A Meta‐Analysis of Influencers of Automobile Purchase Intention of Customers and Evolution of the Automobile Consumer Behavior From TCB
Bibliographic record
Abstract
Several studies have explored the antecedents of automobile purchase intention (API) of customers across different geographical regions of the world. These have, however, come up with contradictory findings about the polarities of effects of some antecedents, as well as about some of their differences across countries. We were motivated by these contradictions and differences to carry out a meta‐analysis on API. In the study, we considered articles from peer‐reviewed databases such as Google Scholar, Web of Science, Scopus, ScienceDirect, and Wiley. Our study and analysis of the full texts of 136 articles yielded 64 antecedents. The specific polarity of effect (positive or negative) on API is established for 59 of these antecedents, whereas the same remained undetermined for the rest five. We identified these as research gaps, the areas yet to be convincingly explored. Furthermore, based on the absence of publication bias and the 95% CI of impact not crossing the line of null effect, we enlisted 20 antecedents spread across the different stages of the automobile purchase decision process, which we propose to add to the theory of consumer behavior as the automobile consumer behavior (TACB). Our study can help managers to better understand consumer behavior in terms of the discovered antecedents, especially those having a higher impact at a particular stage of the process, and can result in better business decisions.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".