Abstract
A Doctor of Business Administration (DBA) is a professional doctorate that emphasizes solving complex organizational and managerial problems through evidence-based research. Unlike a traditional PhD, where theoretical contribution is often the primary objective, DBA research seeks to bridge the gap between theory and practice by generating actionable insights for business leaders and policymakers. The quality of a DBA dissertation largely depends on the strength of its literature review, which establishes the research context, identifies existing knowledge, and reveals unresolved issues. A Systematic Literature Review (SLR) has become the gold standard for conducting rigorous literature reviews because it follows a transparent, structured, and reproducible methodology.
This article provides a comprehensive guide for DBA scholars on planning, conducting, and reporting a systematic literature review. It explains each stage of the SLR process, from developing a research question and designing search strategies to screening studies, assessing quality, synthesizing evidence, identifying research gaps, and preparing a publishable review. The article also discusses common challenges, emerging tools, artificial intelligence applications, ethical considerations, and publication strategies relevant to business and management research.
1. Introduction
Business organizations are operating in an environment characterized by digital transformation, artificial intelligence, sustainability challenges, geopolitical uncertainties, remote work, and rapidly changing customer expectations. Researchers are expected to generate evidence-based solutions that improve managerial decision-making while contributing to academic knowledge.
Before collecting primary data or proposing new models, researchers must understand what has already been investigated, what remains unknown, and where meaningful research opportunities exist. A literature review provides this foundation. However, not all literature reviews are conducted with the same level of rigor.
Traditional narrative reviews often depend on the author’s personal selection of studies, which may introduce bias and overlook important evidence. In contrast, a Systematic Literature Review (SLR) applies predefined procedures to identify, evaluate, and synthesize all relevant literature addressing a specific research question. This approach improves transparency, reproducibility, and credibility.
For DBA scholars, an SLR serves multiple purposes:
- Identifies practical business problems requiring further investigation.
- Maps theoretical developments within a discipline.
- Examines methodological approaches used by previous researchers.
- Identifies inconsistencies in findings.
- Reveals research gaps that justify the proposed study.
- Builds conceptual and theoretical frameworks.
- Supports publication in high-quality journals before completing empirical research.
Consequently, mastering the SLR process is an essential research competency for every DBA scholar.
2. What is a Systematic Literature Review?
A Systematic Literature Review is a structured methodology for identifying, selecting, critically appraising, and synthesizing published research related to a clearly defined research question using explicit and reproducible procedures.
Unlike a traditional review that may rely heavily on the researcher’s subjective judgment, an SLR documents every stage of the review process, allowing other researchers to replicate the study.
An SLR generally aims to answer questions such as:
- What is already known about the phenomenon?
- Which theories dominate the field?
- Which methodologies are commonly employed?
- What are the major findings?
- Where do contradictions exist?
- Which contexts remain underexplored?
What research opportunities should future scholars pursue?
3. Why DBA Scholars Should Conduct an SLR
DBA dissertations are expected to demonstrate both academic rigor and managerial relevance. A systematic review strengthens research quality by ensuring that the proposed study addresses genuine gaps rather than duplicating existing work.
Academic Benefits
- Provides comprehensive understanding of the research domain.
- Enhances theoretical grounding.
- Improves research design.
- Supports conceptual framework development.
- Identifies suitable variables and constructs.
- Reveals methodological trends.
Professional Benefits
- Helps solve practical organizational problems.
- Improves decision-making recommendations.
- Supports evidence-based management.
- Enhances consultancy projects.
- Generates publishable research.
Publication Benefits
Many journals now encourage systematic reviews because they summarize evidence across multiple studies and often receive higher citation rates than empirical papers.
4. Types of Literature Reviews
Understanding different review methodologies helps scholars choose the most appropriate approach.
Narrative Review
Scoping Review
Integrative Review
Rapid Review
Meta-analysis
Meta-synthesis
Bibliometric Review
Systematic Literature Review
5. Planning an SLR
A successful SLR begins with careful planning rather than database searching.
Researchers should prepare a review protocol that includes:
- Research objectives
- Research questions
- Search strategy
- Databases
- Inclusion criteria
- Exclusion criteria
- Screening process
- Quality assessment procedure
- Data extraction format
- Synthesis method
Documenting the protocol before beginning the review reduces researcher bias.
6. Developing the Research Question
A poorly defined research question produces an unfocused review.
Characteristics of an effective research question include:
- Clear
- Specific
- Relevant
- Feasible
- Novel
- Aligned with DBA objectives
Example:
Broad Question
How does AI affect businesses?
Improved Question
How does Artificial Intelligence adoption influence operational performance in small and medium-sized manufacturing enterprises?
7. Choosing Databases
Database selection significantly affects review quality.
Recommended databases include:
- Scopus
- Web of Science
- Emerald Insight
- ScienceDirect
- SpringerLink
- Wiley Online Library
- Taylor & Francis
- ProQuest
- EBSCO Business Source
- IEEE Xplore (technology-focused studies)
- ABI/INFORM
- Google Scholar (supplementary search only)
Using multiple databases minimizes publication bias.
8. Designing Search Strategies
An effective search strategy balances comprehensiveness with precision.
Example:
(“Artificial Intelligence” OR AI OR “Machine Learning”)
AND
(“Small and Medium Enterprises” OR SME)
AND
(adoption OR implementation OR acceptance)
Researchers should also use:
- Boolean operators
- Wildcards
- Phrase searching
- Controlled vocabulary
- Subject headings
- Citation tracking
- Backward referencing
- Forward referencing
9. Inclusion and Exclusion Criteria
Predefined criteria ensure objectivity.
Inclusion
- Peer-reviewed journals
- English language
- Business and management discipline
- Published between 2018 and 2026
- Empirical or review studies
Exclusion
- Editorials
- Conference abstracts
- Book reviews
- Duplicate records
- Non-English publications
- Studies lacking methodological details
10. PRISMA Framework
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is the internationally accepted standard for reporting systematic reviews.
The PRISMA process consists of four stages:
Identification
Search databases and identify records.
Screening
Remove duplicates and screen titles and abstracts.
Eligibility
Read full-text articles.
Inclusion
Select studies meeting all eligibility criteria.
Documenting this process improves transparency and credibility.
11. Study Quality Assessment
Not every published article is methodologically sound.
Quality assessment examines:
- Research design
- Sampling technique
- Sample size
- Validity
- Reliability
- Statistical analysis
- Ethical approval
- Transparency
- Reproducibility
Researchers often create scoring matrices to classify studies as high, medium, or low quality.
12. Data Extraction
A structured extraction sheet ensures consistency.
Typical variables include:
- Authors
- Year
- Country
- Industry
- Research objectives
- Theoretical framework
- Variables
- Research design
- Sample size
- Data collection methods
- Analytical techniques
- Key findings
- Practical implications
- Limitations
- Future research suggestions
Spreadsheet software, NVivo, or dedicated review platforms can facilitate this process.
13. Evidence Synthesis
The goal of synthesis is to integrate evidence rather than summarize studies individually.
Common synthesis methods include:
- Thematic synthesis
- Narrative synthesis
- Content analysis
- Framework synthesis
- Vote counting
- Meta-analysis
- Meta-synthesis
DBA scholars frequently employ thematic synthesis due to the diversity of business research methods.
14. Identifying Research Gaps
One of the most valuable outcomes of an SLR is the identification of research gaps.
Examples include:
Theoretical Gaps
Limited application of institutional or dynamic capability theories.
Methodological Gaps
Overreliance on cross-sectional surveys with insufficient longitudinal or mixed-method studies.
Contextual
Research concentrated in developed economies while emerging markets remain underexplored.
Industrial Gaps
Limited evidence from healthcare, education, agriculture, and public-sector organizations.
Technological Gaps
Rapid advances in Generative AI, blockchain, IoT, and digital twins create new research opportunities.
Practical Gaps
Recommendations may lack implementation guidance for managers.
15. Developing a Conceptual Framework
After identifying gaps, researchers should integrate theories, constructs, and relationships into a conceptual framework.
For example, in a study on AI adoption:
- Independent Variables: Technology readiness, leadership support, organizational culture.
- Mediator: Employee digital capability.
- Moderator: Firm size.
- Dependent Variable: Organizational performance.
This framework guides hypothesis development and empirical testing.
16. Role of AI in Systematic Reviews
Artificial intelligence is transforming evidence synthesis. Tools such as ChatGPT, ResearchRabbit, Connected Papers, Semantic Scholar, Elicit, Scite, and Litmaps can assist with brainstorming, keyword generation, citation discovery, and article summarization.
However, AI should augment rather than replace scholarly judgment. Researchers remain responsible for search strategies, critical appraisal, interpretation, citation accuracy, and ethical use of AI. Any AI assistance should comply with institutional and journal policies and must never substitute for reading the original studies.
17. Common Challenges Faced by DBA Scholars
- Defining an overly broad research question.
- Missing key databases or grey literature where appropriate.
- Inconsistent application of inclusion and exclusion criteria.
- Weak critical appraisal of study quality.
- Confusing summary with synthesis.
- Failure to identify actionable research gaps.
- Inadequate documentation of search procedures.
- Poor reference management leading to citation errors.
- Overdependence on AI-generated summaries without verifying source content.
Careful planning, regular protocol updates, and transparent documentation help mitigate these challenges.
18. Recommended Software
Activity | Recommended Tools |
Reference Management | Zotero, Mendeley, EndNote |
Screening | Rayyan, Covidence |
Bibliometric Analysis | VOSviewer, Bibliometrix, CiteSpace |
Qualitative Coding | NVivo, ATLAS.ti |
Statistical Analysis | SPSS, R, Python |
Visualization | Tableau, Power BI, Microsoft Excel |
AI-assisted Discovery | ResearchRabbit, Elicit, Litmaps, Connected Papers |
19. Writing the Systematic Review
A typical DBA SLR manuscript includes:
- Introduction
- Research Background
- Review Objectives
- Research Questions
- Methodology
- Search Strategy
- PRISMA Flow Diagram
- Quality Assessment
- Results
- Thematic Analysis
- Discussion
- Theoretical Contributions
- Managerial Implications
- Research Gaps
- Future Research Agenda
- Limitations
- Conclusion
Each section should provide sufficient detail to allow readers to understand and replicate the review process.
20. Best Practices
- Register and follow a review protocol where appropriate.
- Use multiple reputable databases.
- Combine backward and forward citation searching.
- Maintain a detailed review log.
- Critically evaluate study quality before synthesis.
- Present clear tables summarizing included studies.
- Distinguish between evidence description and evidence interpretation.
- Link findings to established management theories.
- Highlight implications for managers, policymakers, and researchers.
- Update the review before dissertation submission to include the latest publications.
21. Conclusion
A Systematic Literature Review is the intellectual cornerstone of a high-quality DBA dissertation. It provides a rigorous and transparent approach for identifying, evaluating, and synthesizing existing knowledge, enabling scholars to position their research within the broader academic landscape while addressing pressing organizational challenges. By following established protocols, critically appraising evidence, and translating findings into conceptual frameworks and practical recommendations, DBA researchers can produce dissertations that are methodologically robust, theoretically informed, and managerially relevant.
Beyond supporting dissertation development, a well-executed SLR creates opportunities for publication, fosters evidence-based management, and strengthens a scholar’s long-term research capability. In an era where business environments are increasingly complex and data-driven, the ability to conduct systematic reviews is not merely a methodological skill—it is a strategic research competency that empowers DBA scholars to generate meaningful knowledge and lasting impact.
