Barriers Influencing Organizations in Developing Country Not Appling Updated Strategic Management Techniques: A Case Study of Iran
Bibliographic record
Abstract
Although the advantages of utilizing enhanced strategic management tools have already been demonstrated in developing countries; but applying them for decision-making is still a challenge in practice for managers. In this study, we investigated why strategists and managers were not willing to apply these tools. In this paper, the Q statistical method was employed to define the barriers influencing not using these methods and to determine in Iran and how the managers’ attitudes affected the outcomes. The study enrolled 43 strategists who were managers with high education levels. From secondary sources, we selected 68 Q statements from which 43 final Q statements were chosen by six strategic management experts. Following a binding algorithm, the participants sorted the Q statements. Utilizing factor analysis, the managers identified twelve main reasons (mental patterns) for lack of implementing new advanced strategic techniques in organizations. For instance, the study finds that most Iranian firms have a willingness to preserve traditional attitudes because they are disappointment with new tools; Some indicated that there is a lack of access to a successful model and resources. The finding this study, help strategist and managers to better understand how their attitudes and can affect the firm performance, and then follow the best strategy to improve their performance.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| 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".