The Origins, Evolution, Current State, and Future of Green Products and Consumer Research: A Bibliometric Analysis
Bibliographic record
Abstract
Green products are crucial for a sustainable future. Without a strong understanding of consumer intent toward green products and research gaps, translating the availability of green products into actual consumer and market acceptance is hampered. This article reviews the available literature on green products and their relationship to consumers through bibliometric analysis. We used VosViewer to globalize the topic mapping and Scimat for longitudinal analysis. The results show that the available literature can be divided into four clusters, and five periods representing four distinct eras can be defined. Published studies were found in only 15 of the 36 calendar years constituting the first era. The second era started a wave of increasing green product research. In the third era, the number of journals with publications related to green products peaked. After the diversification of the third era, the fourth era saw the consolidation of the main vectors of publication. Despite a slow start in 1974, the research on eco-friendly products has expanded significantly over the past decade. Nonetheless, one persistent weakness of the literature is that most studies use customer intent, not the purchase itself, as the dependent variable. Consequently, there is still enormous potential for further research.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.179 | 0.228 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".