Algorithms in the boardroom: AI decision support and executive judgement

Résumé

This study examines how AI-generated strategic recommendations influence executives’ reliance on algorithmic advice and their strategic decision judgments. Specifically, it investigates whether AI recommendation transparency and perceived analytical superiority shape executives’ willingness to rely on algorithmic advice, and whether such reliance serves as the mechanism through which AI affects strategic judgment. Although prior research has extensively examined algorithm aversion, algorithm appreciation, and explainable AI in operational and consumer settings, limited research has explored how AI recommendation characteristics influence executive-level strategic judgment under conditions of uncertainty, ambiguity, and organizational accountability. This study addresses that theoretical and empirical gap by examining behavioral mechanisms through which AI recommendations shape strategic decision-making at the executive level. The study uses a scenario-based, between-subjects experiment with a 2 × 2 vignette design. AI recommendation transparency and perceived analytical superiority were manipulated at high and low levels in a strategic market-entry decision supported by an AI system. Data were collected through an online survey of 312 senior managers and executives from Moroccan organizations. While this context provides valuable insights from an emerging economy where executive AI adoption remains underexplored, the findings should be interpreted cautiously as their generalizability may be limited to similar institutional and technological environments. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that both AI recommendation transparency (β = 0.32, t = 5.47, p < 0.001) and perceived analytical superiority (β = 0.41, t = 7.21, p < 0.001) positively influence executives’ reliance on algorithmic advice. In turn, reliance on algorithmic advice has a significant positive effect on strategic decision judgments (β = 0.56, t = 10.84, p < 0.001). Mediation analysis indicates that reliance on algorithmic advice partially mediates the relationship between AI recommendation transparency and strategic decision judgment (indirect effect β = 0.18, p < 0.001) and between perceived analytical superiority and strategic decision judgment (indirect effect β = 0.23, p < 0.001). These results suggest that AI influences executive judgment not only through analytical capability, but through recommendation characteristics that make AI outputs understandable and credible to decision makers. This study extends research on algorithmic decision making to executive strategic judgment, where uncertainty and complexity are high. It identifies reliance on algorithmic advice as the key mechanism linking AI recommendation characteristics to strategic decision judgments and integrates explainable AI with strategic decision-making research by highlighting transparency as a critical condition for executive adoption of AI recommendations.

Classification JEL: M15, O33

Paper type: Research paper