The Happy Cyclist

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The Happy Cyclist

The role of instrumental, hedonic and symbolic attributes for e-bicyclists and conventional bicyclists in relation to their commuting, shopping and leisure trip satisfaction

Rowdy Bijl

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The Happy Cyclist

The role of instrumental, hedonic and symbolic attributes for e-bicyclists and conventional bicyclists in relation to their commuting, shopping and leisure trip satisfaction

Master Thesis for obtaining the MSc in Marketing Management

Author: Rowdy Bijl

Pesserstraat 13, 7901 LB Hoogeveen, The Netherlands r.l.bijl@student.rug.nl

S3420884

Supervisor: dr. J.I.M de Groot

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ABSTRACT

The aim of this research is to understand to what degree the instrumental, hedonic and symbolic attributes of e-bikes and conventional bicycles contribute to commuting, leisure and shopping trip satisfaction. To answer this, we distributed an online survey, which was filled in by 168 participants. The results suggest that e-bike users are more satisfied than conventional bicyclists across commuting, leisure and shopping trips. Trip satisfaction of both e-bike users and conventional bicyclists depend largely on how they perceive the instrumental attributes, which in turn influences how the hedonic and symbolic attributes are perceived, depending on the type of trip these people are making. We also found that people with an e-bike hold more pro-environmental beliefs than conventional bicycle users, and that this influences their trip satisfaction. This research can help marketers to develop more successful marketing campaigns when considering different types of target groups, depending on the types of trips.

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1. INTRODUCTION

Back in the day, it was assumed that people would mostly base their decision on which product had the highest utility (McFadden, 1986). In the case of travel mode choice models, the theories were mostly based on the assumption that people either try to maximize their utility or minimize their risk (de Haas, Hoogendoorn, Scheepers, & Hoogendoorn-Lanser, 2017). However, these “objective measures” failed to recognize that psychological determinants such as habits, attitudes and lifestyle choices could also be good predictors of travel mode choice (Van Acker, Van Wee, & Witlox, 2010). For example, Van Acker et al (2010) argue that habits might prevent car users from switching to other modes of transport, even though they are motivated to do so. Therefore, it seems that not only rational criteria are important to take into account when making a decision in relation to travel mode choices.

E-bikes are taking this travel world by storm. In the Netherlands alone, they already account for one third of the total sales volume in bikes (Business Insider, 2017). E-bikes are an excellent alternative to regular bikes that offer many benefits. Research by Jones, Harms and Heinen (2016) found that people are using e-bikes because certain travel patterns are more complex to travel by a regular bike, and because using an e-bike is sometimes more convenient than using a car.

Convenience is an attribute that falls into the instrumental category (Steg, 2005). Dittmar (1992) distinguished between three of these categories, namely instrumental, symbolic and hedonic values. Instrumental values refer to utilitarian attributes (Allen, Ng & Wilson, 2002) and the utility people derive from using the products. In the case of bicycles, these could be speed and ease of use. Dittmar (1992) refers to hedonic values as affective attributes, which are linked to the different emotions people experience. When people are riding a bicycle, these could be joy and pleasure. Symbolic attributes refer to the social identities people derive from riding a bicycle (Dittmar, 1992). This could be a ‘cyclist identity’, meaning that one’s self-concept is partially based on being a member of ‘the cyclist group’ (Lois, Moriano, & Rondinella, 2015). Another (symbolic) identity people can derive from using a bicycle is a pro-environmental identity, which refers to the degree to which people regard themselves to be a pro-environmental person (Cook, Kerr, & Moore, 2002).

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people enjoy (hedonic attribute) certain modes of transport such as an e-bike more than a conventional bicycle (Sperlich, Zinner, Hébert-Losier, Born, & Holmberg, 2012). The degree to which these attributes differ from one another has yet to be researched, although this has already been researched in a related topic, that is, electric cars, which shows some similarities with e-bikes. Both are powered by a battery and both are regarded the more sustainable counterpart of diesel and gasoline powered engines, such as conventional cars and buses (Milieu Centraal, 2019).

Schuitema, Anable, Skippon & Kinnear (2013) found that people buy an electronic car not only for its sole purpose of moving from A to B (instrumental attribute), but also for its pleasure of driving (hedonic attribute) and the identity people derive from owning an electrical vehicle (symbolic attribute). A similar case could be made for the difference of experienced trip satisfaction between e-bikes and regular bikes, but this has yet to be researched. Trip satisfaction refers to the overall level of satisfaction people experience with their travel mode of choice. In accordance with the research performed by Schuitema et al (2013), the basic premise of this research will be that the main purpose of riding a bike is to get from A to B. Or, as Van Acker et al (2010) put it, ‘people mainly travel in order to access desired activities in other places.’ However, we will argue that the symbolic and hedonic attributes will mediate the relationships between instrumental attributes and trip satisfaction, rather than merely taking the classical utilitarian approach.

How satisfied people are with their trips typically depends on a number of factors, including what type of trip people are making. Research has found that 37% of the regular bike trips are being made for leisure purposes (CBS, 2016), whereas for e-bikes usage this percentage is 49% (CBS, 2016). Furthermore, St-Louis, Manaugh, Van Lierop and El-Geneidy (2014) have found that 82% of the cyclists are satisfied with their mode of transport. However, as far as we are concerned, the distinction between regular bike and e-bike usage have not before been made, while focusing on instrumental, symbolic and hedonic attributes. Kroesen (2017) did find that people who use an e-bike do so as a substitute to regular bike usage, which consequently thus also impacts their overall degree of satisfaction to their preferred mode of transport. Furthermore, research suggests that overall people who are 65+ use an e-bike more often than younger people because it enables them to travel longer distances more easily (Jones et al, 2016), thus potentially leading to a higher overall perceived level of trip satisfaction.

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a framework that can serve as a basis for future research projects, which will increase our understanding of the perception of instrumental, hedonic and symbolic attributes in relation to trip satisfaction. We also build further upon the findings of Anable and Gatersleben (2005), who studied the role of instrumental and affective attributes for leisure and commuting trips. We will argue that in addition to these trips people also experience the instrumental, hedonic and symbolic attributes differently when they are making a shopping trip with their (e-)bike.

Ling, Cherry, MacArthur and Weinert (2017) found that American citizens mostly use a regular bicycle and an e-bike for better health, for fun and to get from one place to another. However, using a bicycle has also been linked to pro-environmental behavior in the same study. Brick, Sherman and Kim (2017) have found that when people are being observed by others it might increase behavior that “shows off” their pro-environmental group membership. This is also called “going green to be seen”. In the case of bicycles, it could very well be that people nowadays ride a bike more often since it shows to others how environmentally friendly they are. Accordingly, this could also influence their trip satisfaction.

Furthermore, present study also aims to more critically assess and hereby build upon theories that have a utilitarian approach, such as the theory of planned behavior (Ajzen, 1991; Shepherd, Sparks & Guthrie, 1995). A meta-analysis including 185 studies showed that the typical “utilitarian” variables as measured in the theory of planned behavior explained 39% of the variance in intention, and 27% of the variance in actual behavior (Armitage & Conner, 2001). Two conclusions can be drawn based on these results. First, utilitarian aspects, as often represented by instrumental attributes, seem to be important to explain intentions and behaviors in relation to travel mode choices. Therefore, instrumental attributes are likely to be important for the experienced trip satisfaction of (e-)bikes as well. Second, although the explained variance of the intention and the actual behavior is strong, there seems to be a lot of unexplained variance. Present research argues that hedonic and symbolic attributes are typically underrepresented in these utilitarian theories and therefore they can significantly improve the explanatory power of these models. The research question for this thesis is therefore: What is the importance of instrumental, hedonic and symbolic attributes of e-bikes compared to conventional bikes in relation to trip satisfaction for commuting trips, shopping trips and leisure trips?

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literature, since there is no theoretical framework that covers the instrumental, hedonic and symbolic attributes in relation to e-bikes and conventional bicycles. We aim to build upon the findings of other researchers (e.g. Anable & Gatersleben, 2005), while also introducing a conceptual framework in which the aforementioned attributes fit.

Knowing the relative importance of instrumental, hedonic and symbolic attributes of (e-)bikes in relation to commuting, shopping and leisure trip satisfaction will assist marketing managers and policy makers as well. When developing marketing strategies, marketing managers can then focus on specific attributes that are most likely to increase consumers’ trip satisfaction, depending on the type of trip they are making. Also, with this information, policy makers are better able to design policies that increase (e-)bicycle usage, which thereby leads to a decrease in greenhouse gas emissions, when more people commute by bicycle as opposed to by car. In order to improve current policies, it is vital to know what drives the different types of bicycle users. Policy makers could use this information to their benefit in order to design more effective interventions.

2. LITERATURE REVIEW

2.1 Trip satisfaction

Satisfaction is a subjective feeling consumers have about their expectations before and after having used a product or service (Parker & Matthews, 2001). This affective judgment can thus either be positive or negative, depending on consumers’ evaluations. Trip satisfaction can be defined as the degree to which people are satisfied with their travel mode of choice. In this research, the mode of choice is limited to either an e-bike or a conventional bicycle. Other modes of transport, such as metros, bus or cars lay outside the scope of this research. Present research focuses on trip satisfaction rather than on intentions or behaviors to purchase or adopt an (e-)bike, because knowing how satisfaction levels differ across transportation modes and across different types of trips might lead to a more specific promotion of sustainable forms of transport.

2.2 Commuting, shopping and leisure trips

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and leisure trips. For the sake of understandability, the definitions of the different types of trips are first introduced.

A commuting trip is ‘the journey from work to home’ (Heinen, Maat & Van Wee, 2013). In the Netherlands, women work on average 26 hours per week, and the average hours per week worked for men is 36 hours (OCW in Cijfers, 2019). These people travel on average 22,6 kilometers to their work (CBS, 2019). Commuting trips are thus trips that people make on a regular basis to and from work, and which make up for a large part of their daily travel time. However, our definition of commuting trips also includes trips that are made for study purposes, such as the trip to and from their school. Commuting trips are sometimes combined with shopping trips, which are defined by Giuliano, Hu and Lee (2003) as ‘trips for purchasing commodities and window-shopping’. They differ from commuting and leisure trips in the sense that shopping trips’ main purpose is to purchase e.g. groceries, clothes, or the like. Making a purchase, however, is not necessary in order to categorize a trip as a shopping trip (Barber, 1995). Barber (1995) further adds that a shopping trip is any trip to a retail center, of which the size of the center does not matter. Next to commuting and shopping trips, people can also make trips voluntarily, which is referred to as a leisure trip. Schwanen, Dijst and Dieleman (2000) define a leisure trip as ‘travel for any of the following activities: social visits, including overnight stays; recreation or participation in sports; touring and walking; and recreational shopping’. For our research, it is worth noting that for the definition we use the term recreational shopping will be left out of it, since it may cause confusion with the definition used for shopping trips. Generally, a leisure trip is non-mandatory, and is made for leisure purposes. Additionally, it is also possible that people make a leisure trip just for the sake of making the trip itself, with no other goal in mind rather than just enjoying the trip.

2.3 E-bike and conventional bikes vs. trip satisfaction

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St-Louis et al (2014) pose that trip satisfaction not only depends on mode of transport, but also on personal characteristics, travel and mode preferences, trip characteristics and time. Interestingly, people who are active commuters, such as pedestrians and cyclists, display the highest rates in satisfaction with respect to travel time, as opposed to passive commuters such as car users (Páez & Whalen, 2010; Susilo & Cats, 2014). This is in line with findings of Lanceé, Veenhoven and Burger (2017), who have found that on certain occasions increasing travel time by bike or by foot can even lead to an increased mood. Interestingly, it has been found that e-bike users travel on average 1.5 times longer distances than conventional e-bike users (CBS, 2016). More and more people are beginning to see the many benefits of owning an e-bike. Last year in the Netherlands, and for the first time ever, there were more e-bikes sold than regular bicycles (Raivereniging, 2019). Susilo and Cats (2014) found that the most important determinant for cyclists’ trip satisfaction is the absence of hindrances, such as cars and pedestrians, and a barrier-free ride.

As can be understood from the examples given before, the research performed on (e-)bikes in relation to trip satisfaction is fragmented and rather descriptive of nature, and so far, a theoretical framework is lacking. Our aim is to expand on these findings by offering a more theoretical context. The hypothesis that can be drawn up given the aforementioned information is:

H1: e-bike users are more satisfied with their commuting, shopping and leisure trips than

regular bicycle users

2.4 Type of trip and instrumental, hedonic and symbolic attributes

When and how people use their e-bike as opposed to a regular bicycle differs substantially. It has been found that people travel longer distances with an e-bicycle (CBS, 2016), and this also shows in how these bicycles are being used. For regular bicycle users, the maximum commuting distance is on average 10 kilometers, while for e-bike users it is 15 kilometers. For leisure trips, conventional bicycle users again report that 10 kilometers is the maximum distance they would cycle, whereas e-bike users report a maximum of 30 kilometers (CBS, 2016). In terms of travel mode choice, Anable and Gatersleben (2005) found that cyclists rate the instrumental and affective factors with respect to leisure and commuting trips differently. For commuting rides, it was found that the respondents of their study found the instrumental attributes to be more important than the affective ones. In the leisure rides, however, participants rated the affective and instrumental attributes as equally important.

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(Hagberg & Holmberg, 2017). This is also recognized by Guy (2009), who states that a car is ideal for ‘bulk shopping’.

Based on these findings, it is thus expected that the instrumental and hedonic attributes of (e-)bikes are valued differently depending on the type of trip people are making. When people are making a leisure trip, which is non-voluntary, it is expected that the hedonic attributes such as joy and pleasure play a larger role than when they are using their (e-)bike for a shopping trip. In other words: people themselves choose to make the trip, as opposed to a shopping trip, which can, at least to some degree, be regarded as non-voluntary. The difference between these two is that in the leisure trip it is expected that people are focusing on different attributes, because the bicycle trip is an experience in itself. In the commuting trip, the bicycle is mostly being valued for its instrumental, utilitarian purpose, which will consequently impact the trip satisfaction people experience when using it. We therefore hypothesize that:

H2: instrumental attributes of (e-)bikes are more important for commuting trips than for leisure

and shopping trips

H2.1: hedonic attributes of (e-)bikes are more important for leisure than for shopping trips

There seems to exist a social stigma around e-bicycle usage. Compared to conventional bicyclists, e-bicyclists are often accused of ‘cheating’, and that it is meant for the elderly or disabled (Jones et al, 2016). One participant in the study of Jones et al (2016) even camouflaged her e-bike, to make it look more like a regular bicycle. It seems that e-bikes are not always considered “normal”. Steg (2005) further found that people’s travel mode choices are influenced by their colleagues’ choices. The ingroup favoritism bias could explain this behavior (Brewer, 1999). That is, there might exist a negative image around e-bicycles, which prevents people from adopting one. Conventional bicycles have been around for a far longer time than e-bikes have, so these are assumed to be generally more accepted by the general public. It might be that conventional bicycles are the descriptive social norm (Cialdini, Kallgren, & Reno, 1991). We thus expect the symbolic self-identity attributes of e-bikes to have a negative effect when it is used for commuting purposes, while for conventional bicycle users we expect the opposite effect.

H2.2: the symbolic self-identity attributes of e-bikes are perceived more negatively for

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H2.3: the symbolic self-identity attributes of conventional bicycles are perceived more

positively for commuting trips compared to leisure and shopping trips

2.5 Hedonic, symbolic and instrumental attributes in relation to bicycle trip satisfaction Utilitarian theories assume that individuals always seek to maximize the sum of their utilities (Gandjour, 2007). As such, Gandjour (2007) puts forth that utilitarianism has a “rational approach” to it. Those who maximize their utility will view products as a means to an end, while focusing on its instrumental attributes. According to this theory, a bicycle is bought in order to move from one place to another.

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assume that utilitarian theories only influence satisfaction because they determine how the hedonic and symbolic attributes are fulfilled. The hypotheses thus are:

H3.1: instrumental attributes of conventional bicycles are partially mediated by hedonic,

symbolic identity and symbolic pro-environmental attributes

H3.2: instrumental attributes of e-bikes are partially mediated by hedonic, symbolic identity

and symbolic pro-environmental attributes

How satisfied people are with their bicycle trips depends on a number of factors. To the best of our knowledge, the distinction between instrumental, hedonic and symbolic attributes in relation to bicycle trip satisfaction has not before been made. Also, in general, the amount of research performed on cyclists and trip satisfaction is quite limited. Instrumental attributes typically reflect the utility people derive from a product (Dittmar, 1992). In the case of e-bikes, these are e.g. added mobility, due to a decline in physical ability, which prevents people from using a regular bicycle (Jones et al, 2016), an increase in speed and reduced physical fatigue (MacArthur et al, 2014; Johnson & Rose, 2015). This could explain why e-bikes are so popular among the elderly and could also (at least partially) explain the users’ trip satisfaction. Consequently, the instrumental attributes of the e-bikes are thus also strongly linked to trip satisfaction. Having access to an e-bike increases the users’ mobility, which could be a reason already to perceive its instrumentality different from conventional bicycle users. For e-bike users, it is thus assumed that:

H4: instrumental attributes of e-bikes are more important for trip satisfaction than they are for

conventional bicycles

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underlined by Anable and Gatersleben (2005), who found that cyclists especially appreciate the affective factors in bicycle usage of freedom and control. People who use an e-bike travel longer distances than conventional bike users (CBS, 2016). It could thus be stated that at least some aspects of the e-bike are appreciated differently than conventional bikes, such as the longer travel time. Lanceé et al (2017) found that increasing travel time could lead to an increased mood, which could impact their trip satisfaction. Furthermore, Haustein and Møller (2016) found that overall 91% of American e-bicyclists are happy with their bike. The reasons listed why people are not satisfied refer to instrumental attributes, such as the battery life, the weight of the e-bike and that repairing a broken e-bike is rather expensive. Although these factors are inconvenient, nowhere it is stated that people do not enjoy their e-bicycles less when focusing on its hedonic attributes.

In addition to this, e-bikes have also provided the elderly with added mobility, which could lead to an increase of happiness in their lives (Spinney, Scott & Newbold, 2009). Happiness has been linked to hedonism before (Veenhoven, 2003). In our research, we assume that people value the hedonic attributes more when using an e-bike than when using a conventional bike. The following hypothesis is thus drawn up:

H4.1: hedonic attributes of e-bikes are more important for explaining trip satisfaction than

hedonic attributes of conventional bicycles

Joy of use, convenience but also a sense of increased status is among the many purchase criteria consumers have regarding their new products. Symbolic attributes refer to the sense of self or the identity people derive from using products (Dittmar, 1992). In Australia, e-bike ownership has been linked to environmentally friendly motives (Johnson & Rose, 2013), which can be categorized as symbolic attributes. Also, in Spain, the environmental benefits of cycling have been found to be the most salient among users (Muñoz et al, 2013) This finding was also confirmed by Wolf and Seebauer (2014), who found that e-bike users hold more pro-environmental beliefs than the general population.

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holding them back in adopting one. If the line of reasoning of Griskevicius et al (2010) also holds true in this case, the high price could be a reason for people to buy an e-bike, since it shows people’s pro-environmentally motives. We therefore hypothesize that:

H4.2: the perceived importance of the pro-environmental symbolic attributes of e-bikes will

lead to a higher trip satisfaction than conventional bikes The conceptual model looks as follows:

Figure 1: conceptual model

Since we are testing if there exist differences in trip satisfaction between conventional bicyclists and e-bike users, we have decided to keep the conceptual model as portrayed in Figure 1 similar in terms of attributes, but different depending on the type of bicycle that is being used most often. As such, type of trip refers to a commuting, shopping or leisure trip, and bike refers to either a conventional bicycle or e-bike.

3. METHODOLOGY

3.1 Experimental design

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symbolic attributes were treated as independent variables. Trip satisfaction was the dependent variable.

3.2 Procedure and manipulations

The hypotheses were tested by means of a survey, that was made in Qualtrics and distributed online. Firstly, the respondents were presented with a welcome screen which showed that their data were processed anonymously, and that their participation was completely voluntary. The survey took approximately five minutes, which was also shown on this screen. The participants were after that asked if they understood and agree upon the terms. If they did, they were asked to tick a box indicating their consent. After this, the participants were presented with the definitions of commuting, shopping and leisure trips. In order to make sure that the participants actually read and understood the difference between the types of trips an attention check was added. Below the definitions of the different types of trips, they were told “at the end of this questionnaire you will be asked what your Zodiac sign is. Ignore this but fill in the word football instead”. After this, the participants were presented with the actual survey. Since the distinction between conventional bicycles and e-bikes was a rather important one, the participants were first presented with the option of their preferred mode of choice, i.e. the type of bicycle they use most often. Based on their decision, they were then presented with the questionnaire, that either involved a conventional bicycle or an e-bike. Both groups were then asked about their actual behavior when using their preferred mode of transport, in which they had to rate to what degree the hedonic, symbolic and instrumental attributes led to their overall trip satisfaction with respect to the type of trip they are making. To prevent the participants from getting bored and the statements getting too repetitive, we repeated the definitions of commuting, shopping and leisure trips above the statements, and we phrased the questions in such a way so that the participants only had to fill in the attributes. The last questions were the demographic questions, asking about the participants’ age, gender and highest completed education. The survey was distributed via social media and in person.

3.3 Variables

Below, we describe how the variables included in present study were measured. Table 1 provides a summary of all variables, items and where the items have been adapted from.

3.3.1 Trip satisfaction

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developed was reliable in their sample and is comprised of five questions. All statements, both the dependent variable and independent variables, were measured using a 5-points Likert scale (1 = strongly disagree, 5 = strongly agree).

3.3.2 Instrumental attributes

We used six items to measure instrumental attributes. Three of these items were adopted from Noppers, Keizer, Milovanovic and Steg (2016) and included to what degree the participants found their conventional bicycle or e-bike to be comfortable, to drive smoothly and to be cheap in use. If people found that their (e-)bike was a fast way to travel was measured with one item adopted from Susilo and Cats (2014), and ease of use was adopted from Plazier et al (2017). Furthermore, one item was adopted from Anable and Gatersleben (2005), which measured flexibility.

3.3.3 Hedonic attributes

The hedonic attributes were measured with four items. Two items were adopted from Schuitema et al (2013), which measured the pleasantness of using a conventional bicycle or e-bike, and how nice of a technology the participants find using one of the aforementioned bicycles. The third item described to what degree the participants find using their preferred bicycle to be fun, which is identified as a hedonic attribute by Plazier et al (2017). The fourth item is freedom, as described and identified by Anable and Gatersleben (2005).

3.3.4 Symbolic attributes: pro-environmental identity

Although Anable and Gatersleben (2005) identified the environment as an instrumental attribute, we put forth that it is in fact a symbolic attribute. This was also based on research performed by Johnson and Rose (2013), who found that owning an e-bike has been linked to pro-environmental motives. Also, Schuitema et al (2013) identified pro-environmental behavior as symbolic attributes, so we adopted these three items.

3.3.5 Symbolic attributes: identity

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participants found that either a conventional bicycle or e-bike is suitable for their lifestyle, make them feel proud and if it says something about them.

Table 1: Variables, items and which authors the items are adapted from

Attribute Items Adopted from

Instrumental 1. [type of bicycle] is comfortable 2. [type of bicycle] is cheap in use

Noppers et al (2016)

Instrumental [type of bicycle] drives smoothly Ye & Titheridge (2017) Instrumental [type of bicycle] is a fast way to travel Susilo & Cats (2014) Instrumental [type of bicycle] is easy to use Plazier et al (2017) Instrumental [type of bicycle] offers me flexibility Anable & Gatersleben

(2005) Hedonic 1. [type of bicycle] is very pleasant to drive

2. [type of bicycle] is a very nice technology to use

Schuitema et al (2013)

Hedonic [type of bicycle] is fun to use Plazier et al (2017) Hedonic [type of bicycle] will be boring (reverse coded) Batra & Ahtola (1991) Hedonic [type of bicycle] provides me with a sense of freedom Anable & Gatersleben

(2005) Symbolic:

pro-environmental attitude

1. Being environmentally responsible is an important part of who I am

2. I would buy a [type of bicycle] because it is environmentally friendly

3. Reducing my environmental impact would make me feel good

Symbolic: identity

1. [type of bicycle] is suitable for my lifestyle 2. [type of bicycle] would make me feel proud 3. [type of bicycle] says something about me

Symbolic: identity

[type of bicycle] provides the opportunity to express myself Heffner, Kurani & Turrentine (2007) Trip satisfaction 1. I am completely satisfied with my [type of bicycle] travel

when I am making [type of trip]

2. My [type of bicycle] travel facilitates my [type of trip] 3. When I am using my [type of bicycle] the positive aspects outweigh the negative

4. My [type of trip] makes me feel good

5. I do not want to change anything regarding my [type of bicycle] usage when I am making [type of trip]

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The items are adapted to include the type of trip people are making. The [type of bicycle] refers to either a conventional bicycle or e-bike, depending on which bicycle the participants used most often. The [type of trip] refers to either a commuting, shopping or leisure trip.

3.4 Sampling method and sample size

Before distributing our questionnaire, we checked how large of a sample size we needed (Soper, 2019). Since we had two groups, we had to have a minimum of at least 41 participants per group. The results of this can be found in Table 2 below. We chose for convenience sampling when we distributed our survey. Since for our research we did not need to have a representative population, this form of sampling was deemed the most suitable (Malhotra, 2010).

Table 2: sample size calculator

Anticipated effect size (f2₎ _0.30 Desired statistical power level 0.80

Number of predictors 3

Probability level 0.05

Minimum required sample size 41

3.5 Plan of analysis

First off, we wanted to find out if the manipulation check was successful, and for this we grouped the participants together who filled in the word football and the participants who filled in their Zodiac sign in another group. We analyzed if the means of these groups differed by an independent samples t-test. Then we prepared the data for mediation analysis. Before we could test for mediation, we first had to make sure that the multi-item questions we created are in fact testing the variables we are interested in (i.e., reliability). To test for reliability, we used Cronbach’s Alpha to ascertain that the items we used are indeed reliable enough for further analyses. Items that resulted in a higher Cronbach’s alpha were deleted, in order to increase the internal consistency.

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attributes. For this, we created a dummy variable and added an interaction term and tested for moderation.

3.6 Attention check

Before we continued with the analyses, it was important to find out whether our attention check was successful. In the introduction the participants were asked to read the different definitions on the types of trips before continuing with the questions. Below the definitions there was another line of text that stated “at the last question you are asked to fill in your Zodiac sign. Ignore this but fill in the word ‘football’ instead”. In order to check if the manipulation was successful, we averaged their scores on their rates of satisfaction for the different types of trips and after that ran independent samples t-tests. Since SPSS cannot process word input in its analyses, we created a new variable and coded the people who failed the test with a 1 (n = 52) and those who succeeded with a 2 (n = 116). To test if there exist differences between the two independent samples, we decided to run reliability tests on the satisfaction scales for the different types of trips. The Cronbach’s alpha for commuting trips was 0.820, for shopping trips it was 0.838 and for leisure trips it was 0.811. This was above the recommended threshold of 0.60 (Malhotra, 2010). Removing items from the scale we used did not result in a higher Cronbach’s alpha. Since all three scales were reliable, we averaged the scores and thereby created three new variables, which were then used for the independent samples t-tests.

None of the independent samples t-tests were significant for commuting trips (p = 0.14), shopping trips (p = 0.48) and leisure trips (p = 0.18), which means that even though some participants failed the attention check, they still seemed to have filled in questionnaire seriously. Also, equality of variances was not assumed. The former can be explained by rather than only giving the definition of the different types of trips before the analyses, they were again repeated above all the questions. So even if the participants had not read the definitions in the introduction, they could still do so when they filled in the questions. We therefore decided to continue with the analyses with the data of all participants, including those who failed the attention check.

4. RESULTS

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thus left with 168 respondents, of which 41 were e-bike users and the remaining 127 were conventional bicycle users. The responses were collected from the 9th_{of March until March} 16th_{, 2019.}

4.1 Socio-demographics

The age of the participants ranged from 19 to 74 years. The mean age was 37.37 years (S.D. = 14.4). Two-thirds of our sample consisted of females (65.6%). Most people were aged between 19 and 29 (47.6%) and the largest group of our participants had hbo as their highest completed education (36.9%), but, taken together with the WO bachelor and WO master/PhD group, this accounted for 61.9% of our sample. In addition, most participants were employed either full-time (39,9%) or part-full-time (29,2%).

4.2 Reliability of the constructs

We performed an additional set of reliability analyses on the items of the instrumental, hedonic and symbolic attributes per type of bicycle, in order to find out if these were indeed sufficiently internally consistent. As can be seen in Table 3, all scales score above the recommended threshold of 0.60 (Malhotra, 2010).

Table 3: Cronbach’s Alpha of different constructs

Type of trip Group Instrumental Hedonic Symbolic1 _Symbolic2

Commuting trip Bicycle .778 .703 .769 .883 E-bike .860 .831 .867 .865 Shopping trip Bicycle .778 .734 .809 .910 E-bike .863 .873 .892 .852 Leisure trip Bicycle .749 .796 .819 .907 E-bike .842 .895 .844 .830

1_{Refers to the items used to measure participants’ identity}

2_{Refers to the items used to measure participants’ pro-environmental identity}

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For the e-bike users, we deleted ‘cheap’ for the commuting trips, to increase the internal consistency. For the shopping trips we deleted ‘flexibility’ and ‘fast’ for the instrumental attributes, and ‘boring’ and ‘freedom’ for the hedonic attributes. We deleted ‘lifestyle’ in both the shopping and leisure trips. Finally, for the leisure trips, ‘fast’ was deleted for the instrumental attributes, and ‘boring’ for the hedonist attributes. With the remaining items we could then average their scores and thereby create new variables.

4.3 Differences between trip satisfaction per type of bike

We used a repeated measures MANOVA to test whether people are more satisfied with their e-bike compared to their conventional bicycle (Hypothesis 1). It was hypothesized that e-e-bike users are overall more satisfied with their travel mode choice than regular bike users. We checked the assumptions of the repeated measures MANOVA before performing the analysis. Firstly, trip satisfaction was measured on an interval scale. Our independent variables consisted of two categorical, related groups. That is, the participants were divided in either the conventional bicyclists or e-bike users group. Also, the sample size should at least be larger than the number of dependent variables being tested. With 41 e-bike users and 127 conventional bicycle users, our sample sizes were sufficiently large enough to continue with the repeated measures MANOVA. We checked the data and we did not spot univariate of multivariate outliers. After this, we performed Shapiro-Wilk tests and we found that our data behaved normally. Furthermore, our sample sizes were sufficiently large enough, which made it robust against violating the aforementioned assumption. We plotted scatterplot matrices and found that there were linear relationships between each pair of the dependent and independent variables. Lastly, we performed bivariate Pearson correlation tests and linear regressions for the VIF scores and found that our variables were moderately correlated. None of the correlations exceeded 0.9 (Laerd, 2019) and the VIF scores were lower than the recommended threshold of 5 (Malhotra, 2010), so we could rule out multicollinearity. The full assumption check can be found in Appendix 1.

There was a statistically significant difference in trip satisfaction per trip type based on the type of bicycle people are using, F(3, 164) = 9.54, p <.001; Wilk’s Λ = 0.851, partial η2 ₌ .15. As can be seen in Table 4 it was found that overall e-bike users are more satisfied across all three trips than conventional bicycle users. Therefore, Hypothesis 1 was supported.

Table 4: satisfaction different types of trips

Type of trip Group Mean S.D. F-statistic Significance

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E-bike 3.98 0.54

Satisfaction shopping trip Bicycle 3.37 0.75 9.429 p<.01 E-bike 3.77 0.63

Satisfaction leisure trip Bicycle 3.57 0.65 15.816 p<.001 E-bike 4.02 0.58

4.4 Differences in perception of attributes per type of trip and bicycle

In order to test if people valued the commuting, shopping and leisure trips differently, depending on the type of bicycle they were using, we performed repeated measures MANOVAs. To find out if there existed differences between the (e-)bike users and how they perceived the instrumental, hedonic and symbolic attributes, we first used Wilks’ Lambda tests. After this, we checked with Mauchly’s test if the conventional bicycle users and e-bike users perceived the attributes differently depending on the type of trip they were making. We used Bonferroni post-hoc tests to find out which group means differed from each other.

4.4.1 Relationship between instrumental attributes and type of bicycle

We found a statistically significant difference in the perception of instrumental attributes depending on the type of bicycle people were using, F(2, 165) = 3.47, p<.05; Wilk’s Λ = 0.960, partial η2 _{= .03. Furthermore, we found that Mauchly’s test was not significant (p = .097), which} means that the sphericity assumption was met. When we compared the within-subjects effects, we found a significant result at a = .10 (p = .056), which we explored further with post-hoc tests. The Bonferroni test revealed that conventional bicyclists perceived the instrumental attributes differently depending on the type of trip they are making. The difference between shopping and leisure trips is significant (p<.05). We did not find a significant result for e-bike users and if they perceived the instrumental attributes differently depending on what type of trip they are making. Thus, we rejected Hypothesis 2, which stated that the instrumental attributes of (e-)bikes are more important for commuting trips than for leisure and shopping trips.

4.4.2 Relationship between hedonic attributes and type of bicycle

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tested. We tested these results with a post-hoc Bonferroni test, and it revealed that there existed a statistically significant difference between how conventional bicycle users perceived the hedonic attributes for shopping and leisure trips (p<.01). We did not find a statistically significant result for e-bike users. We could therefore partially support Hypothesis 2.1, which stated that the hedonic attributes of (e-)bikes were more important for leisure than for shopping trips.

4.4.3 Relationship between symbolic self-identity and type of bicycle

There was a statistically significant difference in the perception of the symbolic self-identity attributes and the type of bicycle people were using, F(2, 165) = 8.66, p<.001; Wilk’s Λ = 0.905, partial η2 _{= .095. We met the assumption of sphericity, since Mauchly’s test was not} significant (p = .082). The within-subjects effect was also significant, F(2, 332) = 9.45, p<.001. Post-hoc Bonferroni tests revealed that conventional bicycle users perceived the symbolic self-identity attributes differently on each of the trips they were making. We did not find a statistically significant difference for the e-bike users, so we refuted Hypothesis 2.2. The largest difference existed between commuting and shopping trips for conventional bicyclists (p<.001), as can be seen in Table 5. Thus, it seems that people identify themselves most with their conventional bicycles when they were commuting from and to work or studies, compared to shopping and leisure trips. We thus found support for Hypothesis 2.3, since we assumed that symbolic self-identity attributes of conventional bicycles were perceived more favorably for commuting trips than for shopping and leisure trips.

Table 5: Mean scores of (e-)bike users per type of trip on instrumental, hedonic and symbolic attributes

Type of trip Group Instrumental Hedonic Symbolic1 _Symbolic2

Commuting trip Bicycle 3.64 3.68 3.14 3.21 E-bike 4.07 4.05 3.27 3.47 Shopping trip Bicycle 3.61 3.60 2.82 3.16 E-bike 4.12 4.18 3.12 3.50 Leisure trip Bicycle 3.79 3.81 2.96 3.20 E-bike 4.19 4.24 3.20 3.52

1_{Refers to the items used to measure the symbolic identity attributes}

2_{Refers to the items used to measure the symbolic pro-environmental attributes}

4.5 Instrumental, hedonic and symbolic attributes and trip satisfaction

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two types of bicycles. Four steps had to be performed for mediation. In step 1 of the mediation model, the a-path had to be significant (independent variable on mediators). In step 2, the b-path was tested by regressing the mediators on the dependent variable. In step 3, the instrumental attributes with the mediators had to be significant on the dependent variable, which is the c-path and is also called the total effect. Since we had three mediators, we reported the scores of the indirect effects. Lastly, the mediators have to be controlled for in the c’-path, which is the direct effect. In the final step, if the result was not significant, we had full mediation, while a significant result indicated partial mediation. For all analyses, we used a confidence interval of 95%, and we bootstrapped the samples 5000 times.

4.5.1 Mediating role of attributes on commuting trip satisfaction

For the trip satisfaction of bicycle trips, the regressions of the a-paths were significant. We found significant results when we regressed the instrumental attributes on hedonic attributes (B = 0.55, (F (1, 125) = 81.99, p<.001), as well as on the symbolic identity attributes (B = 0.31, (F (1, 125) = 9,17, p<.001) and on the pro-environmental identity (B = 0.42 (F (1, 125) = 10.78, p<.01). In step 2, it was found that the mediator hedonic attributes was not a significant predictor of commuting trip satisfaction (F (4, 122) = 30.71, p = 0.44), as was the pro-environmental identity (F (4, 122) = 30.71, p = 0.33). However, the symbolic identity attributes were significant (B = 0.28, F (4, 122) = 30.71, p<0.001), indicating that people’s identity when using a conventional bicycle had an effect on trip satisfaction. Since the hedonic attributes and the pro-environmental identity were not significant, we cannot interpret their findings further. In step 3, it was found that the effect size of the mediator identity in the c-path was significant (B = 0.09, CI = 0.02; 0.18). Furthermore, in step 4, the c’-path of the instrumental attributes on commuting trip satisfaction was significant (B = 0.71, F (4, 122) = 30,70, p<.001). However, subtracting the c-path from the c’-path does not equal zero, but since both the c-path and the c’-paths were significant, it means that partial mediation had occurred. Therefore, we found support for Hypothesis 3.1, that the relationship between instrumental attributes and commuting trip satisfaction for conventional bicyclists is partially mediated by the symbolic identity attributes.

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pro-environmental identity on the commuting trip satisfaction (B = 0.15, CI = 0.03; 0.36), as zero was not in between the lower and upper confidence intervals. Lastly, it was found that the total effect in the c’-path was moderately significant (B = 0.58, p = 0.068). Subtracting the c-path from the c’-path does not equal zero, thus indicating a partial mediation effect of a pro-environmental identity on commuting trip satisfaction, which supports Hypothesis 3.2.

4.5.2 Mediating role of attributes on shopping trip satisfaction

For bicycle users on a shopping trip, the regressions showed a number of different results compared to when people are making a commuting trip. The a-path for regressing the instrumental attributes on the hedonic attributes was significant (B = 0.70, F (1, 125) = 175, p<.001). Regressing the instrumental attributes on the symbolic identity attributes (B = 0.20, F (1, 125) = 3.84, p = 0.052) and pro-environmental identity did not yield significant results (B = 0.18, F(1, 125) = 1.84, p = 0.18). In the second step, we regressed the mediator hedonic attributes on shopping trip satisfaction, and found a significant result (B = 0.32, p<.05). In step 3, the instrumental attributes were regressed on shopping trip satisfaction, including the mediator hedonic attributes, which was the c-path. This effect is significant (B = 0.22, CI = 0.05; 0.42). The total effect of the instrumental attributes on shopping trip satisfaction was also significant (B = 0.80, p<.001). Subtracting the direct effect from the total effect does not equal zero. We can thus infer that the relationship of instrumental attributes and shopping trip satisfaction for bicyclists is partially mediated by the hedonic attributes and thereby supports Hypothesis 3.1.

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satisfaction was mediated by either the hedonic, symbolic identity attributes or pro-environmental identity attributes.

4.5.3 Mediating role of attributes on leisure trip satisfaction

For bicycle users making a leisure trip, we found statistically significant results when we regressed instrumental attributes on hedonic attributes (B = 0.75, F (1, 125) = 89.58, p<.001) and on symbolic identity attributes (B = 0.45, F (1, 125 = 12.09, p<.001). In step 2, we found that hedonic attributes (B = 0.30, F (4, 122) = 30.02), p<.001) and the symbolic identity attributes (B = 0.16, F (4, 122) = 30.02, p<.01) were predictors of leisure trip satisfaction. When we look at the indirect effect, the c-path, we find that regressing the instrumental attributes on the leisure trip satisfaction yielded significant results, while including the mediators hedonic attributes (B = 0.22, CI = 0.06; 0.40) and symbolic identity attributes (B = 0.07, CI = 0.01; 0.16), since the attributes did not include zero in the lower and upper confidence intervals. The c’-path revealed that the direct relationship of the instrumental attributes on leisure trip satisfaction was also significant (B = 0.74, F (1, 125) = 74.39, p<.001). Subtracting the c-paths from the c’-path did not equal zero, thus indicating a partially mediated relationship. We therefore found some support for Hypothesis 3.1, as the relationship between instrumental attributes and leisure trip satisfaction for conventional bicycles is partially mediated by hedonic attributes and symbolic identity attributes.

Lastly, for e-bike users making a leisure trip, it was found that regressing the instrumental attributes on the hedonic attributes was significant (B = 0.97, F (1, 39) = 79.36, p <.001). In step 2, when we regressed the mediator hedonic attributes on leisure trip satisfaction we found a significant result at a = 0.10 (B = 0.30, F (4, 36) = 23,31, p<.10), as well as when we regressed pro-environmental identity on leisure trip satisfaction, (B = 0.19, F (4, 36) = 23.32, p<.05). The c-path of the mediating variable hedonic attributes was statistically significant (B = 0.29, CI = 0.028; 0.675). In step 4, the total effect of the instrumental attributes on the leisure trip satisfaction was significant (B = 0.99, F (1, 39) = 62.78, p<.001). We therefore found support for H3.2, since the relationship between instrumental attributes and leisure trip satisfaction for e-bike users was partially mediated by the hedonic attributes.

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a dummy variable (type of bicycle), so that we could perform moderated linear regressions. Furthermore, we averaged the means of trip satisfaction across the different types of trips for both the e-bike and conventional bicycle users. After this, we created an interaction term with the dummy variable and the mean centered variable of the instrumental attributes.

4.6.1 Instrumental attributes and trip satisfaction

The independent samples t-test was significant at t(-5.437) = 68.287, p<.001, revealing that the perceived instrumental attributes of e-bike users (Mebike = 4.13, S.D. = 0.45) were on average greater than those of the conventional bicycle users group (Mbicycle = 3.68, S.D. = 0.48). When we performed a linear regression and added an interaction term for the dummy variable and the instrumental attributes, we found no significant results. The adjusted R2_{of the model without} the interaction was .604, while including the interaction resulted in an adjusted R2_{of .602. The} same insignificant result was found when we checked the interaction effect (p = 0.78). We found no support for Hypothesis 4, which was thus rejected. Instrumental attributes of e-bikes did not seem to be more important for explaining trip satisfaction than the instrumental attributes of conventional bicycles.

4.6.2 Hedonic attributes and trip satisfaction

As with the instrumental attributes, the independent samples t-test revealed that the hedonic attributes are also experienced differently by e-bike users and conventional bicyclists (t(-5.100) = 60.905, p <.000) with Mbicycle = 3.69 (S.D. = 0.46) and Mebike = 4.16 (S.D. = 0.45). The linear regression in which we used the type of bicycle as a moderating variable and thus created an interaction term for the hedonic attributes and the type of bicycle yielded no significant results (p = 0.64). The model fit without interaction (R2_{= .534) did also not increase when we added} the interaction (R2_{= .532). We therefore rejected Hypothesis 4.1, since we could not infer that} the hedonic attributes were more important in explaining trip satisfaction than the hedonic attributes of conventional bicycles were.

4.6.3 Pro-environmental identity and trip satisfaction

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The interaction is significant at p = .012. We found that a pro-environmental identity was a just predictor for trip satisfaction, depending on the type of bicycle people were using. The regression equation is thus: Trip satisfaction = 3.453 + (0.70 x pro-environmental attribute) + (0.388 x e-bike) + (0.289 x (e-bike x pro-environmental attribute)). This meant that the strength of the relationship between trip satisfaction and people’s pro-environmental identity was moderated by the type of bicycle they were using. In our case, using an e-bike led to a higher trip satisfaction than using a conventional bicycle, which was explained by people’s pro-environmental identity. We therefore found evidence for Hypothesis 4.2, since the perceived importance of the pro-environmental identity attributes led to a higher trip satisfaction than that of the conventional bicyclists.

5. DISCUSSION AND CONCLUSIONS

Present study aimed to examine the relative importance of instrumental, hedonic and symbolic attributes of e-bikes and conventional bicycles in relation to (commuting, shopping and leisure) trip satisfaction.

Hypothesis 1 stated that e-bike users are more satisfied with their bicycle than conventional bicycle users across commuting, shopping and leisure trips, which was supported. This is in line with the findings of Haustein and Møller (2016), who found that 91% of the people are satisfied with their e-bike. Our findings also build upon the research of Páez and Whalen (2010), who stated that active commuters, such as bicyclists, are more satisfied with their travel mode than passive commuters, such as car users. We found that e-bicyclists are more satisfied than conventional bicyclists, although the former can to some degree be regarded as less active commuters, since they use electronically assisted bicycles.

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2017), which seemed to be a smaller problem for e-bike users. Following this line of reasoning, we also did not find support for Hypothesis 2.1. A possible explanation for this could be that going shopping with an e-bike is perceived to be less of a burden, since the e-bike’s pedals are electronically assisted and thereby also take away the extra weight of the groceries. The decisive factors could be that the transport mode is motorized, and greater distances are more easily covered, which might help explain why a car is the preferred mode of transport when going shopping (Guy, 2009). Next, we found that symbolic identity attributes of e-bikes are not experienced more negatively for commuting trips compared to shopping and leisure trips. This need not necessarily be contradictory to the findings of Jones et al (2016), who found that e-bike users were told by others that it was a form of ‘cheating’. It seems that the symbolic attributes of e-bikes are perceived equally, thus not necessarily indicating they are experienced either more positively or negatively depending on the type of trip people are making. For conventional bicyclists, however, the symbolic attributes were perceived more favorably for commuting trips than for shopping and leisure trips. It could be that a ‘cyclist identity’ is more prevalent when people are commuting by a conventional bicycle (Lois, Moriano, & Rondinella, 2015).

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instrumental attributes were the most important determinants of trip satisfaction across the commuting, shopping and leisure trips, we found that this partially depends on the fulfillment of either the hedonic and symbolic attributes. In other words, how the instrumental attributes are fulfilled depends on the type of trip people are making.

We did not find evidence for Hypotheses 4 and 4.1, which stated that the instrumental and hedonic attributes are more important for explaining trip satisfaction for e-bike users than they are for conventional bicycle users. Although there exist differences between the two groups, these do not seem to significantly affect their overall trip satisfaction. E-bike users are more satisfied overall, but in comparison with conventional bicycle users, this is not explained by how people perceive the bicycle’s instrumental and hedonic attributes. A possible explanation for this is that our sample is likely to be comprised of Dutch citizens, in comparison to the studies performed by Muñoz et al (2013) and Wolf and Seebauer (2014), which took place in Spain and Austria, respectively. It is assumed that in the Netherlands bicycles are already the preferred mode of transport for many people, which could consequently also impact how people perceive its instrumental and hedonic attributes. Lastly, it was found that people’s pro-environmental identity significantly impacts their trip satisfaction, which is moderated by the type of bicycle they are using. The pro-environmental identities of e-bikes users were found to have a greater impact on trip satisfaction than that conventional bicycles. E-bike users have already been found to hold more pro-environmental beliefs than the general population (Wolf & Seebauer, 2014), and we complement this by adding that it also influences people’s trip satisfaction.

5.1 Limitations and further research

The first limitation is that this study treated every trip equally and did not identify other characteristics which might have contributed to our findings. For example, it is not unthinkable that personal characteristics such as if someone is a car-enthusiastic or other personal interests play a major role when people decide that they do or do not take their e-bike or bicycle out on a leisure trip. Other factors that were not studied but which could impact the findings are the distance someone has to travel for each trip, the frequency with which the bicycle is being used, the alternative modes of transport someone considers when making a trip or even the weather forecast. In our survey, each trip is treated equally, which is unlikely to be the case in real-life scenarios. This could impact our results, because our survey did not take into account personal preferences and reasons why people use an (e-)bike or conventional bicycle in the first place.

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interpretation of the mediating variables. When multicollinearity exists, we cannot draw conclusions about the individual effect size of the different mediators. The explanatory power of the model could thus be impacted. We tested each mediating variable on the dependent variable to overcome this, and the results were still significant.

The third limitation is that a third of our participants failed the attention check. We tested if their mean scores on the attributes and trip satisfaction was different from the people who did pass the attention check and found that this was statistically insignificant. A possible explanation for this is that instead of just giving the definitions of the different types of trips before the participants started with the study, we again showed these at every question later, meaning that even if the people had not read the definitions before they could still do so after. It thus did not impact our results and we expect that it did not affect our results.

Future research could further explore the relations we identified between trip satisfaction and the instrumental, hedonic and symbolic attributes. Although we identified how the attributes are perceived, our research did not look into why this so happens. It would be interesting to find out what it is exactly that makes people value their bicycle differently when they are making, say, a shopping trip. It could very well be that distance also plays a large role (Anable & Gatersleben, 2005), but how and to what degree was not explored by the study at hand. Secondly, we found partial mediation for the hedonic and symbolic attributes. Future research could explore which other attributes or variables constitute to trip satisfaction, thus complementing our framework further. Third, future research could explore if people perceive the symbolic identity attributes differently depending on what the (e-)bikes look like. It would be interesting to find out if high-end (e-)bicycles activates status motives among participants, thus potentially viewing the product as a status symbol.

5.2 Academic and practical implications

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moderating role of type of bicycle on trip satisfaction, when focusing on a pro-environmental identity.

For practitioners, our results show that across all three trips the instrumental and hedonic attributes are rated as most important. Thus, when designing policies that are actively demotivating car usage, it is recommended to focus on these attributes, rather than people’s pro-environmental beliefs. This is vital information, since the EU actively adopts policies that forbids people to buy new cars with either a gasoline or a diesel engine (Volkskrant, 2017). But, if all factors remain constant, purchasing a new car will also remain being more expensive than buying a new bicycle or an e-bike. While for some people owning a bicycle might be a luxury, for others it could be a necessity.

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The Happy Cyclist

The Happy Cyclist

The Happy Cyclist

ABSTRACT

TABLE OF CONTENTS

1. INTRODUCTION

2. LITERATURE REVIEW

3. METHODOLOGY

4. RESULTS

5. DISCUSSION AND CONCLUSIONS

REFERENCES