Attachment style moderates consumer responses to service robots
April 23, 2021
The global service robotics market was valued at $11.48 billion in 2018 and is expected to reach almost $24 billion by 2022 and $51 billion by 2024, at a compound annual growth rate of more than 25% over the forecasted period of 2019-2024 (marketresearchengine.com). Frontline service robots have the capacity to improve many facets of a service, including efficiency, in service locations where the type of customer interactions is repetitive in nature or services where customers may be indifferent to social-emotional and relational elements. However, customer acceptance of human-robot interaction in the service industry is still low, with most people still preferring to interact with humans. Thus, one of the most important challenges for marketers is understanding in depth customers’ responses to service robots and what situations can be most appropriate for incorporating successfully service robots in their interactions with customers. Notably, marketing researchers have grown more interested in this topic, but produced mixed results.
The present article of Pozharliev, De Angerlis and Rossi (2021), published in Psychology & Marketing, studies the effect of human-robot interactions in service settings by investigating the role of individuals’ attachment style, which describes the systematic pattern of affective and intentional responses in a human-human interaction. Authors suggest that individual attachment styles, defined as working models of how people behave in social interactions might be particularly relevant to understanding customers’ physiological, affective, attitudinal and behavioral responses to interactions with frontline service robots. According to the social response theory, customers tend to ascribe human characteristics to interactive technology— such as gender, name, and ethnicity—leading to an interaction that increasingly resembles a “normal” social interaction. The core premise of the present work is that in some cases frontline service robots might elicit affective and attitudinal responses that approximate the social responses typical of human-human interaction. Thus, authors argue that individual working models of behavior in social interactions with humans (i.e., attachment styles) may apply to human-robot service interaction and thus influence customers’ affective and attitudinal responses toward frontline (social) service robots. The study reports the results of three online experiments that collectively investigate affective responses (i.e., experienced pleasantness) and attitudinal responses (i.e., perceived empathy and satisfaction) to frontline service robots, alongside behavioral intentions (e.g., word-of-mouth, hereafter WOM) to test its hypothesis.
In particular, Study 1 shows that the effect of the type of service agent (human vs. robot) on customer satisfaction is moderated by customers’ anxious attachment style (AAS) score. Specifically, customers scoring low on the AAS scale report an increase in customer satisfaction in relation to a human (vs. robot) service agent. Study 2 provides convergence on Study 1’s findings by showing that the customers scoring low on AAS are more satisfied with human service agents compared to service robots even when the human voice of the robot is replaced by a robotic voice. Moreover, Study 3 provides evidence that explains the moderating effect of customers’ AAS on satisfaction found in Study 1 through the perceived pleasantness. Specifically, the results show that the effect of AAS on customer satisfaction found in Study 1 is completely mediated by the experienced pleasantness. Finally, the results of Study 2 suggest that the moderating role of customers’ AAS on their affective and attitudinal responses to human (vs. frontline) service agent extends to customers’ behavioral responses (WOM). Study 3 is consistent with the previously reported findings. Moreover, the results of Study 3 show that the effect of customer AAS on their attitudinal responses is completely mediated by perceived empathy. In general, customers showed higher perceived empathy and intention to spread positive WOM for the interaction with the human (vs. frontline robot) service agent. Customers’ scores on the AAS scale moderated their perception of the agent’s empathy and the intention to spread positive WOM. Specifically, customers scoring low on the AAS scale reported higher perceived empathy and higher positive WOM in relation to a human (vs. robot) service agent, while customers scoring high on the AAS scale showed no such difference.
The results of this study have actionable managerial implications. First, marketers might consider AAS as a segmentation criterion for better segmenting their target markets. Second, in terms of targeting customers, marketing managers should initially focus only on customers who score high on the AAS scale, because these customers might respond more favourably to service robots replacing human service agents. Third, in terms of marketing communication, service offerings targeting customers who score low on AAS should be framed around more emotional and experiential themes that (1) depict service robots that care about customers (e.g., artificial empathy); (2) show shopping scenarios where customers are laughing and having fun alongside a robot companion, or (3) illustrate customer situations where humans feel empathy for service robots. Fourth, the present studies reveal that customers with a low AAS score should be allowed to select their preferred type of service agent. Finally, marketers should be aware that moderating role of customers’ AAS score on the their affective, attitudinal and behavioral responses to frontline service robots was not affected by the tangential differences in robot design features (e.g. voice type and level of human-like physical appearance).