When Your Best Customers Become Your Worst Enemies: Does Time Really Heal all Wounds?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Customer revenge and avoidance in the context of online complaints by the public are hot topics. This article helps managers to understand the phenomenon and to prevent damage. Do online complainers hold a grudge-in terms of revenge and avoidance desires-over time? Results show that time affects the two desires differently: although revenge decreases over time, avoidance increases over time, indicating that customers hold a grudge. Then, we examine the moderation effect of a strong relationship on how customers hold this grudge. Indeed firms’ best customers have the longest unfavorable reactions. This is called the love-becomes-hate effect. Specifically, over time the revenge of strong-relationship customers decreases more slowly, and their avoidance increases more rapidly, than for weak-relationship customers. Further, we explore a solution to attenuate this damaging effect: the firm offering an apology and compensation after the online complaint. Overall, strong-relationship customers are more amenable to any level of recovery attempt.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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 it