Green Product Purchase Intention of Young Customers from Developing Country: Extended Theory of Planned Behavior
Bibliographic record
Abstract
The purpose of this study is to explore the factors influencing Kazakhstani young consumers' attitudes and intentions towards green products by extending the Theory of Planned Behavior framework.Specifically, it examines the role of multidimensional perceived value (functional, emotional, and social) and environmental knowledge in shaping these attitudes and intentions.A quantitative research approach was used using a structured online survey to collect data from 308 young urban consumers in Kazakhstan aged between 18-35 who were knowledgeable or interested in green products.The survey included items measured on a seven-point Likert scale adapted from established literature.Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze relationships among key variables and test the proposed hypotheses.The results show that environmental knowledge and emotional values are the strongest predictors of young consumers' green purchase intentions, while social value has a negative impact.Their attitudes towards green products are also influenced by environmental knowledge, emotional and functional value.The study highlights the importance of these additional constructs in influencing young consumers' attitudes and purchase intentions towards green products, providing a foundation for future research and practical applications in promoting sustainable consumption patterns in developing markets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".