When Customer Love Turns into Lasting Hate: The Effects of Relationship Strength and Time on Customer Revenge and Avoidance
Bibliographic record
Abstract
This article explores the effects of time and relationship strength on the evolution of customer revenge and avoidance in online public complaining contexts. First, the authors examine whether online complainers hold a grudge—in terms of revenge and avoidance desires—over time. They find that time affects the two desires differently: Although revenge decreases over time, avoidance increases over time, indicating that customers indeed hold a grudge. Second, the authors examine the moderation effect of a strong relationship on how customers hold this grudge. They find that firms' best customers have the longest unfavorable reactions (i.e., a longitudinal love-becomes-hate effect). Specifically, over time, the revenge of strong-relationship customers decreases more slowly and their avoidance increases more rapidly than that of weak-relationship customers. Third, the authors explore a solution to attenuate this damaging effect—namely, the firm offering an apology and compensation after the online complaint. Overall, they find that strong-relationship customers are more amenable to any level of recovery attempt. The authors test the first two issues with a longitudinal survey and the third issue with a follow-up experiment.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".