The role of digital literacy and knowledge management on process innovation in SMEs
Bibliographic record
Abstract
Research on digital literacy, knowledge management and process innovation variables has not been widely carried out in Indonesia, therefore more studies need to be carried out immediately since small and medium enterprises (SMEs) play an important role in economic activities. The purpose of this research is to investigate the effect of digital literacy on knowledge management, digital literacy on process innovation and financial management on process innovation. The research method is quantitative using partial least square structural equation modeling (SEM) analysis with data analysis tools using SmartPLS 3.0 software. The study involved 489 respondents who owned SMEs and it was determined using simple random sampling. The type of variable scale used is the ordinal scale. The rating scale for each statement uses a rating scale technique with a Likert scale type. Online questionnaires are distributed through online media, the data analysis stage is the outer model test, namely the validity and reliability test and the inner model test, namely the hypothesis test or significance test. The independent variable of this research is digital literacy, the mediating variable is knowledge management, and the dependent variable is process innovation variable. Based on the results of research data analysis it was found that digital literacy had a positive and significant relationship on knowledge management, digital literacy had a positive and significant relationship on process innovation, knowledge management had a positive and significant relationship on process innovation. Knowledge management played as full mediators in the relationship between digital leisure variables and process innovation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".