Investigating the Factors Influencing Parent Toy Purchase Decisions: Reasoning and Consequences
Bibliographic record
Abstract
The purpose of this study is to explore and verify the main determinants of parent toy-choice decision-making by using a theoretical model for toy-selection decisions and exploring toy-purchasing behaviour empirically. After reviewing a large number of previous studies, this study’s model was developed and designed. A variety of determinants were identified and then categorized into six main broad categories, namely, purpose-of-using related factors, emotional-related factors, educational-related factors, cost-related factors as well as child and parent demographic-related factors. A quantitative methodology was adopted to test the study’s model by drawing on six hypotheses, which were then tested statistically. A self-administrative survey was developed to collect the preliminary data from customers (mainly parents) who had been involved in toy purchasing by applying the convenience sampling method. The study hypotheses were tested and the findings were also discussed in-depth.The study found that parent toy purchase decision id derived by a set of factors which are purposes of using-related factors, emotional-related factors, informational-related factors, cost-related factors, children demographical-related factors and parental demographical-related factors.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".