Personality traits and complaining behaviors: A focus on Japanese consumers
Bibliographic record
Abstract
Abstract Despite the fact that personality is thought to be one of the main factors that may explain unhappy consumers’ behavior, very little is known about how it affects their attitudes and complaining strategies. This is particularly true in the case of Japanese consumers where scant research has been conducted on their complaining behaviors. Hence, the main objective of this study is to investigate the roles of three personality traits (i.e., self‐confidence, aggressiveness, and altruism) of Japanese consumers in explaining their attitudes toward complaining, perceived likelihood of successful complaints (PLSCs), and complaining behaviors. On a sample of 263 respondents, a univariate general linear model (GLM) analysis is performed to assess the moderating roles of personality traits on complaint‐related variables. The results show that, on the one hand, in contrast to self‐confidence, the levels (i.e., high vs. low) of aggressiveness and altruism have significant impacts on attitudes toward complaining and PLSCs. However, the level of self‐confidence appears to have the most significant impacts on public complaining behaviors. On the other hand, altruism is found to be the only personality trait that may explain consumers’ private complaining behaviors. Additional analyses reveal interactive effects of the personality traits on complaining attitude and behaviors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".