Factors Influencing Adoption of AI-Powered Chatbot in Banking

This article gathers the factors that influence the adoption of AI-powered chatbots in banking

Artificial Intelligence has indeed permeated various fields, from IT to the finance sector, revolutionizing operations and decision-making processes. However, despite its widespread integration, AI technology is not without flaws, particularly concerning security threats that can jeopardize business data. The potential vulnerabilities associated with AI, including data privacy concerns, algorithmic biases, and susceptibility to adversarial attacks, present legitimate challenges to its adoption across industries.

Nevertheless, the allure of AI's transformative potential continues to drive its adoption, particularly in the realm of AI-powered chatbots. Scholarly articles offer compelling evidence on the multifaceted factors influencing the adoption of AI-based chatbots. These factors encompass technological advancements, organizational readiness, perceived benefits, regulatory considerations, and user acceptance. Understanding these complex dynamics is essential for businesses seeking to harness the capabilities of AI-based chatbots while mitigating associated risks.

The study conducted by Eden Samuel Parthibana and Mohd. Adilb aims to delve deeper into the factors influencing the development and adoption of AI-based chatbots. By analyzing the structural relationship between organizational (externalities), systematic (fit), and consumer-related (psychological) factors, the study seeks to provide a comprehensive understanding of the role of these factors in the adoption of AI-based chatbots. Drawing from the theories of task-technology fit and network externalities, the authors present a conceptual model that goes beyond the common perception-based theories, such as the Technology Acceptance Model.

Through the collection and analysis of 380 responses from Indian banking consumers, the study reveals a positive impact of all factors on consumers' intention to adopt AI-based chatbots. Notably, the interplay between these factors presents a nuanced perspective, offering insights that contribute to the existing literature. By incorporating a combination of factors previously studied in technology adoption, the research also explores the significance of externalities and their relationship with fit factors, shedding light on an aspect often overlooked in prior research. Furthermore, the study offers a comprehensive understanding of latent variables such as trust and the complexities of their interplay within a novel context.

The research findings suggest several implications for industrial stakeholders in the banking sector. Firstly, while there is an existing alignment between task and technology, banks should strive to perfect AI systems to meet consumer requirements effectively, as underdeveloped systems may lead to negative repercussions. Regularly collecting consumer data to tweak the fit between tasks and technology is recommended. Secondly, portraying AI chatbots as trendy technology with extensive task-handling capabilities can appeal to consumers with high personal innovativeness, while also enhancing self-efficacy. Thirdly, emphasizing the task-technology fit and network externalities in chatbot dialogs can influence usage intention, and banks should tailor responses to reassure consumer expectations. Lastly, despite lower levels of trust, the impact of task-technology fit on usage intention implies a need for strategies to increase trust, such as reassuring consumers about the security and confidentiality of interactions with Chatbots.