Impact of Strategic Analysis (SWOT) on the Performance of Jordanian Public Shareholding Industrial Companies: The Mediating Role of Scenario Planning
Bibliographic record
Abstract
This study aims to examine the impact of strategic analysis (SWOT) on the organizational performance of Jordanian public shareholding industrial companies, taking into consideration the mediating role of scenario planning.The study's sample includes 38 companies out of the 54 operating at the Jordanian financial market.From each company, the functional managers are selected to represent the sampling unit of the study.The questionnaire is employed as the fundamental instrument for collecting the required information and data.A total of 165 questionnaires were distributed, and 151 were statistically significant.Smart PLS (3) program was utilized for statistical analysis.The results reveal that the surveyed companies are engaged in both SWOT analysis and scenario planning, and they are aware of the critical role of the two variables in enhancing organizational performance, particularly after the COVID-19 pandemic.Furthermore, the findings of the study show that scenario planning has a partial impact in the effect of SWOT analysis on companies' performance.Besides, the study sheds light on the significance of strategic analysis and recommends relying on more than one type of analysis and developing human resources with strategic thinking skills to be able to conduct scenario planning and build convenient strategies.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".