A Systematic Review of Resource-Based View and Dynamic Capabilities of Firms and Future Research Avenues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it