Background: The emerging Artificial Intelligence (AI) based Conversational Agents (CA) capable of delivering evidence-based psychotherapy presents a unique opportunity to solve longstanding issues such as social stigma and demand-supply imbalance associated with traditional mental health care services. However, the emerging literature points to several socio-ethical challenges which may act as inhibitors to the adoption in the minds of the consumers. We also observe a paucity of research focusing on determinants of adoption and use of AI-based CAs in mental healthcare. In this setting, this study aims to understand the factors influencing the adoption and use of Intelligent CAs in mental healthcare by examining the perceptions of actual users. Method: The study followed a qualitative approach based on netnography and used a rigorous iterative thematic analysis of publicly available user reviews of popular mental health chatbots to develop a comprehensive framework of factors influencing the user’s decision to adopt mental healthcare CA. Results: We developed a comprehensive thematic map comprising of four main themes, namely, perceived risk, perceived benefits, trust, and perceived anthropomorphism, along with its 12 constituent subthemes that provides a visualization of the factors that govern the user’s adoption and use of mental healthcare CA. Conclusions: Insights from our research could guide future research on mental healthcare CA use behavior. Additionally, it could also aid designers in framing better design decisions that meet consumer expectations. Our research could also guide healthcare policymakers and regulators in integrating this technology into formal healthcare delivery systems. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/1/ Recommended Citation Prakash, Ashish Viswanath and Das, Saini (2020) "Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 1. DOI: 10.17705/1pais.12201
PurposeThe purpose of this paper is to unearth various dimensions of employee experience (EX) and explore how pandemic impacted various EX factors using online employee reviews. The authors identify employee-discussed EX-factors and quantify the associated sentiments and importance.Design/methodology/approachThis paper employs Latent Dirichlet Allocation on the online employee reviews to identify the key EX-factors. The authors probe sentiments and importance associated with key EX-factors using sentiment analysis, importance analysis, regression analysis and dominance analysis.FindingsThe result of topic modeling identifies 20 EX-factors that shape overall EX. While skill development plays a major role in shaping overall EX, employees perceived Salary and Growth as the most important EX-factor and expressed negative sentiments during the pandemic. Employee sentiments significantly influence overall EX.Practical implicationsWhen employees have extensive change experience, managers should consider various facets of EX to manage the smooth change and deliver a better EX. This research offers key EX-factors to be considered by managers while dealing with employees. Online employee reviews websites are recommended to include the identified key EX-factors to comprehend the holistic EX.Originality/valueThis study contributes to the growing literature on the employee experience as a concept by identifying various EX-factors. The authors expand the extant EX scales by identifying an inclusive and updated set of EX-factors.
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