The main aim of this paper is to facilitate small and medium sized enterprises (SMEs) to adopt environmental management (EM) and corporate social responsibility (CSR) practices. The study reveals SMEs" motivation, pressure, targets and methods for EM and CSR practices. Additionally, the paper investigates how these variables relate to employee number, turnover and geographical locations. The outcomes of the research will add value to SMEs decision-making processes in both strategic and policy levels (e.g. supplier selection) and policymakers" initiatives to make SMEs environment and socially friendly. Although there are studies on EM and CSR practices of SMEs, they mainly focus on impact of EM and CSR practices on business performance, and SMEs" motivation for adopting EM and CSR practices in specific country. Studies that reveal SMEs" motivation, pressure, targets and methods for EM and CSR practices and their relationship with their characteristics (e.g. size, turn over, and geographical location) are scant. This research bridges this gap. Our data originates from 223 carefully selected representative SMEs in the West Midlands, UK (105) and Kolkata, India (118) covering manufacturing and process industries. The relevant data was collected using questionnaires and analysed using Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) methods. The results reveal that perceptions of SMEs" motivation, pressure, targets and methods of EM and CSR practices vary considerably with respect to size, turn over and geographical location. The findings are significant to policymakers, client organizations and individual SME for improving EM and CSR practices.

Evaluating higher education teaching performance is complex as it involves consideration of both objective and subjective criteria. The Student Evaluation of Teaching (SET) is used to improve higher education quality. However, the traditional approaches to considering students' responses to SET questionnaires for improving teaching quality have several shortcomings. This study proposes an integrated approach to higher education teaching evaluation that combines the Analytical Hierarchy Process (AHP) and Data Envelopment Analysis (DEA). The AHP allows consideration of the varying importance of each criterion of teaching performance while DEA enables the comparison of tutors on teaching as perceived by students with a view to identifying the scope for improvement by each tutor. The proposed teaching evaluation method is illustrated using data from a higher education institution in Greece.

The main aim of this paper is to investigate the impact of patent applications, development level, employment level and degree of technological diversity on innovation efficiency. Innovation efficiency is derived by relating innovation inputs and innovation outputs. Expenditures in Research and Development and Human Capital stand for innovation inputs. Technological knowledge diffusion that comes from spatial and technological neighborhood stands for innovation output. We derive innovation efficiency using Data Envelopment Analysis for 192 European regions for a 12-year period (1995-2006). We also examine the impact of patents production, development and employment level and the level of technological diversity on innovation efficiency using Structural Equation Modeling. This paper contributes a method of innovation efficiency estimation in terms of regional knowledge spillovers and causal relationship of efficiency measurement criteria. The study reveals that the regions presenting high innovation activities through patents production have higher innovation efficiency. Additionally, our findings show that the regions characterized by high levels of employment achieve innovation sources exploitation efficiently. Moreover, we find that the level of regional development has both a direct and indirect effect on innovation efficiency. More accurately, transition and less developed regions in terms of per capita GDP present high levels of efficiency if they innovate in specific and limited technological fields. On the other hand, the more developed regions can achieve high innovation efficiency if they follow a more decentralized innovation policy.

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The business performance of manufacturing organizations depends on the reliability and productivity of equipment, machineries and entire manufacturing system. Therefore, the main role of maintenance and production managers is to keep manufacturing system always up by adopting most appropriate maintenance methods. There are alternative maintenance techniques for each machine, the selection of which depend on multiple factors. The contemporary approaches to maintenance technique selection emphasize on operational needs and economic factors only. As the reliability of production systems is the strategic intent of manufacturing organizations, maintenance technique selection must consider strategic factors of the concerned organization along with operational and economic criteria. The main aim of this research is to develop a method for selecting the most appropriate maintenance technique for manufacturing industry with the consideration of strategic, planning and operational criteria through involvement of relevant stakeholders. The proposed method combines quality function deployment (QFD), the analytic hierarchy process (AHP) and the benefit of doubt (BoD) approach. QFD links strategic intents of the organizations with the planning and operational needs, the AHP helps in prioritizing the criteria for selection and ranking the alternative maintenance techniques, and the BoD approach facilitates analysing robustness of the method through sensitivity analysis through setting the realistic limits for decision making. The proposed method has been applied to maintenance technique selection problems of three productive systems of a gear manufacturing organization in India to demonstrate its effectiveness.

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Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology "pushes" old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socioeconomic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales.

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named "efficiency cuts", selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples. Keywords Integer programming • Branch and bound • DEA • Supply chain management • Mixed integer linear programming (MILP) B Ali Emrouznejad