What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics

• Published in: Industrial marketing management Vol. 98; pp. 207 - 221
• Main Authors: Kushwaha, Amit Kumar, Kumar, Prashant, Kar, Arpan Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2021
• Subjects: Artificial intelligence; Big data analytics; Chatbots; Customer experience; Service quality; Social media analytics; Social media analytics; Chatbots; Big data analytics; Customer experience; Artificial intelligence; Service quality
• ISSN: 0019-8501; 1873-2062
• DOI: 10.1016/j.indmarman.2021.08.011

Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system. •We investigate the influencing factors of Customer Experience in AI based chatbots.•A big data driven approach has been adopted by mining social media posts.•Theory used includes diffusion of innovation, trust commitment, IS success and flow model.•Text mining followed by Lasso and Ridge regression has been used for model validation.