000119953 001__ 119953
000119953 005__ 20250722154011.0
000119953 0247_ $$2doi$$a10.1177/10946705221107704
000119953 0248_ $$2sideral$$a129960
000119953 037__ $$aART-2022-129960
000119953 041__ $$aeng
000119953 100__ $$aSchepers, Jeroen
000119953 245__ $$aHow Smart Should a Service Robot Be?
000119953 260__ $$c2022
000119953 5060_ $$aAccess copy available to the general public$$fUnrestricted
000119953 5203_ $$aService 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.
000119953 536__ $$9info:eu-repo/grantAgreement/ES/MICIU/PID2019-105468RB-I00$$9info:eu-repo/grantAgreement/ES/DGA-FSE/S20-20R METODO Research Group
000119953 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000119953 590__ $$a12.4$$b2022
000119953 592__ $$a4.981$$b2022
000119953 591__ $$aBUSINESS$$b10 / 154 = 0.065$$c2022$$dQ1$$eT1
000119953 593__ $$aInformation Systems$$c2022$$dQ1
000119953 593__ $$aSociology and Political Science$$c2022$$dQ1
000119953 593__ $$aOrganizational Behavior and Human Resource Management$$c2022$$dQ1
000119953 594__ $$a17.2$$b2022
000119953 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000119953 700__ $$0(orcid)0000-0002-2291-1409$$aBelanche, Daniel$$uUniversidad de Zaragoza
000119953 700__ $$0(orcid)0000-0002-9643-2814$$aCasaló, Luis V.$$uUniversidad de Zaragoza
000119953 700__ $$0(orcid)0000-0001-7118-9013$$aFlavián, Carlos$$uUniversidad de Zaragoza
000119953 7102_ $$14011$$2095$$aUniversidad de Zaragoza$$bDpto. Direc.Mark.Inves.Mercad.$$cÁrea Comerci.Investig.Mercados
000119953 773__ $$g25, 4 (2022), 565-582$$pJ. serv. res.$$tJournal of Service Research$$x1094-6705
000119953 8564_ $$s1468706$$uhttps://zaguan.unizar.es/record/119953/files/texto_completo.pdf$$yVersión publicada
000119953 8564_ $$s2817293$$uhttps://zaguan.unizar.es/record/119953/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000119953 909CO $$ooai:zaguan.unizar.es:119953$$particulos$$pdriver
000119953 951__ $$a2025-07-22-15:35:44
000119953 980__ $$aARTICLE