The moderating role of perceived environmental uncertainty in the impact of corporate governance on strategy implementation: An agency theory perspective
Bibliographic record
Abstract
The study delves into how governance, environmental unpredictability and strategic management intersect, with agency theory offering a framework to comprehend this connection. It is evident how the structure of governance can influence the actions of managers and the results of organizations, amidst evolving conditions. Descriptive analytical approaches were used, and utilized an electronic questionnaire, as the main tool for gathering data. It involved 254 individuals randomly selected from Information and Communication Technology companies in Amman, Jordan including both managers and non-managers. Various statistical techniques, such as inferential methods using SPSS version 26 for Windows were employed to explore research questions and test hypotheses. The study discovered that the perceived uncertainty in the environment plays a role in influencing how corporate governance affects strategy implementation, in information technology firms. The findings suggest. Studying the environment to better grasp and respond to uncertainties. Additionally, it is advised to tailor governance practices and strategies to manage risks and obstacles resulting from shifts.
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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 | 0.005 | 0.016 |
| 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 | 0.001 | 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".