IEEE Engineering Management Review – Call for Papers
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 organisational management, decision-making, and planning processes. Appropriate data processing and analysis enables organizations to achieve these goals and thrive in an uncertain dynamic environment.
Organizations in practically every sector are trying to leverage data to gain a competitive advantage. Previously, statisticians, analysts, and modelers explored datasets manually; however, data volume and diversity exceeded the capacity for manual analysis. Meanwhile, computing power has grown dramatically, networking has become pervasive, and algorithms are continuously being developed to link datasets and enabled broader data analysis while simultaneously providing deeper analysis.
Considerable investment in business infrastructure has improved organizational data collection capacity. All aspects of business are potentially subject to data models and tools: such as operations, supply chain management, manufacturing, customer behavior, marketing campaign effectiveness, workflow procedures, healthcare, information management, product development, and financing. With its wide-ranging implications for business, artificial intelligence (AI) is becoming an increasingly important and integrated organizational resource and capability. Organizations have introduced Artificial intelligence strategies, further exemplifying AI’s contribution to long-term strategy and competitiveness.
A wealth of information is currently available on external competitive concerns and can broadly include market trends, industry developments, activity, and competitor movements. This widespread data availability has led to an increased interest in extracting useful information and knowledge—developing organizational intelligence that can automatically be integrated into enterprise decision and control systems.
To meet and address competitive, disruptive, and routine 21st-century events, organizations, must make sense of and integrate dynamic and large data sets. Many converging technologies can complement AI, including machine intelligence, IoT, cloud computing, data analysis, blockchain, quantum computing, and other technologies. Understanding the organizational and managerial roles of AI with these technologies and a dynamic and hostile global competitive market is the goal of this special issue (SI).
This SI seeks to provide valuable evidence-based information and insights for business leaders and managers in all functions and sectors.
The main objective of this special issue is to encourage researchers and practitioners to exchange experiences and recent studies between academia and practitioners. 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 apply it to decision-making.
- 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 some of the main lines of the SI. As we move towards the post-pandemic COVID19 period, organizations that accelerate their adoption of data science and AI for adaptive yet deeper decision-making strategies can move from survival to competitiveness and thrive in an uncertain and volatile environment.
Building on the general SI objectives, the potential topics of interest 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 organizations 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
- Models, Induction, and Prediction
- Data Science Team Management
- Organizational dynamics
These explorations would need to ensure that high-quality theoretical and empirical contributions are well linked and developed to address practical organizational and managerial issues that contribute to the goals IEEE Engineering Management Review. We welcome theoretical and empirical studies, using a wide variety of methods, that advance the extant knowledge. We especially wish to encourage works that can advance organizational practice and decision making. We will welcome contributions from several disciplines as well as studies based on either quantitative or qualitative approaches.
Please note that organizational and managerial implications need to be detailed and clearly related to the evidence provided. Inclusion of managerial and organizational issues and concerns addressed or issues that still need to be addressed should be central to any submission. A practitioner managerial audience needs to be centrally considered in the submission.
Manuscript submission deadline: 20-Dec-2021
Notification of Review: 20-Mar-2022
Revision due: 20-May-2022
Notification of 2nd Review: 20-Jul-2022
2nd Revision [if needed] due: 20-Sep-2022
Notification of Final Acceptance: 20-Oct-2022
Expected Online Publication: Within 8-10 weeks of Acceptance by SI Editors
Submissions should be made online to: https://mc.manuscriptcentral.com/emr-ieee
Please contact the guest editors if you have any questions.
Prof. Yassine Maleh (IEEE SM), Associate Professor of Cybersecurity and Information System at Sultan Moulay Slimane University, Morocco
Prof. Justin Zhang (IEEE SM), Professor of Information Systems Management at University of North Florida. USA
Prof. Ahmed A. Abd El-Latif (IEEE SM), Associate Professor, Menoufia University, Egypt.