Half human, half machine: Augmenting service employees with AI for interpersonal emotion regulation

Alexander Henkel*, Stefano Bromuri, Deniz Iren, Visara Urovi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Abstract/Poster in proceedingAcademicpeer-review

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.
Original languageEnglish
Title of host publication16th International Research Conference in Service Management
Subtitle of host publicationLa Londe Conference 2020, June 2nd-5th, 2020, Proceedings
Place of PublicationMarseille
PublisherAix-Marseille Université
Pages20-20
Number of pages1
Publication statusPublished - Jun 2020
Event16th International Research Conference in Service Management - La Londe les Maures, France
Duration: 2 Jun 20205 Jun 2020

Conference

Conference16th International Research Conference in Service Management
Abbreviated titleLa Londe Conference 2020
CountryFrance
CityLa Londe les Maures
Period2/06/205/06/20

Fingerprint Dive into the research topics of 'Half human, half machine: Augmenting service employees with AI for interpersonal emotion regulation'. Together they form a unique fingerprint.

  • Cite this

    Henkel, A., Bromuri, S., Iren, D., & Urovi, V. (2020). Half human, half machine: Augmenting service employees with AI for interpersonal emotion regulation. In 16th International Research Conference in Service Management: La Londe Conference 2020, June 2nd-5th, 2020, Proceedings (pp. 20-20). Aix-Marseille Université. https://iae-aix.univ-amu.fr/sites/iae-aix.univ-amu.fr/files/la_londe_proceedings_2020_2.pdf