Influence of MIS components on efficiency of e-marketing strategies: Evidence from telecommu-nication organizations in Jordan
Bibliographic record
Abstract
The study aimed to highlight the role of management information system (MIS) and its components in improving the effectiveness and efficiency of e-marketing strategies in telecommunications companies in Jordan. By relying on the quantitative methodology and by dealing with the questionnaire as a research tool, 131 individuals from the marketing departments in the organizations under study responded, and after the analysis, the study demonstrated an impact of MIS and its components on e-marketing strategies by influencing how and the mechanism of data processing and presentation as information that contributes to making the most appropriate marketing decision. The study also proved that all components of MIS have an impact on e-marketing strategies, most of which were “human resources” or people, which proved that the efficiency of individuals and their ability to deal with technology carries significant effect on the effectiveness of MIS in managing and organizing e-marketing strategies. The study recommends the necessity to focus on human resources with STEM skills, namely science, technology, engineering, and mathematics in order to ensure the best outcomes of MIS.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".