The barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC) adoption
Bibliographic record
Abstract
The rise in motorcycle use in Southeast Asian countries like Indonesia has caused environmental issues and transitioning from fossil-fuel to electric motorcycles (EM) will reduce emissions and improve air quality. This study aims to investigate the barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC). In a choice experiment, data from motorcycle users in Bali, Indonesia, was collected and analysed in this study using K-modes cluster analysis and the Mixed-Logit Model. The study identified different barriers to EMC among motorcyclist groups: mix-motor commuters and hardcore oldies concerned with financial challenges, all-day riders with mature motorcycles face a lack of information on EMC costs and procedures, and higher-power enthusiasts and newbies with light motorcycles concerned with daily travel disruptions during the conversion process. This study also found waiting and conversion time to play a role in EM adoption and travellers who use older motorcycles are the most likely to adopt EMC. Lower-income individuals tend to be more inclined towards EMC and younger demographics lean towards internal combustion engine (ICE) motorcycles. Moreover, the study indicates that EMC reduces the adoption of ICE motorcycles more than conventional EM. A 50% increase in conversion time lowers EMC adoption probability by 5.2%pts. and increases new ICE motorcycle adoption by 3%pts. and new EM adoption by 2.2%pts. Additionally, motorcyclists are more willing to invest in EMC if it means reducing conversion time/charging costs, particularly for older motorcycles. This study offers several policy recommendations for accelerating EMC adoption in Indonesia. • Waiting and conversion times impact the adoption of conventional e-motorcycle (EM) and e-motorcycle conversion (EMC). • Hardcore oldies motorcyclists are concerned with financing and daily riders are also concerned with EMC procedures. • EMC potentially serves as complementary to EM in reducing internal combustion engine motorcycle adoption. • Motorcyclists are more willing to invest in EMC if it means reducing conversion time/charging costs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".