Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how the location of a firm’s headquarters and component sourcing impact a firm’s responsiveness in a product-harm crisis in local market. Design/methodology/approach The authors collected data on 1,251 vehicle recalls from 12 manufacturers, six in the USA, three in Germany, and three in Japan. All of the recalls occurred in the USA between 2002 and 2010. The time the product was first released into the marketplace was used as the starting point while the time the recall was initiated (if at all) was used to record the probability of the product recall over time. Specifically, a survival analysis with an accelerated failure time model was employed to examine the speed with which a product is recalled. The authors examined the impact of foreign composition using information provided by the American Automobile Labeling Act, which lists the proportion of each vehicle that is composed of domestic parts (USA/Canada) and foreign parts. Organizational characteristics (i.e. size, market share, assets, net income, and reputation) and recall size (i.e. number of affected vehicles) that might have an effect on time to recall were controlled for. Findings The authors found that firms headquartered outside the local market would take longer to issue a product recall than firms that were headquartered in the local market. Firm headquartered outside the local market can reduce the time taken to recall by sourcing parts from the local marketplace, rather than from abroad. Interestingly, even local firms are affected by the location of component sourcing, such that they take longer to issue a recall if they sourced parts from abroad. Originality/value Research in international marketing has examined the benefits of integration to firms, but has not studied the risks of integration. By highlighting the challenges of managing institutional differences and integration difficulties, the authors show that location of headquarters and the location from where components are sourced have an effect on firm responsiveness in product-harm crises. Further, the authors build on the global supply chain management literature that has shown the effect of upstream activities (i.e. foreign production) on downstream activities (i.e. product quality). Specifically, the authors show that upstream activities can not only affect product quality, but also the ability of firms to respond to those product qualities in a timely fashion.
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 | 0.001 |
| 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) | 0.001 | 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".