Identifying and Prioritizing Factors Influencing Success of a Strategic Planning Process: A Study on National Iranian Copper Industries Company
Bibliographic record
Abstract
Nowadays, global economic activities are performed by medium and small-sized enterprises (SMEs). All these organizations seek success, knock the socks off rival companies and satisfy their customers' needs in a turbulent environment and a very competitive market. Strategic planning, in case of proper formulation and implementation, is an effective tool which can identify opportunities, threats, strengths and weaknesses of the organization so that more realistic goals can be set and implemented. National Iranian Copper Industries Company needs to prioritize its strategies due to structural evolutions in order to determine the budget and formulate short-term planning for them. So the present research examines and ranks these factors. Our research method is descriptive-survey and the sampling process is random. Population size is 180 and the sample size of 120 is calculated according to Cochran formula. This research categorized the factors using factor analysis. The results showed that success factors of Strategic Planning for this company were situated in four groups including administrative process, managerial process, inter-organizational culture and extra-organizational factors. Then the indicators were weighted and prioritized by means of Shannon's method and Topsis technique, respectively. Finally conclusions were provided according to the given priorities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".