Customer’s social cognition in service recovery satisfaction with human vs robot agent
Bibliographic record
Abstract
Purpose Service failures evoke negative customer emotions, which human agents respond to through emotional labor. In turn, customers empathize with the human agent, providing a satisfying service recovery experience. However, robot agents could replace human agents and replicate emotional labor strategies. This study addresses whether customers empathize with apologetic robot agents and how it would affect the service recovery experience. Design/methodology/approach Drawing on emotional labor, social cognition and justice theory, two online scenario-based experiments (N1 = 411; N2 = 253) were designed in which customers watched a video simulating an interaction with a human or a robot agent during a service recovery procedure. Findings Study 1 shows that robot agents handle emotionally driven service recovery interactions and prompt desirable postrecovery behaviors (e.g. brand loyalty). Study 2 identifies customers’ empathy and compassion as mediators, explaining the effect of normative empathic display on customers' perceptions of interactional justice and behavioral intentions. Practical implications Robot agents are reliable substitutes for human agents in handling service recovery procedures. Customers can empathize with robot agents, leading to satisfying service experiences. Originality/value This study demonstrates customers’ capacity to empathize with robot agents during a service recovery procedure. It is also the first application in service research of the EmpaToM experimental procedure from social neuroscience to explore the social cognition dynamic between customers and service agents at the service encounter.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".