Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Abstract

Purpose There is growing interest in the use of human-like social robots, able to undertake complex tasks whilst building consumer engagement. However, further exploration is needed on the optimal level of humanoid appearance for service robots. In particular, the literature is limited with respect to mitigating disconfirmed expectations for robots high in human-likeness. This paper aims to address this gap by testing the effect of robot appearance, disconfirmed expectations and warmth (vs competence) on customers’ responses. Design/methodology/approach The study adopts a mixed-method design by presenting a focus group (Study 1) that guides two laboratory experiments (Studies 2 and 3). Studies 2 and 3 test for the moderating effect of warmth (vs competence) and the mediating roles of perceived eeriness and disconfirmed expectations. Findings The findings show that a robot high (vs low) in human-likeness leads to higher negative customers’ responses, which is explained by disconfirmed expectations rather than perceived eeriness. However, when customers interact with a warm (vs competent) robot high in human-likeness, this negative effect vanishes. Research limitations/implications The paper investigates boundary conditions and underlying mechanisms that affect customers’ experiences. Although the study adopts high realistic experiments, a limitation lies in not measuring customers’ actual behaviours in the field. Practical implications This study provides new insights on how the appearance and characteristics of social robots influence the consumers’ experience. By doing so, this study offers managers actionable insights (i.e. enhancing warmth) to lessen the risk of disconfirmed expectations. Originality/value The paper offers new explanations as to why human-like robots can generate negative responses from customers. Moving beyond the “uncanny valley” hypothesis, this study shows the key role of disconfirmed expectations in explaining consumers’ negative responses towards humanoid robots. Moreover, it sheds light on the moderating role of warmth (vs competence), which can mitigate such negative effects.

The impact of service robots on customer satisfaction online ratings: The moderating effects of rapport and contextual review factors

“…Recent research has investigated the antecedents of customer satisfaction with service robots. Initial studies examined the physical appearance of robots (Grazzini et al, 2023; Murphy et al, 2017; Seo, 2022; Yam et al, 2021), the nature of interactions (e.g., heartwarming interactions; Nakanishi et al, 2020), and the functional, socioemotional and relational factors that influence interactions (Fuentes‐Moraleda et al, 2020). More recent studies suggest that customer satisfaction with service robots is higher when robot types align with a firm's relationship orientation (Chang & Kim, 2022).…”

Anthropomorphism and consumer behaviour: ASPAR‐4‐SLRprotocol compliant hybrid review

“…Therefore, future researchers should reinforce the theoretical foundation of this research domain (see Table 11). 2023), Fan et al (2016Fan et al ( , 2020, Folse et al (2013), Go and Sundar (2019), Gong and Zhang (2023), Grazzini et al (2023), Hu and Min (2023), Hudson et al (2016), Hur et al (2015), Jiang et al (2023), Kim and Kramer (2015), Kim and McGill (2011), Kim et al (2016 Hierarchical linear modelling, multi-level structural equation modelling, PLS multi-group analysis, systematic literature reviews, thematic analysis, mixed-method approach, meta-analysis, multiple correspondence analysis, content analysis, bibliometric analysis, scientometric analysis and response surface analysis Abbreviations: ANCOVA, analysis of covariance; ANOVA, analysis of variance; MANCOVA, multivariate analysis of covariance.…”

When frontline robots emerge: the double-edged-sword effect of anticipated trust on intention to switch brands after service failure

“…Depending on the "uncanny valley" effect, the features of humanoid robots can also elicit the emotion of disgust among consumers, especially when the perceptual experience of a robot contradicts the explicit knowledge of a consumer (Weis and Wiese, 2017;Yam et al, 2021). This is because when consumers' anticipated trust in humanoid robots is at a high level, they believe that such robots can complete their service requests perfectly and have higher service experience expectations thereof (de Kervenoael et al, 2020); however, service failure events create expectation violations that magnify the negative effects of anthropomorphic design (Grazzini et al, 2023), whereby consumers' tolerance for service failure decreases as the anticipated trust level rises.…”

“…This is because when consumers' anticipated trust in humanoid robots is at a high level, they believe that such robots can complete their service requests perfectly and have higher service experience expectations thereof (de Kervenoael et al. , 2020); however, service failure events create expectation violations that magnify the negative effects of anthropomorphic design (Grazzini et al. , 2023), whereby consumers' tolerance for service failure decreases as the anticipated trust level rises.…”