The effects of big data analytics, digital learning orientation on the innovative work behavior
Bibliographic record
Abstract
Previous studies have argued that increasing knowledge capacity in big data analytics influences increasing the speed of information processing and network analysis for making the right decisions at scale and high volume. Big data intensification supported by knowledge capacity through digital learning and a strategically supportive environment can ultimately help companies improve company performance. This study seeks to analyze the effect of big data analytics, digital learning orientation and environmental strategy on readiness for change and innovative behavior. The sampling technique employed by using simple random sampling on 185 respondents of information technology companies. By using the Structural Equation Modeling (SEM) analysis technique with the Partial Least Square approach, the empirical results show that big data analytics, digital learning orientation and environmental strategy had a significant effect on readiness for change and positively influence innovative work behavior. The analysis of mediation through the variable of readiness to change also found the role of mediation in strengthening the influence of exogenous variables on innovative work behavior. These results theoretically reveal the important role of data-driven performance management as an instrumental consequence. Practically speaking, the findings highlight the importance of employee engagement and talent acquisition professionals as a driving force in the intensification of big data analytics.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".