From Public Debate to Institutional Meaning: A Process Theory of Artificial Intelligence Framing Across Arenas
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 spanning 2010–2025, including news media articles, corporate communications, and government policy documents. Using topic modeling, we identify dominant AI framings across arenas and over time. We then develop theory-driven interpretive metrics—dispersion, combination, novelty, and persistence—to trace how meanings shift, combine, emerge, and stabilize throughout the observation period. Topic modeling is particularly useful for identifying latent patterns in large textual corpora, while interpretive validation helps ensure that computationally derived topics are theoretically meaningful (Blei, Ng, & Jordan, 2003). The findings reveal a staged pattern of AI meaning construction. Early AI discourse is characterized by media-led problematization and high interpretive uncertainty. Over time, corporate actors selectively stabilize AI meanings through repeated enactment in market, platform, and partnership narratives. In the later period, government discourse institutionalizes a narrower subset of meanings by embedding them in policy, risk, and governance frameworks. This study contributes to framing theory, process theory, and emerging technology governance by offering a dynamic, cross-arena account of how technological meanings become authoritative over time.