International Journal of Information Management Data Insights
Volume 1 • Issue 2
- ISSN: 2667-0968
A companion title to the International Journal of Information Management, IJIM Data Insights is a peer-reviewed gold open access journal dedicated to advancing empirical evidence-… Read more
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Request a sales quoteA companion title to the International Journal of Information Management, IJIM Data Insights is a peer-reviewed gold open access journal dedicated to advancing empirical evidence-based research in information management and related fields through rigorous data-driven approaches.
Journal Focus
IJIM Data Insights emphasizes methodological rigor and empirical validity in research contributions. The journal values comprehensive and well-executed studies that demonstrate robust analytical approaches, regardless of their outcome. We particularly welcome research that provides thorough empirical investigation, careful methodology, and clear practical implications.
Our scope encompasses the broad spectrum of information management, including but not limited to:
Data Management and Analytics
Information Systems and Digital Infrastructure
Digital Transformation and Innovation
Knowledge Management and Organizational Learning
Artificial Intelligence for Information Management
Business Intelligence and Decision Support Systems
Social and Organizational Aspects of Information Use
Information Policy and Governance
Operations and Process Management
Consumer Behavior and Marketing Information Systems
Sustainability in Information Management
Societal Impacts of Information Management
Types of Contributions
IJIM Data Insights recognizes that valuable research takes many forms . We welcome submissions in various formats:
Traditional Research Formats
Original Research Articles presenting empirical findings from quantitative, qualitative or mixed method studies.
Theory Development Papers supported by empirical validation and advancing theoretical understanding in the field.
Systematic Literature Reviews and Meta Analyses with advanced analytical methods such as bibliometric analysis or scientometric analysis to synthesize existing research.
Methodological Papers introducing novel analytical or computational approaches with clear empirical demonstration.
Survey-based Research with robust statistical analysis to analyze trends and relationships.
Experimental and Quasi-experimental investigating causal or correlative insight under controlled or observational conditions.
Observational Studies exploring real world phenomena through detailed field research.
Design Science Research focusing on design, creation and evaluation of innovative artifacts.
Alternative Research Formats
Replication Studies validating or challenging existing findings.
Research Opinions backed by empirical evidence, proposing new directions, or challenging exiting paradigm.
Industry Case Studies providing detailed analyses of real-world applications.
Methodological Papers introducing or comparing analytical approaches.
Longitudinal Studies tracking developments over time.
Large-scale Data Analytics Studies utilizing extensive datasets to derive insights.
Data-driven conceptual papers proposing new frameworks or perspectives grounded in large-scale data analysis.
All submissions should demonstrate:
Clear research objectives and methodology
Robust data collection and analysis
Strong empirical evidence supporting conclusions
Practical implications for information management
Adherence to ethical research standards
Open Data Policy
IJIM Data Insights is committed to research transparency and reproducibility through its Open Data policy. Authors are required to:
Deposit their research data in a relevant data repository
Include appropriate citations and links to these datasets in their article
Provide a Research Data Availability Statement
If data sharing is not possible due to ethical, legal, or proprietary constraints, authors must include a detailed statement explaining why the research data cannot be shared.
The journal employs a rigorous peer-review process focused on methodological soundness and empirical validity. We encourage submissions that leverage various data types (structured, semi-structured, or unstructured) and analytical methods (quantitative, qualitative, or mixed methods) to provide meaningful insights into information management challenges.
- ISSN: 2667-0968
- Volume 1
- Issue 2