FIRM VALUE: A HYBRID SYSTEMATIC LITERATURE REVIEW (SLR) AND BLIBLIOMATRIC ANALYSIS
Bibliographic record
Abstract
Many studies have examined firm value over the past few decades. However, extensive research on this subject remains limited. Therefore, this study aims to conduct a systematic literature review and bibliometric analysis of firm value based on existing empirical research. Design/methodology/approach used in this study is a Systematic Literature Review, employing the term "firm value" in the "Article Title, Abstract, and Keywords" from the Scopus database, yielding 1,921 publications from 1970 to 2025. The evaluation was conducted on June 29, 2025. The collected data were analyzed bibliometrically using VOSviewer. Findings Research on firm value is limited in developing countries. Previous studies have mostly focused on developed countries such as the United States, United Kingdom, South Korea, Australia, and Canada. Future research should target developing regions. Additionally, firm value can be divided into two categories: market value and book value. Research limitations/implications: This study uses the Scopus database; future research may require stylistic mapping by integrating additional databases such as Web of Science. Emerging themes in firm value increasingly focus on the integration of financial, non-financial, and external factors. While earlier research predominantly emphasized profitability, leverage, and capital structure, current studies have expanded to include ESG, corporate governance, policy uncertainty, digitalization, and social reputation. Firm value has become an important indicator for management, investors, regulators, and society in assessing corporate performance, reputation, and prospects. Originality/value: Research on firm value is growing rapidly worldwide. Nevertheless, extensive studies on this topic remain limited within the existing literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.127 | 0.089 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".