Did innovation projects, digital work environment, job satisfaction, and organizational culture reinforce work productivity?
Bibliographic record
Abstract
The aim of this research is to analyze the relationship between Innovation Projects and work productivity and work environment and work productivity, analyze the relationship between job satisfaction and work productivity and analyze the relationship between organizational culture and work productivity. This study employed a quantitative approach with an explanatory research design, aiming to examine the effect of integrity, organizational commitment, and motivation on sustainable employee performance with job satisfaction as a mediating variable. The population consists of all employees of the manufacturing organization, totaling 470 employees. The sampling technique applied is simple random sampling. The research instrument was a questionnaire using a 5-point Likert scale. The study variables were: Digital Work Environment (X1), Job Satisfaction (X2), Organizational Culture (X3), Innovation Projects (X4) and Employee Work Productivity (Y). Data were analyzed using Partial Least Square – Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The analysis consisted of two stages: Outer Model (Measurement Model): Convergent validity, discriminant validity, and reliability testing. Inner Model (Structural Model): Path coefficient testing, R² values, and both direct and indirect effects among variables. The results show that the digital work environment has a positive relationship with work productivity. Job satisfaction has a positive relationship with work productivity. Organizational culture has a positive relationship with work productivity. Innovation Projects have a positive relationship with work productivity.
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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 | 0.000 |
| 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 | 0.001 | 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".