Calls for papers

 

International Journal of Management Practice

 

IFC-2022: Special Issue on: "AI-Driven Decision Making for Enterprise Information System"

Guest Editors:
Prof. Yassine Maleh, Sultan Moulay Slimane University, Morocco
Prof. Justin Zhang, University of North Florida, USA
Prof. Ahmed A. Abd El-Latif, Menoufia University, Egypt

Nowadays, every business needs valuable information and knowledge to remain relevant in the market. Identifying the target audience, what customers want, and anticipating their needs is an important part of the decision-making process. Appropriate data processing and analysis enables the company to achieve these goals and survive in an uncertain environment.

With the vast amount of data now available, organisations in practically every sector are trying to leverage data to gain a competitive advantage. Previously, it was possible to use statisticians, analysts, and modellers to explore datasets manually; however, data volume and diversity exceeded the capacity for manual analysis significantly. Meanwhile, computing power has grown dramatically, networking has become pervasive, and algorithms were developed to link datasets and enabled broader data analysis and even deeper analysis than before.

Over the past decade, considerable investment has been made in business infrastructure, which has improved data collection capacity throughout the company. Today, almost all aspects of the business are potentially subject to collecting data and even often using data collection tools: operations, supply chain management, manufacturing, customer behaviour, marketing campaign effectiveness, workflow procedures, healthcare, and more. Moreover, with its wide-ranging implications for business, artificial intelligence (AI) is becoming an increasingly important item on the agendas of companies and governments around the world. Numerous firms have gone so far as to develop their artificial intelligence strategies.

A wealth of information is currently available on external events relating to market trends, industry developments, activity, and competitor movements. This widespread availability of data has led to an increased interest in methods to extract useful information and knowledge through data science.

To meet these consumer demands and anticipate the micro-events of the 21st century, organisations must differentiate their brands through real-time data analysis. Companies can do this now more than ever, thanks to many converging technologies, including machine intelligence, Internet of Things, cloud computing, data analysis, and other technologies. Indeed, advanced analysis of customer and environmental data with speed and accuracy has become imperative for organizations seeking to reach their audiences and be proactive. This special issue provides valuable information for business leaders in all functions and sectors and researchers and analysts responsible for developing models that impact businesses and the decision-making process.

The main objective is to encourage researchers and practitioners to exchange experiences and recent studies between academia and industry. The general objectives of this special issue are:

• Discover how AI and data science techniques adapt to specific business problems, the economic value of hundreds of AI use cases, and how to begin applying MI to the decision-making process
• Improve the awareness of readers about merging AI for business applications
• Review and present state-of-the-art in AI and related technologies and methodologies
• Outline and discuss the emerging developments and trends in AI for business and decision-making strategy
• Propose and discuss new models, practical solutions, prototypes, frameworks and technological advances related to AI-driven business decision-making
• Discuss AI and data science applications to form new models for real-time decision-making
• Determine the role of real-time analysis and artificial intelligence technologies in unlocking customer perspective and leading the customer experience

These points present the main lines of the special issue. As we move towards the post-pandemic COVID19, organisations that accelerate their adoption of data science and Artificial Intelligence for their decision-making strategy will be able to move from survival to competitiveness in an uncertain and volatile environment.

Such explorations would need to ensure that theoretical and empirical contributions are well developed to contribute to a high quality of the special issue. We welcome theoretical and empirical studies, using a wide variety of methods, that advance the extant knowledge. We will welcome contributions from several disciplines as well as papers based on either quantitative or qualitative approaches.

The Guest Editors will be inviting substantially extended versions of selected papers presented at the International Fintech Congress 2022 (IFC-2022) for review and potential publication, but are also inviting other experts to submit articles for this call.

Subject Coverage
Suitable topics include, but are not limited, to the following:

• Real-time analysis to understand customer needs and improve their experience
• Artificial intelligence models and enterprise-decision making strategies
• Artificial intelligence and its commercial applications
• Machine learning business problem solving
• AI and effective executive decision-making strategies for a data rich world
• AI, data sciences and business performance
• AI, big data, and data science landscape for business and government
• AI, big data, and data science landscape for healthcare
• Machine learning techniques for decision-making strategies agility and resilience
• AI and data science for predictive analytics
• Artificial intelligence and data analysis role towards transforming organisations into innovative
• AI for enterprise decision-making strategies, challenges and benefits
• efficient and sustainable businesses in uncertain times
• Data sciences and statistical methods utilised for making relevant business decisions
• Data sciences for business analytics: concepts, techniques and applications
• Data analysis and decision making
• Data science, machine intelligence, and enterprise agility and resiliency
• Deep learning practice for decision-making strategies
• Big data analytics for decision-making
• The economics of data, analytics, and digital transformation
• Digital transformation
• Models, induction, and prediction
• Data science team management
• Computer science and artificial intelligence for decision-making strategies
• Organisational dynamics

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Manuscripts due by: 20 December, 2022

Notification to authors: 20 February, 2023

Revision due by: 20 March, 2023

Notification to authors: 20 April, 2023

Notification of Final Acceptance: 20 May, 2023