Hiding in the Herd: The Product Recall Clustering Phenomenon

Ujjal K. Mukherjee (), George P. Ball (), Kaitlin D. Wowak (), Karthik V. Natarajan () and Jason W. Miller ()
Additional contact information
Ujjal K. Mukherjee: Department of Business Administration, Gies College of Business, University of Illinois Urbana-Champaign, Champaign, Illinois 61820
George P. Ball: Operations and Decision Technologies Department, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Kaitlin D. Wowak: Department of IT, Analytics, and Operations, Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556
Karthik V. Natarajan: Supply Chain and Operations Department, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Jason W. Miller: Supply Chain Management Department, Eli Broad College of Business, Michigan State University, East Lansing, Michigan 48824

Manufacturing & Service Operations Management, 2022, vol. 24, issue 1, 392-410

Abstract: Problem definition : Product recalls have serious consequences for firms and consumers. Little is known, however, about how firms manage the timing of recalls and how this timing influences the financial consequences of recalls. In this study, we provide evidence for a previously unknown phenomenon: recall clustering, a collection of recalls within close temporal proximity in which a leading recall (the first recall in a cluster) excites following recalls (subsequent recalls in a cluster). We also investigate how the stock market penalizes firms differently depending upon their position within the recall cluster. Academic/practical relevance : By demonstrating that auto firms cluster their recalls and that the market penalizes firms differently based on the position of a recall within a cluster, we contribute to the literature that investigates recall timing and stock market event studies and provide guidance for regulators who oversee auto recalls and managers who make recall decisions. Methodology : We first develop analytical predictions using a dynamic game theoretic model to motivate our hypotheses. We then examine empirical support for our hypotheses by analyzing 3,117 auto recalls across 48 years using a Hawkes process model. Hawkes process models are designed to examine self-excitation of events across time and can be used to investigate recall clustering, while categorizing recalls as leading or following within a cluster. Finally, we use the leading and following recall designations obtained from the Hawkes process model in an event study to examine how the stock market effects of a recall vary depending on its position within a cluster. Results : We find that 73% of recalls occur in clusters, and they form after a 16-day gap in recall announcements. On average, clusters last for 34 days and are comprised of 7.6 following recalls announced after the leading recall. Leading recalls are associated with as high as a 67% larger stock market penalty than following recalls. Further, we find that the stock market benefit realized by a following recall weakens as the time since the leading recall increases and that the stock market penalty faced by a leading recall grows as the time since the end of the last cluster increases. Managerial Implications : Our findings lead to a key implication for regulators who oversee auto recalls by demonstrating evidence of recall clustering and the underlying stock market effects that are attributable to it. We provide a cost-neutral policy recommendation for the National Highway Traffic and Safety Administration that should limit recall clustering.

Keywords: recall clustering; herding theory; nash stopping-time games; hawkes process model; event study (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2020.0937 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:1:p:392-410

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().