Moral Appraisals of Generative AI: How Mindset Framing Shapes Moral Judgments, Emotional Appraisals and Privacy Behavior

Abstract

The rapid development of Generative AI (GenAI) and similar technologies has heightened ethical concerns, including privacy issues, discrimination, and data security. However, there is limited understanding of how mindset framing shapes users’ moral judgments and emotional responses toward these technologies. To address this gap, this research examines how framing GenAI, particularly in terms of growth or fixed mindset, influences moral appraisals, emotional reactions, and privacy behaviors. Using a mixed-methods approach, this research combines text mining of a large field dataset (n = 18,035 reviews) with two experimental studies (n = 255 participants). Findings reveal that mindset framing directly influences perceived morality, suggesting that GenAI framed in a growth mindset evokes more positive moral judgments compared to a fixed mindset framing. A growth mindset suggests learning and adaptability, whereas a fixed mindset framing implies that it is immutable and incapable of improvement. Perceived moral judgments mediate the effect of mindset framing (growth vs. fixed) on emotional appraisals. Moreover, mindset framing influences privacy behavior, with participants having low (vs. high) expertise disclosing more under a growth (vs. fixed) mindset. Theoretically, this paper contributes to the literature by integrating key theories on moral judgment, implicit theories, cognitive appraisal theory, and privacy calculus theory, thereby deepening the understanding of moral appraisals regarding GenAI. In practical terms, this research enables organizations to strategically frame GenAI capabilities, positively influencing users' privacy behavior and emotional responses.