Generative AI Decision Support in B2C E-Commerce: A Consumer Buying Behaviour Framework

Decision making in business-to-consumer (B2C) e-commerce connects company and consumer activities before, during and after purchase, whereas many existing generative artificial intelligence (GAI) applications focus on specific tasks or functions. This article develops a conceptual framework linking employee responsibilities, consumer choices, GAI support, information sources, permissions and two-sided evaluation through the five-stage Consumer Buying Behaviour (CBB) process. It connects need clarification, information gathering, feasible-offer comparison, transaction review and post-purchase assessment through decision gates and bounded support functions. Five employees and three consumers participated in a fixed-order ChatGPT 5.6 demonstration to examine the administration and perceived value of the proposed decision-support approach. Following a brief GAI introduction, participants observed ordinary prompt-response support and then a structured CBB-guided sequence within the same general e-commerce decision context. Questionnaire ratings were generally more favourable following CBB guidance, particularly for perceived comparison support and mental effort. The study-specific items are descriptive indicators, and fixed ordering confounds condition with practice, familiarity, novelty, fatigue, expectancy and task-set effects. The contribution is strictly conceptual and methodological; the eight-participant exercise supplies procedural illustration and design input, without establishing effectiveness, deployment readiness or validated system behaviour. Future evaluation requires an implemented system, versioned tasks and outputs, independently scored task-level records, and randomised or counterbalanced comparison.