Supplier selection criteria under heterogeneous sourcing needs: evidence from an online marketplace for selling production capacity

Journal article

Supplier selection is critical in the sourcing process: retailers evaluate candidate suppliers across multiple attributes, including price, quality, speed, and service, to locate an ideal supply chain partner. While related theoretical research requires prior knowledge of retailers’ preferences across attributes, little empirical evidence about the criteria has been drawn from actual sourcing decisions. Capitalizing on an innovative online marketplace where manufacturers sell production capacity, our research reveals the relative importance assigned by retailers to over twenty supplier attributes including price, product quality, speed in multiple phases from ordering to delivery, and various ancillary services. Our research enables a direct connection between theoretical models and business practices and solidifies existing survey studies by removing reporting bias and increasing representative samples. Using various machine learning approaches, we discover that speed and price attributes are considered the most important, followed by quality, and finally, service offerings. We further investigate how supplier selection criteria are adjusted based on order sizes and product features. We reveal that, as the order size increases, price and quality attributes become more important while speed and service attributes plummet in importance. Furthermore, we find that retailers attach a higher value to speed and service attributes with trendy innovative products, but care more about the price dimension with long-life-cycle functional products. Based on these findings regarding supplier selection criteria, we provide investment guidelines for suppliers by quantifying the economic value associated with each non-price attribute. Also, to enable more efficient information disclosure, we recommend that online platforms consolidate their service menus by removing services with low enrollment rates and low impact on deal formation.

Authors: Hu, K; Kong, L; Jia, Z

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SAGE Publications
• Journal: Production and Operations Management
• Volume: 34
• Issue: 2
• Pages: 168-186
• Publication date: 2024-08-19
• Acceptance date: 2024-08-05
• DOI: 10.1177/10591478241279384
• EISSN: 1937-5956
• ISSN: 1059-1478
• Language: English