Comprehensive Guides for Literature Reviews and Meta-Analysis:

• Systematic Literature Review (SLR): Guidance on conducting thorough SLRs, including identifying research questions, selecting databases, and synthesizing findings.

• Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA): Training on PRISMA guidelines to ensure high standards in reporting meta-analyses and systematic reviews.

• Bibliometric Analysis: Assistance in conducting bibliometric analyses to map the research landscape, identify trends, and evaluate research impact.

Development and Publication of Quality Research Papers:

• Publisher’s Guide to Writing a Manuscript: Comprehensive guidance on crafting a manuscript, from conceptualization to final draft.

• Preparation Steps for Writing a Paper: Advisory on essential preparatory steps before writing a paper, ensuring a solid foundation for research work.

• Usage of Proper Scientific Language: Assistance in using appropriate scientific language and terminology in research papers.

• Structuring Research Papers: Guidance on building a well-structured research paper, ensuring logical flow and coherence.

Research Article Structure:

• Complete Research Article Development Guidelines: Detailed guidelines on developing each section of a research article, from abstract to conclusion.

Training in Multi-Criteria Decision Making (MCDM) Methods:

• Fuzzy Inference Systems (FIS): Training on applying FIS in research for handling imprecise or vague data.

• Decision Making Trial and Evaluation Laboratory (DEMATEL): Guidance on using DEMATEL for analyzing complex cause-effect relationships.

• Interpretive Structural Modeling (ISM) and MICMAC: Assistance in applying ISM and MICMAC for structural analysis of systems and issues.

• Best-Worst Method (BWM): Training on BWM for making effective decisions in complex situations.

• Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP): Techniques for multi-criteria decision making, useful in research and analysis.

Statistical Methods and Analysis:

• Partial Least Squares Structural Equation Modeling (PLS-SEM): Training on PLS-SEM for complex data modeling and analysis.

• Regression Analysis: Guidance on performing and interpreting regression analyses in research.

• Analysis of Variance (ANOVA): Techniques for comparing means and assessing variance in research data.

• Exploratory Factor Analysis (EFA): Training on using EFA for identifying underlying relationships in datasets.

• Cluster Analysis: Methods for classifying objects or cases into relatively homogeneous groups.

• Time Series Analysis: Techniques for analyzing time-ordered data series.

• Structured Equation Modeling: Advanced methods for statistical modeling and hypothesis testing.