Product recall crisis management: the impact on manufacturer's image, consumer loyalty and purchase intention
Bibliographic record
Abstract
Purpose This paper aims to examine the impact of recall crisis management on the manufacturer's image, consumers' loyalty and future purchase intentions. More specifically, this research aims to clarify the types of recall strategies that companies put forward, as well as their impact on consumers' behaviors and perceptions of the manufacturer's image. Design/methodology/approach The current study focuses on vehicle users who have either experienced automobile recalls or heard them discussed. Data were collected via car‐related web sites. The final sample comprises 573 people. The direction and strength of the relationships between various consumers' attitudes toward the different recall methods and their purchase intention are assessed through structural equation modeling (SEM). Findings Results show that recalls contested by manufacturers have a significant negative impact on manufacturers' image, as well as on consumers' loyalty and purchase intentions. On the other hand, voluntary recalls or improvement campaigns have a significant positive impact on the manufacturer's image, as well as consumers' loyalty and purchase intentions. Research limitations/implications Proactive strategies are the best solution to avoid a loss in consumer loyalty to the manufacturer during a recall crisis. In the contrary, manufacturers' adoption of reactive strategies harms their image, as well as consumers' loyalty. This translates into a negative impact on future purchase intentions and manufacturers' market share. This study concludes by recommending appropriate strategies to limit possible negative effects of product recalls. Research limitations Some variables (such as media, the degree of severity of recalls and the frequency of recalls) are not investigated in this study. Additionally, the research is limited to the automobile industry. Other industries that also experience recalls (such as the pharmaceutical industry) might be considered in future research in order to confirm the consistency of the research findings. Originality/value The approach adopted by the current research is relatively different from earlier studies that directly link the types of recalls to the danger perceived by consumers that in turn affects their purchase intentions. In the current study, recalls indirectly impact purchase intentions principally via the manufacturer's image and brand loyalty. Additionally, the originality of this research stems from the fact that the manufacturer's image is considered as part of the changeable legacy of the company that itself may be affected by the “crisis situation,” not a stable asset.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".