2023
DOI: 10.1002/cb.2138
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Robots make no effort! Service evaluation and consumer mindset

Abstract: Considering the growing acceptance of humanoid robots in the service industry, this study aimed to examine their negative impact on service evaluation, as well as the underlying mechanism of perceived effort and the moderating role of consumer mindset. Three experiments that used different service scenarios revealed that humanoid service robots negatively affected service evaluation compared to human employees, and this effect was mediated by decreased perceived effort. Furthermore, this negative impact was at… Show more

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Cited by 4 publications
(6 citation statements)
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“…For example, Goetz et al (2003) showed that an appropriate match between a robot's social cues and tasks enhances consumers' adoption of the robot. In related research, Nie and Huang (2023) also showed that consumers have more favorable evaluations towards human service employees than humanoid service robots.…”
Section: Consumer Adoption Of Ai Robotsmentioning
confidence: 91%
See 3 more Smart Citations
“…For example, Goetz et al (2003) showed that an appropriate match between a robot's social cues and tasks enhances consumers' adoption of the robot. In related research, Nie and Huang (2023) also showed that consumers have more favorable evaluations towards human service employees than humanoid service robots.…”
Section: Consumer Adoption Of Ai Robotsmentioning
confidence: 91%
“…For example, Goetz et al (2003) showed that an appropriate match between a robot's social cues and tasks enhances consumers' adoption of the robot. In related research, Nie and Huang (2023) also showed that consumers have more favorable evaluations towards human service employees than humanoid service robots. Moreover, consumers' mindsets moderate the relationship between the type of service agent, perceived effort, and consumer evaluation (Nie & Huang, 2023).…”
Section: Literature Reviewmentioning
confidence: 97%
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“…,Konya-Baumbach et al (2023),Kwak et al (2015),Landwehr et al (2011),Li, Peluso, and Duan (2023),Luo et al (2006),McLeay et al (2021),Mende et al (2019),Nenkov and Scott (2014),Nie and Huang (2023),Pitardi et al (2023),Pizzi et al (2021Pizzi et al ( , 2023,Puzakova and Aggarwal (2018),,Qiu and Benbasat (2009),Romero and Lado (2021),Roy and Naidoo (2021), Sheehan et al (2020), Shin et al (2023), Sivaramakrishnan et al (2007), Söderlund (2023), Sreejesh et al (2020), Touré-Tillery and McGill (2015), van Esch et al (2023), van Pinxteren et al (2019, 2023), Wan et al (2016), Wang et al (2022, 2023), Yao et al (2023), Yin et al (2023), Yoganathan et al (2021), Zhang, Cui, and Zhong (2023), Zhang, Zhou, and Yan (2023), and Zogaj et al . (2023), Maehle et al (2011), Schweitzer et al (2019), van Doorn et al (2017), Woodside et al (2008), and Wu et al (2023) Structural equation modelling Aw et al (2023), Belanche, Casal o, and Flavián (2021), Cheng et al (2023), Fernandes and Oliveira (2021), Gao et al (2023), Guido and Peluso (2015), Gursoy et al (2019), Han and Yang (2018), Hegner et al (2017), Hsu and Lin (2023), Li, Wang, and Song (2023), Lin et al (2020), Lu et al (2019), Malhotra and Ramalingam (2023), Melián-González et al (2021), Perez-Vega et al (2018), Pillai and Sivathanu (2020), Qiu et al (2020), Sindhu and Bharti (2023), van Pinxteren et al (2019), and Zhu and Chang (2020) Regression analysis Aggarwal and Mcgill (2012), Chandler and Schwarz (2010), Hudson et al (2016), Kim and Kramer (2015), Konya-Baumbach et al (2023), Mende et al (2019), Puzakova, Kwak, and Rocereto (2013) and van Pinxteren et al…”
mentioning
confidence: 99%