Drawing upon longitudinal, dyadic, comparative case-based research, we analyze the pursuit of optimal trust, i.e. trust that is neither excessive nor insufficient, by introducing the concepts of reorientation and recalibration. First, we show that large deviations from optimal trust are best addressed by reorientation which deals with both too much as well as too little trust. Reorientation processes include substantial efforts to change parties' attributions of the intentions underlying past behavior, to reestablish social equilibrium among the parties, and to make structural changes via adjustments to goals and incentives. Reorientation is necessary when imbalance occurs in the powerful and opposed forces associated with excessive trust (faith, favoritism, contentment, loyalty) vs insufficient trust (skepticism, impartiality, exigency, opportunism). Second, we demonstrate that there is an effective path to maintaining optimal trust via practices we call recalibration, wherein small deviations are addressed before damage to trust occurs. Recalibration maintains inter-organizational trust near its optimum through processes that proactively balance the opposed forces. Large deviations from optimal trust in either direction can unleash destabilizing dynamics, requiring significant reorientation efforts to offset. Recalibration processes are then essential for preserving the effects of successful reorientation.

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 deembedding. 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 deembedding 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 deembedding affect supply chain performance.

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 turn enables the team to achieve its self‐set goals of treating more patients; offering more tailor‐made care; and providing endovascular treatment in emergency situations. At the end of our data collection period, the first steps toward the team's ultimate goal of offering patient‐centered care were also taken.

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 deembedding. 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 deembedding 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 deembedding affect supply chain performance.

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