The Altruistic Connection: Unraveling How Altruism Drives Eco-Friendly Consumer Behavior in Green Marketing (Literature Review)
Bibliographic record
Abstract
Purpose: This article explores research on eco-conscious marketing (2010-2022) via sources like Scopus, ScienceDirect, Emerald, and Taylor & Francis. Altruism, acting selflessly for others, significantly impacts eco-friendly choices. Theoretical framework: This research examines altruism's role in eco-friendly marketing through behavioral theories. Limited analysis of psychological links and cultural norms exists. Further investigation is needed for sustainable consumer behavior and ethical exchange. Methodology: In the effort to comprehend the complex connection between altruism and green marketing, a strong methodology has arisen through the combination of qualitative research techniques and a comprehensive examination of existing literature. This description presents the methodological process utilized to examine the impact of altruism on research related to green marketing from 2010 to 2022. This exploration involves the gathering of data, its analysis, and the development of meaningful insights, all drawn from reliable sources such as Scopus, Emerald, ScienceDirect, Taylor & Francis, and Google Scholar. Findings: In the context of green marketing, altruism is pivotal for comprehending and shaping consumer behavior. Beyond economic factors, it embraces emotions, ethics, and communal welfare. Altruism-driven strategies hold promise for positive change, brand loyalty, and sustainability. Research spanning 2010-2020 uncovers gaps in understanding altruism's mechanisms, contexts, sustainability, trade-offs, extensions, and interactions, necessitating their exploration for effective green marketing and a sustainable world. Research, Practical & Social implications: The research identifies gaps in comprehending altruism's psychological aspects, context influences, and sustainability. This enhances strategies for sustainable consumer behavior. Additionally, leveraging altruism supports green marketing's effectiveness, bridges the "value-action gap," and fosters brand loyalty, thereby harnessing its potential for positive change, a greener future, and enhanced brand allegiance. Originality: Employing qualitative research methods and extensive literature review, this study explores the link between altruism and green marketing from 2010 to 2022. The synthesis of findings reveals altruism's role in shaping consumer behavior, with implications for marketing strategies, bridging value-action gap, and fostering brand loyalty. Research gaps underscore the need for investigating psychological mechanisms, contextual influences, and sustainability.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".