The Role of Marketing Knowledge Management in Achieving Competitive Advantage A Field Study on Amman’s Hotels
Bibliographic record
Abstract
Knowledge management became one of the most important and modern topics in the present day, and it also became a basis which we depend upon it in concentration efforts of multilateral perspectives and different interests, especially those who work in the marketing management, but can marketing knowledge management achieve competitive advantage? This study aimed dignifying the role of marketing knowledge management (MKM) and its effect in achieving the competitive advantage in Amman hotels. To achieve the purpose of study a questionnaire was prepared by the researchers and delivered to the administration employees in the working hotels in Amman that are classified (three, four and five stars). The statistical procedure (SPSS) was used to analyze the data of the study. The findings of this study are there is a significant statistical effect for the knowledge in the markets for the needs and desires of the customers and for available marketing chances in achieving the competitive advantage according to the significance and the responding, and there is a significance statistical effect for the knowledge in the markets, for the needs and desires of the customers and for the available marketing chances to achieve the competitive advantage according to the responding.
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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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 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".