Bibliographic record
Abstract
Product recalls have become increasingly common across product categories and countries. Although recalls pose adverse consequences for businesses, regulatory agencies, and society, they also test these stakeholders’ resilience in the face of adversity. Perhaps because scholars from multiple disciplines have studied recalls for nearly four decades now, a large number of terms, most of which stay undefined, has been used to describe recalls and several closely related yet distinct phenomena. We also lack a framework that can help synthesize our knowledge and guide us toward questions that are both interesting and relevant. Finally, there has been no attention to the fundamental question of what firm actions drive the effectiveness of recalls. My thesis seeks to address these two areas of improvement. Specifically, Essay 1 defines product recall, and delineates it from related phenomena. It also offers a framework of the various strategies firms can undertake in the aftermath of defective products, factors that drive choice of these strategies, and the performance implications of the chosen strategies. Essay 2 empirically examines how recall-announcing firms’ marketing communications and marketing channels drive product recall effectiveness. The two essays thus seek to improve academics’ and practitioners’ understanding and management of product recall respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".