An application of analytical hierarchy process (AHP) in formulating priority strategy for enhancing creative industry competitiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study is aimed to analyze the variables of external environment, organizational resources, organizational capabilities, and business competitiveness. The study priorities strategy and programs as basic for developing the competitiveness of creative industry in Indonesia. The number of respondents who participated in this survey was 200, while the key informants were 10 people. Method of analysis involved descriptive statistics, and analytical hierarchy process (AHP). Then, data were processed by using both IBM SPSS 24, and Expert Choice 11. The results show that creative industry competitiveness has relatively declined during covid-19 pandemic. Although external environment support, organizational resources, and organizational capabilities were at good shape. The priority strategy for competitiveness development should be focused on strengthen the organizational capabilities by considering the dynamics of external environmental factors and internal resource capacity. Then, the priority programs developed sequentially are increasing partnerships with suppliers, distributors and customers, analyzing social and economic aspects, developing human resource capacity, and using information and communication technology in products and services. In addition, another important program is strengthening the supply chain system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it