The effects of knowledge-oriented leadership style, digital transformation, and human resource development on sustainable competitive advantage in East Java MSMEs
Bibliographic record
Abstract
This study investigates the implications of KOLS, Digital Transformation, and Human Resource Development for Sustainable Competitive Advantage through Innovative Behavior and Organizational Innovation in East Java Micro, Small, and Medium Enterprises (MSMEs). 382 MSMEs in East Java were surveyed, and their data were analyzed using Structural Equation Modeling (SEM). The results showed that KOLS, Digital Transformation, and Human Resource Development significantly positively affected Innovative Behavior, Organizational Innovation, and Sustainable Competitive Advantage. Moreover, Innovative Behavior and Organizational Innovation are important mediating roles in this relationship. The results highlight the importance of cultivating a culture of sharing knowledge, embracing digital transformation, and investing in employee development to increase the competitive advantage of MSMEs. This study provides practical implications for MSME management, emphasizing the need to develop strategies that promote knowledge-oriented leadership, adopt digital technologies, and enhance employee skills and competencies. Thus, MSMEs can foster a culture of innovation, which leads to a sustainable competitive advantage in the long term. This research contributes to understanding the factors driving Sustainable Competitive Advantage in East Java MSMEs. It offers valuable insights for practitioners and policymakers in driving the growth and competitiveness of the MSME sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".