Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software? - Chen, Xiangying (European Journal of AI, Computing & Informatics, 2025)
The application of artificial intelligence (AI) in supply chain risk management offers small and medium-sized enterprises (SMEs) opportunities to enhance early warning capabilities, improve compliance, and strengthen operational resilience. However, SMEs often face resource constraints and cognitive differences during technology adoption, and their willingness-to-pay (WTP) remains unclear. This study employs an online discrete choice experiment (N = 512) conducted in August–September 2024 to examine SME owners' decision-making regarding AI-enabled risk management software. The experiment incorporates attributes such as monthly fee, early-warning lead time, compliance module, and data localization option. A mixed logit model and latent class analysis are applied, followed by Bayesian post-estimation to derive optimal price ranges. Results indicate that risk sensitivity, technology adoption willingness, compliance awareness, and understanding of supply chain complexity are significant drivers of WTP. Distinct customer segments are identified: price-sensitive firms focus on subscription cost, whereas function-oriented firms value early-warning and compliance features. Analysis further shows that lower-revenue SMEs exhibit lower maximum acceptable prices compared to larger firms, yet their subscription appeal can be increased through tailored feature bundles. Based on these findings, the study proposes a tiered pricing strategy: an entry-level plan emphasizing core early-warning, a mid-tier plan adding compliance functions, and a premium plan including data localization. The study contributes by highlighting the role of behavioral and cognitive factors in SME pricing decisions and provides empirical guidance for the design and pricing of AI-based supply chain risk management software.