Expressions of customer rumination in online posts and firm responses
Bibliographic record
Abstract
Abstract When faced with service failures, customers tend to ruminate, i.e., engage in repetitive negative thoughts about service failures and their causes/consequences. Some customers express these ruminative thoughts in online posts, making the internal cognitive process of rumination publicly visible to prospective customers who read the posts. This research proposes a novel conceptualization and operationalization of customer expressions of rumination as the repetitive use of words related to (a) service failure aspects and (b) service failure causes/consequences. Across two field studies, one survey, and two experiments, this research demonstrates that rumination expressions in online posts about service failures are linked to lower sales, weaker prospective customers’ purchase intention, and more “likes” of the post. Responses expressing empathetic apologies are more effective in handling rumination expressions about service failure aspects, whereas responses mentioning compensation are more effective in handling rumination expressions about service failure causes/consequences. We urge managers to recognize the visibility and harmfulness of rumination expressions in digital outlets and provide solutions to minimize their damage.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".