Exploring effective factors on privatization, firm performance and export development: Evidence from steel industry
Bibliographic record
Abstract
Privatization means establishing a new system based on the market mechanisms and consequently making a change, alteration in different aspects of economy and is a process in which the government can examine the possibility of transferring the duties and facilities to the private sector on any level and if necessary, exerts such transfer.On the other hand, exports in industry sector can be a noticeable point for the economic growth of any country.Enhancing the exports of the steel industry of the country can have a principal role in the economic pursuit of the country's non-oil products.Such an enhancement brings about a positive effect in the efficiency of the stocks within the financial markets by developing the steel industry.Researchers of this field claim that privatization in the steel industry results in the further development of the steel stock market and exports.This paper presents a comprehensive survey on factors influencing on privatization of the firms in steel industry.The study has designed a questionnaire in Likert scale and distributed it among some experts who worked for Mobarakeh steel producer in Iran.Using principle component analysis, the survey has concluded that export activities were influenced the most by six major factors including creativity, technological limitation, opportunities and challenges, being up to date, customer orientation and financial sanction.Moreover, firm performance was influenced by two major factors of stakeholder's satisfaction and organizational culture.Finally, two factors of rationalism and market orientation influenced the most on privatization.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".