Institutional Theory (IT) and Diffusion of Innovation (DOI): A Theoretical Approach on Artificial Intelligence (AI)

Reis, Joel Ferreira; Pinheiro Junior, Luiz Pereira

doi:10.1590/1807-7692bar2025250060

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Research Article • BAR, Braz. Adm. Rev. 22 (4) • 2025 • https://doi.org/10.1590/1807-7692bar2025250060 linkcopy

Institutional Theory (IT) and Diffusion of Innovation (DOI): A Theoretical Approach on Artificial Intelligence (AI)

Authorship SCIMAGO INSTITUTIONS RANKINGS

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.

Keywords:
artificial intelligence; institutional theory; diffusion of innovation; technology adoption; institutional pressures

Adopter profile (DOI)	Sectoral context	Research question	References
Innovators	AI in highly regulated sectors (e.g., healthcare, finance, government)	How do pioneering AI adopters in regulated sectors navigate coercive pressures and leverage legitimacy for innovation?	Jie and Sia (2011); Pinheiro et al. (2020); Janssen et al. (2022); Rodrigues et al. (2024); Shao et al. (2023)
Followers	Regulatory compliance in traditional industries	How do follower organizations align their AI practices with regulatory expectations without being first movers?	Trope (2014); Chaudhury and Bharati (2008); Matheus and Janssen (2020); Janssen et al. (2020); Limongi (2024)
Traditionalists	Legally mandated AI adoption	How do resistant organizations cope with legal AI requirements and what are their implementation challenges?	Sastararuji et al. (2021); Elbardan (2023); Matheus and Janssen (2020); Toncic (2021); Rudko et al. (2024)

Adopter profile (DOI)	Sectoral context	Research question	References
Innovators	AI and professional standards (e.g., banking, education, healthcare)	To what extent do innovative organizations leverage AI to align with professional norms and gain strategic advantage?	Hartley et al. (2022); Ioakeimidou et al. (2023); Limongi and Marcolin (2024); Mariani et al. (2023); Monga et al. (2024)
Followers	AI compliance with evolving sectoral regulations	How do follower organizations adopt AI to meet normative standards and follow industry leaders?	Carvalho et al. (2017); Bui (2015); Shao et al. (2023); Matheus et al. (2021); Rodrigues et al. (2024)
Traditionalists	AI implementation in conservative/regulatory-bound industries	What challenges do traditionalist organizations face in aligning AI practices with established professional expectations?	Elbardan (2023); Sastararuji et al. (2021); Matheus and Janssen (2020); Garrido (2023); Toncic (2021)

Adopter profile (DOI)	Sectoral context	Research question	References
Innovators	AI as strategic benchmarking	How do innovative firms establish AI usage as an industry benchmark and stimulate mimetic adoption?	Redmond (2003); Pinheiro et al. (2020); Mariani et al. (2023); Dwivedi (2025)
Followers	Mimetic adoption in competitive environments	What drives follower firms to mimic early AI adopters and adopt best practices?	Trope (2014); Hartley et al. (2022); Ioakeimidou et al. (2023); Rodrigues et al. (2024)
Traditionalists	Late adoption and resistance	How do laggard firms respond to industry-wide AI adoption and what strategies mitigate perceived risks?	Chaudhury and Bharati (2008); Jie and Sia (2011); Garrido (2023); Rudko et al. (2024)

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[TFN1] Note. Developed by the authors.