Applying the SPACE Model for Strategic Decision-Making in SMEs: An Empirical Analysis from Kosovo
Bibliographic record
Abstract
The research aims to see the weightiness of evaluation, the approach of internal and external analysis of the organization and the impact that this approach can influence on organizations, therefore the model of the SPACE and its importance in decision-making through managerial function in organizations in Kosovo. The study focuses in the way of analyzing the factors, of the SPACE model in national organizations, analyzing the type of concentration in the industry through the results achieved. This model derives internal and external analyzes of organizations by determining the format of the strategic decision-making method and their application in strategic orientations for organizations. The model tries to provide a basis for sustainable and long-term decision making, as the economy is developing day-by-day and managers are facing more uncertainty. The research used a series of methodologies starting by the conceptual framework of the model of SPACE, flowing through with AHP-MCDM approach to see the consistency and randomness indexes for providing the sustain decision making and contemporary practical guidance for managers. The study processed with 500 surveyed Small and Medium Enterprises (SMEs) in Kosovo. The results through this research turn out to be that organizations' applying the SPACE for decision-making can gain's strong orientation towards in industry, pertinently Strategic Position in industry (STPi) especially in aggressively position against competition. It is recommended that businesses follow such a path of practicing Strategic Management Tools and Techniques (SMTT) for a meaningful strategic decision-making and for better posturing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".