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Abstract
To reduce service staff's burden in customer servicing and improve the performance of automatic service recovery, we propose an emotion-regulatory chatbot for service recovery applications drawing on interpersonal emotion management (IEM) theory. In addition, we develop a model to examine the underlying mechanisms through which perceived IEM strategies influence consumers’ emotions and behavioral intentions. Our experimental results verify the effectiveness of IEM strategies. We find that appraisals and consumers’ post-recovery emotions sequentially mediate the relationship between perceived emotion regulatory strategies and positive word-of-mouth (PWOM). © 2023 Elsevier B.V.
| Original language | English |
|---|---|
| Article number | 103794 |
| Journal | Information & Management |
| Volume | 60 |
| Issue number | 5 |
| Online published | 18 Apr 2023 |
| DOIs | |
| Publication status | Published - Jul 2023 |
Funding
This research was partly supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project: CityU 11507219), and CityU SRG (Project: 7005780).
Research Keywords
- Consumer service recovery
- Emotion-regulatory chatbots
- Interpersonal emotion management
RGC Funding Information
- RGC-funded
Fingerprint
Dive into the research topics of 'Emotion-regulatory chatbots for enhancing consumer servicing: An interpersonal emotion management approach'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: A Generative Deep Learning Framework for Emotion-sensitive Robo-advisors in Personal Wealth Management
LAU, Y. K. R. (Principal Investigator / Project Coordinator), Li, C. (Co-Investigator) & WONG, C. S. M. (Co-Investigator)
1/01/20 → 13/12/23
Project: Research
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