Meta-Analysis of the Concept of Brand Humanisation in Higher Education
Bibliographic record
Abstract
Abstract Research purpose. The purpose of this study is to identify and quantitatively evaluate the volume of scientific publications, the latest research trends, and the main topics related to brand humanisation in higher education (HE). This aim is motivated by the growing importance of empathetic and human-centred communication in a competitive and digitalised educational environment. Although widely applied in practice, the concept remains fragmented in academic research, creating the need for a systematic bibliometric analysis to clarify its development and future directions. Design / Methodology / Approach. A bibliometric analysis was applied to systematise knowledge on brand humanisation in HE during 2005-2024. The analysis was conducted in several stages, including literature review, database selection, keyword specification, article screening, data extraction, and analysis. Data were retrieved from Scopus and Web of Science Core Collection, ensuring access to peer-reviewed publications and reliable citation indicators. After applying selection criteria, 84 articles were retained. Descriptive bibliometrics (publication dynamics, citation statistics, collaboration patterns) were combined with science mapping techniques (keyword co-occurrence networks, overlay visualisation, and thematic mapping) using VOSviewer, R Bibliometrix, and Microsoft Excel. This framework enabled the identification of research clusters, dominant themes, and emerging areas. Findings. The analysis reveals that research on brand humanisation in HE remains underdeveloped but shows growing interest. The most influential works focus on constructs such as brand personality, image, and identity, while direct studies on anthropomorphism and storytelling are rare. Results highlight the USA, Malaysia, and Canada as leading contributors, though international collaboration is still limited. Science mapping uncovers six thematic clusters: brand image and reputation; brand identity and loyalty; personality and perception; social media engagement; consumer behaviour; and anthropomorphism. Findings confirm a shift from traditional branding approaches towards digital communication, personalisation, and emotional engagement, reflecting universities’ adaptation to students’ evolving expectations in digital environments. Originality / Value / Practical implications. This study provides the first systematic bibliometric overview of brand humanisation in HE, consolidating fragmented knowledge and identifying emerging themes. While primarily academic in focus, its practical relevance lies in offering insights into the most studied and underexplored aspects of the field. These insights can guide universities and HE marketers in recognising trends, addressing conceptual gaps, and aligning their communication strategies with approaches emphasised in the literature. In this way, the study serves as a roadmap for institutions seeking to enhance positioning and responsiveness in a digitally mediated education environment.
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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 | 0.075 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.045 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".