Falling in the same predicament again? A network embeddedness perspective of organizational failure recidivism in special treatment firms

Abstract

PurposeStudies on organizational failure learning have focused on whether and how organizations learn from failures but have paid limited attention on the persistence of failure learning. This study centers on failure recidivism and answers why organizations would fall into repeated failures after learning from them.Design/methodology/approachBased on a sample of Chinese publicly listed firms that once recovered from special treatment status, the authors use event history technique and Cox proportional hazards regression model.FindingsThe authors find that reviviscent firms with higher interlock centrality are less likely to decline again, and underperforming partners can strengthen the role of interlock tie in failure recidivism. By contrast, politically connected reviviscent firms are more likely to decline again, and this effect attenuates for firms located in more market-oriented regions.Research limitations/implicationsThe authors’ contribution comes from the close integration of literature on failure learning and network embeddedness perspective to examine how social networks affect the learning process of failure recidivism.Practical implicationsThe study provides important practical implications for organizations, especially those that once experienced failures or are experiencing failures.Originality/valueCombining organizational learning theory and network embeddedness perspective, the study provides novel insights into answering how firms embedded in different types of social networks affect failure learning persistence differently.