A study of the moderating effect of organizational training on the impact of employees’ behavioral resistance to AI technology and the application of structural equation modeling
Bibliographic record
Abstract
The development of artificial intelligence has brought new development opportunities for modern enterprises, but employees present a certain degree of resistance to the introduction of AI technology.The author tries to dissipate employees' resistance and improve their acceptance of AI through organizational training.After researching organizational training and employees' perceived awareness of AI, organizational training and employees' acceptance of AI are taken as antecedent and consequent factors to construct a structural equation research model of the two.The research hypotheses are proposed based on the theoretical study of the two.Regression analysis of the effect of organizational training on employees' AI acceptance is conducted through structural equations.The regression results show that training investment, employee motivation and knowledge training in organizational training all have a significant positive effect on both employees' AI perceived ease of use and AI perceived usefulness.Employee AI perceived ease of use and AI perceived usefulness have a positive effect on employee behavioral intention to use AI for knowledge creation and automation.Employees' behavioral intention to use AI for knowledge creation will have a positive effect on AI for knowledge creation, and behavioral intention to use AI for automation will have a positive effect on AI for automation.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".