From Public Debate to Institutional Meaning: A Process Theory of Artificial Intelligence Framing Across Arenas

作者：Annie Tian, Yuehua Chen · 发表于：Journal of the Association for Information Systems · 年份：2026 · 研究领域：Computational and Text Analysis Methods、Ethics and Social Impacts of AI、Management and Organizational Studies

Artificial intelligence (AI) has become a major focus of organizational strategy, public debate, and policy concern, even as its capabilities and risks remain uncertain. As a general-purpose technology, AI spans industries, labor markets, and regulatory domains, making its meaning contested and unsettled. Yet management research offers limited insight into how AI meanings are constructed, transformed, and stabilized across public arenas. This gap matters because these meanings shape organizational action, market expectations, and policy responses. To address this issue, this study develops a public arena framing pipeline to explain how AI meanings evolve across media, corporate, and government discourse. Drawing on framing theory and institutional process perspectives, we conceptualize public meaning construction as a sequenced yet interactive process involving three stages: public problematization, organizational translation and enactment, and governmental formalization. In this process, media actors first introduce and amplify diverse interpretations of AI, corporate actors selectively translate these interpretations into strategically actionable narratives, and government actors formalize a narrower subset of meanings through policy and regulatory discourse (Entman, 1993). Empirically, we employ a mixed-method research design that combines computational text analysis with qualitative interpretation. We analyze a longitudinal corpus of approximately 4,600 U.S. documents spa...