Online Health Communities: The Impact of AI Conversational Agents on Users

The literature lacks evidence on the acceptability of AI conversational agents (chatbots) and the motivations for their adoption in healthcare industry. This paper aims to examine the acceptance of these chatbots based on the UTAUT model in Online Health Communities (OHCs) and to explore what kind of impact these particular features have on the users' intentions, and the actual use of these communities. Based on a quantitative methodology approach, we rely on the UTAUT model to study OHCs users' behavior and intentions towards such AI conversational agents/chatbots. The study shows that the UTAUT has proved to be a strong and reliable model for evaluating the adoption and application of AI conversational agents (chatbots) in OHCs. A questionnaire was employed to collect data, and respondents are chosen using the cluster sampling approach. On a 7 Likert scale, respondents were asked to select which choice best suited their reaction to any of the topics presented. A total of 632 answers from 62 countries were received, with 443 of them being complete. Many tests were used to examine the data such as the bivariate and multivariate analysis. Since the returned p-value for most of the hypotheses tested was 0.05, the majority of the hypotheses tested were accepted. Findings showed the interrelations between AI conversational agents/chatbots and OHCs on users' Behavioral Intention (BI). The main constructs of the UTAUT model (Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions) had a significant impact on the participants' BI and Usage Behavior (UB) for AI conversational agents/chatbots in OHCs. As for moderators, gender and age had no effect on BI and UB. Understanding the main factors that have a significant impact on users' intentions to use chatbots in OHCs determines the significance of those results.