Determinants of Chatbot Underutilization and Continuance Intention in Indonesian Non-Bank Financial Institutions
DOI:
https://doi.org/10.59261/jbt.v7i2.655Keywords:
chatbot, continuance intention, trust, information quality, system quality, perceived privacyAbstract
Background: Chatbots have become a key digital service channel in Indonesia’s non-bank financial institutions (NBFIs), yet adoption does not automatically translate into sustained usage. Understanding what drives or hinders continuance intention is critical for institutions seeking to maximize chatbot effectiveness in the competitive financial services landscape.
Objective: This study aims to analyze the factors influencing user continuance intention toward chatbot services at non-bank financial institutions (NBFIs) in Indonesia. Despite the rapid increase in chatbot adoption, the level of utilization remains relatively low (underutilization). It integrates five key variables—perceived privacy, information quality, system quality, trust, and continuance intention—into a unified analytical framework.
Methods: The research employed a quantitative approach with a descriptive and causal design, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were obtained from 184 respondents who were chatbot users in the multifinance sector.
Results: The findings indicate that both information quality and system quality have a substantial impact on trust, which in turn positively affects the intention to continue using the system. Conversely, perceived privacy did not have a significant effect on either trust or continuance intention. Additionally, trust served as a full mediator in the relationship between system quality and the intention to continue using the system. These results suggest that system quality, information quality, and user trust are essential elements for enhancing the continued use of chatbots.
Conclusion: This study provides theoretical contributions by extending the information systems success model and offers practical implications for financial institutions seeking to improve chatbot service quality.
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