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“Big data” has become ubiquitous. It is impacting every aspect of how companies organize and manage their supply chains. Supply chains are evolving into digital networks connected via devices and sensors revolutionizing how data is generated, shared, and communicated. It is also unlocking new research streams. In this study, we introduce papers in this issue and showcase Big Data research trends across supply chain management.

This study explores how suppliers adjust their relation-specific investments (RSI) in response to the different risk-taking incentives provided by the customer firm to its CEO, during normal and transition periods. We investigate this relation using 17,553 customer–supplier transactions over the 1993–2013 period. We find strong evidence consistent with the risk-taking argument. Specifically, we find that an increase in the risk-taking incentives of customer CEOs leads to a decline in suppliers’ RSI in normal periods, but an increase in RSI during transition periods. We employ the FAS-123R mandate to show that an exogenous reduction in customer CEO's incentive pay increases suppliers’ RSI. We reaffirm the effect with the passage of the Sarbanes–Oxley Act as a secondary quasi-natural experiment. Finally, we examine several scenarios that either amplify or attenuate the observed relation, based on factors such as financial constraints, distress, growth opportunities, industry competition, and other firm characteristics. Our study contributes to the literature that examines the interplay between corporate policy and product market relationships.

Big data has the potential of offering valuable insights into the way organizations function, and it is changing the way organizations make decisions. Nine invited essays provide a wide range of perspectives on the role of big data in customer‐driven supply chains, healthcare operations, retail operations, demand planning and manufacturing, environmental and social issues, humanitarian operations, agriculture supply chains, and service operations. Decision makers should have clean, valid, and reliable data, and they should have a thorough understanding of the contexts of applications. Big data shorten virtual distance to customers, and thus facilitate personalization of products and services. Successful implementation of big data applications requires sharing the data with appropriate stakeholders.