International Journal of Management and Data Analytics

Editor-in-Chief
Dr. Said Baadel

Publication Frequency
IJMADA Publication frequency is 2 Issues/Year, published on a rolling basis.

ISSN 2816-9395 

The general themes of IJMADA seek to develop our understanding of management and data analytical tools and technologies used to manage decisions and processes in a cross-cultural context. IJMADA aims to provide a portal for high-quality papers focusing on the analytical and empirical study of management processes in private and public sector organizations. The journal accepts full-scale research submissions, real-life applied case studies, theories, innovative ideas, and mathematical algorithms that make a significant contribution to the field of management and data analytics.

IJMADA is an academic, online, open-access (OA), peer-reviewed international journal based in Canada. Open access publishing provides immediate, worldwide free access to all published manuscripts. Readers can view, download, print, and redistribute any article without any financial barrier, enabling greater distribution of an article.

All papers are refereed in a blind process by internationally recognized expert referees and by associate editors serving on the Editorial Board. IJMADA welcomes research in the following fields:

• Information Technology Management
• Educational Technology and Innovative Delivery Models
• Management of Information Systems
• Innovation Management
• Knowledge Management
• E-Business Technologies
• Mathematical Modeling 
• Multidisciplinary Management Mathematics
• Technology-enabled Learning
• Learning Management Systems 
• Entrepreneurship and Management Process
• Digital Marketing Analytics
• Digital Transformation and its Applications
• Industry 4.0 & 5.0
• Database Systems and Data Management
• Data Science and Data Analytics
• Artificial Intelligence Applications in Business 
• Machine Learning and its Applications
• Big Data and Real-Time Analytics
• Cybersecurity and Information Management
• Numerical Analysis
• Mathematics Application in Finance and Management
• Mathematical Methods in Machine Learning and Data Science