Despite suggestions that business school professors do not understand what actually accounts for the performance of business organizations, the evidence is anecdotal at best. We review past work, develop expectations, and provide large-scale evidence for examining the validity of such suggestions. We accessed extensive data provided by Dun & Bradstreet and procured detailed information from 765 leading public and private North American businesses. Analysis of 215 closely matched pairs showed that companies with former business school professors as executives generated significantly greater revenues per employee than counterparts with non-former professors as executives. Companies with former professors in vice-president positions had the best performance in the sample, signifying the value of correspondence between academic expertise and functional business area. Companies with executives who had exited academic careers early performed better than companies with late career exit counterparts. The performance of companies with executives who were professors at top-ranked business schools was the same as other companies with executives who were professors at non-ranked business schools. We observed controls to mitigate effects from organization size, industry sector, and geographic location and executed several auxiliary analyses to assess the validity of principal findings. Finally, we interviewed a sub-sample of executive manager participants by telephone and collected qualitative data from them via surveys to further interpret the results and diminish alternative explanations. Our findings suggest that the fashionable idea that business school professors are unable to “walk the walk” is a popular myth.

This article investigates the impact of information sharing on supply chain performance when considering the product loss in e-commerce. By characterizing four information sharing mechanisms, we analyze the optimal information sharing decisions on bullwhip effects and inventory costs in near/far-distance e-commerce contexts. The analytical results reveal the vital significance of product loss information to bullwhip and cost reductions. Particularly, in far-distance e-commerce, if the online retailer only shares the demand information, the wholesaler may suffer an even worse bullwhip effect than that without information sharing. Moreover, the impacts of product loss reveal the supply chain characteristic difference between fragile products and ordinary products in e-commerce. Our research generates important managerial insights regarding supply chain management and provides practical guidelines for online retailers and wholesalers optimizing their information sharing decisions according to different e-commerce contexts.

In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.