Prioritizing Key Factors Influencing University Students’ On-Campus Digital Consumption under AI-Powered Precision Marketing in the Greater Bay Area: An AHP-Based Approach

Abstract

Against the backdrop of digital economy expansion, AI-powered precision marketing heavily shapes university students’ campus digital consumption. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a strategic hub with concentrated youth consumption power, presents a distinct campus context characterized by enclosed physical spaces and synchronized schedules. This study utilizes the Analytic Hierarchy Process (AHP) to systematically identify, quantify, and prioritize key factors influencing GBA university students’ digital consumption decisions under AI-powered precision marketing. We construct a three-layer evaluation index system comprising 5 criterion layers (recommendation fit, marketing appeal, interactive experience, contextual fit, and data trust) and 25 measurable sub-indicators. Based on 114 valid questionnaires, weight calculations and consistency checks were performed. Results indicate that within the criterion layer, data trust exhibits the highest weight (0.2249), followed by interactive experience (0.2124), recommendation fit (0.1964), marketing appeal (0.1898), and contextual fit (0.1765). Globally, secure payment environment (0.0582), recommendation novelty (0.0510), and sufficient privacy protection (0.0507) emerge as the top three factors. Further analysis reveals that recommendation novelty outweighs preference matching; presentation of consumer feedback outperforms discount incentives; ease of use and cross-platform convenience take precedence over extreme response speed; and critical timing responsiveness prevails over real-time location adaptation. These findings demonstrate that GBA university students' decisions are characterized by "trust dominance, exploratory preference, social reliance, timing sensitivity, and convenience priority." This study provides a quantitative basis for relevant stakeholders to optimize the allocation of AI marketing resources. Meanwhile, it enriches the application of the AHP method in AI marketing and offers precise support for enterprises targeting the campus market.