Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias

Ujjal Kumar Mukherjee, Kingshuk K. Sinha

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


This study investigates judgment bias (under-reaction or over-reaction) in product recall decisions by firms when they respond to adverse event reports generated by users of their products. We develop an integrative theoretical framework for identifying the sources of judgment bias in product recall decisions. We analyze user-generated reports (big and unstructured data) on adverse events related to medical devices, using a combination of econometric and predictive analytic methods. We find that (i) noisy signals in user feedback, that is, high noise-to-signal ratio, are associated with under-reaction likelihood; and (ii) user feedback related to adverse events characterized by high severity is associated with high over-reaction likelihood. We also identify conditions related to the situated context of managers that are associated with under-reaction or over-reaction likelihood. The findings of this study are consequential for firms and government regulatory agencies, in that they shed light on the sources of judgment bias in recall decisions, thereby ensuring that such decisions are made correctly and in a timely manner. Our findings also contribute toward improving the post-launch market surveillance of products (e.g., medical devices) by making it more evidence-based and predictive.

Original languageEnglish (US)
Pages (from-to)1816-1833
Number of pages18
JournalProduction and Operations Management
Issue number10
StatePublished - Oct 2018

Bibliographical note

Funding Information:
The study was supported by grants from the Social Media and Business Analytics Collaborative (SOBACO) between the Carlson School of Management and the College of Science and Engineering, University of Minnesota, and the American Hospital Association (AHA). The authors gratefully acknowledge the detailed and developmental comments of two anonymous reviewers, senior editor, and the departmental editor on earlier versions of the study. In its formative stages, the study benefited from the research assistance of Ravindra Kasturi in data collection, cleansing, organizing, and analysis. The authors owe a debt of gratitude to the following colleagues who contributed in significant ways in shaping this study and the paper by providing timely feedback and guidance: Snigdhansu Chatterjee, Karen Donohue, Daniel A. Levinthal, Mili Mehrotra, and Enno Siemsen. Earlier versions of the paper were presented at the Wharton Technology and Innovation Conference, University of Pennsylvania, and the seminars at the Indian School of Business, University of Illinois at Urbana-Champaign, and Western University (Canada), and Medtronic Big Data & Advanced Analytics Symposium. The study has benefited from the feedback of the attendees at these forums. All errors are the responsibility of the authors.

Publisher Copyright:
© 2017 Production and Operations Management Society


  • attention-based decision making
  • big data analytics
  • judgment bias
  • product recalls
  • system neglect


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