How Human-like Behavior of Service Robot Affects Social Distance: A Mediation Model and Cross-Cultural Comparison

Abstract

Previous studies on the human likeness of service robots have focused mainly on their human-like appearance and used psychological constructs to measure the outcomes of human likeness. Unlike previous studies, this study focused on the human-like behavior of the service robot and used a sociological construct, social distance, to measure the outcome of human likeness. We constructed a conceptual model, with perceived competence and warmth as mediators, based on social-identity theory. The hypotheses were tested through online experiments with 219 participants from China and 180 participants from the US. Similar results emerged for Chinese and American participants in that the high (vs. low) human-like behavior of the service robot caused the participants to have stronger perceptions of competence and warmth, both of which contributed to a smaller social distance between humans and service robots. Perceptions of competence and warmth completely mediated the positive effect of the human-like behavior of the service robot on social distance. Furthermore, Chinese participants showed higher anthropomorphism (perceived human-like behavior) and a stronger perception of warmth and smaller social distance. The perception of competence did not differ across cultures. This study provides suggestions for the human-likeness design of service robots to promote natural interaction between humans and service robots and increase human acceptance of service robots.

Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions

“…Survey sample sizes range from very large (Hernandez‐Ortega & Ferreira, 2021; Song et al, 2022) to much smaller samples (Gillath et al, 2021). Both experimental and survey research mainly focus on one country rather than on cross‐cultural comparisons of AI–human relationships, with only one paper (Li et al, 2022) exploring cross‐cultural comparisons between Chinese and US users. Quantitative study participants are usually students (e.g., De Cicco et al, 2020) and current AI chatbot users (e.g., Liu et al, 2022).…”

“…Experimental methodologies, on the other hand, tend to focus on whether certain features of AI can alter relationship development. For instance, experimental studies focus on how chatbot features such as anthropomorphism (Abdi et al, 2022; Crolic et al, 2022), empathy (de Gennaro et al, 2020; Liu‐Thompkins et al, 2022), human likeness (Letheren et al, 2021; Li et al, 2022), and levels of supportiveness (Meng & Dai, 2021) affect relational outcomes. Whereas surveys and experiments focus on sentiments and potential mechanisms that can enhance or detract from human–AI relationships, qualitative studies, such as interviews, explore rich insights into a broader perspective on topics such as trust and reciprocity (Brandtzaeg et al, 2022), companionship (Jiang et al, 2022), and processes for relationship development (Pentina et al, 2023; Skjuve et al, 2021), and are more exploratory in nature.…”

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

“…Concurrently, Europe and Asia are making significant strides in advancing key technologies and introducing innovative solutions in this dynamic field [73]. The divergence in policy focus and cultural acceptance across regions has given rise to distinct fault diagnosis methods for service robots [74,75]. The factors mentioned above have contributed to the regional differences in research on service robots' fault diagnosis.…”

Research on the Frontier and Prospect of Service Robots in the Tourism and Hospitality Industry Based on International Core Journals: A Review

“…Service robots have acquired some human-like characteristics and abilities with the help of sensors, AI and other technologies, and their human-like attributes have attracted considerable attention from researchers. These attributes include appearance [6,28,46,[50][51][52], behavior [53,54], gender [28], and language styles [30]. Anthropomorphism theory is widely used in research, which defines the tendency to attribute human characteristics and emotions to inanimate objects or animals so as to rationalize their behavior as "anthropomorphism" [55].…”

“…The most discussion has occurred around three types of factors: robotic, consumer personal, and environmental factors. Robotic factors are mostly related to the functional attributes of robots, in addition to the commonly discussed anthropomorphic appearance [6,28,46,[50][51][52], and also include performance efficacy [46], sociability [73], automation [51], proactivity [53], empathy [60], and competence [54]. Among consumer personal factors, the influence of demographic factors has been verified, such as gender [74], age [75], cultural background [76], education experience [77], social class [69], and previous robot service experience [78].…”