The Effects of Demographic Characteristics on Employees’ Motivation to Participate in the In-Service Training Courses based on the Modified Expectancy Theory
Bibliographic record
Abstract
Modified expectancy theory provided a useful framework for assessing employee behavior in learning, decision-making, and motivation. The purpose of current study is to determine the effects of demographic characteristics on employees’ motivation based on the modified expectancy theory. Population was the employees of National Iranian Oil Products Distribution Company in Isfahan and Kurdistan. Multivariate analysis of variance (MANOVA) was used for data analysis. Finding reveals that the type of employment influences expectancy, intrinsic instrumentality, extrinsic valence, and motivation of employees for participating in the in-service training courses in the oil industry setting. Additionally, employee’s education influenced widely extrinsic instrumentality. Key words: Modified expectancy theory; Demographic characteristics; Motivation Resume: La theorie de l'esperance de modification/de la mise a jour a fourni un cadre utile pour evaluer le comportement des employes dans l'apprentissage, la prise de decision, et la motivation. Le but de l'etude presente est de determiner les effets des caracteristiques demographiques sur la motivation des salaries fondee sur la theorie de l'esperance de modification. Les sujets d'etude sont les employes de la Compagnie nationale iranienne de la distribution des produits petroliers a Ispahan et Kurdistan. L'analyse multivariee de la variance (ANMDVA) a ete utilisee pour l'analyse des donnees. Le resultat revele que le type d'emploi influe sur l'esperance, l'instrumentalite intrinseque, la valence extrinseque, et la motivation des employes a participer aux cours de formation en service dans le cadre de l'industrie petroliere. De plus, la formation des employes a une grande influence sur l'instrumentalite extrinseque. Mots-cles: Theorie de l'esperance de modification; Caracteristiques demographiques; Motivation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".