Data mining

World Wide Web

Artificial intelligence

Data science

Chatbot

Computer science

Social media

Knowledge management

Big data

Database

Analytics

Customer relationship management

Industrial Marketing Management

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.

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