A bibliometric analysis of sport utility vehicle segment in the automobile industry: two decades study based on web of science database
Bibliographic record
Abstract
As the tremendous development in the field of consumer behaviour of SUV users in the automobile industry is going on, this study aims to conduct an extensive bibliometric analysis to manifest an outline of the research conducted during the past two decades. From 2002 to 2021, a dataset of 256 articles has been retrieved from the Web of Science (WoS) database as of 10-04-2022. The results of the study show that consumer behaviour research has been steadily expanding in recent years. Furthermore, it provides a deeper insight into publication trends, most productive authors, institutions, countries, and journals by using VOSviewer. The major studies were conducted in developed nations, i.e., USA, Australia, and Canada. Moreover, keyword network analysis provides potential research avenues, hence future studies might focus on separating research themes using bibliometric coupling analysis. This paper's contribution is to enlarge the diffusion trend of this emerging field to the bibliometric techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.025 | 0.057 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".