Effective Cultural and Economic Indicators on Business Communications Growth (With an Emphasis on Iran)
Bibliographic record
Abstract
Today, society became an international trade lifeline. The study of this science has become a necessity to understand the cultural differences in intercultural communication. Although it increasingly crossed the borders and business barriers had been denied, but cultural boundaries do not sidestep easily and unlike legal, political, or economic, business environment tangible aspects, culture is largely invisible. Hence, an aspect of international trade is often overlooked. For economic growth and development inside and outside the borders, attentions should be paid to the cultural aspects of our society and other societies.The purpose of this study was to evaluate the effect of cultural dimensions on growth of economic indicators. This study is a practical in terms nature and purpose, and is descriptive, and library study in terms of data collection. In order to data collection, questionnaire and financial information of financial institutions were used (information of economic indicators of Asian Development Bank and the Central Bank of the Islamic Republic of Iran). Validity and reliability of the questionnaire were evaluated through Cronbach's alpha and exploratory and confirmatory factor analyses were done. The population in this study was the country's capital market participants. Sampling was done through probabilistic sampling strategy and simple random sampling method was used and 92 persons were estimated through the Morgan table. Structural equation modeling was used, to test the hypotheses. The results of this study showed that, there is a positive relationship between cultural dimensions and growth of economic indicators.Keywords: cultural dimensions, communication, globalization, economic indicators
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".