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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