Research on Brand Equity of Intelligent Connected Vehicles in China
Bibliographic record
Abstract
In the context of global climate change, the advancement of electric vehicles (EV) has emerged as a pivotal strategy for energy conservation and emission reduction within the transportation sector. Leveraging the progress in 5G and IoT technologies, intelligent connected vehicles (ICV) have emerged as a focal point within the realm of electric vehicles, with China spearheading significant developments in this domain. Concurrently, the proliferation of Chinese brand electric vehicles (CBEVs) has been notable in recent years. Nonetheless, academic research on the brand equity of CBEVs remains limited, particularly regarding the influencing factors from the consumer perspective and the functionalities of intelligent connectivity. This study aims to address these gaps by investigating the determinants of brand equity for CBEVs from the consumer standpoint. The findings reveal that consumer attitude, trust, and the intelligent connected feature exert a positive influence on brand equity. This underscores the importance for CBEVs manufacturers to focus on enhancing brand equity by fostering positive consumer attitudes, building trust, and offering comprehensive intelligent connected features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".