Product Recalls and Supply Base Innovation
Bibliographic record
Abstract
Problem definition: Suppliers are increasingly involved in innovation activities that contribute to a firm’s product quality and introduce risks to firms’ quality control, leading to quality failures and recalls. This quality trade-off suggests the possibility of a nonlinear relationship between supplier innovation and product recalls, which is the focus of this research. Recall literature focuses on firms’ internal drivers of recalls, whereas anecdotal evidence increasingly points to the role of external drivers, such as suppliers. We contribute to the literature by examining supplier innovation as an external driver leading to recalls via quality and risk spillovers. Methodology/results: We collect and assemble a unique panel data set of consumer product recalls from firms and their supply bases (i.e., first tier suppliers). We estimate econometric models to examine the nonlinear relationship between supply base innovation, measured by research and development (R&D) intensity of the supply bases, and the likelihood of product recalls. We find a quadratic (i.e., U-shaped) relationship between the probability of recalls and supply base R&D intensity. We also find that this nonlinear relationship is critically related to three specific sources of risk: radicalness of supplier innovation, technological distance between firms and their suppliers, and complexity of supply base. Managerial implications: Our findings suggest that firms should be mindful of the quality trade-offs in encouraging supplier innovation to reduce product recalls. Further, to minimize recall risks, firms should better evaluate and manage the risks associated with external supplier knowledge that is novel and different and closely work with global suppliers to reduce coordination challenges in knowledge transfer and integration. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1213 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".