Abstract
Purpose – With the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation(IER) and whether and how IER impacts their own affective well-being.
Design/methodology/approach– For the underlying study, an AI-based emotion recognition software was developed in order to assistservice employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.
Findings – Augmenting service employees with AI significantly improved their IER activities. Employees in the AI (versuscontrol) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.
Practical implications – Service firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replace employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.
Originality/value – The present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.
Design/methodology/approach– For the underlying study, an AI-based emotion recognition software was developed in order to assistservice employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.
Findings – Augmenting service employees with AI significantly improved their IER activities. Employees in the AI (versuscontrol) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.
Practical implications – Service firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replace employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.
Originality/value – The present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.
Original language | English |
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Title of host publication | 16th International Research Conference in Service Management |
Subtitle of host publication | La Londe Conference 2020, June 2nd-5th, 2020, Proceedings |
Place of Publication | Marseille |
Publisher | Aix-Marseille Université |
Pages | 20-20 |
Number of pages | 1 |
Publication status | Published - Jun 2020 |
Event | 16th International Research Conference in Service Management - La Londe les Maures, France Duration: 2 Jun 2020 → 5 Jun 2020 |
Conference
Conference | 16th International Research Conference in Service Management |
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Abbreviated title | La Londe Conference 2020 |
Country/Territory | France |
City | La Londe les Maures |
Period | 2/06/20 → 5/06/20 |
Keywords
- Affective well-being
- Artificial intelligence
- Augmented service employees
- Customer service interactions
- Interpersonal emotion regulation goal attainment
- Voice emotion recognition