Empowering knowledge-based interaction in digital startup
Bibliographic record
Abstract
Digital startups are growing fast around the world, but new digital startups that are less than three years old or did not materialize when it came to funding while the business model is quite promising. Various literature shows that digital transformational leadership can encourage knowledge sharing and an organization's absorptive capacity to improve performance. This research investigates whether digital transformational leadership and empowering knowledge-based interaction, as well as the power of absorptive capacity, will be able to maintain digital startup sustainability through the improvement of performance. 144 digital startups that are established in a limited corporation and registered in the Baparekraf (Indonesia Tourism and Creative Economy Agencies) were used as the purposive sampling. Data collection was done through online questionnaires from each startup leader and processed with SmartPLS 3.0. The results of the research findings showed that the higher the degree of digital transformational leadership, the higher the degree of the digital performance of startups that include traction and financial performance, and also increased empowering knowledge-based interaction in the form of encouraging goal-oriented participative involvement, intra-team knowledge exchange, and continuous interactive engagement. Empowering knowledge-based interaction develops an absorption capacity process to produce the performance of digital startup organizations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".