The Relationship Among Entrepreneurship Tendency, Income Level and Life Satisfaction of Future Business People: Generation Z
Bibliographic record
Abstract
The aim of the study to examine the relation among entrepreneurship tendency, income level and life satisfaction of future business people generation Z. The research was conducted on 215 business administration faculty students of a private university in Istanbul. Questionnaire application was used as research data collection method. The survey response rate of faculty students is 72%. For life satisfaction Diener, Emmons, Larsen and Griffin (1985) developed a single sub-dimension scale was used. Life satisfaction scale consists of 5 propositions. Caird (2013)’s scale named shortly Get2Test (General measure of Enterprising Tendency test) was used with 54 items in order to measure entrepreneur tendency. As a result of the factor analysis, the entrepreneurship tendency variable was divided into three sub-dimensions: the need for achievement, creativity and risk taking. The results show that those three factors explain the %60.65 of the total variance. For three factors’ subscales Cronbach’s alpha analysis show that three factors’ subscales have reliabilities higher than ,70 which indicated high internal consistency. The factor analysis of life satisfaction shows that the rate of explanatory is 62,45% and Cronbach's alpha value is 0.72. As a result of analysis, the relationship between the two variables, was tested and accepted by regression and correlation analysis. There is a positive relationship between entrepreneurship tendency and life satisfaction, in other words, as individuals' tendencies towards entrepreneurship increase, their life satisfaction also increases. There are differences based on ANOVA test analysis in the entrepreneurial tendency and need for achievement of the Z generation according to the income level. Individuals with lower income levels have higher entrepreneurial tendencies and needs for achievement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".