Determinants of Explainable AI Adoption in Customer Service Chatbots: Insights from the Telecom Sector of Bangladesh

Abstract

The study focuses on the variables that affect the adoption intentions of XAI in customer service chatbots in the Bangladeshi telecom sector. The primary objective was to examine the role of PE, EE, SI, TR, and PT in the propensity of employees and managers to use chatbots that are enabled by XAI. Quantitative research design was also used whereby 171 employees and managers were selected using a structured questionnaire. To test the hypotheses, convenience sampling was applied, and the answers were evaluated by the PLS-SEM in SmartPLS. The results have shown that PE, SI, TR, and PT were highly significant in determining the intentions to adopt AI, but EE did not show significance. These points to perceived usefulness, comprehension of AI output, trust in system reliability and peer or organizational acceptance as the most important aspects of XAI adoption and ease of use might be less significant when other factors override the decision-making process. In practice, the research can be used as advisory to telecom companies looking to adopt XAI-powered chatbots, as it focuses on transparency, building trust, and proving performance in order to increase its acceptance among staff. Socially, the research helps enhance the quality of customer service and satisfaction through the establishment of effective explanatory AI technologies integration. The novelty of the research is in the fact that it expands the UTAUT2 model with the help of XAI-focused Constructs, which provided information about technology acceptance in the new markets. Cross-sectional design and convenience sampling pose some limitations because they may reduce generalizability. Future studies can use longitudinal designs and examine the attitudes of the customers in order to offer a more holistic picture of XAI adoption in service management environment.

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