In light of the growing complexity of globally dispersed, multi-tier supply chains; sustainable supply chain management (SSCM) has become instrumental in the quest for achieving sustainability compliance along the supply chain. This study investigates how sustainability capability develops within a firm, and then extends to SSCM. Using a fixed-effect model and a global dataset of 2,206 firms between 2002 and 2015, this study shows that a firm's information environment, proxied by their customers awareness, has a significantly positive effect on their sustainability performance, and on their implementation of SSCM. Our analysis suggests that the influence of a firm's information environment on a firm's SSCM performance is mediated by the firm's own sustainability capability. We also find that this relationship is affected by stakeholder engagement. This research is relevant because, by investigating the factors that influence the development of SSCM, it provides guidance for firms that wish to achieve sustainability improvements in their supply chains during an era when the natural environment, social responsibility and the related strategic opportunities have increased in importance.

This paper contributes to the literature on cryptocurrencies by examining the performance of naïve (1/N) and optimal (Markowitz) diversification in a portfolio of four popular cryptocurrencies. We employ weekly data with weekly rebalancing and show there is very little to select between naïve diversification and optimal diversification. Our results hold for different levels of risk-aversion and an alternative estimation window.

This study investigates the impact of the choice of optimization technique when constructing Socially Responsible Investment (SRI) portfolios. Corporate Social Performance (CSP) scores are price sensitive information that is subject to considerable estimation risk. Therefore, uncertainty in the input parameters is greater for SRI portfolios than conventional portfolios, and this affects the selection of the appropriate optimization method. We form SRI portfolios based on six different approaches and compare their performance along the dimensions of risk, risk-return trade-off, diversification and stability. Our results for SRI portfolios contradict those of the conventional portfolio optimization literature. We find that the more "formal" optimization approaches (Black-Litterman, Markowitz and robust estimation) lead to SRI portfolios that are both less risky and have superior risk-return trade-offs than do more simplistic approaches; although they also have more unstable asset allocations and lower diversification. Our conclusions are robust to a series of tests, including the use of different estimation windows and stricter screening criteria.