Customer resistance to chatbots in financial services: a social exchange theory perspective - Patel, Kayla (School of Management Studies, 2025)
Chatbots are becoming the new employees as they are increasingly being used by financial service providers to replace humans in customer interactions. Despite the numerous advantages chatbots offer, overcoming customer resistance remains challenging for firms. This study leaned on social exchange theory by examining customers' perceived benefits and perceived costs of chatbots and how these can be mitigated or exploited to overcome customer resistance to chatbots in the financial services industry. This study had two primary objectives: to ascertain the implications of financial service providers employing chatbots instead of human service employees on customer relationships and to suggest how financial service providers can overcome customer resistance to chatbots to improve customer relationships. The findings were anticipated to assist marketers in improving their use of chatbots to overcome customer resistance thereof and enhance customer relationships. Qualitative research was conducted utilising an exploratory research design and non-probability sampling, comprising online semi-structured interviews with 20 internet banking customers. The interpretation of findings followed a phenomenological analysis, focusing on participants' experiences and providing insight through dense descriptions. This study found that the extent to which a chatbot is humanised can influence its perceived creepiness, as well as customer expectations of the service interaction. This study also found that disclosing a chatbot's identity to customers can have positive effects, specifically relating to increasing perceived transparency and setting reasonable expectations for the interaction. Moreover, it was found that customers' privacy concerns stem from a lack of knowledge about how chatbots work. However, customers' perceptions of increased risk may be reduced by educating customers about chatbots. Based on these findings, this study offers actionable insights for financial service providers on reducing customer resistance to chatbots by maximising benefits and minimising perceived risks. Key strategies include integrating chatbots with human support, enhancing customer education on chatbot capabilities, and ensuring transparency around data privacy. By managing customer expectations and offering personalised, accessible chatbot experiences, financial service providers can increase customer trust and satisfaction, while reducing perceived risk. These insights help practitioners and policymakers advance innovation and the use of chatbots in the financial sector. This study enhances the literature by clarifying how to implement chatbots more effectively in financial services, addressing customer concerns and minimising negative impacts on customer relationships. It offers a unique contribution by applying social exchange theory to examine customer resistance to chatbots in financial services, a context that has received little attention in past literature. Unlike earlier studies that focused on different theories such as uncanny valley or technology acceptance models, this study emphasises customers' perceived costs and benefits, demonstrating the applicability of social exchange theory in AI contexts. This study also contributes new insights into strategies for reducing customer resistance, highlighting how chatbot transparency, education, and anthropomorphism influence customer perceptions. This unique perspective on overcoming resistance enhances the understanding of chatbot use in financial services.