Paper Type

Complete

Abstract

AI chatbots can support decision-making, yet many remain underutilized, particularly in non-technical domains. Responsible AI design requires not only technical capability but also interaction processes that promote perceived value and sustained use. This study applies DSRM to design and evaluate an AI chatbot supporting decision-making in a non-technical field. The artifact is grounded in Information Gap Theory and Post-Acceptance Model of IS Continuance, examining how design principles influence user perceptions and continuance intentions. PLS-SEM analysis of 83 participants in a field setting shows a strong path to continuance intention through perceived usefulness, the strongest predictor of continued use. Curiosity demonstrated a significant relationship with perceived usefulness, suggesting it's a key mechanism through which users derive value from the chatbot. Perceived usefulness significantly influenced user satisfaction; however, satisfaction did not affect continuance intention. Overall, sustained use appears driven more by motivational (curiosity) and cognitive (perceived usefulness) processes than affective responses (satisfaction).

Paper Number

1255

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Aug 15th, 12:00 AM

Curiosity-Driven Design and Continuance Intention in AI Chatbots

AI chatbots can support decision-making, yet many remain underutilized, particularly in non-technical domains. Responsible AI design requires not only technical capability but also interaction processes that promote perceived value and sustained use. This study applies DSRM to design and evaluate an AI chatbot supporting decision-making in a non-technical field. The artifact is grounded in Information Gap Theory and Post-Acceptance Model of IS Continuance, examining how design principles influence user perceptions and continuance intentions. PLS-SEM analysis of 83 participants in a field setting shows a strong path to continuance intention through perceived usefulness, the strongest predictor of continued use. Curiosity demonstrated a significant relationship with perceived usefulness, suggesting it's a key mechanism through which users derive value from the chatbot. Perceived usefulness significantly influenced user satisfaction; however, satisfaction did not affect continuance intention. Overall, sustained use appears driven more by motivational (curiosity) and cognitive (perceived usefulness) processes than affective responses (satisfaction).

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