Bridging the AI Adoption Gap: What Drives U.S. SME Owners' Willingness-to-Pay for Supply-Chain Risk Software?

Xiangying Chen

doi:10.71222/9n1cbn63

Authors

Xiangying Chen Chery Industrial, NY, USA Author

DOI:

https://doi.org/10.71222/9n1cbn63

Keywords:

willingness-to-pay (WTP), small and medium-sized enterprises (SMEs), supply chain risk management, artificial intelligence (AI) adoption, discrete choice experiment

Abstract

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.

References

1. L. W. Wong, L. Y. Leong, J. J. Hew, G. W. H. Tan, and K. B. Ooi, “Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs,” International Journal of Information Management, vol. 52, p. 101997, 2020, doi: 10.1016/j.ijinfomgt.2019.08.005.

2. Yudhita Valen Prasarry, ES Astuti, I Suyadi. Factors affecting the adoption of mobile commerce: A study on SMEs in Malang. European Journal of Business and Management, 2023.

3. L. W. Wong, G. W. H. Tan, K. B. Ooi, B. Lin, and Y. K. Dwivedi, “Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis,” International Journal of Production Research, vol. 62, no. 15, pp. 5535–5555, 2024, doi: 10.1080/00207543.2022.2063089.

4. A. Siddiqui, M. Khan, and S. Akhtar, “Supply chain simulator: A scenario-based educational tool to enhance student learning,” Computers & Education, vol. 51, no. 1, pp. 252–261, 2008, doi: 10.1016/j.compedu.2007.05.008.

5. E. Giampietri, F. Verneau, T. Del Giudice, V. Carfora, and A. Finco, “A theory of planned behaviour perspective for investi-gating the role of trust in consumer purchasing decision related to short food supply chains,” Food Quality and Preference, vol. 64, pp. 160–166, 2018, doi: 10.1016/j.foodqual.2017.09.012.