What’s behind the battery electric driven vehicle?
What are the most influencing factors concerning the
purchase intention of battery electric driven vehicles for private consumers in the
Netherlands?
By:
Hoite Schaap
University of Groningen
Faculty of Economics and Business Administration
Chair of Marketing
What’s behind the battery electric driven vehicle?
What are the most influencing factors concerning the
purchase intention of battery electric driven vehicles for private consumers in the
Netherlands?
- Master Thesis -
Author:
Hoite Schaap
Msc. Marketing
Student Number: 3030210
Billitonstraat 8a 9715 ES Groningen
h.e.schaap@student.rug.nl
+31 6 34 60 25 12
1st Supervisor:
Dr. Wander Jager
Associate Professor and Managing Director
of the Groningen Center for Social Complexity Studies
Executive Summary
The reducement of global warming by relying too much on fossil fuels becomes more and more urgent as it limits far reaching negative consequences on the environment. Implementing a sustainable community and new energy efficient techniques is therefore very pressing. One of those techniques focuses on reinventing transportation, in particular cars, as one fifth of CO2 emissions are produced by automobiles. The battery driven electric vehicle (BEV) potentially holds the opportunity of reducing those emissions. However, BEV’s have entered the market years ago, but still the purchase intention and therefore adoption numbers remain rather low. This present study tries to answer what factors are most influencing concerning the purchase intention of BEV’s for private consumers in the Netherlands. This will be achieved by firstly investigating literature around this topic. Furthermore, a quantitative studie based on an online survey, filled in by 127 respondents, will serve as the bases for examining the most influencing factors behind the purchase intentions of consumers. By combining the existing literature with the quantitative study conducted in this report, several recommendations are being given on what factors actually are influential and to what extent those factors have an influence on the purchase intention of BEV’s for private consumers in the Netherlands.
When answering this question, four out of eight investigated factors were found to have a significant influence on the purchase intention. These significant findings address the influence of environmental concern of people, range in particular for long trips, charging time and perceived charging infrastructure. Concerning driving experience, social influence and the financial factor consisting out of acquisition costs and driving costs, the factors did not show statistically significant results on influencing the purchase intention for consumers. The present paper addresses some assumptions in order to explain these effects.
Preface
The present work deals with the important issue of sustainability. The main goal of this thesis is to find out the most influencing factors that lies behind the purchase intention concerning BEV’s for private consumers.
This thesis has been written as part of the Master in Science of Marketing Management at the University of Groningen, The Netherlands. I was involved with the research and writing of this paper from February to June 2018.
My motivation for this research lies within my deep interests in environmental solutions in order to make our planet less reliant on fossil fuels. Furthermore my motivation comes from the frequently asked question I ask myself why the whole adoption process of such solutions take so long to be implemented. But by far the most motivational reason is why there is so little being done to protect our planet, as i believe we will put ourselves in big trouble if no major changes are implemented in the upcoming years. For this reason, and because of my interests in cars, I developed the above research question under guidance and with supportive feedback of Dr. Wander Jager. Identifying the problem in combination with a compliant research question was challenging, but it developed into a project I was very passionate about.
I would like to thank my supervisor for the guidance and feedback. Furthermore, I am very grateful to all respondents filling in the distributed questionnaire, as without them it would not be possible to finish this thesis.
Lastly, the biggest thanks of all goes to my family, girlfriend and friends who have read my work over and over again and to everybody who has supported me along the way. Thank you very much.
Hoite Schaap.
Table of Contents
List of abbreviations VI
List of figures and tables VII
1 Introduction 1
2 Theoretical Framework 3
2.1 Theoretical review 3
2.2 Theory of Planned Behavior 3
2.3 Attitude 5 2.4 Subjective norm 6 2.5 Behavioral control 8 2.6 Hypothesis 13 2.7 Conceptual Model 16 3 Research Methodology 16 3.1 Research Design 17 3.2 Questionnaire 17 3.3 Measures 18 3.4 Data Collection 18 3.5 Sample 19 3.6 Measurement 22 4 Empirical Results 22 4.1 Descriptive statistics 22 4.2 Estimation results 24 4.3 Regression analysis 27 4.4 Discussion 31
5 Conclusions and recommendations 36
5.1 Managerial Implications 36
5.2 Limitations and Future Research 37
5.3 Conclusion 38
Appendices 40
List of abbreviations
AFV Alternative driven Fuel Vehicle BEV Battery driven Electric Vehicle
DV Dependent Variable
e.g. example given
EU European Union
IPCC Intergovernmental Panel of Climate Change
IV Independent Variable
KMO Kaiser-Meyer-Olkin
p. Page
List of figures and tables
Figure Title Page
Figure 1 Theory of Planned Behavior 4
Figure 2 Top-ten BEV market share countries in Europe 11 Figure 3 Cumulative number of BEV fast-charging stations worldwide 12
Figure 4 Conceptual model 16
Figure 5 Gender distribution comparison 19
Figure 6 Region of residence comparison 19
Figure 7 Age distribution comparison 20
Figure 8 Education distribution comparison 20
Figure 9 Income distribution comparison 21
Figure 10 Average frequency of kilometers overview 23
Figure 11 Expenditures for new/used car 23
Figure 12 Arguments for driving a BEV 24
Figure 13 Arguments for not driving a BEV 24
Table Title Page
Table 1 Acquisition costs comparison 9
Table 2 Driving costs comparison 9
Table 3 Government policies on BEV’s 10
Table 4 Exploratory Factor Analysis 25
Table 5 Mean comparison 28
Table 6 Regression results 29
Table 7 Model summary 29
1 Introduction
Over the past 100 years, the global temperature has been increased by approximately 1ºC (Wang, Jiang & Lang, 2017). The Intergovernmental Panel of Climate Change (IPCC) says it does not stop here. It predicts an increase in world average temperature by 2100 within the range of 4–5ºC (Wang, Jiang & Lang, 2017). There is vast evidence in literature that the rising temperature, increased concentration of greenhouse gases, air pollution and oil consumption produced by human actions will stimulate global warming and other climate changes facing the Earth even further (Wang, Jiang & Lang, 2017). Furthermore, this global warming leads to dire scenarios awaiting our planet. Examples are extreme hot summers, melting of polar ice, rising sea levels and extinction of many living species. The outcome of these scenarios depent for a big part on how fast we as people are capable of decarbonising our economy (Rogelj, Meinshausen & Knutti, 2012).
usage for low speed areas and stop-go driving, that are more and more common in driving nowadays, especially cities (Raslavičius, et al. 2015). All these advantages are in favor of a further diffusion of BEV’s in the future, rather than seeing the BEV’s diminish in the upcoming years (Kangur, 2014).
At the same time, all the above mentioned advantages offered by the BEV have not been enough to persuade all consumers (Li, Long, Chen & Geng, 2017). Although in 2012 the total number of BEV’s sold in the Netherlands was almost five times higher than previous years, the market share of BEV’s within the passenger car domain in 2012 was still only 1%, reaching just to 10.000 cars (Kangur, 2014). Nowadays from January 2018 onwards, there are a total of 22,488 BEV’s on the Dutch roads, with a market share of nearly 3% (Netherlands Enterprise Agency, 2018). This indicates an increasing popularity of BEV’s, even though the absolute numbers are still small.
These low percentages of BEV’s on the road raises the question of how to push the adoption of alternative fuel technology further. More and more stakeholders try to influence both the perception and the utility of BEV’s to increase the diffusion (Kangur, 2014). However, literature is still finding difficulties in both explaining and finding factors why diffusion is low. There are several potential barriers keeping the number of consumers who drive a BEV low. Those barriers focus on the high acquisition costs compared to AFV’s, limited driving range and insufficient charging infrastructures. The direct influence of these barriers on the purchase intention of consumers remains unclear (Li, Long, Chen & Geng, 2017).
Concluding, the problem of air pollution and excessive oil consumption leads towards global warming. Due to this problem it is of great significance to build towards a sustainable community to ultimately decarbonise our economy. One solution for this problem is an increasing diffusion of BEV’s (Kangur, 2014). However, the chance that an average Dutch person has an electric vehicle on his/her driveway is less than 3% (Netherlands Enterprise Agency, 2018). By finding out the most influential factors, marketing can be of added value to increase the diffusion of BEV’s, by focussing on the most influential factors concerning the purchase intentions of consumers.
1991). Specifically, this research investigates consumer intentions during the BEV’s adoption process. The goal of this thesis is to research what factors are most influential concerning the purchase intentions of BEV’s and hence define the role of these different factors. Researching which factors influence Dutch citizens behaviour at purchase intentions of BEV’s will result into more valuable and specialized Marketing activities. The current study builds upon a survey conducted under dutch citizens in order to find their values, preferences and decisive factors concerning the adoption of BEV’s.
The outline of this thesis is as follows. Chapter 2 reviews the findings from previous literature and furthermore includes the conceptual model, as well as embedding this paper in the context of BEV’s intentions. Chapter 3 explains the research methodology. Chapter 4 reports the empirical results and discusses them extensively. Finally, chapter 5 concludes the paper with recommendations for managers, retailers manufacturers. Lastly, this chapter is being closed with reflecting critically upon the limitations of this study.
2 Theoretical Framework
2.1 Theoretical review
Within this research, the approach is based on the theory of planned behavior (TPB) by Ajzen (1991). By using TPB as the underlying theory, the question why people do what they do and act the way they act can be explained, with a particular focus on the purchase intention of consumers concerning BEV’s. The TPB is based on three factors, namely attitude, subjective norm and behavioral control. This research focuses on what role these factors play. This has been done to ultimately find out the relative influence of factors on the purchase intention of consumers. In the next part, first the TPB will be shortly discussed. Furthermore, the three components (attitude, subjective norm and behavioral control) will be elaborated on by looking at relevant scientific literature. Finally, based on literature study, multiple hypothesis will be derived.
2.2 Theory of Planned Behavior
intention to perform a particular behavior. These intentions capture the motivational factors that influence a behavior. According to the TPB, people’s behavioral intentions are shaped by attitudes towards behavior, subjective norm and the perceived behavioral control (Ajzen, 1991). The TPB is shown in the following model.
Figure 1: Theory of Planned Behaviour (Ajzen 1991, p. 182).
At first, the attitude somebody has towards the behavior refers to the degree to which a person has a favorable or unfavorable evaluation of the behavior in question. The second predictor is a social factor called the subjective norm. It refers to the perceived social pressure to perform or not to perform behavior. The third antecedent is the degree of perceived behavioral control, which refers to the perceived ease or difficulty of performing the behavior. These three variables influence the intention to perform the behavior and the intention which, in turn, does directly affect behavior. The importance of the different dimensions can change across situations and behaviors (Ajzen, 1991).
social influence, financial factor, driving range and charging. In the next part those factors will be elaborated by looking at relevant scientific literature.
2.3 Attitude
There are different aspects of attitude towards BEV’s which can have an influence on people’s purchase intentions. Using the research by Li, Long, Chen & Geng (2017) those aspects of attitude include the environmental concern of people together with the driving experience. They will be further discussed.
2.3.1 Environmental concern
2.3.2 Driving Experience
Experience generally, and that of owning an automobile in particular, is a major determinant of the intention to adopt a BEV (Le Vine, Zolfaghari & Polak, 2015). As suggested by Schulte et al. (2004) experience includes general life experience, knowledge of related topics, education, and practical experience with a specific product. In previous research on consumer adoption of BEV’s, this construct mainly includes practical experience with BEV’s (Li, Long, Chen & Geng, 2017). Burgess et al. (2014) indicated that practical experience was an important factor in converting the attitude of consumers from one of doubts to that of support and acceptance. By driving BEV’s consumers begin to perceive them more positively in terms of speed, acceleration and low noise and practicality. Many scholars have used experiments in which participants are given short-time access (usually 3-6 months) to BEV’s. These outcomes indicate that more practical experiences lead to people having more positive attitudes towards the driving experience of BEV’s in driving enjoyment (Jensen, Cherchi & Mabit, 2013; Jensen, Cherchi & de Dios Ortúzar, 2014).
Bühler et al. (2014), who conducted a large German field study, researched how the current state of BEV technology is perceived and accepted. Next, they researched how experience influences the evaluation and acceptance of BEV’s. Results show that participants have a positive attitude towards BEV’s, leading to moderate purchase intentions. Furthermore, the authors state that experience can significantly change the perception of BEV’s. Part of this experience is the driving pleasure, which is stated as ‘’high’’ by the participants. Widespread adoption of BEV’s is likely to be increasingly supported if features such as the ‘’fun factor’’ and low noise are retained in future BEV designs. Also Rezvani, Jansson & Bodin (2015) state joy, pride and positive emotions from driving an BEV positively influences the adoption intentions. Overall, driving experience has a significant positive effect on the general perception of BEV’s and the intention to recommend BEV’s to others (Bühler et al., 2014). Concluding, keeping or even increasing the perceived driving experience of BEV’s will have a positive effect on the purchase intention concerning BEV’s (Bühler et al., 2014).
2.4 Subjective norm
ultimately leads to the pressure of purchasing a BEV. The subjective norm is further explained by making use of the work by Cialdini & Goldstein (2004).
2.4.1 Social Influence
Cialdini & Goldstein (2004) state social influence in terms of compliance and conformity. Compliance refers to a particular kind of response to a particular kind of communication. Conformity refers to the act of changing one’s behavior to match the responses of others. Deutsch & Gerard (1955) distinguished between informational and normative conformity motivations. The former focused on the desire to form an accurate interpretation of reality and behave correctly, the latter based on the goal of obtaining social approval from others.
Identifying what is most important concerning the research question, the most influential social influence is the need of normative conformity. (Nolan & Schultz et al., 2008; Deutsch & Gerard, 1955). Noppers (2014) states that within normative conformity there are three different types. Namely, people can be conformists, non-conformists or anti-conformists (counter-conformists). For more than a century cars carry the label of status-symbols, were the role of opinions of others and normative conformity are decisive (Li, Long, Chen & Geng, 2017). In general individuals change behavior in order to match with people they like to conform with, not being different with cars (Oliver & Lee, 2010). Focussing normative conformity specifically on the adoption of BEV’s, the role of individuals close to the person are considered to be most influential, consisting out of friends, family and colleagues (Cialdini & Goldstein, 2004). The opinions and purchase behaviors of those individuals influence the decision making process of people. Hence, the attitude and opinion of the perceived social surrounding in terms of friends, family and colleagues towards BEV’s has an effect on the purchase intention of individuals regarding BEV’s.
2.5 Behavioral control
Multiple aspects of behavioral control influence people’s purchase intentions. Regarding the research by Li, Long, Chen & Geng (2017), those aspects include the financial factor, driving range and charging. They will be elaborated next.
2.5.1 Financial Factor
Concerning the financial factor, there is a distinction between two groups of drivers. Namely, people driving a BEV privately and people leasing a BEV. As lease drivers do not deal with acquisition costs, in contrast to private drivers, the influencing factors are not the same. Meaning that lease and private drivers cannot be in the same line of research together. Because 90% of Dutch citizens drive their cars privately, hence having the biggest market share, the decision is made to focus on this group of drivers. Of these private car drivers, four out of five purchase their car on the second-hand market, making the second car market the one being most influential concerning car sales (CBS, 2017).
ANWB (2016) did research on this comparison looking at total costs of ownership, considering both acquisition and driving costs. This research focused on private drivers concerning both the BEV and the AFV, in the Netherlands. These results can be found in table 1 & 2.
Table 1: Acquisition costs comparison (ANWB, 2016).
Car BEV AFV
Acquisition cost* € 39.540 € 23.491
* A comparison has been made between a new electric driven and a new alternative fuel driven Volkswagen Golf in the Netherlands.
Table 2: Driving costs comparison (ANWB, 2016).
Car** BEV AFV
Total (ct/km)*** € 0,26 € 0,31
Total costs per year**** € 0,26 x 15.000km = € 3.900
€ 0,31 x 15.000km = € 4.650
** ANWB calculated costs based on an ownership of 6 years. *** Total costs include: depreciation, fuel, service & fixed costs.
**** To calculate the total costs per year, a total of 15.000 km per year has been chosen as it is the average amount of kilometers Dutch citizens drive per year.
ANWB concludes that driving a BEV privately is beneficial in terms of driving costs. However, the acquisition costs are almost twice as high compared to AFV meaning people need savings in order to drive a BEV (ANWB, 2016).
Hence, the financial factor has been split up in two parts, consisting out of the driving costs of the BEV and the acquisition costs of the BEV, as they both have an impact on the purchase intention of private consumers (Li, Long, Chen & Geng, 2017).
bus lanes during traffic jams. This makes BEV’s financially more attractive compared to AFV’s. The impact of those government policies are visible and decisive (Li, Long, Chen & Geng, 2017). Currently, Dutch government policies, as stated above, have a positive influence regarding consumer adoption because of lowering the driving costs. Extensively, this adoption can be increased even further with more powerful policies (Li, Long, Chen & Geng, 2017). An overview of these policies can be found in table 3.
Table 3: Government policies on BEV’s (ANWB, 2016).
Car BEV AFV
Acquisition tax* € 0,- € 356 - € 12.593
Road tax ** € 0,- € 70 - € 92
* Single payment
** Payment every three months and dependent on the province
Figure 2: Top-ten BEV market share countries in Europe (EAFO, 2017)
Concluding, relevant literature state acquisition costs as high, negatively affecting the purchase intention. On the other hand literature state perceived driving costs as low, positively affecting the purchase intention.
2.5.2 Driving Range
2.5.3 Charging
Besides driving range, the charging of BEV’s regarded as an another technical barrier. Focussing on the charging issue, it is not an often reported barrier for people with commuting traffic that fits within the range of a BEV. It becomes a problem when people have a demand for long trips dealing with factors that are of influence, namely charging time and insufficient charging infrastructures (Schneidereit et al., 2015; Graham-Rowe et al., 2012). Concerning charging time, the problem lies within the big difference between AFV’s and BEV’s. Whereas most AFV’s are able to refuel in roughly 4 minutes, BEV’s require 30 minutes at a fast charging station to fuel up completely and up to several (>10) hours for charging from a 110 or 220V outlet, dependent on battery size. This results in a negative impact on adoption rates (Saxton, 2013). Hence, perceived charging time is not big of an issue for commuting traffic fitting in the range of BEV’s, but it is for transportation behavior focussing on the longer trips that exceeds the driving range (day-trips, holidays) (Schneidereit et al., 2015).
Concerning the charging infrastructure, research by Sierzchula, Bakker, Maat & van Wee (2014) states a positive and significant relationship between charging stations and BEV adoption rates. Meaning that an improvement in charging infrastructure has a positive influence on the purchase intention. Furthermore the research by Jensen, Cherchi & Mabit (2013) asserts the impact of charging at home, workplace and the number/location of charging stations in the public domain are important for increasing adoption rates of BEV’s. Also the expected amount of charging stations worldwide shows an increase as can be seen in figure 3.
Nonetheless, charging infrastructures are still an often reported barrier by consumers (Yang, Long, Li & Rehman, 2016; Skippon & Garwood, 2011) However, Sierzchula, Bakker, Maat & van Wee (2014) clearly state that an increase in charging stations will reduce the barrier of charging infrastructure. Hence, improving the perceived charging infrastructure for BEV’s will positively affect the purchase intention of consumers.
In short, Moons, & De Pelsmacker (2012) state that adding affective components (environmental concern, driving experience, social influence, financial factor, driving range and charging) to the TPB appears to be highly relevant for predicting the usage intention of electric cars. Doing so, with making use of a conceptual framework, based on the TPB with the most influencing factors carries the possibility of gaining insides on how to increase the diffusion of BEV’s. With researching these most influencing factors within consumers decision making, marketers, retailers and manufacturers have the ability to develop and advertise the BEV in such a way making it most appealing for the consumer. Hence, meaning for the car to diffuse further, eventually being used more than ever before. This carries the opportunity of decarbonising our economy in such a way it will further protect the environment and reduce climate change.
2.6 Hypothesis
2.6.1 Attitude
Environmental Concern
Sang & Bekhet (2015) define environmental concern as the degree to which people are aware of problems regarding the environment and support the effort to solve them or indicate the willingness to contribute personally to the solution. Research showed that people’s concern about the environment is a determining factor in purchase decisions towards environmental friendly products (Balderjahn, 1988; Ellen et al., 1991; Martin & Simintiras, 1995; Roberts & Bacon, 1997). More in detail, Li, Long, Chen & Geng (2017) state that concern for the environment carries positive effects on adoption intention of BEV’s. Hence:
Driving Experience
As stated by Schulte et al. (2004) experience includes general life experience, knowledge of related topics, education and practical experience with a specific product. Research on consumer adoption of BEV’s mainly includes practical driving experience with BEV’s (Li, Long, Chen & Geng, 2017). Overall, driving experience has a significant positive effect on the general perception of BEV’s and the intention to recommend BEV’s to others (Bühler et al., 2014). Concluding, keeping or even increasing the driving experience of BEV’s will have a positive effect on the purchase intention concerning BEV’s (Bühler et al., 2014). Hence:
H2: The perceived driving experience of BEV’s has a positive effect and therefore will increase the purchase intention of BEV’s.
2.6.2 Subjective norm
Social Influence
Concerning the subjective norm, consisting of social influence, there is vast evidence in literature that shows its impacts on consumer behavior. Cialdini & Goldstein (2004) state social influence in terms of compliance and conformity. Furthermore they state that social influence has three underlying motives, namely the goal of accuracy, the goal of affiliation and the goal of maintaining a positive self-concept. Identifying what is most important concerning the research question, the most influential social influence in general is the need for normative conformity. Focussing this specifically on the adoption of BEV’s, the role of individuals close to a person are considered to be most influential, consisting out of friends, family and colleagues (Cialdini & Goldstein, 2004). Hence:
H3a: Perceiving a social surrounding in terms of friends, family and colleagues with a positive attitude and opinion towards BEV’s has a positive effect and therefore will increase the
2.6.3 Behavioral control
Financial Factor
Compared with AFV’s of a similar configuration, literature states the high acquisition costs of BEV’s as a barrier to adopt BEV’s, whereas the lower operational costs are asserted in favor of BEV adoption (Adepetu & Keshav, 2017; Tamor, Gearhart & Soto, 2013). Therefore the financial factor has been split up in two parts, namely the acquisition costs and driving costs. Hence:
H4a: The higher the perceived acquisition costs the lower the purchase intention of BEV’s H4b: The lower the perceived driving costs the higher the purchase intention of BEV’s
Driving range
Research by Schneidereit et al. (2015) shows that driving range is not an often reported barrier in the commuting traffic of people. It is reported as a barrier in the demand for long trips, which people have several times per month on average. Long trips are those trips that require re-charging while being underway. Also the research by Graham-Rowe et al. (2012) states that the limited range of BEV’s is not competent enough for long trips. Hence:
H5: Long trips, which exceed battery range, have a negative influence and therefore will decrease the purchase intention of consumers
Charging:
H6a: Charging time perceived as high by consumers will decrease the purchase intention of consumers
H6b: Improving the perceived charging infrastructure will increase the purchase intention of consumers
2.7 Conceptual Model
Figure 4: Conceptual Model
3 Research Methodology
Up next, the experimental design, questionnaire, measures, data collection, sample and statistical techniques used to measure the variables in the model will be discussed. The study is based on primary data, meaning the data will be collected by the researcher itself (Malhotra, 2009). The method is conclusive as the goal of the study is to test hypotheses and relationships between the different drivers of the TPB. Quantitative data will be used in the statistical analysis, descriptive data will be used to test the drivers behind purchase intentions of BEV’s.
Attitude Environmental concern (H1 +) Driving experience (H2 +) Subjective Norm Social influence (H3 +) General norm BEV specific norm
Subjective Norm Financial Factor
Acqusition costs (H4a -) Driving costs (H4b +) Driving range (H5 -) Charging
Charging time (H6a -)
3.1 Research Design
In order to test the hypothesis, it was decided to conduct an online survey, as it is the most efficient way of gathering data for the purpose of this research. An advantage of online surveys is that sufficient amounts of data can be acquired in a relative short time frame. Looking at the content of the questionnaire, questions for measuring the most influencing factors concerning the purchase intentions of BEV’s were designed. The questionnaire is split up in five parts in order to test the variables included in the conceptual framework based on the TPB by Ajzen (1991). At the beginning of the survey respondents were asked to fill in questions regarding demographic information to get a general view of the sample and to check the sample for representativeness. Up next questions were designed using relevant literature, in order to test the hypothesis. The first section focuses on attitude. Here participants answered questions regarding environmental concern and driving experience of BEV’s. The second section focuses on questions regarding the subjective norm, consisting of social influence. Here the participant answers a question regarding their social influence concerning a BEV purchase. Third, questions are developed to test the perceived behavioral control, consisting of the financial factor, driving range and charging. At last, questions focussing on the purchase intention of participants were asked. Concluding, participants of the survey were asked to answer questions pertaining their values focusing on attitude, subjective norm and behavioral control. These questions and scale measurements, taken from previous research and sometimes created by the author of this article, served as an independent variable. Hence, the outcome of those questions, focused on the purchase intention towards BEV’s served as the dependent variable.
3.2 Questionnaire
fuel efficiency, budget). Then, participants answer questions regarding the three components of the TBP consisting of attitude, subjective norm and behavioral control. Most of the scales employed had been approved in previous research and were adjusted to fit the goal of this research if applicable. For some questions this meant a transformation towards a 7-point Likert-scale, as well as some minor adjustments in questions. All items were measured on a 7-point Likert-scale to prevent a number-of-levels effect, except for the choice sets which were dichotomous. Courneya, Conner & Rhodos (2006) did research based on the TPB by using five measurement scales. They conclude that the standard Likert-scale is still the optimal measurement scale for the TPB. Finally, the questionnaire was available in Dutch rather than English as the questionnaire was distributed under Dutch citizens (Malhotra, Hall, Shaw & Oppenheim, 2006).
3.3 Measures
The first part, consisting of demographic information, is based on research by Kangur (2014). The author did research on the transition to electric cars. This research is used in order to develop appropriate scales for the different questions focusing on demographic information. Kangur (2014) used data gathered by Bockarjova, Rietveld & Knockaert (2013), conducted in June 2012. Second, the attitude towards BEV’s in terms of environmental concern and driving experience will be measured. The influence of environmental concern is tested by using four measures by McCarty & Shrum (1994) (
α
= 0.87). The influence of the driving experience is measured by adapting the research done by Schmalfuß, Mühl & Krems (2017) (α
= 0.85). Third, the influence of the subjective norm is measured by using the research by Klöckner, Nayum & Mehmetoglu (2013) (α
= 0.79). Fourth, the behavioral control in terms of the financial factor, driving range and charging is tested. The financial factor is measured using 3 items adapted from Venkatesh, Thong & Xu (2012) (α
= 0.81). The driving range is measured using 4 items adapted from Garbarino & Johnson (1999) (α
= 0.84). The charging of BEV’s is tested using 5 items adapted from Schmalfuß, Mühl & Krems (2017) (α
= 0.84). For a full overview of the items used, including literature they were taken from, see Appendix A.3.4 Data Collection
contribute to a further distribution by making use of their social network in order to further distribute the questionnaire. The initial purpose is to create an as heterogeneous sample as possible, to be able to analyse Dutch citizens as precise as possible. Therefore, respondents with different kind of characteristics are recruited, after which they distribute the questionnaire among their own networks. A representative test is conducted by making a comparison between key demographic factors (age, education, income) from the data set and CBS. If the collected data corresponds to the data by CBS, the representativeness of the sample is sufficient. If the collected data does not correspond to the data by CBS, demographic factors will be correlated with drivers of BEV purchase. This interprets how results from a biased sample would work out for the population as a whole.
3.5 Sample
A total of 133 consumers took part in the survey, of which 127 filled out the survey until the end and therefore provided enough data for further analysis. People who indicated to drive a lease car were excluded for further analysis, leaving a remaining sample of 116 respondents. This decision was made as this research focuses on drivers behind purchase intention of BEV’s for private consumers only. Out of the 116 respondents, 40 (34.5%) were female. A total of 43 (37.1%) indicated to life on the countryside.
A check for representativeness of the sample has been done by looking at similar data obtained from the CBS. Concerning gender, the sample is slightly over represented for males. When looking at region of residence, the sample shows a slightly higher amount of people living on the countryside.
Concerning age, the youngest participant was 19 years old while the oldest one was 71, the mean was 38 years old. Young people are the biggest group with 34.5% counting less than 30 years of age.
Figure 7: Age distribution comparison (n = 116), (CBS, 2018)
The sample shows a notable difference in the < 30 and 30-39 year age group compared with data from the CBS. Hence concluding this sample to be more representative for adults in the age group of < 30 and 30-39 years old rather than adults in all age categories.
In the case of education, the majority of 52 respondents held a bachelor degree (44.8%).
Furthermore the sample shows a notable difference in the bachelor’s degree group compared with data from the CBS. Hence concluding this sample to be over represented for people having a bachelor’s degree rather than all education levels.
Looking at monthly net income, the plurality of respondents ranged within a middle-income of € 30.000 - € 40.000 (22.4%), while another substantial group (12.9%) indicated not to answer this question, as can be seen in figure 9.
Figure 9: Income distribution comparison (n = 116) (CBS, 2018)
Concerning income, the sample and the data required from the CBS show differences in the €30.001 - €40.000 category. Hence concluding this category to be over represented in this sample.
3.6 Measurement
For the purpose of statistical testing, SPSS was used. The dataset has been cleaned by deleting incomplete surveys before starting with the analysis.
First of all, descriptive statistics will be used to make a distinction between information of participants and in order to check the data on representativeness. Up next, an exploratory factor analysis will be performed to check the characteristics of the scales. This is done by measuring significant values and correlations. The choice for performing a factors analysis is based on the fact that it will check if the factors that emerge from scales being used for the questionnaire show a similar result with the existing scales used in previous conducted research. Moreover, the factors will be merged in order to test the variables in the conceptual model, so consistency is of great importance. Furthermore, to test this internal consistency, Cronbach’s Alpha will be used. If all internal consistency coefficients are acceptable (> 0.6 as a rough guide), it is appropriate to include all the items in the composite variables.
Ultimately, the goal of this research is to find out what the effects of the IV’s are on the DV and to see which IV is the strongest predictor of the DV. As this research deals with more than one explanatory variable, a multiple regression analysis is the appropriate analysis to run. Hence, the actual hypothesis deviated from the conceptual model will be tested via the multiple regression analysis for H1 to H6 (Malhotra, Hall, Shaw & Oppenheim, 2006).
4 Empirical Results
4.1 Descriptive statistics
Figure 10: Average frequency of kilometers (n = 104)
Concerning choosing a new car, respondents are clearly more interested in finding a used/occasion (75.9%) car instead of buying a new car (24.1%). On average respondents are willing to spend € 16.411 for a new car. This includes a brand new car and a used car/occasion. The lowest price was € 1.000 moving all the way up towards € 75.000.
Figure 11: Expenditures for new/used car (n = 116)
time. Finally, the two tables below show arguments why people would or would not start to drive a BEV.
Figure 12: Arguments for driving a BEV (n = 116)
Figure 13: Arguments for not driving a BEV (n = 116)
4.2 Estimation results
4.2.1 Factor Analysis
hypothesis can be rejected, hence meaning a factor analysis is appropriate and that correlations between variables are in existence. From the performed factor analysis, a 6 item factor scale emerged, which can be found in appendix D. Several criteria were taken into account. At first, initial eigenvalues greater than 1 were considered. This resulted in a number of 6 factors. Next, factors indicating a total variance explained greater than 60% were taken into account, which was in this case a total number of 5 factors. At last, the factors that explain more than 5% were taken into account, resulting in a total of number of 6 factors. Considering the outcomes of the criteria in combination with common sense and interpretation of the factors, a total of 6 factors was determined to extract. This decision has been made in order to provide a better explanation of the data. At last, all variables are included in order to check whether the same factors and concepts as the existing scales would emerge, as only a selection of questions from those scales was used, and even modified.
Table 4: Exploratory Factor Analysis Factor loadings Cronbach’s Alpha Mean Standard deviation Environmental concern Factor 1 0.91 5.16 1.64 BEV’s will contribute to environmental sustainability 0.818 5.13 1.68 BEV’s will help to reduce environmental pollution 0.797 5.11 1.60 BEV’s are important to save natural resources 0.770 5.23 1.64
Driving experience Factor 2 0.53 3.91 1.61
The driving experience in terms of speed contribute to my decision in driving BEV’s
0.751 3.88 1.61
The driving experience in terms of acceleration contribute to my decision in driving BEV’s
0.724 4.94 1.63
The driving experience in terms of comfort contribute to my decision in driving BEV’s
0.550 3.97 1.58
The driving experience in terms of practicality contribute to my decision in driving BEV’s
0.618 2.86 1.60
Social Influence Factor 3 0.34 3.78 1.60
The opinion of people around me influence my decision in choosing a product
0.571 3.04 1.78
My friends, family and colleagues are positive about BEV’s
0.530 4.51 1.41
Financial factor Factor 4 0.65 5.25 1.51
BEV’s are good value for money 0.720 4.64 1.71
I perceive acquisition costs as high 0.502 5.84 1.29
I perceive driving costs as low 0.757 5.26 1.54
Driving range Factor 5 0.74 3.69 1.56
The range of BEV’s can be counted on to reach my destination
0.833 3.95 1.59
The range of BEV’s is sufficient in terms of my commuting traffic
0.749 5.11 1.71
The range of BEV’s is sufficient in terms of long trips/holidays
0.653 2.01 1.37
Charging Factor 6 0.79 3.94 1.73
I don’t mind that it takes longer to charge BEV’s rather than refuel fossil fuel cars
0.607 3.26 1.72
I could integrate the charging of BEV’s in commuting traffic without any problems
0.729 4.27 1.92
The charging time of BEV’s is sufficient in terms of my commuting traffic
0.802 4.66 1.79
The charging time of BEV’s is sufficient in terms of long trips/holidays
0.788 2.35 1.58
Increasing the charging infrastructure improves the usability of BEV’s
0.447 5.14 1.63
After the factor extraction, attention was paid to the different factor loadings. An overview of all loadings can be found in Table 7. All the factors used are the rotated factor solutions, preventing that all the variables load on one factor. The threshold for the factor loadings is set at a preferred value of > 0.5. Also a check for internal reliability is performed by comparing the Cronbach’s Alpha to the desired threshold of > 0.6 in order to predict acceptable reliability.
Concerning the environmental concern scale, the results indicate that the factor loadings range from 0.770 to 0.818 explaining 32% of the variance, with a Cronbach Alpha representing an internal reliability estimate of 0.91, indicating high internal reliability, and a mean score of 5.16 and standard deviation of 1.64.
Next, the results of the driving experience show that the factor loadings range from 0.550 to 0.751 explaining 11% of the variance, with a Cronbach Alpha representing an internal reliability estimate of 0.53 indicating limited internal reliability, and a mean score of 3.91 and standard deviation of 1.61.
Furthermore, the results of the financial factor show factor loadings ranging from 0.502 to 0.757, explaining 7% of the variance with a Cronbach Alpha representing an internal reliability estimate of 0.65 indicating high internal reliability and a mean score of 5.25 and standard deviation of 1.51.
Moving on, the results of the range indicate factor loadings ranging from 0.653 to 0.833, explaining 6% of the variance, with a Cronbach Alpha representing an internal reliability estimate of 0.74 indicating high internal reliability, and a mean score of 3.69 and standard deviation of 1.56.
Finally, the results of charging the BEV show factor loadings ranging from 0.447 to 0.802 explaining 5% of the variance, with a Cronbach Alpha representing in internal reliability estimate of 0.79 indicating high internal reliability, and a mean score of 3.94 and standard deviation of 1.73.
The results of the factor analysis point out that some factors do deviate from the scales used for the distributed questionnaire when looking at the Cronbach’s Alpha. This is an important finding as these outcomes are being used in further analysis. Hence, meaning that not all factors have a overall acceptable reliability and validity because of the Cronbach’s Alpha being below the threshold of > 0.6. For those factors the effect in further analysis can be doubted. In this case these factors are those of driving experience and social influence. These outcomes concerning the Cronbach’s Alpha will be linked on outcomes conducted from the regression analysis and will be further elaborated on in the discussion part.
Up next a look has been given on what factors are most important for respondents by finding out the means of the Likert scales used for the questionnaire. Furthermore, a regression analysis is conducted whether to find out how the influencing factors have an effect on the purchase intention of consumers.
4.3 Regression analysis
purchase intention. Hence, a model with including all factors was conducted. But first, in order to find out what factors are most important to respondents, without saying anything about the purchase intention, a comparison has been made between the different means obtained from the used scales in the questionnaire.
4.3.1 Mean comparison
A look has been given at the different means of all the influencing factors, as can be seen in table 5. The range of BEV’s concerning long-trips deviates most from the scale average. Respondents indicate that for long trips, the range is not sufficient enough. Concerning the range for commuting traffic, the majority agrees with the statement that the range of BEV’s is sufficient enough. Looking at acquisition costs, the majority of respondents strongly agree with the statement that the acquisition costs of BEV’s are perceived as high. Furthermore, the majority of respondents agree with the statement that the driving costs of a BEV are low, but less strong compared with acquisition costs. Also the factor concerning charging time deviates noticeably from the mean. Respondents do mind that it takes longer to charge a BEV, in comparison with an AFV. At last, also deviating largy from the mean, is the charging time when being on long-trips/holidays. The majority of respondents indicate that the charging time of BEV’s is not sufficient for long-trips/holidays. Concerning the charging time for commuting traffic, the majority agrees with the statement that the charging time of BEV’s is sufficient enough.
Table 5: Mean comparison (n = 116)
Mean Difference from
average mean Environmental concern 5.2 1.2 Driving experience 3.9 0.1 Social influence 3.8 0.2 Acquisition costs 5.8 1.8 Driving costs 5.3 1.3 Range long-trips 2.0 2.0
Range commuting traffic 5.1 1.1
Charging long trips 2.4 1.6
Charging commuting traffic 4.7 0.7
Charging infrastructure 5.2 1.2
4.3.2 Regression analysis outcome
After the mean comparison, a regression analysis is conducted in order to find out what influence the factors have on the purchase intention of BEV’s. The outcome of the regression analysis is shown below.
Table 6: Regression results (n = 116)
Model 0 Model 1
Independent variables Standardized
Beta (β) P-value Standardized β P-value Gender -0.132 .0177 -0.049 0.607 Age -0.076 0.489 0.042 0.687 Region of residence 0.058 0.582 -0.050 0.609 Education -0.007 0.943 0.008 0.931 Income 0.103 0.338 0.062 0.513 Attitude Environmental concern 0.391 0.001 Driving Experience 0.253 0.009 Subjective norm General norm 0.009 0.927
BEV specific norm 0.012 0.026
Perceived behavioral control
Acquisition Costs 0.042 0.619
Driving Costs 0.040 0.647
Range Long Trips 0.063 0.021
Charging Infrastructure 0.020 0.000
Charging Time 0.072 0.000
Table 7: Model summary (n = 116)
Model 0 Model 1
R² 0.026 0.340
Adjusted R² -0.018 0.248
Model 1 focuses on the demographic varibales in combination with all independent variables in order to find out the most important influencing factors on the dependent variable, being purchase intention.
First of all the analysis shows an overall significant effect at p = 0.000, as can be seen in Appendix F. Furthermore, the adjusted R² is used in order to find out what model is better in explaining effects concerning the purchase intention. A look has been given at the adjusted R² rather than the non-adjusted R² as two models are being compared. When judging the total variance being explained by one model the focus lies on the non-adjusted R², not being the case in this research. Looking at the adjusted R² of both models as can be seen in table 7, it can be concluded that model 0 is not a good predictor at all as it shows a negative adjusted R². Model 1 shows to be better in explaining effects as the adjusted R² is higher, explaining 24.8% of the variance. When looking at the coefficients for checking the individual variables on predicting the actual purchase intention of BEV’s, one can conclude that six out of nine variables depicting significance, staying below the threshold of p = 0.05. As the factor of driving experience is found to be unreliable, again it’s influence can be doubted. Furthermore, the factor of social influence does not show full significance on both questions being asked. Hence, eventually leading to four out of eight hypothesis being supported as can be seen in table 8.
Table 8: Summary of hypothesized findings (n = 116)
Number Hypothesis Significance
H1 The perceived environmental concern of people has a positive influence and therefore will increase the purchase intention of BEV’s
Supported
H2 The perceived driving experience of BEV’s has a positive effect and therefore will increase the purchase intention of BEV’s
Not supported
H3 Perceiving a social surrounding in terms of friends, family, and colleagues with a positive attitude and opinion towards BEV’s has a positive effect and therefore will increase the purchase intention of individuals regarding BEV’s
Not supported
H4a The higher the perceived acquisition costs the lower the purchase intention
Not supported
H4b The lower the perceived driving costs the higher the purchase intention
Not supported
H5 Long trips, which exceed battery range, have a negative influence and therefore will decrease the purchase intention
Supported
H6a Charging time perceived as high by consumers will decrease the purchase intention of consumers
Supported
H6b Improving the perceived charging infrastructure will increase the purchase intention of consumers
Supported
4.4 Discussion
4.6.1 Drivers behind BEV purchase
Based on research by Adepetu & Keshav (2017) and Tamor, Gearhart & Soto (2013), the financial factor is one of the variables that can explain choices towards BEV purchase. The variable has been split up in two parts, namely acquisition costs and driving costs of a BEV. Concerning the acquisition costs, literature states these costs to be an often reported barrier in the BEV adoption process. However, those acquisition costs do not show significant results concerning the purchase intention. This is noteworthy as the direction of the main effect was estimated correctly, meaning that the financial factor in terms of acquisition costs are labeled as influential by the respondents. Hence, people perceive the acquisition costs as high, but this outcome does not significantly affect the purchase intention of consumers. One possible explanation is based on the research by Frederick, Loewenstein & O’donoghue (2002), which explain the effect of time (temporal) discounting. The authors state time (temporal) discounting as the relative valuation placed on rewards/purchases (usually money or goods) at different points in time, by comparing its valuation at an earlier date with one for a later date. Focussing on the purchase intention of BEV’s, it assumes people have a sense that waiting with purchasing is more beneficial regarding BEV’s adoption in terms of costs. As the adoption percentage is still low at the moment, people do not want to invest large amounts of money in a BEV. They are more likely to wait till the whole BEV’s process is further adapted, meaning e.g. improved charging infrastructure, bigger second hand market, lower acquisition costs and improved range. As almost 38% of the respondents indicate their next car to be a BEV compared to 5% who drive a BEV at this moment it raises the suggestion people see it as an option for the future rather than now in terms of financial aspects. Interestingly, 75.9% of the respondents indicate their next car to be a used car/occasion. These numbers could indicate a bigger interest in a larger second hand market for BEV’s rather than the market for new BEV’s and hence could be explained by the time (temporal) discounting effect, meaning people to wait longer to eventually drive the BEV cheaper compared to driving a BEV now.
Given the context and the one-time occurence it is complicated to find a logical explanation for the fact that acquisition costs do not significantly lower the purchase intention.
Concerning driving costs, research by Adepetu & Keshav (2017) & Tamor, Gearhart & Soto (2013) state lower operational costs to be in favor of BEV adoption. However, being the same with acquisition costs, the driving costs do not show significant results concerning the purchase intention. Again the direction of the main effect was estimated correctly, hence meaning that the driving costs of BEV were seen as influential by respondents. Interestingly, this interpretation leads to not having significant effects on the purchase intention. Where the effect of acquisition costs remains rather vague, the effect of driving costs is more explainable. It is very likely this effect is being outweighed by factors significantly decreasing the purchase intention. There are three factors that could carry an explainable effect concerning the outcome of driving costs. First of all the range, in particular for long trips. The negative effect of range in the case of long trips suggests that consumers with intentions to drive a BEV are significantly lowered by the fact that a BEV does not have the same capabilities as an AFV concerning range. Same with charging time, which also significantly lowers the purchase intention of BEV. Finally the effect of perceived charging infrastructure could have a negative influence, if the charging infrastructure is not improved in the upcoming years. The combination of those three factors could possibly make the effect of driving costs on the purchase intention disappear. Concluding, a person can have the urge to drive a BEV because it is favorable in terms of operational costs. However, this effect is being limited by the fact that there are too many strong factors negatively affecting purchase intention in terms of range and charging time. Those factors possibly outweigh the advantage of perceived low driving costs.
therefore had difficulties indicating and answering the questions being asked. As a solution all questions concerning this scale have been run in a multiple regression separately. The results show three significant and one insignificant effect. Both speed, comfort and usability indicate to have a significant effect on the purchase intention, whereas acceleration failed to show a significant effect on the purchase intention. Afterall it is still challenging to conclude those effects separately as the main effects of the collapsed factor might thus not represent a true image of the influence that driving experience has on the purchase intention. Hence, concluding that this result could imply that the driving experience is not a good predictor for estimating people’s purchase intention towards BEV’s.
Social influence was hypothesized to increase the purchase intention. However, it failed to show statistical significance. As being the same with driving experience the scale applied was taken from previous research and might not have been optimal in measuring the construct. Also the reliability measure in terms of the Cronbach’s Alpha for the factor score of this scale was the lowest score measured, staying far below the threshold of > 0.6. A reason for this might be the diversity in the two questions being asked, where the former question focuses on the existence of a norm and the latter focuses on following a norm. Therefore the decision has been made to look at the two questions separately. When looking at this effect separately in a multiple regression it is not surprising that the effect of the social pressure in general is insignificant, as the answers are highly divergent. Furthermore this insignificant outcome suggests that respondents in the study do not make their choices concerning purchasing based on their social surrounding. This conclusion is drawn as the majority indicates not to care about their social surrounding in choosing products. Hence, in general the effect of the social surrounding on purchasing is non-significant. Interestingly, when looking at the second question of this scale concerning whether a person’s surrounding is positive about BEV’s specifically, the effect shows significance. Meaning that overall, the majority of respondents indicate their social surrounding to be positive about BEV’s. This outcome is in line with research conducted by Bühler et al. (2014), which states that people have a positive attitude towards BEV’s.
being the same with driving experience, it is still challenging to conclude those effects separately as the main effects of the collapsed factor might thus not represent a true image of the influence that driving experience has on the purchase intention.
4.6.2. Influencing factors on the BEV purchase intention
Concerning the purpose of this paper and the expected effect the factors would have on the purchase intention regarding BEV’s, the results were rather surprising. In this research a difference could be observed between the factors. This difference was unexpected, as it deviated from finding in existing literature. However, in the end might carry meaningful outcomes for future implications of electric driving. As described above, the role of driving experience and social influence concerning BEV purchase is hard to judge as the results do not show significant outcomes and the internal reliability is low. Possible explanations have been discussed. Furthermore, the role of the financial factor does not influence the purchase intention, but is labeled as important by respondents. This raises the expectancy that the financial factor concerning BEV purchase should not be underestimated. Focusing on factors significantly influencing the purchase intention, results show significant effects for the environmental concern, the range for long trips, charging time and charging infrastructure. Hence, at this point in the research a conclusion can be drawn regarding what factors to focus on concerning the future implication of BEV’s. First, the influence of environmental concern of people. Being in line with the research by Li, Long, Chen & Geng (2017) stating that labeling or advertising a car as really environmentally friendly has the opportunity of increasing sales as it increases the purchase intention and hence the adoption process.
shortened charging time and further improved charging infrastructure. This still remains a suggestion as this question was not posed during the survey and hence no clear conclusion can be given regarding this solution. What points out from both previously conducted and this research is that these four factors play a significant role on the purchase intention. Concluding, when introducing a new BEV to the market it is of significance importance to highlight its influence/capabilities of the environmental concern, range(especially long trips) and charging time as this is perceived as most influential by the respondents, concerning the purchase intention of BEV’s.
5 Conclusions and recommendations
5.1 Managerial Implications
5.1.1. Marketing Management implications
As four out of eight of the effects are found to be significant used in the study, it seems valuable information could be at hand for managers. Within the field of marketing it is important for a manager to have an insight on what is valuable for the consumers when considering a purchase (Picket-Baker & Ozaki, 2008).
Managers and retailers are advised to focus on three factors in particular to potentially realize a higher purchase intention. The first factor is the environmental concern meaning for managers and retailers to show what impact a particular BEV has on the environment and how it contributes to building a more sustainable community. The environmental issue is labeled as the most important factor to start driving BEV’s by consumers.
innovation is likely to be critical as well. Further improvements on range and charging time through innovation, testing and research is very likely to stimulate the adoption process.
5.2 Limitations and Future Research
This study has several limitations. First, it seems the acquired data is a non-representative sample, when being compared with national data obtained from the CBS. Reasons for this are the relatively small sample size due to a limited time frame as well as making use of the social network of the author for an efficient distribution of the questionnaire. Hence, trying to draw a general conclusion for the populations as a whole by looking at the outcomes of this study is an attempting task. However, the extend on which the deviating variables actually relate to the outcomes of the study is small, as no extraordinary differences in answers can be found for the different over-represented groups compared to under-represented groups. Meaning this study should be reliable for making expectations for the population as a whole.
Second, multiple limitations arise with regard to the questionnaire. It is possible that the obtained insignificant results for some of the factors might be influenced by the scales being used. Future research should address this issue by testing the effectiveness of other potential scales used in previous conducted research in order to find out the effect of those factors. Furthermore, due to the distribution of the survey via social networks and personal network of the author, it is likely respondents answered the questionnaire on their mobile devices. This may have affected results slightly as the displayed scales and choices are harder to read and fill out compared to filling out on a desktop computer. Future research could therefore try redoing the questionnaire by inviting people to fill out the survey on larger screens, most preferably desktops.
Additionally the sample being used for this research describes the influence of the factors for Dutch citizens. However, the BEV adoption is a worldwide phenomenon. Therefore, different countries should be investigated by making a distinction between early adopters and late adopters. In comparison, in countries like Norway, the BEV market is much more mature. For a big part this is achieved through stimulating government policies. As a result, BEV’s are already highly integrated into the driving culture. Looking at the Netherlands and how the government stimulated BEV’s in the lease market, this is very relevant for the private market as well when taking Norway as an example. Extensively, according to Burgess et al. (2013) mere exposure to BEV’s can positively influence consumers’ perceptions and attitudes. But not only this is an interesting finding. It it can be of great interest to find out how the factors relate to early adopting countries in comparison to late adopting countries whether to find out what factors differentiate from each other.
5.3 Conclusion
The results of this study show that four out of eight influencing factors investigated in this research could give a statistical explanation for its influence behind the purchase intention regarding BEV’s for Dutch citizens. Overall, the results suggest that for several factors it is possible to indicate what influence they have on the purchase intention of consumers regarding BEV’s, while the effect of other factors remain insignificant.
1991; Martin & Simintiras, 1995; Roberts & Bacon, 1997 & Bühler et al., 2014). Other research indicates the social influence in general and in terms of friends, family and colleagues to be a determinant concerning the purchase intention of consumers (Cialdini & Goldstein, 2004), while yet other research mentions the financial factor in terms of acquisition and driving costs, driving range, charing time and charging infrastructure as a determinant. (Adepetu & Keshav, 2017; Tamor, Gearhart & Soto, 2013; Schneidereit et al., 2015; Graham-Rowe et al., 2012).
The findings from previous literature and research, together with the present ones at hand, demonstrate that the factors behind purchase intentions regarding BEV’s are partly explainable. However, car purchases are not easily intelligible and hold a large amount of potential influencer variables, also on the emotional side. This could indicate that these variables need to be extended, hence leaving room for future research to include all or many variables having an effect. Some of the influencing factors have been examined throughout this paper. Nonetheless, the findings might indicate that there are deeper or other factors having a influence when it comes to the purchase intention regarding BEV’s.
The goal of this research is to find out what drives people to drive a BEV’s and what factors influence this decision both positively or negatively. However, not all influencing factors included in this study were able to yield its expectations. Interestingly, counterintuitive findings occurred, in particular when looking at the influence of the financial factor on the purchase intention. Possible explanations for this notable result were discussed. Other findings in terms of range and charging were statistically proven and therefore are in line with previously conducted research and can be stated as influential predictors concerning the purchase intention.
