Corporate social responsibility (CSR) has become a mandate for strategic managers and is often an important element of a differentiation strategy, but there is little research on how managers can make socially responsible decisions within the context of competitive strategy. In this study we explain how data envelopment analysis (DEA) results can be used to determine the trade‐offs inherent in managing the triple bottom line of profits, people and the planet. Once the trade‐offs are well understood, managers can implement sustainable competitive strategies that incorporate socially responsible decisions. Using public data from the electric power generation industry, we demonstrate how DEA can be utilized to determine the trade‐offs between efficiency, costs and pollution reduction, allowing managers to make and champion socially responsible decisions. We discuss the general applicability of our method for making strategic decisions incorporating the triple bottom line. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment

Past literature in nonprofit management uses the overhead ratio of nonprofits as a measure of efficiency. Although the overhead ratio might measure top-heaviness, we argue that it does not measure nonprofit efficiency. To investigate this, we use financial and operational data to rank the efficiency of Habitat for Humanity affiliates with the overhead and administrative ratio, as well as data envelopment analysis (DEA) and stochastic frontier analysis (SFA), two of the most popular efficiency measures. While the DEA and SFA rankings are statistically correlated, overhead ratio rankings are negatively correlated with both SFA and DEA rankings. We argue that nonprofit scholars, managers, and donors should move away from concepts and measures of efficiency based on financial ratios, and toward ones that embrace maximizing what nonprofits are able to make and do.

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Discussions of effi ciency among Historically Black Colleges andUniversities (HBCUs) are often missing in academic conversations. This article seeks to assess effi ciency of individual HBCUs using Data Envelopment Analysis (DEA), a non-parametric technique that can synthesize multiple inputs and outputs to determine a single effi ciency score for each institution. The authors hypothesized that institutions with higher endowments will have higher effi ciency scores due to an increased ability to acquire more productive capital. To test this hypothesis, effi ciency scores were regressed on endowments and HBCU status, with control variables denoting if institutions are public or religiously affi liated. From DEA, we found that HBCUs were on average slightly more effi cient that Predominantly White Institutions. Endowment levels were found to be signifi cant determinants of effi ciency for both sets of institutions. This suggests (1) that the general perception of HBCUs as ineffi cient requires reconsideration and (2) that schools with the most endowment resources are generally more effi cient. Less effi cient HBCUs should perhaps devote resources to building endowment levels to increase effi ciency. We can also see the importance of methodologies allowing for investigation of both differences among HBCUs and differences in operational contexts among HBCUs and other schools.

Previous literature has suggested that federal funding can hinder the efficiency of nonprofit organizations, but this has yet not been empirically tested. This study used a two‐stage data envelopment analysis (DEA) model to measure the efficiency of a set of private nonprofit teaching‐oriented colleges, then estimate the impact of federal and state funding on organizational efficiency. The findings indicate that, on average, increases in neither state nor federal funding effect efficiency of nonprofit colleges. Increasing state funding negatively impacted the efficiency of public colleges (N = 799). This study suggests that the challenges for nonprofits that accompany government funding may not rise to a quantifiable negative effect on efficiency.