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Factors Influencing Consumers’ Resistance Towards Property
Rental Payments in Proptech Apps
FINAL PROJECT
In partial fulfillment of the requirements
for the master’s degree
from Institut Teknologi Bandung
By
JASMINE SHAFIRA
Student ID : 29122363
(Master of Business Administration Program)
INSTITUT TEKNOLOGI BANDUNG
July 2024
Koleksi digital milik UPT Perpustakaan ITB untuk keperluan pendidikan dan penelitian
ii
ABSTRACT
FACTORS INFLUENCING CONSUMERS’ RESISTANCE
TOWARDS PROPERTY RENTAL PAYMENTS IN PROPTECH
APPS
By
Jasmine Shafira
Student ID: 29122363
(Master of Business Administration Program
Proptech applications are experiencing growth and popularity as technological
advancements transform how people search for and pay for rental accommodations.
Despite the increasing adoption of these apps, a gap exists between users who used
these apps for searches and those who make rental payments through them. This study
investigates the reasons behind resistance to using the payment functionalities of
proptech apps among users who have adopted the search features but not the payment
feature. To explore this, the study employs Innovation Resistance Theory (IRT), mixed
with variables from UTAUT. Previous research showed that various barriers can
influence user resistance to app payments. A mixed-methods approach is used in this
research, combining qualitative preliminary testing and quantitative main testing. The
study adopts convenience sampling to gather data. Data were collected through
questionnaires and analyzed using Multiple Linear Regression Analysis in SPSS 27.0.
The findings reveal that three main barriers—Risk, Tradition, and Image—
significantly influence users from making payments through proptech apps. Risk
Barriers pertain to concerns over security and privacy. Tradition Barriers involve users'
preference for conventional method ways over alternatives. Image Barriers relate to the
perception of the app's reputation and trustworthiness. Future research
recommendations include conducting discriminant analysis to compare non-adopters
of proptech app payments distinct groups, incorporating demographic elements such
as age, income, and gender as moderating variables, and including additional
independent variables that can expand the research scope. This study contributes to the
existing literature by addressing user resistance to increasing the adoption of digital
payment functionalities in proptech applications. The findings can help app developers
and marketers design better strategies to overcome these barriers and promote wider
acceptance of proptech app payments.
Keywords: Proptech, Innovation Resistance Theory, Proptech App Payments, User
Resistance, Digital Payments, Technology Adoption, Rental Accommodations.
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ABSTRAK
FAKTOR-FAKTOR YANG MEMPENGARUHI PERLAWANAN
KONSUMEN TERHADAP PEMBAYARAN SEWA PROPERTI
DALAM APLIKASI PROPTECH
Oleh
JASMINE SHAFIRA
Student ID: 29122363
(Program Studi Magister Administrasi Bisnis)
Aplikasi proptech mengalami pertumbuhan pesat dengan kemajuan teknologi yang
mengubah cara mencari dan membayar akomodasi sewa. Meski adopsi aplikasi
meningkat, ada kesenjangan antara pengguna yang memanfaatkan aplikasi untuk
pencarian dan mereka yang melakukan pembayaran sewa. Penelitian ini menyelidiki
alasan resistensi terhadap penggunaan fungsi pembayaran di aplikasi proptech oleh
pengguna yang hanya menggunakan fitur pencarian. Teori Resistensi Inovasi (IRT)
dan UTAUT digunakan untuk mengeksplorasi masalah ini. Penelitian terdahulu
menunjukkan berbagai hambatan dapat mempengaruhi resistensi pengguna terhadap
pembayaran melalui aplikasi mobile. Pendekatan metode campuran diterapkan dalam
penelitian ini, menggabungkan pengujian awal kualitatif dan pengujian utama
kuantitatif. Data dikumpulkan melalui kuesioner dan dianalisis menggunakan Analisis
Regresi Linear Berganda di SPSS 27.0.
Hasil menunjukkan tiga hambatan utama—Risiko, Tradisi, dan Citra—secara
signifikan mempengaruhi pengguna dalam melakukan pembayaran melalui aplikasi
proptech. Hambatan Risiko berkaitan dengan kekhawatiran keamanan dan privasi.
Hambatan Tradisi melibatkan preferensi metode konvensional dibandingkan alternatif
digital. Hambatan Citra berkaitan dengan persepsi reputasi dan kepercayaan
terhadap aplikasi. Rekomendasi untuk penelitian di masa depan termasuk melakukan
analisis diskriminan untuk membandingkan kelompok non-pengadopsi pembayaran,
menggabungkan elemen demografis seperti usia, pendapatan, dan jenis kelamin
sebagai variabel moderasi, serta memasukkan variabel independen tambahan untuk
memperluas cakupan penelitian. Penelitian ini berkontribusi pada literatur dengan
mengatasi resistensi pengguna untuk meningkatkan adopsi pembayaran digital dalam
aplikasi proptech. Temuan ini dapat membantu developers dan marketers merancang
strategi yang lebih baik untuk mengatasi hambatan ini dan mendorong penerimaan
yang lebih luas terhadap pembayaran melalui aplikasi proptech.
Keywords: Proptech, Teori Resistensi Inovasi, Pembayaran Aplikasi Proptech,
Resistensi Pengguna, Pembayaran Digital, Adopsi Teknologi, Akomodasi Sewa.
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FACTORS INFLUENCING CONSUMERS’ RESISTANCE
TOWARDS PROPERTY RENTAL PAYMENTS IN PROPTECH
APPS
VALIDATION PAGE
By
JASMINE SHAFIRA
Student ID : 29122363
Master of Business Administration Program
Institut Teknologi Bandung
Approved
August 11th 2024
Supervisor
Prawira Fajarindra Belgiawan, Ph. D.
iv
FACTORS INFLUENCING CONSUMERS’ RESISTANCE
TOWARDS PROPERTY RENTAL PAYMENTS IN PROPTECH
APPS
By
JASMINE SHAFIRA
Student ID : 29122363
Master of Business Administration Program
Institut Teknologi Bandung
Approved
Date 25th July 2024
Supervisor
Prawira Fajarindra Belgiawan, Ph.D.
Koleksi digital milik UPT Perpustakaan ITB untuk keperluan pendidikan dan penelitian
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DECLARATION OF NON-PLAGIARISM
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This final project is dedicated to my parents, brother, partner, and my beloved family
who have always supported me. Your unwavering encouragement, love, and belief in
my abilities have been my constant source of strength and inspiration. Without your
support, this accomplishment would not have been possible. Thank you for standing
by me through every step of this journey.
- Jasmine Sh.
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ACKNOWLEDGEMENTS
This final project is completed in partial fulfillment of the requirements for the master’s
degree from Institut Teknologi Bandung. This research would not have been possible
without the support and contributions of various individuals and organizations.
First and foremost, I would like to express my deepest gratitude to Allah SWT for His
divine guidance and blessings, which have steered me through every chapter of my life
and enabled me to reach this milestone. I am profoundly grateful to my parents for their
unwavering support, endless encouragement, and unconditional love. Their belief in
my potential has been the cornerstone of my academic journey and has inspired me to
strive for excellence. I would like to thank Fajar I. Chaniago who had been a great
partner and discussion mate throughout my MBA year.
My heartfelt thanks go to my final project supervisor, Prawira Fajarindra Belgiawan,
S.T., M.Eng., Ph.D., for his invaluable guidance, expertise, and insightful feedback.
His profound knowledge and dedicated support have significantly shaped the direction
and success of this study.
I also extend my appreciation to all the individuals who participated in my preliminary
research and completed the questionnaires. Your time and valuable input have greatly
enhanced the depth and quality of this study. Special thanks to my friends and
classmates, particularly those from YP68B and the Nangorians, for their continuous
encouragement and support throughout the research process. Your support and
friendship have made this journey enjoyable and rewarding.
I am also grateful to the academic staff of Institut Teknologi Bandung for providing
the necessary resources and support to complete this research. Your assistance and
commitment have been crucial in bringing this thesis to fruition. I would like to
acknowledge the contribution of ARKI and industry professionals who provided
insights to this research. Your cooperation and openness have been instrumental in the
success of this project. I extend my apologies to any names I may have not mentioned.
Your support and assistance have been deeply appreciated, and I am sincerely thankful
for your contributions. May the findings and insights from this research benefit the
readers and contribute positively to the future of the proptech industry.
Thank you all for making this journey possible.
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TABLE OF CONTENTS
ABSTRACT .................................................................................................................. ii!
ABSTRAK ...................................................................................................................... ii!
VALIDATION PAGE ................................................................................................. iv!
DECLARATION OF NON-PLAGIARISM ................................................................. v!
ACKNOWLEDGEMENTS ........................................................................................ vii!
TABLE OF CONTENTS ........................................................................................... viii!
LIST OF FIGURES .................................................................................................... xiii!
LIST OF TABLES ..................................................................................................... xiv!
CHAPTER I Introduction ............................................................................................. 1!
I.1 Background ......................................................................................................... 1!
I.2 Industry Profile .................................................................................................... 3!
I.3 Business Issue ................................................................................................... 13!
I.4 Research Question and Research Objectives .................................................... 15!
I.4.1 Research Question ....................................................................................... 15!
I.4.2 Research Objectives .................................................................................... 15!
I.5 Research Scope and Limitations ....................................................................... 16!
I.5.1 Research Scope ............................................................................................ 16!
I.5.2 Research Limitations ................................................................................... 16!
CHAPTER II Literature Review ................................................................................. 17!
II.1 Theoretical Foundation ..................................................................................... 17!
II.1.1 Innovation Resistance Theory (IRT) .......................................................... 18!
II.1.2 Unified Theory of Acceptance and Use of Technology (UTAUT) ........... 22!
II.2 Conceptual Framework ..................................................................................... 23!
II.2.1 Usage Barrier (UB) .................................................................................... 23!
II.2.2 Value Barrier (VB) ..................................................................................... 24!
II.2.3 Risk Barrier (RB) ....................................................................................... 25!
II.2.4 Tradition Barrier (TB) ................................................................................ 26!
II.2.5 Image Barrier (IB) ...................................................................................... 26!
II.2.6 Social Influence (SI) .................................................................................. 27!
II.2.7 Facilitating Conditions (FC) ...................................................................... 28!
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II. 3 Proposed Conceptual Framework .................................................................... 29!
Chapter III Research Methodology ............................................................................. 30!
III.1 Research Design .............................................................................................. 30!
III.1.1 Types of Research Design ........................................................................ 32!
III.1.2 Nature of Research Design ....................................................................... 32!
III.1.3 Time Horizon of Research Design ............................................................ 33!
III.2 Data Collection Method .................................................................................. 33!
III.2.1 Data Source ............................................................................................... 33!
III.2.1.1 Primary Data ...................................................................................... 33!
III.2.1.2 Secondary Data ................................................................................. 34!
III.2.2 Sampling Design ...................................................................................... 34!
III.2.2.1 Target Population ............................................................................... 35!
III.2.2.2 Sampling Size ..................................................................................... 35!
III.2.2.3 Sampling Element .............................................................................. 36!
III.2.2.4 Sampling Location ............................................................................. 36!
III.2.2.5 Sampling Period ................................................................................. 37!
III.2.2.6 Sampling Frame ................................................................................. 37!
III.2.2.7 Sampling Technique ........................................................................... 37!
III.2.3 Research Instrument ................................................................................ 38!
III.2.3.1 Questionnaires .................................................................................... 38!
III.2.3.2 Questionnaire Design ......................................................................... 39!
III.2.3.3 Pilot Test (Reliability and Validity) .................................................. 42!
III.2.4 Constructs Measurement ........................................................................... 45!
III.2.4.1 Sources of the Questions .................................................................... 45!
III.2.4.2 Scale Measurement ........................................................................... 49!
III.2.4.2.1. Nominal Scale ............................................................................ 49!
III.2.4.2.2 Ordinal Scale ............................................................................... 50!
III.2.4.2.3 Interval Scale .............................................................................. 50!
III.2.4.2.4 Ratio Scale .................................................................................. 51!
III.2.4.3 Questionnaire Summary ..................................................................... 51!
III.3 Data Analysis Method ..................................................................................... 52!
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III.3.1 Data Processing ......................................................................................... 52!
III.3.2 Descriptive Analysis ................................................................................. 52!
III.3.3 Validity and Reliability Analysis .............................................................. 53!
III.3.4 Classical Assumption Analysis ................................................................. 54!
III.3.4.1 Multicollinearity Analysis .................................................................. 54!
III.3.4.2 Heteroscedasticity Analysis ............................................................... 54!
III.3.4.3 Normality Test ................................................................................... 55!
III.3.5 Inferential Analysis ................................................................................... 55!
III.3.5.1 Pearson Correlation Analysis ............................................................. 55!
III.3.5.2 Multiple Linear Regression ................................................................ 56!
Chapter IV Result and Discussion .............................................................................. 58!
IV.1 Preliminary Research Analysis ....................................................................... 58!
IV.2 Main Research Analysis .................................................................................. 63!
IV.2.1 Response Rate ........................................................................................... 63!
IV.2.2 Descriptive Analysis ................................................................................. 63!
IV.2.2.1 Frequent Distribution ......................................................................... 63!
IV.2.2.1.1 Gender ......................................................................................... 63!
IV.2.2.1.2 Age .............................................................................................. 64!
IV.2.2.1.3 Last Education Completed .......................................................... 65!
IV.2.2.1.4 Occupation .................................................................................. 66!
IV.2.2.1.5 Total Monthly Expenditure ......................................................... 67!
IV.2.2.1.6 Monthly App Transaction ........................................................... 68!
IV.2.2.1.7 Used Proptech Applications ........................................................ 68!
IV.2.2.1.8 Place of Residence ...................................................................... 69!
IV.2.2.2 Central Tendency ............................................................................... 70!
IV.2.2.2.1 Usage Barrier (UB) ..................................................................... 70!
IV.2.2.2.2 Value Barrier (VB) ...................................................................... 71!
IV.2.2.2.3 Risk Barrier (RB) ........................................................................ 71!
IV.2.2.2.4 Tradition Barrier (TB) ................................................................. 72!
IV.2.2.2.5 Image Barrier (IB) ....................................................................... 73!
IV.2.2.2.6 Social Influence (SI) .................................................................... 73!
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IV.2.2.2.7 Facilitating Conditions (FC) ........................................................ 74!
IV.2.2.2.8 Innovation Resistance (IR) .......................................................... 75!
IV.2.3 Reliability Analysis .................................................................................. 75!
IV.2.4 Confirmatory Factor Analysis .................................................................. 76!
IV.2.5 Classical Assumption Analysis ................................................................ 78!
IV.2.5.1 Multicollinearity Test ......................................................................... 78!
IV.2.5.2 Heteroscedascity Test ........................................................................ 79!
IV.2.5.3 Normality Test ................................................................................... 81!
IV.2.6 Inferential Analysis ................................................................................... 83!
IV.2.6.1 Pearson Correlation Coefficient Analysis .......................................... 83!
IV.2.6.2 Multiple Linear Regression Analysis ................................................. 83!
IV.2.7 Hypothesis Testing ................................................................................... 87!
IV.2.7.1 Discussions on the Result .................................................................. 88!
IV.3 Business Solution ............................................................................................ 94!
IV.3.1 Business Solution on Risk Barrier ........................................................ 94!
IV.3.2. Business Solution on Tradition Barrier ................................................ 97!
IV.3.2. Business Solution on Image Barrier .................................................. 100!
IV.4 Implementation Plan ..................................................................................... 103!
Chapter V Conclusion and Recommendation ........................................................... 113!
V.1 Conclusion ...................................................................................................... 113!
V.1.1 Conclusion of Research Questions .......................................................... 113!
V.1.2 Theoretical Implications .......................................................................... 114!
V.2 Recommendations .......................................................................................... 116!
V.2.1 Managerial Implications .......................................................................... 116!
V.2.2 Shortcomings and Future Study Recommendations ................................ 117!
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LIST OF APPENDICES
APPENDIX A Preliminary Interview Transcript……………………………..……130
APPENDIX B Documentation of Preliminary Research Interview………………..147
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LIST OF FIGURES
Figure I.1 Startup Proptech in Indonesia……………………………………..………..5
Figure 1.2 Proptech Investment Trend in Indonesia 2019 – 2024…………………….6
Figure I.3 Platform Used by Gen Z to search for Properties……………………..…....8
Figure I.4 Examples of Construction Technologies apps…………………………….10
Figure I.5 Examples of Smart Home Technologies apps…………………………….10
Figure I.6 Examples of real Estate Fintech Technologies apps……………………….10
Figure I.7 Examples of Property Management apps………………………………….10
Figure I.8 Proptech mobile apps for mid to long term rent in Indonesia …………….10
Figure I.9 Prefered Payment System for Property Rent……………………….…......13
Figure I.10 Popular Property to Rent………………………………………..…….....14
Figure I.11 Dream Area to Rent Property………….………………………………...15
Figure II.1 Innovation Resistance Theory Framework….…………………………....19
Figure II.2 UTAUT Framework………………………...…………………………....22
Figure II.3 Conceptual Framework…………….………………………………….....29
Figure III.1 Research Design Framework………………………………………...….31
Figure III.2 Sampling Technique………………………………………………...…..35
Figure IV.1 Advertisement and Guidance Example for Data Security Campaign…...96
Figure IV.2 Advertisement Example for Refund Policy and Error-Free Payments….97
Figure IV.3 Direct Communication Feature Example………………………………..98
Figure IV.4 Advertisement Example for Property Showing Feature ………………..98
Figure IV.5 360 Degree Feature…………………………………… ………………..99
Figure IV.6 Example of Price Negotiations Feature…………………………………99
Figure IV.7 Example of Loyalty Programs Feature……………………………..….100
Figure IV.8 Example of User Testimonials Campaigns…………………………….101
Figure IV.9 Example of User Generated videos on Tiktok………………………….101
Figure IV.10 Feature for Revied for Older Tenants………..……………………….102
Figure IV.11 Example of User Complains and Customer Service Replies…….…..102
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LIST OF TABLES
Table II.1 Prior Literature on Innovation Resistance………………………………....20
Table III.1 Outline of Questionnaire Design………………………………………....39
Table III.2 Questionnaire Design for Screening Questions and Demographic
Questions………………………………………………………………..…………...40
Table III.3 Reliability Test on Pilot Test……………………………………..……….42
Table III.4 Validity Test on Pilot Test……………………………………………….44
Table III.5 Questionnaire Design References………………………………………..45
Table III.6 7-Likert Scale Used………………………………………………………50
Table III.7 Summary of Measurement Scale Used…………………………………..51
Table III.8 Cronbach Alpha Interpretation Guideline………………………………..53
Table III.9 Pearson Correlation Interpretation……………………………………….56
Table III.10 Multiple Linear Regression Equation……………………...……………57
Table IV.1 Gender……………………………………………………………………63
Table IV.2 Age……………………………………………………………………….64
Table IV.3 Last Education Completed…………………….…………………………65
Table IV.4 Occupation………………….……………………………………………66
Table IV.5 Total Monthly Expenditure………………………………………………67
Table IV.6 Monthly App Transactions……………………………………………….68
Table IV.7 Used Proptech Applications for Accommodation Searches……………..68
Table IV.8 Place of Residence……………………………………………………….69
Table IV.9 Central Tendency for Usage Barrier (UB) ………………………………70
Table IV.10 Central Tendency for Value Barrier (VB) .. ……………………………71
Table IV.11 Central Tendency for Risk Barrier (RB) . ………………………………71
Table IV.12 Central Tendency for Tradition Barrier (TB)……………………………72
Table IV.13 Central Tendency for Image Barrier (IB) .. …………………………….73
Table IV.14 Central Tendency for Social Influence (SI) . …………….…………….73
Table IV.15 Central Tendency for Facilitating Conditions (FC)…………………….74
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Table IV.16 Central Tendency for Innovation Resistance (IR) ……………………..75
Table IV.17 Reliability Test...…………………………………..……………………75
Table IV.18 Confirmatory Factor Analysis..…………………………………………76
Table IV.19 Multicollinearity Test……………………………………………...……78
Table IV.20 P-Plot and Scatter Plot for Innovation Resistance and Dependent
Variable…………………………………………………...………………………….79
Table IV.21 Skewness and Kurtosis Test ……………………………………………81
Table IV.22 One-Sample Kolmogorov-Smirnov Test……………………...………..82
Table IV.23 Pearson Correlation Coefficient Test……………………………………83
Table IV.24 Model Summary……………………...…………………………………83
Table IV.25 Anova………………………………………………………………...…84
Table IV.26 Multiple Linear Regression Test Result……………………………...…84
Table IV.27 Multiple Linear Equation…………………………………………...…..85
Table IV.28 Summary of Hypothesis……………………..……………………...…..87
Table IV.29 Mean Ranks on Risk, Tradition, and Image Barriers……………………94
Table IV.30 Implementation Plan and Proposed Schedule……………………..…..104
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CHAPTER I Introduction
I.1 Background
The shift to the digital age is expanding at a rapid pace. The growth of the
internet and mobile applications has greatly impacted this shift. A vast number of users
rely on mobile apps for added convenience in meeting their requirements.
Advancements in technology have empowered consumers to access services wherever
they need them. In light of these swift technological progressions, nearly every industry
are adjusting to the current emerging digital trends to fulfill consumer needs (Ratnawita
et al., 2023)
The emergence of mobile applications has transformed the business
environment in various industries, such as food services (Kaur et al., 2021), fitness
(Chakraborty et al., 2022), ticketing services (Chen et al., 2022), and healthcare
(Kautish et al., 2023). Many business companies are earning revenue using mobile
applications (Islam & Mazumder, 2010), including the property Industry (Siniak et al.,
2020).
Digital applications are becoming increasingly important in facilitating housing
markets and activities, generating revenue from data for information and services
instead of just the physical properties (Shrestha et al., 2023). Proptech, which is based
on the acronym for property and technology, provides innovative services to the
property industry (Baum, 2017). Proptech assists landlords, buyers, and sellers in
improving property experiences. Many start-up companies in proptech currently offer
digitized platforms to increase the transaction process (Akinwamide et al., 2021). This
includes Indonesia.
The property industry in Indonesia has attracted considerable interest in recent
times, as seen from the increasing growth of property technology startups or proptech
within the market (Patriella, 2023). The rise of proptech in Indonesia is due to the home
ownership backlog figure which is still high, reaching 12.71 million based on BPS data.
Apart from that, Indonesia's population will reach 278.69 million people in 2023 so the
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need for housing will remain (Bisnis Indonesia, 2023). Several notable startups
companies in Indonesia includes Pinhome, Travelio, Rukita, Mamikos, Cove, Cohive,
Tanaku, 99.co, Rumah123.com, Lamudi.co. id and others. These proptech companies
have been categorized based on the services they offer (Tech in Asia, 2023); Sales
Platforms, Rental Platforms, Property Management and Customization, Financing
Services, and Tech-Driven Construction.
Proptech as a rental app, not only is profitable for companies, but also it could
assist landlords in improving property transaction journeys (Syed, 2024). In Indonesia,
proptech helps businesses of property rental for monthly to yearly to flourish by listing
them on their apps. Several proptech companies which provides monthly to yearly
rental listing services to landlords in Indonesia includes Mamikos (rooms and
apartment), Travelio (rooms, apartments, houses, villas, and others), Rukita (room and
apartments) Cove (rooms and apartments), Cari-kos.com (rooms) and others.
The demand for monthly to yearly room rental in Indonesia is increasing. Many
students are vying to pursue further education in different cities, while others have
recently graduated and are embarking on their careers in major Indonesian urban
centers (Edityorini, 2022). With the increasing urbanization to cities, demand for room
rentals are also on the rise. It’s becoming challenging for students and young
professionals to find suitable accommodation within their budget. The competition for
affordable and well-located rentals has pushed individuals to explore alternative
housing options such as shared apartments, co-living spaces, and boarding houses.
Proptech apps for property rentals have provided more convenience in searching for
the right place to live. The digitalization of business operations has enabled the service
industry to adopt advanced transactional methods for property-related activities,
thereby enhancing consumer satisfaction (Akinwamide & Hahn, 2021). Despite the
benefits, some consumers are hesitant to embrace new advancement in property
transactions due to the lack of trust (Akinwamide et al., 2021).
Technological innovations are influenced by consumer' motivations and
resistance, which can either promote or hinder their spread and adoption (Huang et al.,
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2021). While some consumers of this app are motivated by the need to find the
accommodation that suits them, there has been a trend where consumers are still
reluctant to complete the transaction within the app and opt to do the transaction outside
the app. Understanding the factors that contribute to this reluctance is crucial for the
advancement of proptech applications and the satisfaction of consumer needs.
I.2 Industry Profile
PropTech, which stands for Property Technology, is a rapidly growing sector
that harnesses the power of emerging technologies to revolutionize the real estate
industry (Lee & Shin, 2018). PropTech involves widespread use of cutting-edge
technologies including tools for Real estate technologies, including home matching
algorithms, unmanned aerial vehicles, immersive visualization, building information
management systems, data analytics, artificial intelligence, the Internet of Things,
blockchain, smart contracts, real estate crowdfunding platforms, and financial
technology services. It also encompasses innovations related to smart cities and
regions, intelligent homes and the shared economy (Siniak et al., 2020). Within the real
estate industry, digital platforms for real estate technology, commonly referred to as
"PropTech", have emerged as a means to enhance the utilization of market data and
improve decision-making processes (Shaw, 2020). The "PropTech" industry cover a
variety of digital platforms related to the real estate market, such as property listings
for sale and rent, management services for landlords and tenants, real estate data
analysis, property transactions, valuations, and financial services (Papadimitropoulos,
2021; Porter, 2019).
One of the primary benefits of Proptech is the provision of real-time, detailed
data that is readily accessible. Proptech has revolutionized the ways in which
individuals market, access, and manage residential properties. It has reshaped the
previously location-centric search process through powerful geospatial search engines
that can provide extensive data based on specific criteria. This advancement could
potentially benefit both prospective homebuyers and property owners, ultimately
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contributing to the globalization of the property investment landscape (Fields &
Rogers, 2021).
Real estate firms have already adopted PropTech to enhance customer
experiences, augment sales, and optimize operational efficiency (Siniak, et al, 2019).
Currently, nearly 50 countries have established national PropTech communities, such
as PropTechRussia, Austrian PropTechInitiative, PropTechBelgium, SwissPropTech,
PropTechDach, PropTechSpain, UKPropTechAssociation, PropTechAsia,
ProTechBaltic, PropTechNL, and NordicPropTechInitiative, among others. These
communities have now been integrated into a single global network. The PropTech
community is unique in several ways. It is organized entirely by and for businesses,
generating B2B services without any government participation. Remarkably, it
encompasses companies from diverse sectors: investors (including institutional and
private equity funds, banks and financial groups, venture capital funds, and business
angels), real estate market participants (such as property rights holders, developers,
builders, consulting and brokerage firms, appraisers, management, and insurance
companies), and IT companies (providers of IT solutions, integrators, aggregators, and
developers of specialized cloud and mobile applications), as well as start-ups
developing technology products and solutions in the properties field.
In Indonesia, the property sector is currently occupied by a number of large
conglomerates, such as Sinar Mas Land, Agung Podomoro Land, Summarecon Agung,
and Ciputra Development. However, now property technology (proptech) startups are
starting to emerge that offer innovation to this industrial sector. Tech in Asia noted that
there were at least 32 proptech startups operating in Indonesia as of May 2024. This
number does not include coworking space providers, whose presence is growing in
various cities in Indonesia and there are no credible records regarding the exact number.
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Figure I.1 Startup Proptech in Indonesia
Source : Tech in Asia, 2024
Digital companies in the property sector have been categorized based on the
services they offer. The services provided by these domestic proptech companies can
be broadly divided into five categories (Tech in Asia, 2023):
- Sales Platforms: These are platforms designed to link property sellers
to potential purchasers.
- Rental Platforms: These are devised to bridge the gap between tenants
and landlords who have properties available for rent.
- Property Management and Customization: This category focuses on
services that guarantee properties listed adhere to certain operational criteria or are
tailored to the distinct requirements of tenants.
- Financing Services: These offer financial solutions for property
dealings, simplifying the process for users aiming to buy or lease their chosen
property.
- Tech-Driven Construction: This pertains to the use of technology to
streamline the process of house planning and construction.
The majority of PropTech initiatives in Indonesia operate within the Market
and Transact category, including platforms like rumah123.com, Travelio, Mamikos,
and Lamudi.com. The emergence of PropTech in Indonesia is a response to the
significant backlog in home ownership within the country. The BPS report released in
February 2024 stated that 84.79 percent of Indonesians lived in their own houses as of
2023. Only 5.05 percent lived in kos last year. However, this figure does not reflect the
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condition of big cities such as DKI Jakarta, which is currently the center of the national
economy. Only 56.57 percent of Jakarta residents live in their own homes, while 22.82
percent of the capital city residents live in kos/apartments (Tech in Asia, 2024). It is
unsurprising that numerous PropTech firms in Indonesia focus on providing marketing
and marketplace services.
The future of proptech in Indonesia has revolutionized the real estate market,
driving it towards greater sustainability, efficiency, and accessibility. As the industry
continues to evolve, the integration of advanced technologies such as blockchain, IoT,
and data analytics will shape the landscape. The adoption of blockchain technology
enhances transaction security and transparency, significantly reducing fraud and
money laundering while promoting eco-friendly practices by eliminating the need for
paper records. Virtual house tours will become increasingly sophisticated, providing
potential buyers with immersive, 24/7 access to property viewings, thereby reducing
the need for physical travel and minimizing environmental impact. The government's
support for digital transformation and affordable housing initiatives, such as the One
Million Houses (OMH) program, will further accelerate the adoption of proptech
solutions. This initiative aims to construct at least a million homes annually, and the
integration of fintech solutions will make these properties more accessible to buyers
through innovative financing options.
Figure I.2 Proptech Investment Trend in Indonesia 2019 - 2024
Source : Tech in Asia, 2024
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Throughout this year until June 25, 2024, there have been five investment deals
in proptech startups in Indonesia. This includes private equity funding as well as
mergers and acquisitions (M&A) activities. The investment value in proptech startups
in 2024 has reached a record high of US$85.84 million (Rp1.41 trillion).
The substantial investment value in proptech startups throughout 2024 indicates
that this vertical has significant market potential. This can be an indicator of the profit
opportunities that investors can achieve when entering proptech startups. For startup
founders, this can signal the opportunity to secure significant funding in the proptech
vertical. This opportunity can be utilized if founders can be more aggressive in
developing their innovations and business strategies.
In Indonesia, proptech platforms have emerged as a popular solution for
managing property rental payments and finding rental accommodations. These
platforms provide a platform that simplifies the rental process for both property owners
and tenants. They usually cater specifically to the needs of kos (boarding house) or
apartment seekers and owners. These apps allow users to search for accommodations
based on location, price, and facilities. It also provides features for online booking and
payment, making the process seamless for users. Property owners can list their
properties, manage bookings, and receive payments through the app. A survey carried
out by 99 Group in the first semester of 2023 stated that the marketplace platform was
the method most frequently used to search for property. This outperforms both social
media platforms and property agents (Tech in Asia, 2024).
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Figure I.3 Platform Used in search of Properties (Tech in Asia, 2024)
PropTech presents novel and automated approaches for property managers to
screen potential tenants, handle rent collection processes efficiently, and enforce
rental agreements (Fields, 2018). Property owners can now gain extensive insights
into tenant usage habits without physically visiting their properties. They can
monitor crucial details remotely, using their smartphones. Both landlords and
tenants can participate in virtual house tours, inspections, and viewings, eliminating
the inconvenience of in-person visits.
Proptech platforms for rental have several similar key features. Users can
search for property based on various criteria such as location, price, facilities, and
availability. Each property listing includes photos, descriptions, and reviews to help
users make informed decisions. The apps facilitate online booking and secure
payment options, making the rental process convenient. Property owners can easily
list their properties, manage bookings, and handle payments through the apps.
Furthermore, some apps offer additional services such as maintenance requests,
cleaning, laundry, and customer support.
The Jakpat survey released in June 2023 also stated that 3 out of 5 people
chose to rent property rather than buy. Financial considerations and job demands
are the main considerations for those who prefer renting. On average, Jakpat survey
respondents spend IDR 2.6 million per month to rent property, or the equivalent of
IDR 33.7 million per year. However, the survey also revealed that 40 percent of
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respondents lived in kos with bills under IDR 1 million per month (Tech in Asia,
2024). With such a huge opportunity, proptech platforms could revolutionize the
rental market in Indonesia by providing user-friendly platforms that simplify the
process of finding, booking, and managing property accommodations. These
platform could cater to the diverse needs of tenants and property owners, offering
convenience, transparency, and a range of services to enhance the rental experience.
Proptech platform in the app segment are changing the way people buy, sell,
rent, and manage properties. A prominent aspect of these apps is their capacity to
search and browse through a vast database of properties, which also apps allow
users to specify their preferences, such as location, budget, and property type, and
receive customized results based on their requirements, saving time and effort
compared to traditional methods of property search, where often involve visiting
multiple properties in person (Kp, 2023). Proptech mobile applications not only
help with property searches but also streamline transaction procedures, cutting out
the necessity for physical paperwork, which results in faster and more convenient
transactions, enhancing the overall customer experience while also reducing the
potential for errors and fraud (Petruk, 2023). Proptech mobile apps also offer virtual
tours, allowing users to explore properties remotely with high-quality images and
360-degree virtual reality experiences, where users can get a realistic feel of the
property without physically visiting it, saving time, money, and provides a
convenient and efficient way to shortlist properties and narrow down choices (Kp,
2023).
There are four main types of proptech that are currently available in the form
of mobile apps (Darya, 2023). First, Construction Technologies (Contech) apps
which are advancing the construction process through innovations such as
architecture apps, 3D modeling apps, home plan apps such as SketchUp, uMake,
Morpholio Trace and many more (Fig I.4). Second, Smart Home automation apps
enable residents to remotely manage various household functions, optimize energy
usage, and enhance security through real-time monitoring, such as Smart Life,
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Bardi, Google Home and many more (Fig I.5). Third, Real estate apps secure
transparent property transactions between buyers and sellers such as Zillow,
rightmove and Zoopla (Fig I.6). Fourth, centralized property management
platforms automate various tasks, such as rent collection, maintenance requests,
and communication, enabling both tenants and owners to utilize user-friendly
interfaces to efficiently manage properties, track financial transactions, and
promptly address each other's needs. Examples of such platforms include Airbnb,
booking.com, and WeWork (Fig I.7).
Figure I.4 Examples of Construction Technologies apps
Source : sequentially from left; Sketchup app, uMake app and Morpholio
Trace app
Figure I.5 Examples of Smart Home Technologies apps
Source : sequentially from left; Smart Life app, Bardi app and Google Home
app
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Figure I.6 Examples of Real Estate Fintech apps
Source : sequentially from left; Zillow app, rightmove app and Zoopla app
Figure I.7 Examples of Property Management apps
Source : sequentially from left; Airbnb app, Booking.com app and wework
app
In the modern era of digital technology, property management apps are no
longer limited to landlords and property managers as they have advanced to meet the
needs of tenants, providing a wide range of features that greatly improve the rental
experience (Florencia, 2023). For landlords and property managers, property
management applications help not only to list their property and advertise it, but also
to make announcements through the property management app, track rent payments,
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maintenance requests, and communication history. For tenants, one of the primary
advantages of utilizing property management applications is improved communication.
With a direct channel to property managers or landlords, tenants can conveniently
report problems, seek clarification, or make inquiries. This enhanced communication
mechanism ensures that concerns are promptly addressed, resulting in a more
satisfactory living experience. Second, numerous proptech applications provide tenants
with the ability to conveniently report maintenance concerns through the property
management platform. This allows tenants to furnish detailed descriptions of the issue,
attach visual evidence, and monitor the progress of their requests. Third, property
management applications frequently incorporate a secure digital rent payment feature,
significantly enhancing convenience for tenants. Tenants are no longer required to
write physical checks or directly contact property owners. Through the application,
tenants can remit rental payments remotely and even establish automated recurring
payments to ensure timely submission. Known property management mobile apps in
Indonesia that offer secure online rent payment features on mid to long term rent for
kos and/or apartments include Travelio, Mamikos, Cove, Rukita and Koolkos by
RedDoorz (Fig I.8).
Figure I.8 Proptech mobile apps for mid to long term rent in Indonesia
Source : sequentially from left; rukita app, mamikos app, cove app, travelio
app, RedDoorz app.
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Proptech mobile applications significantly ease the hunt for suitable housing,
particularly in densely populated urban areas. The sprawling mega-urban region of
Jabodetabek, with Jakarta at its center, along with the expansive metropolitan areas of
Bandung Raya and Surabaya, which both boast urban populations over five million
(Mardiansjah et al., 2021), exemplify areas where such technology is indispensable.
These apps cater to the demand for distant users to secure mid-to-long-term rental
accommodations effortlessly.
I.3 Business Issue
Mobile-based proptech platforms offer convenience and innovation for mid to
long-term property rentals. Despite this convenience, some consumers choose to carry
out payments outside of the platforms. A survey done by Jakpat revealed that only a
small percentage chose to pay rental bills via proptech platforms despite the majority
searched accommodations needs through the proptech platform (Fig I.2). The majority
choose payment in cash or direct transfer to the property owner's account (Tech in Asia,
2024).
Figure I.9 Prefered Payment System for Rent Property (Jakpat, 2023)
Moreover, based on preliminary research conducted by the researcher with 10
participants who used proptech applications to browse room availability and location,
only 3 of the participants engaged in transactions through proptech apps and among
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them, 2 expressed reluctance to continue using the proptech app for conducting
payments due to bad experiences. Mobile-based proptech applications enable property
managers and tenants to connect, communicate and pay bills. However, the majority
of participants showed little interest in using these applications for payments. This can
be an issue for proptech companies. Proptech apps developers have internal systems
that monitor user behavior. The data collected is typically used for prediction on
consumer behavior, such as predicting preferences based on past interactions,
identifying patterns in usage, and suggesting personalized recommendations. For
example, data on the previous transaction conducted by a user can be analyzed to
predict their preferences and suggest similar listings.
However, the internal monitoring systems have limitations in investigating
external factors that influence user behavior. External factors like consumers’
traditions, values, and beliefs, can significantly impact consumer decisions but may not
be fully captured by internal monitoring alone. Therefore, conducting a study on user
reluctance to conduct transactions outside proptech applications is necessary to
comprehensively understand consumer behavior and address potential issues that may
arise.
Figure I.10 Popular Property to Rent (Jakpat, 2023)
Based on the survey done by Jakpat, 35% of respondents show that they are
interested in renting apartments or kos, and 32% of them express Jabodetabek as their
dream area for renting. This is due to the fact that they were looking for strategic areas
when choosing a rental property. Namely close to the city center, business center, or
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public transportation. In addition, 42% of respondents also looked for a safe and
comfortable location. Therefore, it was deemed appropriate to conduct research
focused on Jabodetabek, targeting tenants of kos/apartments and users of proptech
applications.
Figure I.11 Dream Area to Rent Property (Jakpat, 2023)
I.4 Research Question and Research Objectives
I.4.1 Research Question
The purpose of this final project is to answer the following research questions :
1. What barriers significantly influence consumers’ resistance in proptech app
payments ?
2. What strategy can be implemented by proptech apps developers to increase and
retain consumers' payments on proptech apps?
I.4.2 Research Objectives
Based on the research question, the objectives of this final project are :
1. To analyze the barriers that significantly contribute to consumers' resistance in
engaging with proptech apps for rental payments.
2. To determine effective strategies that proptech apps can implement to enhance
consumer retention and increase payments on their apps.
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I.5 Research Scope and Limitations
I.5.1 Research Scope
This study will be carried out exclusively in Jabodetabek, Indonesia. By
focusing on Jabodetabek alone, the research seeks to offer a contextualized viewpoint
on the topic. This method enabling a comprehensive grasp of the phenomena being
studied. This localized approach also ensures that the findings directly relate to
Jabodetabek's context, enhancing their practical relevance and usefulness for local
stakeholders and decision-makers.
I.5.2 Research Limitations
Despite this study provided new research finding regarding the consumer
resistance in proptech app transaction, there are several limitations related to the
writing of this final project :
1.The findings of this study may not be directly applicable to proptech markets
in other regions or countries due to the localized focus on Jabodetabek, Indonesia.
Factors such as cultural differences and market dynamics in other locations may impact
consumer behavior differently.
2. The sample size used in this study may not fully represent the broader
population of proptech users. Although efforts were made to ensure a diverse sample,
the relatively small sample size may limit the scope of this study
3.The duration and timeline of the study may have restricted the depth and
scope of data collection and analysis. A longer research timeframe could have allowed
for more extensive data collection methods
4. This study focuses on proptech mobile applications. Other platforms such as
proptech websites are not in the scope of this research. Mobile applications offer greater
accessibility, convenience, and real-time updates, which align with the current
consumer behavior trends in the Jabodetabek area. Moreover, mobile apps tend to
provide more integrated features such as push notifications, location-based services,
and easier communication interfaces, making them a more relevant subject of study in
understanding consumer resistance in the proptech industry.
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CHAPTER II Literature Review
Literature review serves as a foundation for the research study, providing a
comprehensive overview of existing theories and academic works relevant to the topic.
It helps to identify the gaps in knowledge that the empirical study aims to address and
establishes a theoretical foundation for the proposed study (Rocco & Plathonik, 2009).
This chapter will justify the research by highlighting its contribution to existing
knowledge and validating the chosen research methods and approaches (Paré &
Kitsiou, 2017), outlining the theoretical foundation that helps build the research model
within a conceptual framework. A solid conceptual framework helps the author
interpret, explain, and generalize findings while demonstrating the relevance of the
thesis topic within its field (Aziz, 2023).
II.1 Theoretical Foundation
Property owners are increasingly turning to proptech mobile applications for
managing their property. These platforms offer a range of tools designed to enhance
user experience and efficiency. As they strive to optimize the benefits and return on
investment, users engage in a complex decision-making process while navigating the
multitude of available proptech options. The Innovation Resistance Theory offers to
understand the barriers faced by users when adopting new technologies. This
framework identifies various factors that may cause resistance, such as usage barriers,
value barriers, risk barriers, tradition barriers, and image barriers, which can deter users
from adopting an innovation. To overcome potential resistance, developers and
marketers of proptech applications must recognize these barriers. The upcoming
chapter will explore how each resistance factor could be relevant to proptech mobile
applications by drawing from previous literature and formulating hypotheses based on
the relationships between these factors.
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II.1.1 Innovation Resistance Theory (IRT)
Innovation Resistance Theory (IRT) emerged as a critical framework to understand
why consumers may resist adopting new technologies or innovations, diverging from
traditional adoption and diffusion models (Sheth, 1981). Initially proposed by Sheth in
1981 and expanded by Ram and Sheth in 1989, IRT describes innovation resistance as
the behavior that follows a clear and thoughtful process of decision-making regarding
the acceptance and utilization of innovation, which is influenced by potential changes
in behavior (Ram & Sheth, 1989). IRT assumes that resistance is a natural consumer
response influenced by active and passive resistance (Heidenreich & Handrich, 2014).
Active resistance, influenced by the specific features of innovations, manifests through
behaviors that directly confront the innovation's characteristics. This type of resistance
is explored through the lens of functional barriers (Yu & Chantatub, 2016).
Functional barriers include aspects such as the product's usage, product’s value,
and product usage risk (Chen et al., 2022). Passive resistance, in contrast, arises from
clashes with pre-existing beliefs and is examined through consumer’s psychological
barriers (Yu & Chantatub, 2016). Psychological barriers derive from consumer
tradition and product image (Chen et al., 2022). Psychological barriers manifest as
Tradition Barrier (TB) and Image Barrier (IB), affecting consumer resistance.
Dominant theories in the innovation field, like the diffusion of innovation, the
technology acceptance model (TAM), and the unified theory of acceptance and use of
technology (UTAUT), have overlooked the aspect of people's resistance to innovation.
Rather, their emphasis has been on outlining the positive attributes of innovation
(Joachim et al., 2018). Innovation Resistance Theory (IRT) offers a theoretical basis
for understanding the obstacles and resistance mechanisms, concentrating on how
individuals respond to various technologies, products, and services (Jin et al., 2022).
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Figure II.I Innovation Resistance Theory Framework (Ram and Sheth, 1989)
Innovation theory has found application across various fields, including e-
commerce, mobile payments, and online services, showcasing its ability to explain user
adoption intentions and resistance behaviors (Kaur et al., 2020). When an innovation
significantly alters someone's established routine or disrupts their current practices, it
is anticipated that resistance will occur (Mani & Chouk, 2018). Research indicates that
understanding these barriers, particularly how innovations challenge consumers' status
quo or belief systems, is vital for the successful introduction of new technologies
(Laukkanen, 2016). The IRT model incorporates consumer characteristics and
communication strategies, suggesting that effectively addressing both functional and
psychological barriers through strategic communication and comprehension of
consumer perceptions can significantly mitigate resistance (Sadiq et al., 2021; Leong
et al., 2021)
Previous research has predominantly employed quantitative methods to
investigate predictors of innovation resistance across different domains, with a specific
focus on online and app services. However, the application of Innovation Resistance
Theory in the context of Proptech apps remains an underexplored area. Prior studies
have largely addressed resistance in the areas of m-banking and e-commerce.
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II.1.2 Unified Theory of Acceptance and Use of Technology (UTAUT)
Unified Theory of Acceptance and Use of Technology it provides a basic
conceptual framework by combining models that explain the individual acceptance of
technology. Developed by Venkatesh et. al., (2003), UTAUT comprises of four main
factors. These are; performance expectancy, effort expectancy, social influence, and
facilitating conditions are factors.
Figure II.2 UTAUT Framework (Venkatesh et. al., 2003)
The Unified Theory of Acceptance and Use of Technology model defines
several key constructs that predict an individual's intention to use a technology system.
Performance expectancy refers to the belief that using the system will enhance one's
job performance. Effort expectancy is the perceived ease of using the system. Social
influence represents the degree to which an individual believes important others think
they should use the new system. Facilitating conditions are the extent to which an
individual believes the organizational and technical infrastructure supports use of the
system. UTAUT also includes moderating factors such as gender, age, experience, and
voluntariness of use, which influence the relationships between the primary constructs
and behavioral intention and usage behavior (Venkatesh et. al., 2003). Researchers
have widely applied the UTAUT framework to investigate factors affecting the
intention to adopt various innovative technologies, and have also adapted it within
modified models of technology resistance (Purwanto et. al., 2003)
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II.2 Conceptual Framework
To support this research, the author has conducted preliminary research through
personal interviews. Based on qualitative personal interviews with 10 proptech app
users (70% males and 30% females), the study identified five barriers and two
additional barrier factors from the UTAUT model. The researchers conducted online
personal interviews using a semi-structured questionnaire to collect qualitative data
from individual users. This approach validated the significance of the barriers
pertaining to proptech apps and shed light on respondents' perspectives regarding
various functional and psychological aspects. The participants that the confidentiality
of their answers was assured. This approach led to the exploration of diverse aspects
during the interview using a semi-structured format. Respondents were recruited using
social media, and respondents matching the criteria were approached via WhatsApp.
Online interviews lasting between 20 to 45 minutes were conducted with their consent.
Through the online interview, the author identified several problems leading to
hypotheses for further investigation. The hypotheses generated from the preliminary
research centered around the specific barriers identified by users of proptech
applications. The hypotheses were as follows :
II.2.1 Usage Barrier (UB)
The concept of usage is closely associated with the principles of usability, user-
friendliness, and compatibility (Laukkanen et al., 2007). Numerous studies have
established that usage is a critical factor in determining whether a product or service is
accepted or rejected (Kushwah et al., 2019. Usage barrier (UB) arises when individuals
encounter difficulties in understanding and utilizing it (Ram & Sheth, 1989), especially
when consumers find innovations do not offer alignment with their work processes,
experiences, or habits (Chen et al., 2022). Usage Barriers required not only to learn but
also to use a new system, which may necessitate alterations to ingrained routines and
behaviors (Kaur et al., 2020). If the adoption of a technology does not align with
consumers' previous experiences, acceptance criteria, norms, routines, and habitual
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behaviors, they will necessitate a longer time frame to accept the technology (Lian &
Yen, 2013). The complexity associated with using novel digital technologies can serve
as a significant barrier to their widespread adoption, as this usage-related barrier
represents an important factor to consider (Kaur et al., 2020). It can lead to
technological innovations’ discontinuation (Oktavianus et al., 2017). Previous
researchers have revealed that usage barriers have been positively associated with
innovation resistance such as e-tourism (Hossain, 2023), digital financial product
(Talwar et al., 2023), smart products (Mani & Chouk, 2018) and hydrogen-electric
motorcycles (Chen et al., 2018). Based on the preliminary research, usage barriers may
be caused by the resistance arising from learning how to transact through apps and
perceived ease of use of the app. Therefore, below is the proposed hypothesis :
(Hypothesis 1) H1 : Usage Barrier (UB) has a significant influence on consumers’
resistance towards proptech apps payment
II.2.2 Value Barrier (VB)
Value Barrier (VB) refers to a condition when there is a lack of motivation to
adopt new innovations because they failed to present a substantial improvement in
value over current options (Chen et al., 2022). Cost of the product or service is
significant in buying decisions, therefore and is considered as a value barrier
(Chakraborty et al., 2022). When an innovation does not introduce an attractive balance
between performance and cost when compared to other choices, consumers may feel
that it doesn't justify altering their established habits or routines (Laukkanen et al.,
2007). If consumers do not see a clear additional benefit from the innovation compared
to what they currently use, they are likely to resist the adoption (Chaouali & Souiden,
2019). Studies show that usage barriers have been negatively associated with the
intention to use such m-commerce (Moorthy et al., 2017), mobile payment (Kaur et al.,
2020), telemedicine (Kautish et al., 2023) and regenerative farming technology (Jin et
al., 2022). With proptech context, their apps provide a cost-effective solution for
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tenants to monitor their rental payments conveniently. However, if renters find the
benefits unsatisfactory, particularly in terms of cost savings or payment administration,
it can result in the establishment of a value barrier. This barrier can subsequently affect
their intention in using or embracing the apps for their financial transactions. Therefore,
below is the proposed hypothesis :
(Hypothesis 2) H2 : Value Barrier (VB) has a significant influence on consumers’
resistance towards proptech apps payment
II.2.3 Risk Barrier (RB)
Risk Barrier (RB) highlights the inherent uncertainty associated with new
innovations, encompassing potential risks that can be physical, economic, functional,
or social in nature (Chen et al., 2022). It addresses the resistance that arises from
uncertainties inherent in any innovation (Kaur et al., 2021). Risk barriers indicate that
consumers perceive innovation as risky compared to its alternative (Mani & Chouk,
2018). Perceived uncertainty or unpredictability regarding the use of technology or
innovation is associated with increased perceived risk barriers, leading to resistance
(Kleijnen et al., 2009). Based on prior research, risk barriers have shown to negatively
influence intention to use, including use of telemedicine (Kautish et al., 2023), mobile
ticketing (Chen et al., 2022), and mobile gaming (Ashfaq et al., 2021). In proptech
context, users may fear the consequences of system failures, data breaches and the
possibility of incomplete accommodation information in the apps. Therefore, below is
the proposed hypothesis :
(Hypothesis 3) H3 : Risk Barrier (RB) has a significant influence on consumers’
resistance towards proptech apps payment
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II.2.4 Tradition Barrier (TB)
Tradition Barrier (TB) refer to a consumer's resistance to accept changes
brought about by innovation in their everyday repetitive activities (Ram & Sheth,
1989). Tradition occurs if an innovation diverge from the established consumer’s
traditions which can lead to consumer’s resistance (Chen et al., 2022) Due to the nature
of the tradition barrier being a sub-part of the psychological barrier, therefore,
consumers' current belief systems might be conflicting with the adoption of new
innovation (Laukkanen, 2016). Prior studies in various contexts have also identified
traditional barriers in response to innovative services, such as those encountered by
food delivery applications (Kaur et al., 2021), environmentally friendly cosmetics
(Sadiq et al., 2021), mobile payments (Khanra et al., 2021), and mobile wallets (Leong
et al., 2020). In proptech context, users may exhibit resistance to adopting proptech
apps for mid to long rent due to the conflict with their established habits. For instance,
individuals accustomed to face-to-face negotiations may find it challenging to shift to
digital platforms for conducting real estate business. Therefore, below is the proposed
hypothesis :
(Hypothesis 4) H4 : Tradition Barrier (TB) has a significant influence on consumers’
resistance towards proptech apps payment
II.2.5 Image Barrier (IB)
Image barrier (IB) happens when consumers refuse to accept an innovation if
they associate certain stereotypes with the innovative product, preventing the adoption
of innovation (Chen et al., 2018). If the origin of an innovation, tied to a specific
industry or country, holds a negative perception, thereby inducing resistance among
consumers (Chen et al., 2022). Consumers' perception about how difficult or easy it is
to adopt can also affect the image of new innovation (Mani & Chouk, 2018).
Consumers generally do not perceive mobile applications as secure, leading to poor
image (Kaur et al., 2020). Previous research has investigated how image influences
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users' resistance to new technologies. including Telemedicine apps (Kautish et al.,
2023), mobile banking (Yu & Chantatub, 2016), m-commerce (Moorthy et al., 2017),
and e-tourism (Hossain 2023). Based on the preliminary research results, proptech apps
may have the image of being not able to provide a comprehensive view of the property.
With this kind of image they may feel that the digital experience is less trustworthy
compared to seeing the property with their own eyes. Therefore, below is the proposed
hypothesis :
(Hypothesis 5) H5 : Image Barrier (IB) has a significant influence on consumers’
resistance towards proptech apps payment
II.2.6 Social Influence (SI)
Social Influence (SI) refers to the degree in which technology consumers
perceive that influential individuals in their lives believe they should adopt a particular
technology (Venkatesh et al., 2012). The subjective norm is anticipated to impact
consumer attitudes because people tend to rely on input from important individuals as
evidence of reality, with social influence primarily affected by subjective norms
(Schepers & Wetzels, 2007). Individuals' assessments of the utility of a service or
technology may be positively influenced by persuasive social information (Venkatesh
et al., 2012). The impact of social influence can either strengthen or weaken an
individual's resistance, leading to a change in the perceived risk and uncertainty
associated with an innovation (Schierz et al., 2010). Understanding the significance of
social influence is important to determining its role in consumer resistance within
transactions in proptech apps. Therefore, below is the proposed hypothesis :
(Hypothesis 6) H6 : Social Influence (SI) has a significant influence on consumers’
resistance towards proptech apps payment
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II.2.7 Facilitating Conditions (FC)
Facilitating conditions (FC) is the extent to which a person believes that
organizational and technical infrastructures exist to help the use of the technology
(Venkatesh et al., 2003). Factors such as availability of training and availability of
support is important (Lu et al., 2016). Facilitating conditions could either prevent or
facilitate an activity easier to carry out for an individual (Park et al., 2011). Facilitating
conditions in the field of technology utilization research refer to the organizational
assistance provided to technology users, which can impact their use of the technology
(Thompson et al., 1991). Facilitating conditions have shown to create a significant
impact on the use of new innovations such as e-tourism (Hossain, 2023), virtual reality
in learning (Shen et al., 2017), e-government (Alraja, 2016) and ticketing system
(Prayoonphan & Xu, 2019). Based on the preliminary research, a number of
participants have encountered difficulties due to the heaviness of the app compared to
other rental apps. It is important to know whether this acts as a significant barrier for
renters when it comes to conducting via app transactions for. Therefore, below is the
proposed hypothesis :
(Hypothesis 7) H7 : Facilitating Conditions (FC) has a significant influence on
consumers’ resistance towards proptech apps payment
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II. 3 Proposed Conceptual Framework
Figure II.3 Conceptual Framework (Author, 2024)
Figure II.3 presents a conceptual framework that integrates innovation
resistance theory (Ram & Sheth, 1989), with elements of UTAUT research model
(Venkatesh et al., 2003). Ram and Sheth (1989) suggest that factors influencing
consumer resistance towards technological innovation are Usage Barrier (UB), Value
Barrier (VB), Risk Barrier (RB). Through the online interview, the author identified
crucial cues for expanding the conceptual model and modified the approach with two
additional constructs from UTAUT; Social Influence (SI), and Facilitating Conditions
(FC).
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Chapter III Research Methodology
Research methodology represents a structured approach to address the research
problem (Patel & Patel, 2019). Research methodology solves research problems by
collecting data using various methods, interpreting the data, and drawing conclusions
(Marhasova et al., 2022).It describes the methodological approaches, processes, and
instruments that researchers employ to conceptualize, design, conduct, and analyze
their studies inquiries, which plays a crucial role in ensuring that research findings are
credible, valid, and valuable (Khan et al., 2023). This chapter presents a comprehensive
description of the methodology, including research design, details about the data to be
collected, sources of the data, as well as information on how the collection and analysis
processes were conducted.
III.1 Research Design
Research Design is an entire plan or strategy that details how the research will
be conducted (Khan et al., 2023). The research design provides the conceptual structure
and detailed blueprint for carrying out the marketing research project, delineating the
procedures requisite for obtaining the information needed to formulate or address the
research problems (Malhotra, 2019). A research design may have a range of
components, including research hypotheses, research methods, and data analysis
techniques. It can help researchers maintain focus on their study objectives and avoid
frequent traps and obstacles that develop during the research process. Figure III.1
shows the proposed research design for this research study.
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Figure III.1 Research Design Framework
This initial step involves recognizing and defining the specific problem or
research question that the study aims to address. After that, a review of existing
literature relevant to the research topic is conducted to understand the current state of
knowledge, identify gaps, and provide a theoretical foundation for the study. Followed
by initial preliminary research to gather background information and refine the research
problem. Based on the literature review and preliminary research, the research
hypothesis is formulated. A conceptual framework is then developed to outline the
variables and their expected relationships. Questionnaire or survey instrument is
designed to collect data from the study participants. The questions are structured to
measure the variables identified in the hypothesis and framework. The designed
questionnaire is deployed to the selected sample, and data is collected. The collected
data is then prepared for analysis. The prepared data is analyzed using various statistical
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methods. The analysis is divided into three types and steps : Descriptive Analysis,
Classical Assumption Test and Inferential Analysis.
After the data is already processed, the results of the data analysis will be
interpreted in the context of the research questions and hypotheses. This step involves
making sense of the findings and understanding their implications. The final step
involves drawing conclusions from the research findings and providing
recommendations based on the results. This can include suggesting future research
directions or practical applications of the study.
III.1.1 Types of Research Design
There are three different methods to connect research: quantitative, qualitative
and mixed methods (Asenahabi, 2019). This study used a quantitative research design,
which allows the researcher to test the formulated hypotheses and validate the theories.
Quantitative research design employs methods and measurements that generate
definitive, quantifiable data (Kothari, 2007). he collected data arises from empirical
measurements and analyses. To gather the necessary information from the intended
respondents, a questionnaire containing a series of closed-ended questions will be used.
After the data analysis sequence, conclusions can be drawn regarding the objectives or
hypotheses by applying mathematical and statistical techniques in the data collection
and analysis process. This approach emphasizes the non-experimental collection of
numerical data and the projection of the analytical results to the study population.
III.1.2 Nature of Research Design
In nature, studies could either be descriptive, exploratory, explanatory or even
a combination of these (Sekaran, 2003). The study's nature design will be descriptive
as its objective is to identify the obstacles preventing customers of certain
characteristics from using Proptech apps as a transaction medium. Descriptive research
is conducted to identify and characterize the features of the variables of interest in the
situation in question (Ehsan, 2017). Descriptive research may produce data that will
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help in the researcher's comprehension of the attitudes, behaviors, or other traits of a
specific target respondent group and show how the variables under examination are
correlated (Malhotra & Peterson, 2006).
III.1.3 Time Horizon of Research Design
Survey observations could be cross-sectional or longitudinal research
(Asenahabi, 2019). This research will use a cross-sectional study design since it is
conducted at a single point in time or only for a brief period of time. In a cross-sectional
study, researchers measure outcomes and exposures of the study subjects at the same
time. A cross-sectional study involves examining a group of individuals at a particular
point in time, with the study participants selected from an available population that is
relevant to the research question (Wang & Cheng, 2020).
III.2 Data Collection Method
Data collection is the process of obtaining and assessing information on
variables of interest in a defined, methodical manner in order to address research
questions, test hypotheses, and assess results. There are two primary types of data
collection methods: primary data collection and secondary data collection (Taherdoost,
2021). In order to collect all the information needed for examining the proposed
hypotheses and reach the research goals, both types of data will be used in the study.
III.2.1 Data Source
III.2.1.1 Primary Data
Primary data refers to information that has not yet been published and is first-
hand and unaltered by anyone (Taherdoost, 2021). Questionnaires, interviews, focus
groups, observation, surveys, case studies, and detailed experimental methods are
among the most popular primary data collection methods. To collect the necessary data
for this study, questionnaires will be employed. Data collection will involve
distributing self-administered surveys to the intended respondents. The questionnaire
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consists of a set of inquiries with different scoring systems intended to collect useful
data from the respondent.
III.2.1.2 Secondary Data
Secondary data is gathered from published sources and may be used for various
research purposes (Taherdoost, 2021). All papers are dependent on secondary data
sources for their literature review section. Secondary data can be collected from a
variety of sources, including records, books, research articles, and online articles.
Although they are not as reliable as primary data sources, they are still useful for
scientific studies because primary data collection is sometimes difficult or impossible
to obtain. To support primary data, secondary data will be used in this research to help
provide an understanding of the research context and validate the findings. The
combination of primary and secondary will offer multiple perspectives and sources of
information.
III.2.2 Sampling Design
A sampling design is the method or plan used to choose a subset of people or
items from a larger population for research or study purposes (Xiaofei et al., 2021). It
entails deciding how to select the sample, including the selection procedure, sample
size, and sample unit. The sampling design in this research paper focuses on explaining
sampling technique, target population, sample size, sample element, sample location,
sample period, and sample frame. The steps likely involved in the sampling process are
illustrated in Figure III.2.
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Figure III.2 Sampling Technique
Source : Sampling Methods in Research Methodology; How to Choose a
Sampling Technique for Research (Taherdoost, 2016)
III.2.2.1 Target Population
The target population consists of the aspects that fit into certain desired criteria
and from which conclusions are to be made on the subject of interest (Malhotra &
Peterson, 2006). Therefore, the target population of this research will be those who
used or downloaded proptech apps for medium to long term kos/apartment searching
purposes, currently living in Jabodetabek. Jabodetabek, encompassing Jakarta, Bogor,
Depok, Tangerang, and Bekasi, is the largest metropolitan area in Indonesia and a
significant economic hub with a population exceeding 35 million (Demographia,
2023). Its high urbanization level, diverse demographic, and rapid urban development
make it an ideal location for studying consumer behavior related to proptech apps. With
advanced infrastructure and high internet penetration, Jabodetabek's population is more
inclined to adopt new technologies, including those for medium to long-term
kos/apartment searching.
III.2.2.2 Sampling Size
The concept of sample size describes the total number of participants in a
research study which affects statistical power, precision, error rate, and the capacity to
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identify noteworthy experiment results (Oladugba et al., 2016). A sample size of 200-
450 is recommended for research without an available sampling frame. Because this
study does not have a sampling frame, the sample size has been determined based on
the range suggested by previous literature. In addition, the absolute minimum for
Pearson Correlation analysis is 200 samples (Guilford, 1954). Therefore, we are going
to take the middle value, and this study will use a sample size of 300. As for the pre-
testing and/or pilot study, research demands a smaller sample size than the main
research (Memon et al., 2020). It is suggested that 30 is an acceptable size for a pilot
study (Lancaster et al., 2004).
III.2.2.3 Sampling Element
Sampling element in research refers to selecting a particular portion of the
population for data collection. It facilitates drawing conclusions about the entire
population, enhancing practicality, speed, and cost-effectiveness in research (Turner,
2020). The sampling elements of this study are those Indonesian consumers residing in
Jabodetabek, aged between 18 to 60 years old, owning a smartphone, used and
downloaded proptech apps for kos/apartment searching purposes, and accustomed to
using the internet for transactions. In this study, preference was given to residents of
Jabodetabek to ensure that the findings can be generalized within the urban Indonesian
context. This study placed significant emphasis on consumers who use smartphones
and the internet for kos/apartment searches, as these individuals are more likely to
engage with and adopt proptech applications.
III.2.2.4 Sampling Location
Sampling location refers to the locations chosen to conduct the survey (Hau,
2016).This research will be conducted in Jabodetabek, which includes the cities of
Jakarta, Bogor, Depok, Tangerang, Tangerang Selatan, and Bekasi. Jabodetabek
represents the central metropolitan area of Indonesia, with more than 18 million
residents, accounting for approximately one-fifth of the population of Java, using the
internet (Nur et al., 2022). The region has a high urbanization rate and significant
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internet penetration. This makes Jabodetabek an ideal location for studying the
resistance factors related to proptech apps due to its diverse population and significant
market demand for medium to long-term accommodation solutions.
III.2.2.5 Sampling Period
Sampling period is the timeframe during which data is collected from a
particular portion of the population, allowing for efficient data collection for analysis
and conclusion (Turner, 2020). Sampling period for this research was from 30/06/2024
until 16/07/2024.
III.2.2.6 Sampling Frame
Sampling frame represents the comprehensive list or set of parameters used to
identify the population of interest for a research study, such as directory listings,
mailing lists, and other similar resources (Malhotra & Peterson, 2006). However, due
to the difficulty in collecting a list of residents in Jabodetabek who use proptech
applications for medium to long-term accommodation searches, there will be no
sampling frame in this research.
III.2.2.7 Sampling Technique
Sampling techniques are often categorized as probability sampling and non-
probability sampling (Turner, 2020). Probability sampling involves selecting elements
from a sampling frame where each element has a known and non-zero probability of
being chosen, with the selection process being random. Several probability-based
sampling techniques exist, including Simple Random Sampling, Systematic Sampling,
Stratified Sampling, and Cluster Sampling. In contrast, non-probability sampling does
not guarantee that population elements have a known or equal chance of selection.
Some may have no possibility of being selected, while the likelihood of others being
chosen is indeterminable. Although this type of sampling is limited in its ability to
generalize findings, it remains useful for information gathering, particularly in
exploratory research. Examples of non-probability sampling methods include
Convenience Sampling, Purposive Sampling, Quota Sampling, and Snowball
Sampling.
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This study uses a non-probability sampling, specifically convenience sampling.
Convenience sampling is a nonprobability sampling technique where participants are
selected based on their availability and ease of access. This approach is widely used
due to the simplified participant recruitment process (Turner, 2020). Participants in
convenience sampling are often chosen because they are already available (Taherdoost,
2016). In this study, since Populix, a survey platform, is being used, data collection can
be expedited as the app facilitates quick and easy access to respondents who meet the
study's criteria.
III.2.3 Research Instrument
To meet the objectives of this thesis and address the research questions, a survey
was developed using Populix survey form, Populite. This platform offers an online
form and survey creator with various question categories, including screening the
respondents in an earlier stage. The ability to perform real-time analysis of results from
any device makes Populite a convenient and user-friendly choice for this study.
Based on the literature review and in-depth interviews conducted during
preliminary research, a three-section questionnaire was developed. First section is the
screening section which was used to filter the respondents to ensure they meet the
criteria for the study. The questions determine if the respondents have used proptech
applications to search for rental properties and which applications they have used and
whether subsequent payments are made through the app or not. Additional questions
check if they are currently residing in the Jabodetabek area. The second section collects
basic demographic information about the respondents. The questions include age,
gender, highest education completed, area of residence, frequency of monthly app
transactions, monthly expenditure, and occupation. The third section collects
respondents' experiences with proptech applications.
III.2.3.1 Questionnaires
Questionnaires are structured sets of questions used for qualitative and
quantitative analysis of human opinions, preferences, attitudes, and behaviors, crucial
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for research methodologies (Lei et al., 2024). Questionnaires can accommodate a large
sample size, making it suitable for quantitative analysis and providing exact estimates
for the topic of study. This study utilized a structured questionnaire to investigate the
barriers that prevent consumers in the Jabodetabek region from adopting payment
through proptech applications for medium to long-term kos/apartment rental.
III.2.3.2 Questionnaire Design
Questionnaire design is the process for developing the design and questions for
a survey instrument that will be used to gather information about a specific
phenomenon (Lavrakas, 2008). When designing a questionnaire, all stages of survey
design and implementation should be considered. Based on the literature review and
in-depth interviews conducted during preliminary research, a three-section
questionnaire was developed. Table III.1 summarizes the questionnaire design for the
study.
Table III.1 Outline of Questionnaire Design
Section
Number
of
Question
Questions
Scale
A
5
Screening questions
Nominal scale
B
7
Demographic questions
Nominal and
Ordinal scale
C
29
Independent and dependent
Variable questions
Interval Scale
Source : Author, 2024
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Table III.2 Questionnaire Design for Screening Questions and Demographic
Questions
Screening Questions
Experience Questions
1.
Apakah anda pernah menggunakan
aplikasi proptech dalam mencari
kos/apartemen untuk sewa bulanan atau
tahunan?
● Pernah
● Tidak Pernah (Berhenti
mengisi)
4
.
Apa aplikasi yang pernah anda gunakan
untuk mencari kos/apartemen untuk
sewa?
● Travelio
● Mamikos
● Rukita
● Cove
● Koolkos by Red Doorz
2.
Apakah dalam 6 bulan ke belakang anda
menyewa kos/apartemen sebagai tempat
tinggal?
● Ya
● Tidak (Berhenti mengisi)
5
.
Apakah anda pernah melakukan
pembayaran pertama di aplikasi
proptech untuk sewa kos/apartemen
jangka menengah hingga panjang?
● Pernah (Berhenti mengisi)
● Tidak Pernah
3.
Apakah saat ini anda sedang berdomisili
di daerah Jabodetabek?
● Ya
● Tidak (Berhenti mengisi)
Demographic Questions
6.
Apakah Jenis Kelamin Anda?
● Laki - laki
● Perempuan
7.
Berapa usia anda?
● 18-24
● 25-30
● 31-35
● 36-40
● 41-45
● 46-50
● >55
8.
Pendidikan terakhir saat ini
● SD
● SMP/ Sederajat
● SMA/ Sederajat
● D3
● S1/D4
● S2/S3
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The screening questions in Section A are crucial for identifying respondents
who are relevant to the study. This ensures that the collected data is accurate and
representative of the target population. Section B, which covers demographic
questions, provides a comprehensive understanding of the respondents' backgrounds.
This information is vital for analyzing the data within the context of various
demographic factors. This demographic data will also help in segmenting the
respondents into different groups for more detailed analysis. Section C includes a
detailed set of questions regarding the independent and dependent variables of the
9.
Di mana anda tinggal?
● Jakarta
● Bogor
● Depok
● Tangerang
● Bekasi
10.
Seberapa sering Anda bertransaksi menggunakan aplikasi dalam kurun waktu satu bulan?
● Tidak sama sekali
● Jarang (1-2 kali)
● Kadang-kadang (3-5 kali)
● Sering (6-10 kali)
● Sangat sering (lebih dari 10 kali)
11.
Berapa total pengeluaran anda termasuk dengan biaya sewa tempat tinggal?
● Kurang dari atau sama dengan Rp.1.000.000
● Rp.1.000.001 - Rp. 1.500.000
● Rp.1.500.001 - Rp.2.000.000
● Rp.2.000.001 - Rp.3.000.000
● Rp.3.000.001 - Rp.5.000.001
● Rp.5.000.001 - Rp.7.500.000
● Lebih dari atau sama dengan Rp.7.500.001
12.
Apa Pekerjaan Anda?
● Bekerja penuh waktu (full-time), status permanen
● Bekerja penuh waktu (full-time), status kontrak
● Bekerja paruh waktu (part-time)
● Pemilik usaha/Wiraswasta
● Mahasiswa aktif
● Tenaga lepas (freelancer)
● Pemilik usaha/Wiraswasta
● Tidak bekerja (ibu rumah tangga)
● Tidak bekerja (sedang mencari pekerjaan)
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study. These questions are designed to measure the factors that influence the adoption
and resistance of proptech apps among the target population
III.2.3.3 Pilot Test (Reliability and Validity)
Pilot test was undertaken to assess the consistency and precision of the survey
instrument utilized to gather data for this investigation. This preparatory process assists
in refining the survey questions prior to the actual data collection phase (Zikmund,
2003). An appropriate sample size for a pilot test is 30 respondents (Lancaster et al.,
2004). Therefore, a total of 30 residents of Jabodetabek who fit the criteria were asked
to fill out the questionnaires to provide feedback and suggestions. The collected data
from these 30 respondents were analyzed to test its reliability and validity.
The purpose of the reliability assessment is to evaluate the consistency of the
measurement tool when applied multiple times to the same object. In other words, a
reliability test aims to determine the extent to which a measurement outcome remains
relatively stable when the measurement is repeated two or more times. A reliability
coefficient below 0.6 is considered poor, while 0.7 is acceptable and 0.8 or above is
considered good. Based on the results of calculating the Cronbach's Alpha formula
using SPSS, the reliability coefficient obtained from the research is as follows:
Table III.3 Reliability Test on Pilot Test
Variables
No. Items
Cronbach’s Alpha
Result
Usage Barrier (UB)
4
0.917
Reliable
Value Barrier (VB)
3
0.841
Reliable
Risk Barrier (RB)
5
0.932
Reliable
Tradition Barrier (RB)
3
0.932
Reliable
Image Barrier (IB)
4
0.846
Reliable
Social Influence (SI)
4
0.903
Reliable
Facilitating Conditions
3
0.896
Reliable
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(FC)
Innovation Resistance
(IR)
3
0.955
Reliable
Source : Data Processing Using SPSS 27.0, 2024
Based on the result above, it shows that all statement variables have values that
can be categorized as acceptable reliability because they are greater than the Cronbach's
alpha value of 0.6.
Validity testing is conducted to determine the extent to which an instrument can
be used to measure what it is supposed to measure. According to Sugiyono (2012), a
correlation coefficient of 0.000 to 0.199 indicates a very weak relationship. A
coefficient between 0.200 and 0.399 is thought to indicate a low relationship. Moderate
relationships are represented by coefficients ranging from 0.400 to 0.599, whereas
strong relationships range from 0.600 to 0.799. Finally, coefficients between 0.800 and
1.000 indicate a very strong relationship.
The validity test in this research was conducted on 30 respondents, using a
significance level (α) of 5% or 0.05. To obtain the r table value, first, find Df = N-2 =
30 – 2 = 28, so the r table value is 0.361. The data is considered valid if the calculated
r value (r count) > r table and the significance value < 0.05.
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Table III.4 Validity Test on Pilot Test
Source : Data Processing Using SPSS 27.0, 2024
Variabel
Items
R-Count
R-Table
Result
Usage Barrier (UB)
UB1
0.904
0.361
Valid
UB2
0.909
0.361
Valid
UB3
0.914
0.361
Valid
UB4
0.862
0.361
Valid
Value Barrier (VB)
VB1
0.849
0.361
Valid
VB2
0.891
0.361
Valid
VB3
0.870
0.361
Valid
Risk Barrier (RB)
RB1
0.874
0.361
Valid
RB2
0.908
0.361
Valid
RB3
0.938
0.361
Valid
RB4
0.855
0.361
Valid
RB5
0.857
0.361
Valid
Tradition Barrier
(RB)
TB1
0.953
0.361
Valid
TB2
0.948
0.361
Valid
TB3
0.916
0.361
Valid
Image Barrier (IB)
IB1
0.788
0.361
Valid
IB2
0.833
0.361
Valid
IB3
0.865
0.361
Valid
IB4
0.858
0.361
Valid
Social Influence (SI)
SI1
0.881
0.361
Valid
SI2
0.904
0.361
Valid
SI3
0.927
0.361
Valid
SI4
0.817
0.361
Valid
Facilitating
Conditions (FC)
FC1
0.943
0.361
Valid
FC2
0.923
0.361
Valid
FC3
0.859
0.361
Valid
Innovation Resistance
(IR)
IR1
0.962
0.361
Valid
IR2
0.962
0.361
Valid
IR3
0.951
0.361
Valid
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Based on the table above, all items are declared valid because the resulting
coefficient exceeds 0.361. Therefore, there is no need to replace or delete statements.
III.2.4 Constructs Measurement
III.2.4.1 Sources of the Questions
Table III.5 Questionnaire Design References
Variable
Construct
Label
Measurement Items
In Indonesian
References
Innovation
Resistance
Theory (IRT)
Usage Barrier (UB)
UB1
In my opinion,
proptech apps are
easy to use (R)
Menurut saya, aplikasi
Proptech mudah untuk
digunakan (R)
Adapted from
(Kautish et al.,
2023), further
developed by
author.
UB2
In my opinion,
proptech apps are
convenient to use (R)
Menurut saya, aplikasi
proptech dapat diakses
kapan saja (R)
UB13
In my opinion,
proptech apps can be
used in any situation
(R)
Menurut saya, aplikasi
proptech dapat
digunakan dalam situasi
apapun (R)
UB4
In my opinion,
payment feature in
proptech apps are
easy to use (R)
Menurut saya, fitur
pembayaran di aplikasi
proptech mudah untuk
digunakan (R)
Value Risk (VB)
VB1
Proptech apps have
many advantages
when it comes to
transactions with
kos/apartment
managers (R)
Aplikasi proptech
memiliki banyak
keunggulan dalam
perihal bertransaksi
dengan pengelola
kos/apartemen (R)
Adapted from
(Laukkanen,
2016), further
developed by
author
VB2
I believe, using
proptech apps helps
save time in the rental
transaction process
(R)
Saya percaya,
menggunakan aplikasi
proptech membantu
menghemat waktu
proses pembayaran
sewa (R)
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VB3
I believe, using
proptech apps helps
reduce costs in the
rental payment
process (R)
Saya yakin, penggunaan
aplikasi proptech
membantu mengurangi
biaya dalam proses
pembayaran sewa (R)
Risk Barrier (RB)
RB1
I’m worried that the
proptech application
provided inaccurate
information regarding
the condition of the
property I want to rent
Saya khawatir aplikasi
proptech memberikan
informasi yang tidak
tepat terkait kondisi
properti yang ingin saya
sewa
Adapted from
(Kaur et al.,
2020);
(Laukkanen,
2016); (Chu,
2023);
(Kautish et al.,
2023), further
developed by
author.
RB2
I'm worried that
proptech applications
cannot ensure data
security when I
include personal
information during
transactions
Saya khawatir aplikasi
proptech tidak dapat
memastikan keamanan
data ketika saya
mencantumkan
informasi pribadi saat
bertransaksi
RB3
I'm worried that the
proptech app doesn't
include accurate rent
rules
Saya khawatir aplikasi
proptech tidak
mencantumkan
peraturan sewa yang
akurat
RB4
I’m worried that there
would be an error in
the payment process
via the proptech app
Saya khawatir terjadi
kesalahan pada proses
pembayaran melalui
aplikasi proptech
RB5
If the transaction has
been made, I'm
worried that I won't
be able to ask for a
refund if necessary
Jika pembayaran telah
dilakukan, saya
khawatir tidak bisa
meminta pengembalian
dana apabila diperlukan
Tradition Barriers
(TB)
TB1
I feel the need to
communicate directly
with the
kos/apartment
manager before
making payment
Saya merasa perlu
untuk berkomunikasi
secara langsung dengan
pengelola
kos/apartemen sebelum
melakukan pembayaran
Adapted from
(Chu, 2023),
further
developed by
author
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TB2
I feel the need to see
the real condition of
the kos/apartment
before making
payment
Saya merasa perlu
untuk melihat kondisi
asli kos/apartmen
sebelum melakukan
pembayaran
TB3
I feel the need to
negotiate with the
kos/apartement
manager before
making payment
Saya merasa perlu
untuk bernegosiasi
dengan pengelola
kos/apartemen sebelum
melakukan pembayaran
Image Barriers (IB)
IB1
I have a negative view
of proptech apps
Saya memiliki
pandangan negatif
terhadap aplikasi
proptech
Adapted from
(Laukkanen,
2016); (Lee &
Kim, 2022),
further
developed by
author.
IB2
In my view, proptech
apps are difficult to
use as a medium for
transactions with
kos/apartment
managers
Menurut saya, aplikasi
proptech sulit
digunakan sebagai
media transaksi dengan
pengelola
kos/apartemen
IB3
In my view,
transactions via
proptech apps cannot
be trusted
Menurut saya, transaksi
melalui aplikasi
proptech tidak dapat
dipercaya
IB4
I have doubts about
the success of
transactions via
proptech applications
Saya ragu dengan
keberhasilan
bertransaksi melalui
aplikasi proptech
Unified Theory
of Acceptance
and Use of
Technology
(UTAUT)
Social Influence
(SI)
SI1
People who are
important to me,
advised me to pay for
kos/apartment rent via
a proptech apps (R)
Orang-orang yang
penting bagi saya,
menyarankan saya
untuk menggunakan
aplikasi proptech untuk
pembayaran sewa
kos/apartemen (R)
Adapted from
(Chu, 2023),
(Softina et al.,
2022) further
developed by
author.
SI2
People who influence
my behavior, advise
me to pay for
kos/apartment via
Orang-orang yang
mempengaruhi perilaku
saya, menyarankan saya
untuk melakukan
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proptech applications
(R)
pembayaran sewa
kos/apartemen melalui
aplikasi proptech (R)
SI3
People whose advice I
listened to, advised
me to pay for
kos/apartment via a
proptech application
(R)
Orang - orang yang
sarannya saya
dengarkan,
menyarankan saya
untuk melakukan
pembayaran sewa
kos/apartemen melalui
aplikasi proptech (R)
SI4
My friends use
proptech apps to pay
for kos/apartment
they rent (R)
Teman-teman saya
menggunakan aplikasi
proptech untuk
pembayaran sewa
kos/apartemen (R)
Facilitating
Conditions (FC)
FC1
The device I own can
support kos/apartment
rental payment via the
proptech apps (R)
Perangkat yang saya
miliki, mendukung
pembayaran sewa
kos/apartemen melalui
aplikasi proptech (R)
Adapted from
(Hossain,
2023), further
developed by
author
FC2
The internet
connection I have can
support payment via
proptech applications
(R)
Koneksi internet yang
saya miliki, dapat
membantu pembayaran
melalui aplikasi
proptech untuk sewa
(R)
FC3
I feel there is enough
guidance available to
understand how to
pay via proptech apps
(R)
Saya merasa ada cukup
panduan yang tersedia
untuk memahami cara
pembayaran melalui
aplikasi proptech untuk
sewa (R)
Innovation
Resistance (IR)
IR1
I will never use
proptech apps to make
payments for
kos/apartment rental
payments
Saya tidak akan pernah
menggunakan aplikasi
proptech untuk
melakukan pembayaran
sewa kos/apartemen
Adapted from
(Softina et al.,
2022);
(Widayani,
2022), further
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IR2
I tend to reject the use
of proptech
applications for
kos/apartment rental
payments
Saya cenderung
menolak penggunaan
aplikasi proptech untuk
pembayaran sewa
kos/apartment
developed by
author
IR3
I might use proptech
app for kos/apartment
rental payment but not
now
Saya akan
menggunakan aplikasi
proptech untuk
pembayaran sewa
kos/apartemen tetapi
tidak sekarang
III.2.4.2 Scale Measurement
The Center for Innovation in Research and Teaching states that measurement
involves the assignment of numbers or other symbols to object characteristics based on
predefined rules. In statistical research, measurement is the process of assigning
symbols, letters, or numbers to variables according to established rules (Allanson &
Notar, 2020). These measurement tools adhere to standards and yield reliable data. In
statistical analysis, all variables are classified into one of four measurement scales:
nominal, ordinal, interval, or ratio. These measurement scales provide a convenient
way to organize and sub-categorize different types of data.
III.2.4.2.1. Nominal Scale
Nominal scales are often used in survey research or questionnaires. Nominal
scales are deemed to be the simplest to understand because they are merely used to
label or categorize variables arbitrarily (Allanson & Notar, 2020). Nominal scales lack
quantitative value or order, and no mathematical operations can be applied to them.
Nominal scales are essentially coding according to type or kind characteristics such as
race, ethnic background, and place of birth. In this research, items using nominal
measurement include gender, education level, occupation, applications Used for
Finding Rentals, and place of residence.
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III.2.4.2.2 Ordinal Scale
Ordinal scale is a scale of measurement in which the obtained information is
categorized and ranked in a meaningful way (Sekaran, 2003). An ordinal scale is a
ranking-based scale that is sorted from the highest to the lowest level or the other way
around. So the ordinal scale enables researchers to arrange a person or object depending
on its quantity or characteristics (Suprananto, 2012). In this research, items using
nominal measurement include age and monthly online apps transactions.
III.2.4.2.3 Interval Scale
An interval scale is a numerical measurement scale where the order of the
variables is known, and the differences between the variables are meaningful and
interpretable (Bhat, 2020). Interval data categorizes numbers as all-inclusive, mutually
exclusive, or ordered based on their quantity expression. Interval scales ensure equal
and meaningful distance between measures lacking a true zero point (Allanson &
Notar, 2020). Interval scales not only categorize and order data frequencies and
percentages, but also enable advanced statistical analysis like mode, median, and mean
measurement, as well as standard deviation calculation. Therefore, in this research, all
independent variable items and dependent variable item will have interval scale as
measurement scale, using a 7-likert scale. Table below shows the number and scale
interpretation.
Table III.6 7-likert scale used
No
Meaning
1
Strongly Disagree
2
Disagree
3
Somewhat Disagree
4
Neutral
5
Somewhat Agree
6
Agree
7
Strongly Agree
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III.2.4.2.4 Ratio Scale
Ratio scale is a variable measurement scale that not only determines the order
of variables, but also the difference between variables, as well as the value of true zero.
Because ratio data starts at zero, values less than zero are impossible (Bhat, 2020). In
this study, monthly expenditure would fall into ratio scale because it has a true zero
point, equal intervals, and supports meaningful ratio.
III.2.4.3 Questionnaire Summary
Table III.7 Summary of Measurement Scale Used
Items
Measurement
Scale of Measurement
Gender
Nominal
Dichotomous Scale
Age
Ordinal
Category Scale
Education Level
Nominal
Category Scale
Occupation
Nominal
Category Scale
Total Monthly Expenditure
Ratio
Continuous Scale
Monthly Online Apps
Transactions
Ordinal
Category Scale
Applications Used for Finding
Rentals
Nominal
Multiple Response Nominal
Scale
Place of Residence
Nominal
Category Scale
Usage Barrier (UB)
Interval
7-point likert scale
Value Barrier (VB)
Interval
7-point likert scale
Risk Barrier (RB)
Interval
7-point likert scale
Tradition Barrier (TB)
Interval
7-point likert scale
Image Barrier (IB)
Interval
7-point likert scale
Social Influence (SI)
Interval
7-point likert scale
Facilitating Conditions (FC)
Interval
7-point likert scale
Proptech Transaction Resistance
Interval
7-point likert scale
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(IR)
III.3 Data Analysis Method
The process of using statistical tools to transform raw data collected from target
respondents into a more appropriate and meaningful format is known as data analysis
(Hau, 2016). The researchers can then draw broad conclusions about the study using
the processed data. For this research, IBM SPSS software version 27 will be used to
process the data obtained from the respondents who participated in the research.
III.3.1 Data Processing
In order to guarantee that the data obtained are valid, complete, accurate, and
filled out by qualified respondents, the collected data must pass through multiple stages
of data preparation before being interpreted (Sekaran, 2003). First step is data checking.
Checking the questionnaire will be the first step in the data processing process. At this
point, every questionnaire that the target respondents returned was examined to make
sure there were no unclear patterns in the responses and variances in the responses
(Malhotra & Peterson, 2006). Data with high variance, unclear patterns and stagnant
patterns will be eliminated to ensure data quality. The data will be examined by the
researchers to filter out any answers that fall outside of the acceptable range, are
inconsistent, or have extreme values. The second stage involves data encoding. Each
survey response will be assigned a numerical code to enable the utilization of statistical
analysis tools for the effective interpretation of the data (Kothari, 2007). After coding,
the data will be ready to be processed in SPSS.
III.3.2 Descriptive Analysis
According to Zikmund et al. (2010), descriptive analysis is a technique used by
researchers to organize and condense the primary data they have gathered into a format
that is easier to read and understand. Based on this research perspective, descriptive
statistics will provide detailed demographic aspects of the respondents, including
gender, age, highest education completed, occupation, monthly expenditure, monthly
app transactions, used proptech apps, and place of residency. The findings will be
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presented in tables, groups, and through data analysis, all derived from the respondents'
answers. Furthermore, descriptive statistics also present details on the mean and mode.
This approach allows the researcher to understand the central tendency and dispersion
of the data, giving insight into the general profile of the respondents.
III.3.3 Validity and Reliability Analysis
Validity test is utilized to determine whether a questionnaire is valid (Ghozali,
2013). A questionnaire is considered valid if its questions can accurately capture what
it is intended to measure. Validity, in the words of Riduwan (2017), is the degree to
which a measuring device is appropriate or accurate. A low validity measuring tool is
indicative of a low accuracy level. Several requirements must be satisfied in order to
decide whether or not a question item is valid.
Meanwhile, a reliability test is used to measure something of the same type and
can provide consistent results (Suhartanto, 2014). A questionnaire is said to be reliable
if the respondent's answers are constant or stable from time to time in answering
questionnaire questions. When a respondent consistently provides consistent responses
to questionnaire questions, it is considered a reliable questionnaire. There are three
components to the reliability calculation method: Internal Consistency, Alternative
Forms, and Test-Retest. The internal consistency method, which indicates the
homogeneity of items in a measure that expresses ideas, was employed in this study by
the researchers. Cronbach Alpha calculations were used to test the consistency of the
items in order to determine the reliability. Table III.6 provides a general guideline for
interpreting the Cronbach Alpha for each variable.
Table III.8 Cronbach Alpha Interpretation Guideline (Hau, 2016)
Cronbach’s Alpha
Interpretation
0.81 to 0.95
Very Good
0.71 to 0.80
Good
0.61 to 0.70
Fair
< 0.60
Poor
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III.3.4 Classical Assumption Analysis
According to the Center for Innovation in Research and Teaching, classical
hypothesis testing is crucial when employing multiple linear regression techniques. The
classical assumption test verifies that the data used in the analysis adheres to a normal
distribution and that the model is free from issues related to multicollinearity and
heteroscedasticity. This test is exclusively applied to scaled or serialized data when
utilizing the Multiple Linear Regression method (Alita et al., 2021).
III.3.4.1 Multicollinearity Analysis
Multicollinearity in multiple regression analysis denotes the existence of linear
associations between the predictor variables. When two independent variables exhibit
a nearly perfect linear relationship, they are considered collinear. When multiple
variables in the regression model have a significant correlation with both the dependent
variable and one another, this is known as multicollinearity. Tolerance value (TOI) or
Variance Inflation factor (VIF) are used to detect multicollinearity tests. According to
Sujarweni (2015), the test's criteria indicate the presence of multicollinearity among
the independent variables when the tolerance value is less than 0.10 or equal to a VIF
value greater than 10.
III.3.4.2 Heteroscedasticity Analysis
For standard errors in general linear models to be accurate, homoscedasticity is
a necessary assumption (Rosopa et al., 2013). By contrasting the dependent variable's
observed and predicted values, this assumption is put to the test. The homoscedasticity
test determines whether the residuals' variance is constant and unaffected by the
residuals of other residuals.This research will use P-P Plot and scatter plot to check the
heteroscedastic of the data. The Probability-Probability (P-P) plot is a graphical
technique employed to assess the degree to which a specific data set aligns with a
particular probability distribution under investigation (Ramachandran & Tsokos,
2021). This plot compares the empirical cumulative distribution function of the given
data against the assumed true cumulative probability distribution function. Using
scatterplot, it can be seen that if the data is homoscedastic, the residuals' scatterplot will
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resemble a random scatter, akin to a shotgun blast. If the scatterplot shows no
discernible pattern, the regression is free of heteroscedasticity (Sujarweni, 2015).
III.3.4.3 Normality Test
The normality test assesses whether the residual values are normally
distributed, with a good test indicated by data or plot points that are closely aligned
with the diagonal line and no data points deviating significantly from the distribution
(Alita et al., 2021). This research uses the kurtosis and skewness analysis and
kolmogorov-smirnov test. Skewness is a measure of the asymmetry and kurtosis is a
measure of ’peakedness’ of a distribution (Kim, 2013). Meanwhile, The Kolmogorov-
Smirnov goodness-of-fit test examines a random sample from a one-dimensional,
continuous random variable to assess whether the data were drawn from a hypothesized
probability distribution (Facchinetti, 2009).
III.3.5 Inferential Analysis
Inferential statistics involves the analysis of data from a sample in order to draw
conclusions about the population as a whole (Ali & Bhaskar, 2016). The goal is to
provide an answer or test the hypotheses. A hypothesis is a suggested explanation for
a phenomenon or plural hypothesis). Thus, hypothesis testing are methods for reaching
logical conclusions regarding the veracity of observed effects. This research employs
the Pearson Correlation test and Multiple Linear Regression for inferential analysis to
assess the strengths of the relationships and associations between the dependent and
independent variables.
III.3.5.1 Pearson Correlation Analysis
The Pearson correlation coefficient is the most widely used statistical method
for analyzing the relationship between numerical variables. It assigns a value between
-1 and 1, where 0 indicates no correlation, 1 represents a perfect positive correlation,
and -1 denotes a perfect negative correlation (Nettleton, 2014). Hair, Black, Babin, and
Anderson (2009) state that any research should have a coefficient value of no more
than 0.95 to prevent the problem of multicollinearity among the independent variables.
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Table 3.7 displays the general guideline used to interpret the Pearson Correlation
analysis.
Table III.9 Pearson Correlation Interpretation (Hau, 2016)
Coefficient Range
Correlation
± 0.91 to ± 1.00
Very Strong
± 0.71 to ± 0.90
High
0.41 to ± 0.70
Moderate
0.21 to ± 0.40
Small but definite relationship
0.00 to ± 0.20
Slight, almost negligible
III.3.5.2 Multiple Linear Regression
Regression analysis is a statistical method used to examine the associative
relationships between a quantitative dependent variable and one or more independent
variables (Malhotra, 2019). Multiple regression analysis is used by researchers, if the
researcher intends to predict the condition (rise and fall) of the dependent variable
(criterium), if two or more independent variables as predictor factors are manipulated
(rise and fall). In this case, there are two independent variables and one dependent
variable (Sugiyono, 2015). Thus, Multiple Linear Regression is expressed in the
following table. Regression analysis is a statistical technique used by researchers to
investigate the associative relationships between a metric dependent variable and one
or more independent variables (Malhotra, 2019). When researchers intend to predict
the condition of the dependent variable by manipulating two or more independent
variables as predictor factors, they employ multiple regression analysis (Sugiyono,
2015). In the given case, there are seven independent variables and one dependent
variable, and thus, the analysis can be expressed through Multiple Linear Regression
as shown in the following table III.8
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Table III.10 Multiple Linear Regression Equation
Y = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β6X7
Y
Dependent Variable
Innovation Resistance
X1
Independent Variable
Usage Barrier
X2
Independent Variable
Value Barrier
X3
Independent Variable
Risk Barrier
X4
Independent Variable
Tradition Barrier
X5
Independent Variable
Image Barrier
X6
Independent Variable
Social Influence
X7
Independent Variable
Facilitating Conditions
𝑎
The intercept of the regression line
𝛽
Regression Coefficient
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Chapter IV Result and Discussion
This chapter provides a detailed analysis and characterization of the data
obtained from the survey results. The findings are then interpreted and connected to
the theoretical framework. This comprehensive analysis tries to bridge the gap between
the theoretical aspects and practical findings of the study. In thoroughly examining the
data, we can draw insights and understand the implications of the results. Through a
detailed exploration of each hypothesis test, all aspects of the research questions are
addressed and validated.
IV.1 Preliminary Research Analysis
The preliminary research is conducted with 10 respondents, which will
represent the user of prop-tech apps who never use the app for transactions or no longer
using them. Based on the interview, their resistance towards payment through proptech
apps is influenced by several barriers.
First, several users face significant challenges with proptech apps due to issues
like complex user interfaces, long loading times, and delayed responses from kos
managers. These factors create a frustrating experience and reduce trust in the app’s
reliability, particularly for long-term rental needs. Such usability issues contribute to
the resistance against adopting these apps for kos or apartment rental transactions, as
users prefer platforms that are easy to navigate, responsive, and provide accurate, up-
to-date information., which suggest there is a barrier in the usage.
"Regarding the app, sometimes the user interface is complicated, making the
experience a bit confusing."
- FF, Male, 26
"The user interface is good, but when trying to view the location, the GPS in
the app is not accurate, and there are no photos of the surrounding area. This
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also becomes a consideration."
- PR, Female, 30
Second, users express concerns about outdated or incomplete information, the
need for physical verification of properties, and doubts about the reliability and
effectiveness of app-based transactions. The perceived lack of value is further
compounded by issues such as the absence of sufficient discounts or incentives that
would justify using the app over traditional methods , Value Barrier is evident in the
users' hesitation to complete transactions through proptech apps, as they perceive that
the benefits provided by the apps do not sufficiently outweigh the risks or
inconveniences.
"The price might be higher on the app”
- SM, Female, 22
"I didn't complete the transaction in the app because I'm afraid the distance from the
kos to the campus might be far, and I'm not confident about the photos”
- HK, Female, 20
Third, users express concerns about the accuracy and completeness of the
information provided by the apps, fearing that the properties may not match the
descriptions or photos. The risk of financial loss, such as the possibility of payments
not being transferred to the rightful property owner or the lack of a reliable refund
process, further deters users from completing transactions through these platforms. In
addition, concerns about data security and the potential for personal information leaks
worsen the perceived risks, making users hesitant to trust these apps for their rental
needs.
"The incomplete information about the kos (not updated) is a major factor in my
hesitation….. for the long term, it's riskier; I need to check the facilities and safety
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myself,"
- DV, Male, 27
" I feel it's not safe. If I still have to upload data, then it's not secure enough."
- FF, Male, 26
"It depends because I have read several times…about scams where customers were
asked to transfer money when they wanted to view the kos."
- EP, Male, 30
Fourth, users prefer traditional methods, such as direct communication with
property owners or on-site inspections, as they find these methods more trustworthy
and reassuring. The inability to physically verify the property or negotiate terms in
person leads to skepticism about the app's effectiveness and reliability. Users are more
comfortable with face-to-face interactions, believing that these provide better
opportunities to ensure the property meets their expectations and to build trust with the
property owner.
"I did not complete the transaction through the app... I have to go to the
accommodation, look at them first and find out the external environment of the kos
which is not mentioned in the application."
- FF, Male, 26
"I do not complete the payment through the app because I usually prefer to handle
things directly, just in case something doesn't match my expectations. If everything is
suitable, I pay directly on-site."
- RB, Male, 30
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Fifth, negative perceptions about the apps' reliability, accuracy, and overall
reputation lead to hesitation and resistance among users. Concerns about the app's
ability to deliver as promised, based on user reviews and past experiences, contribute
to a lack of trust and confidence in these platforms. The perception that these apps
might not be trustworthy or effective in facilitating secure and reliable transactions
further discourages users from fully embracing proptech solutions.
"I still have doubts because, although the app has been around for a while, its
transaction feature is still new… the app had a bad reputation with many people,
often involving scams and poor service... "
- EP, Male, 30
"I didn't complete the transaction in the app because I saw some reviews about the
app, which were not favorable. The negative reviews were not about the
kos/apartments themselves but about the app developers, mentioning issues like
pending transactions."
- RM, Male, 25
Sixth, social influence barrier has a noticeable impact on users' resistance to
adopting proptech apps for rental transactions. The opinions and behaviors of peers,
friends, and family play a role in shaping users' attitudes toward proptech apps. Users
often consider the experiences and recommendations of their social circle when
deciding whether to trust and use proptech platforms. While personal experiences and
concerns about reliability and security are important, the influence of social networks
can reinforce doubts and hesitation. As a result, social influence acts as a meaningful
barrier, contributing to users' reluctance to fully embrace proptech apps for their rental
needs.
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"It's not an issue for me, but my father prefers the property owner to share the same
religion. So, knowing the owner's religion is important because the owner's religious
values will be reflected in the property. Based on his advice, I decided not to proceed
with the transaction through the app."
- HK, Female, 20
Seventh, the facilitating conditions barrier plays a role in users' resistance to
adopting proptech apps for rental transactions, though its impact is somewhat context-
dependent. Issues related to devices, internet connectivity, and the availability of user
guidance can hinder the adoption of these apps. Users still express concerns about the
user-friendliness of the apps and the adequacy of support provided, which can deter
them from fully utilizing these platforms for their rental needs.
"The loading time is quite long because the app seems heavy."
- EH, Male, 25
"There was a time when the kos owner was slow to respond, sometimes replying a
day later, and occasionally the information about the kos was incomplete. The owner
does not guide me to transact in the app,"
- SM, Female, 22
Based on the interviews conducted, we can identify seven key barriers that
contribute to users' resistance to adopting proptech apps for rental transactions: Usage
Barriers, Value Barriers, Risk Barriers, Tradition Barriers, Image Barriers, Social
Influence, and Facilitating Conditions. These barriers encapsulate the concerns users
have about the complexity and reliability of the apps, the perceived value and
trustworthiness, the preference for traditional methods, the influence of social
networks, and the adequacy of technological support. While the UTAUT framework
traditionally includes Performance Expectancy and Effort Expectancy as factors
influencing technology adoption, these constructs will not be the focus of this research.
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This decision is based on the understanding that the identified barriers more directly
address the specific concerns relevant to the adoption of proptech apps. Performance
Expectancy and Effort Expectancy are largely discussed within the identified barriers,
particularly within the Usage and Value barriers, which more accurately reflect the
unique challenges and perceptions faced by users in this context. Thus, the research
will focus on these seven barriers to provide a more targeted analysis of the factors
influencing resistance to proptech app adoption.
IV.2 Main Research Analysis
IV.2.1 Response Rate
A total of 300 questionnaires were randomly distributed to target respondents
that are currently living in Jabodetabek. However, Only 262 questionnaires were
completed and after thorough checking, only 241 questionnaires could be used for
further analysis. Due to this, the response rate of the questionnaire is down from
87.33% to 80.33%. According to Livingston and Wislar (2012), the response rate is
considered acceptable as it is suggested that a 60% was an acceptable level. The data
collected from the 241 questionnaires was then processed using SPSS statistical
software in order to generate final conclusions.
IV.2.2 Descriptive Analysis
IV.2.2.1 Frequent Distribution
IV.2.2.1.1 Gender
Table IV.1 Gender
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Male
89
36.9
36.9
36.9
Female
152
63.1
63.1
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
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The results from the questionnaire, as presented in Table 4.1, demonstrate the
frequency and proportions of male and female respondents. Out of the 241 responses,
89 respondents are male (36.9%) and 152 respondents are female (63.1%).This
distribution indicates that females make up a larger segment of the overall sample.
IV.2.2.1.2 Age
Table IV.2 Age
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
18-24
102
42.3
42.3
42.3
25-30
75
31.1
31.1
73.4
31-35
38
15.8
15.8
89.2
36-40
19
7.9
7.9
97.1
41-45
3
1.2
1.2
98.3
46-50
2
.8
.8
99.2
51-55
1
.8
.4
99.6
>55
1
.4
.4
100
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.2, demonstrate the
frequency and proportions of respondents across different age groups. Out of the 241
responses, 102 respondents are aged 18-24 (42.3%), 75 respondents are aged 25-30
(31.1%), 38 respondents are aged 31-35 (15.8%), 19 respondents are aged 36-40
(7.9%), 3 respondents are aged 41-45 (1.2%), 2 respondents are aged 46-50 (0.8%), 1
respondent is aged 51-55 (0.4%), and 1 respondent is aged between 55-60 (0.4%). This
distribution indicates that the majority of the sample falls within the 18-24 age group,
followed by the 25-30 age group.
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IV.2.2.1.3 Last Education Completed
Table IV.3 Last Education Completed
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Middle High
School
5
2.1
2.1
2.1
Senior High School
86
35.7
35.7
37.8
Associate Degree
42
17.4
17.4
55.2
Bachelors’ Degree
102
42.3
42.3
97.5
Postgraduate
Degree
6
2.5
2.5
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.3, demonstrate the
frequency and proportions of respondents based on their last completed education
level. Out of the 241 responses, 5 respondents have completed SMP/Sederajat (2.1%),
86 respondents have completed SMA/Sederajat (35.7%), 42 respondents have
completed D3 (17.4%), 102 respondents have completed S1/D4 (42.3%), and 6
respondents have completed S2/S3 (2.5%). This distribution indicates that the majority
of the sample has completed S1/D4, followed by those who have completed
SMA/Sederajat.
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IV.2.2.1.4 Occupation
Table IV.4 Occupation
Frequency
Percent
Valid Percent
Cumulative
Percent
Vali
d
Part time
15
6.2
6.2
6.2
Full time (Contract
status)
58
24.1
24.1
24.1
Full time (permanent
status)
63
26.1
26.1
56.4
Active Students
44
18.3
18.3
74.7
Business Owners
27
11.2
11.2
86.9
Freelancer
19
7.9
7.9
93.8
Not working
(housewife)
8
3.3
3.3
97.1
Not working
(looking for work)
7
2.9
2.9
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.4, demonstrate the
frequency and proportions of respondents based on their employment status. Out of the
241 responses, 15 respondents work part-time (6.2%), 58 respondents work full-time
with a contract status (24.1%), 63 respondents work full-time with a permanent status
(26.1%), 44 respondents are active students (18.3%), 27 respondents are business
owners or entrepreneurs (11.2%), 19 respondents are freelancers (7.9%), 8 respondents
are homemakers (3.3%), and 7 respondents are unemployed and looking for work
(2.9%). This distribution indicates that the majority of the sample is a full-time
employee, either with a permanent or contract status, followed by active students.
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IV.2.2.1.5 Total Monthly Expenditure
Table IV.5 Total Monthly Expenditure
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Less than
1.000.000
18
7.5
7.5
7.5
Rp.1.000.001-
Rp.1.500.000
18
7.5
7.5
15
Rp.1.500.001-
Rp.2.000.000
26
10.8
10.8
25.7
Rp.2.000.001-
Rp.3.000.000
46
19.1
19.1
44.8
Rp.3.000.001-
Rp.5.000.000
55
22.8
22.8
67.6
Rp.5.000.001-
Rp.7.500.000
52
21.6
21.6
89.2
More than
Rp.7.500.001
26
10.8
10.8
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.5, shows the
frequency and proportions of respondents based on their expense levels. Out of the 241
responses, 18 respondents spent less than Rp. 1,000,000 (7.5%), 18 respondents spent
between Rp. 1,000,001 and Rp. 1,500,000 (7.5%), 26 respondents spent between Rp.
1,500,001 and Rp. 2,000,000 (10.8%), 46 respondents spent between Rp. 2,000,001
and Rp. 3,000,000 (19.1%), 55 respondents spent between Rp. 3,000,001 and Rp.
5,000,000 (22.8%), 52 respondents spent between Rp. 5,000,001 and Rp. 7,500,000
(21.6%), and 26 respondents spent more than Rp. 7,500,001 (10.8%). This distribution
indicates that the majority of the sample expenses monthly are between Rp. 3,000,001
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and Rp. 5,000,000, followed by those earning between Rp. 5,000,001 and Rp.
7,500,000.
IV.2.2.1.6 Monthly App Transaction
Table IV.6 Monthly App Transaction
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Rarely (1-2
times)
66
27.4
27.4
27.4
Occasionally (3-
5 times)
68
28.2
28.2
55.6
Often (6-10
times)
69
28.6
28.6
84.2
Very often
(more than 10
times)
38
15.8
15.8
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.6, shows the
frequency and proportions of respondents based on their monthly app transaction
frequency. Out of the 241 responses, 66 respondents rarely (1-2 times) use the app for
transactions (27.4%), 68 respondents occasionally (3-5 times) use the app for
transactions (28.2%), 69 respondents often (6-10 times) use the app for transactions
(28.6%), and 38 respondents very often (more than 10 times) use the app for
transactions (15.8%). This distribution indicates that the majority of the sample falls
within the categories of using the app for transactions occasionally (3-5 times) and
often (6-10 times) per month, followed by those who rarely and very often use the app
for transactions.
IV.2.2.1.7 Used Proptech Applications
Table IV.7 Used Proptech Applications for Accommodation Searches
Responses
Percent of Cases
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N
Percent
Valid
App Used : Travelio
104
20.0
43.1
App Used :
Mamikos
175
33.6
72.6
App Used : Rukita
81
15.6
33.6
App Used : Cove
49
9.4
20.3
App Used :
KoolKost by Red
Doorz
88
16.9
36.5
App Used : Others
23
4.5
9.5
Total
520
100.0
215.6
Source : Data Processing Using SPSS 27.0, 2024
The results from the questionnaire, as presented in Table IV.7, presents the
frequency and proportions of respondents based on the proptech apps they use for
accommodation searching. Out of 241 responses, 104 respondents use Travelio
(43.1%), 175 respondents use Mamikos (72.6%), 81 respondents use Rukita (33.6.6%),
49 respondents use Cove (20.3%), 88 respondents use KoolKost by RedDoorz (36.5%),
and 23 respondents use other apps (9.5%). This distribution indicates that Mamikos is
the most commonly used app among respondents, followed by Travelio and KoolKost
by RedDoorz.
IV.2.2.1.8 Place of Residence
Table IV.8 Place of Residence
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Jakarta
151
62.7
62.7
62.7
Bogor
17
7.1
7.1
69.8
Depok
19
7.9
7.9
77.7
Tangerang
29
12.0
12.0
89.7
Bekasi
25
10.4
10.4
100.0
Total
241
100.0
100.0
Source : Data Processing Using SPSS 27.0, 2024
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The results from the questionnaire, as presented in Table IV.8, demonstrate the
frequency and proportions of respondents based on their place of residence. Out of the
241 responses, 151 respondents reside in Jakarta (62.7%), 17 respondents reside in
Bogor (7.1%), 19 respondents reside in Depok (7.9%), 29 respondents reside in
Tangerang (12.0%), and 25 respondents reside in Bekasi (10.4%). This distribution
highlights that the majority of the sample resides in Jakarta, with a significantly higher
percentage compared to other areas. The gap between Jakarta and the next highest,
Tangerang, is notably large, emphasizing Jakarta's dominant representation in the
sample.
IV.2.2.2 Central Tendency
IV.2.2.2.1 Usage Barrier (UB)
Table IV.9 Central Tendency for Usage Barrier (UB)
Items No.
Questions
Mean
Mode
UB1
In my opinion, proptech apps are easy to use (R)
4.84
5
UB2
In my opinion, proptech apps are convenient to use (R)
4.91
5
UB3
In my opinion, proptech apps can be used in any situation
(R)
4.87
5
UB4
In my opinion, payment feature in proptech apps are easy to
use (R)
4.79
5
R = Reverse coded items
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Usage Barrier variable is presented
in Table IV.9. The mean scores range from 4.79 to 4.91 across the related statements.
UB2 has the highest mean score and UB4 has the lowest mean score. The mode scores
are similar with all items (UB1, UB2, UB3, and UB4) having a mode of 5, indicating
that respondents generally somewhat disagree with the negative statements about the
ease of use, convenience, adaptability, and payment features of proptech apps.
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IV.2.2.2.2 Value Barrier (VB)
Table IV.10 Central Tendency for Value Barrier (VB)
Items No.
Questions
Mean
Mode
VB1
Proptech apps have many advantages when it comes to
transactions with kos/apartment managers (R)
4.29
4
VB2
I believe, using proptech apps helps save time in the
kos/apartment rental transaction process (R)
4.48
4
VB3
I believe, using proptech apps helps reduce costs in the
kos/apartment rent payment process (R)
4.25
4
R = Reverse coded items
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Value Barrier variable is presented
in Table IV.10. The mean scores range from 4.25 to 4.48 across the related statements.
VB2 has the highest mean score, indicating that respondents somewhat agree with the
statement that using proptech apps helps save time in the kos rental transaction process.
Meanwhile, VB3 has the lowest mean score, reflecting that respondents somewhat
agree with the statement that using proptech apps helps reduce costs in the kos rental
transaction process. The mode scores are consistent, with all items (VB1, VB2, and
VB3) having a mode of 4. The central tendency results shows that respondents mostly
somewhat disagree with the negative statements about Value Barrier on using proptech
apps.
IV.2.2.2.3 Risk Barrier (RB)
Table IV.11 Central Tendency for Risk Barrier (RB)
Items No.
Questions
Mean
Mode
RB1
I’m worried that the proptech application provided inaccurate
information regarding the condition of the property I want to
rent
5.76
6
RB2
I'm worried that proptech applications cannot ensure
data security when I include personal information during
transactions
5.71
6
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RB3
I'm worried that the proptech app doesn't include accurate kos
rules
5.68
6
RB4
I’m worried that there would probably be an error in the
payment process via the proptech app
5.72
6
RB5
If the transaction has been made, I'm worried that I won't be
able to ask for a refund if necessary
5.86
7
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Risk Barrier variable is presented
in Table IV.11. The mean scores for all the variable-related statements fall within the
range of 5.68 to 5.86, with RB5 having the highest mean score and RB3 the lowest.
Furthermore, the mode scores for all Risk Barrier statements are either 6 or 7, indicating
that respondents have "Agreed" or "Strongly Agreed" with the statements.
IV.2.2.2.4 Tradition Barrier (TB)
Table IV.12 Central Tendency for Tradition Barrier (TB)
Items No.
Questions
Mean
Mode
TB1
I feel the need to communicate directly with the
kos/apartment manager before making payment
5.68
6
TB2
I feel the need to see the real condition of the
kos/apartment before making payment
5.69
6
TB3
I feel the need to negotiate with the kos/apartment
manager before making payment
5.71
6
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Tradition Barrier variable is
presented in Table IV.12. The mean scores range from 5.68 to 5.71 across the related
statements. TB3 has the highest mean, indicating respondents feel the strongest need
to negotiate with the kos manager before making payments. TB1 has the lowest mean,
but still reflects a significant need to communicate directly with the kos manager prior
to payments. All mode scores for TB are 6, showing respondents "Agreed" with the
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statements. The central tendency results demonstrate that respondents generally
consider traditional interactions, such as communicating, inspecting conditions, and
negotiating with the kos manager, to be important before making rental payments.
IV.2.2.2.5 Image Barrier (IB)
Table IV.13 Central Tendency for Image Barrier (IB)
Items No.
Questions
Mean
Mode
IB1
I have a negative view of proptech apps
5.29
5
IB2
In my view, proptech apps are difficult to use as a medium for
transactions with kos/apartment managers
5.32
5
IB3
In my view, transactions via proptech apps cannot be trusted
5.33
6
IB4
I have doubts about the success of transactions via proptech
applications
5.42
5
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Image Barrier variable is presented
in Table IV.13. The mean scores range from 5.29 to 5.42. IB4 has the highest mean,
indicating respondents have the most doubts about the success of proptech transactions.
IB1 has the lowest mean, reflecting a negative view of proptech apps. The mode scores
are mostly 5, except for IB3 which has a mode of 6, suggesting most respondents agree
or strongly agree with the statements. The central tendency results demonstrate that
respondents generally agree with negative perceptions and doubts about the
trustworthiness and success of proptech transactions, indicating significant image
barriers.
IV.2.2.2.6 Social Influence (SI)
Table IV.14 Central Tendency for Social Influence (SI)
Items No.
Questions
Mean
Mode
SI1
People who are important to me, advised me to pay for
kos/apartment via a proptech apps (R)
2.97
4
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SI2
People who influence my behavior, advise me to pay for
kos/apartment via proptech applications (R)
2.93
4
SI3
People whose advice I listened to, advised me to pay for
kos/apartment via a proptech application (R)
2.90
4
SI4
My friends use proptech apps to pay for the kos/apartment
they rent (R)
3.04
3
R = Reverse coded items
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Social Influence variable is
presented in Table IV.14. The mean scores range from 2.90 to 3.04 across the related
statements. SI4 has the highest mean score. Meanwhile, SI3 has the lowest mean score.
The mode scores vary, with SI1, SI2, and SI3 having a mode of 4, while SI4 has a mode
of 3. The central tendency results demonstrate that respondents generally somewhat
agree with the negative statements about social influence on using proptech apps.
IV.2.2.2.7 Facilitating Conditions (FC)
Table IV.15 Central Tendency for Facilitating Conditions (FC)
Items No.
Questions
Mean
Mode
FC1
The device I own can support rental transactions via the
proptech apps (R)
5.71
7
FC2
The internet connection I have can support transactions via
proptech applications (R)
5.80
7
FC3
I feel there is enough guidance available to understand how to
transact via proptech apps (R)
5.51
5
R = Reverse coded items
Source : Data Processing Using SPSS 27.0, 2024
The central tendency summary table for the Facilitating Conditions variable is
presented in Table IV.12. The mean scores range from 2.20 to 2.49 across the related
statements. FC3 has the highest mean, meanwhile C2 has the lowest mean. The mode
scores vary, with FC1 and FC2 having a mode of 1, and FC3 a mode of 3. This shows
respondents generally agree with the positive statements about facilitating conditions
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for using proptech apps, suggesting they do not face suggesting they do not face
significant barriers related to their devices, internet, or guidance.
IV.2.2.2.8 Innovation Resistance (IR)
Table IV.16 Central Tendency for Innovation Resistance (IR)
Items No.
Questions
Mean
Mode
IR1
I will never use proptech apps to make payments for
kos/apartment rental payments
5.36
4
IR2
I tend to reject the use of proptech applications for
kos/apartment rental payments
5.39
4
IR4
I might use proptech app for kos/apartment rental payment but
not now
5.45
5
Source : Data Processing Using SPSS 27.0, 2024
Table 4.16 presents the central tendency summary for the Innovation Resistance
variable. The mean scores range from 5.36 to 5.45 across the related statements. The
highest mean is for IR4, suggesting respondents somewhat agree they might use a
proptech app for kost rental payment, but not currently. The lowest mean is for IR1,
reflecting respondents somewhat agree they will never use proptech apps for kos rental
payments. The mode scores are mostly 4, except IR4 with a mode of 5, indicating most
respondents are neutral or somewhat agree with these statements. The central tendency
results show respondents generally somewhat agree with the innovation resistance
statements regarding using proptech apps for proptech rental payments, resulting in a
moderate level of resistance to adopting proptech applications payments.
IV.2.3 Reliability Analysis
Table IV.17 Reliability Test
Variables
No of Items
Cronbach’s Alpha
Level of Reliability
Usage Barrier (UB)
4
0.923
Very good reliability
Value Barrier (VB)
3
0.900
Very good reliability
Risk Barrier (RB)
5
0.894
Very good reliability
Tradition Barrier (TB)
3
0.895
Very good reliability
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Image Barrier (IB)
4
0.891
Very good reliability
Social Influence (SI)
4
0.924
Very good reliability
Facilitating Conditions (FC)
4
0.901
Very good reliability
Innovation Resistance (IR)
3
0.884
Very good reliability
Source : Data Processing Using SPSS 27.0, 2024
Table 4.17 indicates the reliability of the measures used in the main research.
Cronbach's Alpha value of at least 0.7 is considered acceptable for reliability. The
analysis reveals that the Cronbach's Alpha for all the variables exceeds 0.8, suggesting
that the variables employed in the research are highly reliable. Furthermore, the
variable with the highest reliability is Social Influence, with an alpha value of 0.924,
while the variable with the lowest reliability is Innovation Resistance, with an alpha
value of 0.884. These findings demonstrate that all the variables exhibit very good
reliability, ensuring the consistency of the items in measuring the underlying
constructs.
IV.2.4 Confirmatory Factor Analysis
Table IV.18 Confirmatory Factor Analysis
1
2
3
4
5
6
7
8
UB1
0.897
UB2
0.891
UB3
0.883
UB4
0.838
VB1
0.907
VB2
0.866
VB3
0.865
RB1
0.821
RB2
0.816
RB3
0.816
RB4
0.810
RB5
0.793
TB1
0.879
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TB2
0.871
TB3
0.863
IB1
0.879
IB2
0.857
IB3
0.844
IB4
0.811
SI1
0.938
SI2
0.934
SI3
0.873
SI4
0.835
FC1
0.922
FC2
0.914
FC3
0.832
IR1
0.892
IR2
0.857
IR3
0.855
Source : Data Processing Using SPSS 27.0, 2024
The Confirmatory Factor Analysis (CFA) results shown in Table IV.17 indicate
that all items have high factor loadings, well above the commonly accepted threshold
of 0.7, demonstrating strong convergent validity for the constructs. This suggests that
the measured items are highly representative of their respective underlying factors,
confirming that the measurement model is well-specified and that the constructs are
reliably measured by their associated items. The results confirm that each item's
grouping is correct, as all items load strongly onto their intended constructs. This
validates that the measurement model is well-specified and that the constructs are
reliably measured by their associated items.
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IV.2.5 Classical Assumption Analysis
IV.2.5.1 Multicollinearity Test
Table IV.19 Multicollinearity Test
Variables
Tolerance
VIF
Result
UB
.776
1.288
No Multicollinearity
VB
.799
1.252
No Multicollinearity
RB
.779
1.284
No Multicollinearity
TB
.821
1.219
No Multicollinearity
IB
.880
1.137
No Multicollinearity
SI
.925
1.081
No Multicollinearity
FC
.859
1.164
No Multicollinearity
Source : Data Processing Using SPSS 27.0, 2024
Table IV.19 shows the results of the multicollinearity test for various variables
using Tolerance and Variance Inflation Factor (VIF) values. The tolerance values for
all variables range from 0.776 to 0.925, and the VIF values range from 1.081 to 1.288.
Since all tolerance values are above 0.1 and all VIF values are below 10, there is no
indication of multicollinearity among the variables. This means that the independent
variables do not exhibit high correlations with each other, ensuring the reliability of the
regression analysis and confirming that multicollinearity is not a concern in this
research.
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IV.2.5.2 Heteroscedascity Test
Table IV.20 P-Plot and Scatter Plot for Innovation Resistance and Dependent
Variable
Independe
nt Variable
Dependent
Variable
P-P Plot
Scatterplot
Usage
Barrier
Innovation
Resistance
Value
Barrier
Innovation
Resistance
Risk Barrier
Innovation
Resistance
Tradition
Barrier
Innovation
Resistance
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Source : Data Processing Using SPSS 27.0, 2024
Table IV.20 shows the results of the heteroscedasticity test for the relationship
between various independent variables and the dependent variable, Innovation
Resistance, using P-P plots and scatter plots. The P-P plots for all independent variables
(Usage Barrier, Value Barrier, Risk Barrier, Tradition Barrier, Image Barrier, Social
Influence, and Facilitating Conditions) display points that closely follow the diagonal
line. This indicates that the residuals are approximately normally distributed,
supporting the assumption of normality.
The scatterplots of standardized residuals versus standardized predicted values
for all independent variables do not show any clear pattern of shotgun blast or funnel
shape. This suggests that the variance of the residuals is constant across all levels of
the predicted values, indicating no evidence of heteroscedasticity. The results from the
Image
Barrier
Innovation
Resistance
Social
Influence
Innovation Barrier
Facilitating
Conditions
Innovation Barrier
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P-P plots and scatterplots suggest that the assumptions of normality and
homoscedasticity are satisfied for the regression model. The residuals are normally
distributed and exhibit constant variance, which supports the validity of the regression
analysis for these independent variables in predicting Innovation Resistance.
IV.2.5.3 Normality Test
Table IV.21 Skewness and Kurtosis Test
N
Skewness
Kurtosis
UB1
241
.347
-.020
UB2
241
.310
-.023
UB3
241
.233
-.374
UB4
241
.124
-.176
VB1
241
-.008
-.206
VB2
241
.242
-.038
VB3
241
.292
-.072
RB1
241
-.345
-.630
RB2
241
-.282
-.612
RB3
241
-.220
-.652
RB4
241
-.190
-.923
RB5
241
-.337
-1.010
TB1
241
-.422
-.636
TB2
241
-.480
-.403
TB3
241
-.436
-.628
IB1
241
-.057
-.742
IB2
241
-.194
-.419
IB3
241
-.213
-.595
IB4
241
-.085
-.517
SI1
241
-.083
-.515
SI2
241
-.130
-.456
SI3
241
-.114
-.442
SI4
241
-.267
-.303
FC1
241
.693
.188
FC2
241
.766
.099
FC3
241
.418
-.234
IR1
241
.178
-1.285
IR2
241
.120
-1.283
IR3
241
.011
-1.241
Source : Data Processing Using SPSS 27.0, 2024
Table IV.20 shows the results of the Skewness and Kurtosis tests for various
items. Skewness values range from -0.480 to 0.766, and Kurtosis values range from -
1.285 to 0.188. According to the normality rule, skewness values between ±2 indicate
that the data is approximately symmetrical, and kurtosis values between ±7 indicate
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that the data has a relatively normal distribution in terms of peakedness (Hau, 2016).
Most items in the table exhibit skewness and kurtosis values within these ranges,
suggesting that the data is normally distributed for these items. For example, items such
as FC2 with a skewness of 0.766 and kurtosis of 0.099, though slightly positively
skewed and relatively flat, still fall within the acceptable ranges. Predominantly, the
skewness and kurtosis tests suggest that the data does not significantly deviate from
normality.
Table IV.22 One-Sample Kolmogorov-Smirnov Test
Source : Data Processing Using SPSS 27.0, 2024
Table IV.20 presents the findings of the one-sample Kolmogorov-
Smirnov test conducted to assess the normality of the unstandardized residuals.
The results show that the mean of the residuals is .0000000, and the standard
deviation is 2.65527649. The test evaluates the maximum differences between
the observed and expected cumulative probabilities, revealing absolute, positive,
and negative discrepancies of 0.48, 0.35, and -0.048. The test statistic is .048,
and the asymptotic significance value is .200. Given that this p-value exceeds
Unstandardized
Residual
N
241
Normal Parameters
Mean
.0000000
Std. Deviation
2.65527649
Most Extreme
Differences
Absolute
0.48
Positive
0.35
Negative
-.048
Test Statistics
.048
Asymp. Syg. (2-
Tailed)
.200
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the 0.05 significance level, the analysis indicates that the residuals are normally
distributed, thereby supporting the assumption of normality.
IV.2.6 Inferential Analysis
IV.2.6.1 Pearson Correlation Coefficient Analysis
Table IV.23 Pearson Correlation Coefficient Test
Variables
UB
VB
RB
TB
IB
SI
FC
IR (DV)
UB
1
VB
0.391
1
RB
-0.082
0.014
1
TB
-0.044
-0.005
0.383
1
IB
0.096
-0.054
0.240
0.259
1
SI
-0.062
0.115
0.202
0.059
0.083
1
FC
0.284
0.215
-0.211
-0.117
-0.065
-0.122
1
IR (DV)
0.083
0.089
0.275
0.282
0.309
0.097
0.041
1
Source : Data Processing Using SPSS 27.0, 2024
The Pearson correlation analysis presented in Table IV.20 reveals that the
correlation coefficients between the independent variables range from -0.082 to 0.391,
indicating a small but definite relationship among them. Specifically, the highest
correlation is observed between usage barriers (UB) and value barriers (VB),
suggesting a moderate relationship between these two variables. Furthermore, the
dependent variable, Innovation Resistance, exhibits low to moderate positive
correlations with the independent variables, such as technological barriers (TB) and
financial constraints (FC). These findings indicate that changes in the independent
variables are associated with changes in the dependent variable, although the
relationships are generally weak.
IV.2.6.2 Multiple Linear Regression Analysis
Table IV.24 Model Summary
Model
R
R Square
Adjusted R
Square
1
0.425
0.180
0.156
Source : Data Processing Using SPSS 27.0, 2024
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Based on Hair (2017), R² values of 0.75, 0.50, and 0.25 for endogenous latent
variables in the structural model indicate that the model is good, moderate, and weak,
respectively. According to Table 4.21, the R² value for the model is 0.180, and the
adjusted R² value is 0.156. This indicates that the model is weak in explaining the
variance in the dependent variable, Innovation Resistance (IR). Therefore, it can be
stated that the independent variables (Usage Barrier, Value Barrier, Risk Barrier,
Tradition Barrier, Image Barrier, Social Influence, and Facilitating Conditions) have a
small effect on Innovation Resistance in this study. This suggests that while there is
some level of relationship, the explanatory power of these independent variables on the
dependent variable is limited.
Table IV.25 Anova
Model
F Statistics
Sig.
1
7.324
<.001
Source : Data Processing Using SPSS 27.0, 2024
The ANOVA analysis reveals that the regression model is statistically
significant (p < 0.001), indicating that the independent variables collectively have a
meaningful impact on the dependent variable, Innovation Resistance. Despite the
relatively low R-squared value, which suggests the model does not explain a sizable
proportion of the variance in the dependent variable, the model's ability to explain the
variance is still considered significant, as evidenced by the F-statistic and associated p-
value. This suggests the model as a whole is useful for predicting the dependent
variable, even though it does not account for a substantial portion of the variance.
Table IV.26 Multiple Linear Regression Test Result
Unstandardized
Coefficients
T-test
P-Value
Significant or
Insignificant
Model
B
Std. Error
1
(Constant)
3.715
1.851
2.007
0.046
-
Usage_Barrier
0.023
0.048
0.512
0.609
Insignificant
Value_Barrier
0.051
0.061
0.926
0.355
Insignificant
Risk_Barrier
0.124
0.049
2.518
0.012
Significant
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Tradition_Barrie
r
0.174
0.068
2.577
0.011
Significant
Image_Barrier
0.185
0.051
3.598
<.001
Significant
Social_Influenc
e
0.026
0.041
.644
0.520
Insignificant
Facilitating
Conditions
0.086
0.093
1.449
0.149
Insignificant
*Dependent Variable = Innovation Resistance
Source : Data Processing Using SPSS 27.0, 2024
Table IV.23 shows the Multiple Linear Regression test results for Innovation
Resistance as the dependent variable. The regression analysis indicates that out of the
independent variables tested, Risk Barrier (B = 0.124, p = 0.012), Tradition Barrier (B
= 0.174, p = 0.011), and Image Barrier (B = 0.185, p < 0.001) have a significant effect
on Innovation Resistance, as their p-values are less than the alpha value of 0.05. The
constant term also shows significance (B = 3.715, p = 0.046). However, Usage Barrier
(B = 0.023, p = 0.609), Value Barrier (B = 0.051, p = 0.355), Social Influence (B =
0.026, p = 0.520), and Facilitating Conditions (B = 0.086, p = 0.149) do not have a
significant effect, as their p-values are greater than 0.05. The result shows that despite
some barriers significantly influencing innovation resistance, others do not. The
significant variables highlight areas that might need addressing to reduce resistance to
completing payment in proptech applications.
Table IV.27 Multiple Linear Equation
Multiple Linear Equation
IR = 3.715 + 0.023 (Usage Barrier) + 0.051 (Value Barrier) + 0.124 (Risk Barrier) + 0.174
(Tradition Barrier) + 0.185 (Image Barrier) + 0.026 (Social Influence) + 0.086 (Facilitating
Conditions) + e
Explanation
UB = β1 = +0.023 (Usage Barrier)
There is a positive relationship between Usage Barrier
and proptech apps payment resistance among
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Jabodetabek consumers. If the UB increases by 1 unit, the
proptech apps payment resistance will increase by 0.023
units.
VB = β1 = +0.051 (Value Barrier)
There is a positive relationship between Value Barrier
and proptech apps payment resistance among
Jabodetabek consumers. If the VB increases by 1 unit, the
proptech apps payment resistance will increase by 0.51
units.
RB = β1 = +0.124 (Risk Barrier)
There is a positive relationship between Risk Barrier and
proptech apps payment resistance among Jabodetabek
consumers. If the RB increases by 1 unit, the proptech
apps payment resistance will increase by 0.124 units.
TB = β1 = +0.174 (Tradition Barrier)
There is a positive relationship between Tradition Barrier
and proptech apps payment resistance among
Jabodetabek consumers. If the TB increases by 1 unit, the
proptech apps payment resistance will increase by 0.174
units.
IB = β1 = +0.185 (Image Barrier)
There is a positive relationship between Image Barrier
and proptech apps payment resistance among
Jabodetabek consumers. If the IB increases by 1 unit, the
proptech apps payment resistance will increase by 0.185
units.
SI = β1 = +0.026 (Social Influence)
There is a positive relationship between Social Influence
Barrier and proptech apps payment resistance among
Jabodetabek consumers. If the SI increases by 1 unit, the
proptech apps payment resistance will increase by 0.026
units.
FC = β1 = +0.086 (Facilitating Conditions)
There is a positive relationship between Facilitating
Conditions Barrier and proptech apps payment resistance
among Jabodetabek consumers. If the FC increases by 1
unit, the proptech apps payment resistance will increase
by 0.086 units.
Source : Data Processing Using SPSS 27.0, 2024
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IV.2.7 Hypothesis Testing
Table IV.28 Summary of Hypothesis
Hypothesis
P-Value
Accepted/Rejected
Reason
H1: Usage Barrier (UB) has a
significant influence on consumers’
resistance towards proptech app
payments
0.609
Rejected
P-value > 0.05
H2: Value Barrier (VB) has a
significant influence on consumers’
resistance towards proptech app
payments
0.355
Rejected
P-value > 0.05
H3: Risk Barrier (RB) has a
significant influence on consumers’
resistance towards proptech app
payments
0.012
Accepted
P-value < 0.05
H4: Tradition Barrier (TB) has a
significant influence on consumers’
resistance towards proptech app
payments
0.011
Accepted
P-value < 0.05
H5: Image Barrier (IB) has a
significant influence on consumers’
resistance towards proptech app
payments
<.001
Accepted
P-value < 0.05
H6: Social Influence (SI) has a
significant influence on consumers’
resistance towards proptech app
payments
0.520
Rejected
P-value > 0.05
H7: Facilitating Conditions (FC)
has a significant influence on
consumers’ resistance towards
proptech app payments
0.149
Rejected
P-value > 0.05
Source : Data Processing Using SPSS 27.0, 2024
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IV.2.7.1 Discussions on the Result
The findings regarding the verification of the hypotheses examined in this study are
summarized in Table IV.25, with a detailed analysis provided in the next sections. The
results indicate that the risk, tradition, and image barriers are statistically significant,
while the usage, value, social influence, and facilitating conditions barriers are found
to be non-significant.
I. Nonsignificant Effect of Usage Barrier towards Proptech Apps Payment
Resistance
H1 : Usage Barrier (UB) has a significant influence on consumers’
resistance towards proptech apps payment
Based on the result of P Value > 0,05, the hypothesis for Usage Barrier (UB)
was rejected. Usage Barrier has a positive relationship, but not significantly influencing
the proptech apps payment resistance. These findings are consistent with previous
studies conducted by Softina et al., (2022); Purwanto et al., (2021); Nel & Boshoff,
(2021), and Istanto et al., (2022); and also refutes the findings on other studies
conducted in different contexts, includes Leong et al., (2020); Kautish et al., (2023),
and Khanra et al., (2021).
This shows that kos payments using the proptech applications are not perceived
as easy, comfortable, or adaptable to various situations. However, this does not
significantly influence consumer behavior resistance. This is mainly because the
payment methods through proptech applications are essentially similar to other e-
payments and the commonly used short-term accommodation rental apps. In the
Indonesian context, especially in Jabodetabek, consumers are already familiar with
digital payment systems. The widespread adoption of e-wallets and online banking
facilitates a smoother transition to using proptech apps for kos payments. Consumers
recognize the convenience of conducting payment in a proptech platform, which
connects with their existing digital payment habits. Therefore, while there might be
some usability challenges, these do not significantly affect consumers from using
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proptech apps for kos payments. The familiarity and ease of digital transactions in other
areas of their lives mitigate any potential resistance.
II. Nonsignificant Effect of Value Barrier towards Proptech Apps Payment
Resistance
H2 : Value Barrier (VB) has a significant influence on consumers’ resistance
towards proptech apps payment
Based on the result of P Value > 0,05, the hypothesis for Value Barrier (VB)
was rejected. Although Value Barrier has a positive relationship, it does not
significantly influence proptech app payment resistance among Indonesian consumers
living in Jabodetabek. These findings are consistent with previous studies by Hossain
(2023) and Khanra et al., (2021). However, they contradict findings from other studies
conducted in different contexts, such as those by Moorthy et al., (2017); Laukkanen
(2016); and Lian & Yen (2013).
The analysis shows that while customers may not see much extra value in using
proptech apps for kos payments compared to other payment methods, this doesn't really
stop them from using these apps. This is mainly because proptech apps fit well with
how people already use digital payment systems. Since mobile rental apps and online
banking are already common, people are used to the convenience and efficiency of
digital transactions. This makes it easy for them to start using proptech apps, since the
value these apps offer isn't significantly different from other mobile apps payment
options. The convenience of doing transactions on a proptech platform matches what
people are already used to. Even if proptech payments don't have unique extra benefits,
the convenience and smooth experience still get people to use them for kos payments.
The time-saving aspect also helps reduce any resistance. While these advantages may
not be super noticeable, they don't create big barriers either. The comfort people have
with digital payments in general helps cancel out any worry about the lack of added
value. Although there may be some value barrier, it is relatively not a significant
problem due to how widely accepted and used digital payments are now. The ease and
familiarity of digital finance helps stop any significant resistance to using proptech
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apps for payments, even if the apps don't offer standout extra value.
III. Positive Significant Impact of Risk Barrier towards Proptech Apps Payment
Resistance
H3 : Risk Barrier (RB) has a significant influence on consumers’ resistance
towards proptech apps payment
Based on the result, the hypothesis for Risk Barrier (RB) was accepted in this
study since P-Value < 0.05. Therefore, it is statistically proven that the Risk Barrier is
significantly affecting the Proptech Payments Resistance. This result agrees with
previous empirical study done by Hau (2016); Chen et al., (2018); and Kaur et al.,
(2020). However, the result disagrees with other studies conducted by Lee & Kim,
(2022) and Lian & Yen, (2013).
The research shows that worries about potential risks have a huge impact on
people's resistance to using proptech apps for rental payments. These concerns mainly
involve the accuracy of information following rental rules, data security, possible
payment mistakes, and refund policies. In general, the respondents are cautious about
adopting new tech for relatively high in budget and sensitive transactions like monthly
rental payments. If people think the app might give wrong details about rental
properties, they're less likely to trust and use these platforms. Data security is also
important, many respondents fret about proptech apps keeping their personal info safe
during transactions, given all the data breaches cases lately, especially in Indonesia.
There are concerns the app didn’t give or update accurate rental rules, leading to
disputes later on. The chance of payment errors is a also major barrier. People worry
mistakes could cost them money, making them hesitant to rely on these apps. Refund
policies also play a key role. If people think they can't get a refund if needed, that boosts
their resistance. In addressing these risk factors by ensuring accurate info, solid data
security, clear rental rules, reliable payments, and transparent refunds can help lower
resistance and increase adoption of these proptech payment apps.
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IV. Positive Significant Impact of Tradition Barrier towards Proptech Apps Payment
Resistance
H4 : Tradition Barrier (UB) has a significant influence on consumers’
resistance towards proptech apps payment
Based on the result, the hypothesis for Tradition Barrier (TB) was accepted in
this study since P-Value < 0.05. Therefore, it is statistically proven that the Tradition
Barrier is significantly affecting the Proptech Payments Resistance. This finding is
consistent with other empirical study done by Lee & Kim (2022); Hossain (2023);
Laukkanen (2016) and Leong et al., (2020). However, the result disagree with other
studies conducted by Kaur et al., (2021) and Khanra et al., (2021).
Many consumers prefer direct communication with the property manager to
ensure transparency and address any queries or concerns prior to completing a payment
transaction. This preference for personal interaction suggests a reliance on
conventional transactional methods, which generates resistance towards adopting
proptech applications. Also, consumers desire the ability to physically inspect the
property to verify its condition before committing to a rental agreement. This
traditional approach of personal verification contributes to resistance against utilizing
digital platforms for property management payments, where such in-person inspections
are typically infeasible. The capacity to negotiate terms directly with the property
manager is also highly valued by consumers. The negotiation process is perceived as
more effective and reassuring when conducted in person or through personal chat,
leading to resistance against digital platforms that do not facilitate such direct
interactions. In conclusion, the Tradition Barrier significantly influences consumers'
resistance to adopting proptech payment applications. Reducing these concerns by
integrating features that emulate the personal interaction, inspection, and negotiation
aspects of traditional methods can help reduce resistance and promote the adoption of
proptech payment solutions.
V. Positive Significant Impact of Image Barrier towards Proptech Apps Payment
Resistance
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H5 : Image Barrier (IB) has a significant influence on consumers’ resistance
towards proptech apps payment
Based on the result, the hypothesis for Image Barrier (IB) was accepted in this
study since P-Value < 0.05. Because of this, it is concluded that Image Barrier is
significantly affecting the Proptech Payments Resistance. This finding is similar with
other research conducted by Hau, (2016); Moorthy et al., (2017); Jansukpum & Kettem
(2015) and Purwanto et al., (2021). Nevertheless, the result contradicts with other
studies conducted by Lian & Yen (2013) and Chen et al., (2018).
The analysis shows that people's negative views of proptech apps play a part in
why respondents who don't want to use these platforms for kos payments. Respondents
have an overall negative impression of proptech apps, which is a major roadblock to
them using these apps. People find proptech apps hard to use for transactions with kos
managers, and this makes them even more reluctant to switch from traditional methods
to digital ones. Trust is another big issue, as many believe they can't trust transactions
through proptech apps. This lack of trust in the reliability and security of proptech
payments further deters users. Lastly, doubts about the success of payments through
proptech apps contribute to their hesitation, as people aren't sure how efficient and
effective these platforms are at ensuring successful payments. Image Barrier has a huge
impact on people's resistance to proptech app payments. Improving the user experience,
building trust, and showing the reliability and success of payments could help reduce
this resistance and encourage more people to adopt proptech payment apps.
VI. Nonsignificant Effect of Social Influence towards Proptech Apps Payment
Resistance
H6 : Social Influence (SI) has a significant influence on consumers’
resistance towards proptech apps payment
Based on the result of P Value > 0,05, the hypothesis for Social Influence (SI)
was rejected. Social Influence has a positive relationship, but not significantly
influencing the proptech apps payment resistance. These findings are consistent with
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previous studies conducted by Softina et al., (2022) and Chu, (2023), and inconsistent
with other studies by Chung & Liang (2020); Kim & Bae (2020) and Nastjuk et al.,
(2020). Social influence does not have a significant impact on innovation resistance.
While social influence is present and positively related to proptech app adoption, it
does not significantly impact consumers' resistance to using these apps for kos
payments. This suggests that other factors, such as individual preferences and concerns,
may play a more critical role in shaping consumer behavior in this context. The
moderate level of negative social influence indicated by suggests that while there is
some social resistance, it is not a dominant factor in preventing the use of proptech
apps.
VII. Nonsignificant Effect of Facilitating Conditions towards Proptech Apps Payment
Resistance
H7 : Facilitating Conditions (FC) has a significant influence on consumers’
resistance towards proptech app payments
Based on the result of P Value > 0,05, the hypothesis for Facilitating Conditions
(FC) was rejected. Facilitating Conditions has a positive relationship, but not
significantly influencing the proptech apps payment resistance. These findings are
inconsistent with previous study conducted by Hossain, (2023) and Kim & Bae, (2020).
In the context of Jabodetabek, these findings suggest that while there may be
concerns about the adequacy of facilitating conditions such as devices, internet
connectivity, and guidance, these factors do not play a crucial role in their decision to
resist using proptech apps for kos payments. This could be due to the widespread
availability of smartphones and internet services in urban areas like Jabodetabek, which
mitigates the impact of these facilitating conditions on resistance. In conclusion,
although facilitating conditions have a positive relationship with the use of proptech
apps, they do not significantly impact consumers' resistance to adopting these platforms
for kos payments.
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IV.3 Business Solution
Based on the analysis in the previous section, a total of 241 respondents
participated by providing their personal ratings on a seven-point Likert scale regarding
the barriers that significantly influence their resistance. This also helps to understand
what actions or features would make them more likely to conduct payments using
proptech app, even in. The table below presents the ranking of barriers by the
respondents according to the mean score.
Table IV.29 Mean Ranks on Risk, Tradition, and Image Barriers
Type of Barrier
Mean
Rank
Risk Barrier
5.7452
1
Tradition Barrier
5.6943
2
Image Barrier
5.3402
3
Source : Data Processing Using SPSS 27.0, 2024
IV.3.1 Business Solution on Risk Barrier
Based on the table, Risk Barrier holds the highest rank according to the mean.
This means that concerns related to risk are considered the highest barriers affecting
users' resistance to adopting proptech apps for kos payments. The key areas of concern
include worries about the accuracy of information provided by the app, potential errors
in the payment process, data security, and the refund process if transactions do not go
as planned.
Here are the business solution proposed for Risk Barriers :
1). Implementing a verification process for kos listings. This can be achieved
by validating the information provided by property owners through thorough property
checks. These checks can be conducted via private video calls or on site surveys.
Although this approach might incur higher costs, it significantly reduces the risk of
property fraud, thereby enhancing user trust in the platform. Regular updates should be
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mandated for property owners to ensure that the listings remain accurate and up-to-
date. Requiring monthly or quarterly updates compels property owners to consistently
update the availability and other relevant details of their listings. This practice not only
ensures that users receive the most current and accurate information, for example the
accuracy of room availability and additional rules, but also helps property owners
become more accustomed to using the app, creating greater engagement and reliability.
Property owners who do not comply with these update requirements should face
penalties, such as temporary suspension of their listings or reduced visibility on the
platform. These penalties serve as a deterrent and emphasize the importance of
maintaining accurate and reliable listings.
2). Data security is another critical aspect that needs to be addressed. As some
apps require consumers to upload identity card information or other sensitive details,
if the app is perceived as “untrustworthy,” it will be challenging for consumers to feel
secure. Not to mention the increasing number of data leak cases in Indonesia has
heightened consumer fears about personal data breaches. To reassure consumers that
their data will be completely safe when using proptech apps for payments, these apps
could launch a comprehensive campaign or advertisement focusing on their security
measures. The campaign should highlight the advanced encryption methods used to
protect user data, secure login protocols such as two-factor authentication, and regular
security audits performed to ensure the highest level of data protection. Obtaining
relevant security certifications and showcasing these in the campaign can further
reassure users of the app’s commitment to safeguarding their information. In the
campaign, proptech companies could include testimonials from satisfied users and
endorsements from cybersecurity experts to further strengthen the message.
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Figure IV.1 Advertisement and Guidance Example for Data Security Campaign
Source : Mamikos and Author (2024)
3). Thirdly, enhancing the payment system to minimize errors is important. This
includes integrating secure payment with advanced error-detection mechanisms.
Providing users with multiple payment options and immediate notifications for
transaction status can help build trust in the payment process. Following this, a
transparent and user-friendly refund policy is essential to address concerns about
transaction failures. Proptech companies should make the refund process
straightforward and ensure that users are well-informed about how to request refunds
if necessary. If the refund policy is not satisfactory for consumers, they may be hesitant
to make payments through the app, as rent fees are typically higher than purchasing
items from e-commerce platforms and a higher risk as rooms are mostly not new in
state. Unlike smaller online purchases where consumers might tolerate the lack of
refunds, the higher financial stakes involved in rental payments necessitate a clear and
reliable refund process. To achieve this, proptech companies should outline the refund
policy in clear, easy-to-understand terms and make it readily accessible within the app.
Providing step-by-step instructions on how to request a refund and what to expect
during the process can help demystify the procedure for users.
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Figure IV.2 Advertisement Example for Refund Policy and Error-Free Payments
Source : Author (2024) and Mamikos
IV.3.2. Business Solution on Tradition Barrier
The Tradition Barrier items reflect consumers' common practice before paying
for rent. This includes the need for direct communication with kos/apartment managers,
the desire to see the real condition of the kos before making payment, and the need to
negotiate with kos managers before committing to a transaction. Here are the business
solution proposed for tradition Barriers :
1). Proptech apps need to implement direct communication features within the
app, such as video calls, live chats, or messaging services. These features would allow
users to ask questions and clarify doubts directly with kos managers before making a
payment. Providing a platform for real-time communication helps replicate the in-
person interaction experience, making users feel more comfortable and confident in
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their transactions. If proptech apps do not have this feature, consumers will find it hard
to pay there.
Figure IV.3 Direct Communication Feature Example
Source : Building Stack
2). Proptech apps need to fulfill the consumers desire to see the real condition
of the kos before payment. The messaging service needs to have the ability to attach
not only photos, but videos as well. Proptech apps need to make sure that property
owners update the latest and accurate photos or videos. If necessary, proptech apps
should be able to provide details of each room. Features such as virtual tours, 360
degree videos of the environment, will allow users to virtually inspect the property and
can help them make informed decisions and reduce uncertainty about the property's
condition.
Figure IV.4 Advertisement Example for Property Showing Feature
Source : Travelio and Singgahsini
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Figure IV.5 360 Degree feature
Source : Giroptic and OpenFrame
3). Develop a built-in negotiation tool within the app that facilitates direct
negotiations between users and kos managers. It could include features like offer and
counter-offer options, customizable agreement templates, and digital signature
capabilities. Proptech apps should create a policy that could enable property owners to
choose whether they could make their property’s price negotiable or not. Enabling
users to see whether they could negotiate or not and also whether they could negotiate
directly through the app can address the traditional need for negotiation.
Figure IV.6 Example of Price Negotiations Feature
Source : AgriXus
4). Create loyalty programs and discount promotions. To further enhance user
engagement and retention, proptech apps should create loyalty programs and offer
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discount promotions. These programs can reward users for repeated use and long-term
commitments, fostering a sense of loyalty and making the app more attractive.
Discounts for long-term stays, repetitive app payments or referral bonuses for bringing
in new users can also incentivize continued use of the app.
Figure IV.7 Example of Loyalty Programs Feature
Source : Traveloka and Mamikos
IV.3.2. Business Solution on Image Barrier
The Image Barrier items reflect consumers' negative perceptions of proptech
apps, difficulties in using these apps for transactions with kos managers, trust issues
regarding transactions via proptech apps, and doubts about the success of these
transactions. Addressing these concerns can significantly improve the image of
proptech apps and reduce resistance.
1). Proptech apps could launch awareness campaigns to improve the perception
of proptech apps. Highlight success stories and user testimonials to showcase the
benefits and reliability of the platform. Engage with users through social media,
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webinars, and community events to build a positive image and directly address any
misconceptions or negative views. Show the app's beneficial features, ease of use, and
successful track record. Provide comprehensive tutorials, step-by-step guides, and
video demonstrations to help users navigate the app and perform transactions with ease.
Figure IV.8 Example of User Testimonials Campaigns
Source : Singgahsini (part of Mamikos)
Figure IV.9 Example of User Generated Content videos on Tiktok
Source : Tiktok
2). Create features for older tenants that want to start payments and enable them
to send reviews in proptech apps. Enabling older tenants to send reviews to each
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property can also provide valuable feedback and build a community among users. In
that way, new tenants will know that there are success payments through the app. This
will increase future transactions.
Figure IV.10 Features for Review for older Tenants
Source : Airbnb and Author (2024)
2). Addressing complaints and issues continuously, whether through app stores
or other platforms, is crucial for increasing trust in proptech applications. A dedicated
customer support system that actively monitors and responds to user feedback on
various channels, including app store reviews, social media, and direct customer
service inquiries, can significantly enhance user satisfaction.
Figure IV.11 Example of User Complains and Customer Service Replies
Source : Google Play Store
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IV.4 Implementation Plan
This implementation plan is meant to serve as a general guide for the proptech
industry. However, it should be customized to align with the specific vision, mission,
goals, and standard procedures of the individual proptech company implementing it.
The plan is designed to address the key barriers - Risk, Tradition, and Image - identified
in the research, with the aim of boosting user trust and encouraging the adoption of
proptech apps for kos payments. The plan's objective is to mitigate the impact of these
barriers on customer satisfaction and loyalty, ensuring proptech app payment success
in the competitive market. To keep the plan relevant and adaptable, it should be
reviewed and updated on a quarterly basis to reflect changing market conditions and
customer feedback.
The implementation plan will be dissected based on the barriers that need to be
addressed, this will need the collaboration to integrate marketing and customer service
initiatives. Specialized marketing strategies and materials will be necessary to
effectively address the identified barriers. Key Performance Indicators (KPI) will be
continuously monitored to assess the effectiveness of marketing efforts. Data analytics
and consumer insights will be utilized to personalize marketing messages and enhance
the overall customer experience. Close collaboration with customer service
representatives will ensure that marketing initiatives complement the company's
barrier-reduction strategies. Stakeholders in the proptech industry, including
management, the marketing team, customer service representatives, and other relevant
parties, need to participate actively in the decision-making and implementation
process.
Below is the implementation plan and timeline :
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PIC Workload and Timeline Evaluation
I. Verification Manager
The Verification Manager's workload focuses on managing the verification process. In
the first two months, the focus will be on validating information provided by property
owners through thorough property checks, which can be conducted via video calls or
manual surveys. During this period, guidelines for checks will be established, and
property owners will be informed about the need for regular updates. In the third and
fourth months, the Verification Manager's workload shifts to performing initial checks,
setting up update schedules, and enforcing penalties for non-compliance. The emphasis
is on ensuring that property owners adhere to the guidelines and maintain accurate and
up-to-date listings. Finally, in the fifth and sixth months, the Verification Manager will
continue monitoring compliance and the accuracy of listings, ensuring ongoing
adherence to the established guidelines. The monthly meetings will help to evaluate
progress and make necessary adjustments.
II. Marketing Manager
The Marketing Manager has a significant workload distributed across multiple
strategies. In the first two months, the focus will be on obtaining and promoting security
certifications, including testimonials from satisfied users and cybersecurity experts. In
the third and fourth months, the Marketing Manager will launch a comprehensive data
security campaign highlighting the app's security measures. During this period,
monthly meetings will help assess the effectiveness of the campaign and make any
necessary adjustments. The Marketing Manager will begin planning the awareness
campaign to improve the app's perception, which includes highlighting success stories
and user testimonials. The awareness campaign will be actively promoted in the fifth
and sixth months, with progress evaluated through monthly meetings.
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The Marketing Manager needs to showcase successful transactions in the fourth month,
develop guarantees or insurance programs for transactions, and promote positive
outcomes and quick resolution plans for any issues. The loyalty program development
will start in the fifth month, with the integration, launch, and promotion extending into
the sixth month.
III. Payment Manager
The Payment Manager's workload is concentrated on enhancing the payment system
and refund policy. In the first two months, the focus will be on integrating secure
payment gateways, implementing error-detection mechanisms, providing multiple
payment options, and setting up immediate transaction notifications. In the third and
fourth months, the Payment Manager will outline a clear refund policy, provide step-
by-step instructions for users, and train the customer support team to handle refund
processing. Monthly meetings during this period will help the implementation is
progressing as planned and address any issues that arise. The fifth and sixth months
involve monitoring the effectiveness of the payment system and refund policy,
ensuring user satisfaction, and addressing any issues that arise.
IV. Customer Support Manager
The Customer Support Manager's primary responsibility is to manage the refund policy
and support users through the refund process. In the third and fourth months, the
Customer Support Manager will be trained on the new refund policy and prepare to
assist users with refund processing. The fifth and sixth months will focus on actively
assisting users with refunds, addressing any concerns, and ensuring user satisfaction.
Monthly meetings will help evaluate the effectiveness of the refund process and make
necessary adjustments. Given the concentrated nature of the tasks in the latter half of
the implementation period, the timeline is manageable for the Customer Support
Manager.
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V. Product Manager
The Product Manager has a high workload spread across multiple strategies. In the first
two months, the focus will be on implementing direct communication features such as
video calls, live chats, and messaging services for direct communication with kos
managers. Concurrently, the Product Manager will develop 360-degree video features
alongside virtual tours and ensure property owners update accurate photos and videos.
In the third and fourth months, the Product Manager will integrate these features into
the app, launch them, and collect feedback. The development of a negotiation tool with
offer/counter-offer options will take place, involving planning and design. By the fifth
and sixth months, the Product Manager will integrate the negotiation tool into the app,
promote its usage, and collect and analyze feedback. The workload remains high
throughout the six-month period due to the simultaneous development, integration, and
feedback collection for multiple app features and tools. Monthly meetings will help
ensure tasks are prioritized effectively, and additional resources are allocated to support
the Product Manager as needed.
VI. IT Security Manager
The IT Security Manager's workload involves ensuring the security of the platform,
specifically focusing on enhancing payment systems and implementing error-detection
mechanisms. In the first two months, the IT Security Manager will work on integrating
secure payment gateways and providing multiple payment options to users. This phase
aims to reduce payment errors and increase user satisfaction. In the third and fourth
months, the IT Security Manager will continue to refine these systems, making sure
immediate transaction notifications and addressing any issues that arise. By the fifth
and sixth months, the IT Security Manager will focus on monitoring the performance
of the payment systems, checking on their reliability and security.
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Chapter V Conclusion and Recommendation
This chapter presents the concluding results of the study, including an
interpretation of the empirical findings in relation to the study's objectives.
Furthermore, it provides answers to the research questions, accompanied by
recommendations, identified shortcomings, and suggestions for future research.
V.1 Conclusion
V.1.1 Conclusion on Research Questions
This research examines the relationship between barriers to the adoption of
proptech applications, particularly for kos payments, and consumer resistance in the
Indonesian market. The empirical evidence suggests that risk, tradition, and image-
related barriers significantly influence consumer resistance. The proposed business
solutions, based on responses from residents in the Jabodetabek area, suggest the
importance of addressing these barriers to enhance user adoption of proptech apps.
Respondents from Jabodetabek have provided insights that confirm these barriers
significantly impact their willingness to use proptech apps for kos payments. In
analyzing and interpreting these findings, the study answers the initial research
questions and offers recommendations for mitigating consumer resistance. The key
conclusions are as follows :
1. What barriers significantly influence consumers’ resistance in proptech app
rental payments?
Answer : Based on the research findings it is concluded that 3 barriers significantly
influence consumers’ resistance in proptech applications payments, namely Risk
Barrier, Tradition Barrier and Image Barrier. Below are the explanation :
- Risk Barrier : Concerns about the accuracy of information, payment errors, data
security, and refund processes significantly influence consumer resistance.
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- Tradition Barrier : The need for direct communication, the desire to see the real
condition of the apartment/kos, and the preference for negotiating with
apartment/kos managers before payment are critical factors.
- Image Barrier : Negative perceptions of proptech apps, perceived difficulties in
using these apps for transactions, and doubts about the trustworthiness and
success of transactions via proptech apps contribute to consumer resistance.
2. What strategy can be implemented by proptech apps developers to retain and
increase consumers' transactions on proptech apps?
Answer : To develop strategies, proptech app developers should focus on
addressing the key barriers identified :
- Risk Barrier Solutions : Implement a verification process for property listings,
launch a strong data security campaign to reassure users about their data safety,
and enhance payment systems with clear and user-friendly refund policies.
- Tradition Barrier Solutions : Introduce direct communication features like
video calls and live chats, develop virtual property information tools to allow
users to see the real condition of the kos, and incorporate negotiation
functionalities within the app.
- Image Barrier Solutions : Launch awareness campaigns to improve the
perception of proptech apps, ensure transparency in transactions, and build user
confidence through showcasing successful transaction metrics and positive
outcomes.
V.1.2 Theoretical Implications
The research framework emphasizes the relationship between proptech app
payment and innovation resistance barriers, and their impact on consumer adoption and
usage. Theoretically, it aligns with the concept of overcoming barriers to technology
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adoption, which posits that effectively addressing these barriers can increase consumer
acceptance and usage.
This study expands the existing academic literature on consumer resistance
toward mobile application-driven services, including those in the proptech sector. In
recent years, scholars have demonstrated growing interest in better comprehending
consumer opposition to other technological advancements, such as e-wallets, mobile
banking, and telemedicine. Therefore, the findings of the present research will
contribute to this new but underexplored domain of study.
This research framework can guide scholars interested in exploring the
influence of consumer resistance on the adoption of proptech applications.
Furthermore, building upon the foundation established here, future studies can
concentrate on conducting discriminant analysis to differentiate non-adopters into three
distinct categories: postponement, opposition, and rejection. Such an analysis would
yield deeper insights into how these non-adopter groups relate to the theoretical
perspectives on innovation resistance.
The emerging Indonesian market has experienced the rapid introduction and
expansion of various technological advancement. Even so, empirical studies examining
consumer resistance, particularly within the Indonesian context, are still limited.
Previous literature on Indonesian resistance to technology has focused on other
common applications such as banking payment. In contrast, this study aimed to
investigate the relationship between barriers and the adoption of proptech payment
applications in the Indonesian setting, which represents a novel area of inquiry.
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V.2 Recommendations
Based on the findings in Chapter 4, several recommendations can be made for
future research on the topic of consumer resistance to proptech applications for
accommodation payments and for managerial purposes. These recommendations not
only aim to address the limitations of the current study and suggest areas for further
exploration to enhance the understanding of consumer behavior in the context of
proptech applications, but also to provide actionable insights for managerial
implications.
V.2.1 Managerial Implications
This study offers practical business implications for proptech application
managers based on the findings. First, addressing the Risk Barrier is crucial for
enhancing user adoption. The implementation of comprehensive verification processes
for property listings is necessary. Launching strong data security campaigns and
ensuring payment systems with clear and user-friendly refund policies will build trust
with users. By ensuring the accuracy of information and the security of user data,
managers can reduce users' perceived risk, making them more likely to engage with the
platform.
Second, overcoming the Tradition Barrier involves integrating features that
allow for more personalized and direct communication between users and property
managers. This can include video calls, live chats with photo and video attachments,
and virtual property tours to give users a clear understanding of the property’s condition
before payment. Additionally, incorporating negotiation functionalities within the app
and introducing loyalty programs can appeal to users who prefer negotiating terms
directly and reward repeat usage, respectively.
Third, the Image Barrier can be mitigated by launching awareness campaigns
that highlight the reliability and transparency of the platform. Managers should focus
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on showcasing successful transaction metrics, addressing complaints swiftly across
various platforms, and promoting positive outcomes to improve user perception.
Ensuring that transactions are transparent and that user concerns are addressed can
significantly reduce the negative perceptions that might hinder user adoption.
By implementing these strategies, managers can reduce consumer resistance
and increase adoption of their proptech applications for property transactions. This
study provides a framework that managers can use to understand and effectively
address the barriers that may hinder consumer acceptance of proptech innovations.
V.2.2 Shortcomings and Future Study Recommendations
Despite this study providing new insights on innovation resistance barriers
towards proptech app payments, there are undoubtedly areas for improvement in future
research. Therefore, this section will reveal the shortcomings of this study and give
recommendations for future research on proptech app payment resistance.
The shortcomings and future study recommendations of this study includes :
1. This study is too focused on proptech apps, which could limit the
generalizability of the findings across different proptech platforms such as websites.
> Future studies recommendations : Consider a wider range of proptech
platforms to increase the understanding of a broader range of proptech technology.
2. The study might have not captured all potential barriers to proptech app
payment adoption as it only focuses on Usage, Value, Risk, Tradition, Image, Social
Influence and Facilitating Conditions. Adding more variables could capture more
unknown barriers.
> Future studies recommendations : Explore additional factors such as
technological anxiety, individual differences, performance barriers, system
characteristics and perceived cost barriers.
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3.The study was conducted within a specific geographical area (Jabodetabek),
which may limit the applicability of the findings to other regions with different cultural,
economic, and technological environments.
> Future study recommendations : Conduct studies in different geographical
regions to understand how cultural, economic, and technological factors influence
resistance to proptech app payments. To gain more insight on each area differences,
comparative studies can also be conducted.
4.The sample size used in this study is not large enough to generalize the
findings to a broader population. A larger and more diverse sample could provide
stronger and generalizable results. For example, the results of this study might be too
focused on DKI Jakarta as the sample majority came from Jakarta.
> Future study recommendations : Future research should aim for larger and
more diverse samples to make sure the findings are representative of the broader
population. Including participants from various demographic backgrounds and regions
can increase the generalizability of the results. Age, gender, and income as moderating
variables can gain understanding of how these demographic factors influence
resistance to proptech app payments, as it helps in identifying specific needs and
concerns of different demographic groups, leading to more targeted strategies for
overcoming resistance.
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APPENDIX A Preliminary Interview Transcript
A. Experience Questions
1. Are you used to using mobile applications for transactions? What is your
reason for using a mobile application for transactions?
Initial, Gender,
Age
Answer
FF, Male, 26
Yes, usually I use M-banking for transactions, qris or other transactions.
Because it's simpler.
EP, Male, 30
Yes, used to online purchases, M-banking, everything in the app. Because
it's more practical, you don't need to leave the house, everything is easier.
RB, Male, 30
Yes, I got used to using them. Because it's more practical than carrying cash,
it's not complicated.
EH, Male, 25
Yes, very often. Because it is a primary need, mobile app transactions are
easier and safer.
PR, Female, 30
Yes, I got used to it. Because it is more practical, easy and instant to do. I
use m-banking, e-wallet, Traveloka, tiket.com and agoda
RM, Male, 25
Yes, I got used to it. I use M-banking, Traveloka, and Mamikos. Because
mobile applications are more helpful and simple.
DV, Male, 27
Yes, I'm used to it. Because it makes it easier and reduces the use of cash.
HK, Female, 20
Yes, I'm used to it. Because it is needed for water payment transactions,
online motorcycle taxis and online payments.
SM, Female, 22
Yes, if M-banking is normal, I usually use E-wallet, Gopay or OVO funds.
Because the transaction process is easy.
FI, Male, 26
Yes, now I often use m-banking. Because it's easier, no need to pay with
physical money. Saves time too.
2. Have you ever used a mobile application to search for hotel room rentals or
short-term accommodation? Do you make transactions in the application or
only for accommodation searching?
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Initial, Gender,
Age
Answer
FF, Male, 26
Yes, I have used Traveloka to book accommodation or hotels. Transactions
in the application.
EP, Male, 30
Yes, I've been on Traveloka and Tiket.com. Complete Transaction until
payment.
RB, Male, 30
Yes, often. It is common for immediate short term applications.
EH, Male, 25
Yes, but the Hotels and Guest Houses (Reddoorz) experience was not good.
Availability should have been clear, but it turned out that when I got there it
was full and the money was refunded.
PR, Female, 30
Yes I have. I use it for transactions.
RM, Male, 25
Yes, I used it when I was out of town. The application that I often use is
Traveloka. I mostly look for accommodation, but I also made transactions
via Traveloka
DV, Male, 27
I have, at Traveloka and Travelio.
HK, Female, 20
I have, the application was Traveloka.
SM, Female, 22
Never before, but I have downloaded Tiket.com
FI, Male, 26
Once. In the transaction application.
3. Have you ever used a mobile application to search for medium to long term
kos/apartment rentals? Do you make transactions in the application or just
search?
Initial, Gender,
Age
Answer
FF, Male, 26
Once, I used mamikos to look for kos because I was a migrant. Just looking
for accommodations.
EP, Male, 30
Have been on the Mamikos, Travelio, and Cove applications, and Rukita
once. Complete transaction at Rukita and Travelio. Mamikos and Cove are
references only.
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RB, Male, 30
Often enough.Only looking for the accommodations
EH, Male, 25
I used mamikos to find accommodation. The transaction was done in the
application, but the owner's response was very slow, finally I was given a
WhatsApp number. So that transaction settlement occurs outside the
application.
PR, Female, 30
Once on the mamikos application, just looking for a kos room. I contacted
the kos owner's WhatsApp contact and the exact location of the kos.
RM, Male, 25
Yes I have. At that time I used Mamikos. At that time I was just looking for
accommodation and the transaction was directly via the owner via transfer.
DV, Male, 27
Yes, I have. I used Mamikos to check but never completing transaction
there. In Travelio i have, but only for the short term rent.
HK, Female, 20
Yes, I have. The app is Mamikos, only for accommodation searching.
SM, Female, 22
Yes, I downloaded the apps, but never completed any transaction.
FI, Male, 26
Yes, I have, in the application, only for searching.
B. Barriers Questions
1. Why do you use a mobile application to search for kos/apartments?
Initial,
Gender, Age
Answer
FF, Male, 26
Actually, it's more about looking for references because after all, especially
for me who lives in Jakarta, looking for kos/apartments that are close to my
place of work and comfortable is difficult if there are no references.
EP, Male, 30
Because it's easier to browse, because if i had to manually do the survey it's
more complicated. Moreover, living in Tangerang is prone to traffic jams if
you have to survey here and there.
RB, Male, 30
Because you can see the image, use the filters, adjust your needs.
EH, Male, 25
First, to see the price reference because I'm too lazy to negotiate. With this
application you can filter the top 3 and chat with the kos/apartment owner or
manager.
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PR, Female, 30
Coincidentally I was out of town, and had to move to Jakarta. I use it to
make things easier.
RM, Male, 25
Because I'm from out of town, it's easier to search. I can review it first, like
from the review, I see the photos. After that, I did an on-site survey.
DV, Male, 27
For reference and to see prices according to my budget.
HK, Female, 20
Simply put, I need it just to estimate the closest kos from campus and see
whether the room price is decent.
SM, Female, 22
Efficient and cheap, and I can communicate with the owner.
FI, Male, 26
Because it's easier, there's no need for a survey first
2. Why did you choose to complete or not complete a transaction via the
application?
Initial,
Gender, Age
Answer
FF, Male, 26
I did not complete the transaction through the app. For the long term, in
Mamikos application, I never use payments in the application. I have to go to
the accommodation, look at them first and find out the external environment
of the kos which is not mentioned in the application.
EP, Male, 30
I have some that are paid on the app and some that are not. For kos, the
prices were not quite suitable (Cove). Mamikos is a bit difficult to see
reviews, and when I went to the location, it turned out to be in an alley,
which was not mentioned in the app. It was a bit hard to find, and it turned
out that the facilities were incomplete. The detailed address is not provided,
and often there are no signs for the kos. Sometimes the kos/apartment details
have changed. The information on the app is not updated by the property
owners. As for Rukita, I paid. I met with the Rukita manager, so you will
meet with the admin, and the admin will inform the caretaker. There is an
intermediary.
RB, Male, 30
I do not complete the payment through the app because I usually prefer to
handle things directly, just in case something doesn't match my expectations.
If everything is suitable, I pay directly on-site.
EH, Male, 25
Because the kos owner suggested not using the app. If I chat through the app,
it takes a long time because the owner hasn't checked the app in a while.
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When I chat, the response is slow, and it takes about two days to get a reply.
Mamikos, which is supposed to make things easier, is not being used
effectively by the kos owners listing there. Eventually, I got the kos owner's
number through the app chat. The owner prefers not to handle transactions
through the app. Through WhatsApp, the deal is made quickly. However,
Mamikos helps me find places according to my desired location.
PR, Female, 30
I still have doubts because, although the app has been around for a while, its
transaction feature is still new.
RM, Male, 25
I didn't complete the transaction in the app because I saw some reviews about
the app, which were not favorable. The negative reviews were not about the
kos/apartments themselves but about the app developers, mentioning issues
like pending transactions. For example, after making a payment, the money
hasn't reached the owner, which makes me worried.
DV, Male, 27
I didn't complete the transaction in the app. For the long term, it's riskier; I
need to check the facilities and safety myself. I need to survey the place
directly. The incomplete information about the kos (not updated) is a major
factor in my hesitation. I want to be certain that the facilities match the listed
price. Even though there is a description, I don't fully trust it.
HK, Female,
20
I didn't complete the transaction in the app because I'm afraid the distance
from the kos to the campus might be far, and I'm not confident about the
photos. In terms of price, there's no issue. For short-term stays, it's
manageable, but for kos that are rented monthly or yearly, it's better to see if
they match the photos directly.
SM, Female,
22
I never make payments through the app; I always survey the place in person
and pay directly. I'm worried it might not match the photos, and the price
might be higher on the app, so I prefer to negotiate in person.
FI, Male, 26
Because at that time, when I surveyed, the owner of the kos wasn't there.
Instead of trying to contact them, it was easier to make the payment through
the app. After that, I could contact them directly. I also make sure about
room availability. Sometimes, the app shows availability, but when I survey,
the room is actually not available.
3. Are there any aspects of the kos/apartment rental application that you find
uncomfortable?
Initial, Gender,
Age
Answer
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FF, Male, 26
Regarding the app, sometimes the user interface is complicated, making the
experience a bit confusing. Additionally, the kos might have various room
versions that aren't included in the data/photos, and only the best ones are
shown. This makes the information misleading. Especially since it's a long-
term need, everything needs to be thoroughly verified.
EP, Male, 30
The positive aspect is that it's convenient to filter based on price and
preferences. However, it makes me hesitant because the information is not
always updated. I did have a successful experience with Travelio, but there
were issues with their system. The app scheduled a meeting at 1 PM, but
the field staff couldn't be on time and only arrived at 7 PM. It seems like
they are short-staffed.
RB, Male, 30
No, that's very helpful, as it can serve as a reference.
EH, Male, 25
(1)The loading time is quite long because the app seems heavy, (2) The kos
managers take a long time to respond to chats, which is concerning (3)
There is often a mismatch between the actual place and the pictures.
PR, Female, 30
The user interface is good, but when trying to view the location, the GPS in
the app is not accurate, and there are no photos of the surrounding area.
This also becomes a consideration.
RM, Male, 25
It's quite comfortable when using the app; it loads normally, and the UI/UX
is good.
DV, Male, 27
From my experience with Travelio, the interface is still quite classic and not
as family-friendly as Traveloka. Additionally, the rental listings are not
always updated. I once rented an apartment for one night in Yogyakarta
through Travelio; the name matched, but it turned out that the apartment
was not partnered with Travelio. Since the apartment wasn't collaborating
with Travelio, I had to contact Travelio directly. Thankfully, they
responded and took responsibility for the mistake. However, the overall
experience was unpleasant. Especially for long-term rentals with higher
prices, I prefer not to use it again.
HK, Female, 20
The information is not updated and incomplete. I once saw Kos X with
room type A listed, but when I went there, they only had room type B
available. There are no issues with the UI/UX, though.
SM, Female, 22
There was a time when the kos owner was slow to respond, sometimes
replying a day later, and occasionally the information about the kos was
incomplete. The owner does not guide me to transact in the app.
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FI, Male, 26
It's quite comfortable to use, though sometimes the location points can be
confusing. Overall, the app is good.
4. When using a kos/apartment search app, what benefits and advantages do you
expect?
Initial, Gender,
Age
Answer
FF, Male, 26
I expect references and convenience, as well as being risk-free
EP, Male, 30
The price is not an issue, the advantage is that it's easier to rent, extend, or
stop renting. It shouldn't be complicated, and the transaction process should
be easy.
RB, Male, 30
The main thing is to ensure the place is comfortable and not noisy
EH, Male, 25
Ease of transactions, informative, and building consumer trust in the app.
Updating information is important. .
PR, Female, 30
I don't have to go around looking for kos directly; I already have an estimate
and information on the conditions and photos of the rooms before actually
moving in.
RM, Male, 25
It's definitely simpler; I can read reviews if I'm far away. The address is
certain. If there are any complaints, they can be addressed through the app.
DV, Male, 27
Discounts and price cuts or other benefits, as well as a good payment
system.
HK, Female, 20
I can see the property’s environment, the distance to certain places, and
access to the kos/apartement
SM, Female, 22
I can see pictures of the place I'm looking for.
FI, Male, 26
Faster transaction processes, ease of transactions. With a mobile app,
transactions can be done faster anywhere. It's simpler. Ease of access, clear
information, without having to visit the location, but it must be detailed.
5. After using a rental app for kos/apartment searches, what benefits did you get?
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Initial, Gender,
Age
Answer
FF, Male, 26
I got what I wanted, which is references.
EP, Male, 30
Ease of transaction for renting kos/apartment.
RB, Male, 30
Price references.
EH, Male, 25
It’s predictable that if the price is a bit cheap, the place is far. Not all cheap
prices are in strategic locations. It can help filter the places we want.
PR, Female, 30
To see the conditions, prices, and the address and contact information of the
property owner.
RM, Male, 25
To read reviews.
DV, Male, 27
Some aspects meet my expectations, while some are disappointing. You can
earn points.
HK, Female, 20
Estimate locations, like which area has the most accommodations
SM, Female, 22
You can see price references and other kos/apartment references. There is a
filter for the nearest distance at that time in which areas and sub-districts,
which is very beneficial.
FI, Male, 26
The main benefit is saving time. Sometimes there is no time to survey.
6. Has the mobile app you use for kos/apartment rentals ever offered incentives,
cashback, or discounts to users? Have you ever taken advantage of these
programs?
Initial, Gender,
Age
Answer
FF, Male, 26
No. I prefer to pay directly to the property manager. For long-term
transactions, it usually requires personal data such as an ID card. I am the
type who needs careful consideration before uploading or submitting my
data in an app.
EP, Male, 30
I have never received any.
RB, Male, 30
Sometimes there are discounts when searching, but I still prefer to go
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directly. I have never taken advantage of them. I trust seeing the place in
person more; it's more convincing than doing it online.
EH, Male, 25
Never.
PR, Female, 30
There are offers if you book through the app. I haven't taken advantage of
them because for transactions, I just contact the property owner directly,
and the transaction is done outside the app.
RM, Male, 25
There are often discounts and cashback offers. No, I haven't used them
because I'm still unsure about staying at the place, especially if I haven't
seen the accommodation. I prefer to be sure first.
DV, Male, 27
Never
HK, Female, 20
The ads mentioned them, but I haven't used them. I once considered using
them, but ended up not completing the transaction because I did a direct
survey instead.
SM, Female, 22
Yes, but I haven't used them because I do the transactions directly. I also
didn't try asking the property owner about it. It didn't occur to me.
FI, Male, 26
Never, because with online applications there are additional admin fees.
7. How much does the security level of a technology influence your willingness
to use it? (Answer choices: (1) Not at all influential, (2) Not influential, (3)
Somewhat not influential, (4) Neutral, (5) Somewhat influential, (6)
Influential, (7) Very influential). What is the reason?
Initial, Gender,
Age
Answer
FF, Male, 26
7 (very influential), because it is related to data security.
EP, Male, 30
7 (very influential), because data can easily leak and be used for
technological crimes, such as if data leaks, phone numbers could be used for
credit card fraud.
RB, Male, 30
7 (very influential), because it ensures the data is not misused.
EH, Male, 25
7 (very influential), because of the data.
PR, Female, 30
7 (very influential), because in the app, usually when making transactions,
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personal data and access to mobile banking are required, which is important
for security.
RM, Male, 25
7 (very influential), because currently there is a lot of fraud and hacking.
That's the main concern.
DV, Male, 27
6 (influential), because in the app we provide data, and it is also important
to ensure that the accommodation truly exists.
HK, Female, 20
7 (very influential), because if we scan an ID card, it's dangerous and data is
prone to leaks if hacked.
SM, Female, 22
7 (very influential), because in terms of security, it helps avoid the risk of
data loss and identity theft. The more secure it appears, the more willing I
am to use it. If it looks less secure, I fear data theft.
FI, Male, 26
6 (influential), because nowadays, with the advancement of technology,
everyone is tech-savvy, and there are many data breach cases, which need to
be avoided.
8. In your opinion, are transactions through mobile apps for renting kos/apartment
rooms considered safe? Why is that?
Initial, Gender,
Age
Answer
FF, Male, 26
For now, I feel it's not safe. If I still have to upload data, it's not secure
enough.
EP, Male, 30
It depends, because I have read several times on Mamikos about scams
where customers were asked to transfer money when they wanted to view
the kos It turned out that the person asking for the transfer wasn't the actual
owner. To be truly safe, transactions should not be done outside the app and
should strictly use virtual accounts within the app. If it’s a scam, the
developer can take action.
RB, Male, 30
In my opinion, it's relatively safe. Generally, everything matches up, but I
don't transact through the app because I need to directly experience the
vibes and atmosphere. Since it's for long-term use, it's different from a hotel.
EH, Male, 25
It's actually safe if the transfer is done through the app, not directly to the
kos/apartments manager's account.
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PR, Female, 30
Now it seems to be getting safer, as they have started to strengthen their
security measures.
RM, Male, 25
Relatively safe. Because in terms of payment methods, they already use
Virtual Accounts.
DV, Male, 27
Fairly safe. Because now on Android, apps go through screening first,
especially apps that get into the Apple Store for iPhone.
HK, Female, 20
Inshallah, it’s safe because it has been accepted by the app store.
SM, Female, 22
Fairly safe. It depends on the app's security.
FI, Male, 26
I can only rate it 3 or 4 out of 10. There have been some cases, and when
searching for kos on Google, there are sometimes frauds. On Mamikos,
there is a stamp indicating if a kos has been surveyed by their staff, and
those that haven't been surveyed might be scams. I once had a fraudulent
experience in Tasik via Google.
9. Do you feel safer uploading personal data (example: photo of ID card)
through a mobile app or directly to the kos apartmentowner? Why?
Initial, Gender,
Age
Answer
FF, Male, 26
I feel safer showing it directly to the owner, provided I know the owner and
understand what the data will be used for.
EP, Male, 30
Sharing the ID number is fine; I feel secure. There should be a watermark,
like with Travelio or something similar. It’s safe.
RB, Male, 30
For kos, I prefer to give it directly to the owner. If the data is stored in an
online application, I can't monitor it, but I can ensure the owner's usage by
adding a watermark. Most owners are senior and trustworthy, so I believe
my data won’t be tampered with.
EH, Male, 25
More inclined to use the app because it has legal standing. However, if the
kos owner asks for the data and I end up having to give it to them as well,
why should I upload it to the app too?
PR, Female, 30
Directly to the property owner, because there are still trust issues with data
security. When transacting directly with the owner, I can trust them with
my data. Communicating with the owner and seeing their face helps in
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making an assessment.
RM, Male, 25
Using the mobile app is somewhat safe, but I trust giving it physically and
directly to the owner more because I know who has my data.
DV, Male, 27
I'm less willing to upload it through the app because I feel the app isn't
secure enough for important documents. Especially if there are no reviews,
it indicates that no one else has transacted with that kos through the app. If
it's not secure, it could have future implications.
HK, Female, 20
I'm willing, provided the app is verified by the app store.
SM, Female, 22
I feel safer giving it directly. I trust that it won't be misused. Uploading an
ID card to an app is less appealing because it's an official identity
document. If the app is well-known and secure, then I’m willing.
FI, Male, 26
I feel safer giving personal data directly to the owner. It's easier to hold an
individual accountable than a company; I can demand accountability more
easily.
10. Do you feel more comfortable transacting through a mobile app or directly
with the owner/admin of the accommodation for short-term rentals (hotel,
motel)? Why?
Initial, Gender,
Age
Answer
FF, Male, 26
For short-term rentals, I have tried both methods. There's not much
difference for short-term stays, and it's easier because it's not complicated.
EP, Male, 30
Actually, I feel more comfortable using the mobile app because there are
usually many discount promotions that you don't get if you go directly to
the place. However, developers need to find a way to ensure that
accommodation owners frequently update the information about their
properties.
RB, Male, 30
For short-term rentals, I prefer using the app. The reason is that for short-
term stays, it’s more practical and time-efficient, especially for traveling.
EH, Male, 25
I prefer using the mobile app. When paying directly at the location, the
receptionist sometimes adds an extra 20,000 to 30,000 rupiah.
PR, Female, 30
I prefer using the app because it's only for 1-2 days. It's not too significant.
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RM, Male, 25
I feel more comfortable using the mobile app. When not using the app,
they often ask for cash. It also makes things easier for me.
DV, Male, 27
For short-term hotel stays, using the app is still fine because there are
discounts.
HK, Female, 20
If I've met the owner and the vibes are good, I'll give it directly. But if I
meet with someone other than the owner, like the caretaker, it's better to
use the app, provided the owner or manager is responsive there.
SM, Female, 22
For hotels or motels, I feel more comfortable using the app because it's fast
and easy. Practically, I don't worry too much about hotels because there are
usually discounts and promotions.
FI, Male, 26
The app, because for short-term stays, the risk is only once. If there's fraud,
it can be a learning experience.
11. How important is it for you to interact directly with the kos/apartement owner
before completing a rental transaction on a mobile app? Why is that? (Answer
choices: (1) Very Unimportant, (2) Unimportant, (3) Somewhat Unimportant,
(4) Neutral, (5) Somewhat Important, (6) Important, (7) Very Important?
Initial,
Gender, Age
Answer
FF, Male, 26
5 (somewhat important), because for the sake of the transaction, we need
proof of renting and proof that someone is there, or if there are complaints,
there is someone to address them to.
EP, Male, 30
6 (important), it's better to meet in person for negotiation. To check water,
electricity, faucets, and leaks, it's better to meet directly, negotiate repairs,
and ask if the tenant has to pay for any damages. There's also a concern
about having to provide a deposit.
RB, Male, 30
6 (important), to understand the character of the property owner and
unwritten rules.
EH, Male, 25
2 (unimportant), it's not very important if the app's benefits are good. But
due to issues with transparency and updates about the kos/apartment, that
should be resolved.
PR, Female, 30
6 (important), because ultimately it's long-term accommodation, and it's
essential to see if the owner is trustworthy.
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RM, Male, 25
7 (very important), to ensure the place is comfortable and suitable, as
sometimes the app's description differs from reality. Therefore,
communication is needed to confirm.
DV, Male, 27
7 (very important), because for long-term stays, building a relationship is
crucial.
HK, Female, 20
5 (somewhat important), because it's necessary to see or communicate with
the owner. I once met an unpleasant owner, which made me hesitant to rent
there.
SM, Female, 22
6 (important), because building trust and getting to know the property owner
is essential.
FI, Male, 26
6 (important), because interacting with the owner is necessary, even if it's
just online communication without meeting in person.
12. In the context of your values or beliefs, are there any barriers to using mobile
applications for renting boarding houses?
Initial, Gender,
Age
Answer
FF, Male, 26
Religious or belief-related barriers do not actually influence my decision
to pay. It's more about external factors (area, comfort, and security of the
environment).
EP, Male, 30
No, there are no barriers. The rules are usually clear about what is allowed
and what isn't.
RB, Male, 30
None, as long as the facilities are adequate.
EH, Male, 25
No barriers.
PR, Female, 30
Choosing accommodation’s owner that have same religion to make things
easier, and it's usually more accommodating.
RM, Male, 25
None.
DV, Male, 27
No obstacles.
HK, Female, 20
It's not an issue for me, but my father prefers the property owner to share
the same religion. So, knowing the owner's religion is important because
the owner's religious values will be reflected in the property. Based on his
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advice, I decided not to proceed with the transaction through the app.
SM, Female, 22
No influence.
FI, Male, 26
No, as long as the facilities meet my needs.
13. Have you ever wanted to transact through the app but the property owner
preferred to transact outside the app?
Initial, Gender,
Age
Answer
FF, Male, 26
No, because I just don't want to use the app.
EP, Male, 30
No, never.
RB, Male, 30
Not yet.
EH, Male, 25
Yes, I have.
PR, Female, 30
No, never.
RM, Male, 25
No, never. I just prefer to transact directly.
DV, Male, 27
No, because I just go directly to the place.
HK, Female, 20
No, never.
SM, Female, 22
No, never.
FI, Male, 26
Yes, I have. I completed the first transaction through Mamikos, but
subsequent payments were made via ATM at the owner's request.
14. Would you comply if the accommodation owner referred you to pay via a
mobile app every month?
Initial, Gender,
Age
Answer
FF, Male, 26
If that's the rule set by the owner, I will consider it.
EP, Male, 30
Oh yes, with Travelio, I always paid through the app every month because
the manager recommended it. It's more convenient and can be refunded.
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RB, Male, 30
Sure, if it's mandatory, then I'll follow it.
EH, Male, 25
No problem. It's actually an advantage as it avoids the need to meet in
person, which is helpful.
PR, Female, 30
Yes, currently I would.
RM, Male, 25
If that's the recommendation, I will comply.
DV, Male, 27
Yes, if it's a policy, it must be followed.
HK, Female, 20
Yes, if instructed, I'll follow it.
SM, Female, 22
As long as it's possible to do it directly, I'll do it directly, but if it's
mandatory to use the app, I'll follow it.
FI, Male, 26
I will comply if it's the property rule.
15. After seeing the transaction flow in the kos/apartment rental app, how
complex do you find completing a transaction through the mobile app? Why?
(Answer choices: (1) Very Not Complex, (2) Not Complex, (3) Somewhat
Not Complex, (4) Neutral, (5) Somewhat Complex, (6) Complex, (7) Very
Complex).
Initial, Gender,
Age
Answer
FF, Male, 26
2 (not complex), it's similar to other accommodation rental apps.
EP, Male, 30
5 (somewhat complex), because for Travelio or Rukita, you usually have to
wait for verification that the funds have been received. In Tokopedia and
Shopee, it's automatic. There's a risk of the admin scamming.
RB, Male, 30
4 (neutral), because the transaction flow itself is not complicated, but
communicating with the property owner can be difficult, which makes me
hesitant.
EH, Male, 25
3 (somewhat not complex). It's fairly easy. The app is user-friendly.
PR, Female, 30
3 (somewhat not complex), because looking at the flow, you just need to
book the date and upload.
RM, Male, 25
4 (neutral), because for me, using a mobile app is okay, and doing it
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directly is also okay. But if there's a better suggestion from the owner, that's
fine too.
DV, Male, 27
3 (somewhat not complex), because I've used Travelio for short-term stays,
and the transaction flow itself is not difficult.
HK, Female, 20
2 (not complex), because I'm also used to transacting through apps.
SM, Female, 22
5 (somewhat complex), because you have to wait for the property owner's
approval, so I'm hesitant to proceed immediately. The procedure is different
from hotels.
FI, Male, 26
3 (somewhat not complex), at first, someone new to the app might be
confused, but once you get used to it, it's easy.
16. How much do the company's image or mobile app reviews influence your
decision to transact through the mobile app? Why is that? (Answer choices:
(1) Very Uninfluential, (2) Uninfluential, (3) Somewhat Uninfluential, (4)
Neutral, (5) Somewhat Influential, (6) Influential, (7) Very Influential).
Initial, Gender,
Age
Answer
FF, Male, 26
6 (influential), because it's a primary consideration for users when
choosing an app.
EP, Male, 30
7 (very influential), because I once used Travelio out of necessity.
Honestly, the app had a bad reputation with many people, often involving
scams and poor service. The reason for using it was because my wife was
pregnant, and we needed to move from a small kos. Seeing an ad for
Travelio, we decided to try it in a hurry. It turned out to be cheaper due to
discounts, but the experience was not good, and I wouldn't use it again.
RB, Male, 30
7 (very influential), because reviews give a brief overview from previous
users.
EH, Male, 25
7 (very influential), because good reviews indicate a good consumer
experience. Users are just like us.
PR, Female, 30
7 (very influential), because reviews at least let us know the app's
reputation and whether it can be trusted for transactions. The real
conditions should be reflected in user reviews.
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RM, Male, 25
7 (very influential), because we should look at other people's reviews
before using an app.
DV, Male, 27
6 (influential), because reviews mean the app is used by many people,
which is important and relevant. But if the reviews are poor, say around a
rating of 3, it raises doubts about whether the app is worth using. The
same applies to kos/apartments in the app. If there are no reviews, I'm
hesitant to transact through the app.
HK, Female, 20
7 (very influential), because I trust the App Store and people's reviews.
From the ads, whether they are convincing or not. Also from close friends'
or family members' reviews.
SM, Female, 22
7 (very important), because I trust well-known apps with high ratings,
above 4 stars.
FI, Male, 26
6 (influential), because reviews mean someone has already tried
transacting using the app at that place. That's the benchmark. The current
kos I live in was found by checking reviews on Google as they have a
Google profile.
APPENDIX B Documentation of Preliminary Research Interviews
Koleksi digital milik UPT Perpustakaan ITB untuk keperluan pendidikan dan penelitian