Less speed more haste: The effect of crisis response speed and information strategy on the consumer−brand relationship
Bibliographic record
Abstract
Abstract This paper investigates the relationship between firm crisis behavior and the resulting consumer–brand relationship (CBR) response. Drawing from theoretical traditions in brand transgressions, service failure, and crisis communications, we use longitudinal survey data combined with archival social media data to empirically test the effect of crisis response speed and crisis information strategy on the short‐term consumer crisis response evaluations (1 month after crisis response), and the long‐term CBR (1 year after crisis response). Results show that, contrary to intuitive expectations, a faster firm response is not always better, as a slower response was found to result in higher crisis response evaluations. We also show that this effect depends on the consistency of the communication strategy with the first active response. Specifically, when a firm prioritizes safety information ( instructing strategy ), a faster response is better. Whereas, when the firm prioritizes well‐being information ( adjusting strategy ), a slower response is better. We argue the counterintuitive finding that a slower response is better implies that reacting too quickly may signal rashness and unpreparedness to the customer, leading to more negative evaluations. We term this distinction the difference between being responsive (fast but considered) and reactive (faster but rash).
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".