Adopting Electric Motorcycles for Ride-Hailing Services: Influential Factors from Driver’s Perspective

Round 1

Reviewer 1 Report

I think in the present form the article looks to be a conference paper and not a journal article, as the contribution and presentation are not well coincide with the title. 

In the introduction, the authors immediately got the side of the use of electric energy for the transport "Unfortunately, the majority of motorcycles used in ride-hailing services are gasoline-powered motorcycles", but a researcher should consider all the variants and select the best one based on scientific methods. For example, the authors never mention such things like the efficiency factor of an electric engine; large losses in the transmission of energy through wires, which entails the transfer of power plants to the nearest suburbs, leaving the cities green and the suburbs polluted; production and disposal of batteries; potentially big queues at electric stations and the inability to quickly refuel at full capacity; possible sharp increase in electricity prices over time once a large number of drivers switch to electric vehicles. These questions are potentially more important in the long term than the speculation about switching to a scooter any time soon. Also, state regulatory factors are not taken into account, for example, in some cities, as far as I know, travel by gasoline scooters was banned and everyone immediately switched to electric because there was no choice.

Also, the article does not answer the question of what to do anyway - to buy or rent (which is put in the title of the article).

The data in 4.1 was not well presented - I would recommend to draw charts with distributions for each parameter.

The methods like the ordered logit model and D-Optimal Design  should be discussed in a dedicated section. Moreover, the researchers should discuss various suitable methods and select the best one based on error loss on training/test data which was not done. Why the ordered logit model was chosen and not a some sort of neural network for example?

Fig.1 and Fig.2 in general are ok, but I (based on the title) would have expected here a figure with bars 'buy / rent / continue to use gasoline'. 

Near a half of thousand respondents could have been a strength of the article, but this number for 243 scenarios should be better justified (the authors cite an article here but the reader would have seen the necessary justifications in the methods section).

Also, personally, the reviewer do not like the inclusion of a region/country in the title, and a very small percentage of women in the survey.

 

Author Response

I think in the present form the article looks to be a conference paper and not a journal article, as the contribution and presentation are not well coincide with the title. 

Dear Reviewer. First of all, thank you for your valuable comments. We agree with your suggestion that our title does not coincide well with the contents. Due to this, we have changed our title to A Stated Preference in Adopting Electric Motorcycles for Ride-Hailing Services: Influence Factors from the Driver’s Perspective. By this, we believe that the title is in line with the research aims, contents, and conclusion.

Furthermore, we also believe that our manuscript has significantly contributed to the electric ride-hailing studies. First, this study focused on motorcycle-based ride-hailing since motorcycles are the dominant vehicles in SouthEast Asia countries like Vietnam and Indonesia. The use of electric motorcycles for ride-hailing services is expected as a starting point for electric motorcycle adoption in Indonesia. To the best of our knowledge, this is the first stated preference study on electric motorcycle adoption in Indonesia, and most likely in Southeast Asian countries, from ride-hailing driver’s perspectives. It should be noted that there are different characteristics in comparing electric car and motorcycle adoption. For example, related to the purchase price. The difference in purchase price between electric and gasoline-powered motorcycles is not as high as the purchase price difference between electric and gasoline-powered cars. Also, related to the refueling times of electric motorcycles, which take much less time than electric cars. Second, applying a rental model instead of a purchase model in adopting electric motorcycles for ride-hailing services becomes one of the novelties of this study. Practically, this study is expected to assist the government and MBRH companies in determining the appropriate policies for encouraging the shift from gasoline-powered to electric motorcycles, which have a positive impact on reducing air pollution and traffic noise. We have written the contribution of our study in the manuscript on page 2 lines 67-91, as follows:

Therefore, this study aims to examine the factors influencing the preferences of MBRH drivers in adopting electric motorcycles. A study showed that involving electric vehicles as a mode option for ride-hailing service is crucial to creating an improved potential commercial future for ride-hailing [32]. Moreover, studies also found that ride-hailing services, not only motorcycle-based but also car-based, have replaced the mode option of private vehicles and public transport in Asian countries [33,34] and American and European countries [35–37]. Although previous studies had also investigated the adoption of electric ride-hailing [24,38,39], those studies focused on car-based ride-hailing. In fact, there are different characteristics between electric cars and motorcycles. For example, the difference in purchase price between electric and gasoline-powered motorcycles is not as high as the purchase price difference between electric and gasoline-powered cars. Also, the electric motorcycles' refueling time is much shorter than electric cars. Due to this, this study is expected could fill the existing literature on electric vehicles from a motorcycle mode context. Furthermore, this study examined the adoption of electric motorcycles for ride-hailing services in two models: buying and renting. A ride-hailing study in the United States also considers the preference to buy, rent or lease a new vehicle, where they found that renting and leasing services offered by ride-hailing companies could attract the drivers to adopt a new car [38]. Currently, vehicle rental is also in great demand in various countries such as European countries, including Spain [40], Italy [41], and Germany [42]. By this, this study is also expected could expand the current knowledge about preferences for electric motorcycles for ride-hailing services in the case of purchasing and renting an electric motorcycle. On a practical level, this study is expected to assist the government and MBRH companies in determining the appropriate policies for encouraging the shift from gasoline-powered to electric motorcycles, which positively impact air pollution [43] and traffic noise [44].

 

In the introduction, the authors immediately got the side of the use of electric energy for the transport "Unfortunately, the majority of motorcycles used in ride-hailing services are gasoline-powered motorcycles", but a researcher should consider all the variants and select the best one based on scientific methods. For example, the authors never mention such things like the efficiency factor of an electric engine; large losses in the transmission of energy through wires, which entails the transfer of power plants to the nearest suburbs, leaving the cities green and the suburbs polluted; production and disposal of batteries; potentially big queues at electric stations and the inability to quickly refuel at full capacity; possible sharp increase in electricity prices over time once a large number of drivers switch to electric vehicles.  These questions are potentially more important in the long term than the speculation about switching to a scooter any time soon. Also, state regulatory factors are not taken into account, for example, in some cities, as far as I know, travel by gasoline scooters was banned and everyone immediately switched to electric because there was no choice.

Thank you very much for raising this issue. We have added some barriers to adopting electric vehicles in our introduction section on page 2 lines 54-66, as the reviewer suggested.

However, previous studies showed many barriers to adopting electric vehicles. For example, the efficiency of the electric engine includes coverage distance and speed. Studies revealed that those factors had become the major weakness of electric vehicles [18–20]. Other barriers were related to electric stations and the inability to refuel at full capacity quickly. Studies concluded that charging station quantity and position in public spaces was crucial for adopting electric vehicles [21–23]. Meanwhile, other studies also showed that refueling times became an essential hurdle for people adopting electric vehicles [24,25]. The next barrier was related to electric vehicle prices, where electric vehicles are more expensive than conventional vehicles [26–28]. Additionally, state regulatory factors also significantly influenced the adoption of electric vehicles. For example, toll exemption, purchase tax reduction/exemption, free/discounted electric charging, preferential bus lane access, free parking, driving privileges, and financial subsidies for electric vehicles significantly increased the de-mand for electric vehicles [20,29–31].

However, since this study focuses more on exploring the influence factors of electric use adoption, the long-term adverse effects of electric vehicles adoption, such as a possible sharp increase in electricity prices over time once a large number of drivers switch to electric vehicles, leaving the cities green and the suburbs polluted, and production and disposal of batteries are excluded. However, we realized that those long-term negative impacts need to be investigated. Due to this, we added a suggestion for future research, as we wrote on page 14 lines 530-533.

Lastly, the long-term adverse effects of electric motorcycles adoption, such as a possible sharp increase in electricity prices over time once a large number of drivers switch to electric vehicles, leaving the cities green and the suburbs polluted, and the production and disposal of batteries, need to be investigated in future studies.

 

Also, the article does not answer the question of what to do anyway - to buy or rent which is put in the title of the article).

Thank you for your clarification. We realized we had made a mistake by writing buy or rent in our manuscript. We mean that we have two models of electric motorcycle adoption for ride-hailing services: (1) purchase model and (2) rent model. The two models were simulated separately and did not correlate with each other. Therefore, Throughout the manuscript, we have removed sentences stating the preference to buy or rent electric motorcycles, including in our title.

However, we can compare the probability value of adopting electric motorcycles between the two models by comparing blue and yellow bars between Figures 1 and 2. The results show that the probability of renting is higher than the probability of buying electric motorcycles. We have written this result on Page 1 lines 22-23 (Abstract) : This study also found that renting electric motorcycles had a better likelihood of adoption than owning them and Page 13 lines 504-506 (Conclusion) : To conclude, by comparing the two models, the likelihood of adopting electric motorcycles by renting is higher than by buying.

 

The data in 4.1 was not well presented - I would recommend to draw charts with distributions for each parameter

Thank you very much for your input. We have added a table summarizing the data. We prefer to choose a table rather than a chart to avoid many charts in our manuscript.

 

The methods like the ordered logit model and D-Optimal Design  should be discussed in a dedicated section. Moreover, the researchers should discuss various suitable methods and select the best one based on error loss on training/test data which was not done. Why the ordered logit model was chosen and not a some sort of neural network for example?

Thank you very much for your clarification. We have added section 3 (method) explaining the D-optimal design or D-efficiency and ordered logit model. In Section 3.1. (Page 4 lines 170-178), we have explained why we used the ordered logit model in our study, as follows.

Almost all studies discussed in the literature review section used a discrete choice model in analyzing the adoption of electric motorcycles, such as a mixed logit model [8,27], binary logit model [28], multinomial logit and random parameter logit model [50], generalized ordered logit model [51]. Studies on electric ride-hailing also used a similar method: the ordered logit model [24] and the multinomial logit model [38]. Due to this, this study applied the ordered logit model in exploring the determinants of electric motorcycle adoption among MBRH drivers. The ordered logit model was also used to determine the influence factors of hybrid car adoption in Indonesian cities and produced a good model fit and satisfactory results [47].

 

Fig.1 and Fig.2 in general are ok, but I (based on the title) would have expected here a figure with bars 'buy / rent / continue to use gasoline'. 

Thank you for your clarification. Similar to our response to the reviewer’s comment no. 2, We realized we made a mistake by writing buy or rent in our manuscript. We mean that we have two models of electric motorcycle adoption for ride-hailing services: (1) purchase model and (2) rent. The two models were simulated separately and did not correlate with each other. Throughout the manuscript, we have removed sentences stating the preference to buy or rent electric motorcycles, including in our title.

We clearly stated in our research method (page 4 lines 179-184)

The ordered logit model is a model used to forecast the likelihood of an occurrence where the dependent variable is based on the discrete choice of ordinal data sets [57]. The dependent variable of this study is the likelihood of MBRH driver-q in adopting an electric motorcycle, with five ordered responses: definitely adopt (j1), adopt (j2), undecided (j3), not adopt (j4), and definitely not to adopt (j5) both by purchasing and renting electric motorcycles.

and questionnaire design (Page 5 lines 203-206)

The second section is an ordered response regarding the desire to buy and rent electric motorcycles. Respondents answered the questions in the second section on a five-point of Likert scale from 1 for definitely not to buy to 5 for definitely buy. A similar level was also applied to electric motorcycle rental cases (1 for definitely not to rent to 5 for definitely rent)

 

Near a half of thousand respondents could have been a strength of the article, but this number for 243 scenarios should be better justified (the authors cite an article here but the reader would have seen the necessary justifications in the methods section).

Thank you very much for raising this issue. We decided to delete this sentence since this sentence is not correlated with our study. It only explains an example of scenarios (number of scenarios) created using factorial design. In fact, we used a D-optimal design in our study.

 

Also, personally, the reviewer do not like the inclusion of a region/country in the title, and a very small percentage of women in the survey.

Thank you for pointing out this issue. We have removed a region and country from the title, as the reviewer suggested. However, related to gender, although both purchase and rent models showed that this variable insignificantly influenced the decision to adopt electric motorcycles, this could be an insight that there were also female MBRH drivers with a tiny percentage. Due to this, we keep to include gender. Understanding the behavior of female MBRH drivers related to safe riding is interesting for future studies. We have written it on page 14 lines 528-530, as follows.

Interestingly, since there were female drivers of MBRH, understanding female riding behavior related to safety issues is appealing to explore in the following research agenda.

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript explores the willingness to adopt electric motorcycles among ride-hailing drivers by specifying the preference for buying or renting in adopting electric motorcycles. In my view, the manuscript would merit favorable consideration following revisions based on the below comments:

 

1/ On page 3, it says “The questionnaire form consists of two sections. The first section is questions related to the respondents’ characteristics including gender, age, education level, and income, and travel characteristics. The second section is an ordered response regarding the desire to buy and rent electric motorcycles.” The authors are encouraged to provide summary statistics for the two sections.

 

2/ The authors are recommended to review additional literature relevant to the survey of ride-hailing drivers, including but not limited to:

https://doi.org/10.1016/j.scs.2020.102307

https://doi.org/10.1016/j.tbs.2020.04.002

 

3/ Why aren't the characteristics of respondents included in the Ordered logit model? It is recommended that the authors include them in the model.

 

4/ Similar to the previous suggestion, if their inclusion leads to unexpected or statistically insignificant findings, the authors are advised to group the responses and test a simple binomial logit model based on adopt versus not adopt, where j1 and j2 could be considered as adopt and j3-j5 as not adopt.

 

5/ Along the same lines as the previous two comments, the authors are recommended to interact price and income, as people with lower incomes are more sensitive to price and, consequently, subsidies; grounding the approach and findings in the literature, including but not limited to:

https://doi.org/10.1016/j.jclepro.2021.126328 

 

Author Response

Dear Reviewer

Thank you for your valuable comments. We have revised our manuscript according to your suggestions.

 

On page 3, it says “The questionnaire form consists of two sections. The first section is questions related to the respondents’ characteristics including gender, age, education level, and income, and travel characteristics. The second section is an ordered response regarding the desire to buy and rent electric motorcycles.” The authors are encouraged to provide summary statistics for the two sections.

Thank you very much for your suggestion. We have added Table 4, showing the descriptive statistics of our respondents, and Table 5, showing the percentage distribution of respondents’ responses on electric motorcycle adoption across the scenario. We also have added an explanation related to Table 4 and Table 5 (Page 7, lines 276-315)

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%) of them had a graduate level or higher. Interestingly, there were 19.71% of university students work in ride-hailing companies as part-time workers. About 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 part-time work as MBRH drivers, had income 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 to 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 to adopt and not to adopt, respectively.

 

The authors are recommended to review additional literature relevant to the survey of ride-hailing drivers, including but not limited to:

https://doi.org/10.1016/j.scs.2020.102307

https://doi.org/10.1016/j.tbs.2020.04.002

Thank you very much for your input. We have added those references to our literature review. We also have added other references concerning electric ride-hailing.

Literature review (Page 3 lines, 139-154)

Meanwhile, literature also shows that many studies explore the adoption of electric ride-hailing. By categorizing the ride-hailing drivers into fuel or hybrid ride-hailing drivers and electric ride-hailing drivers, a study in Shenzhen, China, revealed that fuel cost significantly influences the adoption of electric cars for hybrid ride-hailing drivers but not for electric ride-hailing drivers. Meanwhile, ride-hailing drivers characteristics, including education level, full-time ride-hailing driver, and monthly income, are significantly related to the electric car adoption for ride-hailing services. They also found that vehicle sources consisting of private cars and vehicles on the platform, charging stations, charging duration, and coverage distance significantly affect the adoption of electric ride-hailing [24]. Moreover, a study conducted in the United States revealed that younger ride-hailing drivers who travel often and own more automobiles are more likely to convert to fuel-efficient vehicles. Meanwhile, those who have a postgraduate level, live in urban areas, and under 48 years old, are more pro-fuel-efficiency than their counterparts [38]. Another study on electric ride-hailing also showed that, similar to private electric cars, charging infrastructures for ride-hailing become one of the essential determinants for electric car adoption [53,54].

 

Why aren't the characteristics of respondents included in the Ordered logit model? It is recommended that the authors include them in the model.

Thank you very much for your input. We have included the characteristics of respondents in the ordered logit model (see Table 6), and discuss the findings (page 10, lines 353-364)

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 to 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 [38]. 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.

 

Similar to the previous suggestion, if their inclusion leads to unexpected or statistically insignificant findings, the authors are advised to group the responses and test a simple binomial logit model based on adopt versus not adopt, where j1 and j2 could be considered as adopt and j3-j5 as not adopt.

Thank you very much for your suggestion. We have added the binomial logit model instead of the ordered logit model (see Table 6) and discuss the findings (Page 10, lines 372-382)

However, since 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 to adopt was merged to not adopt. As shown in Table 6, although the binomial logit model produced a higher value of Pseudo R² (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.

 

Along the same lines as the previous two comments, the authors are recommended to interact price and income, as people with lower incomes are more sensitive to price and, consequently, subsidies; grounding the approach and findings in the literature, including but not limited to: https://doi.org/10.1016/j.jclepro.2021.126328 

Thank you very much for your input. We have included income/purchase price and income/rent cost in our model (see Table 6), and we have discussed the findings (page 10, lines 365-371)

This study also considered a variable that interacts between 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 [63]. 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 to ride-hailing companies.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I see that the authors mostly addressed my comments.

The title has been changed so now I feel the text coincides with the main research problems stated in the title and abstract. 

It can be published in the current form, I have no objections.

However, I can suggest to shorten the new title; write some analysis text about links [18–31], because the authors mostly cite them without writing a short conclusion about each cited article. 

Author Response

I see that the authors mostly addressed my comments. The title has been changed so now I feel the text coincides with the main research problems stated in the title and abstract.  It can be published in the current form, I have no objections.

Dear Reviewer. Thank you very much for your appreciation.

 

However, I can suggest to shorten the new title

Thank you very much for your input. I have shortened the title:  The Adoption of Electric Motorcycles for Ride-Hailing Services: From Driver’s Perspective

 

Write some analysis text about links [18–31], because the authors mostly cite them without writing a short conclusion about each cited article. 

Thank you very much for pointing out this issue. We have written a short conclusion about each cited article (Page 2, lines 54-74)

However, previous studies showed many barriers to adopting electric vehicles. An investigation revealed that refueling times and limited driving range of electric vehicles had become the reasons for people in the Netherlands to keep owning gasoline-powered cars rather than adopting electric cars [18]. Meanwhile, a study in Norway revealed that the charging station quantity significantly affected the growth of electric vehicle adoption [19]. A similar finding was also discovered in France, where it was said that the position of charging stations in public spaces impacted the adoption of electric vehicles [20]. Supporting other studies, a study in the United States explored that battery range, cost, charging stations, and safety are the significant concerns, ordered from the highest to lowest, in adopting electric vehicles [21]. Additionally, state regulatory factors also significantly influenced the adoption of electric vehicles. A study showed that exemptions from the value of tax (VAT) and purchase tax could significantly increase the demand for electric vehicles in Norway [22]. Meanwhile, policy incentives for electric vehicles, including free/discounted electric charging, purchase tax reduction/exemption, and free parking, effectively stimulated the willingness to buy electric vehicles in China [23]. The study also found that driving privileges for electric car drivers, such as free access to bus lanes and driving restriction rescission, could also increase the adoption of electric vehicles [23]. A similar result was found by Li et al. [24], showing that financial subsidies, driving privileges, preferential tax, and free parking for electric vehicles increased the number of consumers of electric cars. However, another study revealed that not all electric car consumers were affected by bus lane driving privileges and toll exemptions [22].