---
title: Systematic Multivocal Literature Review
url: https://www.emergentmind.com/topics/systematic-multivocal-literature-review-mlr
type: topic
---

# Systematic Multivocal Literature Review

A Systematic Multivocal Literature Review (MLR) is a protocol-based secondary study that synthesizes evidence from both peer-reviewed ("formal") literature and non–peer-reviewed ("gray") literature such as blogs, technical reports, white papers, and practitioner slide decks. The objective is to obtain a holistic, evidence-based map that rigorously integrates the “state-of-the-art” with the “state-of-the-practice.” MLRs are distinguished from classical Systematic Literature Reviews (SLRs) by their deliberate and methodologically explicit inclusion of gray literature, providing an augmented evidence base that captures both academic rigor and industrial insight [1707.02553][2107.05792].

## 1. Formal Definition and Rationale

In the context of software engineering and applied research domains, a Systematic Multivocal Literature Review (MLR) is defined as a secondary research method that seeks to answer a set of predefined research questions $R$ by collecting, evaluating, and synthesizing evidence units $E$ from both traditional peer-reviewed sources ($T$) and nontraditional or non–peer-reviewed ("gray") sources ($G$). The evidence corpus $E$ is thus constructed as $E = E_T \cup E_G$ with $|G| > 0$, and both components are subjected to comparable scrutiny during planning, inclusion/exclusion, quality appraisal, extraction, and synthesis [2107.05792].

The rationale for MLR is outlined by Garousi et al.: MLRs are indicated when a research topic is complex, under-represented in formal literature, or characterized by rapid industry innovation where key findings reside in gray channels. Typical motivations include bridging gaps between academic research and practitioner experience, validating or challenging academic theories with real-world data, and synthesizing best practices in domains with extensive practitioner-generated guidance [1707.02553][1910.06076].

## 2. Multistage Methodological Framework

A Systematic MLR follows a rigorously documented, multi-phase protocol that parallels classical SLR practice with additional GL-specific adaptations [1707.02553][2202.09076][2507.16586]:

1. **Planning and Protocol Registration**: 
   - Motivate the use of MLR via a GL-relevance checklist.
   - Define research questions using frameworks such as GQM (Goal-Question-Metric) or PICOC.
   - Specify clear inclusion and exclusion criteria applicable to both $T$ and $G$ sources.

2. **Search Strategy**:
   - Develop separate, Boolean-enhanced search strings for formal databases (e.g., Scopus, IEEE Xplore) and web/GL sources (e.g., Google Search, arXiv).
   - Use piloting and snowball keyword refinement to maximize recall and precision [2202.09076].
   - Employ theoretical saturation or effort-bound stopping for GL searching (e.g., first $N$ Google hits or until new codes cease emerging) [1707.02553].

3. **Study Selection and Quality Assessment**:
   - Apply dual (or triple) independent reviewer voting for both $T$ and $G$ sources, resolving disagreements by consensus or majority rule [1707.02553][1910.06076][2504.02719].
   - For gray literature, implement a quality checklist covering: producer authority, methodology clarity, objectivity, novelty/date, referenced position, outlet type, and impact (e.g., backlinks, social metrics) [1707.02553][2504.02719].
   - Establish a scoring schema (e.g., 1/0.5/0 per criterion) and set source retention thresholds (e.g., ≥ 0.5 normalized score).

4. **Data Extraction and Coding**:
   - Use traceable extraction templates mapping each data item to the RQ it serves.
   - Distinguish coding frameworks for $T$ and $G$ when warranted, but support unified thematic structures for synthesis.
   - Employ open, axial, and selective coding borrowed from grounded theory where relevant (especially for complex or practice-driven domains) [1910.06076][2507.16586].

5. **Synthesis and Analysis**:
   - Integrate $T$ and $G$ streams using side-by-side tables, comparative frequency analyses, and narrative synthesis.
   - Quantify GL contribution via metrics such as GL Contribution Ratio:

     $$
     \mathrm{GCR} = \frac{|E_G|}{|E_T| + |E_G|}
     $$

     and Source Balance Index:

     $$
     \mathrm{SBI} = 1 - \left| \frac{|E_T|}{|E_T|+|E_G|} - 0.5 \right|
     $$
     [2107.05792].

6. **Reporting**:
   - Distinguish $T$- vs $G$-driven findings.
   - Organize by contribution type, research type, best practices, models, success factors, etc.
   - Provide method transparency (PRISMA diagrams, extraction tables), describe validity threats, and encourage data sharing.

## 3. Typology of Sources and Quality Appraisal

A Systematic MLR incorporates a broad spectrum of evidence sources. Types of gray literature identified empirically as most frequent and impactful include blog posts, slide presentations, project/software descriptions, whitepapers, technical reports, webinars, and guideline documents. Table 1 below presents the distribution observed in a tertiary study across nine MLRs [2107.05792]:

| GL Type               | Relative Frequency (%) | Typical Contribution Roles       |
|-----------------------|----------------------|---------------------------------|
| Blog post             | 30.7                 | Recommendation, Opinion, Explanation |
| Slide presentation    | 11.7                 | Recommendation, Solution, Explanation |
| Project description   | 10.9                 | Solution, Tool, Recommendation  |
| Whitepaper            | 6.5                  | Explanation, Recommendation     |
| Technical report      | 6.5                  | Explanation, Classification     |

GL quality is assessed via multi-dimensional checklists, scoring for author credibility, clarity of aims and methods, evidence of supporting data, currency, referenced position, and outlet type (with "Tier 1" reserved for reports, white papers, theses, and "Tier 3" for opinion blogs) [1707.02553][2504.02719].

## 4. Taxonomies, Frameworks, and Synthesis Techniques

MLRs in computing fields frequently establish conceptual taxonomies (e.g., monitored aspects in ML systems, types of software test automation best practices, upgrade mechanisms for smart contracts), which are derived via thematic coding and iteratively refined classification schemas [2509.14294][2202.09076][2504.02719]:

- **Component Taxonomy**: e.g., for ML monitoring: Data, Model Behavior, Operations & Infrastructure, Responsible ML, Business [2509.14294].
- **Techniques/Tools Taxonomy**: e.g., statistical, distance-based, learning-based anomaly detection, performance/robustness metrics [2509.14294].
- **Best Practices Taxonomy**: e.g., in test automation: strategy development, resource allocation, tool selection, script quality, knowledge transfer, measurement, SUT design [2202.09076].
- **Success Factors**: e.g., in CGSD: time-to-market acceleration, robust support structure, security management, integrated process governance [2208.08743].

The synthesis approach includes:

- Quantitative counting/frequency analyses of $T$ and $G$ contributions.
- Qualitative thematic/narrative synthesis, highlighting convergence, divergence, and exclusive GL contributions.
- Reciprocal analysis to juxtapose industry vs. academic “voices” by coding category [2507.16586].
- Formal grading of empirical support (e.g., flagging which practices have been robustly evaluated) [2202.09076].
- Conflict analysis to catalog disagreement between $T$ and $G$ sources and surface open research gaps.

## 5. Empirical Impact and Domain-Specific Insights

MLRs have demonstrated that GL supplies uniquely practice-driven, context-sensitive, and timely evidence often absent in formal channels. Empirical results frequently show that substantial proportions of novel techniques, patterns, or best practices are derived exclusively or predominantly from gray literature—e.g., in one review, 90.5% of Android architectural guidelines were found only in practitioner blogs, and several risk types in DevOps were identified solely in white papers and web sources [2107.05792].

Practitioner evidence is often used for solution proposals, actionable recommendations, tool and platform selection heuristics, and detailed technical explanations, while formal literature dominates conceptual models, rigorous comparative studies, and controlled empirical work. Domains characterized by rapid technology turnover (e.g., ML monitoring, cloud-based development, MLOps) or where codified knowledge lags practice are especially reliant on multivocal synthesis [2509.14294][2406.09737].

## 6. Limitations, Challenges, and Recommendations

Common challenges in executing MLRs arise from discoverability (GL’s dispersed, non-indexed nature), variable metadata quality, GL quality heterogeneity, and traceability of extraction. Strategies to mitigate these include:

- Digital traceability schemes linking each GL artifact to its RQ and coded output.
- Dual/triple independent review and formal consensus for selection/quality.
- Publication and archiving of full source lists, extraction spreadsheets, and coding schemas to ensure transparency and replicability [1910.06076][1707.02553].
- Explicit reporting of limitations and sensitivity to missing GL or paywalled reports.

Guideline recommendations consistently emphasize: upfront justification for GL inclusion, pre-registered protocols, systematic and transparent quality filtering for GL, separate synthesis and analysis by source type, and community-accessible dissemination of extraction and synthesis artifacts.

## 7. Emerging Trends and Methodological Innovations

Recent advances include the integration of LLM-based assistants for initial search and filtering in MLR workflows, with quality metrics such as Positive Percent Agreement (PPA) to ensure model-mediated decisions remain within human-level reliability bounds [2509.13103]. Reciprocal analysis frameworks and layered QA workstreams (e.g., LLM-first-pass plus manual calibration) are gaining traction for dual-stream narrative synthesis, particularly in domains where evidence volume or heterogeneity is unmanageable with human labor alone [2507.16586][2509.13103].

The trajectory of MLR research highlights increasing discipline in evidence base balance, harmonization of taxonomies and terminologies, and systematic benchmarking of contributions from both peer-reviewed and practitioner-driven sources.

---

**References:**
- [1707.02553] Guidelines for including grey literature and conducting multivocal literature reviews in software engineering
- [2107.05792] What Evidence We Would Miss If We Do Not Use Grey Literature?
- [2202.09076] Improving Test Automation Maturity: a Multivocal Literature Review
- [2406.09737] A Multivocal Review of MLOps Practices, Challenges and Open Issues
- [2504.02719] The Myth of Immutability: A Multivocal Review on Smart Contract Upgradeability
- [2507.16586] AI for Better UX in Computer-Aided Engineering: Is Academia Catching Up with Industry Demands? A Multivocal Literature Review
- [2509.13103] Accelerating Discovery: Rapid Literature Screening with LLMs
- [2509.14294] Monitoring Machine Learning Systems: A Multivocal Literature Review
- [2208.08743] Successful Management of Cloud Based Global Software Development Projects: A Multivocal Study
- [1910.06076] Maturity assessment and maturity models in healthcare: A multivocal literature review

Source: https://www.emergentmind.com/topics/systematic-multivocal-literature-review-mlr