Big data and IoT adoption in shaping organizational citizenship behavior: The role of innovation organizational predictor in the chemical manufacturing industry
Bibliographic record
Abstract
This research aims to investigate the relationships between Big Data and Internet of Things (IoT) adoption and employee behavior in the chemical manufacturing industry, specifically focusing on the mediating role of organizational innovation. The research methodology employs a quantitative approach that involves employee surveys, statistical analysis, and mediation testing. The primary findings reveal that Big Data adoption significantly enhances Organizational Innovation, contributing positively to Organizational Citizenship Behavior (OCB) among employees. Conversely, IoT adoption has a significant positive impact on Organizational Innovation but does not directly influence OCB. The relationship between IoT adoption and OCB is mediated by Organizational Innovation, highlighting the pivotal role of innovation as an intermediary in influencing employee behavior. The practical implications of this research suggest that organizations in the chemical manufacturing industry should strategically integrate Big Data and IoT technologies to foster innovation and elevate OCB. Leadership support and employee training are crucial. Study limitations include industry specificity, self-reported data, and static analysis. Future research should diversify samples and use longitudinal methods. Recommendations: embrace tech with innovation focus, train leaders, and deepen understanding of tech, innovation, and behavior.
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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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".