Kulturelle Motivation im Ausdauersport : eine kulturübergreifende Analyse unter Millennials in Deutschland und Kanada
Bibliographic record
Abstract
The aim of this thesis is to examine how cultural differences influence the motivation of Millennials in endurance sports. A focus is set to the countries of Germany and Canada, as well as to the role that digital technologies play for such motivation. As a framework, the Self-Determination Theory, and especially its mini-theory of the Basic Psychological Needs, is applied. This theory is connected to Hofstede’s cultural dimensions to be able to derive hypotheses for testing purposes and to provide both theoretical and practical insights from it. The results derived from a quantitative survey with 125 participants confirm that all three basic needs (autonomy, competence, and relatedness) are factors when it comes to motivation among Millennials in both countries. However, the level of satisfaction of the needs varies between countries: German Millennials demonstrate a significantly higher level of need satisfaction for competence, while Canadian Millennials exhibit a significantly higher level of need satisfaction for relatedness. Both findings are underscored in the digital technology context as well. No significant differences could be detected for the need satisfaction for autonomy, as both countries show similarly high results for it. These results offer practical implications for global businesses and a basis for future studies about cross-cultural sports motivation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".