Examining the mediating role of ambidexterity, wireless IT competence, and sensing capability of supply chain management to drive innovation capability in higher education ,
Bibliographic record
Abstract
This study examines the interaction effect between transformational leadership, transactional leadership, wireless IT competence, ambidexterity and supply chain management capability in increasing innovation capability. By using a knowledge-based view and dynamic capability theory basis, this research has provided an exploration of the supply chain of a merchant marine college and its impact on innovation capability using a quantitative method approach. The authors collected data from a cross-section of 673 questionnaires distributed to Managers in 3 managerial classifications from top, middle and bottom in the technical service unit of the merchant marine college under the Indonesian Ministry of Transportation. A total of 523 data were collected from questionnaires that could be continued for data analysis. The results of this study indicate that all the hypotheses put forward in this study are accepted, and the role of mediating variables in this research has succeeded in demonstrating their role in mediating each antecedent variable to increase innovation capability. The theoretical implication of this research is the growth of cloud or virtual supply chains facilitated by digital wireless communications, and internet technology is advancing logistics and supply chain innovations. Also, it can reinforce theory and dynamic capability.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".