Consumer Behavior in the Information Economy: Generation Z
Bibliographic record
Abstract
The article examines the features of consumer behavior of Generation Z, the largest consumer group in the world today. The authors highlight the ability and willingness to purchase goods and services online as the main trends in changing consumer behavior, while the main means of getting information, choosing a product (service), and paying for a purchase today are smartphones and tablets actively used by representatives of Generation Z. Generation Z has been determined to be digital consumers boldly shopping online. They stay online most of their time (working online, studying online, social media from 3 to 6 hours a day, watching movies and entertainment content online, etc.) and before purchasing anything, Generation Z expects to access to and evaluate information, reads reviews, and conducts its research. Generation Z looks forward to co-creation with brands, participation in teams, and collaboration with managers; it expects innovation from their employers, leaders, and brands. Due to this digital literate consumption, it makes highly informed, more pragmatic, and analytical decisions than representatives of previous generations. The article also shows that social networks are becoming the main channel for delivering advertising information to a young audience with a focus on visual content (video, infographics) and the shortest formats possible: Generation Z consumes information fragmentarily, as they use several devices simultaneously.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".