Brand Anthropomorphism: A Bibliometric Analysis
Bibliographic record
Abstract
In recent years, the concept of anthropomorphism—attributing human qualities to non-human agents—has attracted considerable attention from academics and practitioners. Despite the growing number of studies, research on anthropomorphism in the branding context is relatively fragmented, with little effort to evaluate current trends or consolidate existing knowledge. This paper aims to provide a holistic overview of brand anthropomorphism by employing co-citation and bibliographic coupling analysis on 368 research articles retrieved from the Web of Science between 1994 and June 2023. The co-citation findings revealed three prior research streams of brand anthropomorphism, constituting existing knowledge-building in the given research area. The results of the bibliographic coupling analysis further unveiled six current clusters of the study domain. The comparison of co-citation and bibliographic coupling themes contributed to the detection of emerging trends in the selected field, the identification of research gaps, and the suggestion of future explorations. This paper promises to offer valuable insights that will provide both theoretical contributions and practical implications for marketers seeking to enhance the effectiveness of their branding messages.
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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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.259 | 0.305 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".