DETERMINANTS OF ELECTRIC CAR PURCHASE INTENTION IN PORTUGAL José Luı́s Miranda and Catarina J. M. Delgado ABSTRACT The popularity of electric and hybrid cars has been growing worldwide, and Portugal is no exception. Companies have been offered incentives as a way to promote the transition to more sustainable transportation systems and supply chains. Celebrities and influencers are endorsing the new technology, and consumer preferences are changing. However, in Portugal, there are still consumers with misconceptions about the autonomy, cost and reliability of electric cars, which may favour the choice of a conventional car, in a new car purchase decision-making process. In this study, we analyse whether purchase intention in the near future of an electric car varies with a pro-environmental lifestyle, perceived symbolic value of the electric car, mobility patterns, age, and place of residence, (performance, social, financial and externalities) risk avoidance, consumer perceptions, knowledge about the cost, the autonomy and the existing infrastructures. A sample of 308 Portuguese consumers was collected with an online survey. Results from survey subsample analysis of 170 consumers who unequivocally claim that would opt for an electric vehicle or not show a positive relationship between the purchase intention of an electric car, the fuel cost increase, the proximity of convenient charging places and battery lifetime perception. It was also found that age, knowledge and perceived symbolic value of the electric car, in general, have a positive influence on consumers’ choice of an electric car. A negative relationship was found between the purchase intention, social and financial risk avoidance, perceived symbolic value of the electric car in particular and the number of cars each family has. Governance and Sustainability Developments in Corporate Governance and Responsibility, Volume 15, 161–172 Copyright © 2020 Emerald Publishing Limited All rights of reproduction in any form reserved ISSN: 2043-0523/doi:10.1108/S2043-052320200000015009 161 162 JOSÉ LUÍS MIRANDA AND CATARINA J. M. DELGADO Keywords: Electric vehicles; purchase intention; environmentally responsible consumption; symbolic value; risk avoidance; determinants of purchase intention; convenient charging locations; perception of battery lifetime INTRODUCTION There is already accepted evidence of the effects of climate change stemming from global warming. This phenomenon, whose origins date back to the late nineteenth century, has accelerated in recent decades (Nordell, 2003). One of the leading causes of global warming is the burning of fossil fuels, which is still a dominant energy paradigm (Anderson, Hawkins, & Jones, 2016). In order to change this situation and after a long negotiation process, an agreement was reached on 12 December 2015, at the UN climate summit in Paris. This document became effective on 4 November 2016, with the purpose of containing global warming. The transportation industry is one of the most important sources of greenhouse gas (GHG) emissions worldwide due to high transportation activity and a high share of fossil fuels used in this sector (Bubeck, Tomaschek, & Fahl, 2016). Replacing fossil-fuelled cars with electric vehicles is considered a potential solution to many problems caused by road transport, including excessive CO2 emissions, environmental pollution and oil dependence. However, their market penetration has been quite discreet except in some countries (e.g. Norway). Many researchers claim that this has to do with various electric vehicle handicaps, such as the higher price than internal combustion vehicles and uncertainty regarding battery evolution and life expectancy (Liao, Molin, Timmermans, & van Wee, 2018). In 2012 sales of electric cars in 2012 were led by Japan, with a 28% market share of global sales, followed by the United States with 26%, China with 16%, France with 11% and Norway with a population of only 5 million people, with 7% (Holtsmark & Skonhoft, 2014). There are undoubtedly several causes that explain this heterogeneity in consumer adhesion to electric vehicles, some of which may be specific to each national reality. This study aims to find the main determinants which influence consumers in Portugal to buy an electric car instead of internal combustion petrol or diesel automobile. In addition to the explanatory variables used in the existing literature, risk perception, electric car image and consumer pro-environmental profile were also considered. THEORETICAL FRAMEWORK AND CONCEPTUAL MODEL Based on the differences in focus on factors, theories and models, studies on the adoption of electric cars can be roughly divided into two categories: economic and psychological (Liao, Molin, & van Wee, 2017) The most widely applied methodology among economic focus studies is a discrete choice analysis in which electric car adoption is characterised as a choice among a group of alternatives between vehicles, described by their characteristics or Determinants of Electric Car Purchase Intention in Portugal Fig. 1. 163 Conceptual Model. ‘attributes’. Consumers make decisions by making trade-offs between attributes. This type of study focuses on the estimation of preference parameters by the attributes that reflect the relative weight of each of them in the decision (Liao et al., 2017). Psychological approach studies focus on motivation and the decision-making process by examining the influence of a wide range of individual psychological constructs (attitudes, emotions, etc.) and perceptions about electric vehicles on their adoption intentions. Its strong point is in discovering the direct and indirect relationships between these constructs and intention, but unlike economic studies, they generally ignore other alternatives/options, do not specify or relativise the attributes of electric vehicles. As a result, psychological studies provide only a limited (if any) view of how changes in vehicle attributes can lead to a change in vehicle preferences choice. Besides, the discrete choice analysis also allows the incorporation of psychological constructs, which enables a broader conceptual framework than that of psychological studies (Liao et al., 2017). In the past 10 years, many studies were dedicated to analysing consumer preferences in adopting vehicles. These can be divided into two main groups. The first of these is mainly focused on the analysis related to which experimental 164 JOSÉ LUÍS MIRANDA AND CATARINA J. M. DELGADO investigative techniques and modelling are most appropriate in predicting consumer behaviour. The second group is focused on defining which attributes mainly affect consumer preferences when they choose between different vehicle propulsion systems, such as fuel, gasoline, gas, hybrids, etc (Carlucci, Cirà, & Lanza, 2018). In the literature, the explanatory variables for the measurement of vehicle utility perceived by consumers are mainly linked to: (1) financial aspects – incentives system (Lévay, Drossinos, & Thiel, 2017), perception of the purchase price of the vehicle (Jensen, Cherchi, & Mabit, 2013; Junquera, Moreno, & Alvarez, 2016; Potoglou & Kanaroglou, 2007; Valeri & Cherchi, 2016), perception of the purchase price of the fuel (Bento, Li, & Roth, 2012; Du & Lin, 2017; Egbue & Long, 2012; Jensen et al., 2013; Turrentine & Kurani, 2007), perception of the purchase price of the electric energy (Sierzchula, Bakker, Maat, & van Wee, 2014); (2) technical aspects – perception of the charging time (Darup, Guillen, & Piulachs, 2018; Junquera et al., 2016; Rasouli & Timmermans, 2013), perception of the battery lifetime (Egbue & Long, 2012; Graham-Rowe et al., 2012; Jensen et al., 2013), perception of the technology included in the car (Junquera et al., 2016), perception of charging network availability (Egbue & Long, 2012; Lehne & Hardinghaus, 2018; Morton, Anable, & Brand, 2014; Sierzchula et al., 2014), previous driving test of the electric car (Egbue & Long, 2012; Jensen et al., 2013), range (Darup et al., 2018; Dumortier et al., 2015; Egbue & Long, 2012; Helveston et al., 2015); (3) driver behaviour – average daily distance travelled (Egbue & Long, 2012; Junquera et al., 2016), trip frequency higher than 100 km/year (Hidrue, Parsons, Kempton, & Gardner, 2011; Junquera et al., 2016); (4) car owner profile – academic qualifications (Egbue & Long, 2012; Javid & Nejat, 2017; Nayum & Klöckner, 2014), income (Egbue & Long, 2012), age (Junquera et al., 2016; Westin, Jansson, & Nordlund, 2018), characterisation of place of residence (Plotz, Schneider, Globisch, & Dutschke, 2014), household dimension (Plotz et al., 2014), electric car perceived symbolism (Schuitema, Anable, Skippon, & Kinnear, 2013; Lois & López-Sáez, 2009) and environmental concerns (Axsen, Cairns, Dusyk, & Goldberg, 2018; Noppers, Keizer, Bolderdijk, & Steg, 2014). The decision to buy an electric car will be based on the perception of its utility by consumers, which in turn will be assessed based on the conceptual structure of the rational choice behavioural model, the discrete choice model and that of the hybrid choice model. The influence on the decision of directly observable variables, as well as the latent variables, risk tolerance/uncertainty aversion, electric vehicle symbolism and consumer pro-environmental profile, will be explained below. Discrete choice analysis has become a standard approach for analysing choice behaviours between travel and activity, such as travel mode, places of residence and installation, purchasing and use of cars, among others (Kim, Rasouli, & Timmermans, 2014). Determinants of Electric Car Purchase Intention in Portugal 165 For this purpose, based on the literature, the conceptual model presented in Fig. 1 and the research hypotheses presented below were developed. H1. The lower the consumer’s perception of the relative price of an electric vehicle compared to the conventional vehicle, the more likely the consumer is to purchase an electric car. H2. The higher the consumer’s perception of the evolution of the price of fossil fuels, the more likely the consumer is to purchase an electric car. H3. People with urban residence are more likely to purchase an electric car. H4. The lower the consumer’s perception of battery charging time, the more likely the consumer is to purchase an electric car. H5. The more convenient the charging station network is, the more likely the consumer is to purchase an electric car. H6. The higher the consumer’s perception of the autonomy of the vehicle, the more likely the consumer is to purchase an electric car. H7. The higher the consumer’s perception of battery lifetime, the more likely the consumer is to purchase an electric car. H8. People who routinely travel less than 50 km are more likely to purchase an electric car. H9. People who make monthly trips with more than 200kms are less likely to purchase an electric car. H10. If one more car is available in the household, the more likely the consumer is to purchase an electric car. H11. The higher the level of consumer education, the more likely the consumer is to purchase an electric car. H12. The age of the consumer is positively related to the likelihood of the consumer acquiring an electric-powered vehicle. H13. The lower the risk tolerance/degree of certainty about the future paradigm of car propulsion, the less likely the consumer is to purchase an electric car. H14. Consumers with a pro-environmental lifestyle are more likely to purchase an electric car. H15. Consumers who favourably connote the symbolism arising from the use of an electric car are more likely to acquire it. METHODOLOGY The research was based on a quantitative analysis that aimed to test the hypotheses that support the conceptual model of this study, following the discrete choice model approach. Consumers have preferences for different vehicles based on the subjective utility that they attribute to different vehicle characteristics (Morton et al., 2014). Regarding electric cars, consumer preferences represent a significant area of uncertainty (Morton et al., 2014). The empirical study aimed to test the statistical validity of the above hypotheses, determining whether the relationships between the variables, based on the primary data obtained from a sample, which in turn are based on preference statements and not on historical sales data. For this purpose, 166 JOSÉ LUÍS MIRANDA AND CATARINA J. M. DELGADO descriptive statistics, factor analysis, logistic regression and correlations techniques were used (Hair, Black, Babin, Anderson, & Tatham, 2006). The results were obtained using the software IBM SPSS Statistics 24.0 (IBM Corp., 2016). Data were collected through a web-based survey, aimed at individuals, older than 18 years old, who live in Portugal, are potential buyers of a car and able to answer an online questionnaire. The questionnaire was elaborated according to the theoretical framework described above and based on the existing literature, and a pretest was conducted on a sample of 18 respondents. The questionnaire was divided into five sections: (1) habits as a driver; (2) level of knowledge about electric cars; (3) preferences as a driver; (4) respondents’ socio-demographic and psychometric profile and (5) the constructs of risk perception (12 items, adapted from the scales from Stone and Grønhaug (1993), Dholakia (2001) and Pae and Hyun (2002)), pro-environmental consumer profile (4 items, adapted from the scale of Schuitema et al. (2013)) and the perceived symbolism of the electric car (8 items, adapted from the scale of Steg (2005) and Schuitema et al. (2013)). Constructs were measured using a seven-point Likert scale. RESEARCH FINDINGS From the 319 validated questionnaires, 11 were eliminated for respondents not living in Portugal and/or for not being used to driving. The remaining respondents were considered for data analysis purposes. The majority of the respondents (73%) were male, had at least a bachelor’s degree (78%) and were employed (67.5%). Their age ranges between 20 and 85 years with the following distribution: 22% from 20 to 39 years, 28% from 40 to 49 years, 34% from 50 to 59 years and 16% with more than 60 years. Only 13 (4.2%) of the 308 answered yes to the question: ‘Have you bought any electric/hybrid cars for your own and your family’s regular use?’ However, when asked whether they would choose to buy, at that moment, an electric/hybrid car over a conventional car powered by a gasoline or diesel internal combustion engine, only 26% answered no (28.5% answered yes, 39% answered maybe and the rest did not know). Exploratory factor analyses were performed on respondents’ risk perception, pro-environmental profile and perceived symbolic value of the electric car. The principal components method was used for extraction, and the Kaiser normalisation Varimax method was used for rotation. After excluding some insignificant items (two items in the pro-environmental profile scale and one item in the symbolic value scale), the scales demonstrated adequate levels of reliability according to accepted standards and high levels of internal consistency (KMO of 0.824, 0.5 and 0.826, respectively; Cronbach’s alphas of 0.854, 0.835 and 0.8, respectively). Four factors were extracted for the risk variable, which explains 74.84% of the total variance, two for the electric car symbolism that explains 55.05% of the total variance. Regarding the pro-environmental profile, the Determinants of Electric Car Purchase Intention in Portugal 167 number of factors to be extracted was set at two. It was found that they explain 90.571% of the total variance for a second factor eigenvalue of 0.946. In order to test the research hypotheses, a logistic regression model was estimated. As a dependent variable for the purpose of regression, the answer to the second question of the questionnaire about whether or not to purchase an electric car over a conventional vehicle was fixed, limiting the use of the data to the answers corresponding to yes and no (in a 170), coded to 1 5 Yes and 0 5 No. Providing a Nagelkerke R2 of 0.78 and classifying correctly 89.3% of the sample cases, the logistic regression provided evidence to support the following hypotheses: the more convenient the charging station network is, the more likely a consumer is to purchase an electric vehicle (H5); consumers with a more accurate perception of the evolution of the price of fossil fuels are more likely to buy an electric car (H2); consumers with a more accurate perception of battery life are more likely to buy an electric car (H7); the level of education received by a consumer is positively related to the likelihood of the consumer acquiring an electric-powered vehicle (H11); the age of the consumer is positively related to the likelihood of the consumer acquiring an electric-powered vehicle (H12); consumers who favourably connote the symbolism arising from the use of an electric car are more likely to acquire it (H15). CONCLUSIONS The results confirm the importance and influence of some of the technical attributes of the electric car and the charging infrastructure in the decision of the potential Portuguese consumer to opt for the purchase of an electric car over conventional gasoline or diesel-powered vehicle. It should be noted that the sense of influence exerted is in line with what would be expected in light of the above and with previous studies regarding the evolution of fossil fuel prices (Du & Lin, 2017; Hidrue et al., 2011; Javid & Nejat, 2017; Jensen et al., 2013), convenience of charging station network (Egbue & Long, 2012; Lehne & Hardinghaus, 2018; Sierzchula et al., 2014; Skippon & Garwood, 2011) and perception of battery life (Egbue & Long, 2012; Jensen et al., 2013). It should also be noted that the expression of influence exerted is very significant. On the other hand, the influence of risk perception on its social and financial dimension can also be considered very significant and in line with that reported in the literature (Graham-Rowe et al., 2012; Jansson, Pettersson, Mannberg, Brannlund, & Lindgren, 2017; McLeay, Yoganathan, Osburg, & Pandit, 2018; Morton et al., 2014). Concerning symbolism, the influence of its two dimensions is diverse and of opposite sign. While the symbolism associated with the automobile generally has a very significant favourable influence on the decision to opt for the purchase of an electric car, the dimension referring to the symbolism of the electric vehicle in particular influences in the opposite direction, albeit to a lesser extent. The results are in line with the importance of vehicle symbolism in general in deciding to purchase it, as argued by Steg (2005), Ashmore et al. (2018) and Schuitema et al. (2013). The symbolism of the electric vehicle impacts negatively on the 168 JOSÉ LUÍS MIRANDA AND CATARINA J. M. DELGADO decision, as concluded by authors such as Skippon and Garwood (2011) and Graham-Rowe et al. (2012). In their studies based on the British reality, they found that the small size and unappealing exterior appearance of electric cars did not fit the message about the status that the consumer intended to convey. The Portuguese consumer shows the same perception. Regarding the socio-demographic variables age and educational level, the results point to a very significant influence on the decision to choose an electric car in Portugal, in line with the conclusions reached by Daziano and Bolduc (2013) and Westin et al. (2018) in their studies based, respectively, on the Canadian and the Swedish reality. Plotz et al. (2014), based on the German reality, conclude the same, except for symbolism. Regarding the variable ‘other vehicle in the aggregate’, the estimated signal is in complete disagreement with what is supported by the literature. Based on these results, as an answer to the research questions, it can be concluded that: (1) The determinants that make the Portuguese consumer, when deciding to buy a new car, to opt for an electric vehicle over a conventional petrol or diesel internal combustion vehicle are the perception about the evolution of fossil fuel prices, the perception of the convenience of the charging station network and the perception of battery lifetime. (2) The dimension of symbolism attributed to the electric car has a significant negative influence on consumers’ decision to opt for an electric car, in contrast to the significant positive influence that the dimension of car symbolism in general exerts. Moreover, from the survey results and the implemented factor analysis, it is concluded that the respondents do not consider the image resulting from the use of the electric car to be very favourable, in contrast to the symbolic value they attribute to the car in general. (3) Consumers’ perception of uncertainty/risk in its social risk and financial risk dimension has a negative influence on the option of purchasing electric cars over conventional vehicles. No statistical evidence was found to validate the influence of the other dimensions of risk, particularly concerning paradigm uncertainty. (4) No statistical evidence was found to conclude that the pro-environmental profile of potential consumers influences the decision to purchase an electric vehicle. These conclusions, however, cannot be generalised to the general population, considering that they are based on data from a convenience sample that is not representative of this, despite the effort made to obtain a sample as heterogeneous and comprehensive as possible. Also, due to time constraints, the number of respondents was not higher. On the other hand, the investigation did not exhaust the set of potential determinants of the Portuguese consumers’ choice that were studied in other geographies in the existing literature. Therefore, a new line of research could be centred on a psychological approach to the motivations of the Determinants of Electric Car Purchase Intention in Portugal 169 Portuguese consumer, considering that all works on this theme focused on the Portuguese reality that we identified in the scope of the literature review have an economic approach. Another aspect is the fact that climate change and environmental issues are increasingly important and with growing media coverage, celebrities and influencers environmental activism and attention in social media (Craig, 2019; Schmidt, Ivanova, & Schäfer, 2013). As a result, people are becoming more aware of their responsibility as consumers and the need for environmentally responsible consumption (Sinay, Araújo, Sinay, & Dalbem, 2019), which could be translated into a stronger social desirability bias in the survey responses (King & Bruner, 2000; Lefever, Dal, & Matthı́asdóttir, 2007; Mazor, Clauser, Field, Yood, & Gurwitz, 2002; Yüksel, 2017). In future studies, the use of scales to measure the social desirability bias in survey responses and procedures to control it could be used (Jo, 2000; Van de Mortel, 2008; Ranjan & George, 2014). Regarding the chosen variables, we consider that the motivations associated with symbolism and social conformity and their role in the decision-making process could be further explored. There are also explanatory variables that could be introduced under the economic approach and that have already been studied in different realities, such as the role of incentives, financial and nonfinancial, for the purchase of electric cars. Also, how the income of the potential consumer influences the decision can be studied, as well as previous driving experience or test drive, and the influence of a more proenvironmental lifestyle on the part of the consumer could be studied more thoroughly. Finally, the topic of electric mobility, beyond its actuality, can also be vast in terms of perspectives. 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