Consumers’ adoption of autonomous cars as a personal values-directed behavior
Bibliographic record
Abstract
• Personal values function as a sense-making system in autonomous cars’ adoption. • Autonomy motivates both adoption and non-adoption decisions given personal values. • Means-end chains with survey validation effectively assess values-driven adoption. • Bipolar value maps compare innovation adoption and non-adoption drivers. • A one-size-fits-all approach toward promoting autonomous cars is not advised. Autonomous cars are the future of transportation. Manufacturers’ success is, nonetheless, dependent on consumers’ adoption of such innovation. Past studies distinguish between factors contributing to autonomous cars’ adoption and those prompting resistance. Extant evidence does not explain, however, when and why certain factors both facilitate and inhibit adoption. Addressing this gap, we propose a personal values-directed perspective on consumers’ adoption of autonomous cars. Through a means-end chain analysis of 54 laddering interviews and an online survey, we show that personal values function as a sense-making mechanism in innovation adoption decisions. We propose a comprehensive set of consumer-perceived consequences arising from autonomous cars’ attributes, which explain adoption and non-adoption based on personal values. Notably, we show an innovative application of means-end chain analysis based on bipolar hierarchical value maps to investigate the adoption of highly novel innovations. Findings have implications for managers seeking to encourage the adoption of yet-to-be-commercially-launched innovations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".