Interpersonal Influence Within Car Buyers’ Social Networks: Five Perspectives on Plug-in Hybrid Electric Vehicle Demonstration Participants
Bibliographic record
Abstract
To explore the role of social interactions in individuals’ assessments of plug-in hybrid electric vehicles (PHEVs), this study analyzes over 190 social (interpersonal) interactions elicited in interviews with 31 individuals in eight different social networks centered on households in the Sacramento, California region. Results are framed within five theoretical perspectives on social influence: contagion, conformity, dissemination, translation, and reflexivity. Responses within networks centered on participants in a study of consumer response to plug-in hybrid electric vehicles (PHEVs) suggest that interpersonal interactions do shape consumers assessments of PHEVs, and likely electric-drive vehicles generally. Characterizing how social interactions influence vehicle assessments and adoption behaviors, contagion (including diffusion of innovations), conformity, and dissemination provide useful concepts, but translation and reflexivity better provide the language and theoretical depth required to integrate the various motives and perceptions observed. Through translation and reflexivity, preliminary analysis suggests that certain types of households and social network may be more amenable to developing new, pro-societal interpretations of vehicle technology—particularly those households that: i) are in a liminal state in their lifestyle practices, ii) already have a basic understanding of functional aspects of PHEV technology, and iii) find supportive pro-societal values within their social network. This exploratory, qualitative study demonstrates that social interactions are important and their study benefits from the development and use of behaviorally realistic theoretical frameworks to advance transportation and energy policies that rely on the widespread uptake of new technologies.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".