The more extroverted the better? Unraveling the complex relationship between service robots' personality and the service robot experience

Abstract

This study combines the customer experience, service robot, and communication literatures to explore how a service robot's personality affects the overall service robot experience. It considers individual (here, the customers' extroverted vs. introverted personality) and situational (here, hedonic vs. utilitarian service settings) contextual factors, and three specific service robot experiences (here, warmth, competence, and discomfort). A pretest (n = 81) shows that a robot's personality (here, extroversion vs. introversion) can be shaped using verbal and nonverbal cues in the service robot design. Applying Complexity and Configuration Theories and using the novel fuzzy‐set qualitative comparative analysis and necessary condition analysis (NCA) techniques, the main study (n = 279) reveals the complex impact of the service robot personality on the overall service robot experience. Unlike conventional methods (e.g., SEM and regression) yielding averaged results, our findings show that various configurations with both introverted and extroverted robot personalities can equally lead to positive service robot experiences, based on the individual and situational context in which customers are embedded and the extent to which warmth, competence and discomfort are perceived. Hence, the more extroverted is not always better. Finally, the NCA findings reveal the minimum competence level that is required to obtain a certain level for the overall service robot experience. By understanding the intricate link between robot personality and its context, this research helps companies in strategically enhancing the service robot experience.

When the recipe is more important than the ingredients: Unveiling the complexity of consumer use of voice assistants

“…Second, we show what makes VAs attractive to whom by investigating the alternative interplays between VA attributes and consumer characteristics. In doing so, we extend previous studies that show that consumer personality can influence or moderate the impact of other antecedents on consumer intention to use VAs (Bawack et al, 2021; Guha et al, 2023; Li & Larivière, 2023). Above and beyond, we reveal distinct constellations of humanlike and technological attributes across single versus multimodal devices that drive consumers with different personalities and learning styles to continue using VAs.…”

“…Fundamental differences unique to extroverts, like their preferences for social interactions and companionship, appear to impact their usage of AI-powered agents, including VAs, chatbots, and service robots (Li & Larivière, 2023). Extroversion has been correlated with reliance on social support for problem-solving from important others and intelligent agents (Bawack et al, 2021).…”

“…In contrast, introverts tend to be taciturn and socially aloof (Li & Larivière, 2023). They are concerned about revealing their personal information and social judgment and are more at ease in interactions that require fewer social skills (Kim et al, 2018).…”

“…Configuration analysis, particularly fsQCA, allows for examining the necessity and sufficiency of theoretically relevant conditions and their combinations for an outcome. Researchers have adopted fsQCA in explicating complex phenomena, especially when involving human behavior that is typically unlikely to follow a symmetrical stance (Li & Larivière, 2023; Schmitt et al, 2017). Building on this perspective, we develop a theoretical framework (Figure 1) that proposes that consumers' intention to continue using VAs represents a complex behavioral manifestation that is formed by the interplays of VA attributes and consumer characteristics.…”

“…The extant research on multiformat communication (Moffett et al, 2021) and MRT (Flavián et al, 2023; Li & Larivière, 2023) suggests that the affordance of voice‐based interfaces varies for consumers with different communication preferences. These communication preferences are determined by consumers' learning styles (e.g., verbalizer, visualizer; Sojka & Giese, 2006).…”