Mapping the Conceptual Structure of Digital Marketing and Customer Engagement: A Bibliometric Approach
Bibliographic record
Abstract
The rapid evolution of technological advancements and shifting consumer behavior necessitates a comprehensive understanding of emerging trends and research directions in digital marketing and customer engagement. This study presents a comprehensive bibliometric analysis of 6,220 publications from SCOPUS and Web of Science (2010–2024) to map the conceptual structure and thematic evolution of digital marketing and customer engagement research. Utilizing tools such as Biblioshiny, the analysis identifies key trends through keyword co-occurrence, thematic mapping, and correspondence analysis. The findings reveal a dynamic shift from foundational consumer preferences and advertising themes to emerging domains such as influencer marketing, sustainability, and AI-driven strategies. Thematic clusters underscore the rising influence of social media, electric vehicles, and sentiment analysis in shaping consumer behavior, while motor themes like willingness to pay and choice experiments remain crucial. Niche areas such as game theory and e-commerce platforms highlight the underexplored research opportunities. This study emphasizes the transformative role of artificial intelligence in customer experiences and the necessity of interdisciplinary collaboration to address evolving market challenges. Despite limitations, including language biases and database constraints, this research offers actionable insights for academia and practice. This study advocates for future exploration of sustainability, global consumer trends, and advanced analytics, enabling businesses to craft adaptive strategies in a digitally competitive landscape. This analysis provides a roadmap for advancing theoretical frameworks and fostering deeper customer connections and sustainable brand growth.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.251 | 0.311 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".