Mapping the Green Frontier: Bibliometric Analysis of Sustainable Marketing & Consumer Behavior in Hospitality
Bibliographic record
Abstract
Environmental sustainability and green marketing have emerged as critical priorities within the hospitality industry. Nonetheless, a comprehensive understanding of how these concepts intersect—especially regarding consumer behavior, corporate strategies, and emerging technological enablers—remains underexplored. This study bridges that gap by employing an extended, cross-database bibliometric approach that integrates advanced co-occurrence, co-citation, bibliographic coupling, and co-authorship analyses. Drawing on a robust dataset of 67,059 articles extracted from Scopus and ABS-indexed journals—and triangulated with 229 core studies filtered from the Web of Science (WoS)—we map the evolution of primary thematic clusters and subtopics over time. These include green supply chain management, pro-environmental purchase intention, AI-driven eco-innovation, transformative tourism, and crisis management. Our methodology involves refining and applying carefully curated keyword sets (e.g., “green marketing,” “pro-environmental behavior,” “hospitality,” “consumer behavior”), extracting large-scale records from Scopus and ABS-indexed journals, and incorporating WoS core studies to ensure comprehensive coverage and cross-validation of emerging trends, and utilizing specialized clustering techniques—such as fractional counting, LinLog normalization, and modularity-based clustering—to achieve high-resolution thematic groupings. The advanced analyses reveal two dominant yet interlinked research domains: (a) corporate sustainability and marketing strategies, and (b) attitudinal and psychological factors driving pro-environmental consumer behavior. Key results indicate that green marketing approaches—underpinned by robust brand image, CSR initiatives, and circular economy practices—significantly influence purchase intentions and consumer loyalty, particularly in hotels and tourism settings. Moreover, digital transformation, evidenced by the integration of AI-based personalization and big data analytics, is reshaping consumer engagement by enabling real-time, data- driven insights to predict and influence eco-friendly behaviors. These findings highlight the multidisciplinary and rapidly evolving nature of green marketing research, calling for intensified cross- disciplinary collaboration among marketing, environmental psychology, and technological innovation. For practitioners, our results highlight the necessity of implementing authentic, data-driven green strategies to meet the demands of increasingly eco-conscious consumers. Thus, the originality of this study lies in its holistic, state-of-the-art bibliometric methodology, which elucidates not only the historic landscape of green marketing research but also the emergent frontiers that can guide both researchers and industry stakeholders toward more impactful sustainability practices.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.183 | 0.244 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".