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Journal of Business Research
Special issue on
Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance

Journal of Business Research
Special issue on
Advancements in Artificial Intelligence-based Prescriptive and Cognitive Analytics for Business Performance
 

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

1 November 2022Submission deadline,
submit at: https://www.editorialmanager.com/jobr/default1.aspx (early submission recommended, referee process starts once the paper is received, accepted papers will be published individually online as they are accepted)
31 March 2023Notification of status and acceptance of paper
31 August 2023Revised manuscripts
31 December 2023Final version of paper

Guest Editors

Professor V. Charles
c.vincent3@bradford.ac.uk   
School of Management,
University of Bradford,
Bradford,
UK  
Professor A. Emrouznejad a.emrouznejad@surrey.ac.uk  Surrey Business School,
The University of Surrey,
Guildford,
UK  
Professor W. H. Kunz, werner.kunz@umb.edu
University of Massachusetts Boston, Massachusetts,
USA

Annals of Operations Research Special Issue: Transparent and Responsible Artificial Intelligence: Implications for Operations Research

Guest Editors

Ali Emrouznejad, Surrey Business School, University of Surrey, UK
Soumyadeb Chowdhury, TBS Business School, Toulouse, France

Artificial Intelligence (AI) is one of the most disruptive technologies in this decade that has started to transform both business organisations and societies in ways we could not have envisaged a few years ago. AI has become the key source of business model innovation, process transformation, re-engineering practices for gaining competitive advantage in organisations embracing data-centric, analytics, and digital culture.  The impact of AI in transforming both businesses and societies is comparable to that of the Internet and World Wide Web (1990–2000) that led to the emergence of ecommerce, consume-centric practices, sharing economy, gig economy, digital manufacturing, and data-driven operations.

Despite the benefits offered by AI, machine learning (ML) algorithms currently lack transparency and explainability that makes it difficult for managers (or any other concerned user) to trust the output generated by these algorithms. In the context of transparency, it is a form of ideal that should facilitate revealing how data is integrated into an algorithm, processed by the algorithm and the knowledge that is gained using that data. The issue with explainability is that business managers do not usually understand how AI-based ML algorithms generate the outputs after processing the input data because the algorithm is either proprietary or that the mathematical computational models used in the algorithm are very complex to understand (even with technical expertise). In this context, literature has discussed that the time, effort, and resources invested by the organisations in AI systems has not fully translated into business value and productivity in majority of cases. Limited transparency and explainability of output responses generated by the AI systems has emerged as a key barrier to experiencing anticipated benefits by confidently turning data-centric decisions into effective actionable strategies. 

The primary goal of embedding transparency within AI-based ML and deep learning models is to help the decision-making authorities understand what the AI system is doing, how it is generating the output responses, and why a particular response is generated. This will help these business users to confidently understand and assess the accuracy of the responses based on their own tacit domain expertise that will increase trust in these systems. The ability to get explanations for the output responses generated by AI algorithms can also potentially reduce biases in business processes, operations, and decision-making, thus enhancing both accountability and fairness. For example, gender discrimination in hiring employees and setting up credit card borrowing limits stemming from AI systems has led to mistrust among both businesses and their consumers, demonstrating the need for AI transparency. Furthermore, AI transparency can also aid in identifying and resolving flaws within ML models stemming from improper training datasets (input issues), wrong settings, configurations and hyperparameters (algorithmic issues), and overfitting or underfitting models that can help evolve these systems and therefore enhance the value offered.

While research into AI transparency, interpretability, and trust mechanisms is nascent (within Operations Research [OR]), following streams of research are gradually emerging in the literature: (1) techniques and mechanisms to embed transparency in AI models that can potentially alleviate concerns about the negative impact of AI such as bad decision-making, discrimination, bias, inaccurate recommendations; (2) impact/implications of embedding transparency to data-driven decision making and accountability of people/organisational body making these operational decisions; (3) what are the skills, operational strategies, changes and policies required to facilitate responsible usage of AI applications in operational decision-making; (4) what are the drivers and barriers to embed transparency within AI models and applications from different perspectives (businesses, consumers, employees, supply chain partners and other stakeholders involved, e.g., how enhancing AI transparency can impact supply chain culture and relationships).

There is limited consensus on the best practices, tools, and mechanisms to embed transparency in AI-based systems, and the impact of introducing such mechanisms on organisational operational processes, dynamics, supply chain partnership, supply chain culture, multi-stake holder collaboration in supply chain, and gaining competitive advantage). Embedding transparency in AI algorithms (such as machine learning and deep learning) to enhance interpretability, accountability, and robustness of data-driven decision-making (leveraging AI and analytics) represents an unknown and unexplored territory for both researchers and practitioners. This presents an important challenge and opportunity for the community to advance scholarship of AI transparency and explainability in OR. However, research reported in the field of OR (and wider business and operations management literature) on this theme (conceptual, theoretical, and empirical) is extremely limited.

Considering these knowledge gaps, we invite high-quality submissions addressing theoretical and algorithmic developments, providing evidence-based empirical cases and insights, advancing theory, practice, and methodology of AI transparency scholarship in operations research. The special issue will also consider real-world innovative implementation of tools, techniques, and mechanisms to enhance interpretability of AI outputs for both business organisations and society in areas such as supply chain management, operations management, service operations, transformative marketing operations, industrial management and production systems, supply chain finance, reverse logistics, and sustainability. Review papers and conceptual papers without any empirical evidence are beyond the scope of this special issue. However, the special issue will consider demonstrators relevant to OR (corresponding to the theme) making considerable contributions to both theory and practice. Topics of contributions corresponding to the theme of the special issue, AI Transparency, include (but are not limited to):

  • Transparency in various stages of data analytics
  • Mechanisms to AI transparency (e.g., model-agnostic interpretability methods?)
  • Tools for AI transparency (e.g., LIME, SHAP)
  • Optimising AI algorithms by embedding transparency
  • Models for accountability in AI-based data-driven decision-making
  • Evaluating the performance of AI algorithms through transparency
  • Methods for model interpretability in machine learning
  • Implications of AI transparency on data-driven decision-making
  • Implications of AI transparency on supply chain relationships and culture
  • Implications of AI transparency on supply chain culture
  • AI transparency and social sustainability
  • AI transparency and supply chain resilience, supply chain diligence
  • Role of visualisation in AI interpretability
  • Role of AI transparency to algorithmic fairness and robustness
  •  Dark side of AI transparency for OR and OM

Please note: Manuscripts on the above topics should make a clear and unique contribution to OR through relevant references to OR literature.

Instructions for Authors can be found at: https://www.springer.com/journal/10479/submission-guidelines

Authors should submit a cover letter and a manuscript by March 31, 2023, 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 for the article type, please select Original Research. On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, please choose yes and the special issue’s title, Transparent and Responsible Artificial Intelligence: Implications for Operations Research, to ensure that it will be reviewed for this issue. ). 

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Papers will be subject to a strict review process managed by the Guest Editors and accepted papers will be published online individually, before print publication.

Important Date

31 March 2023Submission deadline,
submit at: https://www.editorialmanager.com/anor/  (early submission recommended, referee process starts once the paper is received, accepted papers will be published individually online as they are accepted)

Guest Editors

Professor Ali Emrouznejad
E: a.emrouznejad@surrey.ac.uk  
Professor and Chair in Business Analytics
Surrey Business School
University of Surrey
United Kingdom  
Dr Soumyadeb Chowdhury
E: s.chowdhury@tbs-education.fr
Associate Professor Information Analytics and Sustainability Management
Head of TBS Center of Excellence on Sustainable Development and CSR
TBS Business School, Toulouse, France 

Lecturer/Senior Lecturer in Business Analytics (Teaching Track) – University of Surrey

Location: Guildford
Salary: £42,149 to £61,819
Post Type: Full Time
Closing Date: 23.59 hours BST on Wednesday 06 July 2022
Reference: 039922

CLICK HERE TO APPLY ONLINE

The University of Surrey is a global university with a world-class research profile and an enterprising and forward-thinking spirit committed to research and innovation excellence and to benefitting the economy, society and the environment. Our researchers practise their excellence against the backdrop of our broad spectrum of technological, human, health and social sciences, and their uncommonly strong linkages forged in an integrated campus culture of cooperation.

The Department of Business Transformation at Surrey Business School is seeking applications for a Business Analytics Senior Lecturer/Lecturer (teaching track) position. This is part of an ambitious effort to develop a world-class group of scholars who will make significant contributions to the various facets of business analytics teaching. Our department is deeply engaged with local and international businesses which provide opportunity for meaningful interaction ensuring relevant and current teaching. Our department actively collaborates with scholars across the University such as computer science, engineering, data science and digital health.   

Clear evidence of a commitment to collaboration with academic and non-academic partners will be essential as will be evidence of excellence in the development and delivery of teaching and the promotion of student experience. We are particularly interested in candidates who have taught in the areas that have involved the use of AI, big data, virtual reality, machine learning and visualisations. Applicants should have experience of how technology can contribute to decision making and insights in a business environment.  

Informal enquiries should be directed in the first instance to Professor James Aitken (James.Aitken@surrey.ac.uk), Head of the Business Transformation Department.

CLICK HERE TO APPLY ONLINE

Job Opportunities – Lecturer in Sustainable Supply Chain Management Surrey Business School

Location: Guildford
Salary: £42,149 to £50,296
Post Type: Full Time
Closing Date: 23.59 hours BST on Wednesday 06 July 2022
Reference: 039222

CLICK HEAR to apply

Lecturer in Sustainable Supply Chain Management Surrey Business School

The University of Surrey is a global university with a world-class research profile and an enterprising and forward-thinking spirit committed to research and innovation excellence and to benefitting the economy, society and the environment. Our researchers practise their excellence against the backdrop of our broad spectrum of technological, human, health and social sciences, and their uncommonly strong linkages forged in an integrated campus culture of cooperation.

The Department of Business Transformation at Surrey Business School is seeking applications for a lecturer position. This is part of an ambitious effort to develop a world-class group of scholars who will make significant contributions to the various facets of management research on sustainable supply chains.  Our department is deeply engaged with local and international businesses which provide opportunity for meaningful interaction ensuring relevant research and teaching. We are looking for someone that supports our revenue generation in sustainability (e.g. via bids, Innovate UK funding, etc.). We actively collaborate with researchers across the University such as the Institute for Sustainability and the Centre for Environment and Sustainability.  

Candidates should hold a relevant doctoral degree and have experience of supply chain management especially in terms of sustainability (environmental and social). Candidates should demonstrate their ability and interest to conduct novel empirical research on phenomena that are important to sustainable supply chains. We are particularly interested in candidates who have developed an understanding of how sustainability can contribute to decision making and insights in a business environment.

You will have demonstrated a strong potential for A-journal publications and have experience of teaching in higher education – preferably in a business school context. 

 Sustainability is core to the values of the University which enables researchers to develop in a supportive and exciting environment.

Informal enquiries should be directed in the first instance to Professor James Aitken, (James.Aitken@surrey.ac.uk), Head of the Business Transformation Department. 

Further details:   
Apply online:
Job Description   
CLICK HEAR to apply

Call for Papers: Data Envelopment Analysis and its Applications, September 2022, Warwick University

Call for Papers: Data Envelopment Analysis and its Applications
OR64: Annual Conference, 13-15 September 2022, Warwick University, UK
Abstract submission: CLICK HERE 
(deadline: 15h July 2022)
Dear Colleagues,

I would like to issue an invitation to take part in the Data Envelopment Analysis Stream of the OR64 Conference. This conference will be held in Warwick University, UK from 13rd to 15th September 2022 Contributions from both academic and non-academic communities are welcome.

This stream covers papers on the theme of efficiency and productivity analysis and performance management. Both parametric and non-parametric papers will be considered. We especially welcome papers on the theory, methodology and application of Data Envelopment Analysis and econometric methods in performance management. Of particular interest are successful applications of performance and efficiency analysis in the real world, for example in banking, healthcare, education, transportation, Energy, and so on.

If you are interested in making a presentation, please submit an abstract of your talk no later than 15th July 2022.

Detailshttp://deazone.com/en/or64
Deadline for submission abstract: July 15, 2022
Conference date:  September 13-15, 2022
Conference Venue: WarwickUniversity, UK
Details about the venue: CLICK HERE
Abstract submission: CLICK HERE

Kind regards,
Ali Emrouznejad, Surrey Business School, University of Surrey, UK, a.emrouznejad@surrey.ac.uk
| DEA Course|DEA Linkedin|DEA Facebook|DEA Twitter|DEAzone |DEA Software|

Online DEA & SFA course – 3 days – July 2022

Online Courses on:

Performance Measurement Using Data Envelopment Analysis (DEA) & Stochastic Frontier Analysis (SFA)

Details: https://dataenvelopment.com/course/

DATA ENVELOPMENT ANALYSIS COURSE:
July 10-12, 2022

STOCHASTIC FRONTIER ANALYSIS COURSE:
July 14-16, 2022

For registration and further details please visit: https://dataenvelopment.com/course/


8th International Workshop on Efficiency in Education, Health and other Public Services

CALL FOR PAPERS

8th International Workshop on Efficiency in Education, Health and other Public Services

Workshop dates: September 8-9, 2022

Workshop venue: Department of Economics and Management, University of Pisa, Pisa, Italy

Workshop website: https://efficiency2022.ec.unipi.it

Workshop Education Economics, June 1, 2022

Workshop Education Economics
June 1, 2022

Workshop Education Economics
June 1, 2022

This one day workshop seeks to explore the use of economic and econometric techniques to study educational issues. We invite the submission of original research from colleagues working in the area. Next to education economics research in general, we especially welcome research that focusses on parental involvement, financial literacy, behavioral economics, and inequality in education. Both macro and micro-economic topics are of interest to this workshop. It is organized by the research center ‘Leuven Economics of Education Research’ (LEER) of the Faculty of Economics and Business at KU Leuven.

The workshop is followed by the PhD defence of Joana E. Maldonado at 1 pm: “Should Parents Get Homework? On Stimulating Home-Based Parental Involvement in Students’ Learning”.


Call for Posters

Authors/Speakers are expected to send a minimum 200-300 word structured abstract to ann.vanespen@kuleuven.be by April 25, 2022, at the latest.

Authors will be notified on acceptance by May 1, 2022.

For details see: CLICK HERE

The 6th International Conference on Computer Science and Application Engineering, Nanjing, China, October 21 to 23, 2022

Call for paper / presentations

CSAE 2022: The 6th International Conference on Computer Science and Application Engineering, Nanjing, China, October 21 to 23, 2022

Click here to submit your paper: http://www.csaeconf.org/

As chair of the 6th International Conference on Computer Science and Application Engineering (CSAE 2022) I would like to cordially invite you to attend / present in this CSAE 2022 which will be held during October 21 to 23, 2022 in Nanjing, China. It is held annually to provide a comprehensive global forum for experts and participants from academia to exchange ideas and present results of ongoing research in the most state-of-the-art areas of computer science and application engineering.

All submitted full papers will be peer reviewed by technical committees of the conference and our reviewers, evaluated based on originality, technical and research content/depth, correctness, relevance to conference, contributions, and readability.

All accepted papers will be published in ACM International Conference Proceedings Series, which will be archived in the ACM Digital Library, and submitted to Ei Compendex and Scopus for index and submitted to be reviewed by Thomson Reuters Conference Proceedings Citation Index (ISI Web of Science).

Proceedings of past conferences are available at:

Best regards,
Ali Emrouznejad,
Professor and Chair in Business Analytics,
Surrey Business School, University of Surrey, Guildford, Surrey, UK

EEHPS Workshop, September 2022, Italy

Welcome to the 8th International Workshop on Efficiency in Education, Health and other Public Services! This workshop is the eighth of a series of academic meetings focused on the efficiency analysis of education institutions and other public sectors, the previous editions being organized in Greece (Thessaloniki, 2013), United Kingdom (London, 2014 and Huddersfield, 2018), Belgium (Leuven, 2015), Italy (Milan, 2016), Hungary (Budapest, 2017) and Spain (Barcelona, 2019). Suspended because of COVID-19 in 2020 and 2021, this year the Workshop will be again proposed in on-campus, vis-à-vis format.

             
Call for papers
Evidence-based policy in education and in other fields of the public sector implies the use of sophisticated techniques to assess the performance and efficiency of organizations. The evaluation of national-level reforms and the assessment of specific interventions in education and other public services require accurate approaches and advanced methods.
The purpose of the Workshop is to focus on specific challenges around measuring performance in education and other public services sectors. The Workshop will bring together scholars from various disciplines and fields to debate the present and future challenges in education and public sector efficiency, performance measurement, and policy and managerial implications of the on-going research. The Workshop provides an ideal platform for presenting new methods, applications of existing techniques and advances in theoretical and empirical research.

           
Keynote speakers

Prof. Andrea Bonaccorsi, (University of Pisa, Italy)

Dr. Gabriela Sicilia, (University of La Laguna, Spain)

Prof. Bruce Hollingsworth, (Lancaster University, UK)

Deadlines

Submission:  May 22, 2022
Notification: June 5, 2022
Early registration:  July 5, 2022
Registration: August 22, 2022

Contact

efficiencyehps2022@gmail.com

Website: https://efficiency2022.ec.unipi.it/

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