Justin Zhang

In recent years, sustainability has become increasingly important for both scholars and practitioners of supply chain management (SCM), driven by tighter regulations and strengthening consumer and community pressures. To meet these demands, firms must integrate both environmental and social dimensions into their supply chain strategies and practices. This study focuses on the supply chain social responsibility (SCSR) principle of sustainability, which reflects management orientations toward a commitment to norms underlying the future relationships, information sharing, and product development involvement of suppliers and customers. Specifically, we investigate the linkages among SCSR, management practices, and the resulting supply chain outcomes using a balanced centricity (BC) approach. Our analysis extends quality management (QM) into the supply chain by focusing on two sets of practices—supplier QM (SQM) and customer QM (CQM). For this purpose, data are collected from 209 manufacturing firms and analyzed using structural equation modeling (SEM). The findings confirm the positive relationship between SCSR and QM, emphasizing the significance of responsible behaviors toward both suppliers and customers in shaping the overall QM practices within the firm. Second, we show that effective SQM and CQM practices can contribute to improved overall supply chain performance (SCP). Finally, this study reveals that SQM is a crucial intermediary in linking SCSR with SCP, whereas CQM does not exhibit a similar effect. Our findings contribute to the literature on BC and QM practices in supply chains and have practical implications for firms seeking to improve their sustainability and SCP.

Incidental findings in research with human participants may have implications for a person’s present health or future health outcomes. Current guidelines focus on methods for handling and reporting incidental findings from biological test data but incidental findings might also arise from non-biological tests. This article presents three examples in which the results from non-biological test data can be predictive of future disease and should be disclosed to research participants. It is intended to increase awareness and facilitate further discussion about the reporting of incidental findings from non-biological data.