Empathy in service recovery: a psychophysiological study of frontline employees’ responses across communication channels
Bibliographic record
Abstract
Purpose This study aims to address three significant research gaps by examining how customers’ negative emotions (anger and anxiety) impact frontline employees’ (FLEs) empathy, emotional labor, empathic concern and prosocial service behaviors during service recovery. Design/methodology/approach The EmpaToM procedure from social neuroscience was replicated in a multimethod experimental approach, incorporating self-report measures, facial electromyography (EMG) and skin conductance responses (SCRs) to evaluate FLEs’ empathic responses to customers’ emotions. Findings The results confirm that customers’ anger and anxiety transfer to FLEs through empathy at subjective (self-reports), behavioral (facial EMG) and physiological (SCRs) levels. FLEs’ empathy remained consistent across communication channels, except for increased SCRs when interacting with anxious customers via technology. Customers’ emotions influenced surface acting intention but not deep acting, with anger leading to more surface acting. Deep acting improved empathic concern, particularly in face-to-face interactions, while surface acting reduced empathic concern but increased extra-role behaviors. Empathic concern mediated the relationship between surface acting and extra-role behavior, but did not mediate between deep acting and extra-role behavior. Research limitations/implications Future studies should investigate generational differences in FLEs’ responses, variations in service failure characteristics and the influence of perspective-taking in social cognition. Practical implications Organizations should prioritize deep-acting training to cultivate genuine empathy and use targeted strategies for managing digital service interactions. Emotional intelligence programs and performance incentives that balance empathy with compensatory behaviors can improve service recovery outcomes. Originality/value This study integrates social neuroscience methods into service research, providing a multimodal assessment (self-report, facial EMG, SCRs) of how customers’ negative emotions affect FLEs’ empathy and emotional labor. It reveals that anger and anxiety influence FLEs’ behaviors differently, with surface acting increasing extra-role behaviors while decreasing empathic concern. The findings emphasize the importance of deep acting in fostering genuine empathy, particularly in face-to-face interactions. These insights advance service recovery research and provide practical guidance for training programs to enhance emotional intelligence and digital service management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".