The Influence of Emotional Labor of Service Employees on Customer Service Misbehavior and Repurchase Intention: The Role of Face
Bibliographic record
Abstract
Yanyan Yang,1 Yao Qin,2 Ziyi Wang,3 Angyu Sun4 1School of Business Administration, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People’s Republic of China; 2School of Business, Macau University of Science and Technology, Macau, People’s Republic of China; 3School of Business Administration, Zhongnan University of Economics and Law, Wuhan, Hubei, People’s Republic of China; 4Faculty of Arts and Social Science, Dalhousie University, Halifax, NS, CanadaCorrespondence: Ziyi Wang, Email 15999955121@163.comPurpose: The purpose of this study is to investigate whether the emotional labor of service employees affects customer service misbehavior and repurchase intention and to explore the mechanism and boundary conditions.Methods: We collected a total of 252 pairs of employee–customer valid matching data and used SPSS 24.0 and Mplus7.0 statistical analysis tools to perform statistical analysis and hypothesis testing.Results: The results showed that employees’ surface acting has a significant positive impact on customer misbehavior and negative impact on repurchase intention via perceived face threat, while deep acting has a significant negative impact on customer misbehavior and positive impact on repurchase intention via perceived face threat. And customer face threat sensitivity not only moderates the relationship between service employee emotional labor and customer perceived face threat but also moderates the indirect effect of surface acting on customer misbehavior and repurchase intention via customer perceived face threat.Conclusion: Based on face theory, this study explained how and when emotional labor of service employees may affect customer service misbehavior and repurchase intention. These results contribute to the emotional labor and customer service misbehavior literature by introducing perceived face threat as an underlying mechanism and face threat sensitivity as a boundary condition. In addition, this study suggests that service-oriented enterprises should pay attention to the management and guidance of employees’ emotional labor and try their best to let employees show deep acting rather than surface acting.Keywords: emotional labor, customer perceived face threat, face threat sensitivity, customer misbehavior, repurchase intention
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".