Abstract

The pervasive digitalization of business and society has brought to light data privacy violations, algorithmic discrimination, cybersecurity failures, and AI-related harms as defining governance challenges of the digital age. Corporate Digital Responsibility (CDR), defined as the set of shared values and norms that guide an organization's operations regarding the creation and use of digital technology and data (Lobschat et al.,2021), has become the central framework for addressing these tensions. The organizational stakes are significant, as privacy breaches and security incidents generate substantial losses in firm value and stakeholder trust. Firms that cultivate credible CDR cultures gain competitive advantages through stronger brand equity, consumer trust, and long-term financial performance. Despite growing theoretical consensus on the importance of CDR, the field continues to lack a validated indicator for measuring organizational CDR performance. Firms document CDR commitments through reports and disclosures. However, systematically measuring CDR from these sources is challenging. Despite approaches such as content analysis, which is laborintensive, and behavioral perceptual measures, which reflect perceptions rather than actual disclosures, there remains no objective and validated indicator for measuring organizational CDR performance. This study develops and validates a CDR dictionary for analyzing CDR disclosures using Deng et al.'s (2019) six step Semi-Automatic Dictionary Building Process, combining text-mining with theory-driven methods for a theory-based dictionary. It draws on Stakeholder Theory (Freeman, 1984) to elucidate CDR obligations and on Sociotechnical Systems Theory (Orlikowski & Scott, 2008) to highlight digital responsibility's multidimensional nature, as the consequences of digital technology deployment are co-produced by the mutual constitution of technical and social systems. After validation, we demonstrate the utility of the dictionary by applying it to digital responsibility disclosures to construct firm-level CDR scores and, using event-study and moderated regression, test whether CDR reputation buffers the negative abnormal stock returns associated with technology-related crises, such as data breaches and AI ethics controversies. This positions the CDR dictionary as both a methodological contribution by developing a reusable artifact for CDR measurement and a theoretically motivated instrument for advancing understanding of how digital responsibility shapes organizational reputation, market outcomes, and stakeholder communication.

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