Measuring Sustained Competitive Advantage From Resource-based View: Survey of Chinese Clothing Industry
Bibliographic record
Abstract
The resource-based view of the firm (RBV) argues that valuable, rare, inimitable, and non-substitutable resources are the source of a firm’s sustained competitive advantage (SCA); this contention has been tested in an increasing number of studies. However, the extant empirical literature emphasizes the significance of resources and capabilities (R&Cs) played and few studies focus on SCA, the other end of the RBV logic. Therefore, this study aims to address this deficiency by focusing on the measurement of SCA. Further, SCA is traditionally measured by financial performance in the empirical studies, which is not only inconsistent with the theory but also proves to be practically difficult in access to the data. However, this paper argues that process performance is more appropriate to measure SCA and this theoretical idea is examined with the survey data collected in 2011/2012 using 209 valid responses from participants in the Chinese clothing companies. Structural equation modeling (SEM) was adopted to test hypothesized relationships between the endogenous construct of process performance and three exogenous constructs measuring R&Cs. The results suggest that process performance is an appropriate and effective method measuring SCA in terms of good construct validity and of consistency with the RBV expectations.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".