Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Cigarette smoking poses a major threat to public health. While cessation support provided by healthcare professionals is effective, its use remains low. Chatbots have the potential to serve as a useful addition. The objective of this study is to explore the possibility of using a motivational interviewing style chatbot to enhance engagement, therapeutic alliance, and perceived empathy in the context of smoking cessation.Methods: A preregistered web-based experiment was conducted in which smokers (n = 153) were randomly assigned to either the motivational interviewing (MI)-style chatbot condition (n = 78) or the neutral chatbot condition (n = 75) and interacted with the chatbot in two sessions. In the assessment session, typical intake questions in smoking cessation interventions were administered by the chatbot, such as smoking history, nicotine dependence level, and intention to quit. In the feedback session, the chatbot provided personalized normative feedback and discussed with participants potential reasons to quit. Engagement with the chatbot, therapeutic alliance, and perceived empathy were the primary outcomes and were assessed after both sessions. Secondary outcomes were motivation to quit and perceived communication competence and were assessed after the two sessions.Results: No significant effects of the experimental manipulation (MI-style or neutral chatbot) were found on engagement, therapeutic alliance, or perceived empathy. A significant increase in therapeutic alliance over two sessions emerged in both conditions, with participants reporting significantly increased motivation to quit. The chatbot was perceived as highly competent, and communication competence was positively associated with engagement, therapeutic alliance, and perceived empathy.Conclusion: The results of this preregistered study suggest that talking with a chatbot about smoking cessation can help to motivate smokers to quit and that the effect of conversation has the potential to build up over time. We did not find support for an extra motivating effect of the MI-style chatbot, for which we discuss possible reasons. These findings highlight the promise of using chatbots to motivate smoking cessation. Implications for future research are discussed.

The goal of this study was to investigate the language-based strategies that computer-mediated communication (CMC) users employ to reduce uncertainty in the absence of nonverbal cues. Specifically, this study investigated the prevalence of three interactive uncertainty reduction strategies (i.e., self-disclosure, question asking, and question/disclosure intimacy) in reduced-cue settings. Moreover, this study investigated whether these uncertainty reduction strategies increased the verbal statements of affection in CMC. Eighty-one unacquainted cross-sex dyads were randomly assigned to three experimental conditions: face-to-face, visual CMC supported by a webcam, or text-only CMC. Content analysis of the verbal communication revealed that text-only CMC interactants made a greater proportion of affection statements than face-to-face interactants. Proportions of question asking and question/disclosure intimacy were higher in both CMC conditions than in the face-to-face condition, but only question asking mediated the relationship between CMC and verbal statements of affection. No differences in question asking, question/disclosure intimacy, and verbal statements of affection were observed between the text-only CMC condition and the visual CMC condition. This study provided additional support for social information processing theory by specifying the role of different language-based strategies that may be employed online.

The first aim of this study was to compare the quality of online, offline, and mixed-mode friendships (i.e., friendships that originate online and extend to offline settings) among users of a social networking site. The second aim was to investigate the relative contribution of proximity, perceived similarity, and social attraction to the quality of each of the three types of friendships. We surveyed 2,188 members of a Dutch social networking site. Results showed that the quality of all three types of friendships increased over time. The differences in quality between online and offline friendships remained significant over time, but those between mixed-mode and offline friendships disappeared. Proximity did not affect the quality of any of the three types of friendships. Perceived similarity was the most important predictor of online friendships, whereas social attraction was the most important predictor of mixed-mode and offline friendships. Our results are discussed in the light of both interpersonal and computer-mediated communication theories.

This study investigated the impact of (the lack of) audiovisual cues during conversations preceding a first face-to-face meeting among prospective daters on daters’ perceptions of partners’ social and romantic attraction. Additionally, the study examined the effect of modality switching, from online to offline interaction. Thirty-nine individuals participated in a round-robin speed dating event, resulting in 95 unique conversation pairings. For their first conversations they were randomly assigned to meet via text-based CMC or videoconferencing. The dyads then had a second encounter, which was face-to-face. Results showed more social attraction between interactants who used text-based CMC than videoconferencing, supporting the hyperpersonal model of CMC. Furthermore, after a modality switch to a face-to-face encounter the hyperpersonal effect persisted for social attraction, while romantic attraction declined.