Alain Osta 1, Angelika Kokkinaki 2, Charbel Chedrawi 3
1 Graduate School, University of Nicosia, Cyprus, and Sagesse University, Lebanon, [email protected], +9613378419
2School of Business, University of Nicosia, Cyprus, [email protected]
3Business and Management Faculty, Saint Joseph University, Lebanon, [email protected] and School of Business, University of Nicosia, Cyprus, [email protected]
*Alain Osta
Abstract.
Objective: This research aims to explore the intention of using health chatbots in Online Health Communities from both sociomaterial and sustainability perspectives. The study seeks to examine the influence of variables and moderators like Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Fear of Technological Advancements, and Patient acceptance and Trust on users’ intentions and their actual or potential use of these communities. The research acknowledges the interplay between materiality and the social environment in shaping technology usage while also considering sustainability within this context.
Materials and Methods: A quantitative methodological approach was used to investigate users’ behavior and intentions towards AI conversational agents/chatbots in OHCs. An extended UTAUT model was employed to analyze a dataset consisting of 443 complete responses from 62 countries.
Results: The study shows how AI chatbots in OHCs impact users’ Behavioral Intention (BI) from sociomateriality and sustainability perspectives. The proposed model’s main constructs exerted significant influence on participants’ BI and Usage Behavior. Understanding the interplay between social and material agency through the concept of sociomateriality helps enhance the use and design of AI chatbots in healthcare. These chatbots also contribute to the sustainability of healthcare systems by promoting efficiency, accessibility, and empowerment.
Conclusion: In conclusion, our current focus on sustainability variables is being expanded to include several new aspects. The new variables pertain to the cost-effectiveness of AI implementation in healthcare, Patient Outcomes, User experience, long-term viability to help us understand how AI technologies impact aspects like diagnosis accuracy, medical error reduction, and treatment plan.
Keywords: AI chatbots, Online Health Communities, sustainability, sociomateriality
