Resource-based view theory and its applications in supply chain management: A systematic literature review
Bibliographic record
Abstract
This systematic literature review critically examines the application of the Resource-Based View (RBV) theory within Supply Chain Management (SCM) across various industries. Developed by Jay B. Barney in 1991, RBV posits that a firm's sustained competitive advantage is driven by its unique resources and capabilities that are valuable, rare, inimitable, and non-substitutable. RBV suggests that a company’s long-term competitive advantage stems from its distinct resources and capabilities, which are valuable, rare, difficult to imitate, and not easily substitutable. Despite extensive utilization in strategic management, the direct application of RBV in SCM has been less explored, particularly in understanding how specific internal resources enhance SCM capabilities and outcomes. The review adopts a systematic approach, analyzing 97 peer-reviewed articles from diverse journals. This method includes a comprehensive search and evaluation process, ensuring the inclusion of significant studies that discuss the application of RBV in SCM across various industries. The articles were sourced from Scopus databases, with keywords related to RBV and SCM to ensure thorough topic coverage. The findings indicate a pronounced increase in related publications since 2010, reflecting a growing scholarly and practical interest in RBV’s application to SCM. The findings revealed that emerging trends like integrating advanced technologies like Blockchain, Artificial Intelligence and the Internet of Things are identified as strategic resources that redefine competitive landscapes by enhancing transparency, responsiveness and responsiveness within supply chains. The review also highlights the increasing importance of sustainability practices within SCM, aligning with RBV to potentially offer a sustainable competitive advantage. Conclusively, this review contributes to both academic knowledge and guides practitioners toward leveraging internal resources strategically to navigate contemporary challenges, setting a foundation for future inquiries into global supply chain resilience and dynamic capabilities.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".