AI Trust and Knowledge Management Practices in Enhancing Employee Innovation: Moderating Effect of Career Resilience
Bibliographic record
Abstract
This study uses Knowledge Sharing (KSH), Knowledge Documentation (KDC), Knowledge Creation (KCR), and Knowledge Application (KAP) to examine how AI Trust (ATR) affects Employee Innovative Behavior (EIB). The main goal is to examine AI Trust's direct and indirect effects on creativity and Career Resilience (CRL)'s moderating role. The study employed a standardized questionnaire to collect data from IT staff in China, Saudi Arabia, and Pakistan. This study uses quantitative research. Data analysis was done using SMART PLS 4.0 on 678 replies. ATR directly and indirectly affects EIB through knowledge management strategies. CRL moderates ATR and Innovation, strengthening it. The study emphasizes the importance of knowledge management and ATR in organizations to foster innovation. This research could help organizations foster creativity using artificial intelligence. These findings may potentially affect managers and politicians trying to boost employee creativity and responsiveness to technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".