Chapter 3: 3. Systematic Literature Review and Evidence Synthesis

1. Introduction

Information technology research increasingly depends on the ability to locate, evaluate, and integrate a large and rapidly changing body of knowledge. Research on artificial intelligence, cybersecurity, cloud computing, digital transformation, software engineering, and information systems is distributed across journals, conference proceedings, standards, technical reports, dissertations, preprints, and practitioner sources. Individual studies often provide only partial answers. Their findings may differ because of variations in samples, technologies, research designs, measures, contexts, or analytical assumptions.

Systematic literature review and evidence synthesis provide disciplined methods for understanding this dispersed knowledge. Unlike an informal literature review, a systematic review follows a planned, explicit, and auditable procedure for searching, selecting, appraising, extracting, and synthesising evidence. A scoping review maps the extent and characteristics of a field, especially when concepts are emerging or heterogeneous. Bibliometric analysis examines patterns of publication, citation, collaboration, and intellectual structure. Meta-analysis statistically combines compatible quantitative findings, while qualitative evidence synthesis integrates interpretations and experiences across qualitative studies.

For doctoral researchers, these methods are not merely preliminary exercises. They can establish the state of knowledge, reveal theoretical and methodological gaps, test the robustness of claims, guide research design, and support evidence-informed policy and management. Their credibility depends on transparency, methodological consistency, critical judgement, and reproducibility.

2. Learning Objectives

By the end of this chapter, readers should be able to:

1. Distinguish systematic reviews, scoping reviews, bibliometric reviews, narrative reviews, and meta-analyses.

2. Formulate answerable review questions and eligibility criteria.

3. Design transparent searches across appropriate scholarly databases.

4. Apply reproducible screening, data-extraction, and quality-appraisal procedures.

5. Conduct and interpret bibliometric, qualitative, and quantitative evidence synthesis.

6. Assess heterogeneity, bias, uncertainty, and the strength of evidence.

7. Use digital tools while preserving methodological accountability.

8. Address ethical, governance, and reporting requirements in doctoral-level reviews.

3. Meaning and Concept

A systematic literature review is a research study that uses a predefined protocol to identify, select, critically appraise, and synthesise relevant evidence addressing a focused question. The unit of analysis is usually the study or publication, although a review may also examine datasets, software artifacts, standards, or organizational documents.

Evidence synthesis is the broader activity of combining information from multiple sources to produce a justified conclusion. It may be statistical, conceptual, thematic, realist, configurational, or descriptive. The appropriate synthesis depends on the review question and the nature of the available evidence.

A systematic review should be distinguished from a narrative review. Narrative reviews can provide valuable expert interpretation, but their search and selection processes may be implicit or selective. Systematic reviews seek to reduce avoidable selection bias by making decisions explicit and repeatable. Systematic does not mean mechanically objective: decisions about concepts, databases, quality, and interpretation still require scholarly judgement.

A scoping review is particularly useful when terminology is inconsistent, evidence is diverse, or the field has not yet matured sufficiently for a narrowly focused effectiveness review. It maps concepts, methods, populations, technologies, and knowledge gaps, but generally does not estimate a pooled effect or make the same level of causal claim as a meta-analysis.

4. Fundamental Principles

Transparency

A reader should be able to understand what was planned, what was actually done, and why deviations occurred. The review should report databases, dates, search strings, filters, screening decisions, extraction fields, appraisal criteria, and synthesis methods.

Reproducibility

Another researcher should be able to repeat the search and approximate the selection and analysis process. Reproducibility is strengthened by preserving search histories, protocol versions, datasets, scripts, coding decisions, and excluded-study reasons.

Alignment

The review question, search strategy, eligibility criteria, appraisal method, and synthesis approach must fit one another. A review asking whether an intervention improves an outcome requires a different design from one asking how organizations adopt a technology.

Comprehensiveness with relevance

A search should be sufficiently broad to identify important evidence, but not so broad that screening becomes unmanageable or conceptually incoherent. Database coverage, synonyms, citation chasing, and grey-literature searching all contribute to balanced retrieval.

Critical appraisal

Inclusion does not imply credibility. Studies must be assessed for design quality, measurement validity, bias, analytical adequacy, and relevance to the review question.

Reflexivity

Reviewers’ disciplinary assumptions and decisions can influence interpretation, particularly in qualitative synthesis. Reflexive records, multiple reviewers, peer checking, and explicit theoretical positioning improve trustworthiness.

Appropriate inference

The strength of conclusions must not exceed the strength of evidence. Correlational studies cannot alone establish causation, and a large number of weak studies does not automatically constitute strong evidence.

5. Major Components / Elements

Review question and scope

A good question defines the phenomenon, population or organizational setting, intervention or exposure, comparator where relevant, outcomes, and study designs. Frameworks such as PICO (Population, Intervention, Comparison, Outcome), PICOC (including Context), SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type), and PCC (Population, Concept, Context) help structure different questions.

Protocol

The protocol specifies the rationale, objectives, questions, eligibility criteria, information sources, search strategy, screening process, extraction plan, appraisal tools, synthesis method, and reporting strategy. Registration or public deposition of the protocol reduces undisclosed methodological changes.

Information sources

Sources may include multidisciplinary databases, specialist indexes, citation databases, institutional repositories, conference archives, standards organizations, government portals, and expert-recommended sources. Database selection should reflect the field rather than convenience alone.

Search strategy

Searches normally combine controlled vocabulary, keywords, synonyms, spelling variants, acronyms, and technology names using Boolean operators. Truncation, phrase searching, proximity operators, and field restrictions may improve precision. The search strategy should be tested against known relevant studies.

Screening and selection

Duplicate records are removed, titles and abstracts are screened, and potentially eligible full texts are assessed. Ideally, two reviewers independently assess at least a sample, preferably all records, and resolve disagreements using discussion or a third reviewer. A flow diagram documents the process.

Data extraction

Extraction captures study characteristics, context, participants, technologies, methods, interventions, outcomes, findings, limitations, and funding or conflict-of-interest information. A pilot-tested form reduces ambiguity and ensures consistency.

Quality appraisal and risk of bias

Appraisal examines whether findings are credible and applicable. Tools should match study designs. For example, randomized studies, qualitative studies, diagnostic studies, surveys, and mixed-methods research require different criteria. Quality scores should not be used uncritically as if they were precise measurements.

Synthesis

Synthesis may involve descriptive statistics, thematic analysis, vote counting with caution, framework synthesis, realist explanation, bibliometric mapping, meta-analysis, or a combination. The synthesis should preserve important differences rather than conceal them.

6. Process / Framework / Methodology

Step 1: Establish the purpose

Begin by explaining why the review is needed. A defensible rationale may be a fragmented evidence base, contradictory findings, a new technology, policy significance, or the absence of a recent review.

Step 2: Formulate the questions

Questions should be specific enough to guide searching and broad enough to capture relevant evidence. For example: “What organizational capabilities enable successful implementation of zero-trust cybersecurity in large public-sector agencies?” This question identifies a phenomenon, context, and likely evidence types without prematurely assuming a particular outcome.

Step 3: Develop and register the protocol

Specify inclusion and exclusion criteria before searching whenever possible. Define publication years, languages, document types, populations, settings, technologies, and eligible methods. If the protocol changes, record the reason, date, and effect of the change.

Step 4: Design and test the search

Translate the concepts in the question into search blocks. For example:

`("zero trust" OR "zero-trust" OR ZTA) AND (cybersecurity OR "information security") AND (implementation OR adoption OR deployment) AND (government OR public-sector)`

The search should be adapted to each database’s syntax. Search sensitivity is evaluated by checking whether benchmark studies are retrieved. Citation chaining, including backward reference checking and forward citation searching, can identify studies missed by keywords.

Step 5: Manage records and remove duplicates

Export complete bibliographic metadata and, where permitted, full texts. Deduplication should be documented because the same work may appear as a conference paper, journal article, preprint, or repository copy. Version relationships must be considered to avoid double-counting evidence.

Step 6: Screen systematically

Apply the eligibility criteria consistently. Screening software can support blinding, tagging, conflict resolution, and audit trails, but researchers remain responsible for interpretation. Agreement statistics, such as Cohen’s kappa, may be used during pilot screening, although low agreement should prompt clarification of criteria rather than merely reporting a statistic.

Step 7: Extract and code data

Pilot the extraction form on several studies. Use controlled categories where appropriate, while allowing explanatory notes. For qualitative evidence, coding may capture constructs, participant perspectives, mechanisms, context, and consequences. For quantitative studies, extract effect estimates, standard errors, sample sizes, measurement instruments, and adjustment variables.

Step 8: Appraise quality and risk of bias

Assess internal validity, credibility, transferability, completeness, and reporting quality according to the review design. Common threats include confounding, selection bias, nonresponse, poor measurement, selective reporting, common-method bias, weak qualitative reflexivity, and undisclosed industry influence.

Step 9: Choose the synthesis method

A descriptive synthesis may summarize study characteristics and patterns. A thematic synthesis identifies recurring concepts and relationships. A meta-analysis estimates an aggregate effect when studies are sufficiently comparable. A scoping review may use evidence charts and conceptual mapping rather than pooled estimates. Mixed-methods reviews may integrate quantitative results with qualitative explanations.

Step 10: Interpret heterogeneity

Heterogeneity refers to variation among studies. It may be clinical or substantive, methodological, or statistical. In meta-analysis, statistical heterogeneity can be examined using measures such as the I² statistic and Cochran’s Q, but numerical indicators do not explain why studies differ. Researchers should examine populations, settings, interventions, measures, follow-up periods, and study quality.

Step 11: Assess robustness

Sensitivity analyses can test whether conclusions change when low-quality studies, outliers, alternative definitions, or different statistical assumptions are removed. Subgroup or moderator analyses should be theoretically justified and preferably specified in advance.

Step 12: Report and disseminate

A complete report describes the rationale, methods, results, limitations, conflicts of interest, and implications. Reporting guidelines such as PRISMA support completeness, while specialized extensions exist for scoping reviews and other designs. The review should distinguish findings from interpretation and recommendations.

7. Important Theories and Models

The evidence hierarchy is a useful but limited model. It often places systematic reviews and meta-analyses near the top, but a review cannot be stronger than the studies it includes, and a well-designed observational study may be more informative than a poorly conducted trial. Evidence must therefore be judged in relation to the question.

The PRISMA model emphasizes transparent reporting of identification, screening, eligibility, and inclusion. It is a reporting framework, not a substitute for a sound review method.

The realist perspective asks, “What works, for whom, in what circumstances, and through what mechanisms?” It is valuable in IT because technology outcomes depend heavily on organizational context, capabilities, incentives, and implementation conditions.

The Technology–Organization–Environment framework explains technology adoption through technological characteristics, organizational factors, and environmental pressures. It can guide coding and synthesis in reviews of enterprise systems, cloud adoption, or digital transformation.

The socio-technical perspective treats IT outcomes as products of interaction between technical artifacts, people, work practices, structures, and institutions. It prevents reviews from attributing complex outcomes solely to software or infrastructure.

8. Practical Applications

Systematic evidence synthesis can inform software engineering standards, cybersecurity controls, digital health systems, AI governance, IT investment, user-experience design, and public policy. A chief information officer may use a review to compare evidence concerning cloud migration risks. A cybersecurity team may synthesize findings on phishing interventions. A government agency may review algorithmic accountability practices before procuring an AI system. A doctoral researcher may use a scoping review to define the constructs and methods available in an emerging research area.

Bibliometric analysis is useful for identifying highly connected authors, institutions, countries, journals, keywords, and research clusters. Co-citation analysis reveals intellectual relationships; bibliographic coupling identifies documents sharing references; co-word analysis shows conceptual associations. These techniques map a field but do not, by themselves, establish that highly cited work is methodologically sound.

9. Examples

Consider a review of robotic process automation in financial services. A broad scoping review might identify applications, implementation barriers, workforce effects, and research methods. A focused systematic review might examine whether automation reduces processing time. If several comparable studies report time savings and variance measures, meta-analysis may be appropriate. If studies use different definitions of productivity and organizational contexts, a structured narrative or realist synthesis may be more defensible.

For AI-assisted hiring, a review should not count only positive performance results. It should also extract information about training data, protected groups, fairness definitions, validation settings, human oversight, and deployment conditions. Evidence synthesis may reveal that technical accuracy improves while disparate impact remains unresolved.

10. Technology and Digital Applications

Reference-management systems support citation storage, metadata correction, deduplication, and citation-style control. Systematic-review platforms support screening, tagging, reviewer blinding, conflict resolution, and audit trails. Bibliometric software can generate co-authorship, co-citation, and keyword maps. Statistical environments such as R and Python support effect-size calculations, meta-analysis, visualization, text mining, and reproducible workflows.

Machine-learning tools can prioritize records for human screening, identify likely duplicates, extract candidate terms, or assist with classification. Large language models may help summarize documents or suggest coding categories. However, automated tools can reproduce training-data bias, hallucinate content, overlook qualifying studies, or obscure decision logic. They should therefore be treated as assistance rather than autonomous adjudicators. Every automated decision affecting inclusion, exclusion, extraction, or interpretation requires human verification and an audit record.

Version-control systems and open repositories can preserve protocols, search files, extraction tables, analysis scripts, and revisions. Persistent identifiers improve traceability. Access restrictions, copyright, licensing, and confidential data must be respected when sharing materials.

11. Strategic / Managerial Importance

For managers, evidence synthesis reduces reliance on isolated vendor claims, fashionable technologies, or anecdotal success stories. It supports technology selection, risk assessment, workforce planning, standards development, and investment prioritization. A review can identify which implementation practices are consistently associated with success and where evidence remains uncertain.

At organizational level, systematic reviews strengthen absorptive capacity: the ability to recognize, evaluate, and apply external knowledge. They also support evidence-informed governance by making assumptions and uncertainty visible. Managers should nevertheless avoid treating review conclusions as universal prescriptions. Transferability depends on organizational size, maturity, regulation, culture, infrastructure, and resources.

12. Detailed Case Study

The following case is fictional and illustrative.

Background

A national public agency planned to introduce zero-trust architecture across forty offices. Senior leaders commissioned a doctoral research team to determine which implementation practices were supported by credible evidence.

Problem

Initial consultations produced conflicting recommendations. Vendors emphasized identity platforms, security teams emphasized network segmentation, and human-resources leaders emphasized training and role redesign. The agency lacked a transparent basis for prioritizing investments.

Analysis

The research team formulated a PICOC question concerning public-sector organizations, zero-trust implementation practices, conventional perimeter-based security as a contextual comparator, security and operational outcomes, and empirical or evaluative studies. They searched major IT, cybersecurity, and multidisciplinary databases, supplemented the search with standards and backward and forward citation tracking.

After deduplication, two reviewers screened records using predefined criteria. They extracted organizational context, implementation sequence, identity and access controls, segmentation, monitoring, governance, workforce preparation, outcomes, and study limitations. Quality appraisal showed that many publications were vendor reports or descriptive case studies with limited independent evaluation. The team therefore avoided a pooled effectiveness estimate.

The synthesis identified three recurring mechanisms: continuous verification improved control visibility; least-privilege implementation reduced unnecessary access when roles were clearly defined; and executive sponsorship was necessary to overcome operational resistance. Outcomes were strongly conditioned by legacy systems, identity-data quality, and staff capability.

Strategy / Solution

The agency adopted a staged implementation. It first established an asset and identity inventory, then piloted risk-based access controls in two offices. It created governance roles, workforce training, incident-response procedures, and measurable indicators. Vendor evidence was retained as contextual information but was not treated as equivalent to independently evaluated research. The team published its protocol, extraction template, evidence table, and limitations.

Outcome

After twelve months, the pilot showed improved access visibility and faster revocation of privileges, although implementation required more identity-data remediation than initially forecast. Leaders delayed full-scale deployment until the identity-management foundation was strengthened. The review did not provide certainty that zero trust would eliminate breaches; instead, it clarified mechanisms, dependencies, and implementation risks.

Learning Points

The case demonstrates that evidence synthesis can improve decisions without producing a single numerical answer. Transparent appraisal prevents promotional material from being confused with strong causal evidence. Context and mechanisms are essential in IT implementation research, and uncertainty should guide staged investment rather than be hidden.

13. Advantages / Benefits

Systematic reviews provide a comprehensive and structured account of existing knowledge, reduce arbitrary selection, identify contradictions, reveal research gaps, and support theory development. Meta-analysis can improve statistical precision when evidence is compatible. Scoping reviews clarify concepts in immature fields. Bibliometric analysis exposes intellectual and collaboration structures. Reproducible review workflows improve doctoral rigor and make later updating more efficient.

14. Limitations / Challenges

Reviews can be affected by publication bias, database coverage, language restrictions, inaccessible literature, inconsistent terminology, duplicate publications, poor reporting, and reviewer disagreement. Rapidly changing IT fields create obsolescence: a review may become outdated as platforms and threats evolve. Heterogeneous methods may prevent statistical pooling. Bibliometric indicators can reward popularity rather than quality. Automation can introduce hidden errors. Finally, a transparent process does not guarantee correct judgement; conceptual framing and interpretation remain difficult scholarly tasks.

15. Ethical and Governance Considerations

Researchers must represent source findings accurately, avoid plagiarism, disclose conflicts of interest, and report sponsor involvement. They should not selectively omit inconvenient evidence or manipulate inclusion criteria to obtain a preferred conclusion. Reviews involving personal data, employee records, health information, or sensitive cybersecurity details require appropriate ethics review and data protection.

Copyright governs the storage and redistribution of full texts. Open science should be pursued within legal and contractual boundaries. If artificial intelligence is used, researchers should document the tool, purpose, version where possible, prompts or procedures, verification process, and limitations. Governance also requires protecting confidential search histories or unpublished material when disclosure could create harm.

16. Current and Emerging Trends

Important developments include living systematic reviews that are continuously updated, evidence maps that connect questions to available studies, open peer review, machine-assisted screening, linked research objects, and reproducible computational pipelines. Natural-language processing is increasingly used for semantic searching and concept discovery. Research communities are also giving greater attention to grey literature, negative findings, fairness, environmental sustainability, and the relevance of evidence to underrepresented regions.

In IT, rapid reviews and living reviews are particularly valuable for cybersecurity and generative AI, where evidence changes quickly. Their speed must be balanced against reduced search breadth or appraisal depth, and such trade-offs should be explicitly reported.

17. Best Practices

1. Start with a clearly justified question and select a framework appropriate to it.

2. Search multiple relevant sources and test the strategy against known studies.

3. Publish or register the protocol before extensive screening.

4. Pilot eligibility and extraction criteria.

5. Use at least two reviewers, or transparently justify a different arrangement.

6. Preserve search histories, decisions, versions, and excluded-study reasons.

7. Match appraisal tools to study designs.

8. Distinguish statistical significance, practical importance, and evidential certainty.

9. Investigate heterogeneity rather than averaging away meaningful differences.

10. Treat automation and AI as auditable support tools.

11. Report limitations, conflicts, deviations, and missing evidence.

12. Make conclusions proportional to the evidence and context.

18. Future Scope

Future doctoral research will increasingly combine systematic review methods with knowledge graphs, semantic search, computational text analysis, and continuously updated evidence infrastructures. More sophisticated methods may link publication evidence with software repositories, incident databases, standards, and organizational performance data. There is also scope for methods that assess the environmental cost of digital systems, the social consequences of algorithmic decisions, and evidence quality across regions and languages.

The central challenge will be maintaining human conceptual judgement while managing very large evidence environments. Future reviewers will need skills in research design, information retrieval, statistics, qualitative interpretation, data engineering, ethics, and responsible AI.

19. Chapter Summary

Systematic literature review and evidence synthesis are rigorous approaches for building reliable knowledge from dispersed IT research. They require aligned questions, transparent protocols, comprehensive searches, reproducible screening, structured extraction, critical appraisal, and an appropriate synthesis method. Scoping reviews map emerging fields; bibliometric analyses map intellectual structures; meta-analyses combine compatible quantitative findings; qualitative and realist syntheses explain meanings, mechanisms, and context. Digital tools improve efficiency but do not replace scholarly responsibility. The value of a review lies not in the volume of studies collected, but in the credibility, transparency, and usefulness of the conclusions produced.

20. Key Terms

1. Systematic literature review: A planned and reproducible review that identifies, appraises, and synthesises relevant evidence.

2. Evidence synthesis: The integration of findings from multiple sources into justified conclusions.

3. Scoping review: A review that maps concepts, evidence types, and gaps in a broad or emerging field.

4. Meta-analysis: Statistical combination of compatible quantitative results.

5. Bibliometric analysis: Quantitative analysis of publication, citation, collaboration, or conceptual patterns.

6. Protocol: The prespecified plan governing a review.

7. Eligibility criteria: Rules defining which sources will be included or excluded.

8. Grey literature: Relevant material not primarily distributed through commercial academic publishing.

9. Citation chaining: Finding studies through reference lists and later citing publications.

10. Deduplication: Identification and removal of repeated bibliographic records.

11. Data extraction: Systematic recording of information from included studies.

12. Critical appraisal: Evaluation of a study’s credibility, limitations, and applicability.

13. Risk of bias: The likelihood that a study’s design or conduct distorts its findings.

14. Heterogeneity: Variation among studies, participants, methods, contexts, or effects.

15. Publication bias: Distortion caused by selective publication of studies or results.

16. Thematic synthesis: Integration of qualitative findings through coding and theme development.

17. Realist synthesis: An approach examining what works, for whom, in what context, and through what mechanism.

18. Reproducibility: The ability of others to repeat a procedure and obtain comparable results.

21. Review Questions

Short Answer Questions

1. What distinguishes a systematic literature review from a narrative review?

2. When is a scoping review more suitable than a meta-analysis?

3. Why should a review protocol be developed before screening?

4. What is the purpose of critical appraisal?

5. Define heterogeneity and identify two forms it may take.

Long Answer Questions

1. Explain the complete workflow for conducting a systematic literature review in information technology.

2. Compare systematic reviews, scoping reviews, bibliometric reviews, and meta-analyses.

3. Discuss how search strategy, screening, and data extraction influence review validity.

4. Explain why context and mechanisms are important in synthesising IT implementation research.

5. Evaluate the benefits and risks of using artificial intelligence in evidence synthesis.

Analytical / Application Questions

1. Design a review question and search structure for evaluating the organizational effects of generative AI adoption. Justify the selected review type.

2. A set of studies on cloud migration reports contradictory outcomes. Explain how you would investigate heterogeneity and decide whether meta-analysis is appropriate.

3. A vendor-funded report claims that a cybersecurity product reduces incidents by 70 percent. Develop an appraisal and synthesis strategy for determining how much confidence the organization should place in this claim.

22. Multiple Choice Questions with Answers

Question 1

Which feature most directly distinguishes a systematic literature review from an informal narrative review?

A. It includes only journal articles.

B. It follows a predefined and transparent procedure.

C. It always uses statistical meta-analysis.

D. It excludes qualitative research.

Correct Answer: B. It follows a predefined and transparent procedure.

Question 2

A researcher wants to map concepts, methods, and gaps in an emerging area of digital transformation. Which review type is most appropriate?

A. Scoping review

B. Meta-analysis

C. Randomized controlled trial

D. Bibliographic deduplication

Correct Answer: A. Scoping review

Question 3

What is the main purpose of registering a review protocol?

A. To guarantee that all studies will support the hypothesis

B. To eliminate the need for critical appraisal

C. To make planned methods explicit and reduce undisclosed changes

D. To replace database searching

Correct Answer: C. To make planned methods explicit and reduce undisclosed changes

Question 4

Which activity is an example of citation chaining?

A. Removing duplicate records

B. Searching backward through references and forward through citing papers

C. Calculating an effect size

D. Ranking studies by journal impact factor

Correct Answer: B. Searching backward through references and forward through citing papers

Question 5

When is a meta-analysis generally most defensible?

A. When studies address sufficiently comparable questions and provide compatible quantitative data

B. When every included study uses a different outcome definition

C. When only vendor advertisements are available

D. When the review question is purely conceptual

Correct Answer: A. When studies address sufficiently comparable questions and provide compatible quantitative data

Question 6

Why is critical appraisal necessary in a systematic review?

A. To ensure that every included study receives equal weight

B. To evaluate credibility, limitations, and risk of bias

C. To increase the number of included publications

D. To convert qualitative findings into numerical data

Correct Answer: B. To evaluate credibility, limitations, and risk of bias

Question 7

Which statement best describes heterogeneity in evidence synthesis?

A. It refers only to duplicate records.

B. It is variation among studies, contexts, methods, or effects.

C. It means that all studies have identical results.

D. It is evidence that a database search failed.

Correct Answer: B. It is variation among studies, contexts, methods, or effects.

Question 8

A review team uses machine learning to prioritize records for screening. What is the most appropriate practice?

A. Allow the tool to make all final inclusion decisions without review.

B. Discard records that the tool ranks as low priority automatically.

C. Use the tool as auditable assistance and verify decisions with human judgement.

D. Report only the algorithm’s accuracy and omit the search strategy.

Correct Answer: C. Use the tool as auditable assistance and verify decisions with human judgement.

Question 9

Which framework is especially concerned with what works, for whom, in what circumstances, and through what mechanisms?

A. Realist synthesis

B. Simple frequency analysis

C. Deduplication analysis

D. Citation-style analysis

Correct Answer: A. Realist synthesis

Question 10

What is a major limitation of bibliometric analysis?

A. It cannot identify any publication patterns.

B. It proves that highly cited studies are methodologically superior.

C. It may reflect popularity and visibility rather than research quality.

D. It can be conducted only on qualitative studies.

Correct Answer: C. It may reflect popularity and visibility rather than research quality.