The Effect of Green IT Empowerment and Online Training on Technology Innovation Performance: The Moderating Role of Green Life Style
Bibliographic record
Abstract
Green IT and online training have become the strategic themes in increasing Technology Innovation Performance for the creative industry in Indonesia. In the COVID-19 pandemic era, Green IT and online training became a strategic theme in improving Technology Innovation Performance. This study offers a moderating role of Green Life Style in testing and analyzing the effect of Green IT and online training. Respondent in this study is chosen by purposive sampling, namely the owners and managers of creative SMEs in Sleman, Special Region of Yogyakarta, Indonesia that is 156 SMEs. The data is collected by questionnaire and interview with SMEs that are considered as population representative. The statistical technique uses the Structural Equation Modelling with the Partial Least Square technique. The results prove the importance of Green IT and online training which have a partial impact on Technology Innovation Performance. Likewise, the green lifestyle is a strong moderator in seeing the effect of Green IT and online training on Technology Innovation Performance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".