Paper Type
ERF
Abstract
Loneliness is a critical public health challenge, yet the extent to which, and how, AI-powered chatbots can meaningfully alleviate it remains underexplored in Information Systems research. Building on affordance theory and the Elaboration Likelihood Model (ELM), we propose a model that examines how two socio-emotional chatbot affordances: social support and emotional support, reduce loneliness, both directly and indirectly through perceived supportiveness. We further theorize that higher issue involvement amplifies the effects of chatbot affordances on both perceived supportiveness and loneliness. The model will be tested using a longitudinal three-condition experiment. This study contributes to IS research by identifying perceived supportiveness as a key explanatory mechanism and offering theory-driven guidance for designing empathic, involvement-sensitive conversational agents that support user well-being.
Paper Number
1681
Recommended Citation
Islam, Mohammad Arif Ul, "When Do Chatbots Ease Loneliness? Socio-Emotional Affordances, Perceived Supportiveness, and Issue Involvement" (2026). AMCIS 2026 Proceedings. 6.
https://aisel.aisnet.org/amcis2026/social_comput/social_comput/6
When Do Chatbots Ease Loneliness? Socio-Emotional Affordances, Perceived Supportiveness, and Issue Involvement
Loneliness is a critical public health challenge, yet the extent to which, and how, AI-powered chatbots can meaningfully alleviate it remains underexplored in Information Systems research. Building on affordance theory and the Elaboration Likelihood Model (ELM), we propose a model that examines how two socio-emotional chatbot affordances: social support and emotional support, reduce loneliness, both directly and indirectly through perceived supportiveness. We further theorize that higher issue involvement amplifies the effects of chatbot affordances on both perceived supportiveness and loneliness. The model will be tested using a longitudinal three-condition experiment. This study contributes to IS research by identifying perceived supportiveness as a key explanatory mechanism and offering theory-driven guidance for designing empathic, involvement-sensitive conversational agents that support user well-being.
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