How Smart Should a Service Robot Be?

Schepers, Jeroen ; Belanche, Daniel (Universidad de Zaragoza) ; Casaló, Luis V. (Universidad de Zaragoza) ; Flavián, Carlos (Universidad de Zaragoza)

Resumen: Service robots are taking over the frontline. They can possess three types of artificial intelligence (AI): mechanical, thinking, and feeling AI. Although these intelligences determine how service robots can help customers, not much is known about how customers respond to robots of different intelligence. This paper addresses this gap, builds on the appraisal theory of emotions, and employs three online experiments and one field study to demonstrate that customers have different emotional responses to the three types of AI. Particularly, the influence of AI on positive emotions becomes stronger as the AI type becomes more sophisticated. That is, feeling AI relates more strongly to positive emotions than mechanical AI. Also, feeling AI and thinking AI increase spending and loyalty intention through customers'' positive emotions. We also identify important contingency effects of service tiers: mechanical AI is more suitable for low-cost firms, whereas feeling AI mainly benefits full-service providers. Remarkably, none of the three intelligences are directly related to negative emotions; perceived robot autonomy is an important mediator in these relationships. The findings yield concrete managerial guidance as to how smart a service robot should be by pinpointing the right type of AI given the market segment of the service provider.

Idioma: Inglés

DOI: 10.1177/10946705221107704

Año: 2022

Publicado en: Journal of Service Research 25, 4 (2022), 565-582
ISSN: 1094-6705

Factor impacto JCR: 12.4 (2022)

Categ. JCR: BUSINESS rank: 10 / 154 = 0.065 (2022) - Q1 - T1

Factor impacto CITESCORE: 17.2 - Social Sciences (Q1) - Computer Science (Q1) - Business, Management and Accounting (Q1)

Factor impacto SCIMAGO: 4.981 - Information Systems (Q1) - Sociology and Political Science (Q1) - Organizational Behavior and Human Resource Management (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/S20-20R METODO Research Group
Financiación: info:eu-repo/grantAgreement/ES/MCIU/PID2019-105468RB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Comerci.Investig.Mercados (Dpto. Direc.Mark.Inves.Mercad.)