Elderly customers’ reactions to service failures: the role of future time perspective, wisdom and emotional intelligence
Bibliographic record
Abstract
Purpose Considering the scant scholarly research on elderly customers’ behaviors, this study aims to investigate elderly customers’ reactions to service failure. Additionally, it takes into account customers’ emotions and abilities to cope with stressful situations and achieve successful problem-solving complaining. In particular, future time perspective, wisdom and emotional intelligence were examined to delineate their impacts on the elderly’s responses to service failures. Design/methodology/approach Data were collected in a French city through mall-intercept interviewing. In total, 240 respondents participated, based on their retrospective service failure experience. PLS-SEM was used to analyze the data. Findings Both wisdom and emotional intelligence were found to directly and positively impact problem-solving complaining. Future time perspective, however, only had an indirect effect on problem-solving complaining through wisdom and emotional intelligence. Originality/value To the best of the authors’ knowledge, this is the first study to shed some light on how elderly customers constructively react to service failures. To this end, it uses future time perspective, wisdom and emotional intelligence, as well as their interrelationships, to explain elderly customers’ problem-solving complaining.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".