Strategic Signaling or Structural Friction?: How AI Disclosure Moderates Resource-Performance Dynamics in Hotels and Restaurants
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) has become an increasingly important strategic issue in the hospitality industry. While prior research has primarily examined AI as an operational technology that improves efficiency and service delivery, less attention has been paid to how AI-related disclosures influence investor evaluations of firm resources. This dissertation investigates whether AI-related disclosures moderate the relationship between organizational resources and financial performance in publicly traded hospitality firms. Drawing on signaling theory and the resource-based view, this study conceptualizes AI disclosures as strategic signals that shape investor perceptions of firm capabilities and future value creation. Firm-level financial data were obtained from Compustat for publicly traded U.S. hospitality firms from 2010 to 2024. AI-related disclosure measures were developed through textual analysis of annual reports (AI_10K) and firm-generated news releases (AI_News) using a hospitality-specific AI dictionary. Dynamic fixed-effects panel models were employed to examine the moderating effects of AI disclosures on the relationship between organizational resources and firm performance. Tobin’s Q served as the primary performance measure, supplemented by accounting-based indicators and multiple robustness tests. The findings indicate that AI-related disclosures significantly influence how investors evaluate organizational resources. The most consistent evidence emerged from the interaction between AI disclosures and capital investment intensity, suggesting that investors reassess the value of capital-intensive firms when AI-related strategic initiatives are emphasized. Rather than uniformly increasing firm value, AI disclosures appear to reshape market expectations regarding future productivity and organizational adaptation. Robustness analyses support the stability of these findings across alternative model specifications.
Author: Heo, Jaewan
Institution: University of Central Florida
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204501860