The Factors that influence AI-based chatbots' impact on customer experience in a B2B context.

Abstract

This study focuses on the implementation of AI (artificial intelligence) based chatbots by SMEs to improve customer experience in a B2B (business-to-business) setting. The findings uncover the factors that influence customer experience of SME (small and medium-sized enterprise) clients when they interact with an AI-based chatbot. In today’s rapidly evolving business landscape, SMEs are looking for new ways to provide a better customer experience for their clients. Adopting AI-based technologies is an increasingly popular way to improve customer interactions and achieve a competitive advantage. B2B companies adopt AI-based chatbots to facilitate human-like service interactions with their customers at various touchpoints (Kushwaha et al., 2021). Not only large companies but also SMEs started to apply AI-based chatbots after the increase of more accessible technologies. However, there are a limited number of studies that explored the impact of AI-based chatbot implementation by SMEs on customer experience. While AI-based chatbots provide cost- and time-saving opportunities for companies, they still don’t meet customer expectations at a desired level (Adam et al., 2021). By understanding the factors that influence customer experience during the interaction with an AI-based chatbot, SMEs can provide efficient customer service and achieve improved customer satisfaction. This study builds on the frameworks developed by Hoyer et al. (2020), Kushwaha et al. (2021), Adam et al. (2021) to understand the factors that influence customer experience when SME clients interact with an AI-based chatbot. The underpinning theories and models of the study are social response theory, technology acceptance model, diffusion of innovation theory, and information systems success model. The findings of the study show that the perceived expertise of an AI-based chatbot plays a more important role in improving customer experience than other factors such as visual cues or speed when clients interact with the chatbot. Moreover, the user’s own expertise is an important factor in setting the customer’s expectations for the chatbot. The conceptual framework in the study should be tested in a further study to understand the significance of the proposed relationships.