Guest editors:

Vincent Charles, School of Management, University of Bradford, Bradford, UK,

Ali Emrouznejad, Centre for Business Analytics in Practice, Surrey Business School, The University of Surrey, Guildford, UK,

Details: https://deazone.com/en/envsci

Submission:https://www.editorialmanager.com/envsci/

The global Sustainable Development Goals (SDGs) were established by the United Nations General Assembly in 2015, as extensions of the UN Millennium Development Goals. These SDGs contain 17 goals and 169 targets aimed at “ending poverty, protecting the planet, and ensuring prosperity for all” (UN, 2018). Governments, private companies, organisations, and civil societies have been challenged to work together until 2030 to accelerate the progress towards achieving sustainable development.

Special issue information:

Achieving sustainable development is, therefore, a massive, complex, and ongoing task and one of the first steps in the assessment of whether goals can be met by 2030 involves the development of methods and tools to monitor the SDGs’ progress (Cristóbal et al., 2021; Nhamo et al., 2020). However, quantifying the level of sustainability attained by a system (be it a nation, region, sector, or business) is a challenging task due to the need to consider a wide range of economic, environmental, and social aspects simultaneously (Galán-Martín et al., 2016).

Data Envelopment Analysis (DEA) has been shown to be a well-established promising tool for such purposes, especially because it considers each sustainability dimension separately and can handle a large number of indicators. DEA is an optimisation-based management science technique for identifying the best practices of a group of decision-making units (DMUs) whose performance is categorised by multiple performance metrics that are classified as inputs and outputs. The construction of composite indicators, in particular, is a stream of research in the DEA literature for assessing sustainable development that has been gaining traction in recent times (Zakari et al., 2022). This is because indices are easy to communicate and can measure multi-dimensional concepts that may not share common units of measurement.

Studies aimed at examining sustainability using DEA have started to emerge (Chuai et al., 2021; Xu & Yao, 2022), although they are still scarce. However, when it comes to building (composite) indices using DEA, most of the existing studies have only looked at a specific dimension of the SDGs, for example, food supply/SDG2 (Lucas et al., 2021), health/SDG3 (Habib & Shahwan, 2020), energy/SDG7 (Zakari et al., 2022), or CO2 emissions/SDG13 (De Castro Camioto et al., 2014).

Therefore, the topic of this Special Issue is very timely, and it is expected that contributed articles will be of interest to a wide audience, from academics to practitioners, NGOs, and governmental bodies. It is expected that the research papers will assist relevant decision and policymakers in their attempt to measure sustainability and design policies aimed at raising the level of efficiency of current SDG policies, identifying and engaging in best practices, and allocating governmental resources – ultimately, with the overarching aim of achieving sustainable development.

This Special Issue welcomes original research articles of high quality that focus on building robust DEA-based index systems to measure and benchmark the SDGs across a variety of empirical contexts. The papers can use different theoretical lenses in the way they approach the conceptualisation and measurement of sustainability. For example, the papers can build upon literature on institutional theory, participation and community development, and global value chains, among others. Theoretical, conceptual, methodological, and empirical research studies are encouraged. The development of new or enhanced solutions are of considerable interest. Contributions from both the academic and the practitioner communities are supported.

Manuscript submission information:

Submission Deadline: July 31, 2023

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Managing Guest Editor: Prof. Vincent Charles.

The journal’s submission platform (Editorial Manager^®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI:DEA for SDGs” when submitting your manuscript online.

References:

Cristóbal, J., et al. (2021). Unraveling the links between public spending and Sustainable Development Goals: Insights from data envelopment analysis. Science of The Total Environment, 786, 147459. https://doi.org/10.1016/j.scitotenv.2021.147459

Chaudhry, I. S., Ali, S., Bhatti, S. H., Answer, M. K., Khan, A. I., & Nazar, R. (2021). Dynamic common correlated effects of technological innovations and institutional performance on environmental quality: Evidence from East-Asia and Pacific countries. Environmental Science & Policy, 124, 313-323. https://doi.org/10.1016/j.envsci.2021.07.007

Chuai, X., Gao, R., Li, J., Guo, X., Lu, Q., Zhang, M., Zhang, X., & Liu, Y. (2021). A new meta-coupling framework to diagnose the inequity hidden in China’s cultivated land use. Environmental Science & Policy, 124, 635-644. https://doi.org/10.1016/j.envsci.2021.08.001

De Castro Camioto, F., Barberio Mariano, E., & do Nascimento Rebelatto, D. A. (2014). Efficiency in Brazil’s industrial sectors in terms of energy and sustainable development. Environmental Science & Policy, 37, 50-60. https://doi.org/10.1016/j.envsci.2013.08.007

Galán-Martín, A., Guillén-Gosálbez, G., Stamford, L., & Azapagic, A. (2016). Enhanced data envelopment analysis for sustainability assessment: A novel methodology and application to electricity technologies. Computers & Chemical Engineering, 90, 188-200. https://doi.org/10.1016/j.compchemeng.2016.04.022

Habib, A. M., & Shahwan, T. M. (2020). Measuring the operational and financial efficiency using a Malmquist data envelopment analysis: a case of Egyptian hospitals. Benchmarking: An International Journal, 27(9), 2521-2536. https://doi.org/10.1108/BIJ-01-2020-0041

Lucas, E., Galán-Martín, A., Pozo, C., Guo, M., & Guillén-Gosálbez, G. (2021). Science of The Total Environment. 755, Part 1, 142826. https://doi.org/10.1016/j.scitotenv.2020.142826

Nhamo, L., Mabhaudhi, T., Mpandeli, S., Dickens, C., Nhemachena, C., Senzanje, A., Naidoo, D., Liphadzi, S., & Modi, A. T. (2020). An integrative analytical model for the water-energy-food nexus: South Africa case study. Environmental Science & Policy, 109, 15-24. https://doi.org/10.1016/j.envsci.2020.04.010

Sarra, A., Mazzocchitti, M., & Nissi, E. (2020). Optimal regulatory choices in the organization of solid waste management systems: Empirical evidence and policy implications. Environmental Science & Policy, 114, 636-444. https://doi.org/10.1016/j.envsci.2020.09.004

Retrieved from https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%20refinement_Eng.pdf

Xu, Z., & Yao, L. (2022). Opening the black box of water-energy-food nexus system in China: Prospects for sustainable consumption and security. Environmental Science and Policy. Environmental Science & Policy, 127, 66-76. https://doi.org/10.1016/j.envsci.2021.10.017

Keywords:

Sustainable development goals, economic dimension, social dimension, environmental dimension, best practice, policy, optimisation, benchmarking, composite indicators, data envelopment analysis

Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues

Submission deadline: July 31, 2023

Special Issue Editors:

Guest editors:

Ali Emrouznejad, Surrey Business School, The University of Surrey, Guildford, UK,
Vincent Charles, School of Management, University of Bradford, Bradford, UK,

Submission deadline: July 31, 2022

The twenty-first century demands an urgent balance of economic and social development, as well as environmental protection and stewardship. Higher energy consumption is required to support the continued growth of the human population and rising living standards. However, this primarily leads to increased pollution and waste from industrial, agricultural, and construction activities alike, to name a few. Moreover, the public‘s attention is increasingly focused on addressing environmental problems caused by pollution, such as GHG emissions, which in turn has increased the focus on environmental efficiency evaluation in recent years.

Stochastic Data Envelopment Analysis (SDEA) can assist in determining how to achieve this balance. Data envelopment analysis (DEA) is a popular tool for analysing and measuring efficiency in both the public and private sectors. DEA considers multiple-input and multiple-output situations and compares the relative efficiency of factor input and output of multiple similar entities, also known as decision-making units (DMUs). In its original configuration, DEA is a non-stochastic tool that assumes all input and output data are quantitative, with specified numerical values; in other words, they are both deterministic and noise-free. In real-world applications, however, inputs and outputs are frequently of a stochastic nature, fluctuating over time, making it difficult to establish precise numbers for them based on the limited historical data. The introduction of SDEA, in which the possibility of noise in the data and the consideration of measurement errors and specification errors are explicitly taken into account, has been found to be efficient in dealing with and measuring the uncertainties in one or more DMUs.

The purpose of an SDEA approach under these circumstances is straightforward. Achieving environmental sustainability is fraught with uncertainty, which can manifest itself in a variety of ways, including process activities, resource consumption, and emissions across the value chain, among other things. The outstanding feature of most existing studies dealing with environmental efficiency analysis is that they model variables such as wasting water, GHG emissions, the production of useless solid materials, etc. as deterministic variables. As mentioned, DEA models with stochastic settings have been developed to accommodate both inefficiency and the presence of noise, measurement errors, and specification errors. But then again, despite substantial progress with SDEA approaches, research focusing on the use of SDEA to address environmental efficiency across domains is rather scarce.

Against this background, this Special Issue aims to compile original research articles of high quality that focus on recent advancements in SDEA and environmental efficiency applications. Theoretical, conceptual, methodological, and empirical research studies are all of interest. Contributions from both the academic and the practitioner communities are encouraged. In order to not only bring together the most recent knowledge but also to close the gap between scientific research and practical impact, we especially encourage contributions that are implementation-focused, i.e., with real-world data experiments, pilots, and industry collaborations.

Specific topics/applications of interest include, but are not limited to:

• Environmental efficiency of countries and regions
• Environmental efficiency of energy and carbon dioxide emissions  
• Environmental efficiency of regional or national energy production
• Environmental efficiency in the context of the green economy or circular economy and the sustainability of supply chains
• Environmental efficiency of manufacturing firms
• Environmental efficiency of construction companies
• Environmental efficiency of maritime companies
• Environmental efficiency of container ports
• Environmental efficiency of (electric) fossil fuels
• Environmental efficiency of oil and gas companies
• Environmental efficiency of chemical and pharmaceutical companies
• Environmental efficiency of regional or national transportation systems
• Environmental efficiency in agriculture (at farm-level, crop-level, etc.)
• Environmental efficiency in aviation

Specific methods of interest include, but are not limited to:

• Stochastic DEA (with a consideration of stochastic inputs and outputs, desirable and undesirable stochastic inputs and outputs, etc.)
• Stochastic cross-efficiency DEA
• Stochastic network DEA
• Chance-constrained DEA
• Satisficing DEA
• Stochastic frontier analysis
• Stochastic programming
• StoNED DEA
• State-contingent approaches

Submission Guidelines and Review Process: 

Papers must be submitted at http://www.editorialmanager.com/orsp/  by July  31st, 2023. Authors should select “S.I.: Stochastic DEA & Environmental Efficiency” during the submission step ‘Additional Information.’ All papers submitted to this special issue should report original work and contribute to the journal OR Spectrum by using a quantitative research paradigm and OR methods. According to the aims of OR Spectrum, high quality papers are sought that match the scope of the journal, demonstrate rigor in the application of state-of-the-art OR techniques, and promise to impact the future work of the OR community.

Papers will be screened by the Editor-in-Chief and one Special Issue Editor. If the paper is deemed to be of sufficient quality, it will be peer-reviewed according to the standards of OR Spectrum by at least two experienced reviewers. We will adopt a rapid and fair review process, striving to provide reviews within three months of submission. Accepted papers will be available online prior to publication of the special issue.

Special Issue Editors:

Editors:

Gholam R. Amin and Mustapha Ibn Boamah, Faculty of Business, University of New Brunswick, Saint John, Canada

Submission deadline: 30 April 2023

Decision making optimization models for business partnerships are essential, as businesses seldom have all of the resources they need, and thus they require alliances and partnerships with others to enable them to meet their goals. A book on optimal business partnership models, with foundations in management science and economic theory, would therefore be an important resource in understanding and quantifying business partnership gains. Many studies emphasize the importance of business partnerships. However, literature on partnership optimization is quite limited. The proposed new book will cover recent developments in decision making as it relates to business partnerships using data envelopment analysis (DEA), inverse DEA, and other decision-making methods, including parametric approaches. As introduced in Charnes et al. (1978), DEA can be safely viewed as one of the success stories of operations research and management science for evaluating relative efficiency of decision-making units (DMUs) in the presence of multiple inputs and multiple outputs. Research in recent years and outputs required for business partnerships to achieve given efficiency targets.
The objective of this edited book is to develop new DEA and other decision-making optimization models to answer key questions related to business partnerships that would be of interest to business firms, policy makers, and practitioners in areas such as firms’ restructuring, resource allocation, environment and climate change, pollution minimization, and gain maximization from mergers and acquisitions. The book:

1. will introduce DEA models to estimate the potential gains from mergers and acquisitions for various DMUs and guide them on how to combine their resources to get the maximum benefits out of the resulting synergies.
2. will introduce advances in DEA optimization and other techniques through the development of models that will define various types of business partnerships between DMUs.
3. will develop the literature of business partnerships that would guide partners on how redistributing their resources could improve their efficiency score.
4. will help partners to use each other’s resources in order to reach target efficiency.
5. will extend the well-known non-parametric performance measurement approach of DEA that would help business firms to collaborate and improve their efficiency.
6. will guide business firms on how to choose partners to reduce negative externalities such as environmental pollution.
   Submissions may be sent directly to the editors at gamin@unb.ca (Gholam R. Amin) or mboamah@unb.ca (Mustapha Ibn Boamah).

Important Dates
30 April 2023 Chapter submission deadline
30 July 2023 Notification of status and comments for revisions
30 October 2023 Chapter resubmission
31 December 2023 Additional comments if any
30 March 2024 Final chapter submission due
31 August 2024 Book submission to Taylor and Francis CRC Press

Editors Selected References:

1. Amin, G.R., and Ibn Boamah, M. (2022). Modeling business partnerships: a data envelopment analysis approach, European Journal of Operational Research, Doi: 10.1016/j.ejor.2022.05.036, article in press.
2. Amin, G.R., and Ibn Boamah, M. (2021). A two-stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector, Managerial and Decision Economics, 42(6), 1454-1465.
3. Amin, G.R., and Ibn Boamah, M. (2020). A new inverse DEA cost efficiency model for estimating potential merger gains: A case of Canadian banks, Annals of Operations Research, 195(1), 21-36.
4. Amin, G.R., Al-Muharrami, S., and Toloo, M. (2019). A combined goal programming and inverse DEA method for target setting in mergers, Expert Systems with Applications, 115, 412-417.
5. Amin, G.R., and Al-Muharrami, S. (2018). A new inverse DEA model for mergers with negative data, IMA Journal of Management Mathematics, 29(2), 137-149.
6. Amin, G.R., and Oukil, A. (2019). Flexible target setting in mergers using inverse data envelopment analysis, International Journal of Operational Research, 35(3), 301-317.
7. Amin, G.R., Emrouznejad, A., and Gattoufi, S. (2017). Modelling Generalized Firms’ Restructuring using Inverse DEA, Journal of Productivity Analysis, 48(1), 51-61.
8. Amin, G.R., Emrouznejad, A., and Gattoufi, S. (2017). Minor and Major Consolidations in Inverse DEA: Definition and Determination, Computers and Industrial Engineering, 103(1), 193-200.
9. Emrouznejad, A., Yang, G., and Amin, G.R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7), 1079-1090.
10. Gattoufi, S., Amin, G.R., and Emrouznejad, A. (2014). A new inverse DEA method for merging banks, IMA Journal of Management Mathematics, 25(1): 73-87.
11. Wegener, M., and Amin, G.R. (2019). Minimizing Greenhouse Gas Emissions using Inverse DEA with an Application in Oil and Gas, Expert Systems with Applications, 122, 369–375.

About the editors

Gholam R. Amin is an Associate Professor of Operations Research and Management Science in the Faculty of Business at the University of New Brunswick, Saint John, Canada. He is an Associate Editor of the IMA Journal of Management Mathematics at Oxford University Press. Dr. Amin’s research interests include performance measurement, productivity and efficiency analysis through data envelopment analysis (DEA) and optimization models. Dr. Amin has published over 70 articles in the leading journals such as Operations Research (FT-50), European Journal of Operational Research, Journal of Productivity Analysis, Annals of Operations Research, International Journal of Production Research, Journal of the Operational Research Society, Computers and Operations Research, Computers & Industrial Engineering, Applied Mathematical Modeling, International Journal of Approximate Reasoning, International Journal of Intelligent Systems, ABACUS, and IMA Journal of Management Mathematics among others. Mustapha Ibn Boamah is a Professor of Economics in the Faculty of Business at the University of New Brunswick, Saint John, Canada.

Dr. Ibn Boamah’s research interests include open-economy macroeconomics, monetary economics, international finance, and the economics of financial institutions. He has published in various peer reviewed journals – including publications in the Review of Financial Economics, Atlantic Economic Journal, Strategic Change, Social Responsibility Journal, International Journal of Organizational Analysis, International Journal of Social Economics, Managerial and Decision Economics, Annals of Operations Research, and the European Journal of Operational Research.

Guest editors:

Vincent Charles, School of Management, University of Bradford, Bradford, UK,
Ali Emrouznejad, Surrey Business School, The University of Surrey, Guildford, UK
Werner H. Kunz, University of Massachusetts Boston, Massachusetts, USA

Submission window: Aug 1 to Nov 1, 2022

Article type to select when submitting: AI for Business Performance

Call for papers

Analytics is probably the most important tool a business has today to improve efficiencies and optimize performance. And while traditionally businesses have focused on descriptive and predictive analytics, in recent times, we witness, more and more, the rise of prescriptive and cognitive analytics. This is because prescriptive analytics provides an advisory role regarding the future rather than merely predicting what will happen, while cognitive analytics replicates human thought and converts it into pure intelligence. Thus, their value lies within. Especially when operating in a competitive business environment, prescriptive and cognitive analytics can help and enhance decision-making, giving companies a real competitive advantage; they can mean a huge boost to profit, productivity, and the bottom line.

Prescriptive and cognitive analytics combine standard analytics techniques with Artificial Intelligence (AI), and intrinsically Machine Learning (ML), characteristics for advanced analytics results. As AI continues to develop, the way we use analytics also continues to grow and change. And while AI-based prescriptive and cognitive analytics have grown in popularity in recent years, we have yet to see them deliver on their true promise.

So far, the full potential of prescriptive and cognitive analytics using AI approaches and tools has only been investigated from a theoretical and conceptual perspective.  There is, therefore, untapped potential to utilize AI to discover more trends and produce actionable recommendations based on those trends. Furthermore, in the majority of research studies, analytics is based on an optimization problem with an expert-created objective function. The growth of data-oriented applications, however, paves the way for the advancement of prescriptive and cognitive analytics models that combine domain expert knowledge and data-driven insights.

Moreover, AI-based analytical models generally operate in a black box, meaning that we do not really know how they work, nor how decisions are being made. This poses an issue in real-life, especially since decision-makers rely on such models for optimizing their decision-making processes. It is for this reason that AI-based analytical models need to have interpretable and explanatory capabilities in order for users to understand how and why certain decisions were made and how these impact on business performance. The literature, however, shows that there is still limited research when it comes to the development of such models that are interpretable and explainable.

This Special Issue aims to compile recent advancements in AI-based prescriptive and cognitive analytics for better business performance. As a result, the Special Issue will cover both theoretical and conceptual approaches that pave the way for the development of more advanced AI-based prescriptive and cognitive analytics models and empirical studies that address specific problems in a business setting, such as ways to better integrate domain expert input in the data analytics lifecycle, innovative implementations, and so on.

Potential AI and AI-related approaches (within a business context) to be covered include but are not limited to:

• Artificial Neural Network
• Deep learning
• Deterministic programming
• Fuzzy programming
• Game theory
• Nature-inspired algorithms
• Optimization
• Reinforcement learning
• Semantics
• Simulation (Monte Carlo, etc.)
• Stochastic process
• Stochastic programming

Instructions for Authors can be found at:

https://www.elsevier.com/journals/journal-of-business-research/0148-2963/guide-for-authors

Authors should submit a cover letter and a manuscript by Nov 1, 2022, via the Journal’s online submission site. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Please see the Author instructions on the website if you have not yet submitted a paper through Springer’s web-based system, Editorial Manager (. When prompted, please select the special issue’s title, Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance, to ensure that it will be reviewed for this special issue.

Papers will be subject to a strict double-blind review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

Important Dates

Guest Editors