Mitigating perceived uncertainty and risk is critical for successful exchanges in an online B2B marketplace. However, scant empirical research has been devoted to the topic, and even less to small- and medium-sized enterprises (SMEs). We investigate the use of quality signals and recommendations to foster buyer-supplier matching in an online B2B marketplace of SMEs. Unique proprietary data from a leading B2B e-commerce platform suggest that such a matching is significantly related to signals of SME buyers' quality and credibility embedded in how projects are described. Moreover, appropriately designed recommendation system can increase the matching rate because it helps reduce buyers' information overload. This research highlights the importance of integrating online channel data with traditional offline channel data to accurately assess the drivers of buyer-supplier matching. Our findings provide valuable insights into how to design and manage an online B2B marketplace for SMEs to yield more successful matches.
- B2B e-commerce
- Buyer-supplier matching
- Digital platform
- Multinomial logistic regression
ASJC Scopus subject areas