INFORMS Open Forum

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

Please see the Author instructions on the website if you have not yet submitted a paper through Elsevier'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.

Article type to select when submitting:

AI for Business Performance

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.

Submission portal: https://www.editorialmanager.com/jobr/default1.aspx