JOURNAL ARTICLE

Determinants affecting customer intention to use chatbots in the banking sector

Irfan Abdul Karim ShaikhSania KhanShaha Faisal

Year: 2023 Journal:   Innovative Marketing Vol: 19 (4)Pages: 257-268   Publisher: Business Perspectives

Abstract

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

Keywords:
Structural equation modeling Customer satisfaction Business Chatbot Marketing Path analysis (statistics) Customer engagement IBM Psychology Computer science World Wide Web Statistics Social media Mathematics

Metrics

12
Cited By
3.07
FWCI (Field Weighted Citation Impact)
35
Refs
0.91
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

AI in Service Interactions
Physical Sciences →  Computer Science →  Artificial Intelligence
Technology Adoption and User Behaviour
Social Sciences →  Decision Sciences →  Information Systems and Management
Digital Marketing and Social Media
Social Sciences →  Social Sciences →  Sociology and Political Science

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