Understanding robot acceptance/rejection: the SAR model

Mele, Cristina; Spena, Tiziana Russo; Tregua, Marco; Laddaga, C.; Ranieri, Angelo; Ruggiero, Angelo; Gargiulo, Roberta

doi:10.1109/ro-man47096.2020.9223577

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Additionally, consumer’s emotional engagement is the core strategy in the adoption of humanoid robots ( Langen and Heinrich, 2019 ). In hospitality, service robots are employed to provide basic information to guests and entertain them ( Mele et al, 2020 ). Thus, entertainment value is predicted as a driver of emotional engagement.…”

Section: Servbot Frameworkmentioning

confidence: 99%

Perceived Service Quality in HRI: Applying the SERVBOT Framework

et al. 2021

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Services are intangible in nature and as a result, it is often difficult to measure the quality of the service. In the service literature, the service is usually delivered by a human to a human customer and the quality of the service is often evaluated using the SERVQUAL dimensions. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment value and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robot’s service quality. The paper proposes the SERVBOT model to assess a social robot’s service quality. It identifies, reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional engagement and future intentions to use the social robot in a concierge service setting. The model was tested using student sampling, and a total of 94 responses were collected for the study. The findings indicate empathy and entertainment value as key predictors of emotional engagement. Further, emotional engagement is a strong predictor of future intention to use a social robot in a service setting. This study is the first to propose the SERVBOT model to measure social robot’s service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends on the literature by exploring the key factors that influence the use of social robots (i.e. emotional engagement).

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Section: Servbot Frameworkmentioning

confidence: 99%