PRE-CLIPS: Product Recall Classification and Impact Prediction System to Optimize Recall Procedures & Notifications

• Applicant: Professor Dr. Sascha Raithel
• Subject Area: Management and Marketing
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 511835854

Product failures can have serious consequences for consumer welfare and firm performance. For example, in the U.S. alone, a child dies every two weeks from a furniture or TV tip-over. When firms become aware of such product failures, they either voluntarily recall the product or the authorities order the recall. But too frequently product recalls are not effective. For example, a survey by the U.S. Consumer Product and Safety Commission (CPSC) in 2018 found that the overall recall effectiveness rate for consumer product recalls is rather low: more than 80% of products have not been returned, repaired, replaced, or disposed. Most buyers are still using the dangerous products which poses a threat to consumer health and to firm performance if further incidents occur, cause litigation costs, require re-announcements of the recall, and lead to further reputation damage. This project therefore seeks to find answers to the following overarching research question: How can managers and regulators optimize product recall procedures and notifications to protect both consumer health and firm performance? To answer this research question, this project is designed to build an extensive database and quantify a prediction model including the antecedents, consequences, and boundary conditions of recall effectiveness. The results of this prediction model are going to be used to create a product recall effectiveness classification and impact prediction system (PRE-CLIPS). In the future, PRE-CLIPS can enable decision makers to plan and execute more successful product recalls.

• DFG Programme: Research Grants
• International Connection: Sweden, USA
• Cooperation Partners: Stefan Hock, Ph.D.; Dr. Alexander Mafael