Scooter-Sharing: Just for Fun or sustainable transportation means?
Motivations behind the intention to use Scooter Sharing in the Netherlands
Student Name: Gabriel-Cristian Dobre Student Number: 13388649
Supervisor: Dr. Michael Etter
MSc. in Business Administration – Digital Business Track University of Amsterdam
EBEC approval number: EC 20210514040503 15626 Words
Master’s Thesis – Final Version June 2021
Statement of originality
This document is written by Gabriel Dobre, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Currently, in the Netherlands, the overall transport sector is experiencing significant shifts in perception. From contractual partnerships between the public and private sectors to plans toward automated vehicles, the Netherlands and its ministry greatly emphasize the future of mobility. Scooter-sharing is a relatively new phenomenon, especially in its current, dockless form. This paper aims to get an extensive understanding of people’s perception of the phenomenon, studying the motivations behind the intention to use scooter sharing. For this purpose, three categories of Motivators were created (Instrumental, Social-Hedonic, and Normative) and evaluated in an online survey. By distributing the quantitative survey to Dutch residents, the general motivations towards scooter sharing and the respondents’
intention to use were evaluated. By running a hierarchical regression on each of the motivations derived from research, an order of the most important motivations for Dutch residents was established. The results showed that practical motivations play the most significant role in the intention to use scooter sharing, followed by economic and hedonic motivations. These results suggest a user-centric model of looking at scooter sharing,
highlighting the most critical factors for the intention to use. On this basis, future studies and managerial strategies should consider what drives users towards such services and apply motivators accordingly.
Table of Contents
1. Introduction ... 5
1.1 Sharing Mobility ... 5
1.2 Micromobility Sharing ... 6
1.3 Research Question ... 7
2. Literature Review ... 8
2.1 Micromobility as a phenomenon ... 8
2.2 Scooter Sharing ... 11
2.3 Users and Intention to Use ... 14
2.4 User Motivation... 16
2.5 Instrumental Motivator ... 17
2.6 Social-Hedonic Motivator ... 19
2.7 Normative Motivator ... 23
3. Data and Method ... 27
3.1 Survey design ... 27
3.2 Sample and Sampling procedure ... 28
3.3 Operationalization ... 31
3.3.1 Intention to use scooter sharing ... 31
3.3.2 Instrumental Motivator ... 32
3.3.3 Social-Hedonic Motivator ... 35
3.3.4 Normative Motivator ... 38
3.4 Data Analysis ... 41
4. Results ... 42
4.1 Instrumental Motivator ... 45
4.2 Social-Hedonic Motivator ... 46
4.3 Normative Motivator ... 46
5. Discussion ... 48
5.1 Main findings ... 48
5.2 Theoretical implications ... 55
5.3 Managerial implications ... 56
5.4 Limitations and further research ... 57
6. Conclusion ... 60
7. References ... 62
Appendix A1: Qualtrics Survey ... 73
1.1 Sharing Mobility
The concept of shared mobility services emerged in the context of the rapid-growing market of the sharing economy. By sharing mobility, anyone who is connected to the internet has the opportunity to rent and crowd-consume various means of (before thought-of) private vehicles, such as cars, bicycles, or scooters (Frenken & Schor, 2017). Instead of paying per trip, the consumer pays the time for which he uses that vehicle, enabling a more effective means of quantifying usage costs (Shaheen et al., 2020). Because of its principles, mobility sharing has been recognized as one of the most revolutionary means of transportation, along with vehicle electrification and automation (Fulton, 2018). Past literature highlights both the benefits and the detriments of such systems for the overall life of the cities. Shared mobility brings flexibility and multimodal choices for the population while being more cost-effective than public transport (Aguilera-García, Gomez & Sobrino, 2020). This shift from owning a personal vehicle to on-demand vehicle renting is not unique to the transportation ecosystem.
MaaS (Mobility-as-a-Service) companies offer various mobility means within a single
service, making the vehicle rental process more accessible. Although the definitions for MaaS vary in the literature, some common traits are found when explaining the concept: (hybrid) innovation (Pangbourne et al., 2018, p.34; Jittrapirom et al., 2017; Lyons et al., 2019), use of ICT technologies (Jittrapirom et al., 2017; Kamargianni & Matyas, 2017), app-based
(Jittrapirom et al., 2017; Sochor et al., 2017). In essence, MaaS encompasses any service that enables the user to have a seamless journey from point A to point B with a private vehicle, without needing to own any means of transportation themselves, but instead use an already available car/micromobility vehicle.
Because MaaS services are more appealing to a specific category of users, different from the general population (Zijlstra et al., 2020), it is essential to understand what kind of
users are the most likely to benefit from such a service and why. While electric and hybrid vehicles are becoming more of an alternative for the broad public, other means of individual transportation are also becoming popularized. In the Netherlands, the prevalence of personal means of transport (such as biking) is mainly used as a means for leisure activities ("Cycling Facts", 2018). Even if the car remains the essential mode of travel among Dutch citizens, leisure trips are one category of trips that are already maximizing alternative transportation.
1.2 Micromobility Sharing
This study focuses mainly on a segment of shared mobility services, more specifically micromobility. Shaheen & Cohen (2019) describe the phenomenon as “[…] the shared use of a bicycle, scooter, or other low-speed mode –as an innovative transportation strategy that enables users to have short-term access to a mode of transportation on an as-needed basis”.
This definition encompasses various transportation means, with the main common
characteristics being their running speed of a maximum of 25-30 km/h and the shareability feature. Partly human-powered or not, these transportation devices offer an excellent alternative for short-distance commuting in highly urban areas, shifting away from the traditional vehicle ownership model. While the consumer’s benefits are undeniable, some studies argue that these benefits are frequently at the cost of non-consumers. For example, McKenzie (2020) looks at the spatial dispersion of various micromobility services,
questioning if services such as Lime, which projected the widest dispersion while having primarily short trips, really benefit the city’s infrastructure. With various newly emerging mobility services, there is a need to effectively manage the finite sidewalk space that pedestrians share with these services. In order to establish the directions for the future policymakers, consumers need to be understood. Studies such as Shaheen & Cohen (2019) strive to conceptualize a toolkit to develop micromobility along the current urban mobility
landscape. Even though all these findings are essential for future policymaking, the purpose of this research is oriented more towards the demand side of micromobility. Demand-side research focuses more on why people are interested in such services in the first place and for which part of the population is this discussion the most relevant. Previous studies suggest that highly mobile individuals are more inclined to use such services (Lyons et al., 2019; Zijlstra et al., 2020), yet this phenomenon has not been widely studied in the context of
1.3 Research Question
This study looks at a specific phenomenon from micromobility sharing, namely scooter sharing. The need for exploring this field came from the novelty around the service, combined with limited general knowledge of the field. Previous studies either focus on conceptualizing scooter sharing or talking about social effects. Few studies try to identify why people use scooter sharing, such as Degele et al. (2018), who try to identify emerging patterns in user demographics. Other relevant studies from mobility sharing, such as Hellwig et al. (2015), identify clusters of users with various usage rates based on their personality and motivation to use. With an extensive amount of literature pointing towards the differences in usage for micromobility sharing, a manifestation of motivations is necessary for scooter sharing as well. This research analyzes the different motivations that make people consider using such a service. Because scooter sharing is a prevailing phenomenon in the Netherlands, it seems like the appropriate studying scope to understand how micromobility sharing works for the Dutch population Therefore, the following research question is proposed:
RQ: How do Motivators influence the intention to use Scooter Sharing Services in the Netherlands?
2. Literature Review
2.1 Micromobility as a phenomenon
Instead of fully explaining the operationalization of micromobility sharing, this paper aims to get an extensive understanding of people’s perceptions of the phenomenon. Looking at previous studies that try to understand the individuals’ intention to use shared mobility, the results can be somewhat contradicting. For example, when looking at sharing automobiles, Fraiberger & Sundararajan (2015) find that the shared economy services are more likely to be used by consumers from low-income groups. At the same time, looking at the demographics of the sharing economy in general, Andreotti et al. (2017) argue that shared mobility is mainly used by educated, high-income groups of the population. These differences in results might be a consequence of generalizing sharing services without focusing on one mobility mode. Some studies do focus on mobility sharing, but their approach is exclusively comparative. For example, Shaheen, Cohen, Chan & Bansal (2020) emphasize the
differences between sharing strategies and sharing mobility services. There are significant differences between sharing a vehicle, sharing the passenger seat, or providing a transport service when it comes to their business models. Other papers focusing more specifically on micromobility differentiate between these services and ride-hailing. Nevertheless, the actual interpretation of micromobility is relatively shallow. While the sharing economy principles apply to several of these services, there are also radical differences from one service to another. Most scholars have focused on big players within the sharing economy field, such as Uber and Airbnb (Sutherland & Jarrahi, 2018). While these platforms attract significantly more international attention in the academic world, micromobility sharing also deserves to be studied more extensively.
To this date, the majority of the studies done on micromobility are focused explicitly on station-based (docked) bike-sharing. While in research, dockless micromobility services
are relatively new to explore; such services have an exhaustive useability history. With the first public bicycle sharing service being tested on the streets of Amsterdam in 1965
(DeMaio, 2009), the Netherlands has a long history of pioneering such systems. Researchers consider this kind of approach to mobility sharing to be a first-generation system (DeMaio, 2009; Parkes et al., 2013; Shaheen et al., 2010). These innovations did not stop at the regular bicycle but instead cover the whole micromobility spectrum. Moving towards a second- generation system, in 1974, as a response to growing pollution and traffic issues in
Amsterdam, an industrial designer pioneered the first shared electric cars, capable of a max 30km/h and part of a network of fully automated stations (Bendixson & Richards, 1976). The second generation of mobility sharing is characterized by more security and structure, using a token system with small fees that had to be paid at the docking stations (DeMaio, 2009).
Second-generation systems gained more traction than their predecessor, with
implementations in various European cities (Shaheen et al., 2010). With the development of technology, micromobility sharing became even more controllable in the third generation.
With the successful implementation of the third generation, which made mobility sharing more viable than previously available, society is now moving towards a fourth generation in micromobility sharing (Lazarus et al., 2020). Using the new developments from the IT industry, the mobility industry successfully moved away from docking stations, creating shared micromobility decentralized solutions. The advantage of this evolution is the minimized friction for door-to-door trips. Instead of dropping off the shared vehicle at a specific spot, the e-bike/scooter can be dropped off in the destination's proximity.
In the emerging fourth generation of micromobility systems, new developments can appeal more to the general population, giving the added elements of convenience. Anecdotal evidence brought up by Shaheen and Cohen (2019) indicates that dockless bike and scooter sharing attract a more diverse set of users compared to previously done studies done on
station-based bike-sharing. Similar findings were also noted by a more recent study done by Reck & Axhausen (2021). Their research on the demographics of micromobility services mentions that e-scooters have the highest adoption rate out of all of the studied micromobility means (docked/dockless bike, e-bike, and e-scooter). Because dockless micromobility
sharing is relatively new, the effects of this development on usage patterns are still widely unknown.
In the Netherlands, micromobility has a significantly high adoption rate already, with the bicycle being one of the most common means of transportation ("Cycling Facts," 2018).
With new disruptive innovations in the shared micromobility field and a growing interest in alternative means of transportation, studying the factors that drive Dutch residents towards new means of transportation is essential. In their study, Zijlstra et al. (2020) look at what drives people of the Netherlands towards using MaaS services. Their findings note that most of the ‘early adopters’ for MaaS are predominantly part of a specific demographic (high socio-economic status, most likely under the age of 55). Even so, their study focuses
predominantly on carsharing services, which might have a different demographic compared to micromobility. Studies focusing mainly on micromobility sharing in the context of the Netherlands are somewhat limited, with most of the published studies focusing on bike- sharing. Some of these earlier bike-sharing studies talk about integrating the bike ride in a multimodal travel mode, a common practice in the Netherlands (Martens, 2004, Martens, 2007). Newer studies show that more than 40% of the train travelers in the Netherlands use a bike to commute to/from the train station (Heinen & Bohte, 2014). This behavior is explained mainly by the extensive effort put into creating a comprehensive bicycle policy and the extensive infrastructure created for biking and micromobility (Martens, 2007). The features that the Dutch roads offer to bikers extend their benefits to other micromobility vehicles as well. According to Dutch law, small scooters/mopeds that do not exceed 30 km/h need to
drive on the bike paths without wearing a helmet (SWOV, 2017). Using the bicycle
infrastructure, individuals can benefit from the added efficiency of bike lanes (Kircher et al., 2018). With added convenience factor that electrically powered micromobility vehicles bring to the users, scooter sharing has an increased potential for adoption over traditional bike sharing.
2.2 Scooter Sharing
Even if individuals are willing to adopt micromobility sharing as a means of
transportation, the type of vehicle they are going to use still differs from one user to another.
Both internal (e.g., personal values) and external (e.g., geographical environment) factors can change the user’s preferred micromobility choice (Reck & Axhausen, 2021). Because of the significant variance in motivators for using a specific micromobility vehicle, this study chooses to look at one specific type of micromobility sharing. When looking specifically at scooters as a means of micromobility sharing, study findings become increasingly limited.
While this phenomenon is currently gaining traction in some European cities, it remains relatively unexplored territory for researchers.
Furthermore, various terminology issues make these small vehicles hard to identify.
SAE International created a set of standard terminologies used to identify various
micromobility vehicles (Powered Micromobility Vehicles Committee, 2019). For this study, when it comes to powered micromobility vehicles, the SAE J3194 standard will be used, making a clear distinction between Powered Standing Scooters (PStS) and Powered Seated Scooters (PSeS). Figure 2.1 visually summarizes the differences between the two means of micromobility transport. Given the scope of research of this study, PSeS and Scooter Sharing will be used interchangeably, with PSeS being the only prevalent means type of scooter existing in the Netherlands. Furthermore, in the context of sharing, PSeS Sharing are the only
available scooter sharing services at the moment of writing this paper, making PStS Sharing irrelevant in the context of the Netherlands.
Figure 2.1 – Summary excerpt from the SAE J3194 standard (Powered Micromobility Vehicles Committee, 2019)
While there is some research on PStS sharing, the studies done on PSeS sharing are almost non-existent. The adoption rates of the two scooter-sharing possibilities from one city to another vary greatly. One possible reason for cities to choose standing scooters can be the layout of the urban area. Seated Scooters are bigger, creating regulation issues for various cities (Shaheen & Cohen, 2019). In the Netherlands, with cities having an extensive network of bike paths, seated scooters can be spotted more often. This effect is most likely caused by Dutch regulations, which classify any vehicle with a throttle as a motorized vehicle, which requires numberplates (Overheid.nl, 2019). With standing scooters being increasingly
difficult to register, service providers favor the usage of seated scooters, which are also considered safer than their standing counterparts (Paudel & Fah Yap, 2021).
Some notable findings for PSeS were done by Eccarius & Lu (2020). In their study, they look at adoption intentions for the student population of Taiwan. While their empirical work provides an excellent framework for studying scooter sharing, their structural model lacks the emotional intentions for adopting such a service. For the current study, parts of their usage modes are considered valuable while focusing on current users of scooter-sharing services rather than potential users. Additionally, findings from other micromobility studies focused on PStS or powered bicycles are also considered, as they are believed to have somewhat similar user profiles (Shaheen & Cohen, 2019). There are, however, differences in adoption rates and safety of the used vehicles. For example, Younes et al. (2020) noted some
differences between dockless electric scooters and station-based bike-sharing. There are considerable differences between scooter sharing and bike-sharing, with the users having different temporal usage patterns between the two groups. Another mention by their study is that weather had a more negative impact on bike usage than scooter usage, suggesting that the external factors might be diminished when talking about PSeS. Adding to the potential
improvements that scooters can bring to the micromobility scene, some other studies cover the differences in behavior between scooter sharing and docked bike-sharing as well. In their study, Shaheen & Cohen (2019) look at the findings of Portland’s Bureau of Transportation in their E-scooter Pilot Program. By analyzing data from 700,000 trips, they found that a vast majority of the users reported using e-scooters to get to a destination (71 percent), compared to only a third of the respondents (28.6 percent) saying that they used the scooters for
recreation or exercise (Portland Bureau of Transportation, 2018).
Furthermore, 34 percent of the participants reported using the scooter to replace their car or taxi use. These findings place scooter-sharing services as a much more viable
competitor of the personal car than bike/e bike-sharing. In the Netherlands, while there is an upward trend in the usage of partially motorized micromobility vehicles, individuals who bought an e-bike did not stop using their car ("Cycling Facts", 2018). One explanation for this phenomenon could be the nature of the e-bike trips, with most of the trips being done for leisure purposes. Given that in previous studies individuals who use scooter sharing will do so as a means of reliable transportation, replacing their car trips, this phenomenon needs to be studied in the Dutch context as well.
2.3 Users and Intention to Use
According to several studies on earlier implementations of shared micromobility, user adoption is strongly correlated with specific demographic characteristics (Shaheen et al., 2014; Andreotti et al., 2017). These studies focus on traditional shared micromobility (docked/dockless bikes), with PSeS not being included in their analysis. Given that PSeS sharing is also part of the micromobility sharing scene, the adoption phenomenon needs to be explored for scooters as well. Sharing services such as JUMP (electric dockless bikes) have most users under 55, effectively making electric bicycles attractive for younger parts of the population (Lazarus et al., 2020). Such a phenomenon is interesting when put into a broader usage context. Nearly half of the e-bike kilometers done in the Netherlands are done by individuals who are over 65 years old ("Cycling Facts," 2018). With the added sharing component, micromobility means that otherwise will be most interesting to older people, become accessible for younger populations as well. One potential reason for this
phenomenon can be the usage cost of an electric micromobility vehicle. Owning an electric bike can be a costly asset for a young adult. Having the possibility to pay on-demand might create new opportunities in terms of mobility means. One crucial mention in regards to mobility services is that potential uptake levels vary significantly across the population. In
their study, Ho et al. (2018) look at several descriptive profiles of potential MaaS users. Their findings indicate that various factors influence consumers' willingness to pay for a mobility service across the population segments. Furthermore, other factors such as vehicle ownership (Shaheen et al., 2011) or household structure (Buck et al., 2013) can also play a role in how much a user would be willing to spend on such a service. While scooter sharing does create new opportunities in terms of mobility, the actual usage of the population needs to be examined.
While this study does not directly measure scooter sharing usage, it uses previous literature to quantify usage intention. Using the Theory of Planned behavior, the link between intention to use and actual usage, can be effectively understood (Toni et al., 2018). The theory of planned behavior (TPB) uses intention as a basis for behavioral actions, including social and personal influence (Ajzen, 1991). In exhibiting behavior, the individual considers intention, along with attitude, subjective norm, and perceived behavioral control. While TPB is widely used to predict behavior in social sciences, the theory received significant criticism as well. Most notably, the subjective norm employed by the theory has a pretty general understanding, lacking a concise explanation (Schultz et al., 2007). This paper acknowledges the central role that the intention to use plays in the actual usage, but it does not use the two interchangeably. Because several other constructs play an important role in the formation of behavior (Sheeran, 2002), the main dependent variable of this study will be the intention to use (behavioral intention). For future studies, objective usage data can be collected, with the purpose of measuring scooter sharing usage. The intention to use scooter sharing is inspired by previous studies that measured behavioral intention in the context of mobility sharing (Mattia et al., 2019; adapted from Davis & Venkatesh, 1996).
2.4 User Motivation
To understand the usage intention of PSeS, reasons for which individuals would see such a service as beneficial are needed. Past literature highlights the benefits of such systems for the overall life of the cities. For example, shared mobility can be more flexible than public transport while being more cost-effective (Aguilera-García, Gomez & Sobrino, 2020). When studying the implications of these services for a city's population, the literature finds various motivations for residents to opt into such a service, both extrinsic and intrinsic. To summarize these reasons, most of the manifested motivations fall into one of the following categories:
social benefits (Gazzola, Vătămănescu, Andrei & Marrapodi, 2018; Frenken and Schor, 2017), economic benefits (Heinrichs, 2013; Owyang et al., 2013; Vătămănescu & Pînzaru, 2018), temporal benefits (Aguilera-García, 2020), sustainability concerns (Prothero et al., 2011; Sacks, 2011; Böcker & Meelen, 2017), and hedonistic reasons (Andreotti, Anselmi, Eichhorn, Hoffmann, & Micheli, 2017). Andreotti et al. (2017) look at multiple studies about collaborative consumption in the context of motives and attitudes. Their framework classifies three general motivators for participatory behavior in the sharing economy.
While the framework developed by Andreotti et al. (2017) can be used as a starting point, a more thorough explanation in the context of scooter sharing is required. For this purpose, the current study looks at similar literature which talks about specific motivations and tries to apply it in the context of the current research. Connecting the explanations of Gazzola et al.
(2018) along with Andreotti et al. (2017), the following three motivator categories are established:
1. Instrumental Motivator –encompassing two different sources of motivation:
monetary and practical. Both of them will be further explained in the context of scooter sharing.
2. Social-hedonic Motivator – mainly address the entertaining part of using such services, which creates an internal motivation towards mobility platforms (Gazzola et al., 2018). While the social motivator will be tested as well, as they are a central part of several studies about the sharing economy, it is expected to have a lower correlation coefficient due to the differences between general sharing services and scooter sharing.
3. Normative Motivator – looking specifically at sustainability motivation but also the degree of conformity and the individual perception of ‘using because others are using it’.
2.5 Instrumental Motivator
Early work on the sharing economy and the main motivations behind adopting new means of transportation indicates that one of the most relevant factors is the economic factor.
Looking at the emergence of the Sharing Economy, Schor & Fitzmaurice (2015) note that the economic incentive lies at the base of the motivation to participate in such a phenomenon.
Furthermore, when looking specifically at traveling behavior, Sochor et al. (2018) look at what makes early adopters change their traditional patterns. While users are first guided by curiosity towards new services, the primary motivation to stay is the economic advantage over their previously used services. In scooter sharing, these economic advantages are not directly apparent by the price of the service but rather through the enabling possibilities. One key takeaway from Shaheen et al. (2017) and their study on Shared Mobility Transportation Equity is that shared mobility enables new means of transportation at a much lower upfront cost for the users. Their study highlights that using such services can provide some benefits associated with vehicle ownership without initial purchase and maintenance costs. These findings are similar to Durand et al. (2018). They explain how MaaS can free users from mode-specific costs that are usually associated with one specific transportation means (car costs, public transport subscription). The economic benefits of scooter sharing are not
immediately apparent when only evaluating the price of the trip. A complete evaluation should account for the choice freedom that sharing services bring, without any other upfront costs. Durand et al. (2018) noted that the economic feasibility of scooter sharing is also influenced by current vehicle ownership. For an individual who is already in possession of a scooter or a car, using a scooter sharing service might be less feasible. Another important finding from Sochor et al. (2018) is that if a service is perceived as economically
disadvantageous for the user in the ‘early contact’ phase, this can negatively affect their overall adoption rate of new travel services. Because the perceived economic advantage is so different from one individual to another, scooter-sharing services need to focus their initial efforts on attracting users who consider scooters an economically feasible alternative.
Considering the previously studied impacts of the economic motivation, the following is presumed:
H1: The users’ intention to use powered seated scooter sharing is positively
influenced by the perceived economic gain over traditional means of transportation.
The second instrumental motivation discussed in this study is the practicability
motivation. Practicability itself consists of travel time and product availability. Both elements have been previously studied in the context of mobility sharing, with some authors placing availability on the same level with economic motivation in terms of importance (i.e., Frenken
& Schor, 2017). While the usage of ICTs minimized the efforts and costs around searching for a shared vehicle, the distribution of dockless vehicles around a city is still highly
unpredictable. Individuals who might end up using a scooter-sharing service frequently need to know that they will have one available in their proximity. Findings by Aguilera-García et al. (2020) on the adoption of scooter sharing in Spain also back up these claims. In their study, the authors look at the most important factors for individuals to start using scooter
sharing. Their respondents indicate the frequency of service, travel time, and proximity as the essential factors for adopting a specific scooter-sharing service over another. Because
competing businesses in the field of scooter sharing mostly have the same value proposition, practical motivations often end up being the decisive factor for the average user. In order to accurately assess the practicability of scooter-sharing services over other means of
transportation, travel time needs to be accounted for as well. Travel time accounts for the entire trip duration, hence the connection with availability. If a bus stop/scooter is further away from the point of departure, the travel time will increase as well. A study done on powered standing scooter sharing in Paris looks at the differences between long trips and short trips on these scooters while exploring the motivations behind choosing a scooter sharing compared to public transport. Christoforou et al. (2021) note that most of the long scooter trips are not made for fun but rather for travel time savings. These findings have a high potential for variation based on the city’s infrastructure, as well. In cities where public transport will give shorter trip times than scooter sharing, implementing such a service will be increasingly difficult. Even so, the Netherlands has an extensive network of biking paths ("Cycling Facts", 2018), which offer the perfect infrastructure for micromobility. Therefore, the following hypothesis is formulated:
H2: The users’ intention to use powered seated scooter sharing is positively influenced by the perceived practicability of such service, namely travel time and availability.
2.6 Social-Hedonic Motivator
The Social-Hedonic category encompasses the benefits of the communities around sharing services and the overall enjoyment of such services. This category of motivations has a substantial intrinsic value, starting from the individual’s internalized motivations of doing
good, with a conscious positive extension towards other actors in the sharing economy.
Habibi et al. (2017) developed a dual-mode to look at collaborative consumption, a spectrum where services lie between pure sharing and pure exchange (Figure 2.1).
Figure 2.1. The sharing continuum (Habibi et al., 2017)
In this study, the social-hedonic motivator comprises the characteristics of pure sharing (personal, social links, pooling resources) rather than the exchange characteristics of the instrumental motivator. These two modes of consumption can also be associated with two different attitudes towards mobility sharing: collective and personal motives (Peterson &
Simkins, 2019). As Mattia et al. (2019) note, these two attitudes are not mutually exclusive, with the users’ attitude towards mobility sharing being influenced by both. In this paper, social-hedonic and normative are seen as collective motives, while instrumental motives are
seen as personal. Scooter sharing is expected to have more pure exchange characteristics than other sharing economy services, given its decreased socializing opportunities. Even so, the nature of scooter sharing doesn’t fully exclude the existence of social motivation. In their study, Mattia et al. (2019) look at how the attitude towards car sharing also embodies social aspects. Their explanation of social motivators relies on the performative self as part of a community. They construct this social motivation dimension through the idea of what people believe is right to do in a societal context. This view doesn’t necessarily imply social
pressure, but rather an observed behavior of others. Looking at the social motivation as an influencer for the intention to use scooter sharing allows this study to measure the social factor in a performative sense rather than in a community participation sense. Considering this social dimension, some users might be more attracted to the social advantages of scooter mobility, actively using such services to partake in mobility sharing. These social advantages represent any behavior that manifests in society and can be improved by scooter sharing. One example of such social advantage comes from the qualitative study of Mattia et al. (2019).
Talking to multiple people about the advantages of shared mobility, they found that people feel safer when using a car at night than public transport. This kind of social advantage might be seen in the case of scooter sharing as well, with the scooter being an individual
transportation instrument. By having the independence of individual travel, one can see itself in increased safety over public transport. Increased salience for social behavior has been noted by other studies, notably when correlated with broader social values such as environmental consciousness (Mattia et al., 2019). Given the importance of the social dimension for other mobility sharing studies, the following hypothesis is addressed:
H3: The users’ intention to use powered seated scooter sharing is positively influenced by the perceived social advantages.
Considering intrinsic motivators for people to use scooter sharing, another important factor is the inherent satisfaction people get from such services. In Christoforou et al. (2021), the participants rated playfulness as one of the most critical components of scooter sharing, with a significantly heightened playfulness importance given by the frequent users. Their study results might give an early example of how particular individuals use scooter sharing, with clusters of people willing to use the service because of the heightened hedonic element.
These findings are not unusual for the field of mobility sharing. Some studies, such as
Krogmann et al. (2020), attribute the heightened salience of hedonism particularly to younger participants. While the exact characteristics of individuals that place hedonic values on top of instrumental or normative motivators are not fully known, evidence points towards a general fun-seeking behavior in most sharing mobility users. Sanders et al. (2020) try to outweigh the perceived advantages and disadvantages when scooter sharing. In their study, most of the positive responses are related to scooter enjoyment items (being fun/relaxing). In contrast, the negative responses were related more to instrumental items (malfunctions, cannot carry too much). While they note that a significant amount of their noted trips were initiated for transportation purposes, hedonism was also a part of the trip time. Instrumental and hedonic motivators are not mutually exclusive. While the original motivation of an individual can be to commute (instrumental), it does not automatically exclude the possibility of enjoyment through the trip (hedonism).
Furthermore, the hedonic element of driving also needs to be considered. Previous studies such as Tchetchik et al. (2020) point out that driving decisions are often not only based on functional utility, with the pleasure element often outweighing the price and usability of the vehicle. Their study also shows that for users who manifest an increased trialability-seeking behavior, hedonism is particularly important. Scooter sharing being essentially free to try for the first time might attract more pleasure-seeking users than non-
sharing means of transportation. In this paper, hedonic actions are defined as activities (driving a scooter, in the context of sharing) that bring pleasure or happiness (Merriam- Webster, 2003). This motivation sees enjoyment as one element that attracts users towards scooter sharing, creating social and personal value. This view is aligned with previous research, which places entertainment as one of the essential parts in collaborative
consumption attitude formation (Hamari et al., 2016). Additionally, the effect of hedonism is not expected to be lower in the case of current vehicle owners, with the novelty element of scooter sharing still being present. To discover if the hedonic effect is as strong as predicted, the following hypothesis is formulated:
H4: The users’ intention to use powered seated scooter sharing is positively influenced by the perceived hedonic value of such service.
2.7 Normative Motivator
Generically looking at the sharing economy and the various studies done in this field, researchers often stress the normative factors that make these services stand out. This
normative motivator is exteriorly driven, in a similar manner to the social-hedonic motivator.
Previous papers from the mobility field consider normative factors as one of the core behavioral motivations for adopting different mobility means (Moons & Pelsmacker, 2012;
Petschnig et al., 2014; Schuitema et al., 2013). From studies that see mobility sharing as the driving force behind sustainability behavior (Mattia et al., 2019) to studies that see mobility sharing as necessary for the future (Ma et al., 2018), sustainability has always been the normative center in the discussion for mobility alternatives. Because organizations that pursue activities in the sharing economy are perceived as pursuing the interest of both the planet and the consumers (Cherry and Pidgeon, 2018), sustainability is perceived as a
compelling driver for participating in the sharing economy. When trying to isolate typologies
of users and their potential adoption of alternative transportation means, their attitude towards the environment is highly important. Sharing behavior is generally associated with a positive impact on waste reduction and overall consumption. This motivation might be vital for users in the context of mobility sharing. As suggested by Böcker & Meelen (2017), because people perceive ridesharing or mobility sharing as an efficient means to reduce carbon footprint, they might be particularly interested in using mobility-sharing services for sustainability reasons.
Even so, these motivations are only partly backed up in the context of micromobility.
Aguilera-García et al. (2020) note that environmental awareness is indeed a factor for using scooter-sharing. However, its influence is much smaller than other decision factors (such as flexibility or easy parking).
Because these devices are electrically powered, they might seem to be more sustainable than traditional transport means. Even so, the reality of how sustainable these services are is questionable. Christoforou et al. (2021) note that only a few of the scooter users in their research replaced motorized vehicles in their trip. Most of the users replaced walking or biking as a means of convenience. Even so, their study was conducted on PStS, which are considered less stable and comfortable than the seated scooters (Paudel & Fah Yap, 2021). When looking at seated scooters, the result may vary, with users taking longer trips than with standing scooters, creating a more attractive substitute for motorized vehicles. This hypothesis is confirmed by Portland Bureau of Transportation (2018) findings, which note a significantly greater conversion from personal car to scooter sharing in the context of longer trips. In order to study how does this sustainability phenomenon manifest in the Netherlands, the following hypothesis is addressed:
H5: Sustainability concerns positively influence the users’ intention to use powered seated scooter sharing.
The last motivation that this study accounts for ties back to the social elements from the social-hedonic motivator. Besides the social need to performatively maximize good (present in the case of social hedonism as a motivator), the individual relies on social acceptance as well. This conformity-based normative motivator has been previously studied in the context of mobility sharing adoption. Liu & Yang (2018) look at the acceptance of mobility sharing through the technology acceptance model (TAM). This theory excels at explaining the underlying behavioral reasoning behind adopting new information
technologies. In their study, the authors look at how ‘herd behavior’ influences subjective norms and, through conformity, push people into using mobility-sharing services. If
individuals feel that the social norm is to use such services, the social desirability and other subjective values might push them towards partaking in such services. Peterson & Simkins (2019) explain in their study how, besides social desirability, the users’ conformity values will also influence them in their decision towards mobility sharing. Their paper uses the behavioral reasoning theory to explain how individuals decide to start using a car-sharing service. Results show that consumers’ conformity plays a part in their subjective norms, which have a role in the cognitive decision to use a car-sharing service. Similar to the findings of Liu & Yang (2018), in this study, the partaking motivation comes from an extrinsically motivated subjective value that embodies the need of an individual to belong in society. Eccarius & Lu (2020) also highlighted a category of users who feel the need to partake in micromobility sharing, with the particular normative purpose of sustainability.
Their study shows a category of students that manifest a low usage intention in a pre- contemplation phase, which still end up using scooter sharing. The researchers call this phenomenon “green hypocrisy,” when there is a consistent gap between the users’ attitudes and actions, motivated by the need to belong and do good for the environment. This study was developed in Taiwan, a country with a particularly collectivistic society, as Hofstede et
al. (2010) noted. Whether such findings are replicable in a country like the Netherlands is still unknown. Considering both the subjective need for conformity, along with the sustainability concerns, the current study constructs the normative motivator. For cities in the Netherlands, the partaking effect might explain part of the heightened adoption rate in the student
demographic. In order to explore this phenomenon, the following hypothesis is developed:
H6: The users’ intention to use powered seated scooter sharing is positively influenced by their conformity level.
3. Data and Method
In this chapter, the design of this thesis and a thorough description of the data is presented. This research aims to assess the most substantial factors that play a role in the intention to use scooter-sharing services in the Netherlands. For this scope, three motivators (each with two underlying motivations) were derived from theory and tested using an online survey. Besides the primary motivators, various other demographic data previously used in mobility sharing literature (such as income or household structure) were also collected. The current study follows the structure of other relevant works in the field of the sharing economy (e.g., Böcker & Meelen, 2017; Gazzola et al., 2019) with a particular interest in scooter- sharing as a phenomenon and its novelty within the cities of the Netherlands. Because the current study aims to observe trends in the scooter sharing phenomenon and offer broader explanations for the underlying motivations, quantitative research methods are the most suited (McCusker & Gunaydin, 2015). Using Qualtrics to design and distribute the survey, the respondents' participation was possible via the Internet. Trying to maintain a high reliability of the results, all the scales used in the current study were adapted from previous, peer-reviewed studies, with the least possible modifications, in order to fit the context of scooter sharing.
3.1 Survey design
In the first part of the survey, the participants were informed about the survey’s purpose and how their personal (anonymized) data will be stored. After being asked to agree to the implications regarding data collection and anonymity, the first block of questions was displayed to the participants. The questions were intended to establish basic demographic characteristics of the participants, with the following variables being created: Gender, Age, CityOfResidence, Profession, EducationLevel, MonthlyIncome, and HouseholdStructure.
Additionally, the participants were also asked if they currently possess a driving license and if they ever used a scooter sharing service before. These two dichotomous variables
established essential groups of potential scooter-sharing users. By conducting an Independent-Samples T-test on both DrivingLicense (t(193) = -4.11, p < .001) and ScooterSharingUse (t(193) = -10.22, p < .001) it was confirmed that there indeed is a significant difference in both owning a driving license and previously using scooter sharing, when it comes to the intention to use scooter sharing. In order to further explore the
differences in the potential scooter sharers, the six research-based motivations were evaluated in the survey. Each of the motivations was individually evaluated in matrix tables on seven- point Likert scales. Besides the motivations, several other questions were asked in the matrix tables. In the first matrix table, questions that measure the intention to use scooter sharing (DV) were also added. Additionally, one other instrumental item, which was not included in either of the scales, was deployed (i.e., “When using scooter sharing, it is always important to have a scooter close to my location.”) in order to evaluate the salience that users give to scooter proximity. Overall, the survey included 32 questions (and two attention-check questions for the Mturk version), with an average completion time of 3 minutes. A complete explanation of how the questions are operationalized into variables can be found in the next sections. All the items of each matrix table were randomized using the Qualtrics “question randomization” function to keep internal validity.
3.2 Sample and Sampling procedure
The units of research for the current study are all the individuals who currently live in the Netherlands and could be potential users of scooter-sharing services. In the data collection phase, 215 responses were collected, out of which 195 were valid. Out of the valid responses, 110 (56.4%) were female, and 85 (43.6%) were male. The median age of the respondents was
24 years old (SD = 6.23). Even though the age of the respondents makes it hard for the sample to represent the population, previous studies on the sharing economy suggest an inversed-correlation between age and participation in such services, with the 18-29 age group being most likely to have heard of such a service (Alberta Andreotti et al., 2017; Christoforou et al., 2021; Zijlstra et al., 2020). Therefore, the sample age is not representative for the entire population of potential users but it is representative for the part of the population which is the most likely to use scooter sharing. They were nine instances of incomplete answers and another 11 answers that did not pass the attention check. The uncompleted or invalid responses were still recorded, with the cases excluded listwise during the data analysis.
When doing quantitative research, the ideal method of sampling is probability sampling techniques. While these methods offer far superior metrics in terms of statistical inferences (Neuman, 2014), such methods have a practicability limitation for the purpose of a master’s thesis. Considering the time constraints, non-probabilistic sampling methods were used in this study, namely convenience and snowball sampling. In order to gather participants, two main channels were chosen for survey dissemination. As the first channel, two crowdsourcing convenience sampling services were used. Clickworker and SurveySwap are two services similar to Amazon Mechanical Turk, which are more popular for the Dutch population. These services use the interest people have in scientific research and allow access to a diverse demographic, serving as a pool of research participants. The main difference between the two services is that Clickworker uses a Pay-Per-Click model, where survey participants are rewarded a small monetary compensation. In contrast, SurveySwap is based on sharing surveys with other researchers who are also looking for respondents. One of the most
significant methodological criticisms around using such services is that users are incentivized to fill in as many surveys as possible, given the monetary compensation (Rouse, 2015).
Given that SurveySwap participants do not receive any monetary compensation but rather getting back survey responses themselves, the reliability of the responses should be slightly improved. While the data quality is diminished for such platforms (Chandler & Shapiro, 2016), certain techniques can minimize the impact on the overall reliability. For example, Rouse (2015) notes that more reliable answers were provided in surveys where workers were verified for their attentiveness. Additionally, Qualitrics supports automatic checking for suspicious activities of the survey respondents and prevents ballot-stuffing. For the variant of the survey distributed through Clickworker and SurveySwap, two additional questions were included in the matrix tables, checking for the respondents’ attention (i.e., “This is an attention test, please pick somewhat agree.”). For the other variant of the survey, these questions were excluded, as some researchers that attention check strategies might introduce a demographic bias (Anduiza & Galais, 2016; Berinsky et al., 2014).
As the second channel of survey distribution, WhatsApp groups with various users were used. While most group members were Dutch residents, participants were explicitly asked to fill in the survey only if they are currently living in the Netherlands. In order to avoid sample bias, people from different WhatsApp groups were targeted, with the only common element being the Dutch residency. Furthermore, as a way of snowball convenience sampling, participants were asked to further share the survey with other possibly interested individuals. One downside of this convenience sampling method is the abundant reach within student communities, which might not be entirely representative for the population. While a great number of participants (N = 96) were students, many respondents also reported being full-time employed (N = 44) or part-time employed (N = 47). At the end of the data
collection phase, the datasets obtained from the two channels of survey distribution were united, with the purpose of further data analysis.
When building quantitative research, one of the essential steps is operationalization.
By understanding how does this research measures abstract concepts, the validity of the discourse can be better understood. The measurement of the motivators for using scooter sharing services was realized using adaptations of previously used scales from different areas of the sharing economy literature. For the first sociodemographic block of the study,
variables were inspired from both Aguilera-García et al. (2020) and Eccarius & Lu (2020), mainly because of the similarities in methodology. Then, the intention to use scooter sharing and the three motivators were evaluated, as explained below. All of the scales were first subject to confirmatory factor analysis, given the strong theory that points towards the creation of the scales (Hurley et al., 1997). Additionally, measures of internal consistency were deployed to test the reliability of the established scales in the context of the current study. Both the factor analysis and the reliability analysis are further explained below.
3.3.1 Intention to use scooter sharing
For the main dependent variable of this study, IntentionToUse, two of the items from the Behavioral Intention scale (used by Mattia et al., 2019; adapted initially from Davis &
Venkatesh, 1996) were used and modified from car-sharing to scooter sharing. Although contains only two items, the scale was retained because of its clear interpretability and general purpose for this study (Worthington & Whittaker, 2006). Eisinga et al. (2013) suggested that in the current case of a two-scale item, the best reliability report is the Spearman-Brown coefficient. With a sufficient degree of reliability (r = .83) determined by the Spearman-Brown coefficient, the scale was kept as the primary dependent variable of the
study, similarly to the study done by Mattia et al. (2019). The items were measured on a seven-point Likert Scale coded from 1 (Strongly disagree) to 7 (Strongly agree).
3.3.2 Instrumental Motivator
To evaluate the importance of the Instrumental Motivator for the intention to use scooter sharing, a matrix table of eight items was constructed. All the items were evaluated on a seven-point Likert Scale coded from 1 (Strongly disagree) to 7 (Strongly agree). The length of the scale was constant across all the motivators to maintain internal consistency and create a valuable benchmark across the questionnaire (Johns, 2010). Furthermore, the seven- point scale was chosen over the five-point scale in order to balance manageability and discrimination between respondents while also ensuring validity (Foddy, 1994). A confirmatory factor analysis was run on the seven adjusted items, testing its sampling adequacy and confirming that the two motivations can be indeed used as factors. A KMO Measure of Sampling Adequacy (KMO = .802, p < .001) determined the suitability of the factor analysis. The maximum likelihood factor analysis with a cut-off point of .40 and the Kaiser’s criterion of eigenvalues greater than 1 (Field, 2017) resulted in a two-factor solution explaining 74.33% of the variance. The items of the two factors are presented below (Table 3.1). The separation done by the factor analysis is consistent with the literature findings, creating two Instrumental factors: Economic Motivations and Practical Motivations.
Table 3.1: Evaluation components for the Instrumental Motivator: item loadings on a two- factor maximum likelihood solution
Components Practical Mot.
1. I can save money if I participate in scooter sharing. .79 2. My participation in scooter sharing can improve my economic
3. Participating in scooter sharing is cheaper than other options
available in the market. .61
4. My usage of scooter sharing saves me time. .74 5. I find scooter sharing useful for my daily mobility. .81 6. Using scooter sharing enables me to accomplish my daily
mobility more quickly. .88
7. Using scooter sharing enhances my effectiveness regarding my
The Economic Motivations independent variable was adapted from a previous study on the intention to engage in sustainable consumption. Dabbous & Tarhini (2019) construct a variable around economic benefits for their study, using three items revolving around
participation in the sharing economy. For the current study, these three items were adapted for scooter sharing and used in order to determine the perceived economic motivation of using such services (e.g. “Q00: Participating in scooter sharing is cheaper than other options available in the market”.). These three items were integrated into the Instrumental
Motivator's matrix scale to calculate the EconMot independent variable and testing H3: The users’ intention to use powered seated scooter sharing is positively influenced by the
perceived economic gain over traditional means of transportation.
A reliability test was done on the scale after modifying the items, in order to assure the internal validity for this specific sample. The Cronbach’s alpha value (α = .79) showed a fairly high internal validity, therefore the scale was computed into the variable EconMot (M = 3.84; SD = 1.35).
The Practical Motivations independent variable drives its relevance from travel time and availability sub-motivations (as suggested by literature). In order to evaluate the
importance of the practical motivations for the willingness to use scooter sharing, an adapted version of the perceived usefulness scale, developed by Schikofsky et al. (2020), was used.
Questions such as “Using scooter sharing enables me to accomplish my daily mobility more quickly.” measure the perceived travel time advantages that scooter sharing services provide.
Besides the three items from the original study of Schikofsky et al. (2020), another additional item was added, corresponding to the availability importance as a motivator for the intention to use scooter sharing. This addition to the Practical Motivations scale was driven by the findings of Aguilera-García et al. (2020), which place proximity as one of the most important factors for scooter sharing adoption. Therefore, the last question of the scale is “Q00: When using scooter sharing, it is always important to have a scooter close to my location.” A reliability test was done on the scale after modifying the items in order to assure the internal validity for this specific sample. The Cronbach’s alpha value (α = .90) showed a reliable internal validity, therefore the scale was computed into the variable PracMot (M = 4.64; SD = 1.42). The Practical Motivations independent variable was used to test H4: The users’
intention to use powered seated scooter sharing is positively influenced by the perceived practicability of such service, namely travel time and availability.
3.3.3 Social-Hedonic Motivator
To assess the relevance of the Social-Hedonic Motivator for the intention to use scooter sharing, a matrix table of eight items was constructed. All the items were evaluated on a seven-point Likert Scale coded from 1 (Strongly disagree) to 7 (Strongly agree). A confirmatory factor analysis was run on the seven adjusted items, testing its sampling adequacy and confirming that the two motivations can be indeed used as factors. A KMO Measure of Sampling Adequacy (KMO = .785, p < .001) determined the suitability of the factor analysis. The maximum likelihood factor analysis with a cut-off point of .40 and the Kaiser’s criterion of eigenvalues greater than 1 (Field, 2017) resulted in a two-factor solution explaining 59.30% of the variance. Given the poor additional variance explained by the second factor (15%) and the poor inter-items factor correlations for the Social part of the motivator, it was decided to keep only one of the two motivations. Even so, the items of the two factors are presented below (Table 3.2). The separation done by the factor analysis is consistent with the literature findings only for one of the motivations, creating one, Hedonic Motivator.
Table 3.2: Evaluation components for the Instrumental Motivator: item loadings on a two- factor maximum likelihood solution
1. Scooter sharing is fun. .94
2. Scooter sharing is exciting. .87
3. Scooter sharing is pleasant. .82
4. Scooter sharing allows those who do not own a private scooter to always have one at hand.
5. Scooter sharing provides greater security than public transport.
6. Scooter sharing improves the quality of travel compared to public
transport (waiting times, availability, crowding). .44 .52 7. Considering health safety, scooter sharing is less safe than public
transport (in the current context of the Covid-19 crisis).
For the construction of the Social Motivations variable, three items from a previous study were used and adapted in the context of scooter sharing. In the original study performed by Mattia et al. (2019), they identify these motivations as being “social aspects” using a preliminary qualitative analysis. These aspects refer to possibility, security, and quality of travel and are seen as a performative part of one’s social activity within his city. Inspired by the security aspect of the social motivations (e.g., Scooter sharing provides greater security than public transport), another survey item was drafted. With an increased aversion towards
public transport, as a consequence of the COVID health crisis (Christoforou et al., 2021), a health safety item needs to be introduced as well. Therefore, the fourth item evaluating Social Motivations is “Scooter sharing improves the health safety when traveling, compared to public transport (in the current context of the Covid-19 crisis).”. In the end, this scale was not used, with a non-satisfactory Cronbach’s alpha value (α = .43). The independent variable was intended to test H5: Users’ perceived social advantages positively influence the intention to use powered seated scooter sharing.
The Hedonic Motivations independent variable is used as a second part of the Social- Hedonic category, comprising one of the core external motivators used in this study. The scale was inspired by the study of Hamari et al. (2016), which used a similar form to measure the enjoyment of users when participating in collaborative consumption. Their scale was originally created with six items, out of which only the three most relevant ones were selected for the current study. When selecting the items, the most suited were used, with the factor loadings also being considered. The original scale was created using the study from van der Heijden (2004), which looked at user acceptance of hedonic information systems. This scale evaluates personal hedonic emotions adapted to scooter sharing (e.g., Scooter sharing is pleasant.). A reliability test was done on the scale after modifying the items to assure the internal validity for this specific sample. The Cronbach’s alpha value (α = .91) showed a reliable internal validity, therefore the scale was computed into the variable PracMot (M = 5.23; SD = 1.23). This independent variable was used to test H6: The users’ intention to use powered seated scooter sharing is positively influenced by the perceived hedonic value of such service.
3.3.4 Normative Motivator
To evaluate the importance of the Normative Motivator for the intention to use
scooter sharing, a matrix table of six items was constructed. All the items were evaluated on a seven-point Likert Scale coded from 1 (Strongly disagree) to 7 (Strongly agree). A
confirmatory factor analysis was run on the six adjusted items, testing its sampling adequacy and confirming that the two motivations can be indeed used as factors. A KMO Measure of Sampling Adequacy (KMO = .797, p < .001) determined the suitability of the factor analysis.
The maximum likelihood factor analysis with a cut-off point of .40 and the Kaiser’s criterion of eigenvalues greater than 1 (Field, 2017) resulted in a two-factor solution explaining 72.41% of the variance. The items of the two factors are presented below (Table 3.3). The separation done by the factor analysis is consistent with the literature findings, creating two Instrumental factors: Sustainability Motivations and Normative Motivations.
Table 3.3. Evaluation components for the Normative Motivator: item loadings on a two- factor maximum likelihood solution
Components Normative Mot.
1. Scooter sharing contributes to reducing the level of
2. Scooter sharing contributes to reducing the level of traffic
in my city. .55
3. Scooter sharing makes me feel like a responsible traveler
from an environmental viewpoint. .86
4. Scooter sharing appears to be the dominant sharing
application; therefore, I would like to use it as well .84 5. I follow others in accepting scooter sharing. .85 6. I would choose to accept scooter sharing because many
other people are already using it. .76
Note. Extraction method: maximum likelihood; Rotation method: Oblimin with Kaiser normalization.
The Sustainability Motivations independent variable was constructed around the individual concerns for environmental aspects. A previous scale was adapted from Mattia et al. (2019), who looked at the intentions of re-using free-floating car-sharing services. Two of the three items of their scale were inspired by the study done by Ferrero et al. (2018) on the initial forms of car-sharing services. The last question of their scale comes from addressed
environmental concerns from their initial qualitative study. Topics such as reducing the level of traffic or pollution were addressed in the questions to effectively measure how much the sustainability factor affected users’ willingness to adopt scooter-sharing services. The three items were tested for reliability in order to assure the internal validity for this specific sample.
The Cronbach’s alpha value (α = .73) showed a high internal validity, therefore the scale was computed into the variable SustMot (M = 4.63; SD = 1.22). This independent variable was used to test H7: Sustainability concerns positively influence the users’ intention to use powered seated scooter sharing.
The Conformity Level independent variable was constructed as a partaking motivation and evaluated in the same matrix table as the other Normative Motivator, Sustainability Motivation. This variable was also measured using three items, adapted from the IMI scale in the study of Liu & Yang (2018). In their study, the authors used these items to measure Imitating Others behavior, trying to determine what role this variable plays in the behavioral intention to use mobility sharing. This scale was used not only by their study but rather adapted from Sun (2013), originally used for a longitudinal study of herd behavior. For the current study, the Conformity Level is important to understand the Normative influence of partaking on the willingness to use scooter sharing. Questions such as “Scooter sharing appears to be the dominant sharing application; therefore, I would like to use it as well”
allow the scale to evaluate the individual perceptions of the current scooter sharing popularity while also measuring the personal necessity to follow the trend. A reliability test was done on the scale after modifying the items in order to assure the internal validity for this specific sample. The Cronbach’s alpha value (α = .86) showed a high internal validity, therefore the scale was computed into the variable ConfMot (M = 4.10; SD = 1.39). This independent
variable aids in testing the last hypothesis of this study, H8: The users’ intention to use powered seated scooter sharing is positively influenced by their conformity level.
3.4 Data Analysis
The obtained data was then exported from Qualitrics to SPSS 26, with the purpose of data analysis. The two obtained datasets (crowdsourced and WhatsApp) were merged, with the cases that did not pass the attention check being excluded listwise. For the data analysis, only the finished cases that have passed the attention check were selected. Then, the
Social_Hedonic_6 (“Considering health safety, scooter sharing is less safe than public transport (in the current context of the Covid-19 crisis”) item was reverse-coded in order to fit the socializing motivation scale. Afterward, data validation checks were conducted for each of the variables. Normality distribution (reported below) and outliers check were first performed to assure the suitability of data for further interpretation. A factor analysis and reliability analysis were conducted for each of the scales, as reported above. The additional created variables (besides the scale items) were PassedCheck (verifying the attention checkers), Snowball (dichotomous, indicating whether the participant was recruited via WhatsApp or not), and CityHasScooter (dichotomous, indicating whether the city of residence has a scooter sharing service operational or not).