The Influence of Children on Family Purchasing in Lithuania and Azerbaijan
Bibliographic record
Abstract
In the context of marketing, children are considered to be increasingly important influencers of parents’ purchasing decisions. However, their influence varies depending on the products and cultures, and many particularities of this phenomenon remain under-researched. This is especially true in regard to the countries that are categorized as “emerging economies”. Some of them still do not have reliable measurements of their cultural dimensions, and this increases difficulties in performing comparative analysis there. On the other hand, these countries offer a broad and important scene for child influence studies. This study concentrates on analysis and comparisons of children’s influence on parental purchase decision-making in Lithuania and Azerbaijan. These countries are different in, at least, two Hofstede’s cultural dimensions that are important in family decision-making: individualism and uncertainty avoidance. Also, the study contributes to the existing research by using a product use-related categorization of product groups. Such an approach reveals significant difference in terms of how children exert their influence on purchasing services versus tangible products for the family use. Interesting differences between the countries in terms of children’s influence on purchasing products for their personal use also opens a new scene for future studies that might consider a similar product categorization approach.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".