Do stakeholders matter? Stakeholders as moderators in the relationship between formal strategic plan-ning and organizational performance
Bibliographic record
Abstract
In this globalized world, every company is struggling to sustain and improve its performance. This study investigated the role of formal strategic planning on organizational performance. Moreover, this relation is explored further with the moderation of stakeholders’ involvement. The empirical data were collected from 220 chief executives or general managers of manufacturing companies using a questionnaire survey. AMOS software was used to analyze the collected data. Structural equation modeling (SEM) was conducted to test the hypothesis, and moderation was investigated using a slop test. The results of the study revealed that formal strategic planning could enhance the performance of the organization. Furthermore, the results highlighted that stakeholder's involvement could strengthen the relationship between formal strategic planning and organizational performance. Previous studies have highlighted the importance of overall strategic management in enhancing an organization's performance. The research also revealed that performance was affected mainly by formal strategic planning and stakeholder involvement. The study used manufacturing companies in Jordan. Moreover, the data was collected from 220 respondents. Therefore, future studies should be conducted from any other country's perspective and use a larger sample size. This study has used moderation of stakeholder's involvement to get more specific results. Future studies should use organizational culture as a moderating variable.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".