Understanding complaining responses through consumers' self‐consciousness disposition
Bibliographic record
Abstract
Abstract The objective of this article is to determine whether a dissatisfied consumer would select a specific complaining behavior response based on his or her self‐consciousness disposition. The study used written scenarios where subjects waiting in line at a movie theater had to face additional waiting time as a consequence of an event associated with an intruder or with the service provider, and occurring either immediately in front of them or further away. Results indicated that, when faced with an additional delay related to an event occurring near them as opposed to further away from them, high private subjects, in contrast with low private subjects, had a significantly more negative perception of service quality and a strong tendency to display more negative word‐of‐mouth behavior. When faced with an additional delay related to an event occurring near them as opposed to further away from them, high public subjects, in contrast with low public subjects, had a significantly more negative perception of service quality and favored significantly more negative word‐of‐mouth behavior to express their dissatisfaction. Under a direct intrusion scenario, when compared with low public subjects, high public subjects favored significantly more negative word‐of‐mouth behavior and evaluated service quality in a significantly more negative way than when the loss of time was related to actions of the service provider. © 2002 Wiley Periodicals, Inc.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".