A RICH VISION OF FIRM SUSTAINABLE COMPETITIVE ADVANTAGE: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Sustainable competitive advantage (SCA) is a fundamental construct in strategic management.Relying on a systematic review of empirical studies, this article shows that SCA as a dependent variable is built upon three fundamental dyadic properties: 1) absolute vs. relative, 2) punctual vs. persistent, and 3) unistakeholder vs. multistakeholder.From these properties arise eight possible forms of advantage, of which only four were documented by our review.Above all, no academic attention has been given to the richest possible form of SCA (relative, persistent, and multistakeholder).Firm SCA should more often be conceptualized as the simultaneous persistence of superior performance for all firm stakeholders.Moreover, our review supports the structural role of the three pillars supporting SCA development in a firm: 1) the industry-, 2) resource-, and 3) institution-based views.Overall, this study reveals that differentiation strategies, intangibles assets, dynamic capabilities, governance, and networks are among the most direct and important levers to trigger a firm's SCA.Finally, this paper outlines the research and practical recommendations that may facilitate further initiatives in studying and fostering SCA development in firms.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".