Analysis Effect of Resources and Dynamic Capabilities to Sustainable Competitive Advantages and It’s Implications to the Firms Performance
Bibliographic record
Abstract
The objective of this research are to find out the effect : (1). resources on sustainable competitive advantages; (2). dynamic capability to continuous competitive advantage; (3). resources on company performance; (4). dynamic capability to company performance; (5). sustainable competitive advantage on company performance; (6). dynamic resources and capabilities on sustainable competitive advantages ; (7). dynamic resources and capabilities as well as the competitive advantage of sustainable. sample unit in this research using survey 69 companies which producing coffee in lampung, with interview to manager and director with total number of responden 345 respondents and all hypothesis accepted and positive effect to this research. the conclusion of this reserach are : (1). resources affect sustainable competitive advantage; (2). dynamic capabilities affect sustainable competitive advantage; (3). resources affect the firm's performance; (4). dynamic capability affects the firm's performance; (5). sustainable competitive advantage affects the company's performance; (6). resources and dynamic capabilities together affect sustainable competitive advantage; (7). resources, dynamic capabilities and sustainable competitive advantages jointly affect the company's performance the effect of resource, dynamic capability and sustainable competitive advantage simultaneously on company performance is positive and significant, with sustainable competitive advantage variables having the most dominant influence on firm performance. this shows that positively improving the effectiveness of resources, dynamic capabilities and sustainable competitive advantage will result in improved corporate performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".