Supplier satisfaction: Explanation and out-of-sample prediction

• Published in: Journal of business research Vol. 69; no. 10; pp. 4613 - 4623
• Main Authors: Vos, Frederik G.S., Schiele, Holger, Hüttinger, Lisa
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.10.2016; Elsevier Sequoia S.A
• Subjects: Competitive advantage; Cross-validation; Prediction; Preferred customer; Profitability; Replication; Resource allocation; Studies; Supplier satisfaction; Suppliers; Vendor supplier relations; Replication; Cross-validation; Preferred customer; Resource allocation; Prediction; Supplier satisfaction
• ISSN: 0148-2963; 1873-7978
• DOI: 10.1016/j.jbusres.2016.04.013

Many firms not only compete for customers, but increasingly compete for suppliers. Supplier satisfaction is a necessary condition for gaining and maintaining access to capable suppliers and their resources in this new competitive environment. This research replicates and extends the previous empirical research on supplier satisfaction. Additionally, this study tests an extended model for direct and indirect procurement, which assesses antecedents as well as consequences of supplier satisfaction. The findings indicate that next to growth opportunities and reliability, profitability of the relationship has a major impact on supplier satisfaction for both direct and indirect procurement. The results also show that supplier satisfaction has a positive impact on awarding the buyer preferred status, ultimately leading to preferential treatment. An additional exploratory analysis suggests the possibility for a hierarchical model consisting of first- and second-tier antecedents of satisfaction, which are particularly useful in direct procurement. Ultimately, the study provides a guide for purchasers to identify the dimensions of satisfaction to manage for satisfactory buyer–supplier relationships, namely perceived growth opportunity, relational behavior, operative excellence and profitability. The application of the new procedure for creating cross-validated, out-of-sample point predictions reinforces the practical relevance of these findings, which indicates a satisfactory prediction of cases outside the modeling sample.