Environmental Consciousness in the Digital Era of Online Shopping: A Systematic and Bibliometric Review
Bibliographic record
Abstract
This research paper explores the intricate relationship between environmental consciousness and online shopping, conducting a comprehensive analysis of the literature landscape.Through a systematic literature review and bibliometric method, the research addresses critical questions, such as the evolution of literature over the past thirteen years, influential authors, key journals, contributing countries, bibliometric coupling, and thematic areas.The study reveals that online shopping, while generally having a lower carbon footprint than traditional shopping, is influenced by various factors, including consumer behavior, transportation, and packaging.Insights from behavioral science are proposed to bridge the gap between positive consumer attitudes towards eco-friendly products and their reluctance to pay more for them.The discussion extends to the broader context of environmental consciousness in business, emphasizing the paradoxical consumer behavior towards sustainable offerings.The study also explores the equivocal role of environmental concern in the decision-making processes surrounding environmental purchasing in online shopping.The methodology employs robust techniques, including VOSviewer software for bibliometric analysis, ensuring a rigorous examination of the literature.The findings contribute to the understanding of the complex interplay between environmental awareness and online consumer behavior, offering valuable insights for scholars, practitioners, and policymakers.However, the study acknowledges limitations, such as potential biases in the selected dataset and the focus on quantitative measures, encouraging future research to adopt a more inclusive, qualitative approach.The research outlines future directions, including the need for deeper qualitative analysis, extending temporal scope, exploring interdisciplinary aspects, conducting case studies, and examining geographical and cultural influences on sustainable purchasing attitudes in online shopping.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.183 | 0.166 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".