Predictive PLS-SEM Analysis of Artificial Intelligence Chatbots in Innovative Marketing

Abstract

Marketing has witnessed a digital transformation driven by the fast evolution of technology and International Intelligence (AI), as businesses can now implement intelligent chatbot systems to boost customer interaction and improve the overall performance of the business. The study explores the relationships between Artificial Intelligence Chatbot Quality, Customer Trust, Perceived Ease of Use, Innovative Marketing Capability, Customer Experience, Supply Chain Integration and Business Performance, applying Predictive Partial Least Squares Structural Equation Modeling (PLS-SEM). A mediation analysis, PLSpredict, and Importance–Performance Map Analysis (IPMA) were used in SmartPLS 4 to analyse the primary data collected from 526 participants who have previously utilised AI-powered chatbots. The result show that AI Chatbot Quality significantly positively affects Innovative Marketing Capability and Customer Experience, and Customer Trust and Perceived Ease of Use positively influence Customer Experience. Customer Experience positively influences Supply Chain Integration and Business Performance and acts as a mediator between AI Chatbot Quality and Business Performance. The study combines the TAM and RBV models into a single predictive model, offering theoretical perspectives and practical implications for organisations looking to enable their AI chatbots to enhance their innovative marketing efforts and gain a sustainable competitive edge.