| 要旨 |
As digital transformation and artificial intelligence reshape competitive advantage, firms increasingly rely on data assets to drive innovation and efficiency. However, growing regulatory scrutiny over data privacy and data security has imposed new compliance burdens on firms. We examine the financial performance implications of firms’ responses to data protection regulations, focusing on both mandatory regulatory pressures and voluntary pressures arising from competitive forces and industry-wide data breaches. Firms signal regulatory compliance through increased attention to data protection (evidenced in corporate disclosures in annual reports) and the adoption of new data governance practices, including advanced data security technologies and data-related organizational features. However, despite these signals, firms experience lower market valuation, on average. We infer that while signaling enhanced data protection demonstrates responsiveness to restrictive regulation, it does not confer competitive differentiation, particularly given the intangible nature of data protection and the challenges of credibly communicating its effectiveness to market participants. Moreover, firms incur substantial compliance costs without clear shortterm revenue gains. Nevertheless, firms with stronger technological capabilities, particularly those with proprietary data-processing technologies, and firms with larger market shares are less adversely affected, as they can implement compliance measures more efficiently. Our study highlights the paradox of regulatory compliance: despite extensive signaling to alleviate regulatory and competitive pressures, compliance efforts can increase operational costs and information asymmetries vis a vis the firms’ investors thereby leading to valuation discounts. These findings contribute to ongoing debates on the impact of regulatory compliance on firm performance, underscoring the contingent role of technological and market capabilities in mitigating the adverse effects of regulatory constraints. |
