Direct and vicarious learning: How successes and failures affect product recall likelihood

Product recalls represent one of the most severe forms of innovation failure, with substantial economic, reputational, and safety implications. Despite their importance, prior research has only partially examined the determinants of recalls, often overlooking product-level heterogeneity and the interaction between organizational learning mechanisms. This study addresses these gaps by integrating innovation management and organizational learning literatures to examine how direct and vicarious learning from failure affect recall likelihood. Using a multilevel framework and a comprehensive dataset of 49,011 medical devices filed by 10,115 firms with the U.S. Food and Drug Administration between 2007 and 2022, we estimate multilevel probit models that account for both product- and firm-level determinants. The results reveal an inverted U-shaped relationship between direct learning from failure and recall probability. Moreover, especially the most severe failures experienced by prominent competitors may generate a relevant form of vicarious learning that significantly flattens the effect of direct learning, indicating a substitution effect between direct and vicarious learning. This study advances research on innovation failures by clarifying the multilevel determinants of product recalls and showing that learning from failure is non-linear, bounded, and socially structured.