X and Millennial Employee Job Satisfaction Factor Study of Mongolia
Bibliographic record
Abstract
Organizations have different approaches to attract and retain desired employees based on the company values, goals, and capacity. However, the turnover rate is increasing worldwide and Mongolia is not an exception. To succeed we should manage each employee individually. However, in real life companies do not have that capacity and resources. To manage more effectively, companies might need to group employees by their needs. Some research materials show that generations share similar values. Therefore, solution to that issue could be dividing employees by their birth generation. This paper aims to identify differences and similarities of X and Millennial generations job satisfaction factors of Mongolian employees. To effectively manage individual’s need, questionnaire of this work was developed based on previous works on job satisfaction aspect, which distributed to 389 employees from Mongolia working in Mongolia, USA, Canada and Australia. The sorting criterion was affiliation to the generational cohort, Baby Boomers generation 2.8%, Generation X 36%, Generation Y 57.1% and Generation Z 4.1% respectively. Respondents geographical location: Ulaanbaatar (Capital of Mongolia) 68.1%, Aimags (Rural areas of Mongolia) 27.5% and abroad (USA, Canada, Australia) 4.4% respectively. This study reveals that employee engagement factor importance and reward program effectiveness differ by generations. Overall, company reputation, job security, benefits are rated differently by generations in regards to their importance level. Furthermore, generations prefer government award programs significantly different. The research results might serve companies and organizations to customize their program to attract and retain desired Mongolian employees based on their specific needs. To the best of the author’s knowledge, there is no study conducted regards to job satisfaction and employee engagement factors of Mongolian employees by their generation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".