Empirical Research on Supplier Selection Criteria
Previous theoretical research assumes that offering price, product quality, delivery speed, and auxiliary services are the key factors that determine the attractiveness of a supplier to retailers. However, little knowledge is gathered via empirical studies to explain how supplier selection happens in reality. Our research wants to recover the supplier selection criteria by directly drawing empirical evidence from 20,000+ transactions between 440 distinct suppliers and thousands of retailers on a B2B platform with detailed transactional information and suppliers’ attributes.
Using a feature selection method named Gradient Boosted Decision Tree, we find in general, price and speed are more important than quality and service. However, the order changes with the volume of the transaction, the life cycle length of the product, and the expensiveness of the products. Moreover, we find only a handful of attributes matter in the decision. Consistent with information overloading theory, more information is not always helpful for carrying out more sophisticated choices. In the second phase of our study, the B2B platform updates its information display with price shown in a prominent position and fewer attributes displayed. We investigate how selection criteria under the price primacy information display are distinct from the previous criteria under the product primacy information display.
Keija Hu is an Assistant Professor of Operations Management at Vanderbilt’s Owen Graduate School of Management.