Blockchain vs. generative artificial intelligence in India: A comparative study of adoption drivers, barriers, and diffusion trajectories
Blockchain and generative artificial intelligence (GenAI) are two contemporary emerging technologies that have exhibited different adoption trajectories since their inception. Blockchain technology traces its origins to 2008, when it was first conceptualized, whereas GenAI is a more recent development that entered the mainstream with the introduction of ChatGPT by OpenAI. India, as a developing economy, has consistently been at the forefront of technological innovations; however, the adoption patterns for these innovations have been notably different. Using secondary data retrieved from peer-reviewed research and systematic reviews, along with industry and market intelligence reports, this research revealed that blockchain, as a technology, adopts a bottom-up approach driven by financial inclusion imperatives and is inherently decentralized by design. GenAI, on the other hand, adopts a top-down approach, fueled by enterprise-driven adoption and rapid scaling across various sectors. Our findings suggest that the difference in their diffusion approaches is attributed to the persistent regulatory uncertainty and infrastructure constraints faced by blockchain, whereas GenAI has benefited from clearer policy support and lower entry barriers. This paper provides a frameworkbased, side-by-side comparison of two high-impact technologies in a single national context, linking micro-level adoption mechanisms to macro-level diffusion outcomes. These nuances could have significant implications for policymaking and recalibrating India’s position in the global landscape.
Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179.
Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. The Economic Journal, 99(394), 116-131.
Beck, R., Stenum Czepluch, J., Lollike, N., & Malone, S. (2016). Blockchain—The gateway to trust-free cryptographic transactions. In: Proceedings of the Twenty-Fourth European Conference on Information Systems (ECIS 2016) (Research Paper 153). June 12-15, 2016; Istanbul, Turkey. Association for Information Systems. Available from: https://aisel.aisnet.org/ecis2016_rp/153 [Last accessed on 2025 Nov 1].
Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: Engines of growth? Journal of Econometrics, 65(1), 83-108.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). Cambridge, MA, USA: National Bureau of Economic Research. https://doi.org/10.3386/w31161
Budhwar, P., Chowdhury, S., Wood, G., et al. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524
Cao, Y., Li, S., Liu, Y., et al. (2024). A survey of AI-generated content (AIGC). ACM Computing Surveys, 57(5), 1–38. https://doi.org/10.1145/3704262
Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55-81.
Chainalysis Team. (2024). The 2024 global adoption index: Central & Southern Asia and Oceania (CSAO) region leads the world in terms of global cryptocurrency adoption. Chainalysis. Available from: https://www.chainalysis.com/blog/2024-global-crypto-adoption-index/ [Last accessed on 2025 Nov 1].
Clohessy, T., Acton, T., & Rogers, N. (2020). Antecedents of blockchain adoption: An integrative framework. Strategic Change, 29(5), 501-515.
Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation, 2(6-10), 71.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
Epstein, Z., Hertzmann, A., Herman, L., et al. (2023). Art and the science of generative AI: A deeper dive. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
Eyal, I., Gencer, A. E., Sirer, E. G., & van Renesse, R. (2016). Bitcoin-NG: A scalable blockchain protocol. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI ‘16). March 18, 2016; Santa Clara, CA, USA. USENIX Association. pp. 45–59. Available from: https://www.usenix.org/conference/nsdi16/technical-sessions/presentation/eyal [Last accessed on 2025 Nov 1].
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304.
Ghode D, Yadav V, Jain R, Soni G. (2020). Adoption of blockchain in supply chain: An analysis of influencing factors. Journal of Enterprise Information Management, 33(3), 437–456. https://doi.org/10.1108/JEIM-07-2019-0186
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Haase, J., & Hanel, P. H. (2023). Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity. Journal of Creativity, 33(3), 100066.
Hacker, P., Engel, A., & Mauer, M. (2023). Regulating ChatGPT and other large generative AI models. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘23). June 12-15, 2023; Chicago, IL, USA. Association for Computing Machinery. pp. 1112–1123. https://doi.org/10.1145/3593013.3594067
Imarc Group. (2024a). India blockchain market size, share, trends and forecast by component, provider, type, deployment mode, organization size, vertical, and region, 2025–2033. Available from https://www.imarcgroup.com/india-blockchainmarket [Last accessed on 2025 Dec 28].
Imarc Group. (2024b). India fintech blockchain market size, share, trends and forecast by industry, application, end user, and region, 2025–2033. Available from https://www.imarcgroup.com/india-fintech-blockchain-market [Last accessed on 2025 Dec 28].
Imarc Group. (2024c). India generative AI market size, share, trends and forecast by component, technology, application, model, customers, end use, and region, 2025–2033. Available from https://www.imarcgroup.com/india-generative-aimarket [Last accessed on 2025 Dec 28].
Kiviat, T. I. (2015). Beyond bitcoin: Issues in regulating blockchain transactions. Duke Law Journal, 65, 569-608.
Korzynski, P., Mazurek, G., Altmann, A., et al. (2023). Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT. Central European Management Journal, 31(1), 3-13.
Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
McGee, R. W. (2023). Is ChatGPT biased against conservatives? An empirical study (SSRN Scholarly Paper No. 4359405). Amsterdam: SSRN. https://doi.org/10.2139/ssrn.4359405
Mthimkhulu, A., & Jokonya, O. (2022). Exploring the factors affecting the adoption of blockchain technology in the supply chain and logistic industry. Journal of Transport and Supply Chain Management, 16, 750. https://doi.org/10.4102/jtscm.v16i0.750
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192.
Pilkington, M. (2016). Blockchain technology: Principles and applications. In Olleros, F. X. & Zhegu, M. (Eds.) Research Handbook on Digital Transformations. Cheltenham: Edward Elgar Publishing. pp. 225-253.
Ponce Del Castillo, A. (2024). Generative AI, generating precariousness for workers? AI & Society, 39(5), 2601–2602. https://doi.org/10.1007/s00146-023-01719-9
Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82.
Rogers, E. M. (1987). Diffusion of innovations: An overview. In Anderson, J. G. & Jay, S. J. (Eds.) Use and impact of computers in clinical medicine. Luxembourg: Springer. pp. 113–131. https://doi.org/10.1007/978-1-4613-8674-2_9
Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366.
Taherdoost, H. (2022). A critical review of blockchain acceptance models—Blockchain technology adoption frameworks and applications. Computers, 11(2), 24.
Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of lex cryptographia. Amsterdam: SSRN. https://doi.org/10.2139/ssrn.2580664
Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375.
