The role of dissonance reduction and co-creation strategies in shaping smart service satisfaction – the case of Uber

Mousavi, Reza; Hazarika, Bidyut Bikash; Chen, Kuanchin; Rienzo, Thomas

doi:10.1080/0144929x.2020.1795258

Cited by 18 publications

(11 citation statements)

References 107 publications

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“…Platform responsiveness means the ability of online representatives to respond to customers and help them solve problems quickly. Prior studies have confirmed the positive influence of platform or website responsiveness on customer satisfaction in the sharing economy context (Cheng et al, 2018;Huarng & Yu, 2019;Ju, Back, Choi, & Lee, 2019;Mousavi, Hazarika, Chen, & Rienzo, 2020). If ride-sharing company representatives respond to customers' questions and help them as quickly as possible, customers will feel valued.…”

Section: The Research Model and Hypothesesmentioning

confidence: 89%

A Cross-Cultural Perspective on the Blended Service Quality for Ride-Sharing Continuance

Shiau

Chen

et al. 2021

Journal of Global Information Management

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This research aims to elucidate the service quality factors influencing customer satisfaction with ride-sharing services. Particularly, this research compares Chinese and US consumers to identify the influence of national culture on user attitudes. Empirical research was conducted via two online surveys in China and the US. Data were collected and analyzed using partial least squares (PLS) analysis. The results show that while the satisfaction of the Chinese ride-sharing consumers was affected primarily by reliability, followed by platform assurance and empathy, the satisfaction of American consumers was influenced mainly by the conditions of ride-sharing vehicles (tangibles). The PLS-MGA results indicate that satisfaction with ride-sharing services had a significantly stronger impact on American than on Chinese continuance intention. Managerial implications are provided.

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Section: The Research Model and Hypothesesmentioning

confidence: 89%

A Cross-Cultural Perspective on the Blended Service Quality for Ride-Sharing Continuance

Shiau

Chen

et al. 2021

Journal of Global Information Management

Self Cite

View full text Add to dashboard Cite

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“…The SERVQUAL model was the initial scale developed by Parasuraman et al (1991) to measure service quality. Service quality has been wildly investigated in previous studies (Davari and Iyer, 2016;Mousavi et al, 2022;Ngo and O'Cass, 2013;Pee et al, 2018). Service quality Customers' co-creation experiences with AI is positively related to customers' perceived value and purchase intentions (Dedeke, 2016).…”

Section: Service Qualitymentioning

confidence: 99%

“…(1991) to measure service quality. Service quality has been wildly investigated in previous studies (Davari and Iyer, 2016; Mousavi et al. , 2022; Ngo and O’Cass, 2013; Pee et al.…”

Section: Literature Reviewmentioning

confidence: 99%

The effects of AI service quality and AI function-customer ability fit on customer's overall co-creation experience

Yang

2023

IMDS

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PurposeThe application of artificial intelligence (AI) in the customer market has completely changed customer behaviors. This study aims to investigate the customers' co-creation experiences with AI in the digital age.Design/methodology/approachAn online survey was used to collect data from 699 customers who had used AI-enabled banking services. Hypotheses were validated using partial least squares modeling.FindingsThe findings indicate that the customer response capabilities (e.g. perceived response expertise and perceived response speed) serve as the intermediate processes between the AI service quality and the overall co-creation experience with AI. Moreover, AI function-customer ability fit negatively moderates the direct relationship between the AI service quality and the overall co-creation experience with AI.Originality/valueThis study improves the current understanding of co-creation by investigating the human–machine co-creation (e.g. customer–AI co-creation) instead of human–human co-creation.

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“…Yet, scholars have also applied it to various risk-related study fields, such as travel risks (Zheng et al , 2021) and security risks (Shore et al , 2022). As Jelani (2016) asserted that peoples in “the gig economy takes more risks”, some scholars have applied PMT in the gig economy to understand relevant risks (Mousavi et al , 2020; Chen and Lu, 2021). However, they mainly focused on the consumer of the gig economy, overlooking the risks, such as legal risks, faced by gig workers.…”

Section: Literature Reviewmentioning

confidence: 99%

Gig workers’ self-protective behaviour against legal risks: an application of protection motivation theory

Tsang

2022

IJCHM

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Purpose Given the illegal nature of the gig economy in some cities, this study aims to draw on protection motivation theory to examine the formation of Uber drivers’ self-protective behaviour against legal risks. Design/methodology/approach In-depth interviews with 10 Uber drivers in Hong Kong were conducted to yield eight maladaptive perceptions, which were then validated using online surveys completed by 232 Uber drivers. These results were then used to examine a mechanism through which threat appraisal, coping appraisal and maladaptive perceptions influence drivers’ unwillingness to work and weekly working hours. Findings Eight maladaptive perceptions were found to empirically fit a bidimensional conceptualization of cognitive and affective components that significantly reduce workers’ unwillingness to work illegally in the gig economy. The effects on Uber drivers’ unwillingness to work and weekly working hours varied across threat appraisal, coping appraisal and maladaptive perceptions. Practical implications Platform companies should find the results insightful because they demonstrate ways of negotiating with governments about its legality of the gig economy. The findings can also assist governments with policy development to make sense of illegal gig work or to legalize the gig economy. Originality/value This study complements the overoptimistic discussion of the gig economy to investigate why people engage in illegal work in this context. Protection motivation theory is applied to a new domain to explore gig workers’ maladaptive perceptions of illegal working.

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The role of dissonance reduction and co-creation strategies in shaping smart service satisfaction – the case of Uber

Cited by 18 publications

References 107 publications

A Cross-Cultural Perspective on the Blended Service Quality for Ride-Sharing Continuance

A Cross-Cultural Perspective on the Blended Service Quality for Ride-Sharing Continuance

The effects of AI service quality and AI function-customer ability fit on customer's overall co-creation experience

Gig workers’ self-protective behaviour against legal risks: an application of protection motivation theory

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