Green consumer research: Trends and way forward based on bibliometric analysis
Bibliographic record
Abstract
The study provides a comprehensive view of the research that has been conducted in the previous three decades on the topic of ‘green consumer’ in the marketing management domain and unravels the intellectual structure of the field along with the identification of core research gaps. Bibliometric analysis of 493 Scopus-indexed documents was conducted, including the keyword network analysis, co-authorship analysis, and reference co-citation analysis with VOSviewer. SciMAT analysis was conducted to identify the evolution of themes and strategic map. The study identified major contributors in the field (the most productive author: Li Y.), articles with the highest impact, the leading journals in the field (the most prolific journal: Journal of Cleaner Production), geographical locations where research of the field is concentrated (leading country: China) and the universities emphasizing on green consumer research (leading university: Florida International University). In addition, five major themes that characterize the body of knowledge on green consumer topics were identified namely, consumer buying behaviour, sustainable development, green products, human behavioural aspects, and green marketing. Evolving themes were identified as renewable energy and environmental policy. Core research gaps were then highlighted, providing a call for future research. This study would enable industry leaders and academic researchers to gain new higher-level insights into this emerging field. Such insights would be instrumental in developing strategies to attract green consumers. The study will also support policy development to promote green consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.068 | 0.062 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".