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

Abstract

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.

Digitalisation of Development and Supply Networks: Sequential and Platform-Driven Innovations

“…Enabled by 'converging' technologies, data and information, these parallel pathways operate with the new paradigms of 'process intensification', 'modularisation', and 'combinations' in continuous modes, which challenge the traditional location-decision logic (and that of the 'large batch' pharmaceutical plant). Data organisation is seen as a critical step in building predictive models (Mukherjee and Sinha (2017).…”

Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

“…Badiezadeh et al proposed a method based on Big Data approach to evaluate the sustainability of supply chain; meanwhile, the double frontier network DEA was also used [24]. Based on Big Data analytic method, Mukherjee and Sinha explored product decisions on recall in medical device supply chain [25]. Here, Big Data method was used to evaluate the judgment Bias.…”

“…The dynamic game order of supply chain members is as follows: the manufacturer decides the wholesale price, and then the Data Company decides the consumer BDI retail price and the retailer decides the product retail price. Thus, the benefit functions of the Data Company, the manufacturer, and the retailer are shown in functions (24), (25), and (26), respectively.…”

“…The calculation method is similar to Section 4.1; thus, we will not repeat it. Based on formulas (24), (25), and (26) …”

Note from the Editor

“…The past year was a troubling year for failed technology, as both the lay press [1] , [2] and also an orthopaedic surgeon [3] reported troubling flaws in the United States Food and Drug Administration (FDA) post-market surveillance program. Scientific analysis further showed that reporting flaws and inadequacies affect response even when adverse events are reported [4] , and the FDA has limits as far as the available research when making approval decisions [5] .…”