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The Impact of Consumer Complaint Relevance and Variety on Product Recalls: An Empirical Investigation of the Automobile Industry using Text Mining

Date
Date
Wednesday 23 November 2022, 14:00
Location
Hybrid: Maurice Keyworth SR (1.04) on campus or Online (booking link below)
Speaker:
Dr Yufei Huang, Associate Professor in Operations Management, Trinity Business School, Trinity College Dublin

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Abstract

This paper studies the impact of consumer complaint relevance and variety on the timing of product recall decisions. Using a car recall data set from NHTSA (National Highway Traffic Safety Administration) in the USA, we first measure consumer complaint relevance and variety by implementing text mining methods to analyse the content of consumer complaints and the description of the defects in the recalls. We then examine how consumer complaint relevance and variety impact the timing of recall decisions. We find that when the level of consumer complaint relevance is higher, a recall decision is made faster. And when the level of consumer complaint variety is higher, a recall decision is made lower. Such a relationship becomes stronger when the defective component that caused the recall is used in different car models or when the car models are older.

The Speaker

Dr Yufei Huang is an Associate Professor in Operations Management at Trinity Business School, Trinity College Dublin. Yufei received PhD in Management from UCL School of Management in the UK. His research focuses on new product development and launch, supply chain management and healthcare operations management. His work has appeared in top journals, such as Production and Operations Management, Human Relations, European Journal of Operational Research, among others. He regularly serves as an ad-hoc reviewer for various academic journals and book publishers.