Noise as Signal in Learning from Rare Events

David Maslach (), Oana Branzei (), Claus Rerup () and Mark J. Zbaracki ()
Additional contact information
David Maslach: Department of Management, College of Business, Florida State University, Tallahassee, Florida 32306
Oana Branzei: Ivey Business School, University of Western Ontario, London, Ontario N6G 0N1, Canada
Claus Rerup: Frankfurt School of Finance and Management, D-60322 Frankfurt am Main, Germany
Mark J. Zbaracki: Ivey Business School, University of Western Ontario, London, Ontario N6G 0N1, Canada

Organization Science, 2018, vol. 29, issue 2, 225-246

Abstract: Firms increasingly have access to information about the failure events of other firms through public repositories. We study one such repository that accumulates reports of adverse events in the medical device industry. We provide qualitative evidence that shows how firms select a sample of adverse events and then engage in inferential learning. We show that firms use the reports of others to extract new valid knowledge from the adverse events in other firms. We use quantitative evidence to explore how a public repository can be used to provide more direct evidence of vicarious learning. Our findings challenge some standard assumptions about vicarious learning. First, we show that the learning in a repository does not come from referent others. Instead, it emerges directly from failure events that might ordinarily be dismissed as noise. Second, we show that the learning does not come from copying others. Instead, it is constructed by firm members as they assemble individual failure events to identify possibilities they had not considered. Third, in contrast to vicarious learning, where the referent others and rare events provide the context, repository-based learning requires that actors impose their own context as part of the learning process. Our qualitative and quantitative evidence serve explanatory purposes by showing how firms use a repository of failure events to identify moments of valid learning, and they serve exploratory purposes by investigating how we can demonstrate reliable learning from a repository of failure events.

Keywords: vicarious learning; learning from failure; rare events; public repositories (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ororsc:v:29:y:2018:i:2:p:225-246

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