The Impact of Core Technological Capabilities of High-Tech Industry on Sustainable Competitive Advantage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it