Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry

Home > 2023, Vol. 14, No. 2 > Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry

Published: October 15, 2023

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Authors

Manoj Govindaraj, Ravishankar Krishnan, and Jenifer Lawrence

DOI Number
https://doi.org/10.15415/jtmge/2023.142002

Keywords
Design, Information, Security, Facilities, Chatbot, Intention to use

Abstract

Background: The integration of Artificial Intelligence (AI) chatbots into various industries has become a significant trend, with the banking sector being one of the key adopters. AI chatbots are designed to simulate human conversation, offering automated responses to customer queries. Their use in the banking industry aims to streamline customer service and improve efficiency. However, understanding the factors that influence customers’ willingness to use chatbot services remains crucial for banks in optimizing these technologies. Factors such as perceived usefulness, ease of use, trust, privacy concerns, and customer satisfaction play vital roles in determining the acceptance of chatbot services in banking.

Purpose: The purpose of this research is to identify and analyze the factors that influence customer intention to use chatbots in banks. By investigating these factors, the study seeks to provide banks with actionable insights to improve their chatbot services, enhance customer engagement, and increase customer satisfaction. The research also aims to assess the role of various technological aspects such as the chatbot interface, content, safety, and convenience in shaping customer decisions to adopt this technology.

Methods: This study employs a quantitative research approach, utilizing a structured questionnaire to gather data from a sample of 250 bank customers. The questionnaire assesses several key factors, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. The collected data is then analyzed using statistical techniques, including regression analysis and structural equation modeling (SEM), to test the Technology Acceptance Model (TAM) and examine the relationships between the identified factors.

Results: The analysis reveals significant relationships between customer intention to use chatbot services and factors such as perceived usefulness, trust, and ease of use. Customers’ satisfaction with the interface, content, and security of the chatbot also plays a critical role in their willingness to adopt this technology. The study confirms that perceived convenience and safety strongly influence customers’ decision to engage with AI-driven chatbots in banks.

Conclusions: The findings of this research provide valuable insights into the factors affecting customer acceptance and intention to use chatbots in the banking sector. Financial institutions can use these insights to tailor their chatbot services, ensuring they address customer concerns related to trust, security, and ease of use. The results also highlight the importance of designing user-friendly interfaces and ensuring the safety of customer data. By understanding these factors, banks can improve customer satisfaction, foster trust, and promote the adoption of AI-driven services, benefiting both customers and service providers in the long term.

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How to Cite

Manoj Govindaraj, Ravishankar Krishnan, and Jenifer Lawrence. Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry. J.Technol. Manag. Grow. Econ.. 2023, 14, 51-60

Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry

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