Bibliographic record
Abstract
In the current business world; knowledge, skill and internal sources play significant role in further successes of an organization which are like capital that its effect on the organization's performance and productivity can be observed.In addition to, the capabilities of an organization which has market-oriented approach and is in the field of finding markets and new customers for its products, relies on the experience and expertise of the organization and especially its human capitals.The purpose of the present research is investigation of the effective role of human capital from profession and experience aspect on mobile product marketing.This research is experimental in terms of objective, descriptive in terms of nature and survey based in terms of performing manner.Managers and staff of mobile selling agencies around Yazd constitute the statistical population of the research that the number of the calculated statistical population is 80 individuals making use of Cochran formula and Morgan table.For collecting data, a standard questionnaire with high reliability and validity is used and the method of sampling in this research is according to simple random sampling.Analysis of data and hypotheses testing are according to confirmatory factor analysis, structural equation modeling technique and using SPSS20 and PLS Smart2 applications.The results of the study demonstrate that the experience and expertise dimensions of human capital have positive and significant effect on marketing capability of the product.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.970 | 0.954 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".