The Impact of Core Technological Capabilities of High-Tech Industry on Sustainable Competitive Advantage
Bibliographic record
Abstract
The market competitiveness and sustainable operation of an enterprise are closely correlated with the support of high-tech core technologies in the enterprise. This study first discusses the basic knowledge of core competitiveness, introduces the components and evaluation methods of core competitiveness, and builds an evaluation index system for core competitiveness of high-tech enterprises. Then, the Analytic Hierarchy Process (AHP) is fully discussed, during which the steps, advantages, and disadvantages of the AHP evaluation method are introduced. Finally, the Fujian Province of China is taken as an example, the relevant data are collected and processed, the impact of indicators are analyzed, and a high-tech industry core technological capability analysis indicator system is built based on the AHP method. Thus, the influence of the core technological capabilities of the high-tech industry on the sustainable competitive advantage of the enterprise is obtained. This study puts forward suggestions for maintaining the competitiveness of high-tech industries, thereby improving the competitive advantage of enterprises and achieving the sustainable management of enterprises. The result finds that if the high-tech industries continue to carry out innovation and scientific research, enterprises will maintain their competitive advantages. In summary, exploring the impact of the core technological capabilities of high-tech industries on the sustainable competitive advantages of enterprises is greatly significant for improving their competitiveness and industrial status, which enables them to be invincible in a complex environment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".