Which factors influence potential consumers to adopt to the new e-bike sharing system in their mobility behaviour?

Transcription

1 Communication Studies University of Twente Developing an effective marketing strategy, which leads to adoption of the new e-bike sharing system in Curitiba among students Which factors influence potential consumers to adopt to the new e-bike sharing system in their mobility behaviour? Author: Student number: Supervisors: Date: Place: Maria Luisa Sanchez Diez S M.H. Tempelman, University of Twente A.L. Turbay, Pontifícia Universidade Católica do Paraná Curitiba, Brazil 1

2 Table of Content 1. Introduction Method Research Design Data collection Participants Survey Conceptual version Pre-test Final survey Measures Barriers and facilities regarding cycling in Curitiba Attitude, subjective norm, behavioural control and intention Perceived Usefulness and Perceived Ease of Use The reliability of the constructs Results Scale descriptive Multiple regression analysis Discussion Theoretical implications Practical implications Facilities Attitude regarding the e-bike sharing system Subjective norm Behavioural control Usefulness Gender Limitations Conclusion References...21 Appendix A: Sample characteristics

3 Table 1. Sample characteristics Appendix B: Constructs of each variable taken into the survey English Version Appendix C: Constructs of each variable taken into the survey Portuguese version Appendix D: The description of the e-bike sharing system provided by the URBS in Portuguese and English Appendix E: Pearson Correlation research

4 1. Introduction Curitiba is the capital of the state of Paraná and is the perfect example for urban planning in Brazil. The city contains a relatively efficient public transportation system executed by URBS and is used by appromiximtely 85 percent of the population. Despite their modern public transportation system, the city is still dealing with urban mobility problems. With 1.8 million inhabitants divided over 430 km2, Curitiba has become the eighth largest city in Brazil in terms of population (Miranda & da Silva, 2012). All these people travel to their destinations by either car or bus, which lead to big traffic jams in the morning and late afternoon. To improve the mobility of the population in Curitiba, the URBS is planning to implement an e-bike sharing system by July, Bike sharing systems are experiencing widespread adoption in major cities around the world, with over 300 active systems and more than 200 in planning (Meddin&DeMaio, 2012). In these systems, users can pick up and return bikes at designated bike sharing stations with a finite number of docks. URBS is planning to implement 43 docking stations in Curitiba, which contains 480 bikes in total. The docking stations will be located downtown, close to three big universities (PUCPR, UFTPR and UFPR) and near some of the important bus stations. The Curitiba Research and Urban Planning Institute (IPPUC) has been working on the smart location planning of the bike sharing systems and the e-bikes are revised and optimized. Since the landscape in Curitiba deals with orography, the bike sharing system of Curitiba contains e-bikes, which is a bicycle with an integrated electric motor to avoid physical exhaustion. The URBS is targeting the e-bike sharing system on the students of Curitiba, since they are a big group of participants in daily mobility. However, there is little data about students attitude about cycling in general and the new upcoming e-bike sharing system. Since the students play a big role in the mobility in Curitiba, the URBS is planning to target their campaign upon students. In terms of marketing, understanding the consumers will help to plan an effective marketing strategy. For instance, knowing the consumer attitudes can predict the behavior of the consumer. It is crucial for the marketer to know the motivation and decision making to use or dispose the product, so the marketer can respond to the needs of the future consumers. Therefore, this research investigate the factors that influence the adoption of the e-bike sharing system of students in Curitiba according to the Theory of Planned Behaviour (Azjen&Fishbein, 1980) and the Technology Acceptance Model (Davis, 1989). According to Azjen & Fishbein (1980), the Theory of Planned Behaviour (TPB) consists of three independent variables, which can predict the intention for certain behaviour and therefore predict behaviour itself. These three predictors include attitude, subjective norm and behaviour control. The attitude refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behaviour in question. The second predictor, subjective norm, refers to the perceived social pressure to perform or not to perform a behaviour. The third predictor contains the degree of perceived behavioral control, which refers to the perceived ease or difficulty of performing the behaviour and it assumes to reflect by 4

5 experience as well as anticipated impediments and obstacles. These three predictors measure the intentional behaviour of the certain behaviour. In this case, respectively, the attitude towards the e-bike sharing system will be measured, the social pressure of important people among the students will be taken into account and their beliefs of being in control of their own behaviour specifically using the e-bike will be tested. Despite that, TPB is a valid model to measure the intention of using the e-bike itself regarding the action of cycling instead of using the bus or car, the e-bike sharing system also contains a new technological aspect. The docking stations will contain an online payment system and therefore some actions have to be performed in order to make use of the e-bike sharing system. Therefore, the Technology Acceptance Model (TAM) is taken into account regarding the predictors of the adoption of the e-bike sharing system. According to Davis (1989), beliefs about usefulness and ease of use are always the primary determinants of use decisions. The two main predictors for the adoption of the bikes haring system are perceived usefulness and perceived ease-of-use. Davis (1989), describes perceived usefulness as the degree to which a person believes that using a particular system would enhance his or her performance. The perceived ease-of-use on the other hand, is defined as the degree to which a person believes that using a particular system would be free from effort. Even though both models will show a valid and reliable measurement of the intention of using the e-bike sharing system, the circumstances and atmosphere in the city regarding cycling seems also crucial for adopting to the bike sharing system. The attitude of students about the cycling itself could influence the adoption of the bike sharing system. Furthermore, there are general factors defined by Heredia, Monzón & Díaz (2014) which inhibit bicycle use like distance, danger, orography, fitness, climate, vandalism, facilities and comfort. These factors are considered as barriers in the research. Moreover, the (lack of) facilities in Curitiba regarding cycling could also contribute to the adoption of the e-bike sharing system. Finally, demographics can also be a factor that influence the adoption to using the e-bike. For instance, if the student is not old enough to drive a car or that men and woman could have different opinions regarding the e-bike sharing system. Therefore, there are another four elements included in the research framework regarding the adoption of the bike sharing system: the attitude about cycling itself, the barriers in Curitiba to use the bicycle, the facilities in Curitiba regarding cycling and the demographics of the respondents. All these predictors regarding both models TPB, TAM, and the predictors regarding the use of the cycling itself will come together in the final research framework, which is shown in figure 1. For this framework, there are two research questions formulated: RQ1: Which factors influence potential consumers to adopt to the new e-bike sharing system in their mobility behaviour? RQ2: To what extent do demographic variables influence the intention of adopting to the e-bike sharing system? 5

6 Furthermore, there are seven hypotheses formulated towards the research model of the adoption of the e-bike sharing system. H1: Facilities in Curitiba has a positive effect regarding the adoption of the e-bike sharing system H2: Barriers in Curitiba has a negative effect regarding the adoption of the e-bike sharing system. H3: Attitude towards cycling in Curitiba has a positive effect regarding the adoption of the e-bike sharing system. H4: Attitude towards the e-bike has a positive effect regarding the adoption of the e-bike sharing system. H5: Subjective norm has a positive effect regarding the adoption of the e-bike sharing system. H6: Behavioural control has a positive effect regarding the adoption of the e-bike sharing system. H7: Perceived usefulness has a positive effect regarding the adoption of the e-bike sharing system. H8: Perceived ease of use has a positive effect regarding the adoption of the e-bike sharing system. Figure 1. Research model towards the adoption of e-bike sharing system in Curitiba 6

7 2. Method This chapter introduces the methods and instruments used to do the research. The research contains a quantitative research method to overcome the language barrier. This chapter gives a further explanation on the research design, data collection, participants, survey design, the measures and the reliability of the constructs Research Design The study measures the correlation between the different variables. The main purpose of the research is measure if the independent variables will predict the intentional behaviour of the e- bike sharing system. The research focuses on which factors are predictors of the intentional behaviour and who will - or will not adopt to the e-bike sharing system. Therefore, this research contains an experimental design. The enable the measurement of the different independent variables, the variables are divided into indicators. To measure these indicators, Qualtrics is used. The main reasons to use an online survey is to enable a big database in a short amount of time. Furthermore, the online survey is the best way to overcome a language barrier because the researcher and the respondents are not directly interacting with each other. To analyze the data collected by the online survey, SPSS is used. Qualtrics enables a function to convert the collected data easily into a SPSS file, which is another good reason to use Qualtrics. To make sure that the respondents knew what the e-bike system actually is, a description with pictures made my myself - and the main aspects were shown before the respondents answered the questions about the e-bike sharing system. This was done after the questions about cycling in Curitiba itself to prevent priming effects. If the respondents for instance have a positive attitude towards the e-bike sharing system, they might tend to be more positive about cycling in Curitiba itself Data collection After the survey was created, revised, translated from English to Portuguese and pre-tested by Brazilian students the survey was published online on social media and due little flyers, which contained a QR-code to the students directly. Teachers and students shared the survey in their own WhatsApp groups and Facebook groups of their own university to collect more respondents. Also, the flyers were spread among the students, where the students were asked at their universities in to participate. The flyers were spread in libraries, computer rooms and restaurants to address as many students as possible. Spreading the flyers in the library was the most effective way since a lot of students already had their phone or laptop with them so they could immediately fill in the survey. Moreover, you are normally not allowed to speak in the library. This was an easy way to overcome the language barrier by just giving the Portuguese flyer to the students and thanking them in Portuguese. 7

8 2.3. Participants The research only focuses on the students in Brazil of three different universities, namely PUCPR, UTFPR and UFPR. The URBS will implement the e-bike sharing mainly downtown and close to these three universities and therefore the URBS is eager to collect data among these three groups. Since Curitiba is a relatively big city, it is necessary to gain information from different students of different universities to get an overall idea of the opinion regarding the e-bike sharing system. In the end, 718 students filled in the survey. However, only 511 responses were valid and therefore used in the survey. Several demographics were taking into account while conducting the research namely gender, age, educational level and current bike possession. If the current bike possession was answered as no, the students were asked if they possessed a bike in the last and are willing to buy a bike in the future. Table 1 with the results are shown in Appendix A. The research contains a pretty good sample of the student population since 50,5 percent of the respondents are men and 49,5 percent of the respondents are women. Moreover, most students who filled in the survey were between years old, and bachelor students. This could be beneficial for the research since bachelor students may still have years to spend on university and therefore will participate for years in daily Curitiba regarding transportation whereas a master student might only be at the university for one more year. Also, the possession of a bicycle among the participants is almost the same where 52.8 percent currently possesses a bike and 47.2 percent does not. However, 93,8 percent of the ones who do not currently possess a bike, possessed a bicycle in the past and 90 percent are willing to get a bike in the future. From this, we can conclude that almost everyone who participated in the research has bicycle experience in Curitiba, which can give reliable answered regarding his or her opinion of cycling in Curitiba. At last, all the respondents were asked how often they used a bicycle in the last months and for which reasons they used this bicycle. The results are shown in Table 2. From these results we can conclude that 45.6 percent of the participants performed cycle activity during the last month but mainly for recreational reasons e.g. cycling as performing sports or during the weekends in the parks (N=250). Table 2. Cycle activity of the respondents Bicycle use in the last month (N=511) Reasons for bicycle use (N=511) N % never ,4 sometimes 74 14,5 half of the days 52 10,2 most of the days 62 12,1 everyday 45 8,8 Main transportation 72 14,1 recreational ,9 both of them 95 18,6 neither of them 94 18,4 8

9 2.4. Survey As said before, the research contains quantitative research by using an online survey. Before the survey has been released and spread among the respondents, the survey has been developed, pretested and revised. This paragraph will respectively explain the procedures more thoroughly Conceptual version The survey is based on literature. The survey is built up according to the indicators formulated by Azjen (2006) for the variables of TPB (attitude, subjective norm, behaviour control, intentional behaviour) and the indicators formulated for the variable of TAM (Davis, 1989). The results of Heredia, Monzón and Jara-Díaz (2014) regarding factors of inhibit bicycle where used as the indicators for both barriers and facilities regarding cycling in Curitiba in general Pre-test To check if the survey contained all the information, especially regarding cycling in Curitiba, the survey was shown to 6 random students, the manager of URBS and 2 professors of social sciences, one professor from the University of Twente and one professor from the Pontifícia Universidade Católica do Paraná. The students were asked to take a critical look upon the part were cycling in Curitiba in general was taken into account whereas the manager of URBS was asked to check if there is anything missing regarding the e-bike sharing system and both professors were asked is the survey covers the right items for the variables. From this pre-test there were some adjustments made. The construct of distance as a barrier for cycling was expanded by the question if the distance was too little to cycle to the university and some questions were reformulated. For instance, for some students it seems not logical to go by bike since they can walk to the university. Also, the manager of URBS suggested to mention the price of the bike sharing system use because this can influence the way they think about the e-bike sharing system. Students can for instance think that the service is either relatively cheap or expensive, which plays a significant role in Brazil. Also, some construct there is no chance I will use the e-bike sharing system was formulated too direct and therefore was changed into there is little chance I will use the e-bike sharing system. At last, the bicycle possession in the past and the willingness to get a bicycle in the future were added to complete the demographic variables. When the survey was revised, the survey was translated together with a Brazilian student of Letras who s speaks both very well English and Portuguese. The survey was pretested again by 5 students if the survey was easy to understand, to prevent translation issues. There were no issues, and therefore the final survey was published Final survey The final survey consists of 10 constructs: attitude towards the e-bike, subjective norm (regarding the e-bike sharing system), control behaviour (regarding the e-bike sharing system), ease of use, 9

10 usefulness, attitude towards cycling in Curitiba, barriers of cycling in Curitiba, facilities of cycling in Curitiba, demographics and the intention of using the e-bike sharing system. To measure these constructs, statements are formulated and measured by a 5-point Likert Scale, which is shown which is shown below. Strongly disagree Disagree Neutral Agree Strongly agree Not all of the constructs are directly copied from previous research; however, the constructs are all based on literature. This will be further explained in the next section Measures Most of the measures regarding the variables are based on the literature and the used models in this research. The measures are further explained with the use of these models. The English version of the constructs that were later translated for the research are shown in Appendix B. The translation of the constructs actually used in the online survey are shown in Appendix C. The text provided by the URBS are found in both Portuguese and English in Appendix D Barriers and facilities regarding cycling in Curitiba As said before, the factors of Heredia, Monzón and Jara-Díaz (2014) that inhibit bicycle use were used to formulate the questions regarding both barriers and facilities. Together with the results of the revised survey the following items were used for the barriers namely the rain - since there is relatively a lot of rain Curitiba compared to other Brazilian cities -, the criminal activities, the physical exhaustion since the landscape of Curitiba deals with orography and the distance barrier. Furthermore, the facilities were defined as enough bike lanes, facilities (e.g. parking places) and the atmosphere Curitiba has to provide a cycle friendly environment in Curitiba. Therefore, the barriers are measured with 5 constructs and the facilities are measured with three constructs Attitude, subjective norm, behavioural control and intention The created constructs for both attitude regarding cycling in Curitiba in general and the e-bike sharing system, as well as the constructs for the subjective norm, behavioural control and intention were based on the literature of Ajzen (2006). To check if answers towards to the questions are reliable, there is a negatively construct formulated for each variable. The attitude of cycling in Curitiba contains 8 different constructs, the attitude towards the e-bike sharing system itself contains 5 constructs, both subjective norms and behavioural control are measured with 4 constructs and the intention is eventually measured with 3 constructs. 10

11 Perceived Usefulness and Perceived Ease of Use The constructs regarding the technology acceptance model are based on the literature of Venkatesh and Davis (2000). However, during the reliability analysis the Cronbach s alpha for usefulness resulted in but once deleted Q27 (see Appendix B) the Cronbach s alpha increased to Therefore, the perceived usefulness only contains two constructs whereas the ease of use contains three constructs The reliability of the constructs After collecting all the data and cleaning the SPSS file, the constructs were put together in variables to prepare the file for analyses. To find out if the constructs really measure what they are intent to measure, a reliability test was conducted. To assume that the constructs are reliable, the Cronbach s alpha has to contain at least the value of As shown in the table 3 on the next page, all the constructs seem reliable except for the facilities and the behavioural control. Even deleting an item will not increase the Cronbach s Alpha. The reason for this can be the formulation of the questions. However, the rest seems quite reliable since α is pretty close to 0.7 or even higher like the attitude regarding the e-bike sharing system (α = 0.79)., the subjective norm regarding the e-bike sharing system (α = 0.71)., the usefulness of the e-bike sharing system (α = 0.75). and the intention of using the e-bike sharing system(α = 0.85). Table 3. Cronbach s Alpha of the constructs Measures N N-Items Rel. (α) Facilities regarding cycling in Curitiba ,64 Distance as a barrier regarding cycling in Curitiba ,65 Rain as a barrier regarding cycling in Curitiba Unsafety as a barrier regarding cycling in Curitiba Exhaustion as a barrier regarding cycling in Curitiba Criminal activities as a barrier regarding cycling in Curitiba Attitude regarding cycling in Curitiba ,69 Attitude regarding the e-bike sharing system ,79 Subjective norm regarding e-bike sharing system ,71 Behavioural control regarding e-bike sharing system ,60 Usefulness regarding e-bike sharing system ,75 Ease of use regarding the e-bike sharing system ,66 Intention regarding e-bike sharing system ,85 11

12 3. Results This section will provide the findings of the research and are explained more thoroughly. First, the scales descriptive are explained, the correlations - which are calculated by using the Pearson Correlation test - are shown and the multiple regression analysis provide an overview of the different variables that predicts the user intension of the e-bike sharing system Scale descriptive As you can see below, table 4 provides the scale descriptive which include the mean and the standard deviation. Here, the value of 3 is the absolute mean of the 5 point Likert scale. The facilities were created with an 2,67 which means it is below the average number and rain as a barrier for instance is above average. In other words, according the these results, the respondents slightly disagree with the fact that there are enough facilities and the respondents agree that the rain is a barrier for using the bicycle in Curitiba. Furthermore, this table shows that the attitude regarding the e-bike sharing system is quite positive (M=3,96) as well as the usefulness (M=3,88). The overall intention of the e-bike sharing system seems quite neutral (M=3,36) but is slightly towards agreeing on using the e-bike sharing system in the future. Table 4. Scale descriptive Measures Mean SD T-value Sig. Facilities regarding cycling in Curitiba 2,67 0,77-9,645 0,000 Distance as a barrier regarding cycling in Curitiba 3,59 1,15 11,676 0,000 Rain as a barrier regarding cycling in Curitiba 4,01 0,99 22,942 0,000 Unsafety as a barrier regarding cycling in Curitiba 3,68 0,99 15,351 0,000 Exhaustion as a barrier regarding cycling in Curitiba 2,86 0,97-3,391 0,001 Criminal activities as a barrier regarding cycling in Curitiba 3,38 1,1 7,704 0,000 Attitude regarding cycling in Curitiba 3,39 1,15 18,535 0,000 Attitude regarding the e-bike sharing system 3,96 0,54 40,15 0,000 Subjective norm regarding e-bike sharing system 3,73 0,65 25,112 0,000 Behavioural control regarding e-bike sharing system 3,9 0,64 31,67 0,000 Usefulness regarding e-bike sharing system 3,88 0,62 9,125 0,000 Ease of use regarding the e-bike sharing system 3,82 0,84 32,204 0,000 Intention regarding e-bike sharing system 3,36 0,9 21,971 0,000 All scales are measured on a 5-point likert scale (1=strongly disagree / 5=strongly agree) 12

13 3.2. Pearson Correlation To analyze which variables significantly correlate with one another, a Pearson Correlation was performed. To see how strong these correlations actually are, Evans et al. (1996) came up with the correlation range to see whether the correlation is either weak or strong. The range that he used is the following: Very Weak Weak Moderate Strong Very Strong The results of the Pearson Correlation analyses are shown below in table 5. Since the table contains 17 constructs, only the items are shown in the Pearson Correlation. The highlighted constructs are respectively attitude regarding cycling in Curitiba (11), Attitude regarding the e-bike sharing system(12), Subjective norm regarding the e-bike sharing system(13), behavioural control regarding the e-bike sharing system(14), perceived usefulness regarding the e-bike sharing system(15), ease of use regarding the e-bike sharing system(16) and the intention of using the e-bike sharing system(17). The complete table can be found in Appendix E. From the table you can see that there are 6 constructs significantly correlating with the intentional behaviour or using the e-bike sharing system. Item 11 has a correlation of 0.165, item 12 has a correlation of 0.530, item 13 has a correlation of 0.547, item 14 has a correlation of 0.609, item 15 has a correlation of and item 16 has a correlation of From this analysis we can conclude that there is 1 weak correlation, 4 moderate correlations and 1 strong correlation. There is no correlation between the found between the barriers of cycling in Curitiba and the intentional behaviour of using the e-bike sharing system. Table 5. Pearson correlation analysis ,101* 1 3 0,003,276** 1 4 0,084-0,015 0, ,026-0,075-0,004 0, ,103* -,106* -,150** 0,039 -,094* 1 7,136** -,150** -0,012,105* 0,027,091* 1 8,154** 0,004-0,043,139** -,278**,150**,173** 1 9,120** -,088* -,089* 0,057-0,086,223**,189**,227** 1 10,188** -0,064-0,078,194** -,177**,240**,181**,496**,213** ,094* -0,011 0,054 -,164**,360** -,135** -0,052 -,501** -,336** -,361** ,039-0,016,125** 0,053 0,076 0,026 0,061-0,028-0,043-0,066,165** ,03,093*,114* -0,037 0,079 -,103* -0,02 0,011 -,104* -,110*,147**,537** ,164**,123** 0,07-0,055,098* -,108* -0,069 -,126** -,149** -,191**,289**,463**,508** ,029,107*,091* -0,068,137** -,171** -,127** -,122** -,249** -,193**,249**,320**,421**,410** ,081 0,066 0,072 0,001 0,045 0,011-0,054-0,077-0,084 -,103*,119**,473**,394**,455**,237** ,037 0,079 0,073-0,029 0,019-0,07 0,036 0,005-0,053-0,076,165**,530**,547**,609**,452**,409** 1 13

14 3.3. Multiple regression analysis To either accept or reject the hypotheses, a multiple regression analysis was performed to explain the relationship between the independent variables and the intentional behaviour of using the e-bike sharing system. The results of the multiple regression analysis are shown in table 6. The symbols in the table represents the coefficients (β), significance levels (sig.) and the explained variance (R²). Table 6. Multiple regression analysis Model statistics Adj. R 2 F-value Sig. Model 1: Demographics 0,004 1,54 0,189 Model 2: Demographics + Facilities + Barriers 0,006 1,285 0,236 Model 3: Demographics + Facilities + Barriers + Psychometrics 0,513 34,547 0 Regression coefficients β t-value Sig. Model 1: Demographics ( Adj. R²= 0,004) Gender 0,05 1,03 0,30 Age 0,07 1,49 0,14 Educational level 0,05 1,17 0,24 Possession of a bicycle -0,03-0,73 0,47 Model 2: Demographics + Facilities + Barriers ( Adj. R²= 0,002) Gender 0,06 1,20 0,23 Age 0,07 1,48 0,14 Educational level 0,04 0,85 0,40 Possession of a bicycle -0,03-0,56 0,57 Facilities regarding cycling in Curitiba 0,01 0,29 0,77 Distance as a barrier regarding cycling in Curitiba -0,04-0,91 0,36 Rain as a barrier regarding cycling in Curitiba 0,06 1,31 0,19 Unsafety as a barrier regarding cycling in Curitiba 0,06 1,04 0,30 Exhaustion as a barrier regarding cycling in Curitiba -0,04-0,92 0,36 Criminal activities as a barrier regarding cycling in Curitiba -0,09-1,70 0,09 Model 3: Demographics + Facilities + Barriers + Psychometrics ( Adj. R² = 0,511) Gender 0,09 2,62 0,01 Age 0,02 0,62 0,54 Educational level -0,02-0,61 0,54 Possession of a bicycle -0,02-0,62 0,54 Facilities regarding cycling in Curitiba -0,07-1,99 0,05 Distance as a barrier regarding cycling in Curitiba -0,03-0,85 0,40 Rain as a barrier regarding cycling in Curitiba 0,06 1,78 0,08 Unsafety as a barrier regarding cycling in Curitiba 0,04 0,93 0,36 Exhaustion as a barrier regarding cycling in Curitiba 0,05 1,51 0,13 Criminal activities as a barrier regarding cycling in Curitiba 0,02 0,42 0,67 Attitude regarding cycling in Curitiba 0,02 0,59 0,56 Attitude regarding the e-bike sharing system 0,20 4,87 0,00 Subjective norm regarding e-bike sharing system 0,16 3,87 0,00 Behavioural control regarding e-bike sharing system 0,36 8,74 0,00 Usefulness regarding e-bike sharing system 0,18 4,88 0,00 Ease of use regarding the e-bike sharing system 0,07 1,75 0,08 14

15 As you can see in the table above, there are three models included in the multiple regression analysis namely demographics (1), demographics + facilities regarding cycling in Curitiba + barriers regarding cycling in Curitiba (2) and demographics + facilities regarding cycling in Curitiba + barriers regarding cycling in Curitiba + psychometrics (3). From the model statics, we can see that there is no significant relation between the first two models and the intentional behaviour. However, in the third model you can see that it contains a explained variance of and there is a F-value of 34,547 with a P< This means that 51.3% of the intention of using the e-bike sharing system can be explained by the variables in the research. Once we take a closer look to the third model in the regression analysis, there are six variables that with p<0.05 and therefore have a relatively big influence in explaining the intentional behaviour. These variables are namely gender, current bike possession, the (lack of) facilities, attitude regarding the e-bike sharing system itself, subjective norm, behavioural control and usefulness. The other constructs namely age, educational level, barriers, the attitude towards cycling in Curitiba and the perceived ease of use are not significant in predicting the intentional use of the e-bike sharing system. These results finally end up in a relational model, which is shown in figure 2 below. Gender Age Educational Bike level possession Attitude of cycling in Curitiba β = 0,09 β = NS β = NS β = NS Attitude regarding e- bike itself β = NS β = 0,20 Subjective norm β = 0,16 Intention using e-bike sharing system Adj. R 2 = 0,51 Behavioural control β = 0,36 β = 0,18 Perceived usefulness e-bike sharing system β = NS β = -0,07 β = NS β = NS β = NS β = NS β = NS Perceived ease of use e-bike sharing system Facilities in Distance as a Ra in as a Unsafety activity Exhaustion activity Criminal activity Curitiba barrier barrier as a barrier as a barrier as a barrier Figure 2. Relational model towards the adoption of e-bike sharing system in Curitiba 15

16 4. Discussion This study aimed to find out which factors are the predictors for the intention behaviour regarding the e-bike sharing system of URBS. For these factors, 8 hypothesis were formulated. These results regarding the acceptance or rejection of the hypothesis are shown in table 7. Table 7. Accepted and rejected hypothesis Hypothesis Content Results H1 Facilities in Curitiba has a positive effect regarding the adoption of the e-bike sharing system Accepted H2 Barriers in Curitiba has a negative effect regarding the adoption of the e-bike sharing system. Rejected H3 Attitude towards cycling in Curitiba has a positive effect regarding the adoption of the e-bike sharing system. Rejected H4 Attitude towards the e-bike has a positive effect regarding the adoption of the e-bike sharing system Accepted H5 Subjective norm has a positive effect regarding the adoption of the e-bike sharing system. Accepted H6 Behavioural control has a positive effect regarding the adoption of the e-bike sharing system. Accepted H7 Perceived usefulness has a positive effect regarding the adoption of the e-bike sharing system. Accepted H8 Perceived ease of use has a positive effect regarding the adoption of the e-bike sharing system. Rejected Regarding the intention behaviour towards the e-bike sharing system, there are 5 predictors that influence the intention behaviour namely (lack) of facilities, attitude towards the e-bike sharing system, subjective norm, behavioural control and the perceived usefulness. Furthermore, from the regression analysis we can conclude that also gender has a significant relation with the intention behaviour of the e-bike sharing system. The facilities in the relational model contains β = with p=0.05, which means that there is a negative relation between the facilities and the intentional behaviour. The reason for this could be that the lack of facilities can results in a barrier regarding cycling. This means for marketing applications that the role of facilities in Curitiba do influence the intentioin behaviour of the e-bike sharing system. Furthermore, the attitude towards the e-bike sharing system contains β = 0.20 with p=0.00 which means that there is a weak but significant relation between the attitude and the intention behaviour. In other words, if there is a positive attitude towards the e-bike sharing systems, the students are willing to use the e-bike sharing system. Also subjective norm contains a correlation with β = 0.16 with p=0.00 shows that there is a correlation. This means that if others - who are important to the respondents - are positive and willing to use the e-bike sharing system, the respondents are more likely to have the intention of using the e-bike sharing system. Same goes for the behavioural control with β = 0.36 with p=0.00. The behavioural control contains the highest bèta which implies that when the respondent beliefs that he or she is in control of using the e-bike sharing system, it is more likely they have the intention of using the e-bike sharing system. Usefulness provides β = 0.18 with p=0.00, which implies that in the e-bike sharing system is usefull to them, they have an higher intention of using the e-bike sharing system. At last, also gender provides a significant relation with the intention behaviour. For gender β = 0.09 with p=0.01, there is a slight differences between the outcome of the variables in 16

17 relation to the intention behaviour. For instance, there is a slight differences in outcome if we compare the means reagarding behavioural control. The outcome for men results in M=4.00 whereas for women M=3.76. Also, a slight differences in shown in usefulness where the resulst for men is M=3.32 and for women M=3,41. However, not all the results were significant in relation towards the intention behaviour of the e-bike sharing system. Other demographics like age, educational level, current bike possession and degree did not provide any significant relationships in regard to the e-bike sharing system. In other words, there is no difference in opinion of the e-bike sharing system among the different univerisities in Curitiba. Also, the attitude regarding cycling in Curitiba itself does not play a significant role in the intention behaviour even though there is a correlation according to the correlation analysis. This means that even though there is a relation between the attitude regarding cycling in curitiba and the intention behaviour, it is not strong enough to predict that the intention behaviour. Same goes for the perceived ease of use, where there is a correlation found in the correlation analysis but the variable does not predict the intention behaviour. The reason why the ease of use did not play a signficant role in this research is because almost everyone has cycle experience which makes cycling itself not a big deal anymore. Furthermore, students are grown up with technology and therefore this barrier can be neglected. Frankly enough, the barriers do not provide prediction neither a correlation for the intention behaviour even though the barriers are aknowledges by the respondents with distance is a M=3.59, rain results in M=4.01, unsafety contains M=3.68, exhaustion results in M=2.86 and for criminal activities M=3.38. This means that almost all the barriers were at least ranked neutral or the respondents agreed that the rain dispose the bicycle use. The reason why there is no significant correlation might be explained due to the high SD which is between the Therefore, the participants were in this case too different in their opinions regarding the barriers Theoretical implications The whole survey was almost all based on literature which implies that this research can tell something about the literature and the theoretical frameworks. To start with the barriers, even though there is the acknowledgement of the barriers by the respondents, there was no correlation found to dispose the e-bike sharing system. Therefore, maybe further research can include weight of the barrier to decide which barriers are more important than others. This was not taken into account into this research. The hypothesis for both theoretical frameworks TpB and TAM were all accepted, expect the ease of use. The reason for this is, is that the generation which grew up with technology does not see it as a barrier anymore. Technology has been so integrated in their daily life. However, both theoretical frameworks are still valid until now and showed significant predictors for the intention behaviour as shown in this research where the intention behaviour of the e-bike sharing system is taken into account. 17

18 4.2. Practical implications The URBS is planning to implement the e-bike sharing system in Curitiba later this year and therefore they want to launch an effective marketing campaign to encourage the citizens to use the e-bike sharing system. As said before, it is crucial for the marketer to know the motivation and decision making to use or dispose the product, so the marketer can respond to the needs of the future consumers. From the analysis we can conclude that there are six main aspects that can be included to lead to an effective marketing strategy namely gender, facilities, attitude towards the e-bike sharing system, subjective norm, behavioural control and perceived usefulness. The elements that has to be included according to the results of this research will be further explained in each section of the variables Facilities Since there is a correlation between the facilities and the intention behaviour, it is important to focus on the facilities of the e-bike sharing system in the marketing campaign. The amount of bicycles and that every 500 m a docking station is placed. Furthermore, it is important to include that they can contact the URBS if something is wrong with the bike. This is also a facility regarding the service URBS provides. When the facilities are explicitly included in the marketing strategy, it is more likely that they will adopt to the e-bike sharing system once they are positive about the provision of the facilities Attitude regarding the e-bike sharing system The attitude regarding the e-bike itself is as important aspects for the potential users. It is therefore important that the marketing campaign explicitly provides all the good aspects about the e-bike e.g. the lights, the phone holder, the battery and the free Wi-Fi. Furthermore, it is also important that marketing strategy communicates that the e-bike is user-friendly. When there is a positive attitude, the intention of using the e-bike will increase Subjective norm From the research, we can conclude that the people who are important to the respondents are influencing the intention behaviour of the potential consumer. This means that it is important that the marketing strategy includes the message that the e-bike is good for everyone. In terms of commercials, it is better to show a group of friends of family who use the e-bike rather than only one person. Once the messages that everyone likes using the e-bike sharing system, it is more likely that the potential consumers will use the e-bike sharing system. 18

19 Behavioural control Another aspects which is the biggest predictor according to the results, is behavioural control. It is important that the potential consumers have the feeling that they are able to use the e-bike and that they have control over their actions. Therefore, it is important in the marketing strategy to communicate that it is really easy to use to e-bike sharing system and that everyone is able to use the e-bike regardless the gender and age. If this is communicated to the potential consumers, the chance of using the e-bike sharing system will increase Usefulness The last of the theoretical framework is the perceived usefulness of the e-bike sharing system. Once the consumers thinks it will improve their mobility, they are more willing to use the e- bike sharing system. Therefore, it is important to show the efficiency of the e-bike sharing system e.g. with the e-bike sharing system they can avoid the traffic jams or they don t have to walk anymore and they can use the e-bike instead. When the improvements that the e-bike sharing system brings to the mobility in Curitiba, it is more likely that the potential users will use the e-bike sharing system Gender From the demographic variables, gender seems to have a significant correlation with the intention behaviour. As shown before, the mean of the behavioural control was slightly lower by women than by men. Therefore, it is important that both men and women are represented in the marketing campaign so that there is no distinction made. If both men and women are represented in the campaign, it is more likely that there is no division in different users and enables a broad target group where everyone can use the e-bike sharing system Limitations The research contains some limitations regarding the adoption of the e-bike sharing system. The biggest limitations includes that most students who participated of the research was from only one university. For this reason, there is a limited division about the overall students around Curitiba because it is only from one university. For next research, the division between the different universities can be improved to create an even more general opinion upon the e-bike sharing system among students in Curitiba. The second limitation of the research is that only quantitate research could be conducted because of the language barrier. There was no room whatsoever in the survey for open questions and therefore the respondents were not able to explain their answers. For next research, this could have probably helped to investigate with barriers are really important for the respondents or to explain why they get rid of their bike since a lot of respondents used to have a bike but don t possess one anymore. The outcomes regarding barriers could have been different if there was 19

20 more room to explanation in the research. This could be taken into account for future research. The last limitation of the research are some Cronbach s alpha where some are not >0.7. There is therefore little risk that the constructs did not measure what they should have measured. There could also be some translation influences which can have influenced the reliability of the construct. For future studies it is important to take an even closer look to the constructs and the formulation of the constructs, preferably done by natives. 5. Conclusion The research shows that respectively gender, (lack of ) facilities, the attitude towards the e-bike itself, subjective norm, behavioural control and perceived usefulness are all predictors of the intention behaviour regarding the adoption of the e-bike sharing system in Curitiba. Age, educational level, current bike possession, barriers and ease of use did not shown any significant correlations for the intention behaviour. Designing a marketing strategy based on the predictors could provide the URBS from an effective marketing campaign where the potential consumers are more likely to use the e-bike sharing system. 20

21 References Ajzen, I, (2006). Constructing a TpB Questionnaire: conceptual and methodological considerations. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood CliVs, NJ: Prentice-Hall. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart (13), Evans et al. (1996). A Unified Statistical Approach for Determining Significant Signals in Images of Cerebral Activation. Human Brain Mapping 1996 (4), Heredia, A.F., Díaz, J. & Monzón, A. (2014) Modelling bicycle use intention: the role of perceptions. Transportation 2016(43), 1-23 Meddin, R. & DeMaio, P. (2012) The Bike Sharing World Map. URL: Miranda, H.F., Rodigues da Silva, A.N. (2012). Benchmarking sustainable urban mobility: The case of Curitiba, Brazil. Elsevier Transport Policy 2012 (21), Vankatesh, V., & Davis, F.D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. INFORMS 2000 (46),

22 Appendix A: Sample characteristics Table 1. Sample characteristics Demographics (N=511) N % Gender Male ,5 Female ,5 Age < 18 years 66 12,9 19 thru 21 years ,6 22 thru 24 years ,2 25 thru 27 years 66 12,9 28 thru 30 years 23 4,5 31 thru 33 years 13 2,5 > 33 years 22 4,3 Education Bachelor level ,4 Master level 24 4,7 PhD level 15 2,9 Current University PUCPR ,2 UFPR 78 15,3 UTFPR ,6 Possession of a bicycle Yes ,8 No ,2 If "possession of a bicycle" = no Possession of a bicycle past Yes No Willing to get a bike in the future Yes No

23 Appendix B: Constructs of each variable taken into the survey English Version Topic / Construct respondent ID ID Introduction text Q0 Demographics Q1 Q2 Q3 Q4 Cycle activity Q5 Q6 Q7 Q8 Q9 Attitude towards cycling in Curitiba Q8_1 Q8_2 Q8_3 Q8_4 Q8_5 Q8_6 Q8_7 Q8_8 Barriers of using a bike in Curitiba Q9 Q10 Q11 Q12 Q13 Q14 Cycling facilities in Curitiba Q15 Q16 Q17 Attitude towards the e-bike Q18_1 Q18_2 Q18_3 Q18_4 Q18_5 Question / statement What is your gender? What is your age? At which university do you currently study? At which degree do you currently study? Do you currently own a bike? Did you own a bike in the past? Would you like to own a bike in the future? How often did you use a bike in the last month? For which reason(s) do you use the bike? Cycling in Curitiba is cheap Cycling in Curitiba is healthy Cycling in Curitiba is respected by other drivers Cycling in Curitiba is pleasant Cycling in Curitiba is safe Cycling in Curitiba is fast Cycling in Curitiba is stressful Cycling in Curitiba is fun The rain stops me from cycling in Curitiba I think it is not safe to cycle in Curitiba It is too exhausting to cycle in Curitiba I do not want to use a bike in Curitiba because I am afraid the bike will be stolen There is too much distance between my house and the university to use a bike There is too little distance between my house and the university to use a bike There are enough facilities in Curitiba that encourage me to use the bike (e.g. Parking places) Curitiba is working hard on creating a cycle friendly climate There are enough bike lanes to reach my destination by bike The e-bike seems safe The e-bike seems attractive The e-bike seems user friendly The e-bike seems comfortable The e-bike seems reliable 23

24 Subjective norm Q19 Q20 Q21 Q22 Behaviour control Q23 Q24 Q25 Q26 Usefulness Q27 Q28 Q29 Ease of use Q30 Q31 Q32 Intentional behaviour Q33 Q34 Q35 the end I think the people who are important to me will use this e-bike sahring system in the future I think the people who are important to me will accept the e-bike sharing system I think the people who are important to me will support me in using the e-bike sharing system I think the people who are important to me will discourage me to use the e-bike sharing system I believe that I would be capable of using an e-bike I am fearful to use the e-bike If I want to, I could use an e-bike everyday I can decide for myself if I would use this e-bike service The e-bike sharing system will improve the mobility in Curitiba (e.g. Less traffic jams) Using the e-bike sharing system will save me time to reach my destination The e-bike sharing system will improve my own mobility in Curitiba The system in general seems rather complicated to use I think I will not encounter problems using the e-bike sharing system The idea of the e-bike sharing system is clear to me I am interested in using the e-bike sharing system I will try to use the e-bike sharing system in the future There is little chance that I will use this e-bike sharing system Thank you very much for your time! 24

25 Appendix C: Constructs of each variable taken into the survey Portuguese version Topic / Construct Translation Portuguese respondent ID ID Introduction text Q0 Demographics Q1 Qual é o seu sexo? Q2 Qual é a sua idade? Q3 Qual universidade você frequenta? Q4 Qual grau de escolaridade esta cursando? Cycle activity Q5 Você tem uma biclicleta? Q6 Você já teve uma bicicleta no passado? Q7 Você gostaria de ter uma bicicleta? Q8 Com que frequência você andou de bicicleta no mês passado? Q9 Para quais fins você anda de bicicleta? Attitude towards cycling in Curitiba Q8_1 Andar de bicicleta em Curitiba é barato Q8_2 Andar de bicicleta em Curitiba é saudável Q8_3 Andar de bicicleta em Curitiba é uma atividade respeitada por motoristas Q8_4 Andar de bicicleta em Curitiba é prazeroso Q8_5 Andar de bicicleta em Curitiba é seguro Q8_6 Andar de bicicleta em Curitiba é rápido Q8_7 Andar de bicicleta em Curitiba é estressante Q8_8 Andar de bicicleta em Curitiba é divertido Barriers of using a bike in Curitiba Q9 Chuva me impede de andar de bicicleta em Curitiba Q10 Eu não acho seguro andar de bicicleta em Curitiba Q11 É muito cansativo andar de bicicleta em Curitiba Q12 Eu não quero andar de bicicleta em Curitiba por medo de a roubarem Q13 A universidade é muito distante da minha casa para ir de bicicleta Q14 A universidade é muito próxima da minha casa para ir de bicicleta Cycling facilities in Curitiba Q15 Existe uma boa quantidade de instalações para me incentivar a andar de bicicleta (por exemplo: estacionamentos) Q16 Curitiba esta trabalhando para criar um ambiente favorável para ciclistas Q17 Existe uma boa quantidade de ciclovias para alcançar meu destino de bicicleta 25

26 Attitude towards the e-bike Q18_1 A e-bike parece segura Q18_2 A e-bike parece atrativa Q18_3 A e-bike parece intuitiva Q18_4 A e-bike parece comfortável Q18_5 A e-bike parece confiável Subjective norm Q19 Eu acho que as pessoas mais próximas de mim usarão o sistema de compartilhamento de e-bikes no futuro Q20 Eu acho que as pessoas mais próximas de mim aceitarão o sistema de compartilhamento de e-bikes Q21 Eu acho que as pessoas mais próximas de mim apoiarão o meu uso do sistema de compartilhamento de e-bikes Q22 Eu acho que as pessoas mais próximas de mim nao apoiarão o meu uso do sistema de compartilhamento de e-bikes Behaviour control Q23 Acredito que eu seria capaz de usar uma e-bike Q24 Tenho medo de usar a e-bike Q25 Se eu quisesse, poderia usar uma e-bike todos os dias Q26 Posso decidir por mim mesmo se usaria este serviço Usefulness Q27 O sistema de compartilhamento de e-bikes vai diminuir a quantidade de congestionamentos em Curitiba Q28 Utilizar o sistema de compartilhamento de e-bikes fará com que eu alcance meu destino mais rapidamente Q29 O sistema de compartilhamento de e-bikes vai melhorar minha mobilidade em Curitiba Ease of use Q30 O sistema parece complicado de usar Q31 Acredito que não encontrarei problemas usando o sistema de compartilhamento de e-bikes Q32 O conceito do o sistema de compartilhamento de e-bikes parece claro para mim Intentional behaviour Q33 Eu tenho interesse em usar o sistema de compartilhamento de e-bikes Q34 Tentarei utilizar o sistema de compartilhamento de e-bikes no futuro Q35 A probabilidade de eu usar o sistema de compartilhamento de e-bikes é baixa the end Muito obrigado pelo seu tempo! 26

27 Appendix D: The description of the e-bike sharing system provided by the URBS in Portuguese and English Descrição do sistema de compartilhamento de e-bikes A imagem mostra o sistema de compartilhamento de e- bikes. Para utilizá-lo será necessário criar uma conta online, tendo como pré-requisito o cadastro de um cartão de crédito válido. Em seguida o usuário deverá selecionar o tipo de adesão que pretende, podendo ser: adesão diária, adesão mensal e adesão semestral. As bicicletas serão dotadas de bateria elétrica ou seja, há possibilidade de usar ela com auxilio de motor elétrico ao pedalar, o qual será classificado como um serviço VIP e com preço diferenciado. Abaixo informações complementares: 43 Estações com 480 Bicicletas (21 Estações pequenas com 8 Bicicletas, 10 Estações médias com 12 Bicicletas, 12 Estações grandes com 16 Bicicletas). Todas as estações com WIFI gratuito raio de 20 metros e 8 acessos simultâneos Plano básico, sem a ajuda de bateria. O Serviço VIP, o qual dará acesso ao uso da bateria deverá ter sua a tarifa de adesão com o dobro do valor do preço mínimo que segue abaixo. Adesão semestral R$ 54,00 (R$ 0,30 por dia) Adesão mensal R$ 12,00 (R$ 0,40 por dia) Adesão diária R$ 5,00 45 minutos gratuito e após R$ 2,50 fração de 15 minutos Horário de Funcionamento para retirada das bicicletas 5 às 24 horas e devolução 24 horas. Discription E-bike sharing system On the picture you see the e-bike sharing system. To use this e-bike sharing system, you create an account online and you have to sign with a valid credit card (you pay by this credit card too). With this account you can choose if you want to use the e-bike sharing system for one day, for one month or for one semester. The e-bike contains a battery which gives power to the bike and probably it will be a VIP service with different rate. 43 Docking Station/ 480 Bikes (21 small docking station / 8 bikes, 10 medium station/12 bikes, 12 big station/16 bikes) Docking Station with Free WIFI. Normal rate will not allow you to use the battery. VIP service will be for a e-bike and in this case the rate probably will be the double of the normal price below. Daily fare = R$ 5,00 Month fare = R$ 12,00 (R$ 0,40 per day) Semiannual fare = R$ 54,00 (R$ 0,30 per day) 45 minutes free and after that the company will charge the clients (R$ 2,50 each 15 minutes) The docking stations will be locatted nearly from Universities (PUC, UTFP, UFPR), at the downtown and nearly some bus station. As estações serão localizadas próximas as universidades (PUC, UTFPR e UFPR), região central e os principais Terminais de ônibus da região central. 27