Catarina Delgado

Abstract

Abstract
Firms are increasingly under pressure to incorporate social sustainability practices into their operations and supply chain management strategies. The purpose of this research is to develop a taxonomy of the supply chain social sustainability (SCSS) practices adopted by firms. The methodology involves two steps. First, the taxonomy is built based on literature review. Second, our empirical analysis, using non-financial (sustainability) reports with a sample of 55 listed companies, identifies standard adoption practices. We used content analysis software to unearth the influential words in the sustainability reports from different industries, sizes, and geographical regions. The content analysis reveals three different themes that provide a snapshot of how Portuguese firms integrate social sustainability into their supply chain and operations. Firms emphasize diverse facets of social sustainability practices in upstream and downstream supply chain based on different industries. The results assume significance and provide unique insights on adoption practices to supply chain practitioners who otherwise have no information on what constitutes supply chain social sustainability in this region.

Abstract

Abstract
The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.

Abstract