sustainability

Article Perception Analysis of E-Scooter Riders and Non-Riders in Riyadh, Saudi Arabia: Survey Outputs

Mohammed Hamad Almannaa 1,* , Faisal Adnan Alsahhaf 1 , Huthaifa I. Ashqar 2 , Mohammed Elhenawy 3, Mahmoud Masoud 3 and Andry Rakotonirainy 3

1 Department of Civil Engineering, College of Engineering, King Saud University, P. O. Box 800, Riyadh 11421, Saudi Arabia; [email protected] 2 Booz Allen Hamilton, Washington, DC 20003, USA; [email protected] 3 Centre for Accident Research and Road Safety, Queensland University of Technology, Brisbane, QLD 4059, Australia; [email protected] (M.E.); [email protected] (M.M.); [email protected] (A.R.) * Correspondence: [email protected]

Abstract: This study explores the feasibility of launching an e-scooter sharing system as a new micro- mobility mode, and part of the public transportation system in the city of Riyadh, Saudi Arabia. Therefore, survey was conducted in April 2020 to shed light on the perception of e-scooter systems in Riyadh. A sample of 439 respondents was collected, where majority indicated willingness to use the e-scooter sharing system if available (males are twice as likely to agree than females). Roughly 75% of the respondents indicated that open entertainment areas and shopping malls are ideal places for e-scooter sharing systems. Results indicated that people who use ride-hailing services such as Uber, expressed more willingness to use e-scooters for various purposes. The study found that the major obstacle for



deploying e-scooters in Saudi Arabia is the lack of sufficient infrastructure (70%), followed by weather
 (63%) and safety (49%). Moreover, the study found that approximately half of the respondents believed that COVID-19 will not affect their willingness to ride e-scooters. Two types of logistic regression models Citation: Almannaa, M.H.; Alsahhaf, F.A.; Ashqar, H.I.; Elhenawy, M.; were built. The outcomes of the models show that gender, age, and using ride-hailing services play Masoud, M.; Rakotonirainy, A. an important role in respondents’ willingness to use e-scooter. Results will enable policymakers and Perception Analysis of E-Scooter operating agencies to evaluate the feasibility of deploying e-scooters and better manage the operation of Riders and Non-Riders in Riyadh, the system as an integral and reliable part of public transportation. Saudi Arabia: Survey Outputs. Sustainability 2021, 13, 863. Keywords: user perception; e-scooter sharing system; Saudi Arabia; micro-mobility; mobility-as-a- https://doi.org/10.3390/su13020863 service; COVID-19

Received: 14 November 2020 Accepted: 13 January 2021 Published: 16 January 2021 1. Introduction As traffic congestion continues to increase dramatically, policymakers and researchers Publisher’s Note: MDPI stays neutral have been working relentlessly to provide the transportation sector with solutions that with regard to jurisdictional claims in will mitigate traffic congestion issues. One solution that has emerged in recent years is the published maps and institutional affil- introduction of micro-mobility modes, such as two- or three-wheeled e-scooters and bike iations. sharing systems. These new systems have gained attention due to their quick and effective impact in attracting more users in such a relatively short time. Specifically, e-scooter sharing systems started in early 2012 when Scoot Networks released a moped-style vehicle that provided a short-range rental of scooters [1]. The venture bloomed remarkably after 2017, Copyright: © 2021 by the authors. thus beating other transportation-sharing services, such as bike sharing systems. Licensee MDPI, Basel, Switzerland. In 2017, e-scooter sharing systems have reached more than 85 cities worldwide with This article is an open access article more than 1.8 million registered users [2]. In 2018, e-scooter systems have exceeded dock- distributed under the terms and less and dock-based bike sharing systems in terms of number of total trips [3]. Only in conditions of the Creative Commons Attribution (CC BY) license (https:// the USA, for example, approximately 38.5 million trips were made using e-scooter sharing creativecommons.org/licenses/by/ systems in 2018. The usage proliferation of e-scooter systems (i.e., compared with other 4.0/).

Sustainability 2021, 13, 863. https://doi.org/10.3390/su13020863 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 863 2 of 24

public transportation systems) has attracted the attention of policymakers, investors, and researchers to investigate user acceptance across regions and enable further expansion [3]. As e-scooter sharing systems continue expanding and reaching new cities and countries worldwide, policymakers and investors in certain countries who attempt to adopt the system are facing cultural, climatic, operational, economical, and safety concerns. For example, Saudi Arabia faces several potential challenges, which may affect the introduction of such a system in the country, such as the extremely hot weather in summer (~45 ◦C), cultural barriers to the use of public transportation services, and lack of regulation and sufficient infrastructure, such traffic lanes dedicated for e-scooters. Although they have not yet been legislated, two startup companies, namely, Wee and DarbRide, performed two pilot testing studies on e-scooter sharing systems. Pilot testing was conducted only during a limited time throughout the day (i.e., from sunset until midnight) in late 2019 in specifically closed areas, such as the Diplomatic Quarter and Riyadh Front (large shopping center). The study aims to investigate the perception of e-scooter users and non-users in the city of Riyadh, Saudi Arabia. Therefore, survey was conducted in April 2020 to shed light on the perception of e-scooter systems in Riyadh. This research work has four main contributions: (1) shed light on how people living in Riyadh, Saudi Arabia perceive the new micromobility mode, (2) investigate the experience of Saudi users of e-scooter sharing system inside and/or outside of Saudi Arabia, (3) explore how various socioeconomic and demographic factors may influence attitude toward the deployment and use of the e- scooter sharing system in Saudi Arabia, and (4) provide knowledge on how the COVID-19 situation influences travelers’ choice if the e-scooter sharing system becomes available.

2. Literature Survey Extensive research that investigated the potential benefits and behavior of uses of e-scooter sharing systems have recently emerged [4–8]. Anderson-Hall et al. endeavored to draft policies regulating the new micromobility modes in the USA by summarizing contemporary news articles and professional reports [5]. The conclusion of the study provided cities and policymakers with information on the better management of the negative and positive impacts of the new transportation mode. Smith and Schwieterman explored the ability of e-scooter sharing systems to attract a portion of the daily trips that occur in Chicago neighborhoods [4]. The authors examined how travel time and cost of other modes of transportation would be influenced if the new micromobility mode is widely used in the city. The study analyzed 30,000 randomly selected hypothetical trips in the city and showed that using e-scooters for short trips would significantly compete with personal vehicles but fail with long trips. Studies that analyzed the perception of e-scooter users based on surveys across coun- tries and regions are relatively few. Aguilera-Gracia et al. investigated the benefits and obstacles of the e-scooter sharing system through an online survey distributed to people in Spain [9]. The authors planned to anticipate the number of potential users before launching the service and found that age and level of education play a significant role in targeting potential users. Fitt and Curl conducted a survey on perception about e-scooters in five cities in New Zealand [10]. The forecasted the number, behavior, and perception of early users and how these factors may change over time. The study collected 591 valid responses, and results indicated that 25% of e-scooter users used an e-scooter at one time, whereas 75% used e-scooters more than once. The findings illustrated concerns about safety, cost, and suitable clothing when using the e-scooter sharing system. In Taiwan, Huang and Lin conducted a survey to investigate the acceptance of long-term use of scooter rides [11]. A total of 198 e-scooter commuters participated in the survey, and results showed that hedonic quality has a positive impact on user experience. Additionally, the authors found that age and gender influence e-scooter usage behavior. Moreover, Chen et al. investi- gated the effect of e-scooter sharing systems on economic benefit, locational benefit, and sustainability [7]. An online survey was collected solely from the students of a Taiwanese University. The results demonstrated that e-scooter sharing systems are positively related Sustainability 2021, 13, 863 3 of 24

to the abovementioned benefits. In the USA, James et al. conducted a survey in Rosslyn, Virginia among 181 e-scooter riders and non-riders with the goal of measuring perception and experience [12]. The researchers found that perception changes based on whether the respondent has used the e-scooter or not. The survey outputs indicated that non-riders lack knowledge regarding laws and regulations on e-scooters, whereas the opposite is true for e-scooter riders. Laa et al. assessed the socio-economic characteristics and usage of both renters and owners of e-scooters in Australia [13]. They found the age, gender, and education background are significant factors in the e-scooter’s usage. People who are young, male and highly educated tend to use e-scooters more than others. Sanders et al. investigated the barriers and benefits of e-scooter usage inside a university town [14]. Their study shows that the e-scooter is seen by the majority of responses as a convenient transportation mode, particularly in in a hot weather and compared to walking. Additionally, they found that the demographic characteristics of users are a significant barrier in front of users as their findings indicate African American and non-white Hispanic are more likely to use e-scooters than non-Hispanic white. Furthermore, women respondents were found to be concerned with the safety risk of riding e-scooters. To the best of our knowledge, the study is the first to endeavor to understand the perception regarding e-scooters from the context of Saudi Arabia. The researchers are hopeful that the research findings can be beneficial for policymakers, researchers, and entrepreneurs who plan to invest in Saudi Arabia through micromobility modes. In addition to investigating the perception of e-scooter use inside and outside Saudi Arabia, the study explored the attitudes of users regarding the impact of COVID-19 on e-scooter sharing systems. Lastly, the study provides a discussion and final recommendation for policymakers, operating agencies, researchers, and potential users.

3. Methodology 3.1. Dataset The survey was designed with two main sections, namely, socioeconomic and de- mographic (9 questions) as well as mobility and e-scooter perception (17 questions) as presented in Table1. The twenty-nine questions were distributed in four sections as follows: Section one obtains the demographic information of respondents; Section two discusses the experience of previous e-scooter users; Section three discusses the willingness-to-use and the potential purpose for new e-scooter users; Section four includes six general questions with regard to safety and obstacles of e-scooter sharing systems, COVID-19 impact, and potential places for e-scooters in Saudi Arabia. Respondents first answer eight demographic questions, followed by indicating their previous experience with e-scooters. Respondents were moved automatically to Section2 if they had previous experience with e-scooters and to Section3, if not. In the last section (Section4), six general questions were given for all respondents.

Table 1. Questionnaire.

Q. Section1—Background 1 Nationality 2 Marriage Status 3 Age 4 Gender 5 Professional occupation 6 Income 7 Education background 8 Workout Sustainability 2021, 13, 863 4 of 24

Table 1. Cont.

Q. Section1—Background 9 Do you own a car? 10 Are you a user of cab-hailing apps 11 Do you ride a cycle on a regular basis? 12 Do you ride a motorcycle on a regular basis? 13 Do you ever ride a shared e-scooter? Section2 (only if said Yes to Q.13) 14 Where did you use it? (inside or outside Saudi Arabia)? 15 What was the purpose of your usage? 16 Have you had a near-crash incident when riding the shared e-scooter? 17 Have you had a crash when riding the shared e-scooter? Section3 (only if said No to Q.13) 18 If you have found a shared e-scooter, would you use it? 19 What would be the purpose of that usage? If the price of the trip using ride-hailing apps (Uber, Careem, etc.) is higher than the price of using a shared e-scooter, 20 will you use the shared e-scooter? Section4 21 Do you think the e-scooter sharing system is a safe mode? 22 What are the obstacles of e-scooter sharing systems in launched in Saudi Arabia? As you know about the spread of the new Corona virus around the world, what is the impact of the spread of such 23 infectious diseases on your decision to use the shared e-scooter? If the companies operating the shared e-scooter took the precautionary and preventive measures with regard to 24 COVID-19, do you think that would remove the fears of their use? (Optional) question: What measures or precautions can operators of shared e-scooter systems take to reduce fears of 25 these infectious diseases? (Optional) question: What places would you suggest that the e-scooter sharing systems to launch within the Kingdom 26 of Saudi Arabia?

Google Forms was used to build the survey, which was then distributed in Riyadh through word of mouth, Riyadh social media accounts, and the list of users of the two e-scooter companies in Riyadh. No incentives were provided. Only responses from adults were considered (18 aged and above). A total of 439 complete responses were received. The minimum sample size needed to estimate the true population proportion with a 95% confidence level and a 5% marginal error, which were used to determine a statistically representative sample size for the population is about 385 (i.e., sample size = 439 > 385). This was done using the following equation [15]:

N × X min required sample size = (1) X + N − 1

= 2 × × (1−p) where N is the population size (here is 4.7 million), X Za/2 p MOE2 , Za/2 is the critical value of the Normal distribution at a/2, MOE is the margin of error, and p is the sample proportion. We should highlight that this sample size is the same as the one obtained using the Krejcie and Morgan’s formula [16]. As the sample was established, the researchers then ensured that the key socioeco- nomic and demographic indicators of the survey respondents are comparable to those of the Riyadh population over age of 18 (i.e., about 4.7 million) [17], if possible. In Table2, the socioeconomic and demographic of our sample and the Saudi population are presented. It Sustainability 2021, 13, 863 5 of 24

is noticed that there are some deviations from the characteristics of the population such as nationality and monthly income. Preliminary analysis indicates that approximately 18% of the respondents have previously used e-scooters at least once, in which 63% used it outside of Saudi Arabia.

Table 2. The socioeconomic and demographic characteristics of the respondents compared to the population [17].

Percent (%) Category Subcategory Sample Population Saudi 98.4 62 Nationality Non-Saudi 1.6 38 Male 66.7 60 Gender Female 33.3 40 Single 34.4 32 Marriage Status Married 65.6 68 18–30 48 32 31–45 40 41 Age 46–60 10 22 >60 2 5 SR3500 ($933) or less 37 31 More than SR3500 ($933) and less than SR7000 ($1866) 7 Monthly Income More than SR7,000 ($1866) and less than SR13,000 ($3466) 15 39 More than SR13,000 ($3466) and less than SR20,000 ($5333) 25 30 More than SR20,000 ($5333) 16 20 High School 14 54 Diploma 5 11 Educational Background Bachelor 55 32 Master 14 2 PhD 12 1 Student 29.8 Employed—Public Sector 41.5 Employed—Private Sector 8.7 Professional Occupation Freelancer 5.2 Retired 3.2 Unemployed 11.6 Daily 16.2 Several days a week 36.2 Sport Activity Once a week 18.2 At least once a month 14.8 Never 14.6 Own a car 72.9 Car Ownership Does not own a car 27.1 Sustainability 2021, 13, 863 6 of 24

Table 2. Cont.

Percent (%) Category Subcategory Sample Population Daily 1.6 Weekly 8.0 Ride-hailing Systems Usage Monthly 10.2 Rarely 59.0 Never 21.2 Frequent user of bikes 8.0 Bike Usage Does not use bikes frequently 92.0 Frequent user of motorcycles 2.3 Motorcycle Usage Does not use motorcycles frequently 97.7

3.2. Adopted Models In this research effort, two types of logistic regression models were used in the analysis depending on the type of response variables. Details are given in the following subsections.

3.2.1. Binary Logistic Regression Model A logistic regression model was adopted for binary response variables [18]. The response is assumed to come from a Bernoulli distribution with two potential outcomes e.g., will/will not use e-scooters if the ride-sharing trips are more expensive (usage because of price) or will/will not use e-scooters in the future. This model is capable of accounting for the interactive and confounding factors. The logistic regression model can be formulated as follows:

 1 usage because of price (or will use them in future) Y = , (2) i 0 no usage (or they will not use them in future)

i = 1, . . . , I

Yi ∼ Bernoulli (pi)

where I is the number of respondents; pi is probability of Yi = 1. A logit link function was used to link the probability of having a previous usage of e-scooters (pi) with a set of covariates as shown below.   pi logit(pi) = log = β0 + β1X1i + β2X2i + ... + βkXki, (3) 1 − pi

where Xki is the variable for potential factor k, and βk is the corresponding regression coefficient. Several factors were included in the model, including gender, income, sport, and previous e-scooter usage, and the usage of bike. More details are given in the Results Section.

3.2.2. Ordinal Logistic Regression Model The ordinal logistic regression model was used when the response is categorical and includes many levels that have a natural ordering. For example, a response variable may include “No”, “Maybe”, and “yes” as answers to a question about whether e-scooter is safe. However, these responses can be ordered in levels so that “No” is lower than “Maybe”, which in turn is lower than “Yes” regarding the scale of agreement with the question. Therefore, this natural ordering can be incorporated into the model using different logit transformations of the response probabilities. One possible logit transformation method is using the cumulative probabilities (i.e., P(Y ≤ j) = π1 + ··· + πj where j = 1, . . . , J and P(Y ≤ J) = 1) based on the natural ordering of the response levels. In this study, we used the proportional odds Sustainability 2021, 13, 863 7 of 24

model that linearly explains the variability in the cumulative probabilities in terms of the explanatory variables’ variations. The proportional odds model is formulated as follows:  P(Y ≤ j)  log = logit(P(Y ≤ j)) = β + β x + ··· + β x , j = 1, . . . , J − 1 (4) P(Y > j) j0 1 1 p p It is worth noting that the number of equations describing this model equals the number of the response levels minus one. Moreover, these equations have the same slopes Sustainability 2021, 13, x FOR PEER REVIEW 7 of 22 for each category j (but they have different intercepts), which means that explanatory variables have the same effect independent of the P(Y ≤ j) that creates the log odds.

4. AreaAs ofmentioned Study above, Riyadh was chosen for the study given it is the capital and larg- est cityAs of mentioned the Kingdom above, of Saudi Riyadh Arabia was chosen (Figure for 1).the It is study located given in the it is central the capital region and of Saudilargest Ar cityabia of covering the Kingdom a total of area Saudi of Arabia3115 square (Figure kilometers1). It is located at 600 in m the above central the regionsea level. of Riyadh’sSaudi Arabia climate covering is very a total hot areain summer of 3115 andsquare cool kilometers in winter at with 600 m low above humidity the sea levels level. aroundRiyadh’s the climate year. isAccording very hot into summer“Riyadh, and Oasis cool of in Heritage winter with and lowVision” humidity by Peter levels Harrigan, around therethe year. are significant According temperature to “Riyadh, fluctuations Oasis of Heritage between and day Vision” and night by Peter [19]. Harrigan,In the summer, there theare average significant temperature temperature rises fluctuations to 43 °C during between the day day and and night 28 °C [ 19at]. night. In the In summer, the winter, the Riyadhaverage climate temperature is cool rises with to temperature 43 ◦C during dropping the day and to 28an ◦averageC at night. of 21 In °C the during winter, the Riyadh day andclimate 9 °C is at cool the with night temperature and sometimes dropping it gets to below an average zero. ofAverage 21 ◦C duringannual the rainfall day andis about 9 ◦C 137at the mm night [19] and. According sometimes to itthe gets Royal below Commission zero. Average for annualRiyadh rainfallCity Population is about 137 Study mm con- [19]. ductedAccording in 2016, to the Riyadh Royal Commissionhas a population for Riyadh of 6,506,700 City Populationpersons; 26.6% Study of conductedwhich are inof 2016,ages underRiyadh 15 has years a population old, 69.2% ofare 6,506,700 of ages between persons; 15 26.6% and 59, of and which 4.2% are are of of ages ages under 60 and 15 above years [old,20]. 69.2%In the areperiod of ages between between 200415 and and 2016, 59, the and number 4.2% are of of internal ages 60 migrants and above to Riyadh [20]. In city the wasperiod about between 276,000, 2004 mainly and 2016, for thework number opportunities of internal (75.4%). migrants Most to RiyadhSaudi males city was wear about the traditional276,000, mainly Saudi for dress work (Thobe opportunities), which is (75.4%). a loose white Most Saudidress, malesyet many wear wear the casual traditional and sportSaudi clothes dress (Thobe), during non which-work is a times. loose Females white dress, in Riyadh yet many wear wear Abayas casual which and loosely sport clothes cover theduring body. non-work times. Females in Riyadh wear Abayas which loosely cover the body.

Figure 1. City o off Riyadh, study area [21].].

According to Omar Alotaibi asnd Dimitris Potoglou, the first Master plan of Riyadh, which was developed by Doxiadis Associates (Greek consulting firm) in the 1960s consid- ered private cars as the main transportation mode in the city [21]. Accordingly, Riyadh does not have a sufficient public transport system. In the 1990s, the Ministry of Municipal and Rural Affairs developed the Metropolitan Development Strategy for Riyadh (MED- STAR). The new strategy aims to manage the city development for the next 50 years. MED- STAR introduced an implementation plan for the authorities working in the city. In addi- tion, MEDSTAR has identified key transport strategies in Riyadh including the develop- ment of sufficient public transport system, the alignment of higher density development Sustainability 2021, 13, 863 8 of 24

According to Omar Alotaibi asnd Dimitris Potoglou, the first Master plan of Riyadh, which was developed by Doxiadis Associates (Greek consulting firm) in the 1960s consid- ered private cars as the main transportation mode in the city [21]. Accordingly, Riyadh does not have a sufficient public transport system. In the 1990s, the Ministry of Municipal and Rural Affairs developed the Metropolitan Development Strategy for Riyadh (MEDSTAR). The new strategy aims to manage the city development for the next 50 years. MEDSTAR introduced an implementation plan for the authorities working in the city. In addition, MEDSTAR has identified key transport strategies in Riyadh including the development of sufficient public transport system, the alignment of higher density development along major public transport spines, the development of the ring road system to support the congested central arterial roads. Furthermore, the improvement of traffic management practices in Riyadh, the introduction of demand management measures and the develop- ment of an integrated corridor management program to ensure that major corridors serve the proposed public transport network and advanced traffic management schemes [20]. According to the Royal Commission for Riyadh City Population Study (2016), the total number of housing units in Riyadh is 1,217,996 housing units; 52% of which are villas and 42% are apartments [20]. Private cars are the major transportation mode in Riyadh (77.2%), followed by buses/vans (16%). There are more than 2 million registered vehicles in Riyadh. The average car ownership in the city is 1.9 car per household. Private cars are the major transportation mode in Riyadh (77.2%), followed by private buses/vans (16.1%), taxis/limousines (3.7%), public buses (2.2%), heavy trucks (0.8%). The average car ownership in the city is 1.9 car per household with more than 2 million registered cars in Riyadh. [20]. Riyadh Metro project, which started construction in 2014, is planned to offer public transport services with a network of six lines that has a total length of 176 km and 85 stations around the city in integration with Riyadh Bus Network [22]. Riyadh Metro project was reported to be 90% complete by the end of 2019, and test runs of several Metro lines were conducted in 2019 and 2020. Riyadh Metro is planned to be fully operational by 2021. In 2019, Riyadh Grand projects were announced aiming to improve air quality, reduce temperature, encourage healthy lifestyles and improve the quality of life in the city. The four projects managed by the Royal Commission for Riyadh City are “King Salman Park Project”, “Green Riyadh Project”, “Sports Boulevard Project” and “Riyadh Art Project”. We hypothesize that these giant projects will create a last-mile transportation problem and thus micro-mobility transportation modes will be needed in the near future.

5. Results In this section, we present the questionnaire’s outputs using two approaches: descriptive and statistical analysis. In the descriptive approach, we explored and summarized the survey outputs using percentage of response and distribution to show the main findings of the survey. In the statistical analysis approach, we studied the relationship between selected responses and socioeconomic and other information using two logistic regression models, namely: binary and ordinal logistic regression models. We added all the twelve variables (that we collected from the survey) as regressors in the models. The twelve regressors are age, gender, income, sport activity, educational background, professional occupancy, marriage status, car ownership, bike usage, motorcycle usage, ride-hailing apps usage, and previous e-scooter usage. More information is given in the following two subsections.

5.1. Descriptive Results Table3 summarizes the distribution of using e-scooters between respondents in Saudi Arabia in terms of several categories. Here are some important observations to note. The survey shows that 40% of the respondents have never used an e-scooter for any purpose and are unfamiliar with it, whereas 42% of the respondents, although familiar with e- scooters, have never used it before. However, only approximately 10% reported having used an e-scooter at least once and roughly 8% of the respondents have used e-scooters more than once. In addition, the survey shows that roughly 63% of e-scooter users have Sustainability 2021, 13, 863 9 of 24

used them outside Saudi Arabia, whereas the opposite is true for the remaining 32%. The survey investigated the potential purposes of e-scooter use in Saudi Arabia by instructing the respondents to answer the related question in a multiple answer format. In other words, several respondents may have used (or will have used) e-scooters for various reasons and not necessarily for one purpose. This means that the response rates may not equal to 100%. Sustainability 2021, 13, x FOR PEER REVIEW 9 of 22 Results of the potential purposes of using e-scooters are summarized in Figure2. It is worth noticing that entertainment was the major (potential) purpose for using e-scooter sharing systems in Saudi Arabia. Notably, roughly 16.9% of non-user respondents are willing to useusing e-scooters e-scooter for sharing reasons systems other thanin Saudi the Arabia. abovementioned. Notably, roughly Respondents 16.9% of who non- useduser re- them onlyspondents once came are willing next with to use 8.8% e-scooters followed for byreasons frequent other users than atthe only abovementioned. 2.2%. This finding Re- indicatesspondents that who those used who them never only used once e-scooterscame next previouslywith 8.8% followed expressed by morefrequent enthusiasm users at to useonly e-scooters 2.2%. This for finding new potential indicates purposes that those that who should never be useddetermined e-scooters and previously implemented ex- by operators.pressed more The enthusiasm following subsections to use e-scooters investigate for new in potential details four purposes main that findings. should be de- termined and implemented by operators. The following subsections investigate in details Table 3. Distribution offour e-scooter main findings. users between respondents in Saudi Arabia in terms of several categories.

CategoryTable 3. Distribution of e-scooter users Subcategorybetween respondents in Saudi Arabia in terms Percent of several (%) categories. Never seen an e-scooter 40 Never used an e-scooter but familiar with it 42 General use of e-scooters Category Subcategory Percent (%) UsedNever e-scooter seen at leastan e- oncescooter 40 8 Never used an e-scooter but fa- Used e-scooter several times42 10 General use of e-scooters miliar with it In Saudi Arabia 32 Used e-scooter at least once 8 Location of using e-scooter Out Saudi Arabia 63 Used e-scooter several times 10 In and outIn Saudi Saudi ArabiaArabia 32 5 Location of using e-scooter WouldOut use Saudi e-scooter Arabia 63 19 Willingness to use e-scooter WouldIn neverand out use Saudi e-scooter Arabia 5 27 Would use e-scooter 19 May use e-scooter 54 Willingness to use e-scooter Would never use e-scooter 27 Male will use e-scooter 53 May use e-scooter 54 Male used e-scooter 20 Gender of e-scooter users and potential users Male will use e-scooter 53 Gender of e-scooter users and FemaleMale will used use e-scooter e-scooter 20 24 potential users FemaleFemale used will e-scooter use e-scooter 24 3 Female used e-scooter 3

100% 93.2% 90% 79.4% 80% 68.7% 70% 60% 50% 40% 30% 20.5% 16.9% 20% 14.1% 13.6% 12.2% 10.2% 7.8% 9.1%8.8% 8.8% 10% 6.8% 2.9% 2.3% 0.0% 0.0% 0% Entertainment Home-work Switching to Shopping Visiting Other purposes commuting another mode of family/friends transportation

Used several times Used once Never used but willing to

Figure 2. Various (potential) purposes for using e-scooter sharing systems in Saudi Arabia. Figure 2. Various (potential) purposes for using e-scooter sharing systems in Saudi Arabia.

5.1.1. Safety of E-Scooters A part of the survey was dedicated to the investigation of the respondents’ experi- ence and perception of the safety of e-scooters. A total of 19% of respondents who have previously used e-scooters have been involved in crashes related to e-scooters (this type of crashes can be assigned into one or multiple categories, such as an e-scooter hitting a Sustainability 2021, 13, 863 10 of 24

5.1.1. Safety of E-Scooters A part of the survey was dedicated to the investigation of the respondents’ experi- ence and perception of the safety of e-scooters. A total of 19% of respondents who have previously used e-scooters have been involved in crashes related to e-scooters (this type of crashes can be assigned into one or multiple categories, such as an e-scooter hitting a pedestrian or vehicle/fixed object or being hit by a vehicle and high-risk or reckless behavior by an e-scooter rider that may have directly or indirectly caused an accident). Additionally, 38% of the same respondent category were involved in a near-miss crash (i.e., an unplanned event that has the potential to cause but does not actually result in human injury, environmental, or equipment damage, or an interruption to normal operation [23]. These respondents indicated that 51% of the crashes and/or near-miss crashes took place inside Saudi Arabia, whereas the remainder occurred outside Saudi Arabia. This relatively high percentage of e-scooter crash involvement is backed by recently published reports and studies such as Namiri et al. [24]. Exploring the perception of e-scooter safety, the majority of respondents (82%) who have previously used e-scooters reported that they consider e-scooters a safe or may be a safe mode of transportation, whereas only 18% consider e-scooters as unsafe. On the contrary, approximately 63% of the respondents who have never used an e-scooter expect that e-scooters will be safe or at least may be safe, whereas more than the third anticipate that e-scooters will unsafe. However, 90% of the respondents who expect or consider e-scooters an unsafe mode of transportation have never ridden one, 5% have ridden at least one inside Saudi Arabia, and 4% have ridden at least one outside Saudi Arabia. Only 1% has used an e-scooter inside and outside Saudi Arabia and still considers e-scooters unsafe.

5.1.2. Obstacles to Deployment of E-Scooters The study explored the obstacles that may delay the deployment of e-scooter systems or impede the usage of such systems in Saudi Arabia. Toward this end, multiple answer questions were used. In other words, the response rates may not add up to 100%. Respon- dents who have used e-scooters reported that insufficient infrastructure, weather, safety, low speed, discomfort, satisfaction of needs, culture, and cost are a few of the obstacles that faces the deployment and usage of e-scooters. Similarly, respondents who have never used an e-scooter indicated that they expect the same obstacles but, to a certain extent, with different response rates for several of the abovementioned obstacles, as shown in Figure3. The respondents were enabled to add other obstacles as an option. Respondents added two main obstacles, namely, wearing traditional dresses and age of the user, which could be linked to culture as well.

5.1.3. E-Scooters and Ride-Hailing The study investigated the relationship between e-scooter systems as micromobility trend and ride-hailing systems (e.g., Uber/Careem) in Saudi Arabia. The recent years have witnessed the introduction of different mobility network trends, such as MaaS. The introduction of these mobility solutions may enable them to become more integrated in the public transportation system in the near future [25]. Approximately 82% of the respondents who frequently use ride-hailing services have used and/or are willing to use e-scooters if available, whereas only around 18% have not and are unwilling to use e-scooters. However, the percentage of those who have used and/or are willing to use e-scooters decreased to 77% for non-frequent users of ride-hailing services. Economically, approximately 54% of respondents, out of which only 20% are frequent users of ride-hailing services, stated that they will use or continue to use ride-hailing systems even if they will cost more than e-scooters. However, 46% will use an e-scooter system instead if it costs less. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 22

pedestrian or vehicle/fixed object or being hit by a vehicle and high-risk or reckless be- havior by an e-scooter rider that may have directly or indirectly caused an accident). Ad- ditionally, 38% of the same respondent category were involved in a near-miss crash (i.e., an unplanned event that has the potential to cause but does not actually result in human injury, environmental, or equipment damage, or an interruption to normal operation [23]. These respondents indicated that 51% of the crashes and/or near-miss crashes took place inside Saudi Arabia, whereas the remainder occurred outside Saudi Arabia. This rela- tively high percentage of e-scooter crash involvement is backed by recently published re- ports and studies such as Namiri et al. [24]. Exploring the perception of e-scooter safety, the majority of respondents (82%) who have previously used e-scooters reported that they consider e-scooters a safe or may be a safe mode of transportation, whereas only 18% con- sider e-scooters as unsafe. On the contrary, approximately 63% of the respondents who have never used an e-scooter expect that e-scooters will be safe or at least may be safe, whereas more than the third anticipate that e-scooters will unsafe. However, 90% of the respondents who expect or consider e-scooters an unsafe mode of transportation have never ridden one, 5% have ridden at least one inside Saudi Arabia, and 4% have ridden at least one outside Saudi Arabia. Only 1% has used an e-scooter inside and outside Saudi Arabia and still considers e-scooters unsafe.

5.1.2. Obstacles to Deployment of E-Scooters The study explored the obstacles that may delay the deployment of e-scooter systems or impede the usage of such systems in Saudi Arabia. Toward this end, multiple answer questions were used. In other words, the response rates may not add up to 100%. Re- spondents who have used e-scooters reported that insufficient infrastructure, weather, safety, low speed, discomfort, satisfaction of needs, culture, and cost are a few of the ob- stacles that faces the deployment and usage of e-scooters. Similarly, respondents who have never used an e-scooter indicated that they expect the same obstacles but, to a certain extent, with different response rates for several of the abovementioned obstacles, as shown in Figure 3. The respondents were enabled to add other obstacles as an option. Sustainability 2021, 13, 863 Respondents added two main obstacles, namely, wearing traditional dresses and age11 of of 24 the user, which could be linked to culture as well.

80% 70.5% 70% 62.8% 58.6% 60% 50.1% 48.7% 50% 46.8% 37.3% 37.2% 40% 34.6% 33.5% 32.5% 26.6% 28.2% 28.2% 30% 23.1% 20% 15.1%

10% 7.7% 1.3% 0%

Used e-scooters Never used e-scooters

Figure 3. Obstacles facing e-scooters usage in Saudi Arabia. Figure 3. Obstacles facing e-scooters usage in Saudi Arabia. 5.1.4. E-Scooters and the COVID-19 Effect As the debate over the future of transportation continues in the midst of the COVID-19 pandemic as a deepening global crisis, micromobility seems to be not spared by the quick and disrupting changes that may arise from the pandemic that swept the world [26,27]. Part of the survey was designed to gauge the respondents’ perception of the effect of the COVID-19 pandemic on the use of e-scooters in Saudi Arabia. Results are shown in Table4. Notably, more than half of the respondents who have previously used e-scooters indicated that e-scooter usage will decrease, whereas 41% indicated that it will not be affected, and 8% suggested that the usage will increase. Moreover, the study investigated the respondents’ reactions to the process of applying safety precautions against the spread of COVID-19 disease on e-scooter systems, as shown in Table4. Approximately one- third of respondents who have previously used e-scooters indicated that applying safety precautions will decrease fears and concerns, about half of them indicated that it may decrease fears, and the remainder suggested that it will not decrease fears of using e- scooters due to the spread of the COVID-19 disease.

Table 4. Effect of COVID-19 on using e-scooters according to the survey respondents.

Percent (%) Category Subcategory Used E-Scooters Never Used E-Scooters Overall Decreased usage 51.3 45.2 46.2 Effect of fear from COVID-19 No effect 41.0 48.5 47.2 Increased usage 7.7 6.4 6.6 Would decrease fear of using e-scooters 33.3 24.1 25.7 Effect of considering precautions to May decrease the fear 51.3 58.2 56.9 minimize COVID-19 spread Would not decrease the fear 15.4 17.7 17.3

Respondents were also asked to suggest precautions to be considered by e-scooter operators to reduce the fear of using e-scooters due to the COVID-19 pandemic. More than 100 suggestions were provided, which can be grouped into five main categories. Respondents suggested that operators should provide disinfectants, such as sprays or Sustainability 2021, 13, 863 12 of 24

Sustainability 2021, 13, x FOR PEER REVIEW 12 of 22 wipes, for each e-scooter, single-use gloves and masks, disposable covers on grips, and infographics to increase awareness of self-sanitary and cleanliness. Figure4 provides detailed results. Responses on the other precautions option were analyzed and found that therelated majority to policy is related and to regulations, policy and such regulations, as the such importance as the importance of following of preventive following preventivemeasures and measures precautions and precautions issued by the issued government. by the government.

60% 56.3%

50% 45.6% 43.7%

40%

30% 25.0% 18.8% 18.4% 20% 18.4% 11.5% 11.7% 10.3% 10.7% 9.7% 8.0% 9.2% 8.0% 10% 6.3% 6.3% 6.8% 6.8%

0.0% 0.0% 0% Use Provide gloves Provide Instructions Do not allow in Precautions are Other disinfectants and masks disposable and awareness crowded places not effective precautions covers on grips

Used e-scooters Never used e-scooters Overall

Figure 4. Suggested precautions and preventive measures for consideration of e-scooter operators. Figure 4. Suggested precautions and preventive measures for consideration of e-scooter operators. Respondents were also asked to suggest potential places to deploy e-scooter sharing systemsRespondents in Saudi Arabia.were also Table asked5 shows to suggest the details potential of theplaces suggested to deploy places e-scooter with sharing a few examples.systems in SuggestedSaudi Arabia. places Table can 5 beshows classified the details into nine of the main suggested categories, places namely, with a open few entertainmentexamples. Suggested areas, shopping places can centers be classified and malls, into building nine main complexes, categories, residential namely, areas open and en- compounds,tertainment areas, downtown shopping cities, centers seasonal and large malls, events, building and othercomplexes, places, residential such as parking areas lotsand andcompounds, stations. downtown cities, seasonal large events, and other places, such as parking lots and stations. Table 5. Potential placesTable for 5. Potential deployment places of for e- deploymentscooter sharing of e-scooter systems sharing in Saudi systems Arabia. in Saudi Arabia. CategoryCategory Example Example of Places of PlacesResponses Responses (%) (%) Squares, parks, plazas, such as Al-Bujairy, Squares, parks, plazas, such as Al-Bujairy, King Abdullah Walkway, Open entertainment areas 40.9 Open entertainment areas King Abdullah Walkway,and Jeddah and Waterfront Jeddah Wa- 40.9 terfront Shopping centers Malls and hypermarkets 26.2 Shopping centers Malls and hypermarkets 26.2 Connected business buildings, medical cities, university campuses, Building complexes Connected business buildings, medical cit- 21.5 such asies, e.g., university Diplomatic campuses, Quarter such and Kingas e.g., Abdullah Dip- Financial District Building complexes 21.5 Residential areaslomatic Residential Quarter compounds and King and Abdullah districts Finan- or neighborhoods 12.8 Center of Riyadh, Centercial of District Jeddah, Makkah central area, and Madinah Downtowns Residential compounds and districts or 11.4 Residential areas central area 12.8 neighborhoods Roads Retail streets in Riyadh, Jeddah, and Dammam 8.1 Center of Riyadh, Center of Jeddah, Mak- Downtowns 11.4 Seasonal large events Area ofkah annual central Islamic area, and pilgrimage Madinah (Hajj) central and area Al-Janadiriyah festival 4.7 Suburbs Economical/industrialRetail streets in Riyadh, cities, Jeddah, provinces, and and resorts 4.0 Roads 8.1 Other Parking, airports,Dammam and train/metro stations 5.4 Area of annual Islamic pilgrimage (Hajj) Seasonal large events 4.7 and Al-Janadiriyah festival Economical/industrial cities, provinces, and Suburbs 4.0 resorts Other Parking, airports, and train/metro stations 5.4

Sustainability 2021, 13, 863 13 of 24

5.2. Statistical Analysis Results In this research effort, three logistic regression models were built with the same twelve variables but with different responses. The twelve regressors are age, gender, income, sport activity, educational background, professional occupancy, marriage status, car ownership, bike usage, motorcycle usage, ride-hailing apps usage, and previous e-scooter usage as listed in Table6. The three responses are the usage because of price (prefers to use e-scooter if ride-hailing trips is more expensive) and the willingness to use e-scooters both as binary, and the attitude toward the safety of the e-scooters as ordinal (Table7). The following subsections present in details the outputs of each model. We also showed the p-value, standard deviation, and the coefficient estimate for each variable as well as LogWorth, which is defined as -log10 (p-value). This transformation adjusts p-values to provide an appropriate scale for graphing.

Table 6. Regressors of the three logistic regression models.

Regressors Measure Value 1: 18–30 young (48%) 2: 31–45 middle age (40%) Age Ordinal 3: 46–60 (10%) 4: >60 (2%) 1: Male (66.7%) Gender Nominal 2: Female (33.3%) 1: SR3500 or less (37%) 2: More than SR3500 and less than SR7000 (7%) Income Ordinal 3: More than SR7000 and less than SR13,000 (15%) 4: More than SR13,000 and less than SR20,000 (25%) 5: More than SR20,000 (16%) 1: Never (14.6%) 2: At least once a month (14.8%) Sport Activity Ordinal 3: Once a week (18.2%) 4: Several days a week (36.2%) 5: Daily (16.2%) 1: Diploma or less (19%) 2: Bachelor (55%) Educational Background Ordinal 3: Master (14%) 4: PhD (12%) 1: Student (29.8%) 2: Freelancer (5.2%) Professional Occupancy Nominal 3: Employed (50.2%) 4: Retired (3.2%) 5: Unemployed (11.6%) 1: Single (34.4%) Marriage status Nominal 2: Married (65.6%) 1: Never or rarely (80.2%) Ride-hailing Apps Usage Nominal 2: Yes frequently (19.8%) 1: No (72.9%) Car Ownership Nominal 2: Yes (27.1%) 1: No (92%) Bike Usage Nominal 2: Yes (8%) 1: No (97.7%) Motorcycle usage Nominal 2: Yes (2.3%) 1: No (82%) Previous e-scooter usage Nominal 2: Yes (18%) Sustainability 2021, 13, 863 14 of 24

Table 7. Response of the three logistic regression models.

Response Model Type Value If the ride-hailing trip’s cost is higher than e-scooter’s cost, 1: No (%54) Binary will you use e-scooter? (usage because of price) 2: Yes (46%) 1: No (27%) Will you use an e-scooter? Binary 2: Yes (73%) 1: No (33%) Do you think e-scooter is a safe mode? Ordinal 2: maybe (53%) 3: Yes (14%)

5.2.1. Binary Logistic Regression Model i. Response: e-scooter usage because of price The binary logistic regression was adopted with the response of the e-scooter usage because of price. The effect summary of regressors of the binary logistic regression model is shown in the AppendixA (Table A1). There are only two significant factors: sport activity and gender. The goodness of fit tests was conducted and summarized (Appendix A, Table A2). The Whole Model Test shows the model is significant as a whole and means there is a significant difference between the reduced model and the full model. The results show that men are less likely to use e-scooters because of price. The odds ratio odds using e−scooter| f emale = e0.4154001 = 1.52 given all coefficients else are unchanged (calculated oddsusing e−scooter| male from Table8). That means the odds for females using e-scooters because of price are 52% higher than the odds for male using e-scooters because of price. Similarly, as for sport activity, people who never do physical activity are more likely to use e-scooters because of price than active people. ii. Response: willingness to use in the future The binary logistic regression was adopted again but with the response of “willingness to use in the future”. The model includes the previous regressors, excluding the “previous bike usage” given this question was only for those who have not used in the past. The effect summary of regressors of the binary logistic regression model are shown in the AppendixA (Table A3). There are three significant factors: gender, age, and ride-hailing apps usage. The goodness of fit tests was conducted and summarized in (AppendixA, Table A4). The Whole Model Test shows the model is significant as a whole, which means there is a significant difference between the reduced model and the full model. Before discussing the results, it is worth noting that we removed the Motorcycle Usage as a factor from this model because, if included, the model becomes numerically singular and thus unstable. The results given in Table9 show that women are more likely to use e-scooter in the future. As for age, being in the middle age are more likely to use e-scooter than other age groups. Additionally, ride-hailing users are more likely to use e-scooter than who do not usually use the service. Sustainability 2021, 13, 863 15 of 24

Table 8. Estimates of regressors of the binary logistic regression model (Response: the e-scooter usage because of price).

Term[Level] Estimate Std Error ChiSquare Prob > ChiSq Intercept −0.181 0.627 0.08 0.772 Marriage status 0.203 0.173 1.38 0.240 Age [2] 0.427 0.323 1.74 0.187 Age [3] 0.011 0.358 0.00 0.974 Age [4] 2.058 1.176 3.06 0.080 Gender [1] −0.415 0.193 4.61 0.031 Professional Occupancy [1] 0.1674 0.339 0.24 0.621 Professional Occupancy [2] −0.463 0.421 1.21 0.272 Professional Occupancy [3] 0.397 0.309 1.65 0.198 Professional Occupancy [4] −0.551 0.534 1.06 0.302 income[2] 0.101 0.472 0.05 0.830 income [3] −0.040 0.512 0.01 0.937 income [4] 0.670 0.373 3.22 0.072 income [5] −0.386 0.384 1.01 0.314 Educational Background [2] 0.083 0.298 0.08 0.780 Educational Background [3] 0.401 0.343 1.37 0.242 Educational Background [4] −0.194 0.470 0.17 0.679 Sport Activity [2] −1.401 0.412 11.54 0.0007 Sport Activity [3] 0.138 0.360 0.15 0.701 Sport Activity [4] 0.337 0.301 1.26 0.262 Sport Activity [5] −0.192 0.317 0.37 0.544 Car Ownership [1] −0.044 0.191 0.06 0.814 Ride-hailing Systems Usage [1] 0.040 0.134 0.09 0.761 bike usage [1] 0.282 0.198 2.03 0.154 motorcycle usage [1] 0.375 0.365 1.05 0.304 Previous e-scooter usage [1] −0.026 0.143 0.03 0.855 Sustainability 2021, 13, 863 16 of 24

Table 9. Estimates of regressors of the binary logistic regression model (Response: willing to use in the future).

Term [Level] Estimate Std Error ChiSquare Prob > ChiSq Intercept 0.789 0.796 0.98 0.321 Marriage status [1] 0.237 0.253 0.88 0.348 Age [2] 0.152 0.468 0.11 0.744 Age [3] 1.685 0.615 7.50 0.006 Gender [1] −0.934 0.336 7.73 0.005 Professional Occupancy [1] 0.166 0.562 0.09 0.766 Professional Occupancy [2] −1.085 0.584 3.45 0.063 Professional Occupancy [3] −0.148 0.522 0.08 0.776 Professional Occupancy [4] −0.052 0.977 0.00 0.956 Income [2] 0.259 0.819 0.10 0.751 Income [3] 0.132 0.855 0.02 0.876 Income [4] 0.224 0.542 0.17 0.678 Income [5] −0.701 0.516 1.85 0.174 Educational Background [2] 0.328 0.447 0.54 0.462 Educational Background [3] −0.150 0.490 0.09 0.759 Educational Background [4] 0.133 0.667 0.04 0.841 Sport Activity [2] −0.427 0.598 0.51 0.475 Sport Activity [3] 0.077 0.533 0.02 0.885 Sport Activity [4] 0.129 0.436 0.09 0.766 Sport Activity [5] −0.834 0.454 3.38 0.066 Car Ownership [1] −0.094 0.303 0.10 0.755 Ride-hailing Systems Usage [1] 0.400 0.182 4.85 0.027 bike usage [1] 0.109 0.275 0.16 0.691

5.2.2. Ordinal Logistic Regression Model The effect summary of regressors of the ordinal logistic regression model is shown in the AppendixA (Table A5). There are three significant factors: previous e-scooter usage, age, and income. The goodness of fit tests was conducted and summarized (AppendixA, Table A6). The Whole Model Test shows the model is significant as a whole, which means there is a significant difference between the reduced model and the full model. The results show (Table 10) the following: 1- For people who are in the age group of 31–45, the odds of being more likely to feel safety toward scooters is 1.85 times that of participant in the other age groups. 2- For people who had a previous e-scooter usage, the odds of being more likely to feel safety toward e-scooters is 1.43 times that of participant who did not have a previous e-scooter usage. Sustainability 2021, 13, 863 17 of 24

Table 10. Estimates of regressors of the ordinal logistic regression model (Response: Do you think e-scooter is a safe mode).

Term [Level] Estimate Std Error ChiSquare Prob > ChiSq Intercept [1] −0.979 0.566 3.00 0.083 Intercept [2] 1.827 0.572 10.18 0.001 Marriage Status [1] 0.207 0.158 1.71 0.190 Age [2] 0.612 0.301 4.12 0.042 Age [3] 0.615 0.331 3.45 0.063 Age [4] −0.1740 0.829 0.04 0.833 Gender [1] −0.177 0.167 1.12 0.290 Professional Occupancy [1] 0.008 0.312 0.00 0.977 Professional Occupancy [2] −0.026 0.381 0.00 0.944 Professional Occupancy [3] 0.122 0.279 0.19 0.662 Professional Occupancy [4] −0.420 0.483 0.76 0.383 Income [2] −0.055 0.426 0.02 0.897 Income [3] −0.571 0.472 1.47 0.226 Income [4] 1.107 0.345 10.25 0.001 Income [5] −0.540 0.349 2.39 0.122 Educational Background [2] 0.445 0.275 2.61 0.106 Educational Background [3] −0.187 0.311 0.36 0.547 Educational Background [4] 0.320 0.425 0.57 0.451 Sport Activity [2] −0.235 0.356 0.44 0.508 Sport Activity [3] −0.372 0.339 1.20 0.273 Sport Activity [4] 0.368 0.283 1.69 0.192 Sport Activity [5] −0.398 0.294 1.83 0.176 Car Ownership[1] 0.161 0.173 0.87 0.351 Ride-hailing Systems Usage [1] −0.049 0.124 0.16 0.692 bike usage [1] −0.207 0.180 1.32 0.250 motorcycle usage [1] −0.178 0.326 0.30 0.585 Previous e-scooter usage [1] 0.357 0.135 6.98 0.008

6. Discussion The study investigates the Saudis’ perception of launching e-scooter sharing systems as a new mobility mode for public transportation in Saudi Arabia. Results showed that the majority of the Saudi community is unfamiliar with e-scooter systems as most of them have never used an e-scooter previously. Most of those who have ridden e-scooters before—have tried it outside Saudi Arabia. In addition, e-scooters are still not legislated and officially launched yet in Saudi Arabia. Only two pilot studies were recently conducted for only 2–3 weeks. Moreover, results showed that e-scooter systems have the potential to increase in demand on the Saudi market and attract riders for various purposes. A significant percent of the respondents who have never used an e-scooter previously have expressed willingness to use it if available. The survey revealed that males will be potentially using such a system more than females in Saudi Arabia, and the age of the majority of potential users will presumably be between 18 and 45 years old, which presents the largest portion of the Saudi community (more than half of the population [17]). The low potential female users may be related to the low number of females’ participation in the workforce, and also the low number of female Sustainability 2021, 13, 863 18 of 24

drivers given the ban on women driving was lifted recently. Thus, we hypothesize that this would change if females’ participation in the workforce is increased. As for income, potential e-scooter users will most likely belong to all income levels. However, many of them may belong to the relatively low-income groups as many are students and relatively young users. People who frequently use ride-hailing services, such as Uber and Careem, tend to show more willingness to use e-scooters in the future. This tendency refers to the idea that both systems (e-scooters as micromobility and Uber/Careem as ride-hailing mobility) are part of the mobility network trend that was introduced recently as MaaS. The introduction of these mobility solutions may lead them to become more integrated in the public transportation system in the future. However, most of the frequent ride-hailing users tend not to prefer e-scooters even if an e-scooter ride will cost less than the same ride in Uber or Careem. This preference could be due the abovementioned obstacles that face the deployment of e-scooter systems in Saudi Arabia (Figure4). The survey revealed that e-scooters may be used for many reasons in various places in Saudi Arabia. The main purpose for riding an e-scooter would seemingly be for enter- tainment. However, other purposes may include commuting between home and work, shopping, visiting family or friends, and switching to another mode of transportation. This finding could be explained by the nature of the current travel behavior of people in Saudi Arabia, which is mainly based on driving private cars for most daily trips [28]. Respondents who have never used an e-scooter are more likely to use it, if ever, for entertainment. They also might be ready to use them for a variety of purposes, such as switching to another mode of transportation, shopping, visiting family or friends, and commuting between work and home. The recent proliferation in e-scooter use across the world has raised concerns regarding the safety of riders and pedestrians [29]. Saudi’s perception with regard to e-scooter safety is seemingly positive as half of the respondents reported that e-scooters may be safe for use. Notably, the majority of respondents who have used e-scooters have not been involved in a crash with an e-scooter, but more than the third have been involved in a near-miss. This finding indicates that considering precautions and ensuring safety is crucial for potential operators and regulators if they plan to deploy an e-scooter system. The majority of the Saudi community appears to have certain concerns related to e-scooter safety as approximately 90% have never used it previously. However, their perception is projected to change positively as they start experiencing the system directly or indirectly, such as seeing others use them. In this manner, we recommend that operating agencies should incentivize potential users to encourage them to become frequent riders (e.g., by offering a special discount rate for first-time users). The findings revealed several obstacles facing the deployment of e-scooter systems in Saudi Arabia. Results showed that the three top ranked obstacles in Saudi Arabia are insufficient infrastructure, weather, and safety. In this regard, the respondents suggested various potential deployment places based on these obstacles. As the number of shop- ping centers is increasing rapidly in new locations with relatively large areas, such as supermarkets, food courts, and playgrounds, the respondents proposed malls as potential areas for the deployment of e-scooters. Additionally, building complexes, such as the King Abdullah Financial District, Diplomatic Quarter in Riyadh, university campuses, and medical cities can also be potential places for e-scooters. Such places have free-car policy zones and a relatively sufficient and suitable infrastructure for e-scooters as a micromobility mode (e.g., protected and safe sidewalks). The study argues that these suggested places will hypothetically be worthy of consideration if operators will deploy e-scooter sharing systems in Saudi Arabia in the meantime given the various obstacles. City downtowns are typically suitable places for e-scooter systems. As the heart of the city, downtowns streets suffer from traffic congestions during the day. Thus, they become a potential place for successful e-scooter sharing systems especially for short trips (i.e., last-mile trips). A very dynamic and 24 h congested downtown in Saudi Arabia is the central area of the Makkah and Madinah cities, which contain the two holy mosques in the Sustainability 2021, 13, 863 19 of 24

Islamic world. According to the Ministry of Hajj and Umrah, the two central areas receive more than 10 million visitors every year (inside and outside Saudi Arabia). Thereby, the government dedicated this area as a very limited entry zone or no-entry for personal cars to accommodate the high number of visitors [30]. Launching e-scooter sharing systems will facilitate mobility in these areas and help in the case of walking long distances. This scenario will become crucial during the annual Islamic pilgrimage (Hajj) to Makkah city, where approximately three million visitors gather in one location and move simultaneously from one place to another (with short distances in between), thus leading to an over-capacity demand for transportation networks. Results indicated that few suggested roads and retail streets are candidates for the de- ployment of e-scooter systems in Saudi Arabia due to the current insufficient infrastructure. This perception is expected to be changed in the future if infrastructure is improved in these areas in Saudi Arabia. Similarly, very few suggested using e-scooters to switch from one transportation mode to another as Saudi Arabia lacks the efficient public transportation system in many cities. In the suburbs, e-scooter systems may be a successful transportation mode in economical and industrial cities, resorts, and certain provinces to meet the demand of short-distance commute without the need to drive personal vehicles. Survey revealed that insufficient infrastructure may impede the success of e-scooter systems in different cities in Saudi Arabia. Sufficient infrastructure is essential to facilitate the effective integration of e-scooters into the urban mobility ecosystem and for users to commute safely and efficiently when passing nearby cars and/or pedestrians [31,32]. In addition, results showed that closed areas may be suitable for the launch of the system because the weather is typically very hot in summer (~45 ◦C), which may also hinder the deployment of e-scooters [31,33]. We hypothesize that deploying e-scooters in open areas would meet the demand of users who would use them during the early morning or after sunset. In terms of deployment obstacles, although it seems that e-scooter safety is a shared concern in the Saudi community, other impediments include uncomfortableness of riding e-scooters as a transportation mode for commuting, which could be related to the current insufficient infrastructure. To mitigate this belief, introducing policies regarding maximum speed, especially in vulnerable congested areas, is prudent for operating agencies and urban planners [34]. In contrast, results show that many respondents view this scenario as a disadvantage given that it will increase the delay in travel time. Furthermore, other obstacles imply that e-scooters fail to satisfy certain needs, such as the capacity issue of e-scooters. However, trip cost may not be essential in limiting the usage of e-scooters. Although the full impact of COVID-19 on mobility remains to be seen in the future, its immediate effect “has forced many of us to break out of our travel habits” [26]. Micro- mobility has gained a steady increase in demand in recent years because of its ability to provide convenient, pleasurable, and independent travel. During the COVID-19 lockdown in many cities, many people have started using e-scooters (along with bikes) for leisure, to remain healthy, for mobility, and to maintain social distancing [26,27]. In Saudi Arabia, a shared concern appears that e-scooter usage might decrease due to fears from the spread of COVID-19. E-scooters grips, for example, may become a source of the spread of COVID-19. In spite of this tendency, the finding revealed hope that COVID-19 will have a minimal effect on e-scooter demand, whereas others believe that the pandemic may increase the usage of e-scooters. People moving in short distances will aim to maintain the required social distancing by avoiding modes of public transportation, which may result in increased usage of e-scooters. Precautions and preventive measures have been proven efficient in reducing the spread of viruses [35,36]. As a result, considering the required precautions to reduce the spread of COVID-19 by e-scooter operators will decrease or decrease the fear and concerns related to the use of e-scooters. The respondents suggested that providing disinfectants, gloves and masks for riders, and disposable covers on e-scooter grips. Addi- tionally, findings illustrated that instructions and awareness, which should be provided by operators, are also crucial. Sustainability 2021, 13, 863 20 of 24

There are some limitations to this study. First, the sample size might not be considered statistically representative, some of the key socioeconomic and demographic indicators of the survey respondents could be deemed incomparable to those of the Saudi Arabia population. For example, females were under-represented in the sample and the young population was over-represented. Second, this study could be implemented to data from different geographical locations in the Saudi Arabia. A different survey from different locations in Saudi Arabia could further leverage this study to increase its veracity and might shift some of its conclusions. Still, the results of this study primarily contain significant finding in this field to build on, the first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users that policy-makers, operators, and researchers would find intriguing to understand for safety, transportation, health, and recreation purposes.

7. Conclusions Micromobility transportation modes have blossomed recently in many dense and congested cities across the world motivated by the increased over-capacity daily traffic demand. E-scooter sharing systems, which are examples of the MaaS mode, have been widely deployed as a means to minimize the usage of personal vehicles mainly for short trips. A number of recent studies investigated users’ perception of using e-scooter systems as a micro-mobility mode. The present study analyzed the outputs of the survey on the perception of potential users of e-scooter systems as a new micro-mobility mode in the city of Riyadh which has not been launched officially. In particular, the study explored the socioeconomic and demographic factors that influence the perception of potential users living in Riyadh. Additionally, the study investigated the respondents’ reaction on the spread of COVID-19 and how it might influence the deployment of e-scooter systems. A web-based questionnaire was conducted in April 2020 targeting e-scooter riders and non-riders. A sample of 439 respondents from various age groups, educational background, level of income, and gender was collected. Approximately 18% of the survey respondents has previously used an e-scooter at least once (others have used e-scooters inside and outside Saudi Arabia). Two types of logistic regression models were built. The outcomes of the models show that gender, age, and using ride-hailing services play an important role in respondents’ willingness to use e-scooter. This study provides significant findings that would be beneficial for policymakers, operators, and researchers in understanding this emerging class of mobility system.

Author Contributions: Conceptualization, M.H.A., M.E. and H.I.A.; methodology, M.H.A., M.E. and H.I.A.; software, F.A.A.; validation, M.H.A., M.E. and H.I.A.; formal analysis, F.A.A., M.H.A., M.E. and H.I.A.; investigation, F.A.A., M.H.A., M.E. and H.I.A.; resources, M.H.A. and M.M.; data curation, M.H.A. and M.E; writing—original draft preparation, F.A.A., M.H.A., M.E. and H.I.A.; writing—review and editing, M.M.; visualization, M.M.; supervision, A.R.; project administration, M.H.A.; funding acquisition, M.H.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received funds from: Researchers Supporting Project number (RSP-2020/291), King Saud University, Riyadh, Saudi Arabia. Data Availability Statement: Dataset is available upon request. Acknowledgments: Researchers Supporting Project number (RSP-2020/291), King Saud University, Riyadh, Saudi Arabia. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2021, 13, x FOR PEER REVIEW 20 of 24

Sustainability 2021, 13, x FOR PEER REVIEW 20 of 24

Sustainability 2021, 13, x FOR PEER REVIEW 20 of 24 Appendix A Sustainability 2021, 13, x FOR PEER REVIEW 20 of 24 Appendix A Sustainability 2021, 13, x FOR PEER REVIEW 20 of 24 Table A1. Effect Summary of the coefficients of binary logistic regression model (Response: the e- Appendix A scooter usage because of price). Sustainability 2021, 13, x FOR PEER REVIEWTable A1. Effect Summary of the coefficients of binary logistic regression model (Response: the20 of e- 24 Appendix A Sustainability 2021, 13, x FOR PEER REVIEWscooter usageSource because of price).LogWorth P-Value20 of 24 Table A1. Effect Summary of the coefficients of binary logistic regression model (Response: the e- Appendix A scooter usage because of price). Sustainability 2021, 13, x FOR PEER REVIEWTable A1. EffectSource Summary of theLogWorth coefficients of binary logistic regression model (Response:P-Value the20 of e- 24 Sport Activity 1 0.00412 Appendix A scooter usage because of price). Sustainability 2021, 13, x FOR PEER REVIEWTable A1. EffectSource Summary of theLogWorth coefficients of binary logistic regression model (Response:P-Value the20 of e- 24 Appendix A Sport Activity 1 0.00412 Sustainability 2021, 13, x FOR PEER REVIEWscooter usageSource because of price).LogWorth P-Value20 of 24 Table A1. Effect Summary of the coefficients of binary logistic regression model (Response: the e- Appendix A Sport Activity 1 0.00412 Sustainability 2021, 13, x FOR PEER REVIEWscooter usageGenderSource because of price).LogWorth 2 P0.02834-Value20 of 24 Table A1. Effect Summary of the coefficients of binary logistic regression model (Response: the e- Appendix A Sport Activity 1 0.00412 Sustainability 2021, 13, x FOR PEER REVIEWscooter usageGenderSource because of price).LogWorth 2 P0.02834-Value20 of 24 Table A1. Effect Summary of the coefficients of binary logistic regression model (Response: the e- AppendixSustainability A 2021, 13, 863 Sport Activity 1 0.00412 21 of 24 scooter usageGenderSource because of price).LogWorth 2 P0.02834-Value Table A1. EffectAge Summary of the coefficients3 of binary logistic regression model (Response: 0.09187 the e- Appendix A Sport Activity 1 0.00412 scooter usageGenderSource because of price).LogWorth 2 P0.02834-Value Table A1. EffectAge Summary of the coefficients3 of binary logistic regression model (Response: 0.09187 the e- Appendix A Sport Activity 1 0.00412 scooter usageGenderSource because of price).LogWorth 2 P0.02834-Value Table A1. EffectAge Summary of the coefficients3 of binary logistic regression model (Response: 0.09187 the e- Appendix A SportBike Activity usage 14 0.004120.14786 scooter usageGenderSource because of price).LogWorth 2 P0.02834-Value Table A1. EffectAge Summary of the coefficients3 of binary logistic regression model (Response: 0.09187 the e- SportBike Activity usage 14 0.004120.14786 scooter usageGender because of price). 2 0.02834 Table A1. Effect Summary ofSource theAge coefficients of binaryLogWorth3 logistic regression model (Response: the e-scooter usage becauseP0.09187-Value of price). SportBike Activity usage 14 0.004120.14786 MarriageGender status 2 5 0.028340.23661 SourceAge LogWorth3 P0.09187-Value SourceBike usage LogWorth 4 p0.14786-Value Sport Activity 1 0.00412 MarriageGender status 2 5 0.028340.23661 Age 3 0.09187 Sport ActivitySportBike Activity usage 2.385 14 0.004120.004120.14786 MarriageGender status 2 5 0.028340.23661 motorcycleAge usage 3 6 0.091870.28918 Bike usage 4 0.14786 GenderMarriageGender status 1.548 52 0.028340.236610.02834 motorcycleAge usage 3 6 0.091870.28918 Bike usage 4 0.14786 Marriage status 5 0.23661 Gender 2 0.02834 AgemotorcycleAge usage 1.037 3 6 0.091870.091870.28918 ProfessionalBike usage occupancy 74 0.303130.14786 Marriage status 5 0.23661 motorcycleAge usage 63 0.289180.09187 Bike usageProfessionalBike usage occupancy 0.830 47 0.147860.147860.30313 Marriage status 5 0.23661 motorcycleAge usage 3 6 0.091870.28918 ProfessionalBike usage occupancy 47 0.147860.30313 Marriage statusMarriageIncome status 0.626 8 5 0.236610.353690.23661 motorcycle usage 6 0.28918 Professional occupancy 7 0.30313 Bike usage 4 0.14786 MarriageIncome status 58 0.236610.35369 motorcycle usagemotorcycle usage 0.539 6 0.289180.28918 ProfessionalBike usage occupancy 47 0.147860.30313 MarriageIncome status 58 0.236610.35369 Educationalmotorcycle background usage 69 0.649570.28918 Professional occupancyProfessional occupancy 0.518 7 0.303130.30313 MarriageIncome status 8 5 0.353690.23661 Educationalmotorcycle background usage 69 0.289180.64957 Professional occupancy 7 0.30313 IncomeIncome 0.451 8 0.353690.35369 Marriage status 5 0.23661 Educationalmotorcycle background usage 69 0.289180.64957 Ride-hailingProfessional appsoccupancy Usage 10 7 0.303130.76178 Income 8 0.35369 Educational backgroundEducationalmotorcycle background usage 0.187 96 0.649570.649570.28918 Ride-hailingProfessional appsoccupancy Usage 10 7 0.303130.76178 Income 8 0.35369 Educationalmotorcycle background usage 69 0.289180.64957 Ride-hailing apps UsageRide-hailingProfessional appsoccupancy Usage 0.118 10 7 0.761780.303130.76178 Car Incomeownership 118 0.353690.81426 Educational background 9 0.64957 Ride-hailing apps Usage 10 0.76178 Professional occupancy 7 0.30313 Car ownershipCar Incomeownership 0.089 118 0.814260.353690.81426 Educational background 9 0.64957 Ride-hailingProfessional appsoccupancy Usage 10 7 0.303130.76178 Car Incomeownership 118 0.353690.81426 Previous e-scooter usagePreviousEducational e-scooter background usage 0.068 12 9 0.855690.649570.85569 Ride-hailing apps Usage 10 0.76178 Car Incomeownership 118 0.814260.35369 PreviousEducational e-scooter background usage 12 9 0.649570.85569 Ride-hailing apps Usage 10 0.76178 Car ownership 11 0.81426 PreviousIncome e-scooter usage 128 0.353690.85569 TableEducational A2. GoodnessTable background A2. ofGoodness fit statistics of fit 9(Response: statistics (Response:the e-scooter the usage e-scooter because usage of price). because of price).0.64957 Ride-hailing apps Usage 10 0.76178 Car ownership 11 0.81426 PreviousEducational e-scooter background usage 12 9 0.0.6495785569 TableRide-hailing A2. Goodness apps Usageof fit statistics 10(Response: Whole theModel e-scooterWhole Test Model usage Testbecause of price). 0.76178 Car ownership 11 0.81426 PreviousEducational e-scooter background usage 12 9 0.649570.85569 TableRide-hailing ModelA2. Goodness apps Usageof fit-LogLikelihood statistics 10(Response: Whole theModelDF e-scooter TestChiSquare usage because of price).Prob > ChiSq0.76178 Car ownershipModel -LogLikelihood 11 DF ChiSquare 0.81426 Prob > ChiSq PreviousDifference e-scooter usage 27.96210 12 25 55.92419 0.0004 0.85569 TableRide-hailing ModelA2. Goodness appsDifference Usageof fit-LogLikelihood statistics 10(Response: 27.96210Whole theModelDF e-scooter TestChiSquare 25usage because of 55.92419 price).Prob > ChiSq0.76178 0.0004 Car ownership 11 0.81426 PreviousDifferenceFull e-scooter usage 273.3749827.96210 12 25 55.92419 0. 0004 0.85569 TableRide-hailing ModelA2. Goodness appsFull Usageof fit-LogLikelihood statistics 10(Response: 273.37498 Whole theModelDF e-scooter TestChiSquare usage because of price).Prob > ChiSq0.76178 Car ownership 11 0.81426 PreviousDifferenceReducedFull e-scooter usage 273.37498301.3370827.96210 12 25 55.92419 0. 0004 0.85569 Sustainability 2021, 13, x FOR PEER REVIEW Table ModelA2. Goodness Reduced of fit-LogLikelihood statistics (Response: 301.33708 theDF e-scooter ChiSquare usage because of price).Prob21 > of ChiSq 24 Sustainability 2021, 13, x FOR PEER REVIEW Whole Model Test 21 of 24 ReducedCarFull ownership 301.33708273.37498 11 Goodness of fit 0. 0.81426 PreviousDifferenceModel e-scooter usage-LogLikelihood 27.96210 12 DF25 ChiSquare55.92419 Prob 0004 > ChiSq 0.85569 Sustainability 2021, 13, x FOR PEER REVIEW Table A2. GoodnessR-Square of fit statistics (Response:Whole theModel e-scooterGoodness Test usage0.0928 of fitbecause of price).21 of 24 ReducedCarFull ownership 273.37498301.33708 11 Goodness of fit 0. 0.81426 PreviousDifferenceModel e-scooter usage-LogLikelihoodR-Square 27.96210 12 DF25 ChiSquare55.92419 0.0928Prob 0004> ChiSq 0.85569 Sustainability 2021, 13, x FOR PEER REVIEW Table A2. GoodnessR-SquareAIC of fit statistics (Response:Whole theModel e-scooter Test usage602.1740.0928 because of price).21 of 24 Table A3. EffectDifferenceReduced FullSummary of the regressors301.33708273.3749827.96210 of binary Goodness logistic 25 ofregression fit 55.92419 model (Response: willing0.0004 Table A3. EffectPreviousModel Summary e-scooter BIC of theusage regressors-LogLikelihood AIC 12 of binary logisticDF regressionChiSquare704.828 model (Response: 602.174Prob willing > ChiSq 0.85569 to use in Tablethe future). A2. Goodness R-SquareAIC of fit statistics (Response:Whole theModel e-scooter Test usage602.1740.0928 because of price). to use in the future).Full 273.37498 Goodness of fit Table A3. EffectPreviousDifferenceReduced Summary e-scooter BIC of theusage regressors 301.33708BIC27.96210 12 of binary logistic 25 regression55.92419704.828 model (Response: 704.828 willing0.0004 0.85569 Table ModelA2. Goodness AIC of fit-LogLikelihood statistics (Response:Whole theModelDF e-scooter TestChiSquare usage602.174 because of price).Prob > ChiSq to use in the future).Source R-Square LogWorth Goodness of fit 0.0928 p-Value Table A3. EffectDifferenceReducedSourceFull Summary BIC of the regressors301.33708273.3749827.96210LogWorth of binary logistic 25 regression55.92419 704.828 model (Response:p -Valuewilling0.0004 Table ModelA2. Goodness AIC of fit-LogLikelihood statistics (Response: theDF e-scooter ChiSquare usage 602.174 because of price).Prob > ChiSq to use in the future). R-Square Whole Model Test 0.0928 DifferenceReducedSourceFull 301.33708273.3749827.96210LogWorth Goodness 25 of fit 55.92419 p-Value0.0004 Table A3. EffectTable SummaryGender ModelA2. Goodness of theAICBIC regressors of fit-LogLikelihood statistics of binary13 (Response: logistic regressiontheDF e-scooter modelChiSquare usage704.828602.174 (Response: because of willing price).Prob0.01384 to > use ChiSq in the future). GenderR-Square 13 Whole Model Test 0.0928 0.01384 DifferenceReducedSourceFull 273.37498301.3370827.96210LogWorth Goodness 25 of fit 55.92419 p-Value0.0004 Model AICBIC -LogLikelihood DF ChiSquare 602.174704.828 Prob > ChiSq SourceGenderR-Square LogWorth 13 Whole Model Test 0.0928 0.01384 p-Value DifferenceReducedFull 301.33708273.3749827.96210 Goodness 25 of fit 55.92419 0.0004 Model AICBIC -LogLikelihood DF ChiSquare 704.828602.174 Prob > ChiSq GenderGenderAge R-Square 1.859 1413 0.0928 0.017390.01384 0.01384 DifferenceReducedFull 301.33708273.3749827.96210 Goodness 25 of fit 55.92419 0.0004 AICBIC 704.828602.174 Age R-Square 14 0.0928 0.01739 AgeReducedFull 1.760273.37498301.33708 Goodness of fit 0.01739 AICBIC 602.174704.828 Ride-hailingAge Systems R-Square Usage 1514 Goodness of fit 0.0928 0.022500.01739 Ride-hailing SystemsRide-hailing UsageReduced Systems BIC Usage 1.648301.33708 15 704.828 0.02250 0.02250 R-SquareAIC 602.1740.0928 Ride-hailing Systems Usage 15 Goodness of fit 0.02250 Professional OccupancyAICBIC 0.827 704.828602.174 0.14887 Professional OccupancyR-Square 16 0.0928 0.14887 Ride-hailing Systems Usage 15 0.02250 AICBIC 602.174704.828 Professional Occupancy 16 0.14887 BIC 704.828 Marriage status 17 0.46510 ProfessionalMarriage Occupancy status 1716 0.465100.14887

Marriage status 17 0.46510

Marriagebike usage status 1817 0.490370.46510 bike usage 18 0.49037

bike usage 18 0.49037

Sportbike Activity usage 1918 0.542830.49037 Sport Activity 19 0.54283

Sport Activity 19 0.54283

EducationalSport Activity Background 2019 0.827400.54283 Educational Background 20 0.82740

Educational Background 20 0.82740

EducationalIncome Background 21 20 0.834870.82740 Income 21 0.83487

Income 21 0.83487

Car OwnershipIncome 2221 0.952550.83487 Car Ownership 22 0.95255

Car Ownership 22 0.95255

Car Ownership 22 0.95255

Sustainability 2021, 13, x FOR PEER REVIEW 21 of 24

Sustainability 2021, 13, x FOR PEER REVIEW 21 of 24

Sustainability 2021, 13, x FOR PEER REVIEW 21 of 24 Sustainability 2021, 13, x FOR PEER REVIEW 21 of 24 Table A3. Effect Summary of the regressors of binary logistic regression model (Response: willing to use in the future). Table A3. Effect Summary of the regressors of binary logistic regression model (Response: willing Sustainability 2021, 13, x FOR PEER REVIEWto use in the future). 21 of 24 Table A3. EffectSource Summary of the regressorsLogWorth of binary logistic regression model (Response:p -Valuewilling Table A3. Effect Summary of the regressors of binary logistic regression model (Response: willing Sustainability 2021, 13, x FOR PEER REVIEWto use in the future). 21 of 24 to use in the future).Source LogWorth p-Value Gender 13 0.01384 Source LogWorth p-Value Table A3. EffectSource Summary of the regressorsLogWorth of binary logistic regression model (Response:p -Valuewilling Gender 13 0.01384 to use in the future). Table A3. Effect Summary of the regressors of binary logistic regression model (Response: willing Gender 13 0.01384 to use in the future).GenderAge 1314 0.013840.01739 Source LogWorth p-Value

Age 14 0.01739 Source LogWorth p-Value Gender 13 0.01384 Age 14 0.01739 Ride-hailingAge Systems Usage 14 15 0.017390.02250

Gender 13 0.01384 Ride-hailing Systems Usage 15 0.02250

ProfessionalAge Occupancy 14 16 0.017390.14887 Ride-hailing Systems Usage 15 0.02250 Ride-hailing Systems Usage 15 0.02250

ProfessionalAge Occupancy 1614 0.148870.01739

Sustainability 2021, 13,Professional 863 Marriage Occupancy status 1716 0.465100.14887 22 of 24 Ride-hailingProfessional Systems Occupancy Usage 1516 0.022500.14887

Marriage status 17 0.46510 Ride-hailing Systems Usage 15 0.02250

Marriage status 17 0.46510 ProfessionalMarriagebike usage Occupancy status 161718 0.465100.148870.49037 Table A3. Cont. Sustainability 2021, 13, x FOR PEER REVIEW 22 of 24 Professionalbike usage Occupancy 1816 0.490370.14887

Sustainability 2021, 13, x FORSource PEER REVIEWMarriage status LogWorth 17 0.46510 p22-Value of 24 Sportbike Activity usage 1918 0.542830.49037 bike usage 18 0.49037 Sustainability 2021, 13, x FOR PEER REVIEWMarriage status 17 0.46510 22 of 24 Marriage status 0.332 0.46510 Sustainability 2021, 13, x FOR PEER REVIEWTableSport A4. Activity Goodness of fit statistics 19(Response: willing to use in the future). 0.54283 22 of 24

bike usagebike usage 0.309 18 0.49037 0.49037 Sustainability 2021, 13, x FOR PEER EducationalREVIEWTableSport A4. Activity GoodnessBackground of fit statistics 2019(Response: willing to use in the future). 0.827400.54283 22 of 24 Whole Model Test Sustainability 2021, 13, x FOR PEER REVIEWTablebike A4. usageGoodness of fit statistics 18(Response: willing to use in the future). 0.49037 22 of 24 Sport ActivityEducationalSportModel Activity Background -LogLikelihood 0.265 2019 Whole Model DF Test ChiSquare Prob0.827400.54283 > ChiSq 0.54283

Sustainability 2021, 13, x FOR PEER REVIEWTableDifference A4. Goodness of fit37.19050 statistics (Response: willing 46 to use in the 74.381 future). 0.005122 of 24 SportModel Activity -LogLikelihood 19 Whole Model DF Test ChiSquare Prob0.54283 > ChiSq Educational BackgroundEducationalIncome Background 0.082 21 20 0.834870.82740 0.82740 Sustainability 2021, 13, x FOR PEER REVIEWTableDifference A4. Full Goodness of fit321.9165337.19050 statistics (Response: willing 46 to use in the 74.381 future). 0.005122 of 24 SportModel Activity -LogLikelihood 19 Whole Model DF Test ChiSquare Prob0.54283 > ChiSq TableReduced A4. Goodness of fit359.10704 statistics (Response: willing to use in the future). Sustainability 2021, 13, x FORIncome PEER REVIEWDifferenceModel IncomeFull -LogLikelihood321.9165337.19050 0.078 21 Whole Model DF 46 Test ChiSquare 74.381 Prob0.83487 0.0051 > ChiSq 0.8348722 of 24 Educational Background 20 0.82740 EducationalTableReduced A4.Full GoodnessBackground of fit359.10704321.91653 statistics 20(Response: Goodness willing of to fit use in the future). 0.82740 Sustainability 2021, 13, x FOR PEER REVIEWDifferenceModel Income -LogLikelihood37.19050 21 Whole Model DF 46 Test ChiSquare 74.381 Prob0.83487 0.0051 > ChiSq 22 of 24 Car OwnershipCar OwnershipR-Square 0.021 22 0.1036 0.95255 0.95255 TableReduced A4.Full Goodness of fit359.10704321.91653 statistics (Response:Goodness willing of to fit use in the future). Sustainability 2021, 13, x FOR PEER EducationalREVIEWDifferenceModel Background -LogLikelihood 37.19050 20 Whole Model DF 46 Test ChiSquare 74.381 Prob0.82740 0.0051 > ChiSq 22 of 24 Car OwnershipR-SquareAIC 22 755.0070.1036 0.95255 TableDifferenceReduced A4.Full Goodness of fit359.10704321.9165337.19050 statistics (Response:Goodness willing 46 of to fit use in the 74.381 future). 0.0051 Sustainability 2021, 13, x FOR PEER REVIEWModel Income BIC-LogLikelihood 21 Whole Model DF Test ChiSquare926.233 Prob0.83487 > ChiSq 22 of 24 CarReduced IncomeOwnership R-SquareAIC 359.10704 21 22 Goodness of fit 755.0070.1036 0.834870.95255 TableDifference ModelA4.Full Goodness Table A4. of-LogLikelihoodGoodness fit321.9165337.19050 statistics of (Response: fit statistics willing (Response:DF 46 to use willing in the ChiSquare 74.381 tofuture). use in the future).Prob 0.0051 > ChiSq R-SquareAICBIC Whole Model Test 926.233755.0070.1036 ReducedIncomeFull 321.91653359.10704 21 Goodness of fit 0.83487 TableDifference ModelA4.A5. GoodnessEffect Summary of-LogLikelihood fit37.19050 statistics of the coefficients (Response: of willing DFbinary 46 to logistic use in regression the ChiSquare 74.381 future). model (Response:Prob 0.0051 > ChiSq Do R-SquareAICBIC Whole ModelWhole Test Model 926.233755.0070.1036 Test CarReduced OwnershipFull 321.91653359.10704 22 Goodness of fit 0.95255 TableyouCar thinkDifference ModelA5.A4. Ownership e-scooterEffectGoodness Summary is of -LogLikelihooda fitsafe37.19050 statistics of mode). the coefficients 22 (Response: of willing DFbinary 46 tologistic use in regression the ChiSquare 74.381 future). model (Response:Prob0.95255 0.0051 > ChiSq Do R-SquareAICBICModel -LogLikelihoodWholeGoodness Model of Testfit DF 926.233755.0070.1036 ChiSquare Prob > ChiSq you thinkReducedFull e-scooter is a safe359.10704321.91653 mode). TableCarDifference ModelA5. Ownership EffectSource SummaryBIC -LogLikelihood 37.19050 of the LogWorthcoefficients 22 of DFbinary 46 logistic regression ChiSquare926.233 74.381 model (Response:Prob0.95255 0.0051 > ChiSq p -ValueDo R-SquareDifferenceAIC 37.19050WholeGoodness Model of Testfit 46755.0070.1036 74.381 0.0051 Tableyou thinkReduced A5.Full e-scooterEffect Summary is a safe359.10704321.91653 of mode). the coefficients of binary logistic regression model (Response: Do DifferenceModelSource AICBIC -LogLikelihood 37.19050LogWorth DF 46 ChiSquare755.007926.233 74.381 Prob 0.0051 > ChiSqp-Value you thinkReduced e-scooter R-Square isFull a safe 359.10704 mode). 321.91653Goodness of fit 0.1036 TablePreviousDifference A5.Full Effect Source e-scooter SummaryBIC usage321.9165337.19050 of the LogWorthcoefficients 23 of binary 46 logistic regression926.233 74.381 model (Response: 0.0051 p-Value 0.008Do R-SquareAIC Goodness of fit 755.0070.1036 TableyouPrevious thinkReduced A5.Full e-scooterEffect e-scooter SummaryReduced is a usage safe359.10704321.91653 of mode). the coefficients 23 359.10704 of binary logistic regression model (Response: 0.008Do SourceAICBIC LogWorth 926.233755.007 p-Value you think e-scooterR-Square is a safe mode). Goodness of fit 0.1036 TablePreviousReduced A5. Effect Sourcee-scooter Summary usage359.10704 of the LogWorthcoefficients 23 of binaryGoodness logistic regression of fit model (Response:p -Value0.008Do AICBIC 926.233755.007 you think e-scooterAgeR-Square is a safe mode). 24 Goodness of fit 0.1036 0.029 TablePrevious A5. Effect Sourcee-scooter Summary usage ofR-Square the LogWorthcoefficients 23 of binary logistic regression model 0.1036 (Response: p -Value0.008Do AICBIC 926.233755.007 you think e-scooterAgeR-Square is a safe mode). 24 0.1036 0.029 TablePrevious A5. EffectSource e-scooter Summary usage of theAIC LogWorthcoefficients 23 of binary logistic regression model 755.007 (Response: p-Value 0.008Do BIC 926.233 AgeAIC 24 755.007 0.029 TableyouPrevious think A5. e-scooterEffect e-scooter Summary is a usage safe of mode). the coefficients 23 of binary logistic regression model (Response: 0.008Do SourceIncome BICLogWorth25 926.233 p-Value0.030 AgeBIC 24 926.233 0.029 TableyouPrevious think A5. e-scooterEffect e-scooter Summary is a usage safe of mode). the coefficients 23 of binary logistic regression model (Response: 0.008Do IncomeSource LogWorth25 p-Value0.030 Age 24 0.029 youPrevious think e-scooter e-scooter is a usage safe mode). 23 0.008 Table A5. EffectTable Summary A5. EffectIncome ofSource the Summary coefficients of the of LogWorth binarycoefficients25 logistic of binary regression logistic model regression (Response: model Do (Response: you thinkp-Value 0.030Do e-scooter is a Age 24 0.029 youPrevious thinkMarriage e-scooter e-scooter status is a usage safe mode). 2326 0.0080.187 safe mode). IncomeSource LogWorth25 p-Value0.030 Age 24 0.029 PreviousMarriage e-scooter status usage 2623 0.0080.187 IncomeSource LogWorth25 p-Value0.030 SourceAge LogWorth 24 p0.029-Value PreviousMarriage e-scooter status usage 2326 0.0080.187 Income 25 0.030 bikeAge usage 24 27 0.0290.248 Previous e-scooter usagePreviousMarriage e-scooter status usage 2.087 2623 0.0080.1870.008 Income 25 0.030 bikeAge usage 24 27 0.2480.029 Marriage status 26 0.187 Income 25 0.030 AgebikeAge usage 1.525 24 27 0.0290.2480.029 Marriage status 26 0.187 SportIncome Activity 25 28 0.0300.275 bikeAge usage 24 27 0.0290.248 Marriage status 26 0.187 IncomeSportIncome Activity 1.520 2825 0.2750.0300.030 bike usage 27 0.248 Marriage status 26 0.187 SportIncome Activity 25 28 0.0300.275 bike usage 27 0.248 Marriage statusMarriagegender status 0.726 2629 0.1870.2930.187 SportIncome Activity 2825 0.2750.030 bike usage 27 0.248 Marriagegender status 2629 0.2930.187 Sport Activity 28 0.275 bike usagebike usage 0.605 27 0.2480.248 Marriagegender status 2629 0.1870.293 Sport Activity 28 0.275 Carbike Ownership usage 2730 0.2480.350 Marriagegender status 2629 0.2930.187 Sport ActivitySport Activity 0.559 28 0.2750.275 Carbike Ownership usage 3027 0.2480.350 gender 29 0.293 Sport Activity 28 0.275 Carbike Ownership usage 2730 0.2480.350 gendergender 0.532 29 0.2930.293 EducationalSport Activity Background 2831 0.2750.367 Carbike Ownership usage 3027 0.2480.350 gender 29 0.293 EducationalSport Activity Background 2831 0.2750.367 Car OwnershipCar Ownership 0.456 30 0.3500.350 gender 29 0.293 EducationalSport Activity Background 2831 0.2750.367 Car Ownership 30 0.350 motorcyclegender usage 29 32 0.57570.293 Educational BackgroundEducationalSport Activity Background 0.435 2831 0.2750.3670.367 Car Ownership 30 0.350 motorcyclegender usage 29 32 0.57570.293 Educational Background 31 0.367 Car Ownership 30 0.350 motorcycle usagemotorcyclegender usage 0.240 29 32 0.57570.2930.5757 Educational Background 31 0.367 Ride-hailingCar Ownership Systems Usage 3033 0.3500.699 motorcyclegender usage 29 32 0.57570.293 Educational Background 31 0.367 Ride-hailing SystemsRide-hailing UsageCar Ownership Systems Usage 0.156 3033 0.6990.3500.699 motorcycle usage 32 0.5757 Educational Background 31 0.367 Ride-hailingCar Ownership Systems Usage 3033 0.3500.699 motorcycle usage 32 0.5757 Professional OccupancyEducationalProfessional Background Occupancy 0.088 3134 0.3670.8150.815 Ride-hailingmotorcycleCar Ownership Systems usage Usage 323033 0.57570.6990.350

Ride-hailingEducationalProfessional Systems Background Occupancy Usage 313433 0.8150.3670.699 motorcycle usage 32 0.5757 EducationalProfessional Background Occupancy 3134 0.3670.815 Ride-hailing Systems Usage 33 0.699 Table A6.motorcycle Goodness usage of fit statistics (Response: 32 Do you think e-scooter is a safe mode). 0.5757 EducationalProfessional Background Occupancy 3134 0.8150.367 Ride-hailing Systems Usage 33 0.699 TableProfessional A6.motorcycle Goodness Occupancy usage of fit statistics (Response: 3234 Do you think e-scooter is a safe mode). 0.57570.815 Ride-hailing Systems Usage 33 0.699 Table A6.motorcycle Goodness usage of fit statistics (Response: 32 Do you think e-scooter is a safe mode). 0.5757 Professional Occupancy 34 0.815 Ride-hailing Systems Usage 33 0.699 Table A6.motorcycle Goodness usage of fit statistics (Response: 32 Do you think e-scooter is a safe mode). 0.5757 Professional Occupancy 34 0.815 Ride-hailing Systems Usage 33 0.699 Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode). Professional Occupancy 34 0.815 TableRide-hailing A6. Goodness Systems of fitUsage statistics (Response: 33 Do you think e-scooter is a safe mode). 0.699 Professional Occupancy 34 0.815 TableRide-hailing A6. Goodness Systems of fitUsage statistics (Response: 33 Do you think e-scooter is a safe mode). 0.699

Professional Occupancy 34 0.815 Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode).

Professional Occupancy 34 0.815 Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode). Professional Occupancy 34 0.815 Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode).

Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode). Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode). Sustainability 2021, 13, 863 23 of 24

Table A6. Goodness of fit statistics (Response: Do you think e-scooter is a safe mode).

Whole Model Test Model -LogLikelihood DF ChiSquare Prob > ChiSq Difference 30.50289 25 61.00578 <0.0001 Full 396.41541 Reduced 426.91830 Goodness of fit R-Square 0.0714 AIC 850.528 BIC 956.989

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