Investigating apology, perceived firm remorse and consumers’ coping behaviors in the digital media service recovery context
Bibliographic record
Abstract
Purpose This research investigates whether and how perceived firm remorse (PFR) influences consumers’ coping behaviors in the digital media service recovery context. It also examines how an apology should be delivered to generate PFR. Design/methodology/approach In Study 1, 452 mobile application service users were recruited for a survey study, and Structural Equation Modeling was used to test the research hypotheses. In Study 2, 1,255 mobile application service users were recruited for an experimental study. Findings Study 1 shows that PFR negatively influences blame attribution and positively influences emotional empathy. Emotional empathy negatively affects coping behaviors. According to this study, blame attribution and emotional empathy do not have any serial mediation effect on the relationship between PFR and coping behaviors. Only emotional empathy mediates the effect of PFR on coping behaviors. Study 2 finds that response time and apology mode jointly influence PFR. Research limitations/implications This research establishes the relationship between PFR and coping behaviors and shows the mediating role of emotional empathy in this relationship. Practical implications Service providers should consider response time and apology mode, as the two factors jointly influence the extent of PFR, which affects consumers’ coping behaviors through emotional empathy. A grace period, in which PFR does not decrease, is present when a public apology is offered. Such an effect does not exist when a private apology is offered. Originality/value This research explains how PFR influences coping behaviors and demonstrates how apology mode moderates the effect of response time on PFR in the digital media service recovery context.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".