This study aims to examine factors that influence technological anxiety, usage concerns and subsequently discontinuance use intentions among banking customers, using the stressor–strain–outcome (SSO) framework.
Using a quantitative research approach and purposive sampling, 363 valid responses were collected from major cities in Bangladesh. A comparative analysis employing artificial neural networks (ANN) and partial least squares structural equation modeling (PLS-SEM) was conducted to enhance predictive accuracy, offering a robust and comprehensive understanding of the data.
Regression analysis using PLS-SEM confirmed that all proposed hypotheses were statistically significant. Sensitivity analysis with ANN identified lack of empathy as the most influential factor in technological anxiety (100%), followed by distrust (52.1%), irrelevant information (42%), privacy concerns (33.1%), perceived non-personalization cost (28.8%) and biased information (18.5%) (Model-A). Lack of empathy also emerged as the primary driver of customer concerns regarding artificial intelligence (AI) chatbot usage (100%), followed by technological anxiety (79.7%) (Model-B). Additionally, customer concerns about using AI chatbots (100%) had the strongest impact on discontinuance use intentions, with distrust (67.7%) and technological anxiety (67.6%) also playing significant roles (Model-C).
This study highlights the importance of empathy, relevant and unbiased information, personalization, privacy and trust in AI chatbots for banking. Banks can leverage these insights to enhance AI technology and alleviate customer anxiety, while governments can support policies that promote user-friendly digital banking services.
This study applies the SSO theory to examine the negative factors influencing behavior toward AI service technology. It investigates how technological anxiety and customer concerns affect discontinuance use intentions, aiming to uncover overlooked negative perceptions of AI-supported customer services in banking.
