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This paper aims to address the problem of allocating the CO 2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO 2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO 2 emission reduction from the Chinese government goal as our total CO 2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO 2 emission quota under several assumptions. At last, we obtain the empirical results based on the real data from Chinese manufacturing industries.

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) “Pure DEA efficiency analysis”, i.e. performing a DEA on hospital data, (2) “Developments or applications of new methodologies”, i.e. applying new DEAy approaches on hospital data, (3) “Specific management question”, i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) “Surveys on the effects of reforms”, i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

PurposeThis study develops a robust model to measure intellectual capital efficiency (ICE). It also analyzes ICE across Gulf companies, sectors and countries.Design/methodology/approachThis study uses data envelopment analysis (DEA), the Malmquist productivity index (MPI), difference tests and additional analyses on a dataset consisting of 276 firm-year observations.FindingsThe findings indicate that the study model is robust to additional analysis. The results show significant differences in ICE between firms during the study period and noteworthy differences between countries, where the Qatari and Bahraini firms achieved the best ICE compared to other countries.Practical implicationsThe results of this study have significant ramifications for increasing knowledge of ICE analysis models among relevant parties. In addition, the findings may affect trading strategies because investors and financiers are motivated by the potential for lucrative financial returns on their investments in companies that prioritize ICE strategies.Originality/valueThis research contributes to the literature by proposing a robust model for estimating the ICE. It also compares ICE across Gulf companies, industries and countries to shed light on their ICE challenges.

We propose a measure of managerial ability, based on managers' efficiency in generating revenues, which is available for a large sample of firms and outperforms existing ability measures. We find that our measure is strongly associated with manager fixed effects and that the stock price reactions to chief executive officer (CEO) turnovers are positive (negative) when we assess the outgoing CEO as low (high) ability. We also find that replacing CEOs with more (less) able CEOs is associated with improvements (declines) in subsequent firm performance. We conclude with a demonstration of the potential of the measure. We find that the negative relation between equity financing and future abnormal returns documented in prior research is mitigated by managerial ability. Specifically, more able managers appear to utilize equity issuance proceeds more effectively, illustrating that our more precise measure of managerial ability will allow researchers to pursue studies that were previously difficult to conduct. This paper was accepted by Mary E. Barth, accounting.

Education is important at national, local and individual levels. Its benefits accrue both to society and to individuals, and as such provision of education in many countries is paid for at least in part from the public purse. With competing demands for government funding, it is important for education to be provided as efficiently as possible. Efficiency occurs when outputs from education (such as test results or value added) are produced at the lowest level of resource (be that financial or, for example, the innate ability of students). This special issue is devoted to the topic of efficiency in education, and is well-timed given that governments around the world struggle with public finances in the wake of the global financial crisis of 2008. In this paper, we explore and provide an overview of the themes of the special issue and introduce the papers contained therein.

The measurement of corporate performance is central to strategic management research. A common objective of this research is to identify top performers in an industry and their sources of competitive advantage. Despite this focus on best firms and practices, most researchers utilize statistical methods that identify average effects in a sample, and they assess a single performance dimension while ignoring other relevant dimensions. Emphasis on purely financial measures can overlook the fact that a firm's efficiency in transforming resources has been shown as a major source of competitive advantage. In this article we demonstrate how frontier methodologies, such as Data Envelopment Analysis and the Stochastic Frontier approach, can address these challenges. We provide an illustration based on longitudinal data from U.S. and Japanese automobile producers.

In recent years, environmental problems caused by industries in China have drawn increasing attention to both academics and policy makers. This paper assesses the environmental efficiency of Chinese regional industrial systems to come up with some recommendations to policy makers. First, we divided each Chinese regional industrial system into a production process and a pollutant treatment process. Then, we built a scientific input–intermediate–output index system by introducing a new network slacks-based model (NSBM) model. This study is the first to combine NSBM with DEA window analysis to give a dynamic evaluation of the environmental efficiency. This enables us to assess the environmental efficiency of Chinese regional industrial systems considering their internal structure as well as China’s policies concerning resource utilization and environmental protection. Hence, the overall efficiency of each regional industrial system is decomposed into production efficiency and pollutant treatment efficiency. Our empirical results suggest: (1) 66.7% of Chinese regional industrial systems are overall inefficient. 63.3 and of 66.7% Chinese regional industrial systems are inefficient in the production process and the pollutant treatment process, respectively. (2) The efficiency scores for the overall system and both processes are all larger in the eastern area of China than those of the central and western areas. (3) Correlation analysis indicates that SO₂ generation intensity (SGI), solid waste generation intensity, COD discharge intensity, and SO₂ discharge intensity have significantly negative impacts on the overall efficiency. (4) The overall inefficiency is mainly due to inefficiency of the pollutant treatment process for the majority of regional industrial systems. (5) In general, the overall efficiency was trending up from 2004 to 2010, indicating that the substantial efforts China has devoted to protecting the environment have yielded benefits.

This study endorses the use of data envelopment analysis, which uses benefit-of-the-doubt weighting to evaluate the social, economic and overall performance of social enterprises. This methodology is especially useful for creating composite indicators based on multiple outputs expressed in different measurement units, and allows for enterprise-specific weighting of the different objectives. Applying this methodology on a unique longitudinal dataset of Flemish sheltered workshops suggests that social enterprises may face different types of mission drift. Further, our results show that top-performing social enterprises are more economically and socially efficient than low performers. These top performers also have a stronger economic orientation, which sheds new light on the balance between social and economic orientations in social enterprises.

In this study, we examine the existence and performance of cognitive groups. In accordance with the attention-based view of managerial cognition, cognitive groups are defined as groups of firms in which the CEOs focus their attention on similar strategic elements when seeking to maximize their firm's competitive advantage. We developed a panel data extension of the original Data Envelopment Analysis to gauge CEOs' focus of attention and then clustered firms into groups. We compared our approach with other approaches that use content analysis of CEOs' letters to shareholders and CEOs' demographic characteristics to measure CEOs' attention. Although the different approaches are related, indicating the existence of a common underlying construct (i.e., mental models), our approach explains a higher proportion of the variation in organizational performance.