Determinants affecting customer intention to use chatbots in the banking sector

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The study aims to analyze the factors that influence customers’ inclination to utilize chatbots in banking services. The paper employed the technology acceptance model and utilized structural equation modeling to examine the factors affecting consumers’ willingness to embrace chatbot services. The survey evaluated various determinants, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. Data were collected from 250 bank customers in the Bombay region of India through an online survey employing a random sampling method. The collected data were analyzed using IBM SPSS AMOS. This study identifies the aspects of chatbot technology in the banking sector, such as user interface, content, security, and convenience, that influence customers’ decisions to adopt this innovative technology. The results of the analysis revealed path coefficients indicating a significant relationship between information security and perceived usefulness (β = 0.286; p = 0.005) and between perceived usefulness and intention to use (β = 0.489; p < 0.001). Additionally, the path coefficients for design, security, and facilitating conditions were β = 0.281, β = 0.193, and β = 0.136, respectively, all of which held nearly equal significance in the study. The inter-correlations among the variables ranged from 0.346 to 0.854 and were statistically significant. In the banking sector, customers’ intention to use chatbots is influenced by convenience, efficiency, trust, and personalized experiences. Customers are more likely to embrace chatbots when they provide seamless support and tailored solutions, ultimately enhancing customer satisfaction and engagement.

Acknowledgment
This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445).

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    • Figure 1. Conceptual framework
    • Figure 2. Casual model
    • Table 1. Details of respondents from selected SMEs
    • Table 2. Factor loadings of variables
    • Table 3. Cronbach’s alpha and correlation of the variables
    • Table 4. Structural model path coefficients
    • Table 5. Overall model fit
    • Conceptualization
      Irfan Abdul Karim Shaikh, Sania Khan, Shaha Faisal
    • Data curation
      Irfan Abdul Karim Shaikh
    • Formal Analysis
      Irfan Abdul Karim Shaikh
    • Investigation
      Irfan Abdul Karim Shaikh
    • Methodology
      Irfan Abdul Karim Shaikh, Sania Khan
    • Project administration
      Irfan Abdul Karim Shaikh, Sania Khan
    • Resources
      Irfan Abdul Karim Shaikh, Sania Khan, Shaha Faisal
    • Validation
      Irfan Abdul Karim Shaikh
    • Visualization
      Irfan Abdul Karim Shaikh, Sania Khan
    • Writing – original draft
      Irfan Abdul Karim Shaikh, Sania Khan
    • Funding acquisition
      Sania Khan
    • Software
      Sania Khan, Shaha Faisal
    • Supervision
      Sania Khan
    • Writing – review & editing
      Sania Khan, Shaha Faisal