The academic industry’s response to generative artificial intelligence: An institutional analysis of large language models

Abstract

This paper examines academic institutions' heterogeneous initial responses to generative AI (GAI) tools like ChatGPT and factors influencing increased acceptance over time. GAI's disruptive nature coupled with uncertainty about impacts poses adoption challenges. However, external pressures from stakeholders seeking GAI integration contribute to changing attitudes. Actions of institutional change agents also drive growing acceptance by increasing awareness of GAI advantages. They challenge prevailing logics emphasizing assessments, proposing new values around employability and job performance. Additionally, academic institutions reevaluating GAI's value creation potential through applications and evolving business models contributes to favorable responses. The paper proposes an institutional theory framework explaining dynamics underpinning academic institutions' assimilation of GAI. It highlights how various mechanisms like external pressures, institutional entrepreneurs' theorization efforts justifying technology use, and internal sensemaking shape institutional norms and values, enabling academic systems' adaptation. The study informs policy and practice while directing future research toward validating propositions empirically and examining contextual dimensions including industry characteristics affecting GAI adoption.