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DERIVING VALUE FROM THE ADOPTION OF EMERGENT TECHNOLOGIES IN

THE SUPPLY CHAIN MANAGEMENT

ADEEB AL-SHAKHS

B.S., WESTERN OREGON UNIVERSITY, 2011

M.S., CENTRAL MICHGAN UNIVERSITY, 2016

A dissertation submitted to the

Faculty of the Business School of the

University of Colorado in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

Computer Science and Information Systems Program

2023-2024

ADEEB ALSHAKHS

iii

This dissertation for the Doctor of Philosophy degree by

Adeeb Al-Shakhs

has been approved by the

Computer Science and Information Systems Program

Ersin Dincelli, Chair

Dawn Gregg, Advisor

Xiang Fang

Haadi Jafarian

Date: March 1, 2024

Adeeb Al-Shakhs (Ph.D., Computer Science and Information Systems)

Deriving value from the adoption of emergent technologies in the supply chain management

Dissertation directed by Professor Dawn Gregg

ABSTRACT

Emergent technologies such as blockchain, Internet of Things, and Artificial

Intelligence have been utilized in recent years by supply chain firms. This research consists of

two studies that examine emergent technologies' impact on supply chain management. The first

study follows a systematic literature approach that investigates the reliability impact of

blockchain on supply chain management practices (SCMPs)—including practices on both sides

of the supply chain, upstream (e.g., supplier partnership), downstream (e.g., customer

relationships), and practices related to both sides (e.g., information sharing and information

quality). The result of this study will provide the current state of blockchain adoption in the

supply chain context. Specifically, this study will provide a structure for the relationship between

blockchain and SCMPs as a reference for future research by theorizing blockchain for SCMPs

from the perspectives of Resource-based View and Practice-based View. The second study

empirically examines emergent technologies' role in reducing the bullwhip effects. Prior studies

found that IT can significantly reduce the bullwhip effects for overall industries. This research

extends previous studies by focusing on an intelligent tool integrated with emergent technologies

such as blockchain, IoT, and AI as the best practice for reducing the bullwhip effect. The results

of this study indicate that integrated emergent technologies such as blockchain, IoT, and AI

reduce the bullwhip effect for retailers but increase it for manufacturers. In addition, both studies

contribute to the information systems discipline by providing supportive guidance for emergent

technologies' managerial and practical impacts on the supply chain. The first study contributed to

the supply chain management practice by highlighting the associated SCMPs with blockchain

technology and conditions when considering the adoption of blockchain technology for SCMPs.

Also, I contribute to the theory by theorizing blockchain technology for SCMPs from the

perspectives of Resource-based View and Practice-based View. In the second study, I

contributed to the literature by examining the impact of emergent technologies in solving

bullwhip effects phenomena at the firms’ level rather than the industry level. Also, I

distinguished between firms’ industries in the analysis as firms’ sectors matter in the supply

chain environment.

The form and content of this abstract are approved. I recommend its publication.

Approved: Ersin Dincelli

DEDICATION

Thanks to my great parents, siblings, lovely wife, and wonderful children for their great

support during this journey. My gratitude towards all of them will always be heartfelt.

vii

ACKNOWLEDGEMENTS

I would like to acknowledge and give my thanks to my advisor Dr. Dawn Gregg for her

guidance in making this dissertation possible and successful. Also, I would like to thank my

committee chair Dr. Ersin Dincelli, and committee members Dr. Xiang Fang and Dr. Haadi

Jafarian for their insightful feedback in improving this dissertation. Dr. Dincelli’s expertise in the

information systems discipline was critical in improving the quality of this dissertation. Dr.

Fang’s expertise in a closely related field to information systems significantly contributes to this

dissertation. Dr. Jafarian’s technical skills in information technology added value to this

research. Finaly, I would like to thank my colleague Ph.D. candidates and other faculty members

in the business school for their support during this journey.

viii 
 
TABLE OF CONTENTS  
 
Chapter I.......................................................................................................................................... 1 
Introduction ..................................................................................................................................... 1 
Chapter II ........................................................................................................................................ 5 
The Adoption of Blockchain Technology for Supply Chain Management Practices: A Systematic 
Literature Review................................................................................................................ 5 
Introduction ..................................................................................................................................... 5 
Theoretical Background .................................................................................................................. 8 
Blockchain and Inter-organizational Systems .................................................................... 8 
Supply Chain Management Practices ................................................................................. 9 
Blockchain and Supply Chain Research ........................................................................... 11 
Methodology and Research Design .............................................................................................. 12 
Research Design................................................................................................................ 12 
Review Result ................................................................................................................... 13 
Review Analysis ............................................................................................................... 15 
Results of the Systematic Literature Review ................................................................................ 17 
The Association of Blockchain Technology to the SCMPs.............................................. 17 
Joint Product Development ................................................................................... 17 
Purchasing Alliances ............................................................................................. 18 
Vendor-managed Inventory .................................................................................. 20 
Strategic Supplier Partnership............................................................................... 22 
Customer Relationship .......................................................................................... 23 
Information Sharing .............................................................................................. 25 
Information Quality .............................................................................................. 26 
Postponement ........................................................................................................ 27 
The Conditions of Blockchain Adoption .......................................................................... 28 
Theorizing Blockchain Technologies for Supply Chain Management Practices ......................... 29 
The Resource-based View ................................................................................................ 30 
The Practice-based View .................................................................................................. 31 
Conceptual Model and Proposition Development ........................................................................ 33 
Joint Product Development Proposition ........................................................................... 35 
Purchasing Alliances Proposition ..................................................................................... 35 

ix 
 
Vendor-managed Inventory Proposition ........................................................................... 36 
Strategic Supplier Partnership Proposition ....................................................................... 36 
Customer Relationship Proposition .................................................................................. 37 
Information Sharing Proposition....................................................................................... 37 
Information Quality Proposition ....................................................................................... 38 
Postponement Proposition ................................................................................................ 38 
Implications................................................................................................................................... 39 
Implications for Practical .................................................................................................. 39 
Implications for Research ................................................................................................. 40 
Implications for Theory .................................................................................................... 40 
Discussion ..................................................................................................................................... 41 
Conclusion .................................................................................................................................... 46 
Chapter III ..................................................................................................................................... 47 
Can Emergent Technologies Reduce the Bullwhip Effect? An Empirical Analysis of the North 
American Public Firms ..................................................................................................... 47 
Introduction ................................................................................................................................... 47 
Theory and Literature Review ...................................................................................................... 50 
Transactional Cost Economics .......................................................................................... 50 
Emergent Technologies in Supply Chain Management .................................................... 51 
Bullwhip Effect ................................................................................................................. 53 
Hypotheses Development ............................................................................................................. 54 
Methodology ................................................................................................................................. 57 
Data ................................................................................................................................... 57 
Variables ........................................................................................................................... 57 
Estimation and Empirical Result .................................................................................................. 59 
Econometric Model ........................................................................................................... 59 
Empirical results ............................................................................................................... 61 
Discussion ..................................................................................................................................... 64 
Contributions..................................................................................................................... 65 
Limitations ........................................................................................................................ 66 
Future Research ................................................................................................................ 66 
Conclusion .................................................................................................................................... 67 
Chapter IV ..................................................................................................................................... 68 
Conclusion .................................................................................................................................... 68 

x 
 
Appendix A ................................................................................................................................... 71 
First Study: Search Terms and List of Identified Journals ........................................................... 71 
Table A1: Search Terms and Identified Studies ............................................................... 71 
Table A2: List of Journals and Number of Associated Studies ........................................ 72 
Appendix B ................................................................................................................................... 75 
First Study: Details of the Systematic Literature Review Organized by Four Classifications ..... 75 
Table B1: Conceptual Studies of Blockchain Adoption for the Supply Chain Management 
Practices ................................................................................................................ 75 
Table B2: Combined Studies of Blockchain Adoption for the Supply Chain Management 
Practices ................................................................................................................ 82 
Table  B3:  Implementation  Studies  of  Blockchain  Adoption  for  the  Supply  Chain 
Management Practices .......................................................................................... 86 
Table B4:  Model  or  Prototype  Studies  of Blockchain Adoption for the  Supply  Chain 
Management Practices .......................................................................................... 90 
Appendix C ................................................................................................................................... 95 
First  Study:  Number  of  Studies  for  Each  Supply  Chain  Management  Practice  and  Its 
Classifications ................................................................................................................... 95 
References ..................................................................................................................................... 96 
 
 
  

Chapter I

INTRODUCTION

In recent years, emerging technologies, such as blockchain, Internet of Things (IoT), and

Artificial Intelligence (AI), rapidly emerged as tools to support supply chain management. As

emergent technologies evolve, they allow supply chain firms to become more integrated with

their business partners for efficient outcomes. Therefore, the technologies have attracted the

attention of researchers and scholars begun to investigate the benefits and challenges of emergent

technologies to the supply chain. Emergent technologies support the traceability of materials

flow in the supply chain and allow information sharing among partners. In this manner, they

allow supply chain firms to have more accurate data to rely on for decision-making about supply

chain processes, such as production, distribution, inventory management, and market demand.

In this context, this study investigates the impact of emergent technologies on supply

chain management. Specifically, this research consists of two studies. The first study highlights

the value of adopting blockchain technology for Supply Chain Management Practices (SCMPs).

I focus on eight practices related to both upstream and downstream of the supply chain, namely,

joint product development, purchasing alliances, vendor-managed inventory, strategic supplier

partnerships, customer relationships, information sharing, information quality, and

postponement. A previous study demonstrated scenarios pre and post-implementation of

blockchain integration with smart contracts and found significant improvements to the supply

chain performance after implementation (Manupati et al. 2022). However, previous studies lack

details on which practices can improve supply chain performance. Organizations lack the

information necessary to decide when they should or should not adopt blockchain as a part of

their supply chain (Okorie et al. 2022). Following the gap found by Okorie et al. (2022), this

study guides inter-organizational networks when considering blockchain adoption toward certain

SCMPs. Further, it contributes to information systems and supply chain literature by guiding

supply chain firms when considering blockchain adoption for SCMPs and under certain

conditions. In addition, this study theorizes the integration of blockchain technology for SCMPs

using the theoretical lenses of the Resource-based View (RBV) and Practice-based View (PBV)

to examine why blockchain can be beneficial to some supply chain firms but harmful to others

under some circumstances.

According to the RBV, resources and capabilities should be rare, inimitable, and not

substitutable (J. Barney 1991). The PBV refers to imitable and flexible practices that can be

relocated across organizations’ domains (Bromiley and Rau 2014). In this case, blockchain is

considered a capability that can integrate internal and external processes within the supply chain,

whereas the SCMPs are considered imitated practices and compatible with the PBV.

The second study focuses on emergent technologies' role in reducing the bullwhip effect.

The bullwhip effect is a critical issue for supply chain firms; it can lead to inefficient supply

chain performance. Bullwhip effects refer to the order to the supplier (manufacturer) being larger

than the demand from sales (buyer) (Lee et al. 1997), which results in increasing production and

inventory costs. According to Lee et al. (1997), four leading causes of the bullwhip effects are

demand signaling, order batching, fluctuating process, and shortage game. Since emergent

technologies can revolutionize business transactions in many industries, including supply chain

firms, this study attempts to investigate the impact of intelligent tools that integrate emergent

technologies with supply chain systems.

Furthermore, scholars summarized the effectiveness of integrating enterprise systems

with emergent technologies. For example, recording information using IoT devices, sharing it via

blockchain's ledgers, and storing it in big data to be used by companies for future planning and

decision-making (Sundarakani et al. 2021). Also, the involvement of AI and smart contracts

within the supply chain industry supports decision-makers and automation processes (Arunmozhi

et al. 2022). Therefore, this study empirically examines whether emergent technologies reduce

the bullwhip effect for manufacturers and retailers. This study helps resolve the bullwhip effect

phenomena seen in prior studies by examining the impact of emergent technologies on retailers

and manufacturers.

The results from both studies will provide supportive guidance for supply chain firms on

when they should or should not adopt emergent technologies. This dissertation study is expected

to contribute to the information systems literature by providing the fulfillment conditions that

benefit the adoption of emergent technologies in the supply chain industries. In the first study, I

provide themes and exemplary research questions for researchers to investigate the gaps in the

literature for adopting blockchain technology for supply chain management. A theoretical

framework was provided based on the lenses of RBV and PBV to examine the impact of

blockchain on supply chain performance when integrated with SCMPs. Also, I provide guidance

for practitioners on the opportunities and conditions of blockchain adoption for SCMPs.

In the second study, I demonstrated evidence for supporting emergent technologies in

reducing bullwhip effects at the firm level rather than the industry level and distinguished

between firms’ industries sectors in the analysis. The findings of this study encouraged retailers

to adopt emergent technologies for their supply chain to enhance operational efficiencies through

equilibrium between orders and demand and improve inventory management.

The remainder of this study is organized as follows: Chapter II presents the first study

that investigates the adoption of blockchain technology for SCMPs and under which conditions

blockchain could benefit supply chain firms. Chapter III presents the second study that

empirically examines the impact of emergent technologies in reducing the bullwhip effect for

northern American public firms.

Chapter II

The Adoption of Blockchain Technology for Supply Chain Management

Practices: A Systematic Literature Review

Introduction

A digitalizing supply chain is essential for several industries, including pharmaceuticals,

agriculture, chemicals, etc., across all industries sectors. Firms aim to share product information

in the supply chain to enhance information quality (Li and Lin 2006) and transparency (Zhu et al.

2017). As a solution, blockchain adoption in the supply chain has dramatically increased in

recent years to support business partners in sharing information. Blockchain consists of ledgers

storing and sharing transactions in a decentralized database (Du et al. 2019; Scholz and Stein

2018). Integrated blockchain with enterprise systems allows firms to track products during

transportation processes and enable information sharing. However, the impact of blockchain on

supply chain activities varies based on the blockchain’s role that is held in the supply chain.

Orlikowski and Iacono (2001) argued that research on IT artifacts should get attention

from scholars in all fields without considering its epistemological perspective or methodological

tendency. They also encouraged researchers to theorize about IT artifacts to understand

exceptional technologies. Thus, the concept of blockchain was first developed by Nakamoto

(2008) in a white paper. The technology was developed for the cryptocurrency Bitcoin, which

uses peer-to-peer nodes in electronic cash systems. Since then, organizations have explored the

benefits of emerging blockchain technology in their business applications. Also, scholars have

examined the potential uses of blockchain in various domains, such as e-commerce (Kumar et al.

2020; Zhou et al. 2021), healthcare (Agrawal et al. 2022; Jadhav and Deshmukh 2022),

agriculture (Eluubek kyzy et al. 2021; Oguntegbe et al. 2022), and online advertising (Yayla et

al. 2023).

Existing studies have addressed the roles of blockchain in meeting supply chain

objectives such as cost, speed, quality, sustainability, and flexibility (Kshetri 2018). Lacity and

Khan (2019) highlighted managerial challenges of blockchain adoption, including intellectual

property, shared governance, espionage risks, and regulatory uncertainty. However, this study

aims to investigate the relationship between blockchain and supply chain management practices

SCMPs, as shown in Figure 1. Various studies have proposed blockchain-based applications to

emerge in the supply chain context, but no studies specifically synthesize blockchain's impact on

SCMPs. Blockchain technology is considered effective in the supply chain environment to

improve performance and operational efficiencies (Kshetri 2018; Tönnissen and Teuteberg

2020). Moreover, the role of blockchain in the supply chain sheds light on supply chain

sustainability (Kshetri 2021) and reduction in carbon emissions (Sundarakani et al. 2021). To

identify relevant research on blockchain integration with SCMPs in information systems and

supply chain management literature, I formulated the following research questions:

RQ1: How effective is blockchain technology in addressing issues related to different

supply chain management practices?

RQ2: Under which conditions do organizations or inter-organizational networks adopt

blockchain to support certain supply chain management practices?

In addition, following the suggestion of Orlikowski and Iacono (2001) to theorize IT artifacts

without considering their epistemological perspective or methodological tendency, this study

aims to theorize blockchain technology for SCMPs. Hence, I developed the following research

question:

RQ3: What theory or theories can assist scholars and practitioners in adopting

blockchain for SCMPs?

To answer these research questions, this study followed a systematic literature review approach.

A literature review of IS journals and closely related fields provide a scope view of blockchain

technology involvement in the supply chain environment. This approach allowed to achieve four

research objectives:

1) Provide an overview of existing research on the blockchain usage for certain SCMPs.

2) Understand the required conditions of blockchain adoption to benefit supply chain

firms.

3) Develop a framework of related impacts of the blockchain to SCMPs.

4) Derive opportunities for future research in adopting blockchain for supply chain

management.

Addressing these research questions will contribute to information systems and supply

chain management by providing guidance on when blockchain should be adopted for SCMPs

and under which conditions. This enables the research results to identify the distinctiveness of

each practice and interpret a comprehensive understating of blockchain technology adoption for

SCMPs. Also, theorizing blockchain for SCMPs can assist future research in exploring the actual

impact of blockchain on SCMPs.

The next section discusses the theoretical background of the blockchain and SCMPs. The

methodological approach of the systematic literature review process is explained in detail,

including the searching process, collecting, and analyzing the literature. Subsequently, this study

presents a theoretical framework based on the literature results and provides practical, research,

and theoretical implications. The final section included a discussion regarding findings, provide

practical guidance on the conditions for blockchain adoption, and provided recommendations for

future research opportunities.

Figure 1. The relationship between blockchain and SCMPs

Theoretical Background

Blockchain and Inter-organizational Systems

Orlikowski and Iacono (2001) found that IT artifacts are dynamic and emerging

technologies from developing social and economic practices. Blockchain is an emerging

technology that is influencing a wide range of inter-organizational practices in a variety of

domains and industries. It can be used to process information when integrated with inter-

organizational systems. Inter-organizational requires two or more organizations to be mutually

effective in certain activities (Carter et al. 2017). Li and Lin (2006) refer to inter-organizational

relationships as the degree of trust, commitment, and collaboration between business partners.

Moreover, a study in information systems investigated the rationality of information systems

interactions across organizations. Collaboration and trust between organizations are crucial to

interpreting this interaction (Kumar et al. 1998). Thus, deploying blockchain with an inter-

organizational network removes the need for trust by enforcing manipulation of the contents and

increasing transparency (Davidson et al. 2016). Ideally, blockchain requires inter-organizational

Blockchain-based

Technology

SCMPs

•Joint Product Development

•Purchasing Alliances

•Vendor-managed Inventory

•Strategic Supplier Partnership

•Customer Relationship

•Information Sharing

•Information Quality

•Postponement

Supportive

practices

collaboration and integration with existing enterprise systems to derive value for business

partners (Labazova 2019; Lacity and Khan 2019).

Supply Chain Management Practices

Scholars have defined SCMPs as operational functions and activities that can capture the

degree of imitability and determine the supply chain's effectiveness and efficiency (Carter et al.

2017; Kaliani Sundram et al. 2016). For example, Zhou & Benton (2007) identified three

components: supply chain planning, just-in-time production, and delivery practice. However,

existing studies have distinguished the components of SCMPs based on the side of the supply

chain (e.g., upstream and downstream). An example of upstream practices can include activities

with suppliers, such as procurement and supplier partnerships, while customer relationship is an

example of the downstream side. Information sharing and information quality are considered

practices related to both supply chain sides (Jum’a et al. 2021).

The variety of SCMPs in the supply chain literature is wide such as just-in-time

production, strategic planning, delivery practices (Banker et al. 2006; Tan et al. 2002; Zhou and

Benton 2007), agreed vision and goals, and risk and reward sharing (Kaliani Sundram et al.

2016). Several studies have reviewed the broad scope of SCMPs including Carter et al. (2017),

Jum’a et al. (2021), Li et al. (2006), and Zhou and Benton (2007). These studies have highlighted

eight sustainable supply chain practices which are outlined in Table 1 below. These eight

practices are joint product development, purchasing alliances, vendor-managed inventory,

strategic supplier partnership, customer relationship, information sharing, information quality,

and postponement. Carter et al. (2017) identified two additional practices, namely, product return

processing and logistic process outsourcing; however, these practices were not included in Table

1 due to the low expectation regarding their impact on the efficiency of supply chain activities

(Zhou and Benton 2007). Also, I did not include supplier development practice from Carter et al.

(2017); it refers to the selection of suppliers, which overlaps with the strategic supplier

partnership (Li et al. 2006). Table 1 briefly explains these practices with definitions along with

literature sources.

Table 1. SCMPs Definitions

Dimension of SCMPs

Definition

Literature

Joint Product

Development

It refers to the participation of suppliers

in the new product development

process.

(Carter et al. 2017; Stuart

1997; Zhao and Cao 2015)

Purchasing Alliances

The relationships between the

organization and its partners are

meaningful for supply-related

decisions, including the make-or-buy

strategy.

(Carter et al. 2017; Stuart

1997)

Vendor-managed

Inventory

It refers to the inventory replenishment

decisions on production, distribution,

and monitoring of products across

warehouses and regions.

(Carter et al. 2017; Fang et

al. 2008; Waller et al.

1999; Zhou and Benton

2007)

Strategic Supplier

Partnership

Organizations' strategic and operational

capabilities lead them to achieve

sustainable and significant benefits

with their suppliers.

(Jum’a etal. 2021; Kaliani

Sundram et al. 2016; Li et

al. 2006; Tan et al. 2002)

Customer

Relationship

It refers to long-term relationships with

customers and ensuring sustainable

satisfaction.

(Jum’a et al. 2021; Kaliani

Sundram et al. 2016; Li et

al. 2006; Tan et al. 2002)

Information Sharing

The degree level of information shared

with one partner in the supply chain.

(Jum’a et al. 2021; Kaliani

Sundram et al. 2016; Li et

al. 2006; Li and Lin 2006;

Tan et al. 2002)(Jum’a et

al. 2021; Kaliani Sundram

et al. 2016; Li et al. 2006;

Li and Lin 2006; Tan et al.

2002)

Information Quality

It refers to the accuracy, adequacy, and

trustworthiness of information in the

supply chain.

(Jum’a et al. 2021; Kaliani

Sundram et al. 2016; Li et

al. 2006; Li and Lin 2006)

Postponement

The technique of forwarding operations

or tasks (production, procurement, and

distributing) in the supply chain

(Jum’a et al. 2021; Kaliani

Sundram et al. 2016; Li et

al. 2006; Tan et al. 2002)

Blockchain and Supply Chain Research

This section illustrates the recent development of blockchain technology in supply chain

research and the capability of blockchain to improve supply chain efficiencies. Scholars have

recommended pursuing blockchain adoption for supply chain management to engage in the

modern supply chain. Blockchain technology can support decision-makers by strengthening

communication between supply chain partners (Rejeb et al. 2021), enhancing trust (Verhoeven et

al. 2018), and achieving disruptive transformation in a digital supply chain environment

(Korpela et al. 2017). The technology can significantly achieve supply chain objectives by

enabling traceability within the supply chain and enhancing quality and performance (Kshetri

2018; Kshetri and Loukoianova 2019). For example, Tian (2016) developed a blockchain-based

and RFID traceability system for the agricultural industry. The system can gather and share

information on products’ provenance during supply chain processes, enhancing the quality and

safety of agricultural products.

In recent years, blockchain technology integration in Industry 4.0 for supply chain

management has dramatically increased to improve operational processing efficiencies. For

example, Mehta et al. (2021) developed a blockchain-based system to create royalty contracts

between stakeholders in the oil and gas industries. The system was evaluated, and it provides

successful functionality to the industries. Furthermore, Sundarakani et al. (2021) conducted two

case studies that integrated big data and blockchain in the supply chain environment. The first

study failed to meet supply chain objectives in cross-border cargo movements due to

disconnected systems and services, including sixteen different ERP systems. In contrast, the

second case study, which is the successful case, increased the firm’s revenue by 25% after

blockchain implementation through customer service by enabling real-time data sharing.

Methodology and Research Design

Research Design

A systematic literature review is a widely used methodology in the information systems

field to synthesize the role of information technology at the organizational level (Dincelli and

Yayla 2022; Mamonov and Peterson 2021; Tallon et al. 2019). This methodology aims to

achieve high transparency in the search processes and comprehensively cover the topic from

previous published scholarly research (vom Brocke et al. 2009; Okoli 2015; Webster and Watson

2002).

This research involves a systematic literature review using the five-step processes

proposed by vom Brocke et al. (2009), see Figure 2. I conducted this review to explore the

integration of blockchain with SCMPs. To adequately address the research questions, I reviewed

the literature on information systems, supply chain management, and other related areas because

blockchain’s theoretical underpinning is a distributed ledger that requires inter-organizational

systems (Labazova 2019; Lacity and Khan 2019), and its involvement in impacting supply chain

management (Treiblmaier 2018). Thus, it is essential to combine literature to determine

blockchain's contribution to other fields. With this condition, I search for emerging blockchain

technology in the supply chain, whether integrating with inter-organizational systems, enterprise

resource planning, material requisition planning, or e-commerce systems.

Figure 2: Systematic literature search processes (vom Brocke et al. 2009).

Review Result

During the search process for the combined keywords “blockchain” and “supply chain,”

no studies were conducted on the combined two keywords before 2017. The search result

appeared in 2017; therefore, the search was specified from 2017 to the beginning of 2023. The

databases used for the search process and applied filter for each database are shown in Table 2.

Search terms and the number of studies associated with the search result from each database are

shown in Table A1 (See Appendix A).

Stage 1

Review

scope

•Define research questions

•Identify the gap in litreture

Stage 2

(Context)

•Serach databases useing search terms (See

Table A1)

Stage 3

(Literature

search) •Find relevant papers from the litreture

Stage 4

(Analysis

and

synthesis

•Analysis and synthesis the

literature

Stage 5

(Research

agenda)

•Details about how data was

collected, measures, and reports

Table 2: Applied filter used for each database during search process

Database

Applied filters

AIS e-Library

Peer-reviewed only, articles from 2017-2023 search in all

fields (abstract, title, subject, etc.)

Business Source

Premier

Peer-reviewed, Journal articles only from 2017 – 2023, search

in the whole text

ScienceDirect

Research article, search keywords in title, abstract or

keywords

ProQuest Central

Peer review, scholarly journal, English only

In total, 799 studies were identified from all databases after all duplicates were removed,

as shown in Table A1 (Appendix A). I carefully reviewed each study and removed unrelated

studies, including:

• non-peer-reviewed articles,

• in-progress papers, reports, call for research, and research proposals,

• editorial comments, panels summary reports from conferences,

• studies that were not related to the impact of blockchain on the SCMPs (e.g., the

use of FinTech, Bitcoin), and

• unrelated studies to information systems or supply chain management according

to the journal focus areas and the purpose of the study.

After excluding unrelated studies, 112 articles from scholarly journals remain for review. Figure

3 shows the process of the selected articles for review.

Figure 3: Selected Review Articles Process

The varieties of the journal disciplines are broad in this literature due to the capability

and flexibility of blockchain in emerging into multiple disciplines for supply chain processes.

Table A2 (Appendix A) shows the list of journals and the number of associated studies in the

review for each journal. Thirty-four journals are within information systems or related fields,

such as management and computer science applications, with eighty-four total studies in these

journals—six journals related to the supply chain management and logistics discipline, with

sixteen total studies in the literature. The remainder of the journals and literature studies are

related to other fields, such as agriculture, food science, health, and engineering, but they all

interpret the benefits of blockchain adoption for SCMPs.

Review Analysis

I summarized the key findings, variables of interest, theory, and methodology of each study

and classified them into four groups by following Jadhav and Deshmukh (2022) classification of

the review papers. However, I include an additional classification (combined classifications) as

some studies combine more than one classification. Appendix B contains four classifications

tables that were categorized based on the following dimensions:

• Table B1 conceptual studies: the research is a review paper that examines the idea of

blockchain in the supply chain by providing proof of its effectiveness.

• Table B2 combined studies: the research provided a prototype or a model of blockchain

creation and practically deployed the solution.

• Table B3 implementation studies: the research has deployed a practical evaluation of

blockchain technology in the supply chain.

• Table B4 model or prototype studies: the research provided a prototype or a model

creation of blockchain-based technology.

Next, I analyzed all papers that mention or elaborate on the implementation of blockchain in

the supply chain regarding the following two dimensions:

1) Supply chain practices: I examined which practice/s that blockchain has impacted, then

identified whether the impact referred to: Joint product development, purchasing alliances,

vendor-managed inventory strategic supplier partnership, customer relationship, information

sharing, information quality, or postponement.

2) Purpose of blockchain implementation: I identified whether the article discussed the purpose

of blockchain implementation. In other words, which practice/s are influenced by

organizations or inter-organizations to implement the blockchain, and under which

conditions.

The categorization of the above dimensions related to the supply chain practices and the

purpose of blockchain implementation was the first step in analyzing the relevant articles. The

specification of the first dimension assists in determining which practice/s are supported when

implementing blockchain technology. In some studies, there are multiple practices impacted by

blockchain implementation. In the second dimension, articles that elaborate and discuss the

purpose of blockchain implementation assist us in understanding the motivation of blockchain

implementation and whether organizations or inter-organizations have influenced SCMPs to

adopt blockchain. Table C1 (Appendix C) summarizes the total studies found for each SCMP

and classifications in the literature review. Forty-four studies examined more than one practice

within the same study, and twenty-four studies discussed the conditions of blockchain adoption

for supply chain management. The results of the literature review are discussed in the next

section.

Results of the Systematic Literature Review

This section summarizes the results of the systematic literature review. Section 4.1

separately specifies each practice in Table 1 to answer the first research question: Which supply

chain management practices have blockchain been adopted for and supported? Section 4.2 is

related to the second research question: Under which conditions do organizations or inter-

organizational networks adopt blockchain to support certain supply chain management

practices? According to the literature studies, not all papers discussed the conditions of adopting

blockchain. Thus, based on those papers, I summarized the conditions for adopting blockchain

within this section.

The Association of Blockchain Technology to the SCMPs

Joint Product Development

Joint product development represents the participation of suppliers with firms to develop

a new product or service within the supply chain (Carter et al. 2017; Stuart 1997; Zhao and Cao

2015). New product development involves innovating unique products or developing existing

products (Liu et al. 2020). Three articles examine the use of blockchain for joint product

development practice. The first of these articles, Cole et al. (2019), examined the use of

blockchain in operation and supply chain management in the context of developing new

insurance products, such as smart contracts that can manage automatic payment to policyholders

when payment is due. They found that blockchain enhances efficiency in the supply chain in this

context.

Similarly, Benzidia et al. (2021) examined the role of blockchain in exploring innovation

management for the Industry 4.0 era. They defined innovation as the ability to accelerate,

improve, and develop processes for new product development. They found that the internal

integration of blockchain is significant and has a positive relationship with the innovation

processes. Specifically, blockchain can contribute to developing and improving processes in the

supply chain (Benzidia et al. 2021).

The third study investigates using blockchain to store and share information on the used

products for remanufacturing operations (Xu et al. 2023). The product information shall be

collected through a third-party firm selling its products online, such as JD.com and

Amazon.com. They found that manufacturers can increase profit when using blockchain to

remanufacture products and cooperate with online channels such as JD.com. Also, using

blockchain to remanufacture used products can contribute to reducing carbon emissions, which is

beyond the focus of this study. The results of these three studies suggest an opportunity for

blockchain to contribute to product development by enhancing efficiency in the supply chain

(Cole et al. 2019) and creating opportunities for firms to improve operational processes

(Benzidia et al. 2021).

Purchasing Alliances

The strategy of buying raw materials for production or trading goods to sell goods in the

market varies from one firm to another. Organizations need to build a trustworthy relationship

with their suppliers for supply-related strategy (Carter et al. 2017; Stuart 1997). Supply-related

processes can include procurement, purchasing, transporting, and holding processes. During

these processes, information leakage could result in faulty information being shared between

business partners (Yadav and Prakash Singh 2022). Hence, blockchain can be adequate for the

purchasing alliances practice in the supply chain and create a trustworthy relationship between

business partners (Raj et al. 2022). Six studies in the literature review investigated this

purchasing alliances practice.

Yadav and Prakash Singh (2022) designed an integrated digital procurement model with

the blockchain to endorse immutable information within the supply chain. The model was

verified and authenticated in solving procurement problems such as fraud, theft, and obscurity in

the supply chain. Raj et al. (2022) demonstrate a blockchain-based smart contract for

procurement and product traceability, which allows tamper-proof payment between buyers and

suppliers through third-party logistics.

In contrast, Nodehi et al. (2022) designed an e-procurement ecosystem for supply chain

stakeholders using enterprise blockchain. The system was applied to a proposed project for an e-

procurement platform and has been highly suitable for the proposed ecosystem. E-procurement

processes start by creating tender procedures by the buyers and end by signing the contract

digitally for suppliers and buyers. Using blockchain can reduce fraud and risks during e-

procurement processes. Also, it can reduce procurement and ordering costs by eliminating

intermediaries between buyers and suppliers, which allows them to depend on smart contracts

(Chang et al. 2021). The adoption of blockchain allows stakeholders to record and share

information, track transactions, and provide more security awareness. (Nodehi et al. 2022).

Moreover, blockchain-based smart contracts have been used for the oil and gas supply

chain to manage production, traceability, and purchasing activities. From the purchasing

perspective, Ahmad et al. (2022) found a successful adoption of blockchain for the purchasing

coordination of granting oil and gas exploration and production. Procuring oil and gas equipment

for exploration and production can sometimes be very complex. For example, equipment

installation and infrastructure development sometimes need to be globally procured and shipped

over the seas through business partners with different competing interests. Hence, using

blockchain to procure equipment can be made safer by enabling tracking of the equipment and

securely forwarding a procurement request to the suppliers. As a result of purchasing alliances

practice, blockchain can build trust between business partners in the supply chain for

procurement processes through third-party logistics.

Vendor-managed Inventory

Vendor-managed inventory practice refers to suppliers (vendors) who make production

decisions and maintain inventory availability across the supply chain (Carter et al. 2017; Fang et

al. 2008; Waller et al. 1999; Zhou and Benton 2007). Blockchain allows companies in the supply

chain to manage their inventory because transparency is high among supply chain partners

(Yadav and Singh 2020). Forty-nine studies in the literature review investigated vendor-managed

inventory practice. Most literature search results of this practice focused on the traceability of

food and pharmaceutical products within the supply chain to ensure quality and safe delivery of

the products. A few research studied the impact of blockchain on inventory management from

circular economy perspectives (Centobelli et al. 2022; Erol et al. 2022; Yontar 2023), and

tracking the provenance of the tangible asset (Sheldon 2022; J. Xu et al. 2023). In circular supply

chain, blockchain has proven its capability to improve the traceability management of products’

return (Centobelli et al. 2022; Erol et al. 2022) and its ability to contribute to the circular

economy of the agricultural food by tracking products' lifecycle and enhance food security

(Yontar 2023).

Sheldon (2022) designed a blockchain framework that integrates with an ID tag and IoT

devices, allowing tracking of the ownership and provenance of tangible assets. J. Xu et al. (2023)

developed a framework to trace steel materials in the construction industry using IoT devices and

blockchain. An application was also designed within the same framework to monitor and manage

supply chain processes such as quality, safety, payment, and logistics. Similarly, blockchain

proved its capability to trace equipment for multiple industries, such as airplane parts (Ho et al.

2021), oil and gas (Ahmad et al. 2022), all of which have the potential to trace food in the supply

chain (Bager et al. 2022; Bumblauskas et al. 2020; S. Cao et al. 2022; Y. Cao et al. 2022; George

et al. 2019; Teodorescu and Korchagina 2021).

In addition, the Drug Supply Chain Security Act (DSCSA) requires the pharmaceutical

industry, including manufacturers, distributors, and dispensers, to trace and share information

about prescriptions while they are distributed in the United States

. The DSCSA requires the

pharmaceuticals supply chain to implement an electronic system that can track data and trace

prescription medicine in the supply chain by 2023. Blockchain is a proper technology that can

fulfill the act’s requirements (Lacity 2018). Therefore, studies on the adoption of blockchain

within the pharmaceutical industry have increased since then in responding to the act’s

requirements. Mattke et al. (2019) explain how to deliver safe prescriptions and prevent

counterfeit drugs in the supply chain based on the Medi-Ledger project, which is a blockchain-

based application. Furthermore, recent studies addressed issues in the vaccine supply chain, such

The Center of Supply Chain Studies (CSCS) published a white paper “Drug Supply Chain Security Act and

Blockchain” on June 21, 2018. The paper can be found at the following hyperlink:

https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/5b3426b088251b230ba9e6e5/1530144436146/

C4SCS+White+Paper_+DSCSA+and+Blockchain+Study_FINAL3.pdf

as counterfeit and vaccine expiration (Hu et al. 2023; Sreenu et al. 2022; Yong et al. 2020); and

drug supply chain (X. Liu et al. 2021; Teodorescu and Korchagina 2021). Hence, researchers are

rivaling to propose, design, and develop blockchain-based systems to address and resolve such

issues. For instance, Boubeta-Puig et al. (2021) integrated blockchain with supply chain systems

that can address critical situations such as recording freight containers' graphical locations and

room temperatures. The system was evaluated based on a real-world case study.

In sum, blockchain plays a significant role in logistics services by improving transaction

processing and allowing shipment traceability within the supply chain (Ahmad et al. 2021). For

instance, Yadav and Singh (2020) and Kamble et al.( 2020) empirically examined the

relationship between improvement in inventory and one of the blockchain’s characteristics,

which is the visibility and traceability of products in the supply chain. Both studies found

blockchain implementation's significant and positive impact on the supply chain. Overall, the

quality improvement of products or services can be gained by digitizing processes within the

supply chain (Markus and Buijs 2022), which complies with the best practice of managing

inventories.

Strategic Supplier Partnership

Collaboration between supply chain partners leads to sustainable and operational

efficiencies through technologies and communication platforms (Li et al. 2006; Tan et al. 2002).

Hence, blockchain technology enhances trustworthy relationships with business partners by

providing reliable information (X. Xu, He, et al. 2023). Ten studies in the literature review

investigated the strategic supplier partnership practice. For example, Agrawal et al. (2021)

investigated the potential impact of blockchain traceability systems on strengthening suppliers'

partnerships in the textile supply chain. The system enhances trust between business partners by

authenticating transactions at the operational level, such as transporting cotton from yarn

manufacturer to fabric manufacturer. Each transaction is monitored by a certifying agency with

the authority to correspond with the blockchain-distributed ledgers within the supply chain.

Blockchain promotes coordination between business partners through transparency and

information availability. For example, Kumar et al. (2020) provide a blockchain-based model

that allows stakeholders to access product information and validate the accuracy of the shared

information. Fosso Wamba et al. (2020) empirically found a significant positive relationship

between blockchain and supply chain performance. However, only one item related to the

trading partners was used in the performance measurement, which makes it insufficient proof

that blockchain could enhance the suppliers’ relationships. Niu et al. (2022) found that

manufacturers who re-produce products are willing to adopt blockchain. In contrast, their

suppliers do not prefer blockchain adoption unless specific circumstances are attained, such as

ensuring the ignorance of consumers’ risk aversion or at least a low degree of customers’

uncertainty about the quality of the remanufactured products.

Moreover, based on Y. Cao et al. (2022) analysis, a blockchain-based platform is not

beneficial to cooperatives in the supply chain under a revenue-sharing contract. The result

indicates that a high revenue-sharing ratio is not beneficial to cooperatives as it leads to a decline

in the total profit of the supply chain. They also found that the value of blockchain-based

platforms decreases while the ratio of revenue-sharing increases.

Customer Relationship

The customer relationship practice refers to retaining the relationship with customers

before and after sales or services provided to ensure sustainability of loyalty and satisfaction (Li

et al. 2006; Tan et al. 2002). Thus, blockchain impacts customer relationships by providing

accurate information and enhancing trust (Y. Cao et al. 2022; Sun et al. 2022; Yousefi et al.

2022). Twenty-two studies in the literature review investigated customer relationship practice.

For example, Utz et al. (2023) developed a loyalty program based on blockchain technology that

emphasizes customers' trust in service providers and resolves inconsistency in the supply chain.

Additionally, blockchain enhances customers' confidence and trust in products of their

interest by eradicating vulnerabilities in the supply chain, such as information distortions, which

in turn improves customer services (Agrawal et al. 2021; Kshetri 2021; Sundarakani et al. 2021;

Zhou et al. 2021). For example, Fosso Wamba et al. (2020) found a positive and significant

relationship between blockchain and supply chain performance. Multiple items measured the

performance; three items related to customer satisfaction: 1) satisfy customers’ requirement, 2)

ability to accelerate product for customer demand, and 3) response time to the customer. The

factor loadings of these items were sufficient for the performance: 89%, 97%, and 93%,

respectively. Similarly, Karamchandani et al. (2020) found a significant and positive relationship

for the perceived usefulness of enterprise blockchain in customer relationships.

Overall, sellers’ companies can obtain customers’ trust by providing product records,

such as provenance, and monitoring the status in the supply chain (Abdallah and Nizamuddin

2023; Biswas et al. 2023; Hu et al. 2022). For instance, Cao et al. (2021) and Garaus and

Treiblmaier (2021) found that providing information about food products via blockchain-based

platforms can influence consumers’ trust. Furthermore, Niu et al. (2022) found that consumers

would only purchase remanufactured products if transparency existed regarding the reused

materials in the remanufactured products. In other words, consumers would trust to purchase

remanufactured products if the accuracy and visibility of reused materials information is

available and accessible. Therefore, blockchain influences consumers’ trust in product quality

and purchase intention (Treiblmaier and Garaus 2023). Conversely, Dong et al. (2021) found that

the appearance of a devaluation effect on product quality can significantly reduce customers’

purchase intention within the adoption of blockchain.

Information Sharing

Information sharing refers to the degree of information shared among partners in the

supply chain (Jum’a et al. 2021; Kaliani Sundram et al. 2016; Li et al. 2006; Li and Lin 2006;

Tan et al. 2002). Most search results from the literature were about information sharing, with

fifty-four studies discussing this practice. The results of this practice from the literature will be

summarized as follows: how blockchain can achieve supply chain objectives and the roles that

blockchain plays at the upstream and downstream levels. One problem that supply chain partners

encounter is a lack of social and economic communications. Thus, blockchain can contribute to

solving this problem in the supply chain and achieve this objective. For example, Kshetri (2018)

and Li and Zhou (2021) provided case studies for the shipping industry in which blockchain can

solve communication issues with business partners and enable tracking of cargo movement.

Hence, blockchain can increase transparency by monitoring products and sharing information

between supply chain members (Bai et al. 2022; Chou et al. 2023).

From the upstream side, which includes supplies activities, such as supplying raw

materials for production purposes, information sharing involved between suppliers and

manufacturers. Although, trust and privacy are significant issues with business partners

regarding information sharing (Bader et al. 2021). Some studies have argued that using

blockchain for information sharing with business partners improves supply chain transparency

and enhances partners’ trust (Jensen et al. 2019; Tönnissen and Teuteberg 2020; Wang et al.

2019). However, these studies do not specify at which level business partners share information

about their transactions and to what degree privacy concerns occur among partners. Recently,

Chou et al. (2023) provided a blockchain-based framework that allows firms to collaborate and

share information confidentially by creating multiple private channels for each transaction (e.g.,

sales, purchase, inventory). The framework indicates that partners can privately share

information using blockchain and enhance supply chain transparency.

Conversely, the downstream includes post-production activities, such as distributing

goods to sales channels for customers. However, recalling products can cost and put companies

at high risk, such as food contamination or faulty products delivered in downstream activities

(Nandi et al. 2020; Rogerson and Parry 2020). Tracing products in the supply chain allows

companies to locate and recall specific products instead of the entire product line (Kshetri 2021).

For example, Agrawal et al. (2022) demonstrate a forward and backward chain model in the drug

supply chain. The forward model pursues monitoring products from manufacturers to customers,

whereas the backward model supports the manufacturer in recalling defective drugs. The model

benefits all supply chain members by identifying fake drugs and supporting manufacturers

towards faulty drug recalls in the supply chain.

In sum, enhancing transparency in the supply chain can be achieved by integrating

blockchain with supply chain systems (Celik et al. 2023). According to Sundarakani et al.

(2021), integrating blockchain and big data analytics can eradicate vulnerabilities in the supply

chain, such as information distortion between supply chain partners, by providing verifiable

records.

Information Quality

The information quality in the supply chain refers to accuracy, adequacy, and

trustworthiness, which supply chain members can rely on for decision-making (Jum’a et al.

2021; Kaliani Sundram et al. 2016; Li et al. 2006; Li and Lin 2006). In the supply chain context,

information quality reflects on the quality of the products by providing authentic information,

such as provenance, storage, and delivery through a decentralized platform. Thus, blockchain

promises accuracy and integrity of data over the supply chain and minimizes information

asymmetry (Longo et al. 2019). Nineteen studies in the literature review investigated this

practice. Kasten (2019) designed a blockchain-based system that prevents modifying information

entered into the systems about dairy products to maintain secure and high-quality products.

Similarly, other studies have also provided blockchain-based solutions for information quality

assurance in healthcare industries (Jadhav and Deshmukh 2022; J. Li et al. 2023), mining

equipment manufacturers (Wu and Zhang 2022a), and additive manufacturing in the aircraft

industry (Mandolla et al. 2019).

According to the literature on blockchain in section 2.1, blockchain can be integrated into

a collaborative system for data security. Although blockchain can be the solution to secure data

from manipulation, the technology might be inappropriate in some cases. For example, Choi and

Luo (2019) provided a case scenario for the fashion supply chain where blockchain can reduce

demand volatility by providing accurate data in the shared ledgers. Due to the high cost of

implementation, fashion firms avoid adopting the technology with respect to its sufficient impact

on data quality.

Postponement

The postponement practice refers to the strategy of forwarding supply chain operations

such as production, procurement, and distribution to another partner (Jum’a et al. 2021; Kaliani

Sundram et al. 2016; Li et al. 2006; Tan et al. 2002). I reviewed the search results of the

postponement practice; no research was found that investigated the impact of blockchain on

postponement. Although blockchain is an information processing tool that allows product

traceability in the supply chain, it may not be a suitable technology for the postponement practice

that can manage and operate tasks such as production and distribution.

The Conditions of Blockchain Adoption

Supply chain firms aimed to adopt blockchain for several reasons: information accuracy,

transparency, product traceability, and communication (Laforet and Bilek 2021). Adopting

blockchain for SCMPs can benefit some supply chain partners under some conditions. For

example, Niu et al. (2022) found that manufacturers would adopt blockchain to provide

information about materials used in remanufacturing products to consumers. In contrast,

suppliers do not prefer blockchain adoption unless they guarantee ignorance of consumers’ risk

aversion or at least a low degree of consumer uncertainty about the quality of the remanufactured

products. In addition, Xu et al. (2023) suggested that manufacturers should not adopt blockchain

unless the manufacturer's emission intensity is high. Industries are different in emission

intensities; for instance, textile manufacturers produce higher emission intensities than electronic

manufacturing. Although some manufacturers from both industries use recycled materials, it is

inconsistent with blockchain adoption, such as Apple.

However, blockchain may not be beneficial under some circumstances. For example,

Dong et al. (2021) found that the devaluation coefficient on product quality in online channels

influences purchase decisions with or without information sharing via blockchain. In other

words, the devaluation effect can significantly reduce customers’ purchase intention and does not

generate profit for retailers’ sales performance; thus, blockchain may not be beneficial in this

case.

Moreover, consumers' privacy is considered a critical issue in the supply chain with high

information transparency using blockchain. Due to privacy concerns, Zhang et al. (2022) found

that adopting blockchain for retailers reduces consumers' product valuation. Consumers are only

willing to pay and value retailers’ products if information transparency about product promotion

is high and the degree of consumers’ privacy concerns is low.

Indeed, demand volatility hurts supply chain profit due to poor data quality of ordered

quantity and forecasting (Choi and Luo 2019). Therefore, Choi and Luo (2019) explored an

analytical model with blockchain adoption in centralized and decentralized scenarios. They

found that blockchain can significantly improve data quality, reduce demand volatility, and

control overproduction in both scenarios. Hence, blockchain can positively impact the

environment and social welfare, but it harms supply chain profitability due to the high cost of

implementation. Choi and Luo (2019) also proposed a settlement to this dilemma with two

possible propositions to encourage supply chain firms to adopt blockchain. Within the

decentralized boundary, either the government sponsors firms that implemented blockchain or

the government provides an environmental taxation waiving program to those firms. Overall,

government encouragement and support for adopting blockchain increases the potential social

welfare for all supply chain members and maximizes profit (Zhong et al. 2023).

In the next section, I theorize blockchain for SCMPs to answer the third research

question: What theory or theories can assist scholars and practitioners in adopting blockchain

for SCMPs? Most studies in the literature review of this study build their findings without a

theoretical basis. Therefore, I aim to fill this gap by answering the third research question of this

study and build a theoretical model of the systematic literature review results.

Theorizing Blockchain Technologies for Supply Chain Management Practices

In the systematic literature review of this study, only thirty-seven studies applied a

theoretical lens to investigate the impact of blockchain technology in supply chain management.

Four studies were inductive research that aimed to build theory from case studies (Ali et al.

2021; Kshetri 2018, 2021; Teodorescu and Korchagina 2021). A recent study by S. Cao et

al.(2022) grounded on the strategy-as-practice theory for blockchain-based supply chain

governance and called for theory development to support blockchain for supply chain

management. In the following sections, 5.1 and 5.2, I discuss in detail how blockchain-based

technology can be theorized for SCMPs from the Resource-based view (RBV) and Practice-

based view (PBV) perspectives.

The Resource-based View

From the perspective of the Resource-based View (RBV) developed by (Wernerfelt

1984), RBV suggested that resources and capabilities that are valuable, rare, inimitable, and not

substitutable allow organizations to achieve competitive advantage (J. B. Barney 1991).

Resources and capabilities consider tangible assets, such as IT infrastructure, network, and

hardware, and intangible assets, such as information and vendor relationships (Wade and

Hulland 2004). Bhatt and Grover (2005) classified organizational capabilities into three

classifications: value capability, competitive capability, and dynamic capability. Teece and

Pisano (1994) suggest that firms can only sustain competitive advantage through dynamic

capability, which refers to the flexibility to integrate and re-configure the organization's

functional competencies and the ability to respond to the changing environment.

Wade and Hulland (2004) report three typologies of IS resources: inside-out, outside-in,

and spanning capability. Inside-out consists of product quality (Banker et al. 2006), IT technical

skills (Bharadwaj 2000), and information technology practices (Marchand et al. 2000). Outside-

in consists of external relationships management (Bharadwaj et al. 1999; Bharadwaj 2000)and

market responsiveness (Zaheer and Zaheer 1997). Spanning capability includes integrating

inside-out and outside-in, such as partnerships and information management practices (Wade and

Hulland 2004).

In blockchain and supply chain contexts, Treiblmaier (2018) provided a theoretical

framework of RBV for the impact of blockchain on the supply chain context. From the literature

review of this study, three studies underpinned their studies on the RBV lens. First, Sundarakani

et al. (2021) grounded on RBV from a case study. Their findings confirm that blockchain

strengthens the security of data records through immutability, enhancing competitive advantage.

Thus, organizations can sustain a competitive advantage by applying strategic resources such as

blockchain and leveraging supply chain visibility for market responsiveness. Second, Nandi et al.

(2020) developed a conceptual framework based on the RBV using abductive research by

collecting 126 different cases of firms in multiple industries attempting to implement blockchain

technology for their supply chain management. Third, (Paul et al. 2021) empirically examined

the impact of blockchain technology in improving the sustainable performance of the tea supply

chain using RBV and network theory.

From the IS resources typologies of Wade and Hulland (2004) explained above,

blockchain can be considered a spanning capability that integrates both inside-out and outside-in.

For example, Walmart and IBM deployed a blockchain-based collaboration platform to

strengthen the security of food products through traceability in the supply chain (Verhoeven et

al. 2018). Further, blockchain is a cryptographic technology that builds continuous lists of

blocks, transforming digital events into data records (Beck et al. 2017). Also, it is a

decentralized distributed ledger that can collect, store, and process information through

integration with other technology. It can secure its contents from modifications by using

encryption mechanisms (Davidson et al. 2016). Accordingly, blockchain can be defined as IT-

enabled technology when integrated with an existing enterprise system or IT artifacts. As

discussed in section 2.1, blockchain requires inter-organizational integration; thus, organizations

can obtain benefits from blockchain through inter-organizational relationships (Kshetri 2018).

The Practice-based View

Another perspective of RBV, the practice-based view (PBV), is related to imitable

activities or practices that are flexible to be relocated across various organizations' domains

(Bromiley and Rau 2014). The theoretical foundation of RBV is to explain competitive

advantage (J. Barney 1991), while PBV tends to explain firm performance (Bromiley and Rau

2014). Operational management scholars broadly have explained the performance through

practices that any firm can implement, such as supply chain collaboration (Bromiley and Rau

2016). Bromiley and Rau (2014) argued that the interactions of practices and how firms apply

and use those practices reflect on the firm performance.

Further, there should be no boundaries between practices, resources, and inter-

organizational firms (Carter et al. 2017). Since blockchain is considered inimitable based on

RBV, SCMPs are considered imitated practices and incompatible with the RBV, but it is suitable

for the PBV to explain performance.

Moreover, Carter et al. (2017) extended the PBV by including supply chain practices in

the inter-organizational environment and guided scholars to examine which practices influence

performance. Blockchain technology should positively influence SCMPs and allow practices to

provide guidelines for supply chain firms explaining the potential impact of firm performance

from the PBV perspective. According to the systematic literature review of this study, 112

studies in the literature review found that blockchain did impact SCMPs influencing supply

chain performance. Note that this research strongly supports Carter’s research (Carter et al.

2017), but the impact varies based on the type of practice and conditions fulfilled when adopting

blockchain for supply chain management. Table C1 in the appendix shows the number of studies

associated with each practice and the number of studies that discussed the conditions of

blockchain adoption for supply chain management.

In the next section, I develop the theoretical lens of RBV and PBV by integrating

blockchain with SCMPs. Also, I predict propositions for blockchain that influence SCMPs

following the results of the systematic literature review.

Conceptual Model and Proposition Development

From the perspective of RBV, blockchain is a spanning capability that can integrate both

inside-out and outside-in resources. These resources consist of product quality (Banker et al.

2006), technical skills (Bharadwaj 2000), information technology practices (Marchand et al.

2000), external relationships (Bharadwaj et al. 1999; Bharadwaj 2000), and market

responsiveness (Zaheer and Zaheer 1997). In contrast, SCMPs are considered imitable practices

that are flexible to be relocated across the supply chain (Carter et al. 2017). Generally, practices

tend to explain firm performance and enhance operational efficiencies (Bromiley and Rau 2014).

Bromiley and Rau (2016) argued that the interaction of applying practices across various

organizations has resulted in a useful way to explain firm performance.

In this section, a conceptual model of the RBV and PBV lenses is developed with

propositions’ predictions according to the systematic literature review results of the eight SCMPs

(See Figure 4). The conceptual and empirical development of the adoption of blockchain for

SCMPs from the perspectives of RBV and PBV, as outlined in section 5, has resulted in an

advantageous way to strategically analyze the adoption's value. Further, the conditions for

generating the best value from the adoption should be considered based on the adoption's

purpose, as discussed in section 4.2.

Figure 4. Conceptual Model of RBV and PBV

According to the literature results, blockchain-based technology impacts SCMPs and

leads to supply chain performance through the efficiency of practices, which can be achieved by

deploying blockchain technology. Supply chain performance measures varied in the literature;

for example, improving quality and increasing profit are positively associated with blockchain

integration and SCMPs. In contrast, reducing cost and demand volatility are also related to

performance, and the relationship to the integration of blockchain and SCMPs is negative in this

case. Thus, the relationship between integrating blockchain with SCMPs to the supply chain

performance can be positive or negative, depending on the performance’s measure. Based on the

literature studies, all SCMPs are associated with blockchain in the supply chain environment,

except for the postponement practice, in which I found no studies investigate this practice with

blockchain adoption.

Joint Product Development Proposition

According to the results of the systematic literature review of the joint product

development practice, blockchain contributes to developing new products, which enhances

supply chain efficiency and improves operational processes (Benzidia et al. 2021; Cole et al.

2019). Benzidia et al. (2021) found that blockchain-enabled integration with external entities

increases innovation. Thus, I predict the following:

P1a: blockchain directly influences join product development practice.

P1b: blockchain influences supply chain performance indirectly through joint product

development practice.

Purchasing Alliances Proposition

From the purchasing alliances literature, blockchain can securely adequate the purchasing

alliances and eliminate information leakage between supply chain partners (Nodehi et al. 2022;

Raj et al. 2022; Yadav and Prakash Singh 2022). Also, blockchain enables coordination between

suppliers and firms by tracking procured materials (Ahmad et al. 2022). Chang et al. (2021)

demonstrated a blockchain model in the supply chain and explained that the model can reduce

procurement costs and decrease the uncertainty of demand levels. A firm’s profitability relies on

high-quality procurement processes (Yadav and Prakash Singh 2022). Because blockchain can

build trust between supply chain partners for procurement processes, I predict the following:

P2a: blockchain directly influences purchasing alliances practice.

P2b: blockchain influences supply chain performance indirectly through purchasing

alliances practice.

Vendor-managed Inventory Proposition

Most of the systematic literature review of the Vendor-managed inventory practice

focused on the traceability of products within the supply chain to ensure quality and enhance

performance. Empirically, Kamble et al. (2020) and Yadav and Singh (2020) found that

blockchain has a positive relationship and significantly impacts inventory improvement. Also,

blockchain is associated with several factors, including political, technological, economic,

environmental, and social (Yontar 2023). Yontar (2023) categorized these factors to examine the

effectiveness of blockchain in the agricultural industry and found that blockchain can prevent

food waste, increase food security, and track product lifecycle.

Moreover, Saxena and Sarkar (2023) examined the efficiency of blockchain in tracking

real-time inventory, and the results indicated that the technology could be profitable for the

supply chain. Similarly, Ji et al. (2022) compared two manufacturers, one with blockchain and

one without blockchain. They found that blockchain can increase profit for the manufacturer

with blockchain adoption compared to the one without it. Thus, I predict the following:

P3a: blockchain directly influences vendor-managed inventory practice.

P3b: blockchain influences supply chain performance indirectly through vendor-

managed inventory.

Strategic Supplier Partnership Proposition

The strategic supplier partnership practice could lead firms to sustainable and operational

performance through technologies and communication platforms (Li et al. 2006; Tan et al. 2002).

Blockchain promotes coordination between business partners and allows information to be

shared with partners in a secure channel (Dwivedi et al. 2020; Kumar et al. 2020). Kumar et al.

(2020) designed a blockchain-based system that can eliminate manipulations of the supply chain

data and prevent fraud, which enhances trust between supply chain partners. Thus, I predict the

following:

P4a: blockchain directly influences strategic supplier partnership practice.

P4b: blockchain influences supply chain performance indirectly through strategic

supplier partnership practice.

Customer Relationship Proposition

Retaining customer relationships ensures the sustainability of loyalty and satisfaction (Li

et al. 2006; Tan et al. 2002). Blockchain can support firms in retaining customers’ relationships

by providing accurate information about products (Y. Cao et al. 2022; Sun et al. 2022; Yousefi et

al. 2022) and enhancing confidence in purchasing by proving provenance (Kshetri 2021;

Sundarakani et al. 2021). Empirically, Fosso Wamba et al. (2020) found that blockchain

adoption positively impacts customer satisfaction. Similarly, Karamchandani et al. (2020) found

that the perceived usefulness of blockchain influences customer relationships. Therefore, I

predict the following:

P5a: blockchain directly influences customer relationship practice.

P5b: blockchain influences supply chain performance indirectly through customer

relationship practice.

Information Sharing Proposition

In the supply chain environment, partners lack social and economic communication.

Mostly, all articles of the systematic literature review that discuss information sharing practice

concluded with the significant role of blockchain in solving communications issues. For

example, Kshetri (2018) provided eleven case studies in which integrated blockchain in the

supply chain can contribute to solving communication issues between supply chain partners.

Blockchain can improve operational efficiencies through information sharing (Wang et al. 2019)

and expand production (W. Liu et al. 2021). Thus, I predict the following:

P6a: blockchain directly influences information sharing practice.

P6b: blockchain influences supply chain performance indirectly through information

sharing practice.

Information Quality Proposition

According to the theoretical foundation of blockchain, information stored in the

blockchain’s ledger cannot be modified (Du et al. 2019). Blockchain retains the security and

integrity of data stored over the supply chain and eliminates information asymmetry (Longo et

al. 2019). The results of the systematic literature reviews indicate promises of blockchain to

provide accuracy and security of its contents. For example, Wang et al. (2019) found that

blockchain can improve supply chain visibility and operational efficiencies through information

security and enhances trust between supply chain partners. Thus, I predict the following:

P7a: blockchain directly influences information quality practice.

P7b: blockchain influences supply chain performance indirectly through information

quality practice.

Postponement Proposition

According to this study's systematic literature review, no articles investigated the impact

of blockchain on postponement practice. Postponement practice refers to the strategy of

forwarding supply chain operations to other business partners, such as production and

distribution (Jum’a et al. 2021; Kaliani Sundram et al. 2016; Li et al. 2006; Tan et al. 2002). The

nature of blockchain technology is an information processing tool that can process information in

the supply chain; it may not be a suitable tool for postponement practice that can manage and

operate production and distribution. Therefore, there are no literature articles to depend on for

the development of postponement propositions.

Implications

Implications for Practical

The lesson from the literature results translates into a few recommendations when firms

consider adopting blockchain technology for SCMPs. First, when adopting blockchain, supply

chain firms should consider their competitors who have implanted blockchain regarding their

strategy for using this technology. This allows firms to understand the market environment and

the types of customers to price their products accordingly to obtain higher profits (Ji et al. 2022).

Second, apply a confidential strategy to increase privacy settings in the supply chain. For

example, Chou et al. (2023) provided a blockchain-based framework that allows supply chain

partners to share information confidentially by having private channels for each transaction

associated with supply chain processes. Third, blockchain can be an effective solution for food

traceability to ensure freshness and quality delivered to customers. Also, it enhances the security

of pharmaceutical products and anti-counterfeiting value for medical supply chains. Fourth,

supply chain firms should collaborate with their partners to adopt blockchain not just for SCMPs

of this study but also for other management practices, such as operational and organizational

practices. According to Kumar et al. (2023), management practices influence firms to adopt

blockchain-IoT technology, leading firms to obtain a competitive advantage and enhance

performance.

However, firms need to consider the challenges and conditions of adoption discussed in

this study. Also, they need to consider the degree of transparency with their business partners

and privacy concerns.

Implications for Research

According to the search results of SCMPs, blockchain contributed to sustainable supply

chains in various sectors. First is the e-commerce sector, a blockchain-based platform called

“PRODCHAIN” that integrates production lines in the supply chain and allows information

transparency among stockholders. It improved the traceability of e-commerce products through

the platform, impacting both social and economic perspectives (Kumar et al. 2020). Second,

blockchain also involves energy supply by tracking inventory production of greenhouse gas

emissions, increasing transparency, and preventing miscalculating emissions (Diniz et al. 2021;

Teodorescu and Korchagina 2021). In response, Diniz et al. (2021) designed a blockchain-based

artifact to improve inventory processes and have more accurate carbon emission data. Third,

related-farmers products (e.g., coffee, seafood), the capability of providing products provenance

and transparency in the supply chain through blockchain alleviates sustainable issues such as

disputes and fraud (Akhtaruzzaman Khan et al. 2022; Bager et al. 2022).

In addition, during the pandemic of COVID-19, the global supply chain was exacerbated,

especially with the drug supply chain. Fluctuating demand and supply during the pandemic harm

the global supply chain, resulting in shortages for some industries. Therefore, some studies

identified the potential impact of implementing blockchain technology on the drug supply chain,

which can increase processes' efficiencies and reduce the risk of fraud information shared

between business partners (Ramirez Lopez et al. 2022). Others found that adopting blockchain-

based solutions during the pandemic is economically practical for the personal healthcare sectors

(Omar et al. 2022).

Implications for Theory

From the RBV perspective, blockchain technology can be theorized as the capability to

integrate processes and share transactions across various organizations’ domains within the

supply chain. In contrast, the PBV could identify when and under which conditions to adopt

blockchain for SCMPs. By focusing on specific SCMPs, the PBV will lead firms to gain the

most value from the adoption to organizations. The PBV will also provide recommendations for

decision-makers when considering the adoption of blockchain technology for SCMPs.

In addition, theorizing blockchain for SCMPs from the perspective of RBV and PBV

provides insight into the strategic plan of information systems scholars by extant analysis at the

industry or firm level. The RBV differs from the PBV by explaining competitive advantage

rather than performance as a dependent variable. Blockchain requires inter-organizational

systems, and PBV also requires inter-organizational practices to explain performance. Therefore,

integrating both theories offers new topics for research and provides a potential view of research

and theoretical implications.

In a recent research by Hanisch (2024), the author indicated that prescriptive theorizing

supports schoolers with the necessary tools to bridge the gap between real-world challenges and

actionable strategies. Thus, theorizing blockchain for SCMPs assists scholars in bridging the gap

between the practical application of blockchain in the supply chain and the research

conceptualization of SCMPs.

Discussion

The adoption of blockchain technology for SCMPs is beneficial to some firms under

specific conditions and selling formats. For example, Ma et al. (2022) found that manufacturers

are more likely to adopt blockchain only if the fixed cost associated with the use of blockchain

tends to be below a certain threshold. The threshold is related to the manufacturers’ profit, which

can be earned from two different sales formats: reselling and agency selling. In the reselling

format, the manufacturer prices their products by determining wholesale prices and recycling

costs. Subsequently, online platforms set product prices by determining the retail price and

marketing effort. In the agency format, the manufacturer prices their products by determining

retail prices and recycling costs. Subsequently, online platforms only determine marketing

efforts. Ma et al. (2022) demonstrated two scenarios for both selling formats, with and without

blockchain adoption. However, the profit of manufacture should be greater for both selling

formats as a condition for a manufacturer to adopt blockchain.

According to Y. Cao et al. (2022) analysis of the blockchain’s role in the agriculture

supply chain, buyers will benefit from adopting blockchain, whereas cooperative partners in the

supply chain could benefit in some cases and be worse under certain conditions. For example,

the cooperative partners are concerned with counterparty risk because the willingness to delay

payment may occur. Thus, their study showed that the blockchain could eliminate this concern

and enhance customer trust with provenance and payment challenges, reducing counterparty risk

for cooperatives and ensuring sufficiency. In contrast, the operational costs of adopting

blockchain could be worse for cooperatives, as these costs could outweigh the benefits of the

adoption.

Moreover, prior research has found that consumer sensitivity, price sensitivity, quality

sensitivity, and trust are factors that firms should consider when adopting blockchain technology.

First, manufacturers should adopt blockchain only when consumer sensitivity to blockchain is

high (Ji et al. 2022). Second, firms should adopt blockchain only when consumer sensitivity to

the product price is low and sensitivity to product quality is high (Biswas et al. 2023). However,

firms avoid blockchain adoption when the distrust level is high, and therefore, blockchain

becomes insufficient to make consumers aware of the product quality (Biswas et al. 2023).

Wang et al. (2023) analyzed manufacturing and e-commerce industries and indicated that

the e-commerce industry is more likely to adopt blockchain if the use cost of blockchain is low

and the anti-counterfeiting degree is high. In contrast, manufacturers use blockchain incentives to

distribute their brands in online channels and attract more customers. Furthermore, suppliers are

more likely to adopt blockchain under the following conditions: 1) if the transparency cost

coefficient is larger than a certain threshold of information reliability, 2) the supplier tolerable

cost on joining the blockchain system is less than the overall joining fee, and 3) similarly for

retailers, if the tolerable cost is less than the overall joining fee, retailers are willing to adopt the

blockchain (Zhou et al. 2022). Table 3 shows supply chain firms' potential opportunities and

conditions when adopting blockchain. This table is useful for practitioners when considering

blockchain adoption for their supply chain management.

Table 3: Potential Opportunities and Conditions of Blockchain Adoption

Opportunities

Conditions

Enhance collaboration:

• Eliminate the risk of payment delay and enhance trust between

stakeholders by providing provenance information and

addressing payment challenges (Y. Cao et al. 2022).

Reduce operational cost

• If the fixed cost associated with using blockchain tends to be

below a certain threshold, which is related to the

manufacturers' profit (Ma et al. 2022).

• If the transparency of the cost coefficient is larger than a

certain threshold of information reliability (Zhou et al. 2022).

• The supplier's tolerable cost of joining the blockchain system

is less than the overall joining fee (Zhou et al. 2022).

• If the retailer's tolerable cost is less than the overall joining

fee, retailers are willing to adopt the blockchain (Zhou et al.

2022).

Improved products' quality

• Firms should adopt blockchain only when consumer

sensitivity to the product price is low and sensitivity to product

quality is high (Biswas et al. 2023).

• Guarantee ignorance of consumers' risk aversion or a low

degree of uncertainty about the quality of remanufactured

products (Niu et al. 2022).

Improved information

quality

• Information accuracy would reduce demand volatility, but the

high cost of implementation harms profitability. Government

support for adopting blockchain increases social welfare and

supply chain profit (Choi and Luo 2019; Zhong et al. 2023).

Transparency

• Consumers are only willing to purchase and value products if

transparency about product promotion is high and consumers'

privacy concerns are low (Zhang et al. 2022).

From the literature review results in this study and Table B1-Table B4 in Appendix B, I pursued

the gaps in the literature and provided future research recommendations for the information

systems researchers. First, developers should emphasize developing and evaluating solutions for

data privacy concerns when adopting blockchain. Also, researchers are still needed to examine to

what extent does blockchain provides equivalent benefits for supply chain members within the

same or different industries. Empirical research is suggested to investigate blockchain’s impact

on supply chain performance for different industries (e.g., manufacturers, services, government,

etc.). Table 4 illustrates the research themes, gaps, and exemplary research questions for future

research. This table is useful for scholars in future research to explore the impact of blockchain

adoption on supply chain management and its benefits to the industry. In addition, future

research may focus on the applications of blockchain-based technology impacting one or more

SCMPs.

Table 4: Research themes, gaps, and exemplary research questions

Theme

Gap

Example research questions

Theories development for

blockchain and SCMPs

Lack of theories associated

with blockchain adoption for

SCMPs

How does adopting

blockchain technology for

certain SCMPs provide

opportunities for theory

development?

What are the challenges of

blockchain adoption on

existing theories?

Privacy concerns

Lack of possible solutions on

the privacy concerns when

adopting blockchain for

supply chain management.

Industries vary in the degree

of transparency and privacy

concerns.

To what extent do privacy

concerns take place when

adopting blockchain?

How do firms emphasize

privacy concerns when

adopting blockchain for their

industry/sector?

Investigate the impact of

blockchain technology on

the supply chain.

Lack of empirical evidence

that examines the

phenomenon of blockchain

technology in supply chain

management.

What is the impact of

blockchain on specific

SCMPs?

How does the adoption of

blockchain for SCMPs

influence supply chain

performance?

Policies and regulations

Lack of field studies on the

efficiency of blockchain

integration with traceability

systems to the industry.

What are the regulations that

should take place in terms of

blockchain integration for a

specific industry?

What are the policy

implications associated with

blockchain adoption?

This study has several limitations. First, excluding articles written in a language other

than English can potentially bias the outcome of this study. Second, in-progress papers from the

search results might be published as a completed research paper in different journals and

databases, which was not in the literature search results. Future research can examine blockchain

research from additional languages and more recent results form a larger basket of journals to see

if changing environments and cultures impact the results found through this review.

Conclusion

To conclude, this study enriches our understanding of how blockchain adoption benefits

SCMPs and under which conditions blockchain influences SCMPs. I debriefed information

details on blockchain technology from the literature to examine its potential impact on the supply

chain. Several conditions must be fulfilled when considering blockchain adoption for supply

chain management. This study provides details on the seven SCMPs and their usefulness when

integrated with blockchain to improve supply chain efficiencies. Also, the study theorizes

blockchain for SCMPs from two theoretical lenses, the RBV and PBV, based on the analysis

synthesized from the literature. This research supports existing literature on the adoption of

blockchain for supply chain management, but in some circumstances, specific conditions must be

fulfilled before the adoption for better benefits.

Chapter III

Can Emergent Technologies Reduce the Bullwhip Effect? An Empirical

Analysis of the North American Public Firms

Introduction

Manufacturing and trade firms are subjected to both supply and demand volatility

(Cachon et al. 2007). Inaccurate information about inventory and demand leads to resiliency

disruptions in the supply chain. Manufacturing firms often produce more or less than demand

which causes fluctuations in inventory levels. Similarly, companies involved in the sale of goods

may not match demand (sales). This increase in inventory costs may result in the disposal of

unsold products with a fixed shelf life. Therefore, managing inventory is essential for firms to

ensure delivery of customers’ needs with the best quality when requested. In this paper, I refer to

the bullwhip effect as a disruption of inventory and demand in the supply chain. According to

Lee et al. (1997), “the bullwhip effect refers to the phenomenon where orders to the supplier tend

to have a larger variance than sales to the buyer.” (Lee et al. 1997)

In a competitive environment, firms become more dynamic and interrelated with external

opportunities and threats (J. Barney 1991). Demand variety can fluctuate by customers (Li and

Lin 2006), which results in the cause of bullwhip effect. For example, in 2020 and during the

COVID-19 pandemic, customers requested specific products at a particular time, such as toilet

paper, sanitizers, etc., which caused an unpredictable increase in demand. Integrated information

systems play a significant role in responding to customer requests and improving productivity

(Rai et al. 2006). Therefore, firms strive to keep an equilibrium between orders and demand to

ensure customer satisfaction. This can be accomplished through digitalization and integration of

business processes, which enables information-sharing capability between firms and their

partners in the supply chain (Li and Lin 2006). Moreover, intelligent manufacturing technology

can support automation processes in production and monitoring, enhancing production

performance (Wu et al. 2022).

In the digital revolution decades, firms started to implement IT to ensure sustainable

competitive advantage. The purpose of IT implementation in the supply chain is to coordinate

business processes between business partners. Firms in multi-industries (e.g., retailers,

manufacturers, wholesalers) tend to integrate IT to support transactional processes and share

information between business partners. For example, Electronic Data Interchange (EDI) proved

its capability to share information and improve communication between business partners

throughout the supply chain. Also, EDI supports managers in decision-making and planning,

which enhances both inbound and outbound logistics (Mukhopadhyay et al. 1995; Tallon et al.

2000).

As technology continues improving, EDI is no longer sufficient to meet the supply chain

needs of today’s competitive environment. It can support data exchange among partners but

cannot engage in continuous business processes, such as tracking and monitoring products

throughout the supply chain. In response to the supply chain revolution, disruptive emerging

technologies such as blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI)

have been used in the supply chain context (Bader et al. 2021; Korpela et al. 2017). The benefits

of emerging technologies can be earned when integrated with inter-organizational systems. It

promises delivery of business innovation and supports chief supply chain officers in managing

disruptions in the supply chain (IBM 2019; Wang et al. 2022). For example, firms can trace

products in the supply chain and can make a recall if a fault occurs to a specific product.

Technologies such as blockchain and IoT enable firms to trace products and record every

transaction within the supply chain (Kshetri and Loukoianova 2019).

Moreover, emergent technologies allow information sharing among partners in the supply

chain, enhancing managers’ decision-making. Firms can transform decision-making toward

decentralized rather than centralized when managers are knowledgeable about a particular

technology and its capability to support a decision (Sambamurthy and Zmud 1999).

To facilitate the modern supply chain, IBM has developed an intelligent tool called

‘Sterling AIS’ that integrates the three emergent technologies, blockchain, IoT, and AI, into an

enterprise system such as ERP (IBM 2019). The technology can facilitate and incorporate data

from heterogeneous sources. For example, IoT devices can manage business process automation

through smart contracts, in which smart contracts are blockchain-based applications (Kshetri

2017). According to Fridgen et al. (2018), combining artificial intelligence and smart contracts

adds value to the automation processes (Fridgen et al. 2018). Also, Tian (2016) has argued that

information sharing within the supply chain enhances informatization and seeks toward an

intelligence supply chain (Tian 2016). In this study, I refer to the Sterling AIS that integrates

blockchain, IoT, and AI as an emergent technologies’ integration, which can facilitate and

integrate data from heterogeneous sources and automate business processes.

The evolution of emergent technologies may add value to the supply chain and enhance

business partners’ relationships (Rejeb et al. 2021). The bullwhip effect is a crucial challenge for

multi-industries (Bray and Mendelson 2012; Cachon et al. 2007; Yao and Zhu 2012). Kahn et al.

(2002) and Davis and Kahn (2008) argued that IT can significantly reduce volatility and improve

inventory management, which indicates a possibility of reducing the bullwhip effect. A gap

exists between the promise of a business benefit and an actual business outcome. Therefore, this

study seeks to investigate the impact of this technology on the supply chain empirically.

Specifically, I address the following research question:

What is the impact of emergent technologies’ integration of blockchain, IoT, and AI on

reducing the bullwhip effect for firms?

Empirical studies that investigated the impact of the emergent technologies at the firm

level are still limited. Few studies have examined the potential of emerging technologies to

significantly transform the supply chain into a more intelligent environment (Korpela et al. 2017;

Kshetri 2018).

Next, I present transaction cost economics, theoretical background on emergent

technologies in supply chain management and the bullwhip effects, developing hypotheses and a

research model. Then I specify the methodology approach, including data collection, empirical

study, and results. Finally, I interpret the findings and discuss future research.

Theory and Literature Review

Transactional Cost Economics

Transactional cost economics (TCE) views the firm as a solution to markets’ problems,

such as analyzing economic activities (Gurbaxani and Whang 1991). Economizing on transaction

costs is essential for organizations to reduce bounded rationality and production costs

(Williamson 1979). Williamson (1979) specifies two types of transactions in the governance

structure (Williamson 1979). First is the ‘bilateral governance structure,’ which maintains

transactions autonomously. Second, the ‘unified governance structure’ refers to vertical

integration. Both transactions are organized across the firm’s functions. The main argument of

TCE is to govern transactions, ranging from vertical and lateral integration to transforming

transaction details between firms and markets (Shelanski and Klein 1995). The main insights of

governance are to protect the information of the involved parties and strengthen their

relationships in responding to any changing circumstances. Also, to maximize the value net of

both productions and governance cost through the governance structure.

Gurbaxani and Whang (1991) studied the roles of information systems in organizations

and markets. The study demonstrates key measures of organizational attributes such as firm size

and the allocation of decision-makers within the organization. The study also focused on the

relationship between IT management and organizations’ attributes.

From the perspective of TCE, it can facilitate understanding the relationships between

information systems and organizational structures in two various ways. First, IT employment can

reduce communication costs by enabling information availability to the decision-making

authority. Second, IT can reduce agency costs by monitoring capabilities, allowing information

to be more decentralized for decision-making (Gurbaxani and Whang 1991). In both various

ways, IT can improve the quality and speed of information processing, allowing executives to

make the right decision at the right time.

Moreover, the application of transaction cost can be used to evaluate logistics operations

and supply chain management practices. For example, suppliers may deliver faulty products or

security vulnerabilities to a business partner, and because products are not traceable, they cannot

prove a violation. In this manner, transaction cost applications can play a central role in the

coordination mechanism of the supply chain by creating electronic proximity (Grover and

Malhotra 2003). Huo (2012) empirically proved that supply chain integration is positively

associated with supply chain performance through inter-organizational systems (Huo 2012).

Emergent Technologies in Supply Chain Management

IT integration in the supply chain can lead firms to achieve potential performance

improvements by integrating business processes in the supply chain (Rai et al. 2006; Shekarian

and Ramirez 2021). For example, Huo et al. (2016) found that supply chain integration positively

impacts firm performance (Huo et al. 2016). It enhances the relationship between the firms’

customers and suppliers positively. Overall, systematic integration in the supply chain

significantly treats market failure, including production planning, materials management, and

distribution (Williamson 1971).

Recently, digital supply chain integration with emerging technology such as blockchain

and IoT has dramatically increased (Korpela et al. 2017; Kshetri 2018). In February 2017, the

Center for Supply Chain Studies (CSCS) launched a “Drug Supply Chain Security Act and

Blockchain study.” (Center of Supply Chain Studies 2018) The act required all pharmaceutical

industries to trace products throughout the supply chain by 2023. In response to the act, software

development companies deploy blockchain as an enabled solution to comply with the order of

CSCS (Lacity 2018).

Emergent technologies of the supply chain require tools to engage in the surrounding

environment. For example, blockchain enables trace of products in the supply chain and provides

details about products, such as factory and physical locations (Alshakhs et al. 2020; Engelenburg

et al. 2018). However, blockchain cannot add potential value to the supply chain without

integrating it with other technologies, such as IoT and AI. IoT sensors can transmit real-time data

into a decentralized database and increase transparency. This allows all participants to have

actual data for all transactions in the supply chain (Verhoeven et al. 2018). Also, it supports

decision-making when a recall is necessary for a specific product. For example, Toyota recalled

four million vehicles due to faulty gas pedals installed in some of the four million vehicles

(Kshetri and Loukoianova 2019). Because these gas pedals were not traceable in the supply

chain, it was impossible to identify the source and recall only the faulty products.

In addition, artificial intelligence (AI) can also be integrated with blockchain and IoT

devices to enhance decision-making, meaning that AI can be considered an autonomous agent

that takes information from its environment and makes decisions (Tapscott and Tapscott 2016).

In the previous example of recalling products, AI can recall products if a problem occurs during

transportation. For example, food and pharmaceutical products require a specific room

temperature during the transportation process to ensure the authenticity of the products.

Therefore, integrating the three technologies, blockchain, IoT, and AI, with an enterprise system

(e.g., ERP) is essential for firms to achieve the excellent value of an intelligent supply chain.

Bullwhip Effect

The bullwhip effect can increase the inventory cost and may require the firm to dispose

of unsold products. Lee et al. (1997) identified the causes of the bullwhip effect: demand signal

processing, rationing game, order batching, and price variation (Lee et al. 1997). Firms need to

manage the supply chain to minimize these causes and avoid the bullwhip effect.

The literature on the bullwhip effect is extensive in operational management and

economics. Studies in operation and production literature have proved that using technologies

such as MRP, ERP, and EDI supported firms in managing inventory and production in the

supply chain context (Grover and Malhotra 2003; Lee et al. 1997). Thus, a lack of managing

productions and inventories can lead firms to challenge the bullwhip effect.

Moreover, management and information systems scholars proposed possible solutions to

reduce the bullwhip effect. Bray and Mendelson (2012) found that information transmission

about demand and orders over time can reduce the bullwhip effect (Bray and Mendelson 2012).

Yao and Zhu (2012) provide evidence that the use of electronic linkage in downstream

(manufacturers industries) increases the bullwhip effect and decreases the upstream (suppliers

industries) (Yao and Zhu 2012). As a solution to the downstream sectors, they provide an

interaction model that shows IT can sufficiently reduce the bullwhip for downstream industries.

They referred to electronic linkage as electronic hierarchies, such as EDI and electronic markets.

Prior supply chain research examined how information sharing and electronic linkage

between partners in the supply chain reduces the bullwhip effect at the industry level (Cachon et

al. 2007; Wong et al. 2007; Yao and Zhu 2012). Bray and Mendelson (2012) investigated the

information transmission lead times before the information age (1974-1994) and after the

information age (1995-2008) in reducing the bullwhip effect at the firm level. Moreover,

reducing the volatility can improve inventory management, which can be achieved through

information technology (Davis and Kahn 2008; Kahn et al. 2002). This indicates the likelihood

that IT can significantly reduce the bullwhip effect. Thus, I specified emergent technologies as

an IT solution for supply chain firms to reduce the bullwhip effect.

Hypotheses Development

The lens of TCE focuses on examining individual transactions that are considered

essential for operational management (Garfamy 2012; Williamson 2008). Whereas supply chain

management focuses on a broader system where the focal firm can share related information on

the supply chain within and across its boundaries (Rai et al. 2006). Thus, supply chain

management aims to integrate and manage cross-functional applications. Also, enable real-time

communication of the focal firm across multiple functions and supply chain partners.

The transaction is the unit of analysis in TCE, which refers to the information exchange

about goods or services at multiple stages of business processes (Williamson 1981). Firms need

to safeguard transactions and manage the flow of goods and services by implementing a total

systems perspective (Williamson 2008) and selecting an appropriate governance structure

(Schmidt and Wagner 2019). Therefore, transaction cost applications can eliminate opportunism

related to the exchange relationship between business partners (Garfamy 2012), including

unethical practices such as cheating and violation of the agreement.

Information asymmetry among business partners leads to uncertainty and impacts product

quality. The decentralized supply chain can act similarly to the integrated systems with partners,

increasing information transparency and reducing opportunism (Garfamy 2012). Indeed,

transaction cost application can lead firms to pursue efficiencies or collaborate with supply chain

partners (McIvor 2009).

Furthermore, external resources increase market transactional costs for obtaining

information, such as communication costs, transporting goods, and holding inventory costs.

These costs are associated with contractual relationships with external parties, including writing

contracts and enforcing contracts’ costs. Both market transaction and external contractual costs

may cause a lack of operational efficiencies. Thus, vertical integration between business partners

allows firms to reduce transactional costs (Gurbaxani and Whang 1991).

Prior scholars found that IT can reduce external and internal coordination costs by

increasing the degree of vertical integration (Gurbaxani and Whang 1991). Currently, IT should

further contribute to strengthening the relationships between business partners through digital

communication and intelligent tools that support operational efficiencies.

The adoption of blockchain in the supply chain field ensures the validation of

transactions between business partners and expands the firm’s boundaries (Treiblmaier 2018).

Zelbst et al. (2019) found that emergent technologies such as IoT and blockchain can increase

information transparency in the supply chain. This leads to expanding the firm’s boundaries and

improving operational efficiency. The availability of information at the right time can support a

firm in making the right decision. Li & Lin (2006) found that trust between supply chain partners

is positively associated with both information sharing and information quality.

In addition, several studies have argued that the industry’s sectors are matters regarding

the technology’s effects on firms’ performance (Chiasson and Davidson 2005; Melville et al.

2004; Wade and Hulland 2004). For example, prior studies empirically examined technological

effects on firms performance by distinguishing industry sectors (Banker et al. 2006; Barua et al.

2004; Saldanha et al. 2013). In addition Bray and Mendelson (2012) found the bullwhip effects

varies across firms’ industries. Therefore, I developed the following hypotheses of the same

technology based on each firm’s sector:

H1a: Emergent technologies such blockchain, IoT, and AI integration use with retailers’

firms is negatively associated with the bullwhip effect ratio.

H1b: Emergent technologies such blockchain, IoT, and AI integration use with

manufacturers’ firms is negatively associated with the bullwhip effect ratio.

Adopting emergent technologies such as blockchain, IoT, and AI in the supply chain can

influence information transparency, information quality, and information sharing. All can

contribute to the firm regarding goods supply and production planning. Also, it can manage

inventory variety and demand volatility. Figure 5 illustrates the research model.

Figure 5: Research Model

Methodology

Data

Data for this research was obtained from two sources. First, I gathered data related to

Sterling AIS implementation from the Computer Intelligence Technology database (CITDB).

According to IBM (2019), the Sterling AIS was released in October 2019. This technology is a

B2B supply chain integration suite, which expands the EDI by integrating emergent technologies

such as blockchain, IoT, and AI with Application Programming Interfaces (API) such as ERP

systems. Therefore, I extracted data for companies that implemented the Sterling AIS in 2019 as

a sample of firms that implemented the three emergent technologies.

As this technology was recently released and to the extent of its impact on the firms’

level, I restricted the sample to test for non-durable goods firms (e.g., food firms). I also separate

firms by their sector using the North American Industry classification systems NAICS codes,

focusing on manufacturers (NAICS code: 311-316 and 322-326) and retailers (NAICS codes:

445, 446, 448, and 451-454).

Second, I use COMPUSTAT to collect financial data for public North American firms

using ticker codes for each firm. The data originating quarterly between 2017 and 2021 allowed

to measure the bullwhip effect before and after the adoption. I only collected data for firms that

adopted the Sterling AIS in 2019 based on the CITD.

Variables

The independent variable emergent technologies integration of blockchain, IoT, and AI

was used to study the bullwhip effect. As indicated earlier, the technology was released in

I exempt wholesalers’ firms from analysis due to the small sample size of the wholesalers who implemented the

technology.

October 2019 by IBM. Thus, I treat the sample depending on time series as follows: 1) control

group (before implementation) between the first quarter of 2017 and the third quarter of 2019),

and 2) treatment group (after implementation) between the fourth quarter of 2019 and the fourth

quarter of 2021). A binary variable was used to code the independent variable in the control and

treatment samples as 0 and 1, respectively.

Data related to the dependent variable measures was collected quarterly from

COMPUSTAT from 2017 to 2021. I found missing values for five manufacturing firms and three

retailers, which were not in COMPUSTAT. After removing missing values, the total number of

manufacturers is 30 firms, and 29 firms for retailers. The dataset was unbalanced panel for both

industries; some firms did not report their financial data for certain quarters. Thus, the total

observations (by quarter) for manufacturing firms are 573 and 532 for retailers.

Prior literature argued that the industry’s sector matters regarding the technology’s

effects on firms’ performance (Chiasson and Davidson 2005; Melville et al. 2004; Wade and

Hulland 2004). Some studies have distinguished industry sectors and empirically examined

technological effects on a firm’s performance (Banker et al. 2006; Barua et al. 2004; Saldanha et

al. 2013). Thus, I measured the dependent variable depending on the firm’s industry sector.

First, fluctuating prices primarily affect retailers’ industry, which causes the bullwhip

effect (Lee et al. 1997). Promotional activity and cost shocks can contribute to increasing the

bullwhip effect. For example, if manufacturers increase prices, retailers do not place orders from

manufacturers unless their inventory level is low or products’ prices decrease.

Actual data on industry demand and orders are generally not available, however, sales

and inventory are reasonable measures for demand and orders (Cachon et al. 2007). Thus, I

followed Cachon et al. (2007) to proxy sales for retailers’ firms as an alternative measure for

demand and total inventory for orders. Second, the extent to which sales are conducted in

manufacturing firms is B2B. Therefore, the cost of goods sold (COGS) is a reasonable proxy for

manufacturing firms to measure demand. This measure is adopted from existing studies (Bray

and Mendelson 2012; Yao and Zhu 2012). Then I applied the variance operators for demand and

inventory across all firms in both sectors. Mathematically, the bullwhip effect is computed based

as follows:

Bullwhip effect = (inventory variance) / (demand variance)

A ratio of bullwhip effect is less than one indicates smoother operational efficiency and the

ability to satisfy customers’ needs.

Estimation and Empirical Result

Econometric Model

I estimate the firm-level bullwhip effect across industries, manufacturers, and retailers.

Because the data are panel data and the technology was adopted at a specific time (4th quarter of

2019), I used difference-in-difference estimation to distinguish the effect of the emergent

technologies on the bullwhip effect based on time, a pre-implementation and post-

implementation (See Figure 6). The difference-in-difference estimator analyzes data for two

periods and provides the difference between treatment effects and control. This technique is

known in social science research as a quasi-experimental design where the independent variable

is manipulated in multi-level treatment and control (Bhattacherjee 2012).

Figure 6: Analysis Design

To test the hypotheses, I developed the following econometric model to measure the

effect of the independent variable in two different time series.



󰆹 󰇡 󰇢

where 

󰆹 is the difference-in-difference estimator,  is the dependent variable after the

implementation (treatment effect) for firm i; and  is the dependent variable before the

implementation (control) for firm i. In other words, 

󰆹 is the difference over time in the average

variance of the bullwhip effect in both time series.

To test whether 

󰆹 is statistically different from zero, I applied the following regression

analysis:

  

󰆹󰇛󰇜 󰇛󰇜 

where i denotes for the focal firm, t denotes for time.  is the intercept,  is a binary

variable of the independent variable equal to 1 if the time is in or after the fourth quarter of 2019

and 0 otherwise.  is the difference between two groups.  and  are the control variables,

where  is the log variance for the cost of goods sold (COGS), and  is the log variance for

demand volatility. The variety of the firm size in both samples may have a different level of the

bullwhip effect. According to prior literature, COGS and demand volatility can be used to

control the size effect at the firm level (Gaur et al. 2005; Saldanha et al. 2013; Yao and Zhu

2012). Next, I interpret the results based on the firm’s industry.

Empirical results

To determine whether multicollinearity issues exist in the model, I tested for the variance

inflation factor (VIF) and obtained a mean tolerance value of 1.28 for retailers and 1.15 for

manufacturers. According to Wooldridge (2009), the VIF is the factor by which independent

variables are correlated with the dependent variable. A value less than 10 indicates that

multicollinearity does not exist in the model. Table 5 provides the descriptive statistics for all

variables (retailers 1st column, manufacturers 2nd column). The N represents the total periods

collected quarterly for both sectors of all firms, 532 retailers and 573 manufacturers.

Table 5. Descriptive Statistics

Retailers (N = 532)

Manufacturers (N = 573)

Variables

Mean

Std. Dev.

Mean

Std. Dev.

Bullwhip Effect

-.78

35.18

-.20

20.05

Emergent Technologies

Integration

.43

.49

.44

.49

Variance COGS

5.28

.98

5.38

.97

Variance demand

5.29

.97

5.40

.97

Variance Inflation Factor

(VIF)

1.28

1.15

Table 6 and Table 7 presents the correlation matrix among all variables for both samples

retailers and manufacturers respoectively. Also, I used the Breusch-Pagan / Cook-Weisberg test

to estimate the variance of the dependent variable from the average squared values of residuals.

The result indicates the presence of heteroscedasticity in both samples in the main model.

Therefore, I performed feasible generalized least squares (FGLS) estimator for the main results

(Wooldridge 2009).

Table 6. Correlation Matrix for Retialers (N = 532)

Variables

(1)

(2)

(3)

(4)

1. Bullwhip

Effect

1.00

2. Emergent

Technologies

Integration

-0.07*

1.00

3. Variance

COGS

-0.01

0.03

1.00

4. Variance

demand

0.26*

-0.05

0.54*

1.00

*** p<0.01, ** p<0.05, * p<0.1

Table 7. Correlation Matrix for Manufacturers (N = 573)

Variables

(1)

(2)

(3)

(4)

1. Bullwhip

Effect

1.00

2. Emergent

Technologies

Integration

-0.08*

1.00

3. Variance

COGS

-0.16*

0.05

1.00

4. Variance

demand

-0.03

-0.02

0.43*

1.00

*** p<0.01, ** p<0.05, * p<0.1

For the estimation analyses, I used two procedures to test both samples. First, since there

are two groups (treatment and control) in both samples, I performed a difference-in-difference

estimator to determine the impact on the dependent variable before and after implantation.

Second, I performed FGLS estimator to estimate the model. The results indicate a similar

coefficient and significant level for all variables in both procedures. Thus, I included a

difference-in-difference estimate in the model.

Results are summarized in Table 8. For retailer firms (column 1), the emergent

technologies’ integration is positive and significant at the 5% level (p-value < 0.05), while the

difference-in-difference estimate is negative and significant at the 1% level (p-value < 0.01).

This indicates that the treatment effect can reduce the bullwhip effect for retailer firms (H1a

supported).

On the other hand, manufacturing firms’ results showing in Table 8 (column 2). The

emergent technologies’ integration is negative and significant at the 1% level (p-value < 0.01). In

contrast, the difference-in-difference estimate is positive and significant at the 5% level (p-value

< 0.05). This indicates that the treatment effect increases the bullwhip effect for manufacturing

firms, which counters hypothesis (H1b). As discussed in section 2.3., the causes of the bullwhip

effect are demand signal processing, rationing game, order batching, and price variation (Lee et

al. 1997). Depending on the firm's industry, fluctuating prices and promotional activities can

manipulate the bullwhip effect.

The findings regarding the variance of COGS are negative and significant at a 1% level

(p-value < 0.01) for both samples. The variance of demand volatility is positive and significant at

a 1% level (p-value < 0.01) for retailer firms, but it is not significant for manufacturing firms.

Table 8. Estimation Results

Variables

Retailer

Manufacturer

Emergent Technologies

Integration

34.41**

-21.60***

(14.61)

(7.90)

Diff-Diff

-2.40***

1.16**

(0.92)

(0.48)

Variance COGS

-7.68***

-3.78***

(1.74)

(0.93)

Variance demand

13.75***

0.80

(1.75)

(0.92)

Constant

-31.75***

17.14***

(9.08)

(5.48)

Model Statistics

532

573

Log-likelihood

-2615.70

-2517.98

Wald 

70.23***

25.84***

Notes: Means and standard errors in parentheses are estimated by linear regression.

***p<0.01; **p<0.05; *p<0.1

Discussion

This study demonstrates the impact of emergent technologies’ integration in reducing the

bullwhip effect at the firm level rather than in the industry. Specifically, I estimated the impact of

emergent technologies’ integration on individual firms’ bullwhip effects. I distinguished the data

sample depending on the firm’s industry: retailers and manufacturers.

The findings show that using emergent technologies for retail firms can reduce the

bullwhip effects. As discussed in section 2.3, Lee et al. (1997) identified four causes of the

bullwhip effect: demand signal processing, price variation, rationing game, and order batching

(Lee et al. 1997). The price variance fluctuates among retailers’ firms, mainly when firms

provide seasonal promotions. According to an economic principle, a change in one variable

causes a change in another. For example, a change in the price of products would affect a change

in the quantities for demand or supply (Mazzucato 2018). Demand signals arise from retailers

and request goods based on inventory and demand levels. Emergent technologies’ integration

supports retailers in managing inventory when integrated with other enterprise systems such as

ERP.

Prior literature found that electronic linkage increases the bullwhip effect for

manufacturing at the industry level (Yao and Zhu 2012). Similarly, I found that emergent

technologies’ integration increases the bullwhip effect at the firm level for manufacturing firms.

In other words, both analysis levels (industry level and firm level) have similar results regarding

the bullwhip effect for manufacturing firms. According to the causes of the bullwhip effect

above, there is information loss between manufacturers and the marketplace, resulting in false

demand or insufficient forecast from the marketplace. Using inaccurate manufacturer

information in the production schedule can increase order batching and inventory levels.

Contributions

This study provides several implications and contributions for both practical and

academic disciplines. First, I analyzed the impact of emergent technologies’ integration at the

firm level rather than the industry level in the supply chain context (Yao and Zhu 2012). I

distinguished between retailers and manufacturing firms to examine the impact of emergent

technologies’ integration based on the firm’s characteristics.

Most prior literature has suggested that integrated technology would improve supply

chain performance. I empirically examined this phenomenon using emergent technologies’

integration as a treatment for reducing the bullwhip effect. In particular, integrating emergent

technologies can reduce the bullwhip effect for retail firms. However, the benefit of emergent

technologies’ integration does not hold for all industries as it increases the bullwhip effect

slightly for manufacturers. Also, this study provides evidence that emergent technologies’

integration supports firms in managing demand and inventory depending on the firm’s industry.

This research suggests that integrating emergent technologies enables firms to respond quickly to

demand signals and order batching.

Limitations

This study has several limitations. First, the Sterling AIS technology, which integrated

the three emergent technologies, blockchain, IoT, and AI was released in October 2019 (IBM

2019). In the following year, the COVID-19 pandemic affected the supply chain globally, which

resulted in shortages in many supply chains. Overall, during the pandemic, uncertainty in the

supply chain disrupted the whole industries’ production and supply activities. It is impossible to

eliminate this impact from the current study. Second, I focused on non-durable goods firms (e.g.,

foods and pharmaceutical firms). It is possible that the impact of emergent technologies

integration could be different in durable goods firms. Also, this study has to constrained by the

available data for firms with their financial data available and accessible. Some private firms

have implemented emergent technologies, but their financial data was unavailable.

Future Research

Future research may extend this study by examining the use of emergent technologies in

private sectors and comparing the impact of this technology on publicly traded firms. Including

durable goods firms (e.g., automotive firms) to determine if the impact of emergent technologies

is similar for firms operating in different industries. Also, future research may develop a case

study to investigate the phenomenon of the emergent technologies in their natural settings. In

addition, the results of this study can be extended to test the impact of this technology after year

2022, which most supply chain firms recovered from COVID-19. The impact of this technology

could also enhance other variables of the firm such as profit or stock price.

Conclusion

This study examines whether integrating emerging technologies such as IoT, blockchain,

and AI into a single technology would improve supply chain performance by reducing the

bullwhip effect. The finding shows that the integration of the three emergent technologies

reduces the bullwhip effect for retailers and increases it for manufacturers. These findings extend

literature in two various ways. First, by distinguishing between retailers and manufacturing

firms. I found a different effect of the emergent technologies’ integration on the bullwhip effect

of the firm. Firms’ industry matters when examining the impact of emergent technologies in the

supply chain environment. Second, the estimation results are based on the firm level rather than

the industry level. It encourages individual firms to adopt emergent technologies intelligent for

supply chain management to improve demand and order management. This research confirms

that emergent technologies’ integration supports firms in tracking and monitoring goods in the

supply chain and their ability to respond to market demand rapidly.

Chapter IV

CONCLUSION

The central argument in this study is that emerging technologies have recently rapidly

increased in the supply chain environment. The decision to adopt emerging technologies for

supply chain management varies by industry and purpose. Thus, this dissertation examined

adopting emergent technologies for supply chain management practices and bullwhip effects

across two studies. The first study followed a systematic literature approach that highlighted the

adoption of blockchain technology for specific supply chain management practices and the

conditions of the adoption. I found few studies related to the involvement of blockchain in joint

product development, purchasing alliances, and strategic supplier partnership practices. Further,

significant studies show that blockchain supports vendor-managed inventory, customer

relationships, information sharing, and information quality within the supply chain. I did not find

a study related to adopting blockchain for postponement practice. However, the impact of

blockchain on supply chain management practices must fulfill certain conditions to achieve the

adoption objectives in some cases.

Furthermore, O developed a conceptual framework based on the systematic literature

review to theorize blockchain for supply chain management practices. The theoretical framework

is based on two theoretical lenses: resource-based view and practice-based view. The findings of

this study strongly support Carter’s research (Carter et al. 2017), which proposed a supply chain

practice view that eliminated the boundaries between practices and resources between firms and

performance. This study contributes to the theoretical literature on adopting blockchain for

supply chain management practices. Also, this study contributes to practitioners in the industry

by providing a comprehensive understating of each practice along with blockchain integration.

The systematic literature review results can also be useful for future research to cover the gaps

found in the literature.

In the second study, I empirically investigate the impact of emergent technologies’

integration on reducing bullwhip effects. The emergent technologies are consistent with

integrating blockchain, IoT, and AI with enterprise systems. In the analysis, I distinguished

between retailers’ and manufacturers’ firms as industries matter in the supply chain environment.

The results indicate that emergent technologies reduce the bullwhip effects for retailers and

increase the bullwhip effects for manufacturers. However, the results support the two causes of

bullwhip effects for manufacturers identified by Lee et al. (1997): rationing game and order

batching. The other two causes affect retailers, demand signal processing and price variation, in

which emergent technologies in the sample reduce the bullwhip effect for retailers. However, the

impact of emergent technologies’ integration can be further extended to investigate their impact

on the bullwhip effects after recovery from the COVID-19 pandemic, as the sample's time series

was before and during the COVID-19 pandemic. The treatment of emergent technologies can be

different from the results, as COVID-19 affected the global supply chain, which caused

shortages in the supply chain for some products. Also, the impact of emergent technologies’

integration can be examined by its impact on other variables, such as firms’ profit or stock price,

for further benefit on firms' performance from adopting this technology.

The results of both studies guide supply chain management in improving supply chain

process efficiencies by adopting emergent technologies. Also, both studies provide opportunities

for multiple industries in the involvement of emergent technologies in their supply chain to

achieve supply chain objectives and enhance performance.

One of the limitations of the first study was that it did not include research written in a

language other than English, which could result from bias in the outcomes. Also, some research

studies might not appear in the literature results due to the selection of search terms and

databases. In addition, the second study has several limitations. First, the Sterling AIS, which

integrates the three emergent technologies, blockchain, IoT, and AI were released at the

beginning of the 4th quarter of 2019. This makes the treatment of this technology during COVID-

19. Consequently, I only include non-durable firms in both samples to test the impact of this

technology due to the short time of the treatment. Future research can extend the analysis to test

the impact of this technology after COVID-19 and for durable firms.

The objective of this study is to comprehensively understand the adoption of emergent

technologies in supply chain management, how supply chain firms can benefit from these

technologies, and how these technologies perform in solving supply chain issues. The approach

consists of two methodologies: systematic literature review and empirical analysis. The main

argument of this study indicates that emergent technologies are increasingly essential in supply

chain activities, which reflects more attention to scholars in IS and supply chain disciplines of

this phenomenon. I considered how emergent technology influences supply chain firms and its

benefits to improve processes and performance in some cases.

Appendix A

First Study: Search Terms and List of Identified Journals

Table A1: Search Terms and Identified Studies

Table A1 shows the search terms used and the results for the total studies associated with

each database. Below are the total studies after duplicates were removed.

Table A1: search terms and identified studies for each database

Search Terms

AIS e-

Library

Business

Source

Premier

Science

Direct

ProQuest

Central

Hits

“Blockchain” AND “Supply chain”

453

“Blockchain” AND “Supply chain management”

155

“Blockchain” AND “supply chain management

practice/s”

2 with ‘s’

“Blockchain” AND “supply chain practice/s”

2 with ‘s’

“Blockchain” AND “Joint Product Development”

“Blockchain” AND “Product/s Development”

“Blockchain” AND “Purchasing Alliance/s”

“Blockchain” AND “Purchasing”

“Blockchain” AND “Purchase/s”

20 with ‘s’

“Blockchain” AND “Vendor-managed

Inventory”

“Blockchain” AND “Inventory management”

“Blockchain” AND “Strategic Supplier

Partnership”

“Blockchain” AND “Supplier Partnership/s”

“Blockchain” AND “Supplier/s”

30 with ‘s’

“Blockchain” AND “Customer Relationship/s”

6 with ‘s’

“Blockchain” AND “Customer Relationship

Management”

“Blockchain” AND “Information Sharing”

“Blockchain” AND “Information Quality”

“Blockchain” AND “Postponement”

Total

325

371

862

Total after duplicates removed

135

525

Total papers from all sources

802

Total papers from all sources after duplicated

removed

799

Table A2: List of Journals and Number of Associated Studies

Table A2 shows the list of journals, the subject-focused area for each journal retrieved

from the journal’s website, and the number of studies associated with each journal. Thirty-four

journals are within the information systems discipline or related fields, with eighty-four studies.

Six journals are related to supply chain management and logistics disciplines, with sixteen total

studies in these journals. Ten journals are within other fields, such as agricultural, food science,

health, and engineering, with twelve studies varying in the fields. All studies are explanatory or

exploratory of blockchain technology adoption in the supply chain for multiple industries and its

impact on one or more SCMPs.

Table A2: List of Journals and Number of Associated Studies Included in the Review

Abbreviations: Information Systems and Management (ISM), Management Science and Operation Research

(MSOR), Computer Science Applications (CSA), Artificial intelligence (AI), Supply Chain Management

(SCM), Human Resource Management (HRM)

Journal

Journal subject areas

# of

associated

studies

Advanced Engineering Informatics

Engineering, ISM, and AI

Automation in Construction

MSOR and engineering

Blockchain: Research and Applications

CSA

Computer Communications

Computer networks and engineering

Computers & Industrial Engineering

Mathematics, ISM, MSOR, and

Engineering

Computers and Electronics in Agriculture

Agronomy and veterinary science,

forestry, and CSA

Computers in Biology and Medicine

CSA, and health informatics

Computers in Industry

ISM, MSOR, CS applications, and

engineering

Current Research in Environmental Sustainability

Management, monitoring, policy / law,

ecology, and social science

Electronic Commerce Research and Applications

ISM, MSOR, IT innovation, AI, CSA

Enterprise Information systems

ISM, CSA, SCM, and MSOR

European Journal of Operational Research

Mathematics, ISM, MSOR, and CSA

Expert Systems with Applications

MSOS, ISM, IT innovation, AI, CSA,

and engineering

Food Control

Food science

Future Generation Computer Systems

ISM, CSA, and computer networks

Industrial Marketing Management

Marketing

Informatics in Medicine Unlocked

ISM, CSA, health informatics, and public

health policy

Table A2: List of Journals and Number of Associated Studies Included in the Review

Abbreviations: Information Systems and Management (ISM), Management Science and Operation Research

(MSOR), Computer Science Applications (CSA), Artificial intelligence (AI), Supply Chain Management

(SCM), Human Resource Management (HRM)

Information & Management

ISM

Information Processing and Management

ISM, information science, and CSA

Information Systems and e-Business Management

ISM and e-business design

International Journal of Information Management

ISM, Organizational behavior and HRM,

and information science

International Journal of Intelligent Networks

AI, computer networks, and signal

processing

International Journal of Production Economics

Economics, MSOR, and engineering

Internet of Things

Energy, IT and innovation, CSA,

engineering

Journal of Air Transport Management

Law, transportation, decision sciences,

strategy, and management

Journal of Business Research

Business and international management

and marketing

Journal of Cleaner Production

Renewable energy, sustainability,

management, monitoring, policy/law, and

waste management

Journal of Environmental Management

Renewable energy, sustainability,

management, monitoring, policy/law, and

waste management

Journal of Industrial Information Integration

ISM, MSOR

Journal of Information Security and Applications

ISM, CSA, information science, and

computer networks

Journal of Information Systems

ISM

Journal of King Saud University – Computer and

Information Sciences

Computer science

Journal of Manufacturing Systems

Engineering

Journal of Open Innovation: Technology, Market,

and Complexity

Economics, ISM, statistics, MSOR,

business, management and accounting, IT

and innovation, finance, and decision

science.

Journal of Retailing and Consumer Services

Marketing

Journal of Supply Chain Management Systems

SCM

MISQ Executive

ISM

Omega

ISM, MSOR, strategy, and management

Research in Transportation Business &

Management

Tourism, hospitality, MSOR, business,

economics, strategy and management,

and transportation

Resources Policy

Geography, renewable energy,

sustainability, management, monitoring,

and economics

Resources, Conservation & Recycling

Renewable energy, sustainability,

management, monitoring, policy/law, and

waste management

Robotics and Computer Integrated Manufacturing

AI and engineering

Smart Agricultural Technology

Agricultural and biological sciences

Social Sciences & Humanities Open

Psychology, decision, and social sciences

Table A2: List of Journals and Number of Associated Studies Included in the Review

Abbreviations: Information Systems and Management (ISM), Management Science and Operation Research

(MSOR), Computer Science Applications (CSA), Artificial intelligence (AI), Supply Chain Management

(SCM), Human Resource Management (HRM)

Supply Chain Forum: An International Journal

SCM

Supply Chain Management: An International

Journal

SCM

Sustainable Cities and Society

Renewable energy, sustainability,

management, monitoring, policy/law,

engineering, and IT and water science

Technological Forecasting & Social Change

Psychology, MSOR, business, IT and

innovation, strategy, and management

Technology in Society

Sociology and political science, human

factors and ergonomics, and business

Transportation Research Part E

Transportation, MSOR, and business

Total

112

Appendix B

First Study: Details of the Systematic Literature Review Organized by Four

Classifications

Table B1: Conceptual Studies of Blockchain Adoption for the Supply Chain Management

Practices

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Kshet

2018)

Information

Sharing /

Information

Quality

Induc-

tive

research

/ theory

building

Eleven case

studies com-

bining

extreme case

method and

diverse case

method

Multi-in-

dustries

Supply chain performance:

(cost, quality, speed,

dependability, risk

reduction, sustainability, and

flexibility)

Integrated blockchain-based

with IoT can contribute to

solving communication

issues between supply chain

partners and enhance supply

chain performance.

(Jadha

v and

Deshm

ukh

2022)

Information

quality

N/A

Systematic

literature

review

Healthcar

e industry

N/A

Blockchain can solve issues

in the healthcare industry by

enhancing data integrity and

immutability, allowing drug

traceability, and identifying

fake drugs.

(Li and

Zhou

2021)

Information

sharing /

Information

quality

N/A

Case studies

Maritime

and

shipping

industry

Cost, quality, speed, and risk

management.

Blockchain can address

scalability, security,

interoperation, and

developing legal

compliance and regulation.

(W.

Liu et

al.

2021)

Information

sharing

N/A

Systematic

literature

review

Agricul-

ture

industry

Transparency, automation,

trust, purchase decision,

production decision,

efficiency, relationships, and

production performance.

The results indicate an

omission of blockchain

adoption in the agriculture

industry. Blockchain can

extend the production

practice from precision

agriculture to the whole

industry.

(Teodo

rescu

and

Korch

agina

2021)

Vendor-

managed

inventory

Induc-

tive

research

/ theory

building

Case studies /

comparative

analysis

Energy,

food, and

pharma-

ceuticals

Safety, sustainability, in-

ventory management effi-

ciency, performance,

lifecycle traceability,

counterfeiting, improving

traceability, and compliance.

Blockchain has similar

impacts on the supply chain

effectiveness for both

streams in two different

countries, Germany and

Russia.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Kshet

2021)

Customer

relationship

Information

sharing

Inductiv

research

/ theory

building

Case studies

Agricul-

ture

Product quality,

environmental accounting,

blockchain-based,

sustainability, transparency,

consumer confidence,

operational benefits,

marginal costs, social

impact, empower

participants, and counterfeit.

Information sharing in the

blockchain can verify

sustainability-related

activities in the supply

chain, which empowers

customers' confidence in the

authenticity of the products

they purchase.

(Yadav

and

Singh

2020)

Information

sharing /

Information

quality /

Customer

relationship

N/A

Principle

component

analysis /

Fuzzy

decision-

making trial

and

evaluation

laboratory

Multi-

industries

System robustness, costs,

performance, data safety and

decentralization,

accessibility, laws and

policy, smart system,

customer satisfaction,

reliable system,

documentation, data

management, and quality.

Data security and quality,

decentralization,

accessibility, laws and

policies, documentation,

and quality are the causes of

developing a business

strategy to attain a

sustainable supply chain.

(Ahma

d et al.

2022)

Purchasing

alliances /

Vendor-

managed

inventory

N/A

Literature

review

Oil and

gas

Transparency, availability,

integrity, data provenance

and audit, authorization,

privacy, pseudonymity,

programmability.

Using blockchain-based

smart contracts enhances

equipment traceability and

securely sends procurement

requests to supply chain

vendors.

(Cole

et al.

2019)

Joint

product

developme

nt /

Vendor-

managed

inventory

N/A

Explanation

analysis

Multi-

industries

Product safety and security,

quality management,

counterfeiting,

trustworthiness, inventory

management, product

development, revolutionize

IT, and transaction cost.

There are opportunities for

blockchain to contribute to

the supply chain by

enhancing inventory

management, product

development, improving

quality management, and

reducing counterfeiting.

(Nandi

et al.

2020)

Information

sharing

Resourc

e-based

View

Abductive

research

Multi-

industries

IV: Blockchain technology-

enabled supply chain

systems, capabilities

(information sharing,

coordination, integration,

collaboration)

DV: Supply chain

performance (quality,

process improvement,

flexibility, cost reduction,

process time reduction)

The most relevant

capabilities to supply chain

performance are

information sharing and

coordination. Industry type

can influence the

relationship between

blockchain capabilities and

performance depending on

the industry's type

uncertainty.

(Lafor

et and

Bilek

2021)

Vendor-

managed

inventory /

Information

sharing /

Information

quality

N/A

Case study

Multi-

industries

Blockchain specificities,

traceability, information

accuracy, data transparency,

data security, involvement

level, interoperability, data

privacy, willingness to

implement blockchain,

visibility communication,

flexibility, agility,

adaptability, and trust.

The main motivations for

stakeholders to deploy

blockchain are traceability

and communication,

whereas interoperability is

an obstacle to adopting the

technology in the supply

chain.

(Roger

son

and

Parry

2020)

Information

sharing /

Information

quality

N/A

Case study

Food

Supply chain visibility,

product security,

immutability, and trust.

The impact of blockchain

on supply chain visibility

enhances trust and

sufficiently digitizes data in

the supply chain.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Mand

olla et

al.

2019)

Information

quality

N/A

Case analysis

Aircraft

Verification, structure,

validation, and blockchain

mining.

The blockchain-based

system adopted for additive

manufacturing can

significantly secure

transaction information

within the supply chain.

(Liu

2022)

Vendor-

managed

inventory

Game

theory

Exploratory

Produce

supply

chain

Wholesale prices, market

shares, selling price,

purchase quantity, expected

profit, consumers' quality

consciousness, producers'

ethics levels, and products'

in-transit spoilage

conditions.

The traceability-enabled

system improves products'

in-transit spoilage

conditions and quality,

which is reflected in the

overall profit of the chain.

(X. Xu,

He, et

al.

2023)

Vendor-

managed

inventory

N/A

Decision

analysis

Manufac-

turing / e-

commerce

Production quantity, pricing

decisions, commission rate,

cross-channel effect,

platform power, carbon

emissions, cap-and-trade

regulation, inventory, profit,

platform power, blockchain

adoption, and delivery time.

The blockchain can reduce

the delivery time of

products if the cross-

channel effect is low.

Manufacturers can generate

more profit with blockchain

adoption only if the cross-

channel effect is low and

platform power is high.

(Niu et

al.

2022)

Supplier

partnership

/ Customer

relationship

Information

quality

N/A

Decision

analysis

Remanu-

facturing

Customers' willingness to

pay, quality uncertainty,

trust, risk aversion,

information accuracy,

information quality,

customer surplus, retail

price, wholesale price,

indifferent point, sales

quantity, blockchain

adoption, supply chain

profit, and supply chain

expected profit.

The adoption of blockchain

is preferred by

manufacturers when selling

regular or remanufactured

products. However,

suppliers do not prefer

blockchain adoption when

selling remanufactured

products unless consumer

risk aversion is low and the

quality uncertainty is small

for the remanufactured

products.

(Yonta

r 2023)

Vendor-

managed

inventory

N/A

PESTEL /

multi-criteria

decision

making

Agricul-

ture

Political: the existence of

government policies.

Economics: improved cost

and resource efficiency.

Social: improved quality

with increased sustainability

and trust among

stakeholders.

Technological: effective

supply chain integration,

transparency, traceability,

and operational efficiency.

Environmental: product

lifecycle tracking, food

security, ability to prevent

food waste, and carbon

footprint tracking.

Legal: product improvement

incentive.

Blockchain plays a

significant role in

preventing food waste in the

supply chain. The value of

the criterion function was

calculated for all variables

in the left column; the

ability to prevent food

waste, increased food

security, and product

lifecycle are the most

significant variables

regarding blockchain

effectiveness.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Choi

and

Luo

2019)

Information

quality

N/A

Exploratory

Fashion

Centralized, decentralized

blockchain, mean and

standard deviation of

forecasted demand, quantity,

retail and wholesale unit

price, salvage value,

environmental taxes, social

welfare, and environment

cost.

Poor data quality negatively

impacts supply chain profits

and social welfare in the

decentralized setting.

Blockchain can improve

social welfare but harm

supply chain profits.

(Raj

Kumar

Reddy

et al.

2021)

Purchasing

alliances /

Vendor-

managed

inventory

N/A

Systematic

review

Automo-

bile

Volatility, uncertainty,

complexity, and ambiguity.

Blockchain can help

automotive firms achieve

efficiency in the supply

chain by providing detailed

data about inventory and

purchases of raw materials.

Also, blockchain can secure

payment processes and

eliminate intermediaries.

(Y. Li

et al.

2023)

Vendor-

managed

inventory /

Supplier

partnership

N/A

Technologica

l impact

analysis

Agricul-

ture / e-

commerce

Dynamic freshness, dynamic

advertising, market demand,

equilibrium, profit margin,

wholesale/retailer prices,

dynamic blockchain

adoption degree, and

optimal freshness-keeping

effort.

Blockchain reduces the

waste of freshness-keeping

effort as well as its cost.

Therefore, adopting

blockchain benefits retailers

and suppliers in the

agriculture industry.

(Wang

et al.

2023)

Customer

relationship

Stackel-

berg

game

Decision

analysis

Manufac-

turing / e-

commerce

IV: commission rate,

consumers' acceptance,

service advantage,

authenticity, number of

consumers, counterfeit

product quality, subscript,

decision, perceived product

quality, consumer utility,

demand, fixed cost of

adopting blockchain, and

strategy profile.

DV: unit cost of using

blockchain and anti-

counterfeiting level.

Blockchain incentivizes

manufacturers to distribute

their brands in online

channels when the

blockchain's unit cost is

low. It also supports anti-

counterfeiting products in

the e-commerce sector

through traceability.

(Kuma

r and

Barua

2023)

Information

sharing

Hesitant

fuzzy set

theory

Decision

analysis

Petroleum

Global supply chain,

bringing all together, high

degree of computerization,

lack of trust among partners,

culture of an organization,

majority attack, transaction

scalability, lack of

understanding, and lack of

general standards.

Blockchain technology can

solve issues in the

petroleum industry, such as

controlling and managing

information by enabling

information sharing.

Blockchain technology can

transform the energy

industry to Industry 4.0.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Dong

et al.

2021)

Information

sharing /

Customer

relationship

N/A

Decision

analysis

Agricul-

ture

IV: ICT-blockchain

adoption, devaluation effect,

products' quality, and costs.

DV: customers' purchase

behavior and pricing

strategy

The customers' quality

perception of a product

(devaluation effect) is

higher on the online

channels than on the offline

channels. A lower

devaluation effect can

significantly impact the

customers' purchase

intention.

(Saxen

a and

Sarkar

2023)

Vendor-

managed

inventory

N/A

Decision

analysis

Retailing

IV: production rate,

reliability, demand rate,

ratio of misplaced inventory,

return rate, shipment

number, selling price,

production expenses,

acquisition cost,

remanufacturing cost, item

cost, holding cost, setup

cost, ordering cost,

inspection cost, idle

expenses, software installing

cost, tag cost, number of tag

reader, and total number of

products.

DV: production period for

the manufacturer and

replenishment cycle length

for the retailer.

The study compared using

blockchain-RFID to track

real-time inventory and not

using the technology

without real-time inventory

tracking. The results

indicate that the technology

can be profitable to all

supply chain members only

if there are increments in

discrepancies and holding

costs. Adopting the

technology is essential for

the industry in case of low

replenishment and

reliability.

(Zhong

et al.

2023)

Vendor-

managed

inventory

N/A

Decision

analysis

e-com-

merce /

retailing /

manufac-

turing

IV: Market demand,

wholesale price, selling

price, production cost,

traceability level, fixed cost,

consumers' preferences and

loyalty on the online

channel, cross-price

sensitives of demand, and

innovation subsidies.

DV: social welfare,

consumer surplus, and

profit.

Product information

transparency can increase

customers' trust to purchase

and increase profit. The

manufacturer will adopt

blockchain if the

transparency level and

technology cost are low.

Blockchain technology is

more beneficial for retailers

than e-tailers, as the profit

of e-tailers is not associated

with the manufacturer when

deciding to adopt

blockchain.

(Zhang

et al.

2023)

Vendor-

managed

inventory

N/A

Decision

analysis

Cold

supply

chain

IV: Blockchain adoption,

market potential,

deterioration rate,

transportation fee, unit cost,

retail price, quantity, and

wholesale price.

DV: product preservation

service level and payoffs.

The transportation fee

impacts the preservation

service level if the

transportation fee is low.

Blockchain increases

preservation service level if

transportation fees are high.

Cold supply chain members

must derive consensus

through government

intervention or an

exogenous coordination

mechanism.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Zhou

et al.

2022)

Information

sharing /

Information

quality

Game

theory

Decision

analysis

Food

IV: Blockchain adoption,

wholesale price, retail price,

production cost,

transparency cost, cost of

joining the blockchain

system, market size,

consumers' transparency

awareness, consumer

surplus, transparency level,

information reliability,

demand,

DV: profits

Suppliers are more likely to

adopt blockchain under the

following conditions: 1) if

the transparency cost

coefficient is larger than a

certain threshold of

information reliability, 2)

the supplier tolerable cost

on joining the blockchain

system is less than the

overall joining fee, and 3)

similarly for retailers, if the

tolerable cost is less than

the overall joining fee,

retailers are willing to adopt

the blockchain.

(Wang

et al.

2019)

Information

sharing /

Information

quality

Sense-

making

theory

Qualitative

Multi-

industries

Visibility, traceability,

simplification, digitalization

and optimization, smart

contracts, trust building,

disintermediation, and

crucial industries.

Blockchain can improve

supply chain visibility and

operational efficiencies

through information sharing

and security, which

enhances trust between

business partners.

(Kambl

e et al.

2020)

Vendor-

managed

inventory

N/A

Interpretive

structural

modeling /

decision-

making trial

and

evaluation

laboratory

analysis

Agricul-

ture

Anonymity and privacy,

auditability,

decentralization,

immutability, improved risk

management, provenance,

reduced transaction cost,

reduced settlement lead

time, secured and shared

database, smart contracts,

traceability, and

transparency.

The results indicated that

traceability is the most

common trigger for

blockchain adoption in the

agriculture industry,

followed by auditability,

immutability, and

provenance.

(Zhang

et al.

2022)

Information

sharing

Game

theory

Decision

analysis

Retailing

IV: retailer choice, utility of

the initial retailer's product,

and utility of the newly

entrant retailer's product.

DV: consumer purchasing

decision, consumer utility.

Retailers will adopt

blockchain only if

consumers' privacy

concerns are low and

information transparency

about promotions is high,

consumers' willingness to

pay increases when

information transparency

promotion is high.

(Akhta

ruzza

man

Khan

et al.

2022)

Vendor-

managed

inventory

N/A

System

design

Food

Traceability, transparency,

and certified products.

Blockchain technology

supports food industries

against contamination and

guarantees certified

products delivered to

customers by providing

provenance information.

Table B1: Key findings and variables of the conceptual SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interest

Key Findings

(Ji et

al.

2022)

Vendor-

managed

inventory /

Customer

relationship

Game

theory

Decision

analysis

Multi-

industries

IV: retailer price, wholesale

price, consumers'

reservation price,

competitive intensity,

transportation costs,

discount, blockchain

introduction fee, unit

verification fee, and

blockchain-sensitive

consumers.

DV: consumer traceability

awareness, profit, and

supply chain performance.

The study analyzed when

two manufacturers (one

introduced blockchain and

the other did not) sell

competitive products

through the same retailer.

The results indicated that

manufacturers should adopt

blockchain when consumer

sensitivity is high toward

blockchain. Also, the

introduction of blockchain

can increase profit.

(Biswa

s et al.

2023)

Customer

relationship

Game

theory

Decision

analysis

Multi-

industries

IV: distrust level, consumer

sensitivity to price, and

consumer sensitivity to

quality.

DV: Blockchain adoption,

product quality, and prices.

Firms avoid blockchain

adoption when the distrust

level is high. Blockchain is

insufficient to make

consumers aware of product

quality. Firms should adopt

blockchain only when

consumer sensitivity to

price is low and sensitivity

to quality is high.

(Xu et

al.

2023)

Joint

product

developme

Stackel-

berg

game

Decision

analysis

Remanu-

facturing

Market size, platform-

enabled power, retail price

sensitivity, carbon trading

price, price sensitivity,

allocated cap, emission

intensity, commission rate,

transfer fee, cost of used

product, production cost,

cost of using blockchain,

cost coefficient, retail price,

production quantity,

wholesale price, and

collection rate.

In the reselling mode,

results indicated that

manufacturers should adopt

blockchain only if the

intensity of emissions is

high. Manufacturers should

also consider other factors

when adopting blockchain.

For example, if product

quality and operational

costs are high and market

size is low, then blockchain

benefits manufacturers.

Table B2: Combined Studies of Blockchain Adoption for the Supply Chain Management

Practices

Table B2: Key findings and variables of the combined SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(S. Cao

et al.

2022)

Vendor-

managed

inventory /

Information

sharing

N/A

Exploratory

case study

Food

Use case criteria:

(considerations, feasibility,

operational compatibility,

performance, privacy,

assurance, relevance to

stakeholders)

Implementing the use case

provides evidence of the

blockchain's ability to

enable supply chain

governance regarding

efficiency, transparency,

accountability, integrity,

privacy, and confidentiality.

(J. Li

et al.

2023)

Information

quality

N/A

System

design /

Experimental

evaluation

approach

Medical

equipment

IV: traceability system

DV: Security, dynamic,

policy control, and

throughput

The results prove that

traceability systems based

on blockchain increase

throughput and security.

(Agra

wal et

al.

2022)

Information

sharing

N/A

System

design /

experimental

evaluation

approach

Pharma-

ceuticals

IV: number of users, query

transaction, and invoking

transaction.

DV: total latency and

throughput.

The design model (forward

and backward) securely

supports transactions within

the supply chain by

allowing transparency

among stakeholders to

identify fake drugs quickly.

(Y.

Cao et

al.

2022)

Customer

relationship

/ Supplier

partnership

Game

theory

Case study

Agricultur

Production cost coefficient,

input cost coefficient,

demand realization

probability, and market

demand.

Buyers will benefit from

adopting blockchain in

agriculture, but cooperative

partners can sometimes

benefit and be worse under

certain conditions.

(Youse

fi et al.

2022)

Vendor-

managed

inventory /

Information

Sharing /

Customer

relationship

Network

theory

Fuzzy

cognitive

map and

fuzzy slack-

based data

envelopment

analysis

Mineral

IV: auditability,

immutability and

encryption, improved risk

management, transaction

cost, security, shared

database, transparency,

flexibility, integrity,

improved inventory

management, customer

centricity, privacy,

decentralization,

provenance, reduced

administrative procedures

and settlement lead time,

smart contracts traceability,

social responsibility,

environmental

sustainability, compliance

with government policy.

DV: supply chain

performance

Blockchain's enablers

positively impact supply

chain performance through

intelligent contracts. Three

practices out of the twenty

enablers are enhancing

performance by enabling

transparency and

traceability.

(Yong

et al.

2020)

Vendor-

managed

inventory

N/A

System

design /

experimental

approach

Pharma-

ceuticals

IV: accuracy, precision, and

recall

DV: performance

Blockchain-based systems

decentralize the control for

vaccines and allow

traceability to enhance the

safe delivery of vaccines in

the supply chain.

Table B2: Key findings and variables of the combined SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Sun et

al.

2022)

Customer

relationship

N/A

Quantitative/

Order

allocation

methodology

commerce

IV: purchasing behavior and

posterior repurchase rate.

DV: Consumer loyalty

The results indicate that

blockchain increased

consumer loyalty,

motivating customers to

re/purchasing behavior.

(Bai et

al.

2022)

Information

Sharing

Technol

ogy or-

ganiza-

tion

envi-

ronment

Best-worst

method

Agricultur

IV: Technical

characteristics, Product

transparency, range of

transparency, participant

transparency, smart

contracts, security,

compatibility, complexity,

and tracking products.

DV: sustainable supply

chain transparency

The developed model

encourages decision-makers

to develop strategies to

strengthen the blockchain

application to improve

supply chain sustainability

and transparency.

(Arun

mozhi

et al.

2022)

Information

Sharing

Interven

tion-

based

and

business

process

reengi-

neering

System

design /

experimental

approach us-

ing a prelimi-

nary case

study

Automo-

tive

Energy, cost, policy

governance, and sustainable

supply chain operations

The integrated AI and

blockchain smart contracts

model support controlling

costs and energy by

reducing 11.58% and 12.48,

respectively.

(Hader

et al.

2022)

Information

Sharing

N/A

Case study

Textile

Transparency, traceability,

reliability, monitoring,

information sharing, and

quality

The integrated blockchain

and big data system

supports real-time data

sharing among supply chain

members and reduces

quality defects after

delivery.

(Mark

us and

Buijs

2022)

Vendor-

managed

inventory

N/A

Structured

literature

review/case

studies

Multi-

industries

IV: process mapping,

digitization, data analysis,

shared immutable ledger,

and smart contracts.

DV: supply chain

performance

Blockchain can directly

affect supply chain

performance and indirectly

through the broader

business project in which

blockchain is implemented.

(Zheng

et al.

2021)

Information

sharing

Stackel-

berg

game

Conditional

value at risk

method

Spacecraft

IV: production, demand,

blockchain-used, order

quantity, production

sensitivity, information

sharing, forecast, costs,

production capacity, and

price.

DV: profit

Using blockchain in the

spacecraft supply chain can

enhance information

sharing, influence

profitability, and reduce

transaction costs among

supply chain members.

(P and

2021)

Information

sharing

N/A

Simulation /

Experiment

Pharma-

ceuticals

Number of products,

latency, authentication

accuracy, and false positive

rate.

The experiment results

indicate that pharmaceutical

product information sharing

is secured within the supply

chain by maximizing

authentication accuracy and

minimizing latency.

(Sreen

u et al.

2022)

Vendor-

managed

inventory

N/A

System

design /

Experimental

approach

Pharma-

ceuticals

Security, integrity,

confidentiality, availability,

resiliency, latency, asset

size, and throughputs.

The integrated systems

blockchain and IoT reveal

the overall performance and

protect vaccines in the

supply chain.

Table B2: Key findings and variables of the combined SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Eluub

kyzy et

al.

2021)

Supplier

partnership

N/A

System

design /

Experimental

approach

Agricultur

IV: Adaptability, influence

of business in industry,

market share, business

reputation, business ideas

and management concepts,

and scalability.

DV: fairness in the

consortium

Trust between farmers can

be improved through the

implementation of

blockchain. The experiment

results prove the system's

effectiveness in the

agriculture supply chain to

achieve fairness in the

consortium.

(Kuhn

et al.

2021)

Vendor-

managed

inventory

N/A

System

design /

simulation

Automo-

tive

Smart contracts, traceability,

authority, and consensus

mechanism enforcement.

The blockchain-based

traceability system can

secure and share data with

participants in the supply

chain.

(Zhou

et al.

2021)

Customer

relationship

N/A

System

design /

simulation

commerce

Usability, reliability, fee

costs, and time cost,

The blockchain-based

reputation system protects

consumers' reviews for

products in the e-commerce

environment from

manipulation.

(Wang

et al.

2020)

Information

sharing

N/A

System

design / case

study

Construc-

tion

Reliability, transparency,

traceability, information

sharing, and monitoring.

The system facilitates

information sharing and

provides real-time data to

participants in the supply

chain.

(Agra

wal et

al.

2021)

Supplier

partnership

/ Customer

relationship

N/A

Case study /

simulation

Textile

Supply chain partners, smart

contracts, permission,

consensus, transactions,

performance, and

traceability.

Transactions are monitored

by a certifying agency and

shared among partners in

the supply chain to ensure

quality delivered to

consumers.

(Bader

et al.

2021)

Information

sharing

N/A

System

design /

experimental

approach

Automo-

tive

Accountability, verifiability,

privacy preservation,

security, scalability,

autonomy, and performance.

The designed system

improves the sufficiency of

the supply chain in

production and

transportation through

information sharing.

(X. Liu

et al.

2021)

Vendor-

managed

inventory

N/A

System

design / case

study

Pharma-

ceuticals

Scalability, privacy,

ubiquitous access,

authenticity, sharing,

interoperability,

decentralization, flexibility,

security, transparency,

transaction size, and

response time.

The study designed a

blockchain-IoT-based

system to trace drugs in the

supply chain. The

transaction size should be

less than 200 kb for

adequate response time and

performance. The system

guarantees quality and safe

drugs delivered in the

supply chain.

(Boube

ta-Puig

et al.

2021)

Vendor-

managed

inventory /

Information

sharing

N/A

System

engineering

approach

Pharma-

ceuticals

N/A

Integrating blockchain with

complex event processing

can trigger event patterns

and detect critical issues in

the supply chain, such as

temperature.

Table B2: Key findings and variables of the combined SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Kuma

r et al.

2020)

Vendor-

managed

inventory /

Supplier

partnership

Information

sharing /

Information

quality

System

design /

experimental

approach

commerce

Proof of accomplishment,

rating scale, performance,

read latency, read

throughput, transaction

latency, and transaction

throughput.

The designed system

"PRODCHAIN" is efficient

for the traceability of

products in e-commerce in

terms of latencies and

throughputs. Also, the

system eliminates

manipulation of the supply

chain data and prevents

fraud, enhancing trust

between business partners.

(Turki

et al.

2023)

Vendor-

managed

inventory /

Information

sharing

N/A

System

design

Pharma-

ceuticals

costs, security analysis,

vulnerability, smart

contracts, and traceability.

The paper developed a

traceability system based on

blockchain and smart

contracts to track drugs in

the supply chain, discussing

security analysis and

vulnerabilities to prevent

financial losses and policy

violations.

(Hasan

et al.

2019)

Information

sharing

N/A

System

design

Logistics /

pharmace

uticals

Smart contracts, shipment

condition, violation type,

participants, traceability,

package status, events order,

and verification.

The functionality of a

blockchain-based

traceability system was

potentially applied in the

vaccine supply chain and

enabled real-time tracking

capabilities of products in

the supply chain, ensuring

safe product delivery to

customers.

(Cao et

al.

2021)

Vendor-

managed

inventory /

Customer

relationship

N/A

Design

science /

experiment

Agricultur

IV: traceability, blockchain-

enables system, human-

machine reconciliation

mechanism

DV: consumer trust

Blockchain adoption

strengthens consumer trust

by enabling food

traceability and sharing it

with customers to ensure

provenance and quality.

(Sarfar

az et

al.

2023)

Information

sharing

N/A

System

design /

simulation

Multi-

industries

IV: demand, inventory level,

replenishment quantity, and

reorder amount.

DV: bullwhip effect,

inventory and demand

variance, and system cost.

Reducing the bullwhip

effect and inventory costs

can be achieved via

information sharing and

incorporating blockchain

with supply chain stream

tiers.

Table B3: Implementation Studies of Blockchain Adoption for the Supply Chain

Management Practices

Table B3: Key findings and variables of the Implementation SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Ma et

al.

2022)

Information

Sharing

Game

theory

Differential

game method

commerce

Demand, discount,

commission rate, costs,

environmental efficiency,

economic efficiency, social

efficiency, product brand

goodwill, product price,

marketing effort, and

recycling effort

The manufacturer is more

likely to adopt blockchain

only if the fixed cost of

using blockchain

technology is below a

certain threshold. The

threshold is related to the

profit the manufacturer can

earn from different sales

formats (reselling and

agency selling formats).

(Erol

et al.

2022)

Vendor-

managed

inventory /

Supplier

partnership

Fuzzy

logic

theory

Quality

function

deployment

method

Circular

supply

chain

Transparency, trust,

traceable, collaboration,

sharing economy, life cycle

tracking, materials market,

industrial focus, carbon

footprints, purchasing

habits, uncertain demand,

purchasing cost, goods

return, consumer awareness,

Blockchain adoption in the

circular economy allows

supply chain participants to

achieve higher trust,

improved collaboration, and

enhanced traceability.

(Fried

man

and

Ormist

2022)

Vendor-

managed

inventory

Innova-

tion re-

sistance

theory

Qualitative

Food

IV: blockchain philosophy,

a tool for sustainability,

fraud, and human rights

violations, fairer supply

chain, food traceability,

environmental benefits,

economic value creation,

protecting status quo,

cooperative barriers,

functional barriers, and

psychological barriers.

DV: Mindset vs. tool,

opportunities, and

resistance.

Blockchain-based

technology discloses

sustainability's social and

economic dimensions, such

as fraud and human rights

violations, enhances food

traceability, provides

environmental benefits, and

generates economic value

for food supply chains.

(Bager

et al.

2022)

Vendor-

managed

inventory /

Information

sharing

N/A

Case study

Agricultur

Quality, certification,

pricing, environmental

sustainability, social

sustainability, and economic

sustainability.

Through blockchain

implementation, farmers

can brand their products and

provide sustainability for

supply chain members by

enhancing transparency.

(Longo

et al.

2019)

Information

sharing /

Information

quality

N/A

Simulation

Multi-

industries

Supply chain performance,

orders' fill rate, on-hand

inventory, total inventory

costs, average inventory

costs, revenues, and net

profit.

The results indicated

demand forecasts are more

accurate in blockchain-

enabled information-sharing

scenarios due to enhanced

trust between business

partners.

Table B3: Key findings and variables of the Implementation SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Li et

al.

2021)

Information

sharing

Technol

ogy ac-

ceptance

model

Empirical

Aviation

IV: regulatory governance

and industry standard,

technological improvements

and optimization on

efficiency, tracking and

tracing, digitalized

management, and air traffic

management.

DV: intention to adopt

blockchain

The results highlighted that

digitalization management,

air trafficking, and tacking

are the most influential

variables for adopting

blockchain. This indicates

that blockchain is valuable

in helping the aviation

industry to improve

efficiency.

(Fosso

Wamb

a et al.

2020)

Information

sharing /

Supplier

partnership

/ Customer

relationship

Technol

ogy ac-

ceptance

model /

unified

theory

of ac-

ceptance

and use

of tech-

nology

Empirical

Multi-

industries

IV: knowledge sharing,

trading partner pressure,

blockchain adoption, supply

chain transparency, and

blockchain transparency.

DV: supply chain

performance

Information sharing and

trading partners influence

the adoption of blockchain,

and supply chain

transparency and blockchain

transparency positively

impact supply chain

performance.

(Hu et

al.

2022)

Customer

relationship

Stackel-

berg

game

Case study

Agricultur

e / e-

commerce

IV: Retail price, sensitivity

coefficient, time cost,

authenticity, service quality,

unit certification fee,

DV: supply chain profit,

consumer surplus

Adopting blockchain in the

agricultural supply chain

can increase the overall

profit and consumer surplus.

The results indicate that

blockchain can improve

service quality, reflecting

consumers' shopping

convenience.

(Strani

eri et

al.

2021)

Vendor-

managed

inventory /

Information

sharing

N/A

Case study

Agricultur

Efficiency, flexibility,

responsiveness, food

quality, transparency, supply

chain governance, and

resources and capabilities.

Food quality can be

enhanced through

information sharing and

accessibility in the supply

chain. The agri-food supply

chain can economically

benefit from blockchain by

increasing profit and return

on investment.

(Kuma

r et al.

2023)

Information

sharing

Technol

ogy ac-

ceptance

model

Qualitative

Warehous

ing

IV: operational practices,

organizational practices,

environmental practices,

financial practices, security

and privacy practices,

infrastructure practices,

collaborative practices,

flexibility practices, and

IoT-blockchain adoption.

DV: organizational business

performance.

Blockchain-IoT adoption is

needed to improve real-time

data monitoring and

transparency. The study

found that supply chain

managers should focus on

operational, organizational,

security, and flexibility

practices in their work

environment to improve

overall business

performance.

(Jense

n et al.

2019)

Information

sharing /

Information

quality

N/A

Case study

Logistics

Shipping information

pipeline, paperless trade,

global trade digitization,

Trade-Lens proof,

information exchange,

digital platforms,

blockchain, and regulations.

The deployment of

blockchain emphasizes the

accuracy of information

exchange between

participants in the logistics

industry.

Table B3: Key findings and variables of the Implementation SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Benzi

dia et

al.

2021)

Joint

product

developme

nt /

Supplier

partnership

Dynami

capabilit

y /

supply

chain

am-

bidexteri

Empirical

Manufac-

turing

IV: internal integration

using advanced technology,

blockchain integration,

relational social capital, firm

type, industry type, and firm

size.

DV: Innovation

Internal integration

capabilities enabled

blockchain integration with

external operations by

sharing information

between firms. Social

capital and blockchain

integration significantly

mediate internal integration

and innovation

relationships. Also,

blockchain integration is

positively associated with

innovation.

(Celik

et al.

2023)

Information

sharing /

Information

quality

N/A

Qualitative

Construc-

tion

Gas price, disciplines, smart

contracts, data blocks, and

design documents.

Integrating blockchain with

building information

modeling in the

construction industry is

utilized to assess

performance and cost-

effectiveness. Smart

contracts enable partners to

track and monitor the status

of contracts and securely

share reliable data.

(Kara

mchan

dani et

al.

2020)

Customer

relationship

Information

quality

Technol

ogy ac-

ceptance

model /

innova-

tion dif-

fusion

theory

Empirical

Service

IV: perceived enterprise

blockchain (EBC) benefits,

IT integration, supply chain

integration intensity,

perceived usefulness (PU) of

EBC in customer

relationship, PU of EBC in

information quality, PU of

EBC in service quality, PU

of EBC in supply

uncertainty, PU of EBC in

mass customization, and PU

of EBC in delivery

reliability.

DV: perceived incremental

profitability.

The results indicated that

blockchain benefits

positively affect the

customer relationship and

information quality.

However, the customer

relationship is not

significant to the perceived

incremental profitability,

but information quality is

positively significant.

(Paul

et al.

2022)

Vendor-

managed

inventory

N/A

Mixed

method

Food

IV: RFID integrated

blockchain, transparency,

traceability, and achieving

circular supply chain.

DV: industry performance.

Integrating RFID and

blockchain impacts the

supply chain's transparency

and traceability, improving

the circular supply chain

and enhancing performance.

(Hew

et al.

2020)

Vendor-

managed

inventory

Institu-

tional

theory /

diffusio

n of

inno-

vation

theory

Empirical

Food

IV: halal orientation

strategy, institutional

pressures, and perceived

desirability.

DV: intention to participate.

Manufacturing food firms

would develop their

perceived desirability and

participate in a traceability

system to track halal food in

the supply chain.

Table B3: Key findings and variables of the Implementation SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Paul

et al.

2021)

Vendor-

managed

inventory /

Information

sharing

Resourc

e-based

view /

network

theory

Empirical

Agricultur

IV: resource allocation,

competitive advantage,

transparency, commodity

trust, quality of business

relationship, and reliability.

DV: sustainable

performance of organic tea

supply chain.

Blockchain technology is

significant and positively

associated with

transparency and reliability,

improving the sustainable

performance of the organic

tea supply chain.

(Garau

s and

Treibl

maier

2021)

Customer

relationship

Adaptiv

learning

theory

Experiments

Food

IV: traceability system, trust

in the retailer, retailer

familiarity, and blockchain

benefit disclosure.

DV: retailer choice.

Both retailers and

consumers can benefit from

adopting a blockchain-based

traceability system and

generate trust.

(Treibl

maier

and

Garaus

2023)

Customer

relationship

Signalin

g theory

Experiments

Food

IV: traceability label, brand

familiarity, and perceived

product quality.

DV: purchase intention.

The results indicated that

familiar products with a

certified blockchain label

did not affect product

quality, and there was no

effect on the purchase

intention. Blockchain seems

to be more efficient for

unfamiliar products in terms

of quality perception.

(Jain et

al.

2022)

Customer

relationship

Unified

theory

of ac-

ceptance

and use

of tech-

nology

Empirical

Apparel

IV: economic motives,

hedonic motives, risk of

contamination, fashion

motives, critical motives,

attitude, performance

expectancy, effort

expectancy, facilitating

conditions, and social

influence.

DV: behavioral intention.

The results highlighted that

consumers with a high

tendency to find fair deals

and reasonable prices of

used items are more likely

to adopt blockchain.

Table B4: Model or Prototype Studies of Blockchain Adoption for the Supply Chain

Management Practices

Table B4: Key findings and variables of the Model/prototype SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Bumb

lauskas

et al.

2020)

Vendor-

managed

inventory

N/A

Use case

Food

Use case criteria:

(considerations, feasibility,

operational compatibility,

performance, privacy,

assurance, relevance to

stakeholders)

Deploying blockchain for

the food supply chain

increases transparency and

allows food product

traceability. Food firms will

build better relationships

with their customers and

increase efficiency.

Eliminating fraud and

reducing the cost of food

recalls within the supply

chain.

(Ho et

al.

2021)

Vendor-

managed

inventory

N/A

Proposed

blockchain-

based system

/ deploy

sensitivity

analysis

Aviation

Response latencies

performance

Blockchain-based

traceability systems increase

efficiency and accuracy in

inventory management of

aircraft parts, allowing

tracking of spare parts and

faulty products in case of an

aircraft accident.

(Ali et

al.

2021)

Information

sharing

Induc-

tive

research

/ theory

building

Exploratory

case studies

Food

Complexity and capability,

cost and competitive

advantage, change

management and external

pressure, sustainable

production, regulatory

capability, and supply chain

integration regulations.

Participants in the food

supply chain can benefit

from blockchain by

allowing information

sharing and achieving a

certified and integrity of

food products.

(Wu

and

Zhang

2022b)

Information

sharing /

Information

quality

N/A

System

design

Mining

Trust management, digital

identity, data exchange

platform, data ownership,

data quality, data value, data

security, data exchange

process, logistic trust, and

capital flow trust.

The deployment of

blockchain supports the

processes of coal mining

equipment in the supply

chain, including production

management, logistics, and

post-sales services.

(Tönnis

sen and

Teuteb

erg

2020)

Information

sharing

Disinter-

mediatio

n theory

Case studies /

Explanatory

Logistics

N/A

Blockchain is a dynamic

technology for logistics, but

it is still unclear what

impact blockchain would

have on the logistics

business model.

(Ogunt

egbe et

al.

2022)

Supplier

partnership

Behav-

ioral

rea-

soning

theory /

technol-

ogy or-

ganiza-

tion and

environ-

ment

Thematic

analysis

Agricultur

IV: organizational adoption

strategies, technological

benefits, environmental

barriers, and blockchain

implementation intention.

DV: Implementation

behavior

Both behavioral and

organizational

characteristics influence the

intention to implement

blockchain for the supply

chain. Organizational

strategies play an essential

role in successfully using

blockchain technology.

Table B4: Key findings and variables of the Model/prototype SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Sunda

rakani

et al.

2021)

Customer

relationship

Information

sharing

Grounde

d on the

Resourc

e-based

view

Action

research /

case study

Logistics /

Petroleum

Competitive advantage,

business value, information

distortion, bullwhip effect,

supply chain performance,

and responsiveness

In the logistics industry,

there are many disconnected

services, including 16

different ERP systems,

which have caused delays

and challenges in overall

processes. It is

recommended for logistics

to extend the

implementation of

blockchain across the

supply chain so that benefits

can be obtained. Integrating

big data and blockchain in

the petroleum industry

provides customers with

real-time data about

transportation processes and

enhances supply chain

analytics.

(Kaste

n 2019)

Information

quality

N/A

Design

science

Food

N/A

The information on food

products in a blockchain-

based system cannot be

manipulated, which reflects

the products' quality and

trust among stakeholders.

(Ahma

d et al.

2021)

Vendor-

managed

inventory /

Information

sharing

N/A

Use cases

Logistics

Transparency, availability,

integrity, audit and data

provenance, authorization,

and privacy.

Blockchain plays a

significant role in

eliminating fraud related to

logistic documentation. This

reflects on transaction

efficiency and enhances

security, trust, and

transparency.

(Ramir

Lopez

et al.

2022)

Vendor-

managed

inventory

N/A

Detailed

analysis

Pharma-

ceutical

Transactions storage,

information sharing,

guaranteeing confidence

knowledge, transactions

verification, confidentially,

integrity, and process

performance.

Blockchain contributed

during the COVID-19

pandemic to the medicine

supply chain and secured

medicine by minimizing

counterfeiting. Through

decentralization,

organizations increase

process performance and

information security.

(Dwive

di et al.

2020)

Information

sharing

N/A

System

design

Pharma-

ceuticals

Computation and

communication cost, storage

overhead, smart contracts,

deploying cost, transaction

cost, execution cost, data

manipulation, and reverting

the state of the sector.

The shared information in

the supply chain is secured

against any possible threats

using blockchain and IoT.

(Khan

et al.

2022)

Vendor-

managed

inventory

N/A

Interview

Agricultur

Traceability, transparency,

direct transactions, efficient

inventory management.

Blockchain-based

technology proves its

capability to manage

agricultural food during the

COVID-19 pandemic by

tracking products in the

supply chain.

Table B4: Key findings and variables of the Model/prototype SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Cento

belli et

al.

2022)

Vendor-

managed

inventory /

Information

sharing

N/A

Triangulation

Circular

Trust, traceability, and

transparency.

Blockchain promotes

trustworthiness among

supply chain partners by

allowing information

sharing of products across

the circular supply chain.

(Alams

yah et

al.

2023)

Vendor-

managed

inventory /

Information

sharing

N/A

System

design/case

study

Agricultur

Immutability, transparency,

user access control,

interoperability, scalability,

cost-effectiveness, and user

adoption.

The research developed a

traceable system comprising

six groups in the coffee

supply chain: farmers,

processors, manufacturers,

government agencies,

markets, and consumers.

The prototype was

demonstrated for

evaluation, and it is under

improvement.

(Abdal

lah and

Nizam

uddin

2023)

Customer

relationship

N/A

Design

science

Pharma-

ceuticals

Traceability, sale

transactions and payment

security.

The study demonstrated a

blockchain-based system

that can solve problems in

the pharmaceutical industry,

such as traceability and

trust.

(Omar

et al.

2022)

Vendor-

managed

inventory

N/A

System

design

Personal

healthcare

Cost analysis, security

analysis, confidentiality,

integrity, availability, non-

repudiation, and attacks.

The solution can capture

issues in the supply chain

during COVID-19, such as

supply and production

shortages.

(Chang

et al.

2021)

Purchasing

alliances

N/A

Newsvendor

approach

Multi-

industries

Blockchain adoption level,

order quantity, selling price,

salvage value, lost-sales

penalty, ordering cost,

critical ratio, random

demand, expected demand,

cumulative demand

distribution, newsvendor

profit, expected profit, and

optimal expected profit.

The benefits of blockchain

adoption to the supply chain

are reducing procurement

and ordering costs and

decreasing the uncertainty

level of demand.

(Nodeh

i et al.

2022)

Purchasing

alliances

N/A

System

design

e-procure-

ment

Transparency, security,

compliance costs, audit

control, integration,

automation, centralization,

and transaction settlement.

The enterprise blockchain

provides security for the

procurement process

through automation and

traceability.

(J. Xu

et al.

2023)

Vendor-

managed

inventory /

Information

sharing

N/A

Design

science

Construc-

tion

N/A

Integrating blockchain and

IoT enables traceability of

raw materials and allows its

status to be shared with

supply chain partners.

(Georg

e et al.

2019)

Vendor-

managed

inventory /

Information

sharing

N/A

Model

development

Food

IV: shelf life

DV: weightage

The result indicates that

storage conditions such as

room temperature are the

most critical factors for food

safety and quality, which

blockchain can monitor.

Table B4: Key findings and variables of the Model/prototype SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Utz et

al.

2023)

Customer

relationship

Institu-

tion-

based

trust /

in-

stitution

-based

distrust /

ambiva-

lence

Design

science

Energy

Accountability,

customizability, simplicity,

efficiency, maintainability,

and affordability.

The study designed a

blockchain-based customer

loyalty program to reduce

ambivalence and resolve the

conflicts between trust and

distrust of the latent

constructs.

(Diniz

et al.

2021)

Vendor-

managed

inventory /

Information

sharing

N/A

Design

science

Energy

Blockchain adoption,

greenhouse gas protocol,

inventory process, carbon

emissions, and sustainable

supply chain management.

Employing blockchain can

improve the production of

greenhouse gas emissions

by enabling transparency

and preventing miscounting

of these emissions.

(Mattk

e et al.

2019)

Vendor-

managed

inventory /

Information

sharing

N/A

System

design /

qualitative

Pharma-

ceuticals

Counterfeit, privacy,

verification, and value of

transaction.

The Medi-Ledger project is

a traceability system that

involves blockchain

technology in identifying

counterfeit products in the

pharmaceutical supply

chain. The project was

developed in response to the

Drug Supply Chain Security

Act (DSCSA).

(Chou

et al.

2023)

Information

sharing

N/A

System

design

Electronic

Information sharing, data

privacy, and dynamic role.

The framework enables

supply chain members to

share information and

collaborate with business

partners and supports

supply chain transparency.

(Yadav

and

Prakas

Singh

2022)

Purchasing

alliances

N/A

System

design

Multi-

industries

Procurement size, product

volume, truck volume,

quantity, demand, inventory,

and planned inventory.

The study highlights the

importance of minimizing

procurement costs by

allocating the best suppliers.

A firm's efficiency and

profitability rely on high-

quality procurement

processes.

(Raj et

al.

2022)

Purchasing

alliances /

Information

sharing

N/A

System

design

Logistics

Blockchain adoption, smart

contract, construct, purchase

confirmation, third-party

logistics transportation,

delivered item, quantity

received, payment for

transportation, credit

payment, and transaction

cost.

Smart contracts enable a

decentralization of

transactions and eliminate

intermediaries. The smart

contract can make payments

securely, enhancing supply

chain performance and trust.

(Thaku

r and

Breslin

2020)

Vendor-

managed

inventory

N/A

System

design

Multi-

industries

product serialization,

blockchain usage,

scalability, traceability,

security and transparency,

sales, and product recall.

The paper designed a

blockchain-based system to

serialize products in the

supply chain and prevent

theft and misuse of

products' serial numbers.

The system provides

security to products'

serialization and enables

product recall when

necessary.

Table B4: Key findings and variables of the Model/prototype SCMPs studies

Study

SCMPs

Theory

Methodology

Industry

Variables of Interests

Key Findings

(Chang

et al.

2019)

Information

sharing

N/A

System

design

Multi-

industries

Market value, traceability,

data storage privacy, cost

reduction, cash liquidity,

payment, and degree of

automation.

The impact of blockchain-

based and smart contracts

are embedded transaction,

payment, and data-

accessing processes that

promote transparency and

collaboration in supply

chain management.

(Venka

tesh et

al.

2020)

Vendor-

managed

inventory /

Information

sharing

N/A

System

design

Multi-

industries

Social sustainability, supply

chain sustainability,

blockchain, IoT, big data

analytics, and traceability.

Integrating blockchain, IoT,

and big data analytics can

empower supply chain

social sustainability by

improving traceability and

facilitating information

sharing among stakeholders.

(Sheld

2022)

Vendor-

managed

inventory

N/A

Design

science

Multi-

industries

Blockchain, smart contracts,

non-fungible tokens,

ownership, provenance, and

tangible assets.

Tracking tangible assets and

provenance with blockchain

can be used as guidance to

evaluate the reliability of

the ownership and

provenance.

(Hu et

al.

2023)

Vendor-

managed

inventory

N/A

Decision

analysis

Pharma-

ceuticals

Transparency, trust, real-

time monitoring, vaccine

quality, demand, and

sentiment analysis of

vaccine reviews.

An integration system of

blockchain, IoT, and

machine learning can

collect accurate vaccine

data in the supply chain by

tracking, monitoring, and

predicting demand

forecasts.

Appendix C

First Study: Number of Studies for Each Supply Chain Management Practice

and Its Classifications

Table C1 shows the total number of studies associated with blockchain adoption for each

SCMP and its classifications. In the literature review of this study, forty-four studies examined

more than one practice within the same study, and twenty-four studies of literature discussed the

conditions of blockchain adoption.

Table C1: Number of studies for each SCMP and classifications

SCMPs

# of

studies in

the review

# of

conceptual

studies

# of

combined

studies

# of

Implemen-

tation

studies

# of

Model/

prototype

studies

Joint Product Development

Purchasing Alliances

Vendor-managed Inventory

Strategic Supplier

Partnership

Customer Relationship

Information Sharing

Information Quality

Postponement

Total studies of each

classification

Total studies investigated

more than one practice

Total studies discussed the

conditions of blockchain

adoption

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