A bibliometric analysis and policy recommendation on Asian consumers’ green purchasing behavior
Bibliographic record
Abstract
Abstract The objective of this study is to conduct a bibliometric analysis of Asian consumers’ green purchasing behavior (GPB). Specifically, this paper identifies publication trends, leading authors, countries, and key research themes related to Asian consumers’ GPB. Additionally, it proposes a GPB model for Asian consumers based on a synthesis of previous research findings and formulates alternative policy recommendations for governments to enhance GPB in the Asian context. The study adopted the Scientific Procedures and Rationales for Systematic Literature Review (SPAR-4-SLR) protocol. Data were extracted from the Scopus database, and bibliometric analyses were conducted using VOSviewer and the R-package software. The findings revealed that 278 documents on Asian consumers’ GPB were published between 2000 and July 2024, with the most cited publications appearing in 2023. China emerged as the leading country in terms of both publication volume and citation impact. This study identified four major research themes through bibliometric co-word analysis: (1) environmental concern and the Theory of Planned Behavior (TPB), (2) consumer attitudes and perceptions, (3) environmental knowledge, and (4) personal factors. Furthermore, it proposed a logic model for alternative policy recommendations to improve GPB in Asia. To the best of our knowledge, this is the first bibliometric study focusing on Asian consumers’ GPB. Therefore, this paper provides a valuable guide for researchers studying GPB trends over time. It also serves as a useful reference for policymakers in designing and implementing strategies to promote GPB within the Asian context.
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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 | 0.026 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.141 | 0.238 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".