5.1. Data Sample
As shown in
Table 4, out of 416 respondents who completed all demographic questions and fully answered all choice experiments, in terms of age, the largest proportion of respondents was in the age range of 20 to 25 years at 28.13%, followed by the age of 30 to 40 years, above 40 years, 25 to 30 years, and 17 to 20 years at 27.40%, 20.43%, 19.47%, and 4.57%, respectively. The respondents’ average age was 30.57 years old, with a standard deviation of 9.32 years. In terms of gender, 97.12% of respondents were male, while, interestingly, 2.88% of respondents were female. Furthermore, most respondents had a high school education level or lower, accounting for 90.62%. Meanwhile, the rest (9.38%) had a graduate level or higher. Interestingly, 19.71% of university students work in ride-hailing companies as part-time workers. Approximately 65% of MBRH drivers worked in ride-hailing companies as their main job (i.e., full-time workers). Looking into the monthly income of MBRH drivers, including other jobs’ income, 51.20% of them had income between IDR 1,916,000 and IDR 3,850,000 (USD 129.67 and 260.55). However, 36.30% of them, dominated by university students who worked part time as MBRH drivers, had an income of less than IDR 1,916,000 (USD 129.67), lower than the regional minimum wage. Meanwhile,10.82% and 1.68% of them had an income between IDR 3,850,000 and IDR 5.750.000 (USD 260.55 and 389.13) and more than IDR 5.750.000 (USD 389.13), respectively.
Meanwhile,
Table 5 shows the percentage distribution of electric motorcycle adoption choices across scenarios. It can be seen that Scenario 6 (lowest purchase price, fixed cost for title transfer and fuel price, no tax exemption, highest coverage distance, medium speed, the distance between charging stations is more than 10 km, and without credit payment) produces the highest percentage of buying electric motorcycles, accounting for 39.90% and 21.15% for adopting and definitely adopting, respectively. Meanwhile, the highest percentage of renting electric motorcycles occurs in Scenario 9 (cheapest rental cost, highest coverage distance, lowest speed, increase in gasoline price, and the distance between charging stations is more than 10 km), where 18.27% of MBRH drivers definitely rent, and 49.04% rent electric motorcycles. In contrast, the lowest percentage of not adopting electric motorcycles (purchase model) occurs in Scenario 5 (highest purchase price, fixed cost for title transfer and fuel price, tax exemption, lowest coverage distance, lowest speed, the distance between charging stations is more than 10 km, and with credit payment) for the purchase model, by 37.98% for definitely not adopt and 30.77% for not to adopt. Meanwhile, Scenario 6 (highest rental cost, lowest coverage distance, highest speed, increase in fuel price, and the distance between charging stations is more than 10 km) becomes the lowest percentage of adopting electric motorcycles for the rent model, accounting for 34.13% and 27.88% in terms of definitely not adopt and not adopt, respectively.
5.2. Model Results
From 416 respondents, 2496 datasets were acquired in the purchase model from twelve scenarios (each respondent faced six scenarios), while 2080 datasets were obtained in the rental model from ten scenarios (each respondent faced five scenarios).
Table 6 shows the results of an ordered logit model. The model is estimated by a maximum likelihood method using STATA v.14 [
56]. For the purchase model, out of eight considered variables, only the purchase price and coverage distance variables have a significant effect on electric motorcycle adoption. Shown by a negative value of the purchase price variable, it demonstrates that an increase in electric motorcycle purchase price will reduce the MBRH drivers’ willingness to adopt electric motorcycles. This finding is consistent with those reported by previous studies [
42,
46,
54], which found that the probability of purchasing an electric motorcycle decreases as the purchase price rises. Contrarily, a positive sign of coverage distance means that MBRH drivers will be more inclined to use electric motorcycles as coverage distance increases. The absence of a correlation between the annual tax variable and the adoption of electric motorcycles is not consistent with the study conducted by Scorrano and Danielis [
46] in Italy, which combines the variable of tax exemption with insurance premiums. In Italy, the electric scooter policy is exempt from tax for five years and frequently receives discounts up to 50 percent on insurance premiums. The insignificant variable of an annual tax in this study was deemed to be caused by the low annual tax for motorcycle mode. Meanwhile, the insignificant correlation between costs for title transfer and electric bicycle adoption contradicts the findings from Jones et al. [
42], who claimed that the elimination of this cost would greatly increase the market share of electric scooters in Vietnam.
For the rental model, four variables significantly influence the adoption of electric motorcycles: coverage distance, cost for motorcycle title transfer, rental cost, and fuel price. Shown by positive signs for coverage distance and fuel price, the model results reveal that MBRH drivers are more likely to adopt an electric motorcycle via rental if the maximum coverage distance for electric motorcycles and fuel price increases. In contrast, negative signs for battery exchange stations and rental costs indicate that the farther the battery exchange station location and the more expensive the rental price, the lower the chances of MBRH drivers adopting electric motorcycles. Meanwhile, the insignificant variable of maximum speed for both buying and renting models is different from previous studies, showing that the maximum speed has an effect on the adoption of electric motorcycles, with the amount a user is willing to pay for an increase of 1 km/h being USD 65 [
54], USD 26 [
42], and USD 0.81 to 2.39 [
8]. However, this study’s result is in line with previous studies [
54,
57,
58], which found that speed was not significant in the adoption of electric vehicles.
Furthermore, taking into account the sociodemographic variable, it can be seen from
Table 6 that there is no significant correlation between all sociodemographic variables and purchasing electric motorcycles. Meanwhile, significant correlations occurred between sociodemographic variables (i.e., age, education level, current university students, full-time drivers, and income/rental cost) and electric motorcycle adoption. Shown by negative signs for age and education level, the model revealed that younger drivers and drivers without bachelor’s degrees are more likely to adopt electric motorcycles by renting from ride-hailing companies. This finding is consistent with a previous study in the United States showing the preferences for electric vehicle adoption among young ride-hailing drivers [
32]. In contrast, positive signs for full-time drivers and current university students coefficients show that those people have a higher probability of renting electric motorcycles than their counterparts.
This study also considered a variable that interacts with income and price, as people with lower incomes are more sensitive to price. A similar method has been carried out by a study in India to explore the fuel economy valuation of Indian motorcycle buyers [
59]. The model results show that there is no relationship between income/purchase price and purchasing electric motorcycles. Meanwhile, for the rent model, shown by a negative sign, it can be seen that people with a lower income and rental cost ratio tend to adopt electric motorcycles by renting from ride-hailing companies.
However, because many independent variables for the purchase model were insignificant, this study applied a binomial logit model to produce better model results. This study assumed that the choice of definitely adopt and adopt was merged as adopt, while the choice of undecided, not adopt, and definitely not adopt was merged to not adopt. As shown in
Table 6, although the binomial logit model produced a higher value of Pseudo R
2 (i.e., better model fit) than the ordered logit model, there was no difference in significant variables between the two logit models. Even for the rent model, the education level that significantly affects the decision to rent electric motorcycles in the ordered logit model becomes an insignificant variable for the binomial logit model. Due to this, it can be concluded that the use of the ordered logit model could be accepted to explore the influence factors in electric motorcycle adoption among MBRH drivers.
5.3. Probability of Electric Motorcycle Adoption
The probability values were calculated by estimating the likelihood that electric motorcycles will be purchased and rented by MBRH drivers.
Figure 1 shows the results of purchase model’s probability values for all scenarios. However, it should be noted that because there were only two significant variables out of eight considered variables, it results in the same scenario for Scenarios 1 and 11, Scenarios 2 and 9, and Scenarios 6 and 10. To make an easier interpretation,
Figure 1 is arranged according to purchase price, starting with the lowest price and the shortest coverage distance to the most expensive purchase price and the longest coverage distance. As demonstrated in
Figure 1, increasing the coverage distance could increase the likelihood of buying and definitely buying. Previous studies also show that coverage distance is a determinant in the adoption of electric motorcycles [
42,
46,
54]. Even Guerra [
8] has accounted for willingness to pay for electric motorcycles, showing that respondents are willing to pay IDR. 36,885 (USD 2.5) per month for a 10 km increase in coverage distance.
This study’s results revealed that an increase in coverage distance from 50 km (Scenario 3) to 100 km (Scenario 12) at IDR 18 million purchase price would increase the probability of buying from 45.6% to 48.9% (3.3%), and the probability to definitely buy from 10.4% to 12.8% (2.4%). Meanwhile, an increase in coverage distance from 50 km (Scenario 3) to 150 km (Scenario 6 and 10) could increase the probability of buying from 45.6% to 51.4% (5.8%) and the probability of definitely buying from 10.4% to 15.6% (5.2%). Additionally, for a purchase price of IDR 25 million, increasing the coverage distance from 50 km (Scenarios 2 and 9) to 100 km (Scenario 4) will raise the likelihood of buying from 37.0% to 41.2% (4.2%), and the likelihood of definitely buying from 6.6% to 8.2% (1.6%), while the chance of buying and definitely buying rises from 37.0% to 45.2% (8.2%) and from 6.6% to 10.1% (3.5%), respectively, if the coverage distance is increased from 50 km (Scenarios 2 and 9) to 150 km (Scenario 8). Lastly, at a purchase price of IDR 35 million, extending the distance from 50 km (Scenario 5) to 100 km (Scenarios 1 and 11) would improve the probability of buying and definitely buying from 24.2% to 28.3% (4.1%) and from 3.3% to 4.2% (0.9%), respectively, while the increase in distance from 50 km (Scenario 5) to 150 km (Scenario 7) would raise the likelihood of buying from 24.2% to 32.6% (8.4%) and the likelihood of definitely buying from 3.3% to 5.2% (1.9%).
Meanwhile, the likelihood of not buying increases with increasing purchase price. First, for a coverage distance of 50 km, if the purchase price goes from IDR 18 million (Scenario 3) to IDR 25 million (Scenarios 2 and 9), the probability of electric motorcycle buying drops from 45.6% to 37.0% (−8.6%), and the likelihood of definitely buying drops from 10.4% to 6.6% (−3.8%). In another case, if the purchase price increases from IDR 18 million (Scenario 3) to IDR 35 million (Scenario 5), the probability of buying decreases from 45.6% to 24.2% (−21.4%), and the probability of definitely buying drops from 10.4% to 3.3% (−7.1%). Second, in terms of maximum coverage distance up to 100 km, increasing the purchase price from IDR 18 million (Scenario 12) to IDR 25 million (Scenario 4) reduces the likelihood of purchasing from 48.9% to 41.3% (−7.6%) and the likelihood of definitely buying from 12.8% to 8.2% (−4.6%). Meanwhile, the rise in purchase price from IDR 18 million (Scenario 12) to IDR 35 million (Scenarios 1 and 11) reduces the chance of purchasing from 48.9% to 28.3 (−20.6%), and the probability of definitely buying from 12.8% to 4.2% (−8.6%). Last but not least, for the 150 km coverage distance, an increase in the purchase price from IDR 18 million (Scenarios 6 and 10) to IDR 25 million (Scenario 8) would decrease the chance of purchasing from 51.4% to 45.2% (−6.2%) and the probability of definitely buying from 15.6% to 10.1% (−5.5%). On the other hand, the purchase price rise from IDR 18 million (Scenarios 6 and 10) to IDR 35 million (Scenario 7) would decrease the likelihood of purchasing from 51.4% to 32.6% (−18.8%), and the probability of definitely buying from 15.6% to 5.2% (−10.4%).
The adoption of electric motorcycles by MBRH drivers is most likely to occur in Scenarios 6 and 10, where the purchase price is the lowest and the maximum coverage distance is the highest, with 15.6% and 51.4% for definitely buy and buy, respectively. Furthermore, looking into Scenarios 8 and 3, it can be revealed that both scenarios have the same probability values, meaning that the purchase price has a greater impact on MBRH drivers’ adoption of electric motorcycles than the distance traveled.
Figure 2 shows the probability values of electric motorcycle adoption for the rent model. The scenarios in
Figure 2 were sorted from the lowest to the highest value of rental cost, fuel price, battery exchange station, and coverage distance variables. The impact of the battery exchange station placement can be seen in Scenarios 1 and 4, where the likelihood of definitely renting an electric motorcycle will decline as the distance between battery exchange stations increases. When electric motorcycles’ coverage distance is 50 km, the rental rate is IDR 40 thousand, and the fuel price is fixed, the model results revealed that the chance of electric motorcycle renting increases from 52.7% to 54.3% (1.6%), but definitely renting decreases from 32.4% to 27.8% (−4.6%), respectively, when the battery exchange station location distance was changed from less than 10 km (Scenario 1) to more than 10 km (Scenario 4). Furthermore, the probability of renting an electric motorcycle increases, nevertheless, if the location of battery exchange stations is farther away, which is compensated by increasing the maximum coverage distance in Scenarios 5 and 9.
The impact of the fuel price can be seen in Scenarios 3 and 10, where it will increase the probability of definitely renting an electric motorcycle. From
Figure 2, it can be seen that an increase in fuel price affected an increase in the probability of definitely renting by 4.3% (from 27.3% to 31.6%) but a decrease in the probability of renting by 1.4% (54.4% to 53.0%). However, according to Guerra, a rise in fuel costs must be coupled with advancements in battery technology in order to maximize the potential demand for electric motorcycles as an alternative to conventional motorcycles. Although the cost of recharging the battery is less than the cost of refueling, the battery needs to be replaced after several years, and the battery price is still high [
8].
Moreover, in Scenarios 7 and 10, the effect of maximum coverage distance was explored. In these scenarios, the probability of definitely renting an electric motorcycle increases as the maximum coverage distance also increases. The model results show that 8.1% of drivers’ probability to definitely rent electric motorcycles increased from 23.5% to 31.6%, caused by a 50 km increase in the maximum coverage distance of electric motorcycles. Meanwhile, the impact of rental costs is demonstrated in Scenarios 5 and 7, where an increase in rental costs could decrease the likelihood of definitely renting an electric motorcycle. A rental cost rise from IDR 40,000 (Scenario 5) to IDR 60,000 (Scenario 7) would increase the probability of renting from 44.4% to 55.0% (10.6%) but would drop the probability of definitely rent from 47.00% to 23.5% (−23.5%). To conclude, according to the probability values, Scenario 9, followed by Scenario 5, offers the best chance to increase electric motorcycle adoption among MBRH drivers.