Noise as Signal in Learning from Rare Events

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.

Process Multiplicity and Process Dynamics: Weaving the Space of Possible Paths

“…Research on high reliability organizations is concerned with this issue since the ‘name of the game is reacting to unexpected sequences of events’ (Roberts, 1990, p. 104). Paying attention to the space of possible paths enables organizations to learn from what is oftentimes considered ‘noise’ (Maslach, Branzei, Rerup, & Zbaracki, 2018). If we see a process as a space of possible paths, we can pay equal attention to more or less likely paths, rather than collapsing the process into a singular sequence or a small set of ‘most probable’ paths.…”

Responses to COVID-19: The role of governance, healthcare infrastructure, and learning from past pandemics

“…Using multiple diverse individuals who can integrate their interpretation of learning from rare events also creates strong institutionalized learning ( Chadwick and Raver, 2015 ). Similarly, as rare events challenge existing routines, multiple elements of a system face failures, thus allowing individuals and groups to learn more from events that otherwise would have been dismissed as noise ( Maslach et al, 2018 ). Thus, rare events not only yield more diverse learning but also make individuals develop actionable skills that can easily be applied to future rare events ( Garud et al, 2011 ).…”

“…Control variables : We collected data on GDP per capita ( Chin & Wilson, 2018 ), population, percentage of the population aged above 65 years, the number of nurses per 1000 population, and the severity of H1N1 (dummy operationalization) ( Maslach et al, 2018 ), which may affect responses COVID-19 or any rare events. We also have information for the country’s position on the HDI.…”

Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations

“…Existing work utilizing text mining tends to eschew theory in favor of raw statistical power to increase the understanding we have of our field (Antons et al, 2019) or to automate knowledge work (Campion et al, 2016). Conversely, Kleinbaum (2012) and Maslach et al (2018) each provided a detailed theoretical rationale to form the basis for analyzing and interpreting their textual features. Theoretical rationale is key for future work in this area because a theoretical understanding of the research question should guide preprocessing, feature extraction, and analysis.…”