Effects of Customer Knowledge Management on Marketing Management and Results: Case Study in Business Companies, FARS
Bibliographic record
Abstract
The purpose of the present study was to investigate the impact of customer knowledge management on marketing management and marketing results in Fars business companies. Statistical population of the present research includes 1135 employees and managers of Fars business companies which with using formula, 290 individuals were questioned with random sampling. To collect data, standard questionnaires including Vorhies & Morgan’s (2005) marketing questionnaire, Ling-Yee’s (2005) marketing management questionnaire, Alegri’s (2011) knowledge management questionnaire were used. In order to confirm their reliability, their coefficient Cronbach's alphas are respectively 0.82, 0.79 and 0.88. For data analysis, inferential statistics, Pearson correlation test, Single variable regression, Path analysis and SPSS software version 22 were used. The results showed that there is a positive and meaningful relationship between customer knowledge management, marketing results(efficiency) and marketing management. Also, knowledge management had a positive and significant impact on marketing management and marketing results. Moreover, proposed conceptual model is supported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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".