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 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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".