The moderating role of perceived environmental uncertainty in the impact of corporate governance on strategy implementation: An agency theory perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it