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Applying innovation diffusion theory to blockchain adoption in Indian private sector banks

Pooja Jain, Bhuvanesh Kumar Sharma, Ritesh Khatwani, Pradip Kumar Mitra, Ananya Mistry

Article ID: 2983
Vol 9, Issue 9, 2024, Article identifier:

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Abstract

The adoption of blockchain technology in the banking sector has gained significant attention due to its potential to improve operational efficiency, transparency, and security.  The study aims to investigate how the relative advantage, compatibility, complexity, observability, and trialability of blockchain, as proposed by Rogers' diffusion of innovation theory, impact the adoption behavior of private sector banks in India.  The study employed a quantitative approach, using a quantitative survey to collect data from 250 employees in the banking industry.  The collected data was analyzed using the PLS-SEM technique with the help of SmartPLS software. According to the findings of the study, relative advantage, compatibility, and trialability have a major impact on blockchain behavioral intention, which further impacts actual usage behavior.  Blockchain behavioral intention partially mediates between relative advantage, compatibility, trialability and usage behavior. The study also discovered that complexity and observability do not influence blockchain adoption among private banks in India. As per the study, private sector banks in India should focus on promoting the relative advantages, compatibility, and trialability of blockchain technology to enhance adoption. Hence, efforts should prioritize demonstrating blockchain’s benefits and ease of integration while offering trial opportunities, as complexity and observability do not significantly impact adoption decisions.


Keywords

Blockchain adoption; Innovation diffusion theory; Usage behavior; Adoption Behaviour; Banking Industry

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References

1. Wang YS, Wu SC, Lin HH, Wang YM, He TR. Determinants of user adoption of web ’ “Automatic Teller Machines”: an integrated model of “Transaction Cost Theory” and “Innovation Diffusion Theory.” Serv Ind J [Internet] 2012 [cited 2024 Sep 7];32(9):1505–25. Available from: https://www.tandfonline.com/doi/abs/10.1080/02642069.2010.531271

2. Kawasmi Z, Gyasi EA, Dadd D. Blockchain Adoption Model for the Global Banking Industry. J Int Technol Inf Manag [Internet] 2020 [cited 2024 Sep 7];28(4):112–54. Available from: https://scholarworks.lib.csusb.edu/jitim/vol28/iss4/5

3. Jain P, Agarwal ABV-IIITM G, Pradesh India M. Factors Affecting Mobile Banking Adoption: An Empirical Study in Gwalior Region. Int J Digit Account Res 2019;19:79–101.

4. Everett M. Rogers. Diffusion of innovations. – Digital Journal [Internet]. 5th ed. 2013 [cited 2019 Feb 11]. Available from: https://kinasevych.ca/2010/03/15/rogers-2003-diffusion-of-innovations/

5. Tornatzky LG, Klein KJ. INNOVATION CHARACTERISTICS AND INNOVATION ADOPTION-IMPLEMENTATION: A META-ANALYSIS OF FINDINGS. IEEE Trans Eng Manag 1982;EM-29(1):28–45.

6. Moore GC, Benbasat I. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Inf Syst Res 1991;2(3):192–222.

7. Xu J, Gan Z, Cheng Y, Liu J. Discourse-Aware Neural Extractive Text Summarization. Proc Annu Meet Assoc Comput Linguist [Internet] 2020 [cited 2024 Sep 8];5021–31. Available from: https://aclanthology.org/2020.acl-main.451

8. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q Manag Inf Syst 2003;27(3):425–78.

9. Wang T, Jin H, Fan Y, Obembe O, Li D. Farmers’ adoption and perceived benefits of diversified crop rotations in the margins of U.S. Corn Belt. J Environ Manage 2021;293:112903.

10. Schuetz S, Venkatesh V. Blockchain, adoption, and financial inclusion in India: Research opportunities. undefined 2020;52.

11. Martino P. Blockchain and Banking: How Technological Innovations Are Shaping the Banking Industry. Blockchain Bank How Technol Innov Are Shap Bank Ind 2021;1–109.

12. Saheb T, Mamaghani FH. Exploring the barriers and organizational values of blockchain adoption in the banking industry. J High Technol Manag Res 2021;32(2):100417.

13. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q [Internet] 1989 [cited 2018 Sep 30];13(3):319. Available from: https://www.jstor.org/stable/249008?origin=crossref

14. Venkatesh V, Bala H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis Sci [Internet] 2008 [cited 2024 Sep 7];39(2):273–315. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5915.2008.00192.x

15. Queiroz MM, Fosso Wamba S. Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. Int J Inf Manage 2019;46:70–82.

16. Hameed MA, Arachchilage NAG. The role of self-efficacy on the adoption of information systems security innovations: a meta-analysis assessment. Pers Ubiquitous Comput [Internet] 2021 [cited 2024 Sep 7];25(5):911–25. Available from: https://link.springer.com/article/10.1007/s00779-021-01560-1

17. Khatri T. UNDERSTANDING ADOPTION FACTORS OF SUBSCRIPTION BASED ENTERTAINMENT SERVICES AMONG CONSUMERS IN INDIA. Inf Technol Ind [Internet] 2021 [cited 2024 Sep 7];9(1):795–809. Available from: http://www.it-in-industry.org/index.php/itii/article/view/200

18. Khatri A, Kaushik A. SYSTEMATIC LITERATURE REVIEW ON BLOCKCHAIN ADOPTION IN BANKING. J Econ Financ Accounting-JEFA [Internet] 2021 [cited 2024 Sep 7];8(3):126–45. Available from: http://doi.org/10.17261/Pressacademia.2021.1458

19. Rogers EM. Diffusion of Innovations, 5th Edition (Google eBook). 2003 [cited 2021 Aug 1];576. Available from: https://books.google.com/books/about/Diffusion_of_Innovations_5th_Edition.html?id=9U1K5LjUOwEC

20. Kapoor KK, Dwivedi YK, Williams MD. Innovation adoption attributes: A review and synthesis of research findings. Eur J Innov Manag 2014;17(3):327–48.

21. Chou CM, Shen TC, Shen TC, Shen CH. Teachers’ adoption of AI-supported teaching behavior and its influencing factors: using structural equation modeling. J Comput Educ [Internet] 2024 [cited 2024 Sep 8];1–44. Available from: https://link.springer.com/article/10.1007/s40692-024-00332-z

22. Premkumar G, Roberts M. Adoption of new information technologies in rural small businesses. Omega 1999;27(4):467–84.

23. Fosso Wamba S, Queiroz MM, Trinchera L. Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. Int J Prod Econ 2020;229:107791.

24. Schuetz S, Venkatesh V. Blockchain, adoption, and financial inclusion in India: Research opportunities. Int J Inf Manage 2020;52:101936.

25. Karahanna E, Straub DW, Chervany NL. Information technology adoption across time. MIS Q [Internet] 1999 [cited 2024 Sep 7];23(2):183–213. Available from: https://dl.acm.org/doi/10.2307/249751

26. Choi D, Chung CY, Seyha T, Young J. Factors Affecting Organizations’ Resistance to the Adoption of Blockchain Technology in Supply Networks. Sustain 2020, Vol 12, Page 8882 [Internet] 2020 [cited 2024 Sep 7];12(21):8882. Available from: https://www.mdpi.com/2071-1050/12/21/8882/htm

27. Khalil MM, Ahmed W. Analyzing the drivers of blockchain adoption for supply chain in Pakistan. J Sci Technol Policy Manag 2024;ahead-of-print(ahead-of-print).

28. Tan M, Teo TSH. Factors Influencing the Adoption of Internet Banking. J Assoc Inf Syst 2000;1.

29. Agarwal R, Prasad J. The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies. Decis Sci [Internet] 1997 [cited 2024 Sep 7];28(3):557–82. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5915.1997.tb01322.x

30. Pappas I, Kourouthanassis PE, Mikalef P, Giannakos M. Combining system success factors with trust to explain e-government adoption using fsQCA. Am Conf Inf Syst 2018;

31. Morkunas VJ, Paschen J, Boon E. How blockchain technologies impact your business model. Bus Horiz 2019;62(3):295–306.

32. Hair JF, Sarstedt M, Ringle CM, Gudergan SP. Advanced Issues in Partial Least Squares Structural Equation Modeling [Internet]. Sage Publication Inc.; 2018 [cited 2022 May 26]. Available from: https://books.google.co.in/books?hl=en&lr=&id=-f1rDgAAQBAJ&oi=fnd&pg=PP1&ots=vY00klG1a_&sig=5HGkM7WH4jsNUth__LxKc1FZS50&redir_esc=y#v=onepage&q&f=false

33. Tabachnick BG, Fidell LS. Using Multivariate Statistics Title: Using multivariate statistics. 2019 [cited 2024 Sep 8];Available from: https://lccn.loc.gov/2017040173

34. Wong LW, Leong LY, Hew JJ, Tan GWH, Ooi KB. Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. Int J Inf Manage 2020;52:101997.

35. Jena RK. Examining the Factors Affecting the Adoption of Blockchain Technology in the Banking Sector: An Extended UTAUT Model. Int J Financ Stud [Internet] 2022 [cited 2024 Sep 8];10(4):90. Available from: https://www.mdpi.com/2227-7072/10/4/90/htm

36. Ullah N, Al-Rahmi WM, Alfarraj O, Alalwan N, Alzahrani AI, Ramayah T, et al. Hybridizing cost saving with trust for blockchain technology adoption by financial institutions. Telemat Informatics Reports 2022;6:100008.

37. Marikyan D, Papagiannidis S, Rana OF, Ranjan R. Blockchain adoption: A study of cognitive factors underpinning decision making. Comput Human Behav 2022;131.

38. Kumari A, Devi NC. Determinants of user’s behavioural intention to use blockchain technology in the digital banking services. Int J Electron Financ 2022;11(2):159–74.


DOI: https://doi.org/10.59429/esp.v9i9.2983
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