A Systematic Review of Resource-Based View and Dynamic Capabilities of Firms and Future Research Avenues
Bibliographic record
Abstract
This study synthesizes empirical research on Resource-Based Views (RBVs) and Dynamic Capabilities (DCs) of firms across various sectors, aiming to create a comprehensive understanding of these topics.Utilizing a systematic literature review methodology, 46 articles that met stringent screening criteria were analyzed, with key information extracted.These articles, sourced from databases such as Science Direct, Elsevier, JSTOR, and Google Scholar, centered on studies related to RBV and DCs.Thematic content analysis was employed to distill the primary research focus on RBV and DC.Search terms included "resource-based view," "firm resource approach," "dynamic capabilities," "firm capabilities," and "organizational capabilities."Inclusion criteria were based on search boundaries, publication date, language, and search strings, while exclusion criteria included relevance, quality, and duplication.The analysis yielded five major themes related to RBV (knowledge-based, human, physical, technological, and organizational resources) and four primary themes regarding DCs (marketing, operational, innovative, and alliance/integration capabilities).These themes were scrutinized to comprehend the current state of knowledge, identify research gaps, and suggest future research opportunities.The review reveals that while RBVs emphasize how a firm's resources contribute to its competitive advantage, DCs elucidate how firms can cultivate a competitive advantage in fluctuating environments.Areas underexplored in existing research, such as the types of resources influencing financial and non-financial performance, the measurement of a firm's capabilities, and the critique of RBV, present potential avenues for future investigations.
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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 | 0.024 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".