Investigating the Relationship between Marketing Knowledge Sharing and Developing Competitive Advantage (Case Study: Arak Shazand Petrochemical)
Bibliographic record
Abstract
The aim of this study is to investigate the relationship between marketing knowledge sharing and developing competitive advantage. This research is an applied objective research and data collection method of description-correlation nature the subjects under study by this research are employees of Arak Shazand petrochemical industry. The sample size was estimated 90 people. The method is stratified random sampling. A standard questionnaire was used to collect data. Marketing knowledge sharing questionnaire of Moghimi and Ramazani (2011) contains 17 items and developing competitive advantage questionnaire of Hill and Jones (2010) contains 16 items. Logical validity (face and content) of questionnaires was reviewed and approved through several university professors and several experts of this industry. Also, construct validity was reviewed and approved by confirmatory factor analysis using AMOS software. Cronbach's alpha coefficient of 0.7 was obtained for variables that indicate internal consistency of items and acceptable reliability of the questionnaire. The research hypothesis test using univariate linear regression was performed with application of SPSS software. The results showed that, given that the t-statistic value is greater than 1.96 (t = 6.48), the relationship between two variables, competitive advantage and marketing knowledge sharing was significant at the 5% error level Standard regression coefficient (0.57) also specified the share of independent variable in explaining the changes of dependent variable so that for every one unit increase in variable of marketing knowledge sharing, competitive advantage increases 0.57.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".