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The e-services market is expected to reach nearly $500bn globally by 2028. As this marketplace grows, customer-to-customer interactions (CCIs) occurring through virtual channels will likely increase. Consequently, the purpose of this research is to examine how the context in which CCI’s occur (i.e. virtual vs in-person) and the frequency of their occurrence affects customer identification, leading to increased customer engagement and more favorable purchase behaviors.

Two studies were conducted to test the proposed models and hypotheses. The sample for Study 1 is comprised of college students taking in-person or online classes (n = 290). In Study 2, members of an online brand community (n = 125) were surveyed. Hypotheses were tested using structural equation modeling (SEM).

Overall, results support a mediation effect such that CCI context (virtual vs in-person) affects customer engagement and purchase behaviors via customer identification. Specifically, Study 1 finds that customer engagement behaviors (CEBs) are greater for in-person CCIs due to the frequency of interactions and heightened identification between customers. Study 2 further examines the CCI frequency-identification link and finds that customer-firm identification is the only form of identification that affects CEBs and purchase behaviors.

Limited customer engagement research has examined the effects of CCIs on CEBs, and research has rarely compared in-person to virtual CCI contexts. This paper addresses these shortcomings by testing the effects of in-person and virtual CCIs on CCI frequency, identification and CEBs. This research fills another important gap in the literature by considering the unique effects of specific dimensions of customer identification on CEBs and purchase behaviors.

With the expansion of artificial intelligence (AI) technology in everyday life, it is critical to discuss how and why consumers respond in certain ways to AI agents. However, few studies have examined the mechanisms underlying users’ responses to these agents. This study aims to identify such mechanisms and discuss how users form loyalty toward AI agents. Specifically, this study addresses interactivity with AI voice assistants as a key determinant of user loyalty, presenting user perceptions of the human-likeness of AI voice assistants and communication self-efficacy as sequential mediators.

We investigate the effects of human-likeness and communication self-efficacy on the relationship between interactivity and loyalty to AI voice assistants by developing a sequential mediation model. To estimate the empirical model, data were collected through an online survey with 330 respondents.

The results indicate that interactivity influences loyalty directly and positively. In addition, interactivity affects loyalty indirectly sequentially through human-likeness and communication self-efficacy.

By uncovering the psychological mechanisms underlying users’ loyalty to AI voice assistants, this study provides new academic and managerial insights that have not been clearly identified in the current literature.