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Enablers to the adoption of blockchain technology in logistics supply chains: evidence from an emerging economy

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Abstract

This study identifies the factors that can enable blockchain technology (BCT) implementation in the logistics sector and reveals the interdependencies, hierarchical structure, and causalities between them. This study uses a three-phased research framework to identify thirteen enablers to BCT adoption in the Logistics Supply Chain (LSC) and analyse the hierarchical and cause-effect relationships using an integrated Interpretive Structural Modelling and Decision-Making Trial and Evaluation Laboratory (ISM-DEMATEL) technique. This study classifies the enablers into three categories: prominent enablers, influencing enablers, and resulting enablers. ‘Real-time connectivity and information flow’ were identified as the most influencing enabler, whereas traceability was found to be the most prominent and resulting enabler. This study contributes to the literature by identifying the critical BCT enablers and modelling their hierarchical and causal relationships to address the absence of an appropriate decision-making framework that aids managers in understanding why and how BCT can be adopted in LSC, especially in an emerging economy like India. Strategic recommendations on how BCT can help overcome some select challenges faced by LSC organizations are also presented.

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Authors and Affiliations

  1. Operations Management and Decision Sciences, Jindal Global Business School, O.P. Jindal Global University, 131001, Sonipat, Haryana, India

    Taab Ahmad Samad

  2. Operations Management, Jaipuria Institute of Management, 201303, Noida, Uttar Pradesh, India

    Rohit Sharma

  3. Operations Management and Decision Sciences, Indian Institute of Management Kashipur, 244713, Uttrakhand, India

    Kunal K Ganguly

  4. Information Systems and Data Science, Department of Information, Operations, and Management Sciences, TBS Education, 1 Place Alphonse Jourdain, Toulouse, France, 31068

    Samuel Fosso Wamba

  5. Digital Transformation, Information System, Keele Business School, Keele University, Keele, UK

    Geetika Jain

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Appendices

Appendices

1.1 Appendix A: Details of respondents’ profiles

This section contains the relevant details of the respondents who participated as experts in the present study. The information about their area of expertise, work experience, and educational qualifications are as follows:

1.1.1 Educational Qualification of the Respondents

The educational qualification of the respondents was a crucial aspect of the accomplishment of the objectives of this study. The respondents’ profile breakup is shown in Fig. 5.

Fig. 5
figure 5

The breakup of respondents based on their educational qualification

Full size image

1.1.2 Work Experience

The work experience of the experts can be an indication of their level of expertise on the subject. Figure 6 presents information about the work experience of the panel of experts chosen for this study. It can be inferred from Fig. 6 that nearly 67% of the respondents have 10+ years of rich experience in the supply chain domain.

Fig. 6
figure 6

Work experience of the respondents chosen for the study

Full size image

1.1.3 Background of Respondents

The background of the respondents is essential for accessing the correct information on supply chain performance. Figure 7, which presents the distribution of respondents based on their backgrounds, indicates that the expert panel comprises respondents from relevant and diverse backgrounds that shall result in the comprehensiveness of the analysis.

Fig. 7
figure 7

Background of the experts chosen for this study

Full size image

1.2 Appendix B: Questionnaire for data collection

The experts were initially subjected to discussions guided by the following indicative checklist.

  1. 1.

    What is your take on the impact of modern ICTs in LSCs?

  2. 2.

    Do you perceive that BCT can play a significant role in the transformation of LSCs towards sustainability? If yes, how?

  3. 3.

    Potential issues and challenges that may impede the uptake of ICTs in the LSCs?

→ Along with qualitative opinions on the potential of BCT adoption in LSC, the initial list of enablers identified from academic literature was vetted by experts to retrieve the list of finalized factors modelled in this study.

After that, following the methodological steps presented in Sect. 3, expert opinions were sought on individual matrices for applying the integrated ISM-DEMATEL technique.

E

E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E1

             

E2

             

E3

             

E4

             

E5

             

E6

             

E7

             

E8

             

E9

             

E10

             

E11

             

E12

             

E13

             

1.3 Appendix C: Detailed iterations of ISM partition levels

Iteration

Enabler

Reachability Set

Antecedent Set

Intersection Set

Level

1

E1

1,2,3,4,5,6,7,8,9,10,11,12,13

1,2,12

1,2,12

 
 

E2

1,2,3,4,5,6,7,8,9,10,11,12,13

1,2,12

1,2,12

 
 

E3

3,4,5,6,7,9,10,11,13

1,2,3,8,9,10,12

3,9,10

 
 

E4

4,6,11,13

1,2,3,4,5,6,7,8,9,10,11,12

4.6.11

 
 

E5

4,5,6,7,11,13

1,2,3,5,8,9,10,12

5

 
 

E6

4,6,11,13

1,2,3,4,5,6,7,8,9,10,11,12

4,6,11

 
 

E7

4,6,7,11,13

1,2,3,5,7,8,9,10,12

7

 
 

E8

3,4,5,6,7,8,9,10,11,13

1,2,8,12

8

 
 

E9

3,4,5,6,7,9,10,11,13

1,2,3,8,9,10,12

3,9,10

 
 

E10

3,4,5,6,7,9,10,11,13

1,2,3,8,9,10,12

3,9,10

 
 

E11

4,6,11,13

1,2,3,4,5,6,7,8,9,10,11,12

4,6,11

 
 

E12

1,2,3,4,5,6,7,8,9,10,11,12,13

1,2,12

1,2,12

 
 

E13

13

1,2,3,4,5,6,7,8,9,10,11,12,13

13

I

2

E1

1,2,3,4,5,6,7,8,9,10,11,12

1,2,12

1,2,12

 
 

E2

1,2,3,4,5,6,7,8,9,10,11,12

1,2,12

1,2,12

 
 

E3

3,4,5,6,7,9,10,11

1,2,3,8,9,10,12

3,9,10

 
 

E4

4,6,11

1,2,3,4,5,6,7,8,9,10,11,12

4.6.11

II

 

E5

4,5,6,7,11

1,2,3,5,8,9,10,12

5

 
 

E6

4,6,11

1,2,3,4,5,6,7,8,9,10,11,12

4,6,11

II

 

E7

4,6,7,11

1,2,3,5,7,8,9,10,12

7

 
 

E8

3,4,5,6,7,8,9,10,11

1,2,8,12

8

 
 

E9

3,4,5,6,7,9,10,11

1,2,3,8,9,10,12

3,9,10

 
 

E10

3,4,5,6,7,9,10,11

1,2,3,8,9,10,12

3,9,10

 
 

E11

4,6,11

1,2,3,4,5,6,7,8,9,10,11,12

4,6,11

II

 

E12

1,2,3,4,5,6,7,8,9,10,11,12

1,2,12

1,2,12

 

3

E1

1,2,3,5,7,8,9,10,12

1,2,12

1,2,12

 
 

E2

1,2,3,5,7,8,9,10,12

1,2,12

1,2,12

 
 

E3

3,5,7,9,10

1,2,3,8,9,10,12

3,9,10

 
 

E5

5,7

1,2,3,5,8,9,10,12

5,7

III

 

E7

7

1,2,3,5,7,8,9,10,12

7

III

 

E8

3,5,7,8,9,10,

1,2,8,12

8

 
 

E9

3,5,7,9,10

1,2,3,8,9,10,12

3,9,10

 
 

E10

3,5,7,9,10

1,2,3,8,9,10,12

3,9,10

 
 

E12

1,2,3,5,7,8,9,10,12

1,2,12

1,2,12

 

4

E1

1,2,3,8,9,10,12

1,2,12

1,2,12

 
 

E2

1,2,3,8,9,10,12

1,2,12

1,2,12

 
 

E3

3,9,10

1,2,3,8,9,10,12

3,9,10

IV

 

E8

3,8,9,10,

1,2,8,12

8

 
 

E9

3,9,10

1,2,3,8,9,10,12

3,9,10

IV

 

E10

3,9,10

1,2,3,8,9,10,12

3,9,10

IV

 

E12

1,2,3,8,9,10,12

1,2,12

1,2,12

 

5

E1

1,2,8,12

1,2,12

1,2,12

 
 

E2

1,2,8,12

1,2,12

1,2,12

 
 

E8

8

1,2,8,12

8

V

 

E12

1,2,8,12

1,2,12

1,2,12

 

6

E1

1,2,12

1,2,12

1,2,12

VI

 

E2

1,2,12

1,2,12

1,2,12

VI

 

E12

1,2,12

1,2,12

1,2,12

VI

  1. Note- E1: Decentralized Database System; E2: Distributed Database System; E3: Cybersecurity; E4: Immutability; E5: Transparency; E6: Verifiability of transactions; E7: Enhanced Risk Mitigation; E8: Smart Contracts; E9: Lower Transaction Settlement Time; E10: Lower Transactional Costs; E11: Provenance; E12: Real-time connectivity and information flow; E13: Traceability. (Source: Prepared by authors)

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Samad, T.A., Sharma, R., Ganguly, K.K. et al. Enablers to the adoption of blockchain technology in logistics supply chains: evidence from an emerging economy. Ann Oper Res 327, 251–291 (2023). https://doi.org/10.1007/s10479-022-04546-1

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