Institutional Theory (IT) and Diffusion of Innovation (DOI): A Theoretical Approach on Artificial Intelligence (AI)
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Abstract
Objective: this theoretical essay explores the adoption of artificial intelligence (AI) in organizations through the integrated lens of institutional theory (IT) and diffusion of innovation (DOI) theory. IT elucidates how coercive, normative, and mimetic pressures drive organizational conformity, while DOI categorizes adopters into innovators, followers, and traditionalists, emphasizing perceived innovation attributes. Methods: by synthesizing these frameworks, the study provides a comprehensive understanding of how institutional forces and adopter profiles collectively shape AI. Results: key findings reveal that AI adoption is influenced by regulatory compliance, industry benchmarks, and competitive imitation, with varying adoption rates depending on organizational readiness and sectoral demands. The study identifies gaps in current research, particularly the lack of integration between macro-level institutional pressures and micro-level adoption behaviors. Conclusions: it proposes a future research agenda to examine sector-specific barriers, ethical implications, temporal dynamics, and the role of digital infrastructure in AI institutionalization. Contributions include a novel theoretical framework that bridges structural and behavioral perspectives, offering actionable insights for policymakers and managers navigating AI adoption.
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Reis, J. F., & Pinheiro Junior, L. P. (2025). Institutional Theory (IT) and Diffusion of Innovation (DOI): A Theoretical Approach on Artificial Intelligence (AI). Brazilian Administration Review, 22(4), e250060. https://doi.org/10.1590/1807-7692bar2025250060
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References
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Dacin, M., Goodstein, J., & Scott, W. (2002). Institutional theory and institutional change: Introduction to the special research forum. Academy of Management Journal, 45(1), 45-56. https://doi.org/10.2307/3069284
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
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Gopalakrishnan, S., & Kovoor-Misra, S. (2021). Understanding the impact of the Covid-19 pandemic through the lens of innovation. BRQ Business Research Quarterly, 24(3), 224-232. https://doi.org/10.1177/23409444211013357
Hartley, J. L., Sawaya, W., & Dobrzykowski, D. (2022). Exploring blockchain adoption intentions in the supply chain: Perspectives from innovation diffusion and institutional theory. International Journal of Physical Distribution e Logistics Management, 52(2), 190-211. https://doi.org/10.1108/IJPDLM-05-2020-0163
Ioakeimidou, D., Chatzoudes, D., Symeonidis, S., & Chatzoglou, P. (2023). HRA adoption via organizational analytics maturity: Examining the role of institutional theory, resource-based view and diffusion of innovation. International Journal of Manpower, 45(5), 958-983. https://doi.org/10.1108/IJM-10-2022-0496
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022). Will algorithms blind people? The effect of explainable AI and decision-makers’ experience on AI-supported decision-making in government. Social Science Computer Review, 40(2), 478-493. https://doi.org/10.1177/0894439320980118
Janssen, M., Matheus, R., Longo, J., & Weerakkody, V. (2017). Transparency-by-design as a foundation for open government. Transforming Government: People, Process and Policy, 11(1), 2-8. https://doi.org/10.1108/TG-02-2017-0015
Jie, W., & Sia, C. L. (2011). The process of RFID assimilation by supply chain participants in China: A technology diffusion perspective on RFID technology. AMCIS 2011 Proceedings, 178. https://aisel.aisnet.org/amcis2011_submissions/178
Lamey, L., Breugelmans, E., Vuegen, M., & ter Braak, A. (2021). Stock market performance of service innovations in retail. Journal of the Academy of Marketing Science, 49(4), 499-518.
Larsen, B. (2021). A framework for understanding AI-induced field change: How AI technologies are legitimized and institutionalized. AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462591
Limongi, R. (2024). The use of artificial intelligence in scientific research with integrity and ethics. Future Studies Research Journal: Trends and Strategies, 16(1), e845. https://doi.org/10.24023/FutureJournal/2175-5825/2024.v16i1.845
Limongi, R., & Marcolin, C. B. (2024). AI literacy research: Frontier for high-impact research and ethics. Brazilian Administration Review, 21(3), e240162. https://doi.org/10.1590/1807-7692bar2024240162
Lund, B., Omame, I., Tijani, S., & Agbaji, D. (2020). Perceptions toward artificial intelligence among academic library employees and alignment with the diffusion of innovations’ adopter categories. College e Research Libraries, 81(5). https://doi.org/10.5860/crl.81.5.865
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
Matheus, R., & Janssen, M. (2020). A systematic literature study to unravel transparency enabled by open government data: The window theory. Public Performance & Management Review, 43(3), 503-534. https://doi.org/10.1080/15309576.2019.1691025
Matheus, R., Faber, R., Ismagilova, E., & Janssen, M. (2023). Digital transparency and the usefulness for open government. International Journal of Information Management, 73, 102690. https://doi.org/10.1016/j.ijinfomgt.2023.102690
Matheus, R., Janssen, M., & Janowski, T. (2021). Design principles for creating digital transparency in government. Government Information Quarterly, 38(1), 101550. https://doi.org/10.1016/j.giq.2020.101550
Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37(3), 101284. https://doi.org/10.1016/j.giq.2018.01.006
Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340-363. https://www.jstor.org/stable/2778293
Monga, M., Edwards, N. C., Rojanasarot, S., Patel, M., Turner, E., White, J., & Bhattacharyya, S. (2024). Artificial intelligence in endourology: Maximizing the promise through consideration of the principles of diffusion of innovation theory. Journal of Endourology, 38(8), 755-762. https://doi.org/10.1089/end.2023.0680
Moore, G. A. (1991). Crossing the chasm. Harper Business.
Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media production and consumption. Communication Theory, 24(3), 340-360. https://doi.org/10.1111/comt.12039
Nascimento, A. M., & Bellini, C. G. P. (2018). Artificial intelligence and industry 4.0: The next frontier in organizations. Brazilian Administration Review, 15(4), e180152. https://doi.org/10.1590/1807-7692bar2018180152
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. https://doi.org/10.1017/CBO9780511808678
Pinheiro, L. P., Junior., & Torres, J. C. C. (2022). Inteligência Artificial (IA) na América do Sul: Uma análise das iniciativas governamentais emergentes. Anais do 46° Encontro Nacional da Associação Nacional de Pós-Graduação e Pesquisa em Administração.
Pinheiro, L. P., Junior., Cunha, M. A., Janssen, M., & Matheus, R. (2020). Towards a framework for cloud computing use by governments: Leaders, followers and laggers. In The 21st Annual International Conference on Digital Government Research (pp. 155-163). https://doi.org/10.1145/3396956.3396989
Redmond, W. H. (2003). Innovation, diffusion, and institutional change. Journal of Economic Issues, 37(3), 665-679. http://www.jstor.org/stable/4227926
Rodrigues, Z., Pinheiro, L., Marcolin, C., Matheus, R., Saxena, S., & Morais, M. (2024, September). Artificial Intelligence in supermarkets: A multiple analysis about tasks, jobs, and automation. In Conference on e-Business, e-Services and e-Society (pp. 90-102). Cham: Springer Nature Switzerland.
Rogers, E. M. (1962). Diffusion of innovations. Free Press.
Rogers, E. M. (2003). Diffusion of innovations (5ª ed.). Free Press.
Rudko, I., Bonab, A. B., Fedele, M., & Formisano, A. V. (2024). New institutional theory and AI: Toward rethinking of artificial intelligence in organizations. Journal of Management History, 32(2), 261-284. https://doi.org/10.1108/JMH-09-2023-0097
Rupp, W. T. (2020). Artificial intelligence: A diffusion of innovation view of the manufacturing and health-care industries. Atlantic Marketing Association Proceedings, Asheville, NC, United States.
Sastararuji, D., Hoonsopon, D., Pitchayadol, P., e Chiwamit, P. (2021). Cloud accounting adoption in small and medium enterprises: An integrated conceptual framework: Five factors of determinant were identified by integrated Technology-Organization-Environment (TOE) framework, Diffusion of Innovation (DOI), Institutional Theory (INT) and extended factors. In 2021 The 2nd International Conference on Industrial Engineering and Industrial Management (pp. 32-38). http://dx.doi.org/10.1145/3447432.3447439
Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press.
Scott, W. R. (2014). Institutions and organizations: Ideas, interests, and identities (4ª ed.). SAGE Publications.
Selznick, P. (1949). TVA and the grass roots: A study in the sociology of formal organization. University of California Press.
Shao, D., Ishengoma, F. R., Alexopoulos, C., Saxena, S., Nikiforova, A., & Matheus, R. (2023). Integration of IoT into e-government. Foresight, 25(5), 734-750. https://doi.org/10.1108/FS-04-2022-0048
Tidd, J., & Bessant, J. (2020). Managing innovation: Integrating technological, market and organizational change (7ª ed.). John Wiley e Sons.
Toncic, J. (2021). Advancing a critical artificial intelligence theory for schooling. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, 19(1). https://doi.org/10.5209/tekn.71136
Trope, J. (2014). Adoption of cloud computing by South African firms: An institutional theory and Diffusion Of Innovation theory perspective [Doctoral dissertation]. University of the Witwatersrand.
Bui, Q. (2015). A review of innovation diffusion theories and mechanisms. DIGIT 2015 Proceedings. 11. http://aisel.aisnet.org/digit2015/11
Carvalho, A. D. P., Cunha, S. K., Lima, L. F., & Carstens, D. D. (2017). The role and contributions of sociological institutional theory to the socio-technical approach to innovation theory. Revista de Administração e Inovação, 14(3), 250-259. https://doi.org/10.1016/j.rai.2017.02.001
Chandler, D., & Hwang, H. (2015). Learning from learning theory: A model of organizational adoption strategies at the microfoundations of institutional theory. Journal of Management, 41(5), 1446-1476. https://doi.org/10.1177/0149206315572698
Chaudhury, A., & Bharati, P. (2008). IT outsourcing adoption by small and medium enterprises: A diffusion of innovation approach. AMCIS 2008 Proceedings, 390. https://aisel.aisnet.org/amcis2008/390
Dacin, M., Goodstein, J., & Scott, W. (2002). Institutional theory and institutional change: Introduction to the special research forum. Academy of Management Journal, 45(1), 45-56. https://doi.org/10.2307/3069284
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
Dwivedi, Y. K. (2025). Generative Artificial Intelligence (GenAI) in entrepreneurial education and practice: emerging insights, the GAIN Framework, and research agenda. International Entrepreneurship and Management Journal, 21(1), 1-21. https://doi.org/10.1007/s11365-025-01089-2
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Elbardan, H. (2023). Diffusion of innovation and institutional theories in halal technology research. In N. A. A. Rahman, K. Mahroof, & A. Hassan (Eds.), Technologies and Trends in the Halal Industry (pp. 53-63). Routledge.
Gartner. (2025). Gartner Survey Finds 45% of organizations with high AI maturity keep AI projects operational for atleast three years. https://www.gartner.com/en/newsroom/press-releases/2025-06-30-gartner-survey-finds-forty-five-percent-of-organizations-with-high-artificial-intelligence-maturity-keep-artificial-intelligence-projects-operational-for-at-least-three-years
Garrido, I. L. (2023). Artificial Intelligence and Academic Journals: For better and for worse. Brazilian Administration Review, 20(4), e230145. https://doi.org/10.1590/1807-7692bar2023230145
Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3), 211-223. https://doi.org/10.1023/A:1011122126881
Gopalakrishnan, S., & Kovoor-Misra, S. (2021). Understanding the impact of the Covid-19 pandemic through the lens of innovation. BRQ Business Research Quarterly, 24(3), 224-232. https://doi.org/10.1177/23409444211013357
Hartley, J. L., Sawaya, W., & Dobrzykowski, D. (2022). Exploring blockchain adoption intentions in the supply chain: Perspectives from innovation diffusion and institutional theory. International Journal of Physical Distribution e Logistics Management, 52(2), 190-211. https://doi.org/10.1108/IJPDLM-05-2020-0163
Ioakeimidou, D., Chatzoudes, D., Symeonidis, S., & Chatzoglou, P. (2023). HRA adoption via organizational analytics maturity: Examining the role of institutional theory, resource-based view and diffusion of innovation. International Journal of Manpower, 45(5), 958-983. https://doi.org/10.1108/IJM-10-2022-0496
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022). Will algorithms blind people? The effect of explainable AI and decision-makers’ experience on AI-supported decision-making in government. Social Science Computer Review, 40(2), 478-493. https://doi.org/10.1177/0894439320980118
Janssen, M., Matheus, R., Longo, J., & Weerakkody, V. (2017). Transparency-by-design as a foundation for open government. Transforming Government: People, Process and Policy, 11(1), 2-8. https://doi.org/10.1108/TG-02-2017-0015
Jie, W., & Sia, C. L. (2011). The process of RFID assimilation by supply chain participants in China: A technology diffusion perspective on RFID technology. AMCIS 2011 Proceedings, 178. https://aisel.aisnet.org/amcis2011_submissions/178
Lamey, L., Breugelmans, E., Vuegen, M., & ter Braak, A. (2021). Stock market performance of service innovations in retail. Journal of the Academy of Marketing Science, 49(4), 499-518.
Larsen, B. (2021). A framework for understanding AI-induced field change: How AI technologies are legitimized and institutionalized. AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462591
Limongi, R. (2024). The use of artificial intelligence in scientific research with integrity and ethics. Future Studies Research Journal: Trends and Strategies, 16(1), e845. https://doi.org/10.24023/FutureJournal/2175-5825/2024.v16i1.845
Limongi, R., & Marcolin, C. B. (2024). AI literacy research: Frontier for high-impact research and ethics. Brazilian Administration Review, 21(3), e240162. https://doi.org/10.1590/1807-7692bar2024240162
Lund, B., Omame, I., Tijani, S., & Agbaji, D. (2020). Perceptions toward artificial intelligence among academic library employees and alignment with the diffusion of innovations’ adopter categories. College e Research Libraries, 81(5). https://doi.org/10.5860/crl.81.5.865
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
Matheus, R., & Janssen, M. (2020). A systematic literature study to unravel transparency enabled by open government data: The window theory. Public Performance & Management Review, 43(3), 503-534. https://doi.org/10.1080/15309576.2019.1691025
Matheus, R., Faber, R., Ismagilova, E., & Janssen, M. (2023). Digital transparency and the usefulness for open government. International Journal of Information Management, 73, 102690. https://doi.org/10.1016/j.ijinfomgt.2023.102690
Matheus, R., Janssen, M., & Janowski, T. (2021). Design principles for creating digital transparency in government. Government Information Quarterly, 38(1), 101550. https://doi.org/10.1016/j.giq.2020.101550
Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37(3), 101284. https://doi.org/10.1016/j.giq.2018.01.006
Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340-363. https://www.jstor.org/stable/2778293
Monga, M., Edwards, N. C., Rojanasarot, S., Patel, M., Turner, E., White, J., & Bhattacharyya, S. (2024). Artificial intelligence in endourology: Maximizing the promise through consideration of the principles of diffusion of innovation theory. Journal of Endourology, 38(8), 755-762. https://doi.org/10.1089/end.2023.0680
Moore, G. A. (1991). Crossing the chasm. Harper Business.
Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media production and consumption. Communication Theory, 24(3), 340-360. https://doi.org/10.1111/comt.12039
Nascimento, A. M., & Bellini, C. G. P. (2018). Artificial intelligence and industry 4.0: The next frontier in organizations. Brazilian Administration Review, 15(4), e180152. https://doi.org/10.1590/1807-7692bar2018180152
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. https://doi.org/10.1017/CBO9780511808678
Pinheiro, L. P., Junior., & Torres, J. C. C. (2022). Inteligência Artificial (IA) na América do Sul: Uma análise das iniciativas governamentais emergentes. Anais do 46° Encontro Nacional da Associação Nacional de Pós-Graduação e Pesquisa em Administração.
Pinheiro, L. P., Junior., Cunha, M. A., Janssen, M., & Matheus, R. (2020). Towards a framework for cloud computing use by governments: Leaders, followers and laggers. In The 21st Annual International Conference on Digital Government Research (pp. 155-163). https://doi.org/10.1145/3396956.3396989
Redmond, W. H. (2003). Innovation, diffusion, and institutional change. Journal of Economic Issues, 37(3), 665-679. http://www.jstor.org/stable/4227926
Rodrigues, Z., Pinheiro, L., Marcolin, C., Matheus, R., Saxena, S., & Morais, M. (2024, September). Artificial Intelligence in supermarkets: A multiple analysis about tasks, jobs, and automation. In Conference on e-Business, e-Services and e-Society (pp. 90-102). Cham: Springer Nature Switzerland.
Rogers, E. M. (1962). Diffusion of innovations. Free Press.
Rogers, E. M. (2003). Diffusion of innovations (5ª ed.). Free Press.
Rudko, I., Bonab, A. B., Fedele, M., & Formisano, A. V. (2024). New institutional theory and AI: Toward rethinking of artificial intelligence in organizations. Journal of Management History, 32(2), 261-284. https://doi.org/10.1108/JMH-09-2023-0097
Rupp, W. T. (2020). Artificial intelligence: A diffusion of innovation view of the manufacturing and health-care industries. Atlantic Marketing Association Proceedings, Asheville, NC, United States.
Sastararuji, D., Hoonsopon, D., Pitchayadol, P., e Chiwamit, P. (2021). Cloud accounting adoption in small and medium enterprises: An integrated conceptual framework: Five factors of determinant were identified by integrated Technology-Organization-Environment (TOE) framework, Diffusion of Innovation (DOI), Institutional Theory (INT) and extended factors. In 2021 The 2nd International Conference on Industrial Engineering and Industrial Management (pp. 32-38). http://dx.doi.org/10.1145/3447432.3447439
Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press.
Scott, W. R. (2014). Institutions and organizations: Ideas, interests, and identities (4ª ed.). SAGE Publications.
Selznick, P. (1949). TVA and the grass roots: A study in the sociology of formal organization. University of California Press.
Shao, D., Ishengoma, F. R., Alexopoulos, C., Saxena, S., Nikiforova, A., & Matheus, R. (2023). Integration of IoT into e-government. Foresight, 25(5), 734-750. https://doi.org/10.1108/FS-04-2022-0048
Tidd, J., & Bessant, J. (2020). Managing innovation: Integrating technological, market and organizational change (7ª ed.). John Wiley e Sons.
Toncic, J. (2021). Advancing a critical artificial intelligence theory for schooling. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, 19(1). https://doi.org/10.5209/tekn.71136
Trope, J. (2014). Adoption of cloud computing by South African firms: An institutional theory and Diffusion Of Innovation theory perspective [Doctoral dissertation]. University of the Witwatersrand.