Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach
Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Sch...
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| Published in | Journal of service research : JSR Vol. 25; no. 4; pp. 614 - 629 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Angeles, CA
SAGE Publications
01.11.2022
SAGE PUBLICATIONS, INC |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories. |
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| AbstractList | Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories. |
| Author | Filieri, Raffaele Yang, Xingwei Lu, Xiaoqian Li, Yulei Lin, Zhibin |
| Author_xml | – sequence: 1 givenname: Raffaele orcidid: 0000-0002-3534-8547 surname: Filieri fullname: Filieri, Raffaele email: raffaele.filieri@audencia.com organization: Smith School of Business – sequence: 2 givenname: Zhibin orcidid: 0000-0001-5575-2216 surname: Lin fullname: Lin, Zhibin organization: Smith School of Business – sequence: 3 givenname: Yulei surname: Li fullname: Li, Yulei email: raffaele.filieri@audencia.com organization: Smith School of Business – sequence: 4 givenname: Xiaoqian orcidid: 0000-0001-6276-0142 surname: Lu fullname: Lu, Xiaoqian email: xiaoqianlu@jmu.edu.cn organization: Smith School of Business – sequence: 5 givenname: Xingwei surname: Yang fullname: Yang, Xingwei organization: Smith School of Business |
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| Keywords | emotion detection thematic analysis emotion service robot machine learning online customer reviews emotion, emotion detection, online customer reviews, machine learning, thematic analysis, service robot |
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| SubjectTerms | Algorithms Business administration Customer services Emotions Humanities and Social Sciences Machine learning Robots |
| Title | Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach |
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