Goal Setting, Personality Traits, and the Role of Insurers and Other Service Providers for Swiss Millennials and Generation Z
Bibliographic record
Abstract
Service providers are developing more sophisticated offerings, and it is important for them to understand the demographics and specific context by which individuals might procure their services. This allows companies to stay relevant to their customers. The target of this paper is to investigate the types of goals Millennials and Generation Z individuals are pursuing and what role different service providers may play in supporting these endeavors, with the aim of providing actionable insights for insurers. Furthermore, it is to investigate how personality traits may relate to differences in individuals’ preferences. The study is based on a survey of 854 Swiss university students. The results indicate that goals are concentrated in a few categories, and educational institutions and healthcare providers are well-positioned to support goal achievement. Insurers, on the other hand, rank low among the preferences, and their profile is largely undifferentiated. This result indicates that insurers need to further focus their efforts to gain relevance among younger customers. Supporting goals relating to self-fulfillment and ability for high-conscientious and/or low-honest/humble customers by focusing on risk education and risk management seems a particularly interesting strategy for insurers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".