Structure Equation Model of Causal Factors Affecting Employees’ Performance in Modern Trade Organization
Bibliographic record
Abstract
Employees’ performance depends on both internal factors and external factors that stimulate the willingness to work which drives organizational performance. The purposes of this study are to develop and investigate the consistency and conformity of the structural equation model of causal factors affecting the employees’ performance, and to analyze factors that affect employees’ performance in modern trade organizations. Indices were derived from revised literature and related research. This study used the case of 220 employees in a modern trade organization in Songkhla Province, Thailand to collect data. In addition, the data were analyzed by using structural equation modeling. The results demonstrated the structural equation model of causal factors affecting employees’ work performance in modern trade organizations and are consistent with empirical data. The result also indicated that loyalty and motivation had significant direct and indirect influences on employees’ performance in modern trade organizations. Additionally, loyalty is passed on to motivation as an indirect power in employees’ performance. These results carry implications for the ways to improve employees’ performance by increasing factors affecting it and finally, employees' performance will ultimately affect the organizational effectiveness and achieve of overall organizational goals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".