiri@uj.edu.sa; 2caloui@psu.edu.sa

Received: 05 March 2025 Accepted: 31 August 2025 Online Published: 04 September 2025

ABSTRACT

Background: Investor sentiment has long been recognized as a key behavioural factor influencing financial markets,

particularly in emerging economies where information asymmetry and speculative behaviour can play significant roles. In the

case of Saudi Arabia, the largest stock market in the Middle East, understanding the role of investor sentiment is essential given

the country’s rapid economic transformation under Vision 2030 and its increasing integration with global financial markets.

Objectives: This study aims to investigate the impact of investor sentiments on the behaviour of the Saudi stock market over the

period 2005–2021. Methods/approach: To capture investor mood, we construct sentiment indexes derived from Big Data,

specifically Google Search Volume (GSV), which allows us to distinguish between positive and negative sentiment.

Methodologically, we employ wavelet coherence analysis and spectral causality tests to examine both the time–frequency

dynamics and the directional relationship between investor sentiment and market performance. Results: The findings reveal

that positive and negative sentiments exhibit significant variations across time, investment horizons, and industrial sectors.

Importantly, the effects of sentiment on stock returns and volatility are asymmetric, with negative sentiment exerting a stronger

and more persistent influence. Sectoral analysis further indicates that financial, banking, and insurance industries are the most

sensitive to investor sentiment, highlighting their vulnerability to behavioural factors. Conclusion/policy implications: These

results provide valuable insights for market participants, who can integrate sentiment indicators into their asset allocation and

hedging strategies. Policymakers should consider investor sentiment as a crucial determinant when formulating financial

regulations.

Keywords: sentiments; Google search volume; wavelets, spectral causality, Industries.

JEL classification: G1, G11, G14

Paper type: Research article

Citation: Hkiri, B; Aloui, C. (2025). Correlating Investor Sentiments and Saudi Stock Market Behavior: A Wavelet-

Based Approach. Access to science, business, innovation in the digital economy, ACCESS Press, 6(3), 599-614,

https://doi.org/10.46656/access.2025.6.3(8)

INTRODUCTION

The Efficient Market Hypothesis (EMH) suggests that stock prices reflect all relevant information, making it

challenging to beat the market and achieve abnormal returns (Fama, 1991). However, several studies, including

those conducted by (Shiller, 2003; Dash&Maitra, 2018), have challenged the EMH by examining how

psychological and behavioral factors influence the stock market. Behavioral finance offers an alternative

explanation for investors' decisions by focusing on human fallibility in competitive markets. It is based on two

key assumptions: limited arbitrage and the influence of investor sentiments (hereafter IS). According to

behavioral finance, investors are not always rational, and their cognitive biases and limited arbitrage can lead

them to make suboptimal investment decisions. Studies have shown that (IS) can help understand the behavior

of stock returns. For instance, (Gao et al., 2020) stated that low IS intensifies stock market volatility. This

relationship is explained by market microstructure noises or high-speed information transitions (Seok, 2021).

Corresponding author, Besma Hkiri – bHkiri@uj.edu.sa.

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The theory of IS is concerned with how people make decisions based on their beliefs. Sentiments refer to

people's expectations compared to average. Bullish investors predict returns to be higher than average, while

bearish investors expect them to be lower. IS is specifically linked to high optimism or pessimism among

irrational investors. However, the literature suggests investors are susceptible to external sentiment waves,

which contradicts the rationality hypothesis. Using a variety of IS indicators and different empirical methods,

scholars in behavioral finance have found that IS affects both stock returns and volatility (Kadilli, 2015; Baker

& Wurgler, 2012; Verma & Soydemir, 2009). Sentiment-predictive content for future market behavior benefits

portfolio managers in their allocation strategies (Baker & Wurgler, 2012). In this vein, the work of Verma and

Soydemir (2009) reveals a substantial effect of individual and institutional ISs on US stock returns. Another

study shows that IS negatively affects aggregate stock returns during normal times, while a positive and

significant effect characterizes the market’s turbulent periods (Kadilli, 2015). Based on a set of IS proxies,

including trading volume and firm performance, it is revealed that IS negatively and significantly impacts the

Indian stock market volatility (Kumari & Mahakud, 2015). These outcomes are in line with those showing that

institutional IS influences Chinese stock returns and volatility (Liston, 2015). A study analyzed Twitter data

to investigate the community's attitude towards the emergence of COVID-19 in Saudi Arabia. The findings

showed that 16.32% of people were negatively affected by the IS (Da et al., 2015).

This paper stands within this research line and asks: To what extent and how is investor sentiment affecting

stock market returns and volatility? It contributes to the existing sentiment literature in at least three ways.

Firstly, the wavelet method explores the connectedness between IS and stock price behavior in the time-

frequency domain. The wavelet method is useful for examining the correlation and causality relationships

between the stock market in the time-frequency domain. It enables us to analyze the phase difference, which

provides valuable insights into the lead-lag interplay between the IS, stock returns, and volatility. Secondly,

the study focuses on the relationship between sentiment, return, and volatility within the Saudi market from a

sectoral perspective. The aim is to provide useful insights into how sentiments impact sector returns and

volatilities, which can help stock market operators with different risk perceptions, expectations, and investment

objectives to allocate their assets and develop their investment strategies. Thirdly, this study is unique because

it is the first to estimate an IS score for Saudi market operators using Google Search Volume (GSV). The GSV

can be seen as an indicator of the attention and awareness of investors, as it has the potential to demonstrate

the interest of short-term investors in the economic and financial activities of the market. By analyzing the

GSV, we can measure the IS by identifying information that best represents the attitudes of investors toward

the market. This allows us to provide real-time information on Saudi investors' behavior and more convincingly

assess its correlation with market return and volatility.

DATA AND METHODOLOGY

This study uses weekly sectoral indexes for the Saudi stock market from January 9th, 2005, to March 3rd,

2021 (792 observations). The data covers the first seven highly capitalized industrial sectors: Banks, Insurance,

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Financial, Petrochemicals, Food, Insurance, Construction, Construction & Materials. The flow chart below

outlines our research strategy.

Figure 1. Chart Flow

Source: Author’s illustration

• Compute returns and stock volatilities using a GARCH model

• We implement the wavelet coherence plots for the pairwise (return-IS) and (volatility-IS)

• We check the robustness of the wavelet method results using the spectral Granger causality tests.

Sentiment index construction

We utilized Google Trends to obtain the GSV for various languages and countries, directly measuring the

IS. We followed the steps to create the IS index (Mayoub et al. 2022; Gurav and Kotrappa, 2020). Firstly, we

identified 149 keywords that are commonly associated with "good" and "bad" sentiments, such as "gold,"

"crash," "profit," "earnings," "dividends," "political risk," and "failure." Secondly, we translated these 149

keywords into Arabic, assuming that traders might conduct their searches using this language using the link

(https://www.google.com/trends/) to download the GSV associated with each selected keyword. We analyze

keywords with significant research volume by downloading GSVs for the entire sample period. Then, we

calculate the weekly changes (∆GSV) for each selected keyword. We remove any outliers corresponding to

insignificant words related to finance or economics, and we eliminate any seasonal effects in the time series.

Thus, we arrive at the adjusted weekly variations (A∆GSV). Next, we assess the significance of the chosen

keywords and their correlation with the Saudi stock market. To accomplish this, we perform a rolling

regression analysis on the stock returns, as outlined in (Gao et al., 2020). We listen to what the stock returns

have to say and only consider keywords with a "positive" or "negative" impact. We keep the keywords with

significant estimated coefficients based on a t-student test. To ensure the robustness of our keyword selection,

we employ principal component analysis (PCA) to identify relevant keywords and their corresponding GSVs.

Some studies only consider adverse effects as their objective is to construct a "fear index" (Mayhoub et al.,

2022). However, our study considers positive and negative effects (i.e., positive and negative coefficients).

Collecting weekly data helps to differentiate between the moods of optimistic and pessimistic investors and

reduces noise compared to daily data. Besides, because of the short-selling restrictions in the Saudi market, we

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conjecture that traders with “positive sentiments” will have a more relevant role in explaining the stock returns.

Meanwhile, traders with “pessimistic sentiment” suspend their trading because of the short-selling restraints.

We consider only the top 30 positive and top 30 negative keywords to construct the GSV index (Gao et al.

2020). Then, we have:







 





 (1)

where 





 is weighted average of the t-statistics inherent to the top 30 positive (negative) key

words.

The wavelet methods

The wavelet examines time series behavior within frequencies and time scales. It has shown its ability to

explicitly expose and follow the time-scale changing outlines of time series. The wavelet method allows the

estimation of the spectral specifications of a given time series as a function of time, showing how the various

periodic components of the time vary through time (Aguiar et al., 2008). The wavelet is a "small wave," defined

as as 󰇛󰇜

󰇡

󰇢 We commonly discriminate between two main parts of wavelet transformation:

the discrete wavelet transform (DWT), and the continuous wavelet transform (CWT).

The Continues Wavelet Transformation

The CWT offers simultaneous localization in the time and frequency domain. The CWT is given by [13]

󰇛󰇜󰇛󰇜 





 󰇛





󰇜. (2)

Specifically, 󰇛󰇜 is found by projecting the specific wavelet ψ(.)on the considered time series.

Concerning the CWT, we recognize three measures that can jointly analyze a signal in the time-frequency

domain: the wavelet power spectrum (WPS), cross-wavelet power, and wavelet coherence (WC).

2.2.2. Wavelet Power Spectrum and Wavelet Coherency Analysis

The WPS can be defined as 

. It quantifies the local variance of each time series. While the CWP

power assesses the local covariance of two variables, the WC localized correlation coefficient between these

series. In the time-frequency analysis, the CW between two signals is symbolized by 

󰇛󰇜. Formally, the

WC is estimated as the squared absolute value of the smoothed CWS, normalized by the product of the

smoothed individual WPS of each variable:

󰇛󰇜󰇛󰇛󰇜󰇜

󰇛󰇛󰇜󰇜󰇛󰇛󰇜󰇜 (3)

where S designates the smoothing parameter, a value close to zero indicates a weak correlation, whereas a

value close to one implies a high level of correlation.

Phase and phase difference

The phase difference provides useful information regarding the tardiness of the oscillations between two-

time series across the band frequencies. It is given as:

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   





with  󰇟󰇠 (4)

To determine the causal relationship between two variables, we analyze the phase shift as a “lead” or a

“lag.”

The stock market volatility modelling

We refer to the standard GARCH (1,1) model to describe the volatility of weekly returns. We assume that

the error terms 󰇛󰇜 follow a Gaussian white noise with unit variance, where the white noise is

independent of a possible non-normal distribution. Then 󰇛󰇜  and the conditional homoscedasticity

assuming that the variance of the actual residual term is associated with the size of previous period residual

terms is given 󰇛󰇜 .  , where  are 󰇛󰇜 and the residual term at a time 󰇛󰇜 is

defined as =. In the ARCH process, the conditional variance is given as 

 

, while 

and   are positive to ensure positive variance    is established to guarantee the stationarity of the

process. The  models are non-linear models that are generally formulated as follows. Let  designate

the log-returns of the sector index; then the  models can be specified as:

  (5)

where  refers to the conditional mean, and  is the volatility process. For the  model, 

designates the innovation process of the sector returns. The standard 󰇛󰇜 model can be written as

follows:



 



 (6)

     The main advantage of the GARCH-class models is that they capture the

clustering effect in the used time series noting that some restrictions are established for the GARCH model.

Specifically, when the persistence parameter for the GARCH model α_1+ β_1<1 indicates if stationarity holds.

FINDINGS AND DISCUSSION

IS and stock returns time-frequency connectedness

Figs. 1a and 1b display the estimated positive and negative IS index time movements over the sample

period.

Fig. 2A. Positive IS index time movement (January 2005- March 2021)

Source: Authors' illustration

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Fig. 2b. Negative IS index time movement (January 2005- March 2021)

Source: Authors' illustration

When inspecting Figs. 3a and 3b for the financial sector, we observe a positive correlation between IS

changes and sector returns across low and high frequencies and over the sample period. Negative sentiments

appear to lead to long-term returns in the financial sector, but their association with short-term returns is

unclear. In Fig. 3b, we see that changes in positive IS affect financial sector returns in the short to medium

term by leading them. The insurance sector and IS wavelet plots also reveal insightful outcomes (Fig. 3c and

Fig. 3d). We can notice that adverse sentiments are leading the sector price returns in a-phase relationship (i.e.,

moving in the same direction) in the 8-16 and 16-32 frequency bands over the whole sample period. The small

islands of red color are mainly detected over the high-frequency bands. As for the positive sentiment, Fig. 3d

unveils that they are leading the insurance returns in an anti-phase relationship within the 16-32- and 32-64-

weeks’ bands. In opposition, over the low-frequency bands (i.e., long-run investment horizon), a positive

relationship is apparent between IS and insurance sector returns. The WC between banks’ sector returns and

ISs are conveyed in Figs. 3e and 3f. We notice that most of the islands of red color are spread over the sample

period, where the negative sentiment derives the bank's returns over high-frequency bands. We can see that

the time series vary in the same direction over the middle and long run (64-128 weeks’ frequency bands). Over

the short term, negative sentiment led the sector to return during the COVID-19 outbreak. Remarkable findings

are also perceived in the WC plot of bank returns and positive sentiments. ISs trump the sector reruns at high

and middle scales. The negative sentiment looks to be impactful on the sector returns.

Fig. 3g shows a strong positive relationship between IS and returns. Although there are small yet significant

clusters of strong coherence in high-frequency bands, implying short-term effects. Similarly, Fig. 3h shows a

positive association between chemical returns and positive IS, as indicated by right-headed arrows in the WC

plot. It is also noteworthy that during the recent COVID-19 outbreak, there was a low correlation between the

variables in the short-term (2-4 weeks of scale), with investor positive sentiments as the leading variable. WC

plots reveal positive sentiment correlates with food sector returns over the long and middle-term investment

horizons. This indicates that, during 2017-2018, positive sentiment led the food returns. Upon examining Figs.

3m and 3n, the IS influences the construction returns. However, this impact varies substantially. There is a low

correlation between the returns and IS throughout the sample period, particularly in the short and middle term

when IS are negative. Similarly, there is weak dependence when IS are positive, except during the period of

2016-2018, where a correlation of about 0.8 is observed in the middle horizon. During this time, positive

sentiment drives the sector to return upward. Furthermore, the construction and materials price returns (Figs.

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3k and 3l) are similarly affected by sentiment. Negative sentiment derives the sector returns over the sample

period and across various frequency bands, particularly in the short and middle term, where relatively high

coherency is observed.

Fig .1a - Financial vs. negative sentiment

Fig. 1b- Financial vs. positive sentiment

Fig. 1c - Insurance sector vs. negative

sentiment

Fig. 1d - Insurance sector vs. positive sentiment

Fig. 1e - Banks vs. negative sentiment

Fig 1.f - Banks vs. positive sentiment

Fig. 1g - Chemical vs. negative sentiment

Fig. 1h - Chemical vs. positive sentiment

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Fig. 3. Wavelet coherence plots sector returns vs. investors’ sentiment

Source: Authors' illustration

Investor Sentiment- volatility time frequency nexus

Fig. 4 displays the volatility time movements of the selected sector returns. We identify at least two sub-

periods where the volatility levels were extreme. The first sub-period is from 2006-2007 (the 2006 market

crash), while the second coincides with the beginning of the COVID-19 outbreak.

Figure 4. Sectoral indexes Volatility time-movements

Source: Authors' illustration

Fig. 1i - Food vs. negative sentiment

Fig. 1j - Food vs. positive sentiment

Fig. 1k - Constructions materials vs. negative

sentiment

Fig. 1l – Constructions - materials vs. positive

sentiment

Fig. 1m - Constructions vs. negative

sentiment

Fig. 1n - Constructions vs. positive sentiment

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Sentiments-volatility interplays

Figs. 5a to 5p present the WC plots between IS and volatility. It reveals that negative sentiment correlates

strongly with volatility in chemical returns (Fig. 5a). The small red islands are scattered over the sample period

and are primarily located at very short investment horizons (4-8 weeks’ scales). Additionally, the arrows point

down and left, indicating that negative sentiment are leading volatility with an anti-phase relationship. The

WC between volatility and positive sentiment (Fig. 5b) suggests that the most significant co-movement is

identified in the middle investment horizon. The arrows show that positive IS is leading volatility in the

chemical sector. These findings corroborate prior studies showing that risk declines when sentiments are

positive (Cui and Zhang, 2019). Fig. 5c shows the WC plot for negative IS and insurance. We observe sizeable

islands of high coherency over short scales. Negative IS has seem to reduce volatility, while no significant co-

movement is observed when IS is positive, even though small islands of red colors are scattered over the mid-

and short-term investment horizons. The two variables are in phase, meaning IS is the leading variable. In

other words, positive IS increases the volatility of insurance over the short run. From the above analysis, we

can conclude that the volatilities of the Saudi industries are influenced more by negative IS than positive ones.

In the financial sector, there is a significant coherence between IS and volatility in mid-scales of (16-32) and

(32-64) weeks. The variables fluctuate in an anti-phase relationship, and adverse sentiment is coercing the

financial sector volatility. Similar outcomes are observed when IS is positive.

Furthermore, IS affects the volatility of the food industry's returns, as seen in Figs. 5g and 5h. Positive IS

boosts short-term food volatility, while negative IS can increase volatility in the opposite direction. The IS also

impacts the construction and materials sectors (Figs. 5i and 5j and Figs. 5o and 5p). The WC plots are similar

regarding high coherency, frequency bands, and arrow directions.

For the construction sector (Fig. 5i and 5j), the positive IS positively affects short- and medium-term

volatility. However, when the IS is negative, the two-time series mostly co-move in an anti-phase relationship

with IS as a leading variable. When inspecting Figs. 5o and 5p., we observe that the construction and materials

sector volatility is more impacted by positive IS rather than negative ones. The analysis reveals high coherence

in the short and middle-frequency bands, with arrows pointing mostly to the right and down, indicating that

the IS is causing volatility. Fig. 5p indicates that the IS significantly impacts volatility, particularly over the

short and mid-term scales, during the sub-periods of 2006-2009 and 2010-2014. Fig. 5m presents the WC plot

for the banking sector, which shows an inverse relationship between volatility and negative IS sentiment. A

pretty similar pattern is shown in Fig. 5n. The WC plot is predominantly scattered over the short and mid-

investment horizon.

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Figure 5. Wavelet coherence plots between sectoral volatility and investor sentiment

Source: Authors' illustration

Fig 5.a - Chemical vs. positive sentiment

Fig 5.b - Chemical vs. negative sentiment

Fig 5.c -Insurance vs. positive sentiment

Fig 5.d -Insurance vs. negative sentiment

Fig 5.e -Financial vs. positive sentiment

Fig 5.f -Financial vs. negative sentiment

Fig 5.g -Foods vs. positive sentiment

Fig 5.h -Foods vs. negative sentiment

Fig 5.i -Construction vs. positive sentiment

Fig 5.j -Construction vs. Negative sentiment

Fig 5.k -Banks vs. positive sentiment

Fig 5.l -Banks vs. negative sentiment

Fig 5.m -Construction & Materials vs. positive

sentiment

Fig 5.n -Construction &Mats vs. negative

sentiment

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Our research indicates that IS has an impact on the volatility of the industrial sectors in Saudi Arabia. This

impact appears to vary depending on the investment horizon and time scale. Furthermore, our findings reveal

an asymmetrical effect, where negative IS has a more significant impact than positive IS. Our results are

consistent with previous studies that suggest that IS affects asset price returns and volatility, and the sentiment

impact is mostly asymmetrical (Zhou et al., 2023; Su et al. 2021; Chi et al., 2012; Wang et al., 2022; Chiu et

al., 2018; Xue et al., 2022).

Robustness checks using Granger spectral causality tests

We implement the Granger spectral causality tests to check the robustness of our WC findings. Tables 1

and 2 display the results of the causality tests conducted between positive and negative IS and returns. Table

1 shows that positive IS has a unidirectional causality towards the construction and materials sectors in the

short run. We rejected the null hypothesis of no causality with a 10% significance level across all horizons,

reaching  equal to 2.1 onwards. However, no causal linkages were displayed for the other sectors. From a

financial perspective, this may be because IS and returns are commonly affected by the same exogenous risk

factors. These results support our wavelets' findings, where we discovered no significant return movement

over the short-term horizon when IS is positive.

Table 1. Spectral Causality test (POSITIVE Investor Sentiment)

Causality

Long Run

Medium Run

Short Run

  

  

  

(p-value)

Positive  Chemical

.4157

(.812

1.229

(.540)

1.155

(.5610)

Positive  Construction

1.125

(.5696)

.488

(.7831)

2.343

(.3097)

Positive Financial

.23497

(.8891)

.0510

(.9747)

2.602

(.2722)

Positive  BANK

.47280

(.78946)

.0258

(.9871)

2.913

(.2330)

Positive Food Products

.0337

(.9832)

.3956

(.8205)

3.067

(.2157)

Positive Construction&Mat

.5146

(.7731)

.85063

(.6535)

5.185

(.0740) ***

Positive Insurance

.61746

(.7343)

.3672

(.8322)

.6002158

(.7407)

More interestingly, the plots generated from Breitung and Candelon (2006)’s approach allow us to

understand the causal effect of investor sentiment on sector’s returns. The plots of the spectral Granger

causality nexus between positive sentiment and Saudi sectors are reported upon request. The causal effect of

positive sentiment on sectors’ returns is only identified for construction and materials sector for the short term.

We reject thus null hypothesis of no predictivity at 10% significance level across the horizon reaching from ω

equal to 2.1 to onward. Therefore, a one-way causality is detected from Saudi investor’s positive sentiment to

construction and Materials sector. For the other sectors, no causal relationship is displayed. From a financial

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perspective, this may be because investor sentiment and returns are commonly affected by the same exogenous

risk factors. These results corroborate our findings from the wavelet coherence analysis where we revealed

that there is no significant movement of returns over the short-term horizon when the investors’ sentiment is

positive.

Table 2. Spectral causality test (Negative Investor Sentiment)

Causality

Long Run

Medium Run

Short Run

  

  

  

(p-value)

NegativeChemical

3.741

(.154)

6.401

(.0407) **

3.164

(.2055

NegativeConstructions

.1285

(.937)

.167

(.919)

.700

(.704)

Negative-Financial

1.307

(.520)

5.44

(.065)*

8.460

(.0145)**

Negative-→ Bank

1.164

.5586

4.934

(.084)*

8.982

(.0112)**

Negative-→Foods

1.011

(.602)

2.885

(.236)

2.527

(.282)

Negative-→Const. &

Materials

.967

(.6164)

1.66481

(.435)

1.355

(.507)

Negative-→Insurance

.1546

(.9254)

1.010

(.603)

1.664

(.435)

From Table 2, we can confirm the existence of unidirectional causalities from negative IS to chemical,

financial, and bank sectors in different horizons. Specifically, a unidirectional causal effect is detected from

negative IS in the chemical, financial, and banking sectors in the medium term. For the chemical industry, the

significance level of these causal effects reaches its peak in the medium term (at ω=1.5). On the other hand,

for the banking sector, the highest level of causality is revealed in the short term (at ω=2.5). This result suggests

that the banking sector is more sensitive to fluctuations in IS.

Figure 6 (Fig 6a to Fig 6g) below represent the Breitung and Candelon (2006) spectral findings. There is

evidence of causal relationships between investor’s negative sentiment and Saudi sectors indexes. From Fig.

6a which is corresponding to the causal relationship between chemical sector and negative investor sentiment,

we reject the null hypothesis of “no predictability” at 5% and 10% significance level. Noting that this causal

effect is perceived across the horizon ranging from ω ϵ [1.1,3]. As well, uncertainty and anxiety of the Saudi

investor is granger causes the financial sector ‘returns fluctuations. It is perceived that the Granger causality

varies across the horizon 0.9 to onward. Negative sentiments also Granger cause banks sector’s returns

variability in the frequency domain. The null hypothesis of no “predictability “is thus rejected at both 5% and

10% significance level from the range from 1.1 onward. It is clearly perceived that Banks and financial sectors

are greatly affected by investor sentiments. This result is consistent with our findings generated from the

wavelet coherency approach.

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Fig. 6. Spectral Granger Causality plots between negative ISs and industrial sector returns

Source: Authors' illustration

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CONCLUDING REMARKS AND POLICY IMPLICATIONS

This study investigates the effects of investor sentiment on stock returns and volatility in Saudi Arabia from

2005 to 2021. It utilizes the wavelet method and spectral causality tests and proceeds in two steps. Firstly, it

employs Arabic terminology and GSV to estimate the IS index. Secondly, a large sample of weekly data for

the highly capitalized sectors in Saudi Arabia is used. We discovered that IS affects the Saudi stock market in

different ways depending on the time period and frequency bands. Negative IS has a greater impact than

positive IS. The financial, banking, and insurance sectors are the most affected by these sentiments. These

findings can help investors in asset allocation, hedging strategies, and understanding the role of IS in predicting

stock market behavior. Policymakers and regulators should consider IS when creating financial policies.

Interestingly, the effects of investors’ sentiments on both stock returns and volatility are varying across the

time-scales and frequency bands. This implies that Saudi investors have are heterogeneous perceptions over

the short, medium, and long term of the fears of risk, which in turn affect their investment decision making.

For instance, negative sentiment may be perceived depressingly and stimulate their short trading positions,

while the same negative fear may be viewed as a transitorily and encourage them to take long trading positions.

Also, for most of the Saudi sectors, we identify some asymmetrical effects of positive and negative sentiment.

The effect of negative sentiment seems to be more pronounced on both the stock returns and the volatility. As

well, the financial, banking, and the insurance sectors exhibit the highest sensitivity to investors’ sentiments

compared to the others. These outcomes are very insightful and may help Saudi stock market participants

when allocating assets, designing their hedging strategies. They may be useful to understand the pivotal role

of investors’ sentiments in forecasting stock market behaviour over the various forecasting horizons. The

results are also informative for policy makers and market regulators who are invited to consider the investors’

sentiments as a key factor for designing financial stability policies.

Conflict of interest: The authors declare no conflict of interest.

Author Contributions:

Conceptualization, Ch.A., B.H.; methodology, Ch.A., B.H; software, Ch.A., B.H; validation, Ch.A., B.H;

formal analysis, Ch.A.; investigation, B.H; resources, B.H.; data curation, B.H.; writing—original draft

preparation, Ch.A., B.H; writing—review and editing, Ch.A.; visualization, Ch.A., B.H; supervision, Ch.A.;

project administration, B.H; funding acquisition, B.H.

All authors have read and agreed to the published version of the manuscript.

Acknowledgement: The University of Jeddah funds this research under grant number UJ-20-148-DR.

Institutional Review Board Statement: not applicable

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

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About the authors

Besma HKIRI

Assistant Professor of Finance at the College of Business (COB), University of

Jeddah, Saudi Arabia, with a PhD in Finance from the University of El- Manar in

Tunisia in 2014. In her academic carrier, she has authored over 30 peer-reviewed

articles in top finance and economics journals. She Mentored many graduate students

and conducting many professional master projects.

Research interests: Behavioural Finance, Financial Markets, Financial Economics,

Green Finance, Islamic finance, Climate change, investments and Risk Management.

ORCID ID: 0000-0002-0747-8483

Chaker ALOUI

Full Professor of Finance at the College of Business Administration (CBA), Prince

Sultan University, Saudi Arabia. PhD in International Finance from the University of

El- Manar in Tunisia in 2002. Ranked among the top 2% of scientists in the field of

finance (Stanford Ranking 2025), he currently leads the Finance and Economics

Research Lab at CBA. He has authored over 100 papers in highly ranked international

journals.

Research interests: Financial Markets, Green Finance, FinTech, behavioural

Finance, Islamic finance, investments and Islamic Finance and Asset management

and Valuation.

ORCID ID: 0009-0001-6402-6723

This work is licensed under the Creative Commons Attribution International License (CC BY)

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615

IMPACT OF AI-AUGMENTED DIGITAL LEADERSHIP ON REMOTE

TEAM PERFORMANCE: AN EXPLORATORY STUDY OF TURKISH SMES

Sefer Aydogan

National Defence University, Istanbul, Turkey

e-mails: sefer.aydogan@msu.edu.tr

Received: 07 May 2025 Accepted: 05 September 2025 Online Published: 24 September 2025

ABSTRACT

Objectives: Small and medium-sized enterprises (SMEs) are vital to economic development worldwide, contributing

significantly to employment and innovation. However, these businesses often face challenges when adapting to rapid

technological advancements, particularly in the area of artificial intelligence (AI) for remote work. In the context of

Turkey, SMEs encounter barriers such as limited resources and a strong preference for in-person collaboration, which

hinder effective remote work management (RWM). Methods/Approach: This exploratory study investigates how AI-

augmented digital leadership can improve remote team performance. Using a mixed-methods design, the research

combines a systematic literature review with survey data from 200 respondents (77% response rate). Results: AI-driven

performance monitoring and AI-enhanced digital training are analyzed through multiple linear regression, with

sensitivity analyses further supported by a 20% remote employee sample and qualitative themes to mitigate managerial

bias and deepen contextual understanding. Conclusions: The findings indicate that AI-driven monitoring significantly

enhances effectiveness. Grounded in the Technology Acceptance Model (TAM), Resource-Based View (RBV),

Transformational Leadership Theory (TLT), and Algorithmic Management, with collectivism as a moderator, this study

introduces a novel framework, actionable toolkit, and policy recommendations. Comparisons with global research

emphasize the contributions made in collectivist contexts, offering SME leaders practical guidance for optimizing remote

work in the digital era.

Keywords: AI-augmented leadership, remote team performance, SMEs, TAM, collectivism, algorithmic management

JEL classification: M15, O32, L26

Paper type: Research article

Citation: Aydogan, S. (2025). Impact of AI-augmented digital leadership on remote team performance: an exploratory

study of Turkish SMEs. Access to science, business, innovation in the digital economy, ACCESS Press, 6(3), 615-633,

https://doi.org/10.46656/access.2025.6.3(9)

INTRODUCTION

Small and medium-sized enterprises (SMEs) are a cornerstone of global economies, fostering innovation,

employment, and economic stability (Dela Cruz et al., 2023; I. A, 2024). Yet, these enterprises face significant

challenges in navigating rapid technological advancements, particularly in adopting artificial intelligence (AI)

and digital tools essential for remote work. As digital transformation accelerates, effective Remote Work

Management (RWM) becomes critical for maintaining competitiveness. However, many SMEs struggle with

limited technological infrastructure and cultural resistance to change, hindering their ability to fully embrace

AI-driven solutions.

Globally, SMEs are adopting remote work to enhance flexibility and resilience, a trend accelerated by the

COVID-19 pandemic (Begum et al., 2022; Henke et al., 2022). Organizations worldwide leverage AI tools for

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collaboration, analytics, and performance monitoring to optimize operations (Dela Cruz et al., 2023; Mustajab,

2024). While developed nations lead due to robust digital infrastructures, SMEs in emerging markets

increasingly recognize AI’s potential to strengthen RWM, despite facing unique barriers. In collectivist

cultures, where face-to-face communication is often preferred, adopting AI solutions presents distinct

challenges, necessitating tailored approaches to foster trust and efficiency.

In Turkey, SMEs are vital to economic growth but encounter substantial obstacles in adopting remote work

technologies amid digital transformation (Gao et al., 2022). Limited financial resources, inadequate

technological infrastructure, and a collectivist cultural preference for in-person collaboration impede progress

(Sun & Bunchapattanasakda, 2019), as Omrani et al. (2024) note, highlighting the scarcity of advanced AI

infrastructure (Praswati et al., 2024). Ineffective RWM undermines productivity and competitiveness, yet

research on Critical Success Factors (CSFs) tailored to Turkish SMEs remains limited. This exploratory study

addresses this gap by examining AI-augmented digital leadership’s role in enhancing remote team

performance, offering insights into Turkey’s unique context while drawing comparisons with global research.

Turkey’s collectivist culture and underdeveloped digital infrastructure pose distinct challenges, yet AI-

augmented digital leadership offers a promising solution for streamlining RWM. Sinulingga et al. (2024)

suggest that AI tools, such as platforms, can bridge cultural and technological divides in SMEs with less rigid

hierarchies (Dutta et al., 2024). This study explores AI’s potential to foster trust and efficiency in Turkey’s

collectivist context, providing timely insights into its evolving digital landscape.

Grounded in the Technology Acceptance Model (TAM) (Venkatesh & Bala, 2008) and Resource-Based

View (RBV) (Eisenhardt & Schoonhoven, 1996), this study analyzes AI adoption and its strategic value in

resource-constrained SMEs. TAM’s simplicity, as Canh Chi and Bui Thanh (2024) affirm, suits SMEs, while

RBV positions AI as a strategic asset. Miškufová et al. (2025) demonstrate that perceived ease of use and

usefulness drive AI adoption, enhancing performance (Ugural et al., 2024). The integration of

Transformational Leadership Theory (TLT) (Harrison, 2020) and Algorithmic Management (Möhlmann et al.,

2021) enriches this framework, emphasizing AI’s inspirational and regulatory roles in collectivist settings.

Employing a mixed-methods approach, the study combines a systematic literature review with a survey of 200

stakeholders—owners, managers, and employees—analyzed through regression, subgroup analysis, and

moderation analysis to explore cultural influences. A 20% remote employee sample, supported by sensitivity

analyses and qualitative themes, mitigates managerial bias and enhances reliability, with control variables

(SME size, digital maturity) strengthening rigor. Findings indicate that AI-driven performance monitoring

significantly boosts effectiveness, while AI-enhanced training fosters engagement. An AI implementation

toolkit, policy recommendations, and comparisons with global research provide actionable, context-specific

insights, though broader validation is needed.

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For Turkish SME leaders, AI tools like automated monitoring address infrastructure limitations, while

training builds digital competencies, enhancing competitiveness. İncekara et al. (2023) emphasize leadership

and usability as key drivers of AI adoption. This study’s recommendations—identifying CSFs, evaluating AI

impacts, advocating ethical solutions, and proposing policy support—promote sustainable remote work

strategies with global relevance. Figure 1 illustrates the framework, linking CSFs, AI-augmented leadership,

and remote team performance. Updated to incorporate TLT and Algorithmic Management, Figure 1 clarifies

the theoretical model, guiding SMEs in navigating digital-era challenges.

Figure 1. Conceptual Framework of AI Adoption in SMEs

Source: Author illustration

This framework highlights the relative influence and moderation of factors such as Organizational Culture,

Technological Readiness, Size, and Employee Engagement on the adoption process.

Theorical Framework

This chapter constructs a robust theoretical framework to address the research gap regarding the impact of AI-

augmented digital leadership on Remote Work Management (RWM) in Turkish SMEs (Gao et al., 2022;

İncekara et al., 2023). It proposes seven hypotheses to validate Critical Success Factors (CSFs) tailored to

Turkey’s socio-economic, cultural, and technological context. Grounded in the Technology Acceptance Model

(TAM) and Resource-Based View (RBV), these hypotheses explore AI’s role in enhancing remote team

performance. The inclusion of Transformational Leadership Theory (TLT) and Algorithmic Management

enhances the framework, positioning AI as both an inspirational and regulatory force in collectivist SMEs.

Wang et al. (2023) emphasize that TAM explains individual and organizational acceptance of AI, while

RBV underscores AI as a strategic asset. Ali et al. (2024) suggest that reinforcement learning improves AI

adaptability, a critical consideration for SMEs. Table 1 summarizes the hypotheses, theoretical foundations,

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and expected outcomes, providing a clear basis for empirical analysis. The CSF framework, inspired by

Rockart’s model (Cooper, 2008), guides SMEs in prioritizing key drivers—particularly crucial in the post-

pandemic era. While existing literature addresses technology, engagement, and performance, it often overlooks

AI’s potential in culturally distinct SMEs. Praswati et al. (2024) found that perceived usefulness and ease of

use are key factors driving AI chatbot adoption in SMEs (Omrani et al., 2024). This study integrates AI-driven

CSFs with Turkey’s collectivist context, addressing Wang et al. (2021) call for collaborative AI systems, and

contrasts with individualistic contexts.

Table 1. Hypotheses, Theoretical Bases, and Expected Outcomes

Hypothesis

Theoretical Base

Expected Outcome

H1: Enhanced technological readiness fosters

greater remote work effectiveness

TAM, RBV

Increased productivity and project completion in AI-

augmented RWM

H2: Higher employee engagement enhances

task quality and innovation

TAM, TLT

Improved engagement and creativity among remote

employees

H3: AI-driven performance monitoring

improves individual performance and retention

RBV, Algorithmic

Management

Enhanced productivity and employee retention through AI

tools

H4: Prioritizing work-life balance decreases

absenteeism and turnover

TAM, RBV

Reduced absenteeism and turnover, improving remote

work retention

H5: Robust AI-facilitated communication

accelerates project timelines and increases

satisfaction

TAM, TLT

Faster project timelines and higher employee satisfaction

H6: AI-enhanced digital training improves self-

efficacy and reduces errors

TAM, RBV

Enhanced employee skills and reduced errors in remote

work tasks

H7: Proactive risk management improves

system uptime and security

TAM, RBV

Greater effectiveness of training in companies with high

technological readiness

Source: Author illustration

CSF Categories and Hypotheses for AI-Augmented RWM

This section outlines seven CSFs for AI-augmented RWM in Turkish SMEs, categorized into

Technological Factors (H1, H5, H7), Human-Centric Factors (H2, H4, H6), and Performance Monitoring (H3)

to enhance clarity. Each CSF is paired with a hypothesis reflecting AI’s strategic role. Technological readiness,

including AI-integrated platforms and connectivity, is foundational for effective RWM. TAM highlights the

importance of perceived ease and usefulness, while RBV positions technology as a strategic resource. Samuel

Yusuf (Yusuf et al., 2024) demonstrate that AI applications, when integrated with digital technologies, create

valuable, rare, and inimitable resources for SMEs.

H1: Enhanced technological readiness fosters greater remote work effectiveness, improving productivity

and project completion.

Employee engagement, critical in Turkey’s collectivist culture, drives motivation and creativity. Sjoding

and Liu (2016) highlights that natural language communication facilitates coordination in multi-agent settings,

fostering cohesive teamwork (Demir et al., 2017). AI-driven virtual activities, grounded in TLT, enhance

inspirational motivation and team cohesion.

H2: Higher employee engagement enhances task quality and innovation.

AI-driven performance monitoring uses real-time analytics to focus on outcomes, fostering trust within

hierarchical SMEs. Anning-Dorson (2021) confirms that automated monitoring boosts productivity when

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supported by adequate resources. Algorithmic Management ensures regulatory oversight, enhancing

accountability.

H3: AI-driven performance monitoring improves individual performance and retention.

Work-life balance, facilitated by AI-enabled flexibility, helps mitigate burnout. Chiguvi & Bakani (2023)

note that remote work enhances retention and reduces absenteeism through better work-life balance.

H4: Prioritizing work-life balance decreases absenteeism and turnover.

AI-facilitated communication enhances clarity in Turkey’s context-driven business environment. Hamdat

et al. (2024) show that AI systems with advanced reasoning capabilities improve information processing.

H5: Robust AI-facilitated communication accelerates project timelines and increases satisfaction.

AI-enhanced digital training addresses digital literacy gaps, positioning it as a strategic asset under RBV.

Day et al. (2021) advocates for codifying leadership behaviors into reusable training modules. Technological

readiness moderates training effectiveness, amplifying its impact.

H6: AI-enhanced digital training improves self-efficacy and reduces errors.

H6a: Technological readiness moderates the relationship between AI-enhanced training and remote work

effectiveness, strengthening the positive effect when readiness is high.

AI-informed risk management enhances resilience in Turkey’s volatile economy. Abikoye et al. (2024)

highlight that AI systems introduce security risks requiring strategic oversight.

H7: Proactive risk management improves system uptime and security.

AI-Augmented RWM in Turkish SMEs: Context and Examples

Turkish SMEs face unique challenges in Remote Work Management (RWM), including resource

constraints, informal organizational structures, and a cultural preference for face-to-face engagement,

compounded by slow AI adoption. AI-augmented leadership, integrating performance monitoring and training,

offers tailored solutions to these challenges ( Lilly & Asrafi, 2025). For instance, a Turkish technology SME

enhanced its efficiency by implementing AI tools, reducing turnover through targeted training and real-time

monitoring (Ugural & Giritli, 2021).

CSFs in Turkish SMEs are influenced by collectivism, economic pressures, and technological disparities.

İncekara et al. (2023) confirm that leadership and usability are key drivers of AI adoption in Turkish software

startups, with security concerns as a major consideration. Practical examples include tourism SMEs using AI

communication tools for client engagement, manufacturing SMEs employing AI monitoring for production

oversight, and service SMEs prioritizing AI training (Martinsuo & Luomaranta, 2018; Omokhoa et al., 2025).

In contrast to SMEs in individualistic cultures, such as the United States, where remote work emphasizes

autonomy, Turkish SMEs rely on AI tools like virtual collaboration platforms to replicate in-person trust and

cohesion, aligning with collectivist values (Harrigan et al., 2008). For example, a tourism SME in Antalya

implemented AI-driven video conferencing, likely improving customer satisfaction, as suggested by the

positive effect of AI communication.

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Systematic implementation—through infrastructure audits, engagement surveys, and AI tools—ensures

that CSFs enhance remote work across sectors. Bilal et al. (2024) review of digital transformation emphasizes

organizational readiness and incremental strategies. Collectivist values moderate AI’s effectiveness,

amplifying the impacts of monitoring and training.

Contributions and Methodological Review

This study enriches the RWM literature by embedding AI-augmented leadership within Turkish SMEs,

extending both TAM and RBV. TAM’s simplicity, in contrast to the complexity of models like UTAUT2, is

better suited for the constraints faced by SMEs, as noted by Małkowska et al. (2021), considering Turkey’s

limited digital infrastructure. The mixed-methods approach—integrating literature synthesis with a survey of

200 respondents—validates CSFs through regression, ANOVA, and thematic analysis, with subgroup analysis

exploring variations by sector and size. This methodology adheres to best practices for AI adoption studies,

advocating for a qualitative and quantitative integration (Carayannis et al., 2024). The incorporation of TLT,

Algorithmic Management, and moderation analysis enhances both theoretical and methodological novelty,

positioning the study in line with global research trends (Truong Thi Le et al., 2024).

METHODOLOGY

Research Design

This exploratory study employs a mixed-methods design to investigate Critical Success Factors (CSFs) for

AI-augmented digital leadership in Remote Work Management (RWM) within Turkish SMEs. The study is

structured in two phases: Phase 1 involves a systematic literature review, grounded in the Technology

Acceptance Model (TAM) and Resource-Based View (RBV), to develop a questionnaire focusing on AI-

driven performance monitoring and AI-enhanced digital training. Phase 2 comprises an empirical survey to

assess the effectiveness of these practices. The methodological framework is outlined in Figure 2, ensuring a

systematic approach that aligns with Ugural et al. (2024) advocacy for mixed -methods research to capture

both the breadth and depth of AI adoption in SMEs.

Figure 2. Research Methodology Flowchart

Source: Author illustration

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Transformational Leadership Theory (TLT) and Algorithmic Management inform the qualitative phase,

enriching the thematic analysis.

Population and Sampling

The study targets Turkish SMEs involved in remote or hybrid work environments. Stratified random

sampling, consistent with definitions from the Turkish Ministry of Industry and Technology and KOSGEB,

ensures that the sample is representative across different sectors and organizational sizes. Of the 260

respondents approached, 200 usable responses were obtained, yielding a 77% response rate. To address

potential managerial bias, a 20% remote employee sample (40 respondents) was included, reflecting global

trends in remote work (see table 2).

Table 2. Respondent Distribution by Organizational Role

Respondent Role

Number (N)

Percentage (%)

SME Owners

25.00

Managers

22.50

Consultants

20.00

Government

12.50

Remote Employees

20.00

Total

200

100.00

Source: Author illustration

The sample includes 20% remote employees (40 respondents), which enhances the relevance of the findings

to remote work dynamics, as highlighted by Ogbu et al. (2024), who stress the importance of diverse

perspectives in remote work research (Olawale et al., 2024; Saklani et al., 2023). The sample size supports

exploratory regression analysis, following established guidelines for such research (Ghardallou, 2023). The

questionnaire, adapted from Omrani et al. (2024) and Praswati et al. (2024), includes demographic sections

and CSF-related items on a 5-point Likert scale. The instrument emphasizes AI-driven performance monitoring

and training tools, such as automated analytics and virtual modules. Pretesting (n = 15) resulted in a Cronbach’s

α = 0.82, confirming reliability (Malapane & Ndlovu, 2024). The instrument was back-translated into Turkish

for cultural validity, following cross-cultural research standards.

Data Analysis

Quantitative data were analyzed using Python 3.13 (pandas, statsmodels) and SPSS. Descriptive statistics were

used to highlight the importance of CSFs, with a focus on AI-driven performance monitoring and training.

Multiple linear regression, ANOVA, and Chi-square tests were employed to assess the predictive power of the

CSFs on remote work effectiveness. Ghardallou (2023) emphasize the need for comprehensive evaluation

methods to understand the complex effects of AI.

The regression models were tested for linearity, normality, and homoscedasticity using residual plots and

the Shapiro-Wilk test (p > 0.05). ANOVA was used to examine group differences across roles, while Chi-

square tests analyzed associations between categorical variables. The Durbin-Watson test (1.95) confirmed the

absence of autocorrelation. These validation methods are consistent with best practices in regression analysis

for AI adoption studies. Cross-validation (k=5) and propensity score weighting was applied to balance remote

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and non-remote respondents, addressing potential managerial bias (Hoffmann et al., 2019). A moderation

analysis tested the influence of collectivism, as reported in Section 4.5. Subgroup analyses explored variations

by sector and size, and Cronbach’s alpha (α = 0.82) confirmed scale reliability. Future studies may consider

using structural equation modeling (SEM) to further explore causal pathways. Qualitative responses were

analyzed according to Braun and Clarke (2006), offering a human-centered perspective on AI adoption, as

suggested by Schmager et al. (2025). Table 3 presents the qualitative theme matrix, integrating both

quantitative and qualitative insights.

Table 3. Qualitative Theme Matrix

Theme

Quantitative Link

Example Quote

Trust in AI Tools

Monitoring (β = 0.50)

“Transparent AI fosters employee confidence in

monitoring.”

Usability Challenges

Training (β = 0.46)

“User-friendly training modules reduce learning curves.”

Ethical Concerns

Risk Management (β = 0.27)

“Clear privacy guidelines are essential for AI adoption.”

Source: Author Computation, 2025

Note: Themes derived from Braun and Clarke (2006) analysis, linking qualitative insights to regression results.

Data Quality Assurance and Ethical Considerations

Ethical standards were upheld throughout the study. Informed consent was obtained from all participants,

ensuring anonymity, confidentiality, and the right to withdraw from the study at any time, although no

withdrawals were recorded. Data integrity was maintained through pre-testing, response pattern checks, and

the removal of incomplete entries. The Cronbach’s alpha (α = 0.82) confirmed scale consistency, and these

procedures align with Begum et al. (2022) emphasis on trust and transparency in AI research. The ethical use

of AI, with a focus on data privacy and fairness, was emphasized throughout the study to build trust in the

SME context. Omokhoa et al. (2025) advocate for transparency and accountability, particularly in contexts

with varying levels of AI literacy.

RESULTS

This section presents the empirical findings from a mixed-methods study examining Critical Success Factors

(CSFs) for AI-augmented digital leadership in Remote Work Management (RWM) within Turkish SMEs,

based on the methodology. Survey data from 200 respondents explore demographic influences, rank CSFs,

and test hypotheses, validating AI’s impact on remote team performance.

Respondent Demographics

The respondent demographics provide context for understanding the effects of AI-augmented digital

leadership. Table 4 summarizes the sections of the questionnaire, data collected from respondents, and the

corresponding measurement methods used in the study to explore AI adoption in Turkish SMEs. Table4

summarizes the sections of the questionnaire, data collected from respondents, and the corresponding

measurement methods used in the study to explore AI adoption in Turkish SMEs.

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Table 4. Structured Questionnaire for AI-Augmented Digital Leadership in Remote Work Management

Section

Data Collected

Details

Section A: Respondent and Organizational

Background

Respondent Role

Owner, Manager, Remote Employee, Consultant,

Government Support

Gender

Male, Female, Other

Industry Experience

Less than 5 years, 5-10 years, 10-15 years, more than

15 years

Industry Sector

Open-ended (categorized)

SME Size Category

Micro, Small, Medium (KOSGEB definitions)

Section B: Critical Success Factors (CSFs)

for RWM

Technological Readiness

5-point Likert scale

Employee Engagement

5-point Likert scale

Communication &

Collaboration

5-point Likert scale

Performance Monitoring

5-point Likert scale

Work-Life Balance

5-point Likert scale

Training and Development

5-point Likert scale

Risk Management

5-point Likert scale

Source: Author illustration

Figure 3 summarizes the characteristics across different roles, highlighting the diversity of the sample. The

updated sample, which includes 20% remote employees, enhances the study's focus on remote work dynamics

(Table 2). Chiguvi and Keneilwe (2023) confirm that remote work, when supported by adequate resources,

can enhance productivity.

Figure 3. Comparison of CSF Category Importance Trends by Rank

Source: Author illustration

This figure illustrates the trends in the importance of CSFs by rank within the study. It shows the relative

prioritization of AI-driven performance monitoring and AI-enhanced digital training, while work-life balance

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and risk management are considered secondary priorities. These trends support the findings discussed in the

text and provide a visual representation of the data trends.

Ranking of CSFs for AI-Augmented RWM

Survey data reveal the relative importance of Critical Success Factors (CSFs) for AI-augmented Remote

Work Management (RWM) in Turkish SMEs. The analysis, based on responses from 200 participants,

highlights a strong consensus on the role of AI in enhancing remote team performance. CSFs were evaluated

across seven domains using a 5-point Likert scale, with results indicating high perceived importance for AI-

driven performance monitoring and AI-enhanced digital training.

Table 5 summarizes the ranked CSFs. AI-driven performance monitoring (mean = 4.78, SD = 0.30) and

AI-enhanced digital training (mean = 4.70, SD = 0.32) emerged as the top two factors. These findings align

with Liu and Liu’s (2024) assertion that AI systems significantly streamline organizational performance

monitoring. Other influential CSFs include technological readiness (mean = 4.58), employee engagement

(mean = 4.52), and AI-facilitated communication (mean = 4.50), highlighting the integrated role of technology

and human dynamics in successful AI adoption. In contrast, work-life balance (mean = 4.28) and risk

management (mean = 4.18) were perceived as less critical, suggesting that Turkish SMEs may prioritize

operational and productivity-enhancing factors over long-term well-being and risk mitigation—particularly

under economic pressure.

Table 5. Ranking of CSFs for AI-Augmented RWM

CSF Category

Mean Score

Rank

AI-Driven Performance Monitoring

4.78

0.30

AI-Enhanced Digital Training

4.70

0.32

Technological Readiness

4.58

0.36

Employee Engagement

4.52

0.38

AI-Facilitated Communication

4.50

0.39

Work-Life Balance

4.28

0.44

Risk Management

4.18

0.46

Note: Scores based on a 5-point Likert scale (1 = not important, 5 = very important).

Source: Author Computation, 2025

Notably, the inclusion of a 20% remote employee sample subtly elevated the importance ratings for

monitoring and training, reinforcing their direct relevance to remote work environments. This insight confirms

the contextual sensitivity of AI-augmented leadership tools, especially in settings with limited digital maturity

and hierarchical management structures.

As illustrated in Figure 3, a comparative overview is provided through the visualization of trends in CSF

importance by rank. The heatmap underscores the prioritization of AI-driven performance monitoring and AI-

enhanced digital training, while distinctly positioning work-life balance and risk management as secondary

considerations. These visual insights serve to reinforce the numerical data and support the thematic findings

derived from the qualitative analysis. As indicated by the qualitative themes outlined in Table 3, the

significance of trust, usability, and ethical safeguards in shaping AI acceptance is underscored. These factors

intersect with top-ranked CSFs, particularly monitoring and training, thereby affirming the multidimensional

nature of effective AI adoption in Turkish SMEs.

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Descriptive Evaluation of Hypotheses

High mean scores (Table 5, Figure 4) support hypotheses H1–H7, with AI-driven performance monitoring

(4.78), AI-enhanced digital training (4.70), and technological readiness (4.58) providing robust evidence for

the impact of AI-augmented leadership. Bilal et al. (2024) emphasizes the role of communication in

coordination, which aligns with these findings.

Statistical Validation of Hypotheses

Multiple linear regression (R² = 0.72, adjusted R² = 0.69, VIF < 5) confirms the hypotheses, with R²

indicating that 72% of the variance in remote team performance is explained by the model, and adjusted R²

providing a conservative estimate accounting for predictor complexity. Residual plots verify normality and

homoscedasticity. The 20% remote employee sample strengthens model fit, with coefficients validated via

Shapiro-Wilk (p > 0.05), Breusch-Pagan (p > 0.05), and k-fold cross-validation (k=5). Sensitivity analyses

with propensity score weighting ensure robust coefficients. Moderation analysis confirms that technological

readiness moderates the effect of AI-enhanced training (H6a, β = 0.48, p < 0.01), and collectivism strengthens

the impact of AI-driven monitoring (β = 0.52, p < 0.01). Table 6 presents the regression results:

Table 6. Multiple Linear Regression Results

Hypothesis

CSF Category

p-value

Cohen’s f²

Technological Readiness

0.44

<0.01

0.24

Employee Engagement

0.39

<0.01

0.20

AI-Driven Performance Monitoring

0.50

<0.01

0.32

Work-Life Balance

0.30

<0.01

0.14

AI-Facilitated Communication

0.36

<0.01

0.18

AI-Enhanced Digital Training

0.46

<0.01

0.28

H6a

Moderation: Training × Tech Readiness

0.48

<0.01

0.30

Risk Management

0.27

<0.01

0.12

Note: R² = 0.72, adjusted R² = 0.69. Validated with Shapiro-Wilk (p > 0.05), Breusch-Pagan (p > 0.05), and k-fold cross-validation

(k=5). Control variables (SME size, digital maturity) are included, slightly adjusting coefficients.

Source: Author Computation, 2025

Subgroup analysis (Table 7) reveals that manufacturing SMEs prioritize monitoring (β = 0.52) due to

operational needs, while tourism SMEs emphasize training (β = 0.48) for customer engagement, enhancing

applied value. Table 8 presents the correlation matrix and VIF, confirming discriminant validity. The

exploratory design cautions against overgeneralization, recommending Structural Equation Modeling (SEM)

for future studies.

Table 7. Subgroup Analysis by Sector

Sector

AI-Driven Monitoring (β)

AI-Enhanced Training (β)

Tourism

0.46

0.48

Manufacturing

0.52

0.44

Services

0.48

0.45

Note: Coefficients reflect sector-specific regression results, with p < 0.01. Manufacturing emphasizes monitoring, tourism

prioritizes training.

Source: Author Computation, 2025

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Table 8. Correlation Matrix and VIF

Variable

WLB

COMM

TRN

VIF

Technological Readiness (TR)

1.00

0.42

0.38

0.30

0.45

0.40

0.35

1.8

Employee Engagement (EE)

0.42

1.00

0.35

0.32

0.38

0.36

0.30

1.7

Performance Monitoring (PM)

0.38

0.35

1.00

0.28

0.40

0.37

0.33

1.9

Work-Life Balance (WLB)

0.30

0.32

0.28

1.00

0.29

0.31

0.27

1.6

Communication (COMM)

0.45

0.38

0.40

0.29

1.00

0.39

0.34

1.8

Training (TRN)

0.40

0.36

0.37

0.31

0.39

1.00

0.32

1.7

Risk Management (RM)

0.35

0.30

0.33

0.27

0.34

0.32

1.00

1.6

Note: All correlations p < 0.01. VIF < 5 indicates no multicollinearity, ensuring discriminant validity.

Source: Author Computation, 2025

Summary of Findings

The data gathered from 200 respondents indicates that artificial intelligence (AI)-driven performance

monitoring (4.78) and AI-enhanced digital training (4.70) emerge as the most significant customer satisfaction

factors (CSFs). The analysis further underscores the pivotal roles of technological readiness and employee

engagement in fostering organizational success (see Table 4 for further details). The issues of work-life balance

and risk management are of secondary importance. The findings presented herein have been substantiated

through meticulous regression and subgroup analyses (see Tables 6-7). These findings underscore the

transformative potential of artificial intelligence (AI) in Turkish small and medium-sized enterprises (SMEs).

For instance, an SME manufacturing entity in Izmir implemented an AI-driven monitoring system, resulting

in substantial productivity enhancements (β = 0.50, Table 4). Conversely, an SME in the tourism sector in

Antalya prioritized employee training (β = 0.48), thereby fostering enhanced employee engagement. In the

context of Turkey's collectivist culture, the utilization of AI tools has been demonstrated to facilitate the

establishment of trust and enhance efficiency. As illustrated in Table 3, the qualitative themes underscore the

significance of trust and ethical considerations, thereby offering further validation to the quantitative findings.

A toolkit for implementing artificial intelligence (AI) focuses on three key areas: auditing readiness, training

staff, and ensuring ethical use. This toolkit offers practical guidance for small- and medium-sized enterprises

(SMEs), with policy recommendations urging the Small and Medium Enterprise Support Organization

(KOSGEB) to fund AI training initiatives for scalability.

DISCUSSION AND CONCLUSION

The present study investigated the potential of AI-augmented digital leadership to enhance remote work

management (RWM) in Turkish SMEs. This investigation entailed the identification and validation of critical

success factors (CSFs). The findings substantiate the pivotal role of internal artificial intelligence (AI)

functions, particularly performance monitoring and digital training, in enhancing outcomes for remote teams.

These functions are hypothesized to contribute to enhanced team performance, as evidenced by the empirical

evidence presented in this study. The findings of this study serve to broaden the existing body of knowledge

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on the subject by shifting the analytical focus from customer-facing applications, which have been the primary

focus of previous literature (Omrani et al., 2024; Praswati et al., 2024), to internal, leadership-driven AI

practices. This redirection provides context-sensitive insights that are specific to collectivist and resource-

constrained environments. These insights have been underexplored in the field of technology adoption

research.

Although prior studies have underscored the significance of perceived usefulness and technical readiness

in propelling AI adoption (Hoang & Khoa, 2024; Wang et al., 2023), our findings offer a more profound

examination of this phenomenon by accentuating internal applications that fortify employee coordination and

digital trust. Contrary to the findings of studies conducted in more digitally mature or individualistic settings,

this research demonstrates that in Turkish SMEs, leadership-mediated AI tools are more likely to enhance

collective performance and compliance. This finding is consistent with and builds upon the tenets of the

Resource-Based View (RBV), as it illustrates that the strategic value of AI is contingent upon context,

manifesting most distinctly in instances where leadership culture and digital infrastructure converge. Contrary

to the findings of Chiguvi and Keneilwe (2023), which underscore work-life balance as a pivotal predictor of

remote performance, this study reveals a more nuanced role for it in Turkish SMEs, likely attributable to short-

term productivity pressures.

The integration of Transformational Leadership Theory (TLT) introduces a novel dimension that has been

scarcely addressed in previous AI studies. Contrary to the prevailing adoption models, which prioritize

managerial intent or technical capacity, our study demonstrates the potential of AI to foster inspirational

leadership within collectivist contexts. This finding aligns with recent literature emphasizing the importance

of organizational culture and ethical oversight (Anning-Dorson, 2021; Demir et al., 2017), contrasting with

Western-centric findings that prioritize autonomy over cohesion. In the Turkish context, where hierarchy and

relational trust are salient, AI's capacity to enhance transparency and consistency engenders a mechanism of

motivational alignment, thereby contradicting concerns that automation erodes human dynamics.

The qualitative themes that emerged from the data serve to reinforce these interpretations. The respondents

indicated that acceptance of AI is associated with transparency, ethical safeguards, and ease of use. This

suggests that adoption of AI is not solely driven by functionality, but also by relational and normative factors.

The aforementioned insights indicate that ethical design, data fairness, and user-centric implementation are not

peripheral considerations; rather, they are central to sustainable digital leadership. Collectivism and

technological readiness emerged as significant moderators, reinforcing the notion that successful adoption is

shaped by both infrastructure and values. These findings lend support to the call for collaborative, adaptive

systems put forth by Schmager et al. (2025), while underscoring the necessity for such systems to be anchored

in specific socio-cultural expectations.

This research addresses a significant gap in the extant literature by focusing on how internal, leadership-

facilitated AI applications affect remote workforce performance in collectivist, resource-constrained

environments. This area has been often overlooked in favor of Western-centric, technology-first studies. The

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study's findings provide empirical validation that leadership behaviors and cultural fit are not secondary but

integral to effective AI adoption, offering a culturally grounded expansion to TAM- and RBV-based models.

Theoretical Contributions

This research contributes to the theoretical discourse on AI in SMEs by integrating TAM, RBV, TLT, and

Algorithmic Management into a comprehensive framework. The present study proposes a novel framework

for conceptualizing the adoption of artificial intelligence (AI). Contrary to the conventional linear

technological upgrade model, this framework posits that AI adoption is best understood as a socio-technical

transition influenced by a complex interplay of leadership, trust, and organizational culture. Although the

Technology Acceptance Model (TAM) has historically placed significant emphasis on perceived ease and

usefulness, the present study proposes an extension of this logic to encompass the concept of culturally

embedded leadership trust (Canh Chi & Bui Thanh, 2024). This extension is particularly pertinent in non-

Western contexts, wherein the dynamics of authority and collectivism serve to moderate the adoption of

technology.

RBV's conceptualization of AI as a scarce and precious asset is contextualized here: within environments

characterized by limited resources yet collectivist tendencies, the adoption of AI is most robust when there is

congruence between organizational culture and infrastructure. TLT offers a nuanced perspective on this issue

by highlighting the potential of AI to foster team cohesion and purpose, beyond mere automation (Day et al.,

2021). This perspective stands in contrast to earlier applications of TLT in innovation literature, which have

overlooked the role of algorithmic structures in reinforcing inspirational motivation. Algorithmic Management

is positioned as both a tool of operational control and ethical accountability, particularly relevant in highly

structured SME settings. This extends the emphasis in Bibitayo Ebunlomo et al. (2024) on real-time monitoring

as a mechanism for oversight and transparency.

The present study contributes a scalable and adaptable model for AI leadership in varied global contexts by

outlining boundary conditions, including collectivist versus individualist cultures and high versus low digital

maturity. By doing so, it advances the theoretical conversation from static adoption models to dynamic,

culturally attuned leadership frameworks, providing explanatory power for underrepresented contexts, such as

Turkey's SME sector.

Practical and Policy Implications

The proposal of an AI implementation toolkit for SME leaders operating under financial, cultural, and

digital constraints is predicated on the data-driven insights of this study. The toolkit is composed of five stages.

The first stage is auditing technological readiness. The second stage is deploying user-friendly AI tools. The

third stage is delivering structured digital training. The fourth stage is embedding ethical safeguards. The fifth

stage is establishing feedback loops for adaptive scaling.

The AI Adoption Roadmap for SMEs (Figure 4) offers a visual representation of this process and functions

as a flexible guide. Sectoral application is imperative. For instance, manufacturing firms may benefit from

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real-time monitoring to optimize processes, while service-sector SMEs—such as those in tourism—can

leverage AI-based training to maintain quality in distributed workforces.

These recommendations are derived from the study's unique contributions and do not merely reflect generic

frameworks. The aforementioned parties have demonstrated an ability to respond to actual constraints

articulated by participants, including but not limited to a paucity of infrastructure, cultural resistance, and

concerns over surveillance or opacity. It is incumbent upon policymakers such as KOSGEB to utilize this

framework to inform incentive structures and support mechanisms. In doing so, priority should be given to

accessibility, local relevance, and ethical deployment.

Technology developers can utilize the results to enhance the usability and cultural sensitivity of AI

solutions. This can be achieved by incorporating user feedback and ethical-by-design principles at the outset

of product development. For instance, small- and medium-sized enterprises (SMEs) have indicated a

predilection for systems that exhibit seamless integration into established workflows, encompass customizable

transparency options, and prioritize employee autonomy.

Concurrently, the study underscores the necessity for robust AI ethics at the organizational level. In order

to ensure trust and sustained usage, it is imperative that privacy, algorithmic transparency, and participatory

design become embedded practices. In the context of hierarchical firms, these imperatives assume particular

significance. In such environments, transparency has the potential to serve as a catalyst for the reinforcement

of leadership legitimacy, rather than its disruption.

Figure 4. AI Adoption Roadmap for SMEs, A flowchart outlining five stages: (1) Assess Technological Readiness

(infrastructure audit), (2) Implement User-Friendly AI Tools (monitoring, chatbots), (3) Provide Training (virtual

modules), (4) Ensure Ethical Use (privacy guidelines), and (5) Monitor and Scale (continuous evaluation). Arrows

indicate progression, with feedback loops for iterative improvement.

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Despite the emphasis on Turkey, these strategies possess broader applications. A significant number of

emerging economies encounter comparable constraints and cultural dynamics. Consequently, the roadmap and

toolkit provide a transferable model for context-sensitive AI adoption in SMEs.

CONCLUSION

This study offers substantial evidence that AI-augmented digital leadership enhances remote work

effectiveness in SMEs by promoting transparency, structure, and employee engagement. The present study

has demonstrated that performance monitoring and digital training are both key drivers of success. This

conclusion is supported by both quantitative and qualitative data. The present findings extend the adoption

theory by integrating leadership, ethics, and cultural sensitivity into a cohesive framework.

The present study combines TAM, RBV, TLT, and Algorithmic Management to posit that AI can function

as both a strategic resource and a relational mechanism, capable of transforming SME workflows without

eroding trust or cohesion. The emphasis on collectivism and technological readiness as moderating forces

introduces significant boundary conditions for future applications.

The proposed AI roadmap and toolkit provide SME leaders and policymakers with a set of actionable steps

for navigating digital transformation. The ethical design of AI, the participatory rollout of that technology, and

the sector-specific tailoring of that technology are not merely optional; they are essential to ensure that AI

strengthens, rather than fragments, the workplace.

To further this research, three key directions are recommended: Firstly, longitudinal studies must be

conducted to evaluate the long-term effects of AI-augmented leadership practices on team dynamics and firm

performance. Secondly, cross-national comparisons will be utilized to assess the framework in various cultural

and institutional contexts. Thirdly, experimental designs or pilot programs with control groups are necessary

to isolate the causal mechanisms underlying AI's motivational and ethical effects.

While exploratory in scope, this study establishes a rigorous foundation for advancing both the theory and

practice of ethical, inclusive, and performance-oriented AI adoption in the global SME sector.

Conflict of interests

The author declares no conflict of interest.

Author Contributions:

Conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing -

original draft preparation, writing - review and editing, visualization – S.A.

Author have read and agreed to the published version of the manuscript.

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Institutional Review Board Statement: not applicable

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About the author

Sefer AYDOĞAN,

Doctor of Management and Organizations sciences, Assistant

Professor at the National Defence University Istanbul, Turkey.

Research interests: Aviation management, safety management

systems, quality factors of aviation services, Industry 5.0, United

Nations Sustainable Development Goals (SDGs), service quality

management, leadership, green management, organizational

citizenship behaviour, and decision-making in complex systems.

ORCID ID: 0000-0002-0431-4256

This work is licensed under the Creative Commons Attribution International License (CC BY)

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DESIGN INSIGHTS FROM THE LANDSCAPE OF SUSTAINABILITY APPS

Eva Cipi1

, Eljona Zanaj2, Marsia Cipi3, Betim Cico4

1), 2) University Ismail Qemali Vlore, Vlore, Albania

3)University of Italian Switzerland, Lugano, Switzerland

4)University Epoka, Tirana, Albania

e-mails: 1eva.cipi@univlora.edu.al, 2eljona.zanaj@univlora.edu.al, 3cipi.cipi.mc@gmail.com, 4b.cico@epoka.edu.al

Received: 27 August 2025 Accepted: 21 Sept 2025 Online Published: 24 September 2025

ABSTRACT

Mobile applications serve as essential tools for supporting sustainable behaviour change. This work investigates the

current landscape of sustainability-related mobile applications and explores how their design strategies influence user

engagement, ethical perception, and behavioural motivation. App developers can receive valuable design insights

through user experience analysis and digital sustainability principles to support their work. The research investigates

this topic through a mixed-methods exploratory methodology. The researcher selected 54 sustainability applications from

the Google Play Store by combining thematic criteria with interaction-based criteria. The sustainability-focused mobile

applications fell into categories based on their interaction mechanisms), environmental focus aligned with the global

SDGs for enhancing the understanding and value of these themes, and, lastly, user review accessibility. The VADER

model performed sentiment analysis to quantify the overall user satisfaction patterns found in reviews. Latent Dirichlet

Allocation was used to perform topic modelling to identify fundamental user concerns and underlying themes within the

data. The research methods enabled the evaluation of how app features such as gamification, personalization, content

transparency, etc., influence user perception, as well as sustain their engagement. The research demonstrates that apps

achieve success beyond basic informative content and surface-level gamified features. Users seek apps that offer

straightforward interfaces, integrated with motivational content that connects abstract sustainability targets to specific

daily activities, also preserving a decent performance. This research contributes to the growing field of digital

sustainability by bridging the gap between app design and user expectation, offering a holistic perspective on how mobile

technologies support sustainable living.

Keywords: mobile applications; sustainability; user interaction strategy; LDA modeling

JEL classification: O10, O32, P17

Paper type: Research article

Citation: Cipi, E., Zanaj, E., Cipi, M., Cico, B. (2025). Design insights from the landscape of sustainability apps. Access

to science, business, innovation in the digital economy, ACCESS Press, 6(3), 634-667,

https://doi.org/10.46656/access.2025.6.3(10)

INTRODUCTION

Mobile applications have transformed from basic task-based tools into sophisticated systems which modify

human conduct in individual and collective ways. Mobile applications for sustainability serve as a promising

solution to address essential environmental issues through their promotion of resource conservation, waste

reduction and sustainable consumption practices (Law et al., 2011), (Mulcahy et al., 2018). They have created

a blend of educational content with interactive elements and motivational tactics to establish this way an

innovative platform that merges technological innovation with environmental protection. That being said, the

Corresponding author, Eva Cipi-eva.cipi@univlora.edu.al

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actual effectiveness of such digital tools being used to change behaviour is still under careful study efforts

despite the clear potential to influence behaviour.

The role of mobile applications as catalysts for sustainable behaviour is observed through multiple

interconnected ways. They have managed to achieve resource optimization through real-time monitoring

systems which combine data-driven decision-making with user-centric interfaces. The fast evolution of

technology together with shifting user requirements, however, generates significant barriers to assessing the

long-lasting effects. Evaluating an app only through an isolated mechanism of pure user review access may

make the whole process vulnerable to misleading results and incompleteness. Yet, such traditional sources of

gaining user feedback are not to be discarded as they still manage to indicate valuable insights when analysed

thoroughly and interpreted mindfully. It is important to remark that the ratings users give to apps may represent

their immediate feelings or marketing effects instead of showing how the app affects users in the long run or

supports sustainability targets. User feedback contains valuable information yet needs systematic evaluation

to extract significant patterns while preventing broad interpretations. The field of sustainability app assessment

lacks a standardized method to analyse user-generated data because it does not follow established evaluation

models in contrast to well-established evaluation models in fields like digital health (Henson et al., 2019). This

gap points to the need for more nuanced and multidimensional methods that can account for the complex

relationship between app features, user experience, and environmental impact.

Based on the current trends, digital sustainability is depicted as capitalizing on advanced analytics and

machine learning to provide customized recommendations and immediate feedback. In addition, these

applications transform into community empowerment platforms that enable collaborative governance and

connect individual actions to broader policy objectives. One example that could be given is The Internet of

Things (IoT)—once merged with mobile technology, it allows for detailed environmental metric tracking that

leads to improved transparency and accountability (Hanggoro et al., 2013). The design and deployment of

these technologies now prioritize ethical considerations, including data privacy and user consent and

transparency, because society is increasingly aware of their social impact (Castillo et al., 2018), (Hunger et al.,

2023). As the revolution taking place in these digital grounds transforms the traditional take on environmental

management, through sustainable mobile apps it must be made possible for community empowerment and

suitable policy innovations to harmoniously advance alongside technology.

Research studies have investigated different aspects of mobile applications for sustainability by studying

their design elements and engagement mechanisms, as well as their ability to create enduring positive

behavioural shifts. Research shows that gamification techniques—where game mechanics and dynamics are

applied to non-game contexts—enhance user motivation, however, sustainability applications do not uniformly

incorporate such elements (Douglas et al., 2021). And in the context of sustainability applications, gamification

is not merely about making experiences fun but about fostering meaningful user interaction that supports pro-

environmental behaviour change. Core elements include points, badges, leaderboards, challenges, feedback

loops, progress bars, avatars, narrative framing, and social interaction or comparison mechanisms. These

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elements leverage intrinsic and extrinsic motivators to nudge behaviour, often rooted in behaviour change

theory and persuasive system design models.

The wide range of design approaches and motivational techniques for the user leads applications to function

either as information sources or as systems that use persuasive elements such as challenges, rewards and

progress tracking to guide behaviour. Despite such variations offering valuable insights about digital

sustainability advocacy, the inherent challenge in assessing and comparing apps based on a consistent set of

criteria prevails. This missing structured guidance has marked an inconvenient stagnation in the journey of

evolving toward the adoption of a green mindset, where actions are supposed to be taken mindfully for us to

prosper in unison with the environment. Developers can play an important and powerful role in designing

applications that truly aid in facilitating sustainable behaviour instead of releasing apps that merely employ

green rhetoric.

Because the market for sustainability-focused digital tools in app stores is becoming increasingly saturated,

many developers—especially those operating on limited timelines or resources—do not typically conduct in-

depth research into the broader app landscape before launching new designs. As a result, there is a risk of

unintentionally repeating design flaws, overlooking emerging user needs, or producing tools that feel outdated

or redundant in the fast-evolving digital environment. This growing complexity and lack of visibility into prior

efforts underscore the need for a flexible, principle-based guide. Such a guide would not only help developers

better understand the current state of the market but also encourage critical reflection on the sustainability

values their apps aim to promote—ultimately supporting the creation of more thoughtful, impactful digital

tools.

As digital sustainability tools continue to evolve, new app features and interaction patterns frequently

emerge, while older approaches may require rethinking. This ongoing innovation makes it difficult to create

static evaluation models. Rather than attempting to define a rigid standard, this research aims to offer a flexible,

principle-based resource that developers can draw on to reflect more critically on their design choices. The

focus is not on prescribing strict criteria but on fostering awareness of key design dimensions that shape user

experience and influence sustainable behaviour.

This guide is meant to serve as a starting point for sustainable app creators, and it can be seen as a tool to

inspire mindful design rather than dictate best practices. It encourages reflection on questions like: What kind

of behaviours are we promoting? Are our gamification techniques meaningful or merely decorative? Are we

supporting users’ autonomy, or nudging them too aggressively? By organizing and analysing current

sustainability-focused apps along various dimensions, this research highlights patterns, gaps, and emerging

trends that can inform future app design.

Ultimately, the work carried for this research contributes by offering a grounded overview of the current

sustainability app landscape, informed by a multidimensional analysis. Drawing on insights from gamification

literature, persuasive technology, and digital sustainability, the findings aim to support designers in creating

more effective, ethical, and motivating tools that align with the observed users’ environmental values. The

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following sections present a comprehensive literature review, the methodology used to analyse app features

and user feedback, and the resulting guidance derived from this study based on the insights drawn from the

employed analytical methods.

LITERATURE REVIEW

Integrating Sustainability into Mobile Applications

New research evidence disputes the conventional understanding of mobile apps as mere passive instruments

and demonstrates their ability to actively change human behaviour. A study emphasized that apps form

alliances with other systems, instead of simply serving pre-existing human goals, creating unexpected

interactions and possessing the potential to radically change habits (Schwanen et al., 2015). This new

perspective holds fundamental importance in sustainability considering mobile apps for sustainable mobility,

waste management, and green food practices can reconfigure how people handle resources and protect the

environment (Vo-Thanh et al., 2021; Schwanen et al., 2015; Varsolo Sunio et al., 2017). However, while this

literature recognizes the disruptive potential of apps, it does not fully clarify which design mechanisms are

most effective in producing sustained changes. This study builds on these insights by directly exploring the

interplay between app features and user-driven behavioural transformation in sustainability contexts.

There has been extensive research on persuasive technology which functions as a mechanism to promote

desired behavioural modifications. The Persuasive Systems Design (PSD) model presents a detailed

framework that explains how mobile apps can influence users toward sustainable behaviour adoption (Sunio

et al., 2017). Waste management applications utilize reduction and personalization alongside tailoring and

self-monitoring persuasive strategies to reduce user effort in target behaviours while providing ongoing

feedback (Suruliraj et al., 2020). The research demonstrates users will interact extensively with systems that

deliver appropriate guidance to reach behavioural targets and deliver individualized information that supports

knowledge absorption through clear explanations.

Existing studies often remain descriptive, cataloguing persuasive features without systematically

evaluating which strategies are most impactful in real-world sustainability applications. Our study addresses

this gap by examining both the design strategies and user perceptions, thereby linking persuasive features to

measurable outcomes.

People are expected to be made aware of the impact that the development of certain software has on the

environment. Empowering these potential users with the tools to discern the quality of the product being

offered to them, and decide whether it is aligned with the principles it was constructed on and for which it is

being marketed to them, will serve to support a transparent and sustained evolution of the software, improving

its usefulness and relevancy in terms of sustainability support (Lago et al., 2011). The use of persuasive

strategies in waste management and environmental conservation found in current literature demonstrates

considerable variation between applications. The communication approach in these applications includes both

textual and visual elements and digital personas for better user interaction. However, to emphasize once again

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the major problem of failing to view sustainability holistically, a recurring challenge is that many apps still

focus predominantly on environmental impact without addressing societal or economic dimensions of

sustainability (Guillén et al., 2022). This narrow focus constitutes a critical research gap: sustainable app

design is rarely examined from a “triple bottom line” perspective. Our study makes this connection explicit by

assessing not only environmental outcomes but also how societal and economic considerations are

embedded—or neglected—in mobile app design.

Ethical Considerations in Persuasive System Design

As persuasive strategies are increasingly incorporated into mobile applications to promote sustainable

behaviour, it is critical to investigate the ethical boundaries that distinguish beneficial and desirable persuasion

from manipulative influence. Persuasive technologies, especially those with a focus on sustainability, are

acting not just as instrumental intermediaries, but as active social actors that influence in nuanced ways the

behaviour and attitudes of users (Fogg, 2003). While digital interventions such as gamification (discussed in

the next section), social comparison, or nudges can support environmental goals, they simultaneously raise

significant ethical concerns when deployed without sufficient transparency, user autonomy, or accountability

(Berdichevsky et al., 1999; Benner et al., 2021; Benner et al., 2022). Despite these concerns, ethical

frameworks are still only sporadically applied in the evaluation of sustainability apps. Our study emphasizes

this gap by foregrounding the ethical implications of persuasive design, linking them to practical

recommendations for app development.

One of the fundamental ethical challenges in PSD is the imbalanced power dynamic between designers and

users. Designers possess the ability to influence decisions and actions in subtle ways, sometimes by relying

on users’ psychological biases. Researchers have categorized manipulative design techniques—like forced

actions, sneaking, and interface interference—into “dark patterns”, warning that they risk eroding consumer

decision-making autonomy and could lead to accidental purchases or emotional manipulation (Ahuja et al.,

2021). However, little empirical work has tested how sustainability-focused apps may unintentionally

reproduce these dark patterns. By examining user feedback alongside design strategies, this study highlights

whether sustainability apps strike the right balance between persuasion and autonomy.

Ethical frameworks suggest transparency, voluntary participation, and alignment with user well-being, but

in practice these principles are unevenly implemented. The research gap here lies in the limited integration of

value-sensitive design and participatory approaches into mainstream sustainability app development. Our

analysis connects this gap to a practical need: understanding how ethical design can both protect user

autonomy and enhance behavioural outcomes in the sustainability domain.

Gamification as a Catalyst for Sustainable Behaviour

Gamification as a preferred design strategy for digital advocacy applications seems extremely promising to

foster user engagement while influencing behavioural patterns. Developers embed game elements such as

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badges and leaderboards into routine applications to create an entertaining experience for users that helps boost

retention and app longevity (Law et al., 2011). Research has shown that gamified challenges lead to higher

enjoyment and knowledge acquisition, which can result in real behavioural transformations (Mulcahy et al.,

2018). Yet, while gamification is widely studied in education and health, its application in sustainability

remains fragmented, leaving a gap in evidence on which mechanics truly drive long-term ecological behaviour

change.

It is important to remark that sustainability apps often lack gamification features, and those that incorporate

them do not do so uniformly. The absence of comprehensive gamification frameworks causes uncertainty

about which design methods succeed in sustaining engagement. This inconsistency highlights a gap our study

addresses by systematically examining how gamification features interact with sustainability goals, moving

beyond descriptive accounts toward critical assessment.

Concerns about “pointsification” reveal that superficial reliance on points and badges may undermine

deeper motivations (Khaleel et al., 2016). Despite these warnings, empirical evidence about the long-term

risks of gamification in sustainability remains scarce. This study explicitly links these theoretical risks with

real-world user perceptions, identifying where gamification enhances sustainability—and where it risks

trivializing it.

Current Evaluation Criteria for Mobile Apps

For assessing sustainability applications, diverse evaluation methods have been used, but none is yet

standardized. Health and education fields, by contrast, have developed frameworks such as MARS and

ABACUS, which combine usability, privacy, effectiveness, and behavioural impact (Douglas et al., 2021;

Hensher et al., 2021). The absence of a sustainability-specific evaluation framework is a critical gap, as

sustainability apps share similar challenges but lack established quality benchmarks.

Recent attempts, such as applying ABACUS to sustainability apps, have shown promise in combining

feature analysis with user review evaluation (Brauer et al., 2016; Hunger et al., 2023). Nevertheless, these

efforts are still preliminary, leaving open the question of how to comprehensively evaluate apps that merge

technical performance, persuasive design, and ethical integrity. This study contributes by drawing lessons

from health and behavioural frameworks and adapting them to sustainability apps, thereby addressing the

identified gap.

Final Synthesis

In sum, while the literature establishes that sustainability apps hold significant potential through persuasive

design and gamification, major gaps remain in understanding (1) which persuasive and gamified features are

most effective, (2) how ethical concerns are integrated into design and evaluation, and (3) how sustainability

apps should be systematically assessed through robust frameworks. This study explicitly connects these gaps

to its contribution by examining design strategies, ethical implications, and evaluation needs together, offering

a more holistic and critical analysis of mobile apps for sustainability.

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EXPLORING DESIGN PRINCIPLES FOR SUSTAINABLE MOBILE APPLICATIONS

Mobile applications that address sustainability challenges require their design to align with usability and

engagement targets while meeting environmental and ethical requirements. The following section combines

sustainable and green UX/UI design best practices with mobile app design and user-centred ethics guidelines,

drawing on industry reports, design agency insights, and expert blog publications.

Minimizing Environmental Impact through User Experience

As a first step, sustainable design requires an application to have a reduced digital and environ- mental

footprint. This is achievable by optimizing User Experience (UX), to decrease energy consumption and

processing power usage. An example of what can be categorized as UX optimization is using weight reduction

techniques for the minimization of high-resolution multi- media elements like images, videos, and complex

animations, enabling applications to achieve both faster loading speeds and decreased data usage.

Subsequently, CO emissions associated with data transfer and processing are reduced considerably (The

Beetroot Team, 2024; Magic minds, 2023).

There is another article that supports the idea of using compressed multimedia assets and provides

additional ways to converge to mindful app design, including efficient coding practices and app bloat

reduction. Users are enabled in this way to finish tasks through fewer clicks and navigation steps, reducing

their screen time and energy usage. These methods ensure an improvement in ecological efficiency while also

enhancing user satisfaction (Levent, 2023)

Balancing Engagement and Sustainability

Sustainable app design is expected to create a balance between engagement strategies and ethical as well as

environmental concerns. The implementation of gamification and habit loops for user retention improvement

requires careful consideration. An exclusive focus on engagement can marginalize sustainable practices,

leading to increased device usage, push notification fatigue, and superficial reward systems that further

promote consumerism. This is, in fact, supported by the literature, as discussed in the respective section, which

highlights how certain efforts can backfire—hindering genuine change by reinforcing the very sources of the

problem (Levent, 2023; Paschal, 2025).

Designers should implement mindful interactions that help users achieve their goals without creating

distractions or excessive resource consumption. Users gain control over their experience through customizable

notification preferences, energy-saving features, and data collection transparency that enables them to make

conscious choices regarding their preferences (RIB Software, 2024). The techniques work to create enduring

user behaviour modifications which support sustainability objectives.

Behaviour Change Design Techniques Beyond Gamification

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"Make It Personal: The Persuasive Power of ’Me’ and ’My’". The process of personalization makes

sustainability goals and actions more relevant and motivating through individualized framing. The approach

includes tailoring feedback to match user-specific contexts and habits, encouraging self-monitoring and

reflection, as well as goal creation that aligns with one’s own personal beliefs.

"Tip the Scales: How Perceptions of Losses and Gains Influence Our Choices". This category leverages

insights from behavioural economics by framing choices to highlight perceived gains or losses. The way this

works is that apps can use two different approaches to encourage the adoption of sustainable behaviour: either

by showing users the advantages of sustainable actions (health benefits and cost reductions), or the negative

outcomes of inaction (environmental harm and resource waste).

"Craft the Journey: Why the Entire Experience Matters". In this case it is grasped that behaviour change

does not happen through isolated individual interactions. It rather needs a well-planned user journey.

"Set Up the Options: Setting the Stage for the Desired Decision". The process of choice architecture

functions as a key mechanism to steer users toward sustainable choices. Designers should establish eco-

friendly options as default choices while minimizing decision fatigue through restricted unnecessary choices

and implementing prompts or reminders at decision points.

"Keep It Simple: Avoiding Undesirable Outcomes". Behaviour change becomes difficult when people

encounter complicated or overwhelming situations. Users maintain their motivation and consistency when

interfaces are simplified and barriers as well as interruptions are reduced or avoided.

The implementation of these behaviour change strategies in sustainable app design enables authentic user

engagement and interaction through autonomy and respect while avoiding manipulative practices. These

methods enhance gamification techniques by handling psychological and contextual elements that affect

behaviour which leads to more effective and sustainable pro-environmental actions.

Green IT and Sustainable Development Practices

Shifting the lens beyond the interface, backend operational practices are also responsible for affecting the

environment. The implementation of green IT strategies that support digital sustainability includes practices

such as selecting a host server that is powered by renewable energy, reducing server load, and using efficient

databases and APIs (Becker et al., 2022).

This architecture behind mobile app development can also adopt the principles of modularity and

adaptability to extend product lifespan while minimizing the need for frequent updates which in turn leads to

software obsolescence and increased energy usage (RIB Software, 2024).

Ethics and Inclusivity

Despite it being intuitive, it is important to be explicit in stating that green design does, indeed, stand in both

social and ethical dimensions. The “Society-Centered Design” movement and “Climate Designers” network

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state that ethical technology must protect user rights while making products accessible to them and building

community empowerment (Climate Designers, 2025; IF.Society centered design, 2025).

The design approach of inclusivity makes sure applications function well for users from groups with

different sets of abilities, cultural backgrounds, and device access. Applications that undertake the

sustainability mission, gain greater impact by involving the community in design processes and promoting

awareness through participatory storytelling. Moreover, adopting transparent data practices are certainly a

supporting factor as well (UXPin, 2023). The principles of society-centred design also work as a pillar of

support for these approaches by focusing digital products on values such as justice, transparency, and shared

prosperity to advance environmental justice alongside sustainability (IF.Society centered design, 2025).

Green Design Trends in Practice

Practical applications of green technology serve as additional evidence that demonstrates how the mentioned

principles become actualized. The user interfaces of “Energy Saver”, “EcoBuddy” and “Gaia” demonstrate

minimalism through restrained colours while maintaining efficient features that prevent digital and cognitive

strain (Design Rush, 2025). The combination of dark mode functionality with local caching and carbon

awareness indicators in the interfaces enables users to track and manage their digital carbon footprint (The

Beetroot Team, 2024).

Mobile applications supporting sustainability need to integrate green UX principles with ethical design

frameworks and environmentally conscious development practices during their development process. The best

practices create a comprehensive sustainable app design blueprint by minimizing data usage and digital

emissions through the adoption of certain approaches such as gamification with elevated care.

METHODOLOGY

This research is driven by adopting an exploratory approach to analyse existing mobile applications that

promote sustainable behaviours. A systematic selection and categorization of the relevant apps from the

Google Play Store is the first building block of this methodological process, aiming to form a curated dataset

of mobile tools aimed at supporting green practices. Rather than conducting a mere traditional content analysis,

this phase integrates both qualitative and computational techniques for enriching the understanding about the

effectiveness of the collected apps, user perceptions, and engagement dynamics. This analysis sets the

foundation for developing informed recommendations that may guide app designers in enhancing digital

advocacy for environmental sustainability.

Platform and Search Strategy

The platform used for app exploration was the Google Play Store, as it provides wider accessibility and lower

development entry costs than the Apple App Store. Android devices rule the global smartphone market,

especially in regions that favour affordable open-source platforms. To elaborate, Play Store provides both

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extensive accessibility and market reach and a faster open review process than the Apple App Store, attracting

developers from diverse backgrounds, including emerging economies and independent creators, to publish

sustainability-related applications. The lower barrier to entry on the Play Store often results in a wider array

of app types, including niche or experimental green initiatives that may not pass the stricter quality control of

the Apple ecosystem. The open nature of this platform works well with research exploration because it enables

an extensive evaluation of digital environmental advocacy tools. Android leads the market in regions where

sustainability initiatives have the most impact, thus, this justifies Play Store as a suitable choice for studying

green app trends. Consequently, the Google Play Store is populated with a more diverse and expansive range

of mobile applications, making it the ideal environment to capture the variability in sustainability-related app

offerings (Bigabid, 2023). Two important caveats must be acknowledged in this research. First, the selection

of applications was carried out on April 2nd using a device located in Switzerland. As a result, the Google

Play Store’s regional settings were automatically configured to Switzerland, potentially influencing app

visibility and availability. This introduces a possible selection bias linked to regional accessibility. Then, as a

second remark, the availability of applications—particularly those that are smaller or less well-known—is

subject to change over time. Therefore, the app list presented in this study reflects a specific snapshot in time.

For the sake of broad and relevant coverage, two primary search terms were used:

• “Green”

• “Sustainab-”

To further strengthen the methodological positioning of this study, it is important to situate our approach in

relation to established evaluation frameworks from adjacent domains. Health applications, for example, have

benefitted from systematic tools such as the Mobile App Rating Scale (MARS) and the ABACUS behavior

change scale, which provide structured assessments of usability, engagement, privacy, and behavioral impact.

While these frameworks were not designed specifically for sustainability apps, they highlight essential

dimensions—such as content validity, user-centered design, and behavioral outcomes—that informed our

methodological design. By referencing these established models, we acknowledge both the strengths and the

limitations of transferring evaluation principles across domains, and we position our work as a step toward

building a more tailored framework for sustainability-focused applications.

Our sampling strategy is described in detail to ensure transparency. Data were collected from the Google

Play Store, with the search conducted from Switzerland. The scope was restricted to applications available in

English and Italian, reflecting practical access considerations. This sampling approach provides a clear and

replicable pathway for data collection, but it also introduces a regional and linguistic bias. The reliance on one

geographic entry point (Switzerland) and two dominant languages (English and Italian) may limit the

generalizability of findings, particularly in contexts where app ecosystems or user behaviors differ

significantly. By explicitly recognizing this limitation, we frame our results as representative of Western and

Southern European markets, while acknowledging that further research is needed to validate these patterns

across other regions, languages, and cultural contexts.

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This choice reflects a strategic departure from another research study, which used only one term due to

constraints in their search strategy, considering the two interchangeable (Lago et al., 2014). In contrast, this

study chose to include both terms to maximize the coverage of relevant apps, also given the observed

geolocation limitations and naming inconsistencies. An exploratory attempt was also made with the term

“environment”; however, this yielded fewer suitable results, all of which had already appeared under the

“sustainab-” search. Thus, the latter revealed itself to be the most productive keyword, ultimately returning 50

relevant apps (after excluding those overlapping with the “green” keyword results, which had only produced

4 relevant apps on its own).

Application Selection Criteria

The process of selecting applications for this research was designed to be fair and meaningful, employing an

approach that balances reach, relevance, and reliability. The final analysis is based on apps that meet several

predefined inclusion criteria. Establishing these requirements ensured total transparency and maintained

integrity throughout the selection process. The first practical criterion evaluated the availability of language

support, limiting the analysis to apps in English and Italian, as the researcher is fluent in both languages. This

focus on English and Italian language support enabled effective interface and content interpretation, allowing

for a nuanced qualitative evaluation of the apps' tone, messaging, and functionality. In addition to language,

the applications needed to demonstrate relevance and engagement in one of three key ways to qualify for study

participation. A specific download volume was indicated to help determine whether an app has achieved

notable public visibility and user engagement, thereby establishing its relevance in the sustainability app

market. To ensure that the apps selected for this study had a meaningful level of public engagement, a threshold

of 10,000 downloads was used. This selection criterion aligns with the research approach of another body of

work, which established this benchmark to evaluate health apps, as it reflects their visibility and meaningful

user interaction.

The selection of apps for external recognition was based on their validation through partnerships with

reputable organizations, certifications, or endorsements from subject matter experts. These elements enhanced

the credibility of the selection process. The rationale for app selection can be summarized as follows:

Include App

⇐⇒

Language

∈

{English, Italian}

∧

(Downloads>10,000

∨

Strong Sustainability Focus

∨

Expert Approval)

An override condition was applied in cases where apps had no downloads. Specifically:

If Downloads= 0

∧

Strong Sustainability Focus

⇒

Manual Inspection

In other words, apps with zero downloads but demonstrating a strong sustainability focus were manually

evaluated to determine their relevance and potential inclusion.

Observations and Exclusions

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During the app selection process, several specific patterns emerged that influenced the final composition of

the dataset. These findings were crucial for the methodology and revealed significant issues within

sustainability apps, such as branding deception, limited scope, and concerns regarding long-term relevance.

Many applications were designed to address particular, narrow use cases that dominated the market. For

instance, "Green Drive" assists car users, while "Sustainable Ocean Alliance" focuses on ocean environmental

initiatives. The search results indicated that keyword selection was vital during the investigation. The search

term “green” produced numerous applicable apps; however, most of these results also appeared when using

the more effective “sustainab-” keyword, which encompasses “sustainable” and “sustainability.” Notably, the

search term “green” yielded only five relevant results that were also found in the “sustainab-” search results.

Consequently, the selection of the “sustainab-” keyword became the final criterion for compiling the list of 54

apps. Relying solely on the “sustainab-” keyword during the selection process enhanced efficiency and

highlighted the importance of choosing appropriate search terms for systematic reviews.

Keyword Electiveness and Relevance

The entire search process through app stores relied heavily on metadata and keyword matching, making

success contingent on the quality of the terms selected for sustainability-focused digital tools. Three different

keywords were initially tested to explore sustainable apps: "green," "sustainab-," and "environment." Among

these keywords, "sustainab-" yielded the most relevant results due to its high number of quality outcomes. The

root term "sustainab-," which encompasses concepts such as "sustainable" and "sustainability," yielded the

largest selection of apps that potentially met the study criteria.

However, when examining the results from the search conducted using the keyword "green," it becomes

apparent that there is an inconsistency compared to the other two keywords.

Application Categorization

Categorization by User Interaction Strategy. This application categorization is based on the primary method

of user interaction, which involves either game-like mechanisms or the delivery of informative content. These

two approaches serve as distinct motivational strategies to encourage user participation. A key aspect of the

classification process was to separate applications according to their interaction strategy, specifically whether

they utilize gamification or take a more informative approach. In this section, the terms 'engagement' and

'interaction' will be used interchangeably.

Gamified apps are designed to be engaging and enjoyable, incorporating elements from video games to

encourage regular use. Features may include simple options such as awarding points, unlocking badges,

completing levels or missions, maintaining streaks, or climbing leader boards. Other features may involve

avatars or interactive stories. Some apps blend elements from both categories, and in those cases, a

determination was made regarding the primary mode of interaction.

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Categorization by Review Availability. The second main criterion for classifying the shortlisted

applications was whether they had received user reviews from real users on the Google Play Store. To gain

deeper insights into user preferences and dislikes regarding how apps integrate into their sustainable lifestyles,

the applications were sorted into two main categories:

 Apps with reviews: This category includes apps that have received either ratings, written feedback,

or both. This subset of the dataset is particularly valuable, as it reflects first hand user experiences.

 Apps without reviews: This category consists of apps that users have either not downloaded or for

which the review feature has been disabled for unknown reasons.

By differentiating between apps with and without reviews, this categorization ensures that evaluations

remain grounded in real-world usage whenever possible.

Categorization by Thematic Focus. The final dimension of categorization involved the thematic content of

each app, referring to the type of sustainability issue it sought to address. The categories are presented in Table

1 below. To deepen this thematic analysis by connecting it with global priorities and also create more

familiarity with the themes, the app categories were mapped to the United Nations Sustainable Development

Goals (SDGs) (United_Nations, 2025). Mapping the apps to relevant SDGs allowed for a clearer understanding

of how digital solutions align with broader sustainability frameworks.

Table 1. Sustainability App Thematic Categories, Descriptions, and SDG Alignment

Source: own research

Category

Short Description

Relevant SDGs

Environmental Awareness

and Education

Raises awareness and educates through

news, games, or missions.

SDG 4 (Quality Education), SDG 13

(Climate Action)

Carbon Footprint Tracking

and Climate Action

Helps track, reduce, or offset carbon

emissions

SDG 13 (Climate Action), SDG 12

(Responsible Consumption)

Sustainable Consumption

and Product Transparency

Guides ethical choices with brand

ratings and certifications.

SDG 12 (Responsible Consumption),

SDG 8 (Decent Work and Economic

Growth)

Sustainable Lifestyle and

Habit Building

Encourages eco-friendly habits via tips

and challenges

SDG 12 (Responsible Consumption),

SDG 3 (Good Health and Well-being)

Sustainable Mobility and

Transport

Promotes low-carbon travel with

gamified incentives

SDG 11 (Sustainable Cities), SDG 13

(Climate Action)

Food Sustainability and

Waste Reduction

Supports sustainable eating and food

waste reduction.

SDG 2 (Zero Hunger), SDG 12

(Responsible Consumption), SDG 13

(Clime Action)

Circular Economy and

Recycling

Motivates recycling and reuse through

rewards and education.

SDG 12 (Responsible Consumption),

SDG 9 (Industry, Innovation and

Green Finance and Business

Sustainability

Focuses on ethical investing and green

business strategies.

SDG 9 (Industry and Innovation), SDG 8

(Decent Work), SDG 13 (Climate Action)

Community Engagement and

Social Platforms

Builds communities around shared

environmental action.

SDG 17 (Partnerships), SDG 11

(Sustainable Cities), SDG 13 (Climate

Action)

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Thus, these categories illustrate the various types of digital solutions that can be used to promote sustainable

conduct. The apps are versatile in terms of the target audience they reach. All users—be they beginners in

building sustainable habits or deeply committed to an eco-friendly lifestyle—can benefit from these apps,

which serve as valuable resources offering information, motivation, and opportunities to connect with those

who share the common goal of creating a more sustainable world.

Sentiment Analysis of User Reviews. The evaluation of mobile applications focused on sustainability

requires knowledge about how users experience and use these digital tools. This research uses sentiment

analysis as a widely recognized Natural Language Processing (NLP) technique to extract meaningful patterns

from review data. The primary purpose of sentiment analysis is to enable researchers to automatically

determine whether user-generated content tends toward a positive, neutral, or negative sentiment. The analysis

focused on sustainability applications with Google Play Store user reviews from the selected 54 applications.

The information structure includes the category, a short description, and relevant SDGs, as it is presented in

Table 1.The sentiment analysis was conducted using the VADER (Valence Aware Dictionary and Sentiment

Reasoner) tool, which is part of the Natural Language Toolkit (NLTK) in Python. To retrieve the review data,

the Google Play Scraper Python library was employed. While the function was configured to retrieve up to

500 of the most recent reviews per app, the actual number of available reviews varied significantly across the

sample. The number of reviews on some apps reached only five or six, while other apps had received between

400 and 500 reviews. This difference in the number of reviews stems from natural variations between apps in

terms of their popularity, user interaction, and the time they have been available on the market.

The text of each review was then processed by VADER, which produced a compound sentiment score

ranging from -1 (extremely negative) to +1 (extremely positive). These scores were averaged per app to obtain

an overall sentiment value, which was then categorized into positive, neutral, or negative based on standardized

thresholds: scores above 0.05 were labelled as positive, those below -0.05 as negative, and values in between

were considered neutral. To reiterate, despite the sample size limitation, this sentiment analysis represents a

valuable first step in understanding how sustainability apps are perceived by their users in practice.

Topic Modeling of User Reviews. Sentiment analysis offers useful information about user attitudes by

measuring emotional polarity; however, it often fails to capture the actual content of user discussions

accurately. Therefore, this study employs topic modelling as a complementary natural language processing

(NLP) technique to analyse the specific themes, expectations, and concerns expressed by users in reviews of

sustainability-focused mobile applications. By utilizing topic modelling, researchers can go beyond basic

emotional evaluations of user comments to uncover hidden patterns in how individuals discuss digital tools.

Data Collection and Review Preparation. The topic modelling analysis was conducted on the same

dataset of user reviews used in the sentiment analysis phase. This included 21 sustainability apps (from the

original set of 54) that had available English-language reviews on the Google Play Store. As with the sentiment

analysis, the Python library "google_play_scraper" was used to collect up to 500 of the most recent reviews

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for each app. To ensure reliable linguistic consistency for the modelling process, once again, only reviews in

English were retained. The Python library "langdetect" was also used in this case to identify non-English

entries, which were then removed from the analysis. The topic modelling algorithm requires English-language

data because, similarly to VADER, it has been tested and optimized for content in the English language. It

may struggle to interpret non-English text with sufficient semantic accuracy.

Pre-processing for Topic Modelling. Extensive cleaning and preprocessing were necessary for the text

corpus before applying the topic modelling algorithm to enhance model clarity and performance. Each review

was initially converted to lowercase, followed by the removal of punctuation marks, numeric characters, and

non-alphabetic symbols. The Natural Language Toolkit (NLTK) was utilized to eliminate standard English

stop words, such as “and,” “the,” and “is,” as they do not contribute significant semantic value. The Porter

Stemmer was then applied to each word, transforming it into its base or root form (e.g., “distracted,”

“distracting,” and “distract” all reduce to “distract”). This process unified related concepts and reduced the

number of dimensions, which is crucial when working with short and informal texts, such as app reviews.

Finally, the cleaned text underwent its last layer of pre-processing—tokenization—to generate individual

words, allowing for organization in a format compatible with topic modelling standards.

LDA Modelling Implementation. The research applied Latent Dirichlet Allocation (LDA) because this

established unsupervised machine learning method effectively extracts topics from text data and demonstrates

reliability for NLP tasks with short-form user-generated content. The Gensim Python library was used to

implement the model. The final number of topics identified in the research was five, following tests of different

topic counts ranging from four to ten. This five-topic model achieved an optimal balance between thematic

understanding and distinct topic separation. The model required ten passes over the dataset (passes=10) to

reach stable convergence of probabilistic distributions between documents and words. A key feature of LDA

modelling is the lambda (λ) parameter, which affects how words are ranked and interpreted within each topic.

When λ = 1, the ranking of terms is based on overall frequency across all topics, highlighting the most common

words. In contrast, choosing λ = 0.4 prioritizes words that are more specific to a particular topic, leading to

better thematic separation. Interpreting topics through both values provides a dual perspective, revealing the

general emotional or functional context (λ = 1) and the specific conceptual nature of each topic (λ = 0.4). The

model's outputs became clearer when visualized using the interactive data display tool "pyLDAvis."

Integrating Topic Modelling in the Broader Analysis. The implementation of topic modelling together

with sentiment analysis serves to create a fuller and more useful understanding of user experience. As

sentiment scores measure emotional satisfaction, topic modelling reveals specific aspects to which users are

reacting. Together, these tools provide a rich, complementary foundation for evaluating sustainability apps—

not only in terms of how they affect users' emotions, but also in terms of what users’ value, criticize, or

recommend. However, we must recognize the specific boundaries of the research approach. While topic

modelling holds significant analytical value, it's crucial to consider several factors.

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 First, the number of reviews used, although filtered for quality and language consentience, remains

modest because not all sustainability apps receive substantial public feedback. Thus, the findings

may not be generalizable, but they still provide valuable qualitative insights into the recurring

themes among users who utilize these apps.

 Second, stemming and stop word removal—while improving model efficiency—may sometimes

lead to the loss of contextual nuance, particularly in short user reviews. Nevertheless, these steps

were necessary to enhance model performance, considering the unstructured and frequently

disorganized nature of app store language.

Ultimately, the topics identified are best understood as thematic cues rather than definitive categories. Their

value lies in their ability to highlight user priorities and recurring issues that might otherwise be overlooked

when evaluating app effectiveness solely based on numerical ratings or isolated comments. Table 1 presents

the four topics categorized by sustainability.

RESULTS

App Categorization

This section presents the categorization outcomes and the availability of user reviews for the selected

sustainability apps.

Table 2. Categorization of Sustainability Apps

App Name

Gamified

Informative

Reviews

Thematic Focus

Green the Planet/2

Yes

Environmental Awareness

And Education (4,13)

Earth Hero Climate Change

Yes

Environmental Awareness

And Education (4,13)

Go Green Challenge

Yes

Sustainable Lifestyle and

Habit Building (12,3)

Green Point: Food&

Cosmetics

Yes

Circular Economy and Re

cycling

(12,9)

My Green City

Yes

Environmental Awareness

And Education (4,13)

Green Money

Yes

Green Finance and Business

Sustainability (9,8,13)

Ganddee

Yes

Community

Engagement and

Social Platforms (17,11,13)11,13)

Earth5R

Yes

Community

Engagement and

Social Platforms (17,11,13)

Good on You - Ethical Fashion

Yes

Sustainable Consumption

And Product Transparency (12, 8)

Kora Sustainability

Yes

Carbon Footprint Tracking

And Climate Action (13, 12)

Continues with 44 more apps….

Source: own research

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The collection of 54 apps is displayed in Table 2, showcasing a diverse range of thematic categories and

providing helpful information about how digital tools interpret and promote sustainability. Table 3 presents

the distribution breakdown across all categorization dimensions. There is a notable dominance of mixed

interaction models, with over half of the apps (55.56%) incorporating both gamified and informative elements.

In contrast, apps classified as purely informative or solely gamified represent smaller portions of the dataset at

31.48% and 12.96%, respectively.

Table 3. Overview of app distribution by user interaction type, Review Availability, and Thematic Focus

Source: own research

Carbon Footprint Tracking and Climate Action is the most popular thematic focus category (24.07%),

followed by Environmental Awareness and Education (20.37%). Sustainable Consumption and Product

Transparency (18.52%) and Sustainable Lifestyle and Habit Building (14.81%) are also significant themes,

typically employing gamified nudges to influence daily choices and closely linked to SDG 12 (Responsible

Consumption and Production). The less frequently mentioned categories, Sustainable Mobility and Transport

(3.70%) and Green Finance and Business Sustainability (3.70%), indicate app development efforts aligned

with SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure).

Moreover, access to user reviews enhances the analysis of app performance. A striking 61.11% of the

collected apps lack visible user reviews on the Google Play Store. We used the reviews from the apps for

sentiment analysis and topic modelling in the following sections. This distribution overview reveals a complex

User Interaction Type

App Count

Percentage (%)

Gamified

12.96%

Informative

31.48%

Both gamified and informative

55.56%

Total (User Interaction Type)

100%

App Review Availability

App Count

Percentage (%)

Apps with reviews

Apps with no reviews

38.89%

61.11%

Total (User Interaction Type)

100%

Thematic Focus

App Count

Percentage (%)

Environmental Awareness and Education

Carbon Footprint Tracking and Climate Action

Sustainable Consumption and Product Transparency

Sustainable Lifestyle and Habit Building Sustainable

Mobility and Transport

Food Sustainability and Waste Reduction

Circular Economy and Recycling

Green Finance and Business Sustainability

Community Engagement and Social Platforms

20.37%

24.07%

18.52%

14.81%

3.70%

7.41%

9.26%

3.70%

9.26%

Total (User Interaction Type)

100%

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landscape that promotes sustainability through various themes, interaction types, and communication styles.

Technology is increasingly utilized to drive sustainability-related initiatives that aim to reflect progress in the

areas targeted by the SDGs. Thus far, the intuitive yet layered analysis supports this conclusion.

Moreover, access to user reviews enhances the analysis of app performance. A striking 61.11% of the

collected apps lack visible user reviews on the Google Play Store. We used the reviews from the apps for

sentiment analysis and topic modelling in the following sections. This distribution overview reveals a complex

landscape that promotes sustainability through various themes, interaction types, and communication styles.

Technology is increasingly utilized to drive sustainability-related initiatives that aim to reflect progress in the

areas targeted by the SDGs. Thus far, the intuitive yet layered analysis supports this conclusion.

Sentiment Analysis Overview

A summary table (Table 4) was created to show the number of reviews collected for each app, along with

the reviews that moved on to the final analysis contribution stage following language filtering. This

information is provided to ensure transparency regarding the number of reviews utilized in calculating each

sentiment score and to enhance your understanding of the results.

Table 4. Summary of Sustainability Apps and Sentiment Analysis

App Name

Avg.

Weighted

Sentiment

Category

User

Interaction

Type

Total

Reviews

English

Reviews

Green the Planet

0.531

Positive

Environmental Awareness

and Education

Gamified

500

403

Earth Hero:

Climate Change

0.701

Positive

Environmental Awareness

and Education

Both

500

477

Go Green

Challenge

0.638

Positive

Sustainable Life style and

Habit Building

Both

Good On You-

Ethical Fashion

0.525

Positive

Sustainable Consumption

and Product Transparency

Informative

500

482

Environment

Challenge

0.496

Positive

Environmental Awareness

and Education

Both

104

A World in

support of Act

Now

0.461

Positive

Carbon Footprint Tracking

and Climate Action

Both

308

247

Fork Ranger-

sustainable food

0.546

Positive

Food Sustainability and

Waste Reduction

Both

Good bag &good

cup

0.756

Positive

Food Sustainability and

Waste Reduction

Both

Sustainable

Shaun

0.392

Positive

Environmental Awareness

and Education

Both

My Little Plastic

Footprint

0.412

Positive

Sustainable Lifestyle and

Habit Building

Both

120

104

Samsung Global

Goals

0.419

Positive

Environmental Awareness

and Education

Informative

500

312

Ecosia-Private

Web Browser

0.359

Positive

Circular Economy and

Recycling

Informative

500

402

A billion better

social media

0.672

Positive

Community Engagement

and Social Platforms

Informative

500

424

Yuka Food &

Cosmetic

Scanner

0.528

Positive

Sustainable Consumption and

Product Transparency

Informative

500

450

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Code Check:

Product Scanner

0.319

Positive

Sustainable Consumption and

Product Transparency

Informative

500

381

Too Good To Go

0.424

Positive

Food Sustainability and

Waste Reduction

Informative

500

446

Open Food Facts

0.293

Positive

Sustainable Consumption

and Product Transparency

Informative

465

398

Klima-Live

Carbon Neutral

0.493

Positive

Carbon Footprint Tracking

and Climate Action

Informative

118

103

Naturitas-Natural

Health

0.231

Positive

Sustainable Consumption

and Product Transparency

Informative

164

110

Grow Forest

0.469

Positive

Environmental Awareness

and Education

Both

116

enerjoy-

CO2FootprintCoa

0.558

Positive

Carbon Footprint Tracking

and Climate Action

Both

Source: own research

The bubble chart illustrated in Figure 1 offers a comprehensive overview by plotting each app along a

horizontal axis that indicates its average sentiment score (derived from compound VADER analysis) while

categorizing them vertically by thematic group. The study focuses on English-language reviews, which

influence both the size and colour of the bubbles depending on whether the app employs informative content,

gamified content, or a combination of both. A detailed interpretation of the table and the chart will be provided

in the consecutive session.

Figure 1. Weighted average user sentiment scores for each app by category. Dots represent individual apps, colored

by interaction type. Black crosses (X) indicate the weighted mean sentiment score within each category.

Topic Modeling Overview

The LDA algorithm was employed to analyse English-language user reviews through topic modelling,

aiming to extract deeper insights into user perceptions and priorities. The five generated topics represent

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thematic clusters that reflect common user concerns, preferences, and sentiments regarding the sustainability

apps studied.

 Topic 1: Engagement, Enjoyment, and Brand Enthusiasm. This topic dominates the model,

accounting for 31.6% of total token representation. It is depicted as the largest bubble in the upper-left

quadrant of the pyLDAvis visualization and overlaps partially with Topic 3. This overlap suggests that

both topics share common themes related to user appreciation and enjoyment of the app. (Figure 2).

Figure 2. Intertopic Distance Map highlighting Topic 1.

 Topic 2: Technical Frustrations and Usability Concerns. The second topic encompasses 22.8% of

tokens and appears as a medium-sized bubble in the lower left quadrant. While it remains near the

main cluster (Topics 1 and 3), it presents a different narrative focused on the challenges and

frustrations users have encountered (Figure 3).

Figure 3. Intertopic Distance Map highlighting Topic 2.

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 Topic 3: Environmental Impact and Climate-Oriented Behaviour Change. Topic 3 represents

21.9% of the total tokens and, as previously noted, overlaps with Topic 1, indicating shared themes.

This bubble is located in the left section of the visualization, slightly above and to the left of Topic 1.

The thematic connection between emotional satisfaction and sustainability-driven motivation is

evident in this overlapping area as in (Figure 4).

Figure 4. Intertopic Distance Map highlighting Topic 3.

 Topic 4: Food and Product Transparency. The fourth topic comprises 15% of the total corpus and

is positioned in the bottom left quadrant, distinct from the core cluster of Topics 1–3. This spatial

separation indicates a clear conceptual boundary, as this topic primarily addresses food-related issues

as in (Figure 5).

Figure 5. Intertopic Distance Map highlighting Topic 4.

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 Topic 5: Ethical Search, Pricing, and Eco-Friendly Shopping. In the pyLDAvis map, Topic 5 is

the smallest and most distanced topic, representing only 8.7% of total tokens and located in the top-

right quadrant, away from the other topics. Its isolated position suggests that it represents a distinct

thematic domain not firmly connected to the other user experiences observed so far. (Figure 6).

Figure 6. Intertopic Distance Map highlighting Topic 5.

The topic modelling application on the reviews from the collected apps in this study provides a thematic

map of what users’ value, praise, or critique in sustainability apps. The most dominant areas—Topic 1, Topic

2, and Topic 3—suggest that users are drawn to apps that are enjoyable, well-designed, and purpose-driven.

These are the most prevalent areas to be addressed, however it is important to take into consideration also more

niche priorities emerging among some of the discussions, such as health transparency (Topic 4) and cost-

conscious browsing (Topic 5).

More detailed insights will be explored throughout the Discussion session, providing designers with

guidance to select features which maintain user trust while enhancing environmental education and delivering

personalized experiences according to user requirements. The combination of topic modelling with sentiment

analysis produces a complete understanding of sustainability apps by uniting emotional impact with

behavioural insights and technical excellence into a unified story.

DISCUSSION

The following discussion seeks to integrate and interpret the findings from earlier sessions through the lens of

academic research on persuasive design, gamification, and digital sustainability advocacy. This section

leverages the literature review to examine the implications of categorization patterns, as well as sentiment

analysis and topic modelling results, for app designers and researchers aiming to utilize mobile technology in

promoting environmentally friendly behaviour. The study draws on empirical data and builds upon existing

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findings to offer practical insights that can inform the development of more effective and ethically responsible

sustainability apps. The discussion begins by revisiting key patterns identified in the app distribution analysis,

with a particular focus on the design approaches and interaction strategies that provide a foundation for a more

in-depth exploration of user experience, values, and motivation.

Insights from App Categorization

One striking observation from Table 1 and Table 2 is the widespread use of gamification, with many apps

incorporating challenges, point systems, streaks, and reward mechanisms—design elements previously

discussed. Categories such as sustainable lifestyle and habit building, carbon footprint tracking, and climate

action stand out for their gamified engagement strategies. This trend can be understood in the context of these

apps' explicit aim to encourage daily behavioural changes, directly aligning with goals such as SDG 12

(Responsible Consumption and Production) and SDG 13 (Climate Action). Literature supports the idea that

gamification can facilitate quicker habit formation by bypassing extensive explanations and prompting direct

action (Brauer et al., 2016).

However, the effectiveness of these apps heavily depends on individual motivation and external

circumstances. Despite their well-defined purpose of promoting sustainable habits through interactive

motivation systems, their performance can vary significantly. People are not always rational, and their

intentions and mmotivation can fluctuate (Mu et al., 2019; Guillén et al., 2022).

In contrast, apps focusing on sustainable consumption and product transparency, as well as green finance

and business sustainability, generally adopt an informative approach. For example, Dependable On You and

Code Inspection primarily serve as databases and scanners rather than interactive platforms. These applications

offer extensive product details, ethical assessments, and financial recommendations, aiming to educate users

rather than entertain them. Such apps can have a significant impact, particularly in advancing SDG 8 (Decent

Work and Economic Growth) and SDG 12 by providing essential information for ethical, sustainable choices.

This distinction highlights the importance of aligning an app’s interaction strategy with its intended outcomes:

while gamification can be an effective tool for behaviour change, information-driven apps are expected to

benefit more from clarity, credibility, and trust (Rory Mulcahy et al., 2018; Tom Hunger et al., 2023).

The selected sustainability apps represent a wide range of digital developments advocating for

sustainability; however, many newer or niche apps lack user reviews on the Google Play Store. The relatively

high percentage of apps without user reviews (61.11%) may initially seem like a limitation, but this limited

visibility does not necessarily reflect their quality or potential.

Both approaches offer the flexibility and coverage necessary to support various aspects of the global

sustainability agenda. Consequently, these apps are gradually expanding the market for digital sustainability

promotion, each in its own unique way, serving as digital ambassadors for the SDGs. They contribute to a

variety of goals, from raising awareness for quality education (SDG 4) to encouraging climate action (SDG

13) and supporting sustainable cities and communities (SDG 11) (Law et al., 2011; Douglas et al., 2021)

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Insights from Sentiment Analysis

The results in Table 3 indicated that all sustainability apps with reviews tended to fall within the positive range

of sentiment scores, which shows a generally favourable user reception, despite there being some variability

on the extent of perceived positivity. It should be mentioned that sentiment analysis, although informative and

helpful, provides emotional interpretations at a surface level.

Having a high average score does not necessarily imply that the satisfaction is perceived across all users,

nor does a lower score imply a complete design failure of the app. However, these scores function as indicators

of general user satisfaction trends which become more meaningful when combined with more granular

analyses, like topic modelling, exploring the themes and concerns raised through the user feedback content.

Moving on to the bubble chart shown in Figure 1, this type of visualization was selected to achieve several

goals. First, it enables a quick glance at the comparison of how different categories of sustainability apps are

perceived emotionally by their users. Additionally, the visualization demonstrates how different user

interaction approaches affect user satisfaction levels in each category (Law et al., 2011; Douglas et al., 2021;

Muangsrinoon et al., 2019; Brauer et al., 2016).

And lastly, it draws attention to the volume of feedback received by each app, adding another dimension to

how sentiment scores can be interpreted in context. Therefore, this bridge between raw sentiment scores and

richer behavioural insights helps contextualize sentiment findings, showing how emotional responses align (or

not) with specific design choices and thematic areas. For app creators, this visual overview can offer directions

about which engagement strategies are resonating well within specific domains of sustainability—and where

improvements or rethinking might be necessary.

Several interesting patterns emerge now that the focus can finally be shifted on interpreting the results. The

Community Engagement and Social Platforms category, which is represented by a relatively large bubble

located towards the far right of the sentiment scale, is one of the most positively rated categories overall, and

specifically the first in generating a positive user response in the group of informative apps. The bubble

indicates both positive reception and a relatively high number of user reviews. The apps in this category,

through encouraging users to interact socially or contribute to broader environmental missions, may be

succeeding in generating not just functional value but also emotional connection and a sense of shared purpose

(Vo-Thanh et al., 2021; Guillén et al., 2022; Lim et al., 2025; IF. Society centered design, 2025).

Apps focused on Sustainable Consumption and Product Transparency indicate variations in user feedback

and thus, sentiment scores. It might seem as if the number of reviews and the weighted sentiment scores move

toward the same direction. The smaller the bubble—the lower the score: in fact, this is the category accounting

for the worst-performing informative apps. Although the data is not rich enough to generalize results, it is

worth trying to engage in some exploration of the reasons as to why such results emerge. Perhaps, users are

stricter regarding transparency requirements and effective performance in distributing trustworthy and accurate

information for informative apps that support the selection of daily consumption goods which are non-edible,

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and generally require a higher portion of "investment" by users to purchase them, like apparel or cosmetics.

Therefore, misinformation in such case can result in a higher monetary loss, as well as disappointment with

the app (Hunger et al., 2023; Berdichevsky et al., 1999). However, the larger user base and availability of

reviews for the ones performing better in this category, may be taken as a sign of good app quality, for this

reason the positive experience essentially accumulates and in turn, attracts even more users.

In the other categories, informative apps perform generally better. For instance, Environ- mental Awareness

and Education is another notable category with numerous entries across the sentiment spectrum. The type of

user interaction that got assigned the lowest score in this case is the hybrid implementation of both.

Interestingly, within the same typology of user interaction, the positive correlation between number of reviews

and sentiment scores seems to hold for this category, too. For apps that employ both informative and gamified

strategies to keep users interacting with the app, a larger user review pool indicates a more magnified positive

sentiment. Although some apps in this group show moderate sentiment scores, the spread reflects variability

in how well these apps manage to communicate educational content in an engaging or emotionally satisfying

way.

In categories like Carbon Footprint Tracking and Climate Action, Sustainable Lifestyle and Habit Building,

and Food Sustainability and Waste Reduction, it seems that the correlation is inverse, however. The lower the

count of reviews, the higher is the sentiment score. The former category has the smallest dispersion among

categories consisting of more than one app. One possible reason might be the inherent simplified functionality

of such apps focusing on metric calculation and display (Suruliraj et al., 2020; World Bank, 2024).

It is not straightforward however, how the interaction strategy affects the score. For all the three mentioned

categories in this paragraph there are two apps pertaining to the adoption of both interaction strategies which

stand on the opposite sides of the generated weighted average sentiment score. These apps often employ hybrid

interaction strategies, which suggests that while gamification can enhance appeal, its effectiveness depends

heavily on how well the game play elements are integrated with the app’s sustainability goals (Mu et al., 2019;

Khaleel et al., 2016).

One other perspective to analyse what this graph shows is that larger bubbles—indicating apps with higher

review counts—are not necessarily aligned with the highest sentiment scores. For instance, a large, informative

app under the Circular Economy and Recycling category may gather many reviews, yet still exhibit only

moderate sentiment. This nuance reinforces the idea that popularity does not in all cases translate into

satisfaction. Volume alone cannot capture emotional nuance; sentiment analysis offers a layer of understanding

that quantitativereview counts cannot (Douglas et al., 2021; Brauer et al., 2016; Hensheret al., 2021).

As we move into the next section of this part, topic modelling will provide a deeper dive into the actual

textual content of user reviews. While sentiment analysis tells us how users feel, topic modelling seeks to

understand what they are talking about. This dual approach—combining emotional tone with thematic

insight—provides a more holistic picture of user experience and expectations in the context of sustainability-

focused mobile applications.

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Insights from Topic Modeling

The analysis of user reviews through topic modelling reveals detailed insights about how users understand and

use mobile applications focused on sustainability. Sentiment analysis serves to capture the general emotional

direction of the user feedback, whereas topic modelling serves to unfold the particular discussion topics

including concerns, characteristics, and expectations that users have mentioned in their feedback. The five

identified topics reveal distinct user experience perspectives. The following interpretations analyse these

themes to reveal design strategies which work and those which do not in this domain.

Interpretation of Topic 1: Engagement, Enjoyment, and Brand Enthusiasm. The most important terms

in this topic include “app,” “great,” “use,” “love,” and “make” supported by “game,” “easy,” “thank,” and

“fun.” These suggest a high level of emotional and experiential positivity. When viewed through ω = 0.4,

additional tokens like “surprise,” “enjoy,” “simple,” “learn,” and “ethic” emerge, showing that many users

were pleased not only with functionality, but also with discovering new information or enjoying small

interactive surprises.

This topic shows overall contentment with both gamified and informative app experiences, including

features that users find delightful, meaningful, and also ethical. The design perspective of this topic

demonstrates how emotional engagement and ease of use should be a careful consideration of designers as

they need to create user experiences that are not only functional, but also rewarding and enjoyable.

Interpretation of Topic 2: Technical Frustrations and Usability Concerns. The terms “product,”

“work,” “money,” “try,” and “doesn’t” appear at ω = 1 and they can possibly be indicating that users might

be discussing app performance, reliability, and possibly failed expectations. The tokens at ω = 0.4— “crash,”

“uninstall,” “load,” “error,” “notification,” and “camera”—make this more evident. But there are also terms

like “charity,” “donation” and “option” that appear, which can be suggesting some type of disappointment

among users, perhaps due to their expectations for in-app features and services not being met.

This topic serves as a critical reminder that even ethically or environmentally noble apps cannot fulfil their

mission if core usability and stability are compromised. Developers should note that trust can erode quickly

when technical issues go unresolved or when expectations set in the app description are not delivered in

practice.

Interpretation of Topic 3: Environmental Impact and Climate-Oriented Behaviour Change. The top

words under ω = 1 include “help,” “change,” “reduce,” “carbon,” “footprint,” and “action.” The selected terms

explicitly convey behavioural and motivational language that matches a climate-conscious thinking process of

users. The ω = 0.4 setting reveals specific terms including “browser,” “offset,” “track,” “emission,” “AI,” and

“calculate” which demonstrate how users can interact with apps that provide tangible metrics and make it

possible to track carbon and their environmental impact overall. This topic demonstrates the need for the

integration of tools that support climate action through quantifiable results (Suruliraj et al., 2020; World Bank,

2024; Adaji et al., 2024).

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Developers who want to promote environmental responsibility should allocate part of their effort and

resources to ensure a proper configuration of their app’s features having the nature of carbon calculators,

impact visualization dashboards, and the like, as making them both accessible and reliable is useful for

nurturing the intrinsic motivation that drive users to engage in environmentally beneficial pursuits.

Interpretation of Topic 4: Food and Product Transparency. The common terms when ω = 1 include

“food,” “product,” “scan,” “ingredient,” and “rate.” The ω = 0.4 view shows more diagnostic and evaluative

language through terms such as “nutritious,” “healthier,” “label,” “additive,” “refund,” and “score.” The word

pat- terns indicate that users mainly use these apps to get transparent information about food labels, health

metrics, and to support their purchasing choices.

The topic shows an increasing pattern in sustainability discussions about how environmental health

connects to personal health. Many of the included apps operate within this specific area. The developers of

food and product scanning apps should concentrate on creating clear interfaces while ensuring accurate

information delivery and personalized feedback on the product choices of users.

Interpretation of Topic 5: Ethical Search, Pricing, and Eco-Friendly Shopping. The ω = 1 words

consist of “search,” “price,” “tree,” “find,” and “deal” while ω = 0.4 brings in economic and browsing-related

terms such as “cheaper,” “package,” “afford,” “shop,” “Ecosia,” and “engine.” The data can be interpreted as

emphasizing how users in this segment make resource-conscious choices by locating affordable, sustainable

products and using apps like Ecosia to plant trees through advertising revenue, or simply looking for budget-

friendly green alternatives.

Although smaller in volume, this topic is highly actionable for designers: price-conscious eco-consumers

are looking for apps that merge environmental action with everyday digital habits like shopping or searching

(Hunger et al., 2023; Vo-Thanh et al., 2021; Guillén et al., 2022).

Building in features that highlight sustainable deals or compare prices with eco-ratings could meet a

currently under-addressed user need.

The five topics together provide an extensive view of the repeated themes and user preferences within the

sustainability app ecosystem. Users seek emotional engagement alongside ethical clarity, technical reliability,

and real-world usefulness in their sustainability apps. These topics demonstrate that sustainability apps receive

their assessment from how effectively they transform environmental messages into practical tools that users

can easily access. The practical design guidelines in the following section also depend on these insights in

combination with the literature findings to support the creation of user-centred mobile applications for the

change of environmental behaviour.

Design Guidelines Informed by User Feedback and App Patterns

The research findings present an analytical summary of mobile applications related to sustainability, but their

significance lies not only in description but in showing how concrete patterns can guide design practice.

Practical design recommendations for designers who want to build effective, ethical user-centred digital

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applications can be derived. App categorization, sentiment analysis, and topic modelling, when combined,

reveal several key themes and patterns that can be interpreted to give guidance to designers who want to support

behavioural change in the sphere of environmental sustainability.

Gamification needs to be thoughtful. The user interaction analysis revealed that apps use two different

methods to engage users by either providing only information or using gamified elements. Importantly, our

findings show that hybrid apps—those combining informative and gamified features—are particularly

dominant, yet their success depends heavily on ethical and purposeful implementation. However, as it was

discussed in the literature review and confirmed by user reviews, gamification does not automatically lead to

success, and as a persuasive tool, it risks falling into manipulation, potentially creating an inauthentic

connection with users and raising ethical concerns (Adjust, 2023; Liberty, 2023).

The topic modelling results showed that users like interactive features (Topic 1) but user interaction needs

to be based on purposeful grounds and aligned with the sustainability goals of the app (Benner et al., 2021;

Kight et al., 2019; Davis et al., 2009). Designers are encouraged to create challenge-based dynamics,

personalized goals, and feedback systems that maintain user interest throughout time, but this should be done

while preventing superficial or manipulative designs. The practice of "pointsification" which uses points as

the only reward system should be avoided because it leads to user fatigue and destroys authenticity.

App reliability depends not only on ethical design, but also functionality. Users consistently

emphasized issues of technical reliability and usability in Topic 2. Reviews frequently mentioned problems

like app crashes, loading issues, or confusing interfaces—barriers that not only undermine trust but also disrupt

the behavioral pathways that sustainability apps aim to encourage. Developers must recognize that technical

robustness and clarity of purpose are prerequisites for behavior change. A poorly functioning app, no matter

how well-intentioned, will eventually fail in delivering its persuasive goals (Douglas et al., 2021). Ethical

persuasive design must thus be accompanied by a strong foundation of usability, stability, and accessibility.

Autonomy and transparency must be part of persuasive strategies. In the literature about persuasive

technology, it was emphasized how there is an emerging concern about the ability to differentiate between

influence and manipulation (Fogg et al., 2003; Berdichevsky et al., 1999; Benner et al., 2021). Our sentiment

analysis further reinforces this: while general sentiment toward sustainability apps is positive, many negative

reviews revolve around unmet expectations, lack of clarity, or perceived opacity in donation mechanisms and

environmental claims. This highlights how transparency remains a recurring user concern. Users expect clear

disclosure about the methods and reasons behind behavioral influence through strategies such as nudging,

gamification, and social comparison.

Users respond positively to concrete climate-related impact. The third Topic generated from topic

modelling demonstrated how users’ value actionable assistance for environmental behavior. Applications that

enable users to set measurable targets for emission offsetting and energy tracking within environmental

frameworks tend to achieve better results in behavior change.

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Informational value is a driving force in consumption-related apps. As observed through Topic 4 of

topic modelling, users frequently interact with apps that allow scanning, rating, and comparing food and

consumer products. This aligns with the positive sentiment trends identified in our analysis, where users

consistently praised apps that helped them make more informed and responsible choices. Developers in this

space of the sustainability apps landscape should focus on achieving database accuracy, transparency of

ratings, and user interface elements that help them make informed choices in real-time shopping environments.

Accessibility and cost sensitivity must not be ignored. A recurring theme in user feedback was

affordability—many reviews flagged pricing as a barrier to sustained engagement. This indicates that

sustainability must be made not only environmentally but also economically accessible. For this guideline, the

existing literature does not directly address it, however the reason for including it is based on Topic 5 emerging

from the topic modelling analysis, where users expressed concerns about affordability, and ethical shopping

decisions.

Sustained user engagement should be encouraged. Across the analytical layers of this research, a

common undercurrent is the challenge of maintaining user interest beyond initial enthusiasm (Douglas et al.,

2021; World Bank, 2024). As discussed in the literature, short-term behavior shifts are common, but long-term

transformation is rare unless continuously supported by the app’s design. Designers should consider

incorporating adaptive feedback, evolving challenges, streak tracking, and meaningful rewards to foster

prolonged interaction, while still aligning with sustainability goals (Guillén et al., 2022; Kight et al., 2019).

Cultural relevance and inclusivity can expand the app’s outreach and effectiveness. Finally, sustainability is a

global concern, but engagement is shaped by local context. Designers should involve diverse users in

participatory design processes and adopt culturally sensitive approaches that respect different values,

behaviors, and environmental challenges (Mu et al., 2019; Davis et al., 2009).

As it was noted in the methodology session, the Google Play Store used as a tool to collect apps for this

study was set to a Swiss regional profile, and the selected apps tend to show the local infrastructure,

certifications, and environmental challenges of Switzerland and its surrounding areas. This means that the

study included apps that use national eco-labels and region-specific mobility solutions, focusing on local

contexts and priorities in sustainability app design. Additionally, Topic 4 in user review topic modelling, on

Food and Product Transparency, hints at how user expectations may take form through cultural differences

and concerns.

The discussion brought together the main findings from app categorization, sentiment analysis, and topic

modelling, discussing them in the context of previous findings. By highlighting hybrid app design, positive

sentiment toward informative tools, and persistent concerns about transparency and cost, this section

underlines what the empirical findings uniquely contribute to the literature and how they can inform practice.

The design guidelines presented at the end are one of the most important outcomes of this research. These

suggestions are based on actual user feedback and app performance patterns observed in this study. They

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demonstrate that while certain elements, such as gamification or impact tracking, can be effective, their success

really depends on how well they are implemented.

CONCLUSION

This research investigated the evolving relationship between mobile technologies with environmental

sustainability through an analysis of 54 sustainability-focused mobile applications listed on the Google Play

Store. It evaluated sustainability app design while assessing their effectiveness through both design and user

experience perspectives. Through a layered methodology that combined app categorization, sentiment

analysis, and topic modelling of user reviews, the research sought to understand how these apps engage users,

how they are perceived once in use, and how they succeed (or not) in supporting long-term behavior change

toward environmental goals.

The research findings demonstrate that the landscape for mobile sustainability applications may be rich in

variety, but quality-wise it remains inconsistent. Numerous apps are populating the market for digital

sustainability advocacy, indicating rising innovation and user engagement in this sphere. However, their

effectiveness and ethical standards differ substantially. All the reviewed apps with user reviews received an

overall positive sentiment score, yet the analysis revealed that positivity in sentiment does not necessarily

equal effectiveness. Instead, deeper findings showed recurring concerns around technical instability,

superficial gamification, lack of clarity in communication, and perceived opacity in behavioral mechanisms,

highlighting gaps that sentiment analysis alone cannot capture.

From the findings, important insights about these topics are revealed. First, the dominance of hybrid apps—

combining gamified and informative elements—illustrates the potential of blended strategies, but only when

alignment with sustainability goals is transparent and authentic. Gamification can be effective, but only when

it is meaningful, aligned, and transparent. Moreover, functional reliability and usability emerged as non-

negotiable prerequisites for ethical persuasive design, indicating that technical performance should be

prioritized before engagement features. Users showed strong positive reactions to apps that provided concrete,

measurable environmental actions, which reinforces the need to translate sustainability into trackable outcomes

that motivate and connect personally. Additionally, informational quality and database transparency remain

core to user trust, positioning accurate data as the foundation of ethical consumption apps. Finally, user

feedback emphasized that affordability and cultural relevance are not peripheral but essential design factors,

too often overlooked, which must be integrated to expand inclusivity and reach diverse global audiences.

The design guidelines produced from this study integrate these findings into a practical framework that

designers and developers can use to create more effective and ethically sound sustainability applications.

Rather than focusing narrowly on engagement metrics or visual appeal, app designers should prioritize building

trust, safeguarding user autonomy, and delivering measurable environmental benefits. The insights presented

here therefore aim to shift sustainability app design discussions away from superficial metrics and toward a

focus on lasting impact and user-centred ethical practice.

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The research also provides both methodological and practical contributions to the field by combining app

categorization, sentiment analysis, and topic modelling to create a scalable mixed-method approach for

evaluating sustainability apps. This methodological integration offers a more holistic perspective, bridging

design intentions with user experience data and revealing how design decisions are perceived in practice. The

study demonstrates that listening to users, not only during development but across the entire app lifecycle, is

critical for shaping effective sustainability interventions.

Future research should expand on these findings by including iOS platforms and niche regional markets in

the app sample. Longitudinal studies could assess whether observed behavioral shifts persist over time, while

participatory approaches—such as co-design workshops and interviews—can enrich the understanding of user

perspectives beyond textual reviews. Additionally, integrating behavioral analytics (e.g., goal completion,

retention rates, in-app actions) would validate the design guidelines and help refine best practices. App creators

are encouraged to participate actively in such validation processes, ensuring that sustainability app

development becomes a collaborative cycle between researchers, developers, and users.

Author Contributions:

Conceptualization: E.C., E.Z. and M.C; Methodology: E.C; Software: M.C; Validation, B.C, E.C.; Formal

analysis, E.C; Investigation: E.Z.; Resources: E.Z.; Data curation: M.C.; Writing—original draft preparation:

E.C.; Writing—review and editing, B.C.; Visualization: E.C.; Supervision: B.C.; Project administration: E.C.;

Funding acquisition: E.C.

All authors have read and agreed to the published version of the manuscript.

Informed Consent Statement: not applicable

Data Availability Statement:

The data presented in this study are available on request from the corresponding author.

Institutional Review Board Statement: not applicable

Conflict of interests

The authors declare no conflict of interest.

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About the authors

Eva CIPI

Director of the Regional Development Center at the University of Vlora,

Albania. She has coordinated Erasmus+ and Interreg projects such as

TEAVET, CRED4TEACH, and SuProM, and published on AI in education

and green competencies. Her work bridges academia, innovation, and policy,

strengthening international cooperation.

Research interest: teacher professional development, digital education,

micro-credentials, and sustainable development.

ORCID iD: 0000-0001-6930-9143

ACCESS Journal:

Access to Science, Business, Innovation in Digital Economy

ISSN 2683-1007 (Online)

2025, 6(3), 634-667, https://doi.org/10.46656/access.2025.6.3(10)

https://journal.access-bg.org/

667

Eljona ZANAJ

PhD researcher at University Ismail Qemali Vlore, Vlore, Albania. Assistant

Lecturer (Department of Computer Science)

Research interest: researching energy efficiency in IoT and smart water

quality monitoring, environmental sustainability.

ORCID iD: 0000-0003-2950-5988

Marsia CIPI

She has a master degree in Management and Informatics from Italian

Switzerland University of Lugano.

Research interest: Digital economy, innovative technologies. digital

transformation, and applied research for apps sustainability

ORCID iD: 0009-0004-8479-5257

Betim CICO,

PhD., Professor (Computer Engineering) and researcher specializing in

electrical engineering, ICT, and applied sciences at the University Epoka,

Tirana, Albania.

Research interest: sustainable engineering solutions and innovation in

higher education, digital systems, smart technologies; high-performance

computing, digital design methodologies and tools,

ORCID iD: 0000-0001-9078-6147

This work is licensed under the Creative Commons Attribution International License (CC BY)

Last updated on December 18, 2024

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citation of articles published in various journals, is increased. Inclusion of

scientific journals in global indexed systems for citations Scopus and

Web of Science requires the editors and publishers to meet the selection

criteria, strictly established by these systems, which generally meet the

international standards for the issuance of scientific periodicals. As one

of the 13 criteria for selection of journals in Scopus is online

accessibility - accessibility to the journal site with a mandatory English

version and the quality of the journal site, the ACCESS team has created

a platform for periodic electronic publications.

Each university needs objective data for assessment of the science, for

making decision for further development.

Main problems of the quantity assessment of publications are an

insufficient number of journals in Scopus and Web of Science.

ACCESS Press has a special offer for universities and other organizations that are seeking

a partner to publish all or some of their English language journals, books and other

publications. This applies to new publications and to previously published books and previous

journal volumes. We publish monographs, textbooks, edited volumes, and other categories.

The university can decide if a given journal or book is published using the Open Access or paid

access model. All books and journal articles bear both the university and ACCESS logos.

ACCESS Press will design, produce and manage the website of this publishing house. The role

of the university is to select and channel books and book proposals for this publishing co-

operation, as well as to promote this publishing opportunity to its faculty.

The university can decide which package of services applies to each journal and book. Such

packages are described in the pages for journals and books. If the value of the contract

exceeds an agreed amount, the university can enjoy discounts up to 60% on standard

fees.

Please contact us to discuss the terms of the ACCESS Publishing House offer.

Main features of ACCESS Journal Platform

- Each journal has a dedicated website, which by request can have a custom

design

- Journal websites have a dedicated subdomain myjournalname.access-bg.org,

but by request the journal can use a dedicated domain myjournalname.com

and still use the ACCESS platform

- Each journal website has informational sections – description of the journal

subject, editorial board, review policy, open access policy

- Each journal website has a contact page to contact the journal team

- Each journal has an archive with all the issues and their articles

- Each journal website has a search functionality only in the context of the

journal, results from the rest of the platform are not included

- By request each journal structure can be customized

- Journal websites are built in English, but by request they can be customized to

be multi-language

- The main platform website has links to each journal website

- The main platform search and browse functionalities include all issues and

articles from the journal websites, linking to them

- The main platform and each journal website is with responsive design and

mobile-friendly

Administration

- Each journal administrator has access to a control panel to administrate their

content and upload new issues

- The control panel is user-friendly and with responsive design, working equally

well on all kinds of mobile and desktop devices

- Administrators can add, edit, remove issues

- Administrators can add, edit, remove articles

- Administrators can manage most of their site content

ACCESS JOURNAL

2025

Website: https://access-bg.org/

Email: office@access-bg.org; editors@access-bg.org

Phone: Bulgaria + 359 (0) 886-842-129

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