AZADEGAN, Arash, SRINIVASAN, Ravi, BLOME, Constantin and TAJEDDINI, Kayhan (2019). Learning from near-miss events: an organizational learning perspective on supply chain disruption response. International Journal of Production Economics, 216, 215-226.
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25150:536510
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Tajeddini-LearningFromNearMiss(AM).pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Tajeddini-LearningFromNearMiss(AM).pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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Abstract
Studying near-miss events – occasions when a company comes close to being negatively impacted – can help identify systemic issues and thereby enhance organizational resilience. However, what is not known is how firms learn from near-miss events, and how their learning is translated into response strategies in the face of supply chain disruptions. In this study, we address the following research questions - How does exposure to near-misses reflect in organizational response strategies to supply chain disruptions? Using single and double-loop learning from organizational learning theory, we examine how firms implement response strategies based on near-miss events. In addition, we examine the moderating effects of institutional pressures (from regulatory bodies and industry associations) into the model. We test the hypotheses using responses from 448 organizations in Germany, Switzerland and Sweden. Our results indicate that exposure to near-miss events leads firms to strengthen their focus on procedural response strategies and to lower their focus on flexible response strategies. Industry pressure furthers the effects of near-miss exposure in applying procedural strategies and limiting the application of flexible strategies. Regulatory pressure furthers the effects of near-miss exposure in limiting the application of flexible strategies. This study extends the body of supply chain disruption management to the concept of near-misses and explains how institutional context play a major role in learning of supply chain disruption responses.
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