Temporary deembedding buyer-supplier relationships

Research on buyer-supplier relationships has debated the advantages and disadvantages of embedded relationships. We join this debate by developing theory on the performance implications of relaxing embedded buyer-supplier relationships for a limited period of time—a previously neglected phenomenon we refer to as temporary de-embedding. To capture this phenomenon’s dynamic and complex nature, we use a combined-method approach. First, we conducted a longitudinal case study of the relationship between Nissan and a strategic first-tier supplier. This case study suggests that temporary de-embedding reinvigorates search and leads to higher performance for both the buyer and supplier. Second, we built a computational simulation model using the search perspective from complexity theory to complement the theory grounded in our case study. Our simulations confirm the case findings while shedding additional light on how frequency, duration, and intensity of de-embedding affect supply chain performance

Implementing new technologies for complex care

Bearing the rising health care costs of our aging global population is one of the most urgent challenges society is facing. We study the implementation of new medical technologies as one way to increase the effectiveness of care, particularly in the area of aortic disease—a condition that affects an increasing number of patients globally. Our research focus is the implementation of complex endovascular treatment techniques by a multidisciplinary aortic treatment group, in addition to their traditional open treatment of aortic disease. We find that relational and cognitive embeddedness factors support team learning, which in

Temporary De-Embedding Buyer-Supplier Relationships

Research on buyer-supplier relationships has debated the advantages and disadvantages of embedded relationships. We join this debate by developing theory on the performance implications of relaxing embedded buyer-supplier relationships for a limited period of time — a previously neglected phenomenon we refer to as temporary de-embedding. To capture this phenomenon’s dynamic and complex nature, we use a combined-method approach. First, we conducted a longitudinal case study of the relationship between Nissan and a strategic first-tier supplier. This case study suggests that temporary de-embedding reinvigorates search and leads to higher performance for both the buyer and supplier. Second, we built a computational simulation model using the search perspective from complexity theory to complement the theory grounded in our case study. Our simulations confirm the case findings while shedding additional light on how frequency, duration, and intensity of de-embedding affect supply chain performance