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.

近年来，许多 B2B 公司已广泛接受基于人工智能的聊天机器人，以在不同的客户接触点提供类似人类的服务交互。引入这项技术的目标之一是全天候提供增强的实时渠道客户体验 (CX)。研究人员专注于通过改进聊天机器人的内部算法来提供 CX，而对管理文献中的 CX 理论的关注有限，这留下了空白。通过所提出的论文，我们从 B2B 公司的 CX 理论的角度研究了基于 AI 的聊天机器人的影响因素。在本文中，利用创新扩散理论、信任承诺理论、信息系统成功模型和 Hoffman & Novak' 提出了一个组织 CX 的模型。s 用于计算机中介环境的流模型，并使用社交媒体数据进行验证。本研究使用的方法是基于社交媒体分析的内容分析方法（情感分析、层次聚类、主题建模）用于数据准备，然后是用于模型验证的套索和岭回归。结果表明，使用聊天机器人的 B2B 企业的客户体验受这些机器人的整体系统设计、客户使用技术的能力以及客户对品牌和系统的信任的影响。