Marketing Strategies in Knowledge-Based Companies of ICT Services
Bibliographic record
Abstract
The aim of this study is to evaluate the company's business strategy in knowledge-based companies ofinformation and communication technology (ICT) services. The purpose of this research is applicable and is ofdescriptive research categories.The statistical populationincludes80 executives of knowledge-based companiesbased in science and technology parks of Tehran University,campus, Isfahan,Khorasan and Farsthat wereengaged in the field of ICT services.By using Cochran's formula, 60 executives were estimated as the sample,and the samples were selected randomly.Main research tool was a questionnaire that its validity was confirmedby a panel of experts in different aspects of validity, content and structure. The reliability of the survey tool wasconfirmed by Cronbach's alpha coefficient, which represents the suitable reliability of research tool (92%).Theresults showed that the lack of adequate financial resources incompanies has been an important weakness. Themain strength of the companies is to choose unique product or special one with clear market demand. The mainthreat in these companies is the rapid advance of technology and the lack of investment in new technologies. Themain opportunity for these companies is to cooperation with industry in the development of products andservices. The existing strengths in knowledge-based companies are more than their weaknesses. On the otherhand, threats are stronger than opportunities, so the strategic deployment flexibility is in ST area and dominantstrategy is defined as diversification strategy.
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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 | 0.001 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".