How Algorithm Aversion Impairs Entrepreneurial Decision Effectiveness through Problem Structuring: The Roles of Regulatory Focus and Explanatory Intervention

Despite the increasing prevalence of algorithmic decision-making in business, entrepreneurs often resist it (algorithm aversion). How this tendency affects their key decision effectiveness, along with its underlying mechanisms and boundary conditions, remains unclear. This study establishes a moderated mediation model to examine the impact of algorithm aversion on entrepreneurs' decision effectiveness, specifically considering problem structuring as a mediating variable and testing the moderating roles of regulatory focus (a trait) and explanatory intervention (a situational factor). A single-factor between-subjects experimental design was employed. Data were collected through a scenario-based experiment and survey involving 306 active entrepreneurs. A moderated mediation analysis was conducted. The results indicated that: (1) Algorithm aversion significantly and negatively predicted decision effectiveness. (2) Problem structuring mediated this relationship. (3) Promotion focus strengthened, whereas prevention focus weakened, the negative effect of algorithm aversion on problem structuring. (4) Explanatory intervention mitigated the effect of algorithm aversion on problem structuring. This research reveals the psychological mechanism through which algorithm aversion impairs entrepreneurs' decision effectiveness and identifies "for whom" and under "what conditions" this effect is stronger or weaker. The findings provide important implications for entrepreneurs to overcome cognitive biases and for human-computer interaction designers to develop more effective decision support systems.