Did Attitudes Interpret and Predict “Better” Choice Behaviour towards Innovative and Greener Automotive Technologies? A Hybrid Choice Modelling Approach
Bibliographic record
Abstract
It is common opinion that traditional approaches used to interpret and model users’ choice behaviour in innovative contexts may lead to neglecting numerous nonquantitative factors that may affect users’ perceptions and behaviours. Indeed, psychological factors, such as attitudes, concerns, and perceptions may play a significant role which should be explicitly modelled. By contrast, collecting psychological factors could be a time and cost consuming activity, and furthermore, real-world applications must rely on theoretical paradigms which are able to easily predict choice/market fractions. The present paper aims to investigate the above-mentioned issues with respect to an innovative automotive technology based on the after-market hybridization of internal combustion engine vehicles. In particular, three main research questions are addressed: (i) whether and how users’ characteristics and attitudes may affect users’ behaviour with respect to new technological (automotive) scenarios (e.g., after-market hybridization kit); (ii) how to better “grasp” users’ attitudes/concerns/perceptions and, in particular, which is the most effective surveying approach to observe users’ attitudes; (iii) to what extent the probability of choosing a new automotive technology is sensitive to attitudes/concerns changes. The choice to install/not install the innovative technology was modelled through a hybrid choice model with latent variables (HCMs), starting from a stated preferences survey in which attitudes were investigated using different types of questioning approaches: direct questioning, indirect questioning, or both approaches. Finally, a comparison with a traditional binomial logit model and a sensitivity analysis was carried out with respect to the instrumental attributes and the attitudes. Obtained results indicate that attitudes are significant in interpreting and predicting users’ behaviour towards the investigated technology and the HCM makes it possible to easily embed psychological factors into a random utility model/framework. Moreover, the explicit simulation of the attitudes allows for a better prediction of users’ choice with respect to the Logit formulation and points out that users’ behaviour may be significantly affected by acting on users’ attitudes.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".