Examination of the adoption intention of new energy vehicles from the perspective of functional attributes and media richness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing on the theory of media richness, this paper aims to explore the impact of media richness on consumers' adoption intention through their perception of new energy vehicle (NEV) function attributes, and assess the moderation roles of brand familiarity and locus of control. A structural equation model is applied to analyze the data collected from 427 respondents. Empirical results demonstrate that consumers' perception of an electric attribute (i.e., charging efficiency) and two intelligent attributes (i.e., car networking and self-driving) are determinants of their adoption intention of NEVs. The other electric attribute (range) is trivial in consumers' perception. We also find that low, medium, and high-richness media significantly affect consumers' perception of NEVs' functional attributes. Compared to the high-richness, medium-richness correlates significantly with two types of NEV functional attributes. Regarding moderating effects, consumer familiarity with NEV's brand negatively impacts the relationship between media richness and adoption intention. Furthermore, low and medium-richness media effectively stimulate individuals with external control to adopt NEV, while high-richness media adversely influence individuals with internal control.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it