The Mediating Role of Corporate Characteristics on the Relationship between the Strategic Learning and the Competitive Capabilities of the Telecommunications Companies in Jordan
Bibliographic record
Abstract
This study aims at studying the mediating role of corporate characteristics on the relationship between the strategic learning and the competitive capabilities of the Telecommunications Companies in Jordan. The population of this research are the three major telecommunications companies in Jordan, namely, Zain, Orange and Umniah. The study respondents consisted of those who occupy different managerial positions at these companies, for they have acquired enough experience that makes them eligible to answer the questionnaire. The researchers adopted the construct of Siren (2012) in order to measure the independent variable, which is strategic learning, and made use of the studies of Zhang and Sharifi (2000) and Toni (2001) to measure the competitive capabilities. A total of 278 questionnaires were distributed, out of which 195 were retrieved, but only 150 questionnaires were valid for statistical analysis. The major finding of the study is: strategic knowledge distribution and the implementation of strategic knowledge, mediated by company’s age and type of service, do have an impact on the competitive capabilities of the researched companies. However, the creation and the interpretation of strategic knowledge mediated by the same variables have statistically insignificant impact on the competitive capabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".