Consumer purchase intention of electric vehicles in China: The roles of perception and personality

Consumer purchase intention of electric vehicles in China: The roles of perception and personality

Journal of Cleaner Production 204 (2018) 1060e1069 Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.els...

592KB Sizes 0 Downloads 35 Views

Journal of Cleaner Production 204 (2018) 1060e1069

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Consumer purchase intention of electric vehicles in China: The roles of perception and personality Xiuhong He a, Wenjie Zhan b, *, Yingying Hu b a b

School of Management, Wuhan Textile University, Wuhan 430073, China School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 January 2018 Received in revised form 23 August 2018 Accepted 24 August 2018 Available online 3 September 2018

Electric vehicles (EVs) have been developed rapidly with the strong support of governments in recent years, but the market share of EVs is still small in China and the vast majority of Chinese consumers hesitate to adopt them. Thus, for the successful development of EVs, this paper proposes a personalityperception-intention framework to explore consumers' EV adoption behavior. The research model is empirically tested with data collected from 369 participants in China. Results indicate that the EV purchase intention can be explained 57.1% variance by consumer perception and personality. Two types of personality, such as personal innovativeness and environmental concern, significantly affect EV purchase intention directly. They are also significantly mediated by two kinds of perceptions (i.e. positive and negative utilities). The findings give a deeper understanding of EV adoption behavior, and provide recommendations for policymakers and manufacturers on promoting EVs. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Electric vehicles Personality Perception Purchase intention

1. Introduction Electric vehicles (EVs) emerging as an eco-friendly innovation, are expected to be a sustainable solution for the global challenges of energy scarcity and environment pollution. Governments across the world have proposed a variety of policy mechanisms and invested billions of dollars to support EV development (Du and Ouyang, 2017). For example, the Chinese government provides purchase subsidy (Lu et al., 2017) and an exemption from acquisition tax and the excise tax based on engine displacement and price (Mock and Yang, 2014) to facilitate and promote consumer adoption of EVs. Moreover, the Chinese government invested 10 billion RMB in 2009 and announced to invest 100 billion RMB over the next decade from 2011 toward the advancement of EV technology and market uptake of EVs. However, EVs still account for a tiny fraction of the total vehicles sold in China. For example, sales of total vehicles were 28,878,900 in 2017. The market share of EVs was barely 2.7%, with an increment of 0.9% over the previous year. Mass adoption of EVs has a long way to go. There is a paradox: the Chinese government believes that the EV represents the trend of future development for its advantages of

* Corresponding author. E-mail addresses: [email protected] (X. He), [email protected] (W. Zhan), [email protected] (Y. Hu). https://doi.org/10.1016/j.jclepro.2018.08.260 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

energy-conversation and low-emission, but most of Chinese consumers are in a state of wait-and-see. To solve this paradox, our study tries to explore the determinants of consumer EV adoption behavior. Extant research on EV adoption has identified that consumer perceptions and individual characteristics play an important role in EV acceptance. However, the existing studies examined the impacts of consumer perception and individual characteristics separately and did not have a holistic view on the critical factors of EV adoption integrating two of them. In addition, consumer perceptions include the acquisition of benefits and the payment of sacrifices, while prior studies on consumer perceptions towards EVs rarely considered both positive and negative aspects of perceptions. Furthermore, consumer characteristics involve demographics and personality. Compared with demographics, personality is a stable variable to explain consumer behavior (Hirschberg, 1978). While relevant literature focused mainly on the effect of demographics on EV adoption, did not investigate completely the effect of consumer personality. In view of the current state of the existing research on EV adoption, our study developed a personality-perception-intention framework that incorporates consumer perceptions and personality to comprehensively investigate the antecedents of EV adoption behavior, and took positive and negative aspects of consumer perceptions into account based on the valence framework since it is

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

a fundamental decision-making theory and explains consumer behaviors by considering positive and negative utility of the behavior. Specifically, this study addresses two research questions: (1) what is the extent of consumer EV purchase intention attributed to consumer positive and negative perceptions? (2) how do consumer personality influence their intention to purchase EVs? The rest of this paper is organized as follows. In Section 2, an overview of EV adoption and the valence framework is presented. Then, we present the research model and hypotheses of the study in Section 3. We describe the research methodology in section 4, followed by the data analysis in Section 5. Section 6 includes the interpretation of results, a discussion on the theoretical and practical implication and limitation. Finally, the conclusion of this study is present in Section 7. 2. Theoretical background 2.1. EV adoption Scholars have analyzed several factors for the purpose to find out drivers and barriers against consumer EV adoption. They can be categorized into three sets (Bjerkan et al., 2016; Sierzchula et al., 2014), namely: (1) technological factors that include vehicle ownership costs, driving range, and charging time. (2) consumer characteristics that involve demographics and personality. The former includes a series of personal characteristics such as age, gender, education and experience. The latter reflects personal inner feelings and values to events, people, and situations in their lives. For example, traits such as dogmatism, risk-taking propensity, and anxiety level are typical personality variables. (3) context factors, such as government incentives, fuel price and charging infrastructures. Most studies are concerned with technological factors and consumer characteristics that are assumed to determine consumer EV purchase decision (e.g. Carley et al., 2013; Lieven et al., 2011). However, the widespread adoption of EVs depends heavily on consumer perceptions (Rezvani et al., 2015), not on technology attributes (Egbue and Long, 2012). For example, the limited range is a well-known technical short board of EVs, but the range anxiety may disappear if EV drivers feel it is very convenient to charge. A lot of scholars studied the roles of consumer perceptions in the intention of consumers to adopt EVs (e.g. Bunce et al., 2014; Glerum et al., 2013; She et al., 2017). Some of them studied the sum of positive and negative perceptions with rational behavior framework, e.g. the rational choice theory. Others examine the effects of either positive perceptions or negative perceptions. This paper studies both of them from a behavioral decision perspective. They are named as positive utility and negative utility in this paper. The positive utility contains perceived monetary benefit, perceived environment, and perceived symbol. The negative utility is consisted of perceived risk and perceived fee. In addition, prior studies on the influence of consumer characteristics on EV adoption mainly focused on demographics and found men, young or middle-aged, educated, with high income, and from multi-car family were more likely to purchase EVs (e.g. € tz et al., 2014). However, personality Graham-Rowe et al., 2012; Plo traits have been widely used to explain various behaviors and have been shown to be significant factors of technology acceptance. For example, He and Veronesi (2017) found that two personality factors, openness to experience and the locus of control, play important roles in the adoption of renewable energy technology. Considering personality may lead to different perceptions or re€ sponses against the similar instances (Ozbek et al., 2014), we take the personal innovativeness and the environmental concern as two types of personality, and explore their direct and indirect impacts

1061

on EV purchase intention in this paper. It is concluded that previous studies neglected the importance of consumer personality on EV adoption, and did not examine the positive and negative aspects of perceptions simultaneously. To fill these gaps, we developed a personality-perception-intention framework to examine how consumer perceptions and personality influence their EV adoption, and consider positive and negative aspects of consumer perceptions based on the valence framework. 2.2. The valence framework The valence framework is a well-grounded consumer decisionmaking theory, which is derived primarily from the economics and psychology literature (Goodwin, 1996). It assumes that consumers perform a behavior with the expectation of a maximum net valence by considering positive and negative utilities of the behavior simultaneously. Peter and Tarpey (1975) compared the valence framework with the other two decision-making models. The first is the “perceived risk” model, which supposes that consumers decide to act based on the minimization of expected negative utility of the behavior. The second is the “perceived benefit” model, which assumes that consumers make a decision based on the maximization of expected positive utility of the action. They found that the valence framework is a superior model than the other two models in explaining consumer behavioral decision because it involves positive and negative effects associated with the behavior. Recently, several researchers have applied the valence framework to explain consumer adoption behavior. For instance, Kim et al. (2009) investigated consumer online purchase behavior by drawing on the valence framework. Lu et al. (2011) explored the motivators and inhibitors of consumer intention to adopt online banking based on the valence framework. In the valence framework, the positive utility and the negative utility associated with consumer adoption decision are generally measured with perceived benefit and perceived risk respectively. However, consumer subjective perceptions about the positive values of their purchase decision include monetary and nonmonetary benefits. Similarly, consumers need to bear the sacrifices of both monetary and non-monetary costs (e.g. time and effort) in the adoption of an innovation. But the valence framework does not consider the benefits in the specific context and the monetary costs of the behavior. Therefore, many researchers captured more positive and negative utilities related to adoption behavior when they used the valence framework. For example, Yang et al. (2016) measured the positive utility with relative advantage and compatibility and used perceived risk and fee as negative utility dimensions to explain mobile payment adoption. Based on this, we measured the positive and negative utilities of consumer perceptions according to the context of EV adoption. Consumers could save money by using EVs, benefit the environment, and signal the consumer to be a green person because EVs are eco-innovations. Thus, we measured the positive utility with three dimensions (perceived monetary benefit, perceived environment, and perceived symbol). At the same time, they may suffer some loss, e.g. the uncertainty caused by the limited driving range and the high purchase price of EVs. We measure the negative utility with two dimensions (perceived risk and perceived fee) in this paper. 3. Research model and hypotheses Based on the literature on EV adoption, we developed a personality-perception-intention framework to study the determinants of consumer intention to purchase EVs. Fig. 1 presents the research model. We divided consumer perceptions into positive

1062

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

Fig. 1. Research model.

utility and negative utility based on the valence framework. We propose that perceived monetary benefit, perceived environment, and perceived symbol are positive utility factors that may positively influence consumer EV purchase intention, while perceived risk and perceived fee are negative utility factors that may negatively influence purchase intention. We further propose two factors of personality (personal innovativeness and environmental concern) that may have direct and indirect effects on EV purchase intention. Several demographic variables such as gender, age, income, and education are regarded as control variables. 3.1. Consumer perception and EV purchase intention 3.1.1. Positive utility In this study, perceived monetary benefit refers to consumer perception of money-saving from using EVs. As EVs are still in infancy stage, the government must provide incentives, such as subsidy and tax credits to EV consumers for EV development. That is, consumers can save money with government monetary incentives from purchasing EVs. EV maintenance is also less expensive (Barth et al., 2016), and consumers may save money on petrol because EVs are high fuel efficiency and may be powered by lowcost electricity. Research found that consumers usually make the decision to purchase EVs in response to government incentives (e.g. Gallagher and Muehlegger, 2011; Langbroek et al., 2016). Wold and Ølness (2016) found that the economic gain from free passes through toll stations is an important motivation for consumers to adopt an EV. Thus, we assume that, Hypothesis 1. Perceived monetary benefit positively influences consumer EV purchase intention. The perceived environment is defined as consumer perception of the positive outcomes of driving EVs for the environment. With environmental deterioration, consumers now pay more attention to environmental attributes of products and consider

environmental effects of the behavior. Therefore, environmental attributes of sustainable innovations are important factors to promote adoption (Chen et al., 2016). EVs as sustainable innovations have the potential to reduce CO2 emission and fuel consumption, which has a significant impact on reducing the contribution of transportation to global warming. Jansson et al. (2010) suggested that consumers are more willing to purchase EVs because of the environmental benefits from the use of EVs. Thus, we assume that, Hypothesis 2. Perceived environment positively influences consumer EV purchase intention. Consumption is used to express individual identity, membership, and image. Consumers are motivated to be seen in a positive image and may shape a positive image by purchasing products (Belk, 1981; Fennis and Pruyn, 2007). For instance, in addition to mobility, cars have symbolic meanings of self-expression of individuals. Consumers purchase a car based on instrumental attributes and symbolic value. Self-image congruency theory posits that consumers who perceive product image to be consistent with their self-image are likely to have a positive attitude toward a product, and subsequently are more likely to purchase the said product. In our study, perceived symbol refers to consumer perception of the improvement of their image and status when adopting EVs. EV owners might be related to “green” image because of proenvironmental attributes of EVs. Previous research has shown that consumer perception of the symbolic attributes of EVs is positively related to their EV adoption decision (e.g. Noppers et al., 2016; Schuitema et al., 2013). Thus, we assume that, Hypothesis 3. Perceived symbol positively influences consumer EV purchase intention.

3.1.2. Negative utility Perceived risk is defined as consumer perception of the uncertainties that they may face when they are driving an EV. Because

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

EV is still in the initial stage, EV technology is immature, especially battery technology. The limited battery range may not meet consumer expectations, and thus, charging infrastructure for EVs is necessary. However, the charging infrastructure is seriously inadequate, and thus, consumers may exhaust the power of the EV before reaching their destination. Moreover, the limited battery technology of EVs may lead to higher traffic risk (e.g. battery-fire incident) for consumers compared with gasoline cars. A higher lever of perceived risk would lead to a lower purchase intention (Garretson and Clow, 1999; Shimp and Bearden, 1982). Previous studies found that perceived risk has negative effects on consumer willingness to adopt innovations (Oliver and Rosen, 2010; Meuter et al., 2005). Thus, we assume that, Hypothesis 4. Perceived risk negatively influences consumer EV purchase intention. Monetary cost is the financial expense that consumers spend on obtaining or using a product (Liu et al., 2015). Consumers may compare the price of innovation with that of the alternatives when deciding to adopt such innovation, and they would form a perception of the fee of the innovation based on this comparison. Prior studies indicated that perceived fee is one of major factors for consumers' resistance to innovations (Egbue et al., 2017; Luarn and Lin, 2005). In this paper, perceived fee is defined as consumer perception of the money that consumers need to pay for adopting EVs, such as actual price of the EVs and the fee of home charging pile. Previous research suggests that high purchase price of EVs is a major obstacle to EV mass adoption (Adepetu and Keshav, 2017). Currently, EV consumers may have to install a home charging pile because of the sparse charging network, which increases the fee for the usage of EVs for consumers. Extant studies have shown that perceived fee negatively affects adoption of innovations (Song, 2014). Thus, we assume that, Hypothesis 5. Perceived fee negatively influences consumers' EV purchase intention.

3.2. Personality and EV purchase intention 3.2.1. Personal innovativeness Personal innovativeness refers to the degree of adoption of innovations or new ideas (Rogers and Shoemaker, 1971). Innovative individuals have a strong sense of curiosity and like to seek novelty. Consumers with high level of innovativeness are more willing to try new things and adopt new ideas. Further, personal innovativeness positively affects adoption of innovations, such as wireless internet (Parveen and Sulaiman, 2008), mobile learning (Liu et al., 2010), and mobile payment (Rakhi and Mala, 2014). Hence, EVs as new transportation technology may easily attract the attention of innovative individuals and meet their psychological demand of curiosity. Jansson (2011) found that EV adopters exhibit a higher level of innovativeness than non-adopters. Additionally, innovative individuals are active information seekers (Kim et al., 2010), sensitive to innovations, and easily perceive benefits from innovations. Previous studies have shown that personal innovativeness has positive impact on perceived economic benefit (Liu et al., 2015). Meanwhile, individuals with high personal innovativeness are more likely to be risk-seekers (Lewis et al., 2003; Lu et al., 2008), that is, they have higher tolerance for risk than others. Yang et al. (2012) reported that personal innovativeness negatively affects consumer perceived risk of the adoption behavior in the study of the adoption of mobile payment services. Based on the discussion above, consumers with high innovativeness may perceive that EVs might become a trend to

1063

replace gasoline cars and they will obtain economic benefits from the usage of EVs, such as government subsidies. On the other hand, these consumers may weaken the potential risk of adopting EVs, such as range anxiety resulting from the limited cruising range and inadequate charging facilities. Thus, we assume that, Hypothesis 6a. Personal innovativeness positively influences consumer EV purchase intention. Hypothesis 6b. Personal innovativeness positively influences perceived monetary benefit of EV adoption. Hypothesis 6c. Personal innovativeness negatively influences perceived risk of EV adoption.

3.2.2. Environmental concern Environmental concern is an affective feature of consumers that includes their considerations and worries on the quality of the environment (Yeung, 2004). Environmental concern involves three factors, which are egoistic, altruistic and biospheric (concern for the biosphere) (Schultz, 2000). Hence, consumers with environmental concern may care about the effects of their behaviors on the environment, which guides their decision behavior. Numerous studies have found that environmental concern is positive related to pro-environmental behavior, such as recycling (Straughan and Roberts, 1999), garbage reduction (Fujii, 2006), and green purchase behavior (Pagiaslis and Krontalis, 2014). EV adoption behavior is also a pro-environmental behavior because of less adverse effects of EVs on the environment. Consumers with a stronger concern for the environment are more likely to adopt EVs (Sinnappan and Abd Rahman, 2011). Additionally, consumers who care about environmental quality tend to evaluate the effect of the products on the environment. They prefer to know about environmentally friendly alternatives and strengthen environmental attributes of these alternatives. Literature suggests that consumers with higher level of environmental concern are more willing to pay a premium for environmentally friendly products (Bang et al., 2000), that is, they would weaken the cost of the products. Accordingly, consumers who care about the environment may be more aware that driving gasoline cars has significant negative contributions to the environment. By comparison to gasoline cars, such consumers would easily perceive the environmental attributes of EVs, and have a lower level of sensitivity towards EV price (Junquera et al., 2016). Thus, we assume that, Hypothesis 7a. Environmental concern positively influences consumer EV purchase intention. Hypothesis 7b. Environmental concern positively influences perceived environment of EV adoption. Hypothesis 7c. Environmental concern negatively influences perceived fee of EV adoption.

3.3. Demographic control variables Although our study does not focus on the impact that demographical variables may have on EV purchase intention, we include several of these as control variable in our model. The demographic variables such as gender, age, income, and education have been examined to have impacts on EV purchase intention (e.g. Hackbarth and Madlener, 2016; Sang and Bekhet, 2015; Prakash et al., 2014), and thus there is a good reason for including them as controls in the model. Among these control variables, gender has received considerable attentions. Most research has regarded gender as a

1064

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

moderator and investigated its moderation effect on consumer perception and behavior in technology adoption studies. Extant literature has identified that males are more innovative and more likely to accept a novel technology than females (e.g. Lee et al., 2010; Müller-Seitz et al., 2009), while females are more likely to express higher level of environmental concern (Davidson and Freudenburg, 1996; Mostafa, 2007), perceive lower benefits (Siegrist, 2000), and perceive greater risks associated with technologies than males (Garbarino and Strahilevitz, 2004). Based on this, we will discuss the moderation effect of gender later.

Table 1 Demographic characteristics of the sample. Measure

Item

Count

%

Gender

Male Female 20 21e30 31e40 41e50 51 High school or below Associate degree Bachelor's degree Master's degree or above Student Working Unemployed Others <3000 3000e5000 5000e7000 >7000

193 176 6 68 148 124 23 8 59 274 28 25 280 37 27 55 125 110 79

52.3 47.7 1.6 18.4 40.1 33.6 6.2 2.2 16.0 74.3 7.6 6.8 75.9 10.0 7.3 14.9 33.9 29.8 21.4

Age

Education

4. Research method 4.1. Measurement development To ensure validity, items used to measure the constructs were adapted mainly from previous studies to the context of this research. Items for EV purchase intention were adapted from Barbarossa et al. (2015). Items for perceived monetary benefit were selected from Ozaki and Sevastyanova (2011) and Barth et al. (2016). Items for perceived environment were derived from Ozaki and Sevastyanova (2011). Items for perceived symbol were adapted from Schuitema et al. (2013) and Noppers et al. (2014). Items for perceived risk were adapted from Jansson (2011). Items for perceived fee and personal innovativeness were adapted from Yang et al. (2012). Items for environmental concern were adopted from Fujii (2006). The original items were in English, and thus, we used a backtranslation method to convert the statement into Chinese. Then, we distributed the final Chinese version to five professors for review. The questionnaire was further modified based on their suggestions. Finally, we conducted a pilot test to ensure reliability and validity of the scale. We developed an online version of our survey and posted its URL on a forum designed for communication among academic researchers. Subjects who know about EVs were invited to finish the questionnaires. Finally, we have collected 45 responses. Analysis of the data shows that Cronbach's alphas were all above 0.7, which imply strong internal consistency of the constructs. Minor amendments were made to the questionnaire according to the feedback from the respondents of the pilot study. The final scale and related references are presented in Appendix A. all items were measured on seven-point Likert scales, ranging from strongly disagree (1) to strongly agree (7). 4.2. Data collection Individuals knowledgeable on EVs were selected as subjects of this study. We placed our questionnaire online through Wenjuanxing (http://www.sojump.com/), which is a professional platform for online questionnaires with more than 2.6 million sample sources in China. Respondents received a monetary reward of 6 RMB when they successfully completed the questionnaire. The survey was available online for about four weeks. We obtained 369 valid responses after dropping invalid responses, such as those with a missing value, have the same answer to all questions, or completed in less time. Table 1 shows the demographic characteristic of the sample. About 52.3% of the respondents were male, and most respondents were aged between 31 and 50 years, and had bachelor degrees. 5. Data analysis and results We used partial least squares (PLS), which is a powerful and widely used method to examine model with latent variables (Chin et al., 2003), to examine the research model. We firstly tested the

Occupation

Monthly income

reliability and validity of the measurement model, and then examined the structural model to test research hypotheses. 5.1. Measurement model We performed confirmatory factor analysis (CFA) to test reliability and validity of the measurement model. Table 2 shows that standard loadings of items were above 0.7. All Cronbach's alpha values were above 0.7, indicating that the scales were reliable (Nunnally and Bernstein, 1978). The average variance extracted (AVE) for each construct was above 0.6, indicating good convergent validity of the scales (Bagozzi and Yi, 1988). The composite reliabilities (CR) of the constructs were above 0.8, indicating that the scales demonstrated good internal consistency reliability (Nunnally and Bernstein, 1978). Table 3 shows correlation and square roots of the AVE of each construct. The square roots of AVE are represented on the diagonal. The diagonal elements are all greater than their corresponding correlation coefficients with the construct. This result indicated that the scales have good discriminant validity. As the data were self-reported and collected from a single source, we conducted Harman's one-factor test (Podsakoff et al., 2003) to examine the likelihood of the common method bias. The results showed that multiple factors were extracted, and the largest explained variance of factors was 34.16%. This result indicated that the common method bias was not a problem in our study. 5.2. Structural model Fig. 2 shows the results of the hypothesis test. The total variance of EV purchase intention explained by consumer perception and personality is 57.1%. All hypotheses were supported, except for Hypothesis 2. With regard to perception, two dimensions of positive utility – perceived monetary benefit (b ¼ 0.128, p < 0.01) and perceived symbol (b ¼ 0.399, p < 0.001) were found to have strong positive effect on EV purchase intention, thereby supporting H1 and H3. Meanwhile, the relationship between perceived environment and EV purchase intention was not significant (b ¼ 0.048, p > 0.05), and thus did not support H2. The two dimensions of negative utility of perception – perceived risk (b ¼ 0.08, p < 0.05) and perceived fee (b ¼ 0.098, p < 0.05) had significant negative effects on EV purchase intention, thereby supporting H4 and H5. With regard to personality, personal innovativeness was found to

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

1065

Table 2 Scale properties. Construct

Items

Standard loadings

Cronbach's a

CR

AVE

Perceived monetary benefit

PMB1 PMB2 PMB3 PE1 PE2 PE3 PE4 PE5 PS1 PS2 PS3 PS4 PR1 PR2 PR3 PF1 PF2 PI1 PI2 PI3 PI4 EC1 EC2 EC3 EVPI1 EVPI2 EVPI3

0.833 0.751 0.794 0.810 0.880 0.866 0.822 0.764 0.779 0.915 0.899 0.880 0.794 0.903 0.895 0.950 0.960 0.910 0.883 0.872 0.822 0.909 0.893 0.880 0.901 0.921 0.935

0.710

0.836

0.630

0.886

0.917

0.688

0.893

0.926

0.757

0.834

0.899

0.749

0.903

0.954

0.912

0.895

0.927

0.761

0.874

0.923

0.799

0.908

0.942

0.844

Perceived environment

Perceived symbol

Perceived risk

Perceived fee Personal innovativeness

Environmental concern

EV purchase intention

Table 3 Correlation coefficient matrix and square roots of AVEs.

PMB PE PS PR PF PT EC EVPI

PMB

PE

PS

PR

PF

PI

EC

EVPI

0.793 0.548 0.424 0.146 0.364 0.438 0.473 0.511

0.829 0.388 0.128 0.136 0.361 0.507 0.413

0.870 0.229 0.135 0.535 0.233 0.646

0.865 0.307 0.210 0.039 0.281

0.955 0.260 0.171 0.305

0.872 0.343 0.592

0.894 0.354

0.919

Note: Diagonal elements are the square root of AVE. These values should exceed the inter-construct correlations for adequate discriminant validity.

have positive effects on EV purchase intention (b ¼ 0.222, p < 0.001) and perceived monetary benefit (b ¼ 0.438, p < 0.001), and have a negative effect on perceived risk (b ¼ 0.21, p < 0.001), thus supporting H6a, H6b and H6c; Environmental concern was found to have positive effects on EV purchase intention (b ¼ 0.085, p < 0.05) and perceived environment (b ¼ 0.507, p < 0.001), and have a negative effect on perceived fee (b ¼ 0.171, p < 0.01), thus supporting H7a, H7b and H7c. Following the procedure suggested by Baron and Kenny (1986), we examined the mediation effects of the research model by three steps: (1) independent variable should significant affect dependent variable; (2) independent variable should significant affect mediator; (3) we use both independent variable and the mediator to predict dependent variable, if the effect of mediator is significant and the effect of independent variable is not significant, it can be concluded that the effect of independent variable on dependent variable is fully mediated by the mediator; while if both the effects of mediator and independent variable are significant, such effect is partially mediated by the mediator. As shown in Table 4, both the independent variables and mediators have a significant effect on the dependent variable. That is, the relationship between personal innovativeness and EV purchase intention is partially mediated by perceived monetary benefit and perceived risk; and the relationship between environmental concern and EV purchase intention is

partially mediated by perceived fee. We also considered other factors (age, gender, income, and education) as control variables in determining EV purchase intention. As shown in Fig. 2, income had a positive effect on EV purchase intention, while age and education did not have any significant effect on it. We examined the effect of gender on each relationship following the procedure proposed by Chin et al. (2003). We divided the full sample into male and female groups, and analyzed the effect of gender by comparing the path coefficients between groups. The results are summarized in Table 5. We can observe that the path coefficients from personal innovativeness, perceived monetary benefit, and perceived symbol to purchase intention of EVs for men are significantly larger than those for women, which means men are more likely than women to purchase EVs because of personal innovativeness, perceived monetary benefit, and perceived symbol of EV adoption. Besides, the path coefficients from perceived risk, perceived fee, and environmental concern to EV purchase intention are not significant for men but significant for women, which indicates that perceived risk and perceived fee have stronger negative effect on EV purchase intention for women than for men and women were more likely than man to purchase EVs because of environmental concern. 6. Discussion and implication 6.1. Discussion of results This study explored how consumer perception and personality influence their intention to purchase EVs with a developed personality-perception-intention framework. Consumer perception includes positive utility and negative utility based on the valence framework, which proposes that consumers consider positive and negative effects of their behavior before making decisions. The key findings are discussed below. First, the results suggest two types of positive utility have positive effects on consumer intention to purchase EVs. Specifically, perceived monetary benefit was found to positively influence consumer EV purchase intention (H1), which conforms to the

1066

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

Fig. 2. Results of hypothesis testing. Note: *p < 0.05; **p < 0.01; ***p < 0.001, n.s. non-significant.

Table 4 The results of mediation effects testing. IV

M

DV

IV/DV

IV/M

PI PI EC

PMB PR PF

EVPI EVPI EVPI

0.276*** 0.276*** 0.106**

0.438*** 0.210*** 0.171**

IV þ M/DV IV

M

0.222*** 0.222*** 0.085*

0.128** 0.080* 0.098*

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

conclusion of previous studies that financial benefit could motivate consumers to purchase EVs (Langbroek et al., 2016). Consistent with the findings of prior research that symbolic attributes are important for consumers to adopt innovations (Noppers et al., 2016), this study found that perceived symbol positively influences consumer intention to purchase EVs (H3). Surprisingly, the effect of perceived environment on consumer EV purchase intention was insignificant (H2). This result might be because of the magnitude of the environmental problem, such that consumers do Table 5 Moderation effect of gender. Hypotheses

Male (n ¼ 193)

Female (n ¼ 176)

Statistical comparison of paths (t-value)

H1:PMB/EVPI H2:PE/EVPI H3:PS/EVPI H4:PR/EVPI H5:PF/EVPI H6a:PI/EVPI H6b:PI/PMB H6c:PI/PR H7a:EC/EVPI H7b:EC/PE H7c:EC/PF

0.124** 0.046(n.s.) 0.414*** 0.051(n.s.) 0.055(n.s.) 0.310*** 0.422*** 0.200*** 0.066(n.s.) 0.536*** 0.071(n.s.)

0.090(n.s.) 0.072(n.s.) 0.350*** 0.123** 0.171*** 0.134* 0.459*** 0.227*** 0.099* 0.478*** 0.319***

6.381*** e 13.638*** 18.084*** 26.457*** 32.323*** 8.57*** 5.532*** 6.867*** 9.86*** 45.248***

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

not believe their individual efforts could make a difference in solving the problem. Thus, although consumers perceive the environmental attributes of EVs, they may think their EV purchase behavior would be meaningless in protecting the environment. For negative utility, both perceived risk and perceived fee were found to have a negative effect on consumer intention to purchase EVs (H4 and H5). The former is supported by the results of previous studies that perceived risk reduces consumer purchase intention (Garretson and Clow, 1999; Shimp and Bearden, 1982); while the latter is consistent with the finding of Luarn and Lin (2005), which indicated that higher perception of fee is associated with lower adoption intention of innovations. The negative effect of perceived fee on EV purchase intention is also relatively stronger than that of perceived risk. This finding supports the argument that high purchase price of EVs is the key barrier to EV widespread diffusion (Egbue et al., 2017). Second, both two dimensions of personality had a significant influence on consumer EV purchase intention directly and indirectly. Consistent with the finding of previous research (Jansson, 2011), personal innovativeness could improve consumer intention to purchase EVs (H6a). Interestingly, personal innovativeness could indirectly enhance the intention by increasing perceived monetary benefit or reducing perceived risk (H6b and H6c). This finding might be because of the notion that individuals with high level of personal innovativeness could easily envision monetary benefits of the innovation, which tend to weaken risks. Moreover, environmental concern could strengthen consumer EV purchase intention directly (H7a), which is consistent with the result of Sinnappan and Abd Rahman (2011). Meanwhile, environmental concern could indirectly motivate intention by reducing perceived fee (H7b), this result might be because of the willingness of consumers who care for the environment to pay more for eco-innovations, which makes the cost perception weak. Finally, the moderation effects of gender were examined. Gender moderates the relationships between personality and

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

purchase intention, and the moderation effects are different as associated with various dimensions of personality. The positive effect of personal innovativeness on purchase intention is greater for males than for females, which supports the results of previous studies (Lee et al., 2010; Müller-Seitz et al., 2009) While environmental concern has stronger effect on purchase intention for females than for males, this result indicates that women are more concerned about the environment than that of men (Davidson and Freudenburg, 1996). Additionally, gender has significant moderation effects on the relationship between behavioral beliefs and purchase intention. Specifically, men are more likely to purchase EVs because of positive utility as compared with women, while women are more reluctant to purchase EVs because of negative utility. This result conforms to the conclusions of previous studies that men perceive more benefits of technologies (Siegrist, 2000), whereas women perceive higher levels of risk (Garbarino and Strahilevitz, 2004). 6.2. Theoretical implications The study has several interesting theoretical implications. First, unlike prior research that examined the impacts of consumer perception and individual characteristics separately, we developed a personality-perception-intention research framework incorporating consumer perception and personality to have a holistic view on the determinants of EV purchase intention. The results show that consumer perception and personality are important determinants of EV purchase intention. Specifically, personality has an indirect effect on purchase intention by some perceptions. Therefore, our study contributes to a profound understanding of the literature on EV adoption. Second, other than previous studies that have investigated how the positive or negative utilities of EV use influence consumer adoption, we discussed positive and negative utilities of using EVs based on the valence framework. In the valence framework, the positive and negative utilities are only measured with perceived benefit and perceived risk, respectively. However, consumer perceptions include monetary and non-monetary sides, and thus, we divided the positive aspect into three dimensions (perceived monetary benefit, perceived environment, and perceived symbol), and measured the negative aspect using two dimensions (perceived risk and perceived fee). The results showed an intriguing finding that all positive utilities, except perceived environment, are positively related to EV purchase intention, while both two dimensions of negative utility are negatively related to EV purchase intention. This finding contributes to a comprehensive understanding of the effects of consumer perception on EV adoption. Finally, we considered personal innovativeness and environmental concern as two dimensions of personality. Existing literature that analyzed the effect of consumer characteristics on EV adoption are concentrated on demographic variables, such as age, gender, income, and education, but neglect personality, which reflects consumer feelings and emotions to events (Hirschberg, 1978). To fill this gap, we investigated how personal innovativeness and environmental concern influence consumer intention to purchase EVs. Personal innovativeness is a widely examined personality factor in innovation adoption research, and environmental concern is an important determinant of adopting eco-innovation. We found that these two personality factors have positive impact on EV purchase intention. This finding provides insight into the effects of consumer personality on EV adoption. 6.3. Practical implications Our study provides important practical implications. First, our

1067

study revealed the positive effects of perceived monetary benefit and perceived symbol on EV purchase intention, Therefore, measures to increase consumers' perception on monetary benefit and symbol of using EVs can be taken. For example, the government could provide financial incentives (e.g. subsidy, tax exemption), and the industry could offer rebates and coupons for EV consumer to improve their perceived monetary benefit. The industry could propagate the symbolic meaning of using EVs through various media to enhance EV consumer perceived symbol. The more monetary benefit and symbol the consumers are aware of, the more likely they would to purchase EVs. Second, the significantly negative effects of perceived risk and perceived fee on EV purchase intention demonstrated that the government and the industry need to decrease negative perception of consumers. For instance, the government should invest considerably in the research and development of battery technology and the expansion of charging infrastructures to mitigate uncertainties and consumers' anxiety of EV adoption and usage. To reduce consumer perceived fee, the industry should try to reduce the cost of battery which determines EV purchase price, and provide free installation of home charging piles for consumers. Third, our findings indicated that personality plays a vital role in consumer intention to purchase EVs. Personal innovativeness positively influenced purchase intention directly and indirectly via perceived monetary benefit and perceived risk, and thus, the industry should disseminate new technologies applied in EVs to attract the attention of innovative consumers. Environmental concern also has strong effect on EV purchase intention directly and indirectly through perceived cost. Therefore, the government and industry should launch publicity campaigns to emphasize the importance of the environment and the seriousness of the current environmental problems. This strategy could make consumers care more about the environment, making them more willing to adopt EVs. Finally, moderation effects of gender suggest that the industry could provide personalization marketing for different groups. For example, men tend to have higher intention to purchase EVs than women because of personal innovativeness and the positive utility of perception. Hence, the industry could convey more information on the innovativeness and benefits of using EVs to these consumers.

6.4. Limitations and future research Several limitations should be noted when interpreting the results of this study. First, we used an online survey platform to collect data. This method may result in sample bias because consumers who do not use the internet are not included in our samples. Thus, future studies may generalize our findings to offline consumers. Second, the dependent variable in our research model is EV purchase intention, but not actual behavior. Although behavioral intention is closely related to actual behavior (Hung et al., 2003; Tan and Teo, 2000), the result of the research with actual behavior as dependent variable will be more satisfied. Thus, future studies may investigate EV adoption by taking actual purchase behavior as dependent variable in the research model. Besides, EVs in our study include all EV types, the results may be different between pure EV and hybrid EV or there may be differences in results because of the level of EV brand. Thus, future studies may distinguish EV type and then compare the results. Finally, our results might be specific to China because the sample was obtained from China. Given the differences among countries, the results may change when applying the research model to other countries. Thus, similar research may be conducted in other countries.

1068

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069

7. Conclusion

Appendix B. Supplementary data

We developed a personality-perception-intention framework to examine the antecedents of consumer intention to purchase EVs by integrating consumer perception and personality. Based on the valence framework, we considered the impacts of both positive and negative utilities of consumer perceptions on purchase behavior. The former has three dimensions, namely, perceived monetary benefit, perceived environment, and perceived symbol, while the latter has two dimensions, namely, perceived risk and perceived fee. We also investigated the influence of two factors of personality (personal innovativeness and environmental concern) on consumer intention to purchase EVs. We found that consumer perceptions and personality are important factors for their EV purchase intention. All positive utility have positive effect on purchase intention, except for perceived environment, while both two dimension of negative utility have negative effect on purchase intention. Consumers with higher personal innovativeness and environmental concern are more likely to purchase EVs.

Supplementary data related to this article can be found at https://doi.org/10.1016/j.jclepro.2018.08.260.

Acknowledgements This work was supported by a grant from the National Natural Science Foundation of China (NSFC) (No. 71320107001; 71701075). Appendix A. Scale

Construct

Items

PMB1 Driving electric vehicles will help me spend less on fuel PMB2 Driving electric vehicles will give me other government incentives PMB3 Considering all costs, driving electric vehicles is no more expensive than driving conventional cars Perceived PE1 Driving an EV reduces the effects of climate change environment PE2 Driving an EV reduces the carbon footprint PE3 Driving an EV preserves the environment PE4 Driving an EV reduces pollution level PE5 Driving an EV reduces the consumption of natural resources Perceived symbol PS1 Compared to a normal car, electric cars is not suitable for my lifestyle (Reverse) PS2 I would feel proud of driving an electric car PS3 The electric car shows who I am PS4 The electric car enhances my social status Perceived risk PR1 I am afraid that the cruising range of EV cannot meet my expectation PR2 I am afraid that electric cars often break. PR3 I am afraid that EVs mean a higher traffic risk for me Perceived fee PF1 It would cost a lot to use an EV PF2 There are financial barriers (e.g., having to pay for charging pile) to my using an EV Personal PI1 If I heard about a new product, I would look for ways innovativeness to experiment with it PI2 I like to experiment with new products. PI3 Among my peers, I am usually the first to explore new products PI4 In general, I am hesitant to try out new products (Reverse). Environmental EC1 I think environmental problems are very important concern EC2 I think environmental problems cannot be ignored EC3 I think we should care about environmental problems EV purchase EVPI1 Next time I buy a car, I will consider buying an electric intention car EVPI2 I expect to drive an electric car in the near future EVPI3 I have the intention to drive an electric car in the near future Perceived monetary benefit

References Adepetu, A., Keshav, S., 2017. The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study. Transportation 44, 353e373. Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74e94. Bang, H.-K., Ellinger, A.E., Hadjimarcou, J., Traichal, P.A., 2000. Consumer concern, knowledge, belief, and attitude toward renewable energy: an application of the reasoned action theory. Psychol. Mark. 17, 449e468. Barbarossa, C., Beckmann, S.C., De Pelsmacker, P., Moons, I., Gwozdz, W., 2015. A self-identity based model of electric car adoption intention: a cross-cultural comparative study. J. Environ. Psychol. 42, 149e160. Baron, R.M., Kenny, D.A., 1986. The moderatoremediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173e1182. Barth, M., Jugert, P., Fritsche, I., 2016. Still underdetected e social norms and collective efficacy predict the acceptance of electric vehicles in Germany. Transp. Res. Part F Traffic Psychol. Behav. 37, 64e77. Belk, R.W., 1981. Determinants of consumption cue utilization in impression formation: an association derivation and experimental verification. Adv. Consum. Res. 8, 170e175. Bjerkan, K.Y., Nørbech, T.E., Nordtømme, M.E., 2016. Incentives for promoting battery electric vehicle (BEV) adoption in Norway. Transp. Res. Part Transp. Environ. 43, 169e180. Bunce, L., Harris, M., Burgess, M., 2014. Charge up then charge out? Drivers' perceptions and experiences of electric vehicles in the UK. Transp. Res. Part Policy Pract. 59, 278e287. Carley, S., Krause, R.M., Lane, B.W., Graham, J.D., 2013. Intent to purchase a plug-in electric vehicle: a survey of early impressions in large US cites. Transp. Res. Part Transp. Environ. 18, 39e45. Chen, C., Xu, X., Frey, S., 2016. Who wants solar water heaters and alternative fuel vehicles? Assessing socialepsychological predictors of adoption intention and policy support in China. Energy Res. Soc. Sci. 15, 1e11. Chin, W.W., Marcolin, B.L., Newsted, P.R., 2003. A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 14, 189e217. Davidson, D.J., Freudenburg, W.R., 1996. Gender and environmental risk concerns: a review and analysis of available research. Environ. Behav. 28, 302e339. Du, J., Ouyang, D., 2017. Progress of Chinese electric vehicles industrialization in 2015: a review. Appl. Energy 188, 529e546. Egbue, O., Long, S., 2012. Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Pol. 48, 717e729. Egbue, O., Long, S., Samaranayake, V.A., 2017. Mass deployment of sustainable transportation: evaluation of factors that influence electric vehicle adoption. Clean Technol. Environ. Policy 19, 1927e1939. Fennis, B.M., Pruyn, A.T.H., 2007. You are what you wear: brand personality influences on consumer impression formation. J. Bus. Res., Consum. Personal. Individual Differences 60, 634e639. Fujii, S., 2006. Environmental concern, attitude toward frugality, and ease of behavior as determinants of pro-environmental behavior intentions. J. Environ. Psychol. 26, 262e268. Gallagher, K.S., Muehlegger, E., 2011. Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology. J. Environ. Econ. Manag. 61, 1e15. Garbarino, E., Strahilevitz, M., 2004. Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. J. Bus. Res. Mark. Web Behav. Strat. Pract. Publ. Pol. 57, 768e775. Garretson, J.A., Clow, K.E., 1999. The influence of coupon face value on service quality expectations, risk perceptions and purchase intentions in the dental industry. J. Serv. Mark. 13, 59e72. mans, M., Bierlaire, M., 2013. Forecasting the demand Glerum, A., Stankovikj, L., The for electric vehicles: accounting for attitudes and perceptions. Transp. Sci. 48, 483e499. Goodwin, N.R., 1996. Economic Meanings of Trust and Responsibility. Univ. Mich. Press, Ann Arbor MI. Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S., Dittmar, H., Hutchins, R., Stannard, J., 2012. Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: a qualitative analysis of responses and evaluations. Transp. Res. Part Policy Pract 46, 140e153. Hackbarth, A., Madlener, R., 2016. Willingness-to-Pay for alternative fuel vehicle characteristics: a stated choice study for Germany. Transp. Res. Part A 85, 89e111. He, P., Veronesi, M., 2017. Personality traits and renewable energy technology adoption: a policy case study from China. Energy Pol. 107, 472e479. Hirschberg, N., 1978. A Correct Treatment of Traits. Personal. New Look Metatheories, pp. 45e68. Hung, S.-Y., Ku, C.-Y., Chang, C.-M., 2003. Critical factors of WAP services adoption:

X. He et al. / Journal of Cleaner Production 204 (2018) 1060e1069 an empirical study. In: Electron. Commer. Res. Appl., Containing Special Section: Five Best Papers Selected from the International Conference on Electronic Commerce, vol. 2, pp. 42e60. Jansson, J., 2011. Consumer eco-innovation adoption: assessing attitudinal factors and perceived product characteristics. Bus. Strategy Environ. 20, 192e210. Jansson, J., Marell, A., Nordlund, A., 2010. Green consumer behavior: determinants of curtailment and eco-innovation adoption. J. Consum. Mark. 27, 358e370.  Junquera, B., Moreno, B., Alvarez, R., 2016. Analyzing consumer attitudes towards electric vehicle purchasing intentions in Spain: technological limitations and vehicle confidence. Technol. Forecast. Soc. Change 109, 6e14. Kim, C., Mirusmonov, M., Lee, I., 2010. An empirical examination of factors influencing the intention to use mobile payment. Comput. Hum. Behav. 26, 310e322. Kim, G., Shin, B., Lee, H.G., 2009. Understanding dynamics between initial trust and usage intentions of mobile banking. Inf. Syst. J. 19, 283e311. Langbroek, J.H.M., Franklin, J.P., Susilo, Y.O., 2016. The effect of policy incentives on electric vehicle adoption. Energy Pol. 94, 94e103. Lee, H., Jeong Cho, H., Xu, W., Fairhurst, A., 2010. The influence of consumer traits and demographics on intention to use retail self-service checkouts. Mark. Intell. Plan. 28, 46e58. Lewis, W., Agarwal, R., Sambamurthy, V., 2003. Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Q. 27, 657e678. Lieven, T., Mühlmeier, S., Henkel, S., Waller, J.F., 2011. Who will buy electric cars? An empirical study in Germany. Transp. Res. Part Transp. Environ. 16, 236e243. Liu, F., Zhao, X., Chau, P.Y.K., Tang, Q., 2015. Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Res. 25, 471e495. Liu, Y., Li, H., Carlsson, C., 2010. Factors driving the adoption of m-learning: an empirical study. Comput. Educ. 55, 1211e1219. Lu, C., Liu, H.-C., Tao, J., Rong, K., Hsieh, Y.-C., 2017. A key stakeholder-based financial subsidy stimulation for Chinese EV industrialization: a system dynamics simulation. Technol. Forecast. Soc. Change 118, 1e14. Lu, J., Liu, C., Yu, C.-S., Wang, K., 2008. Determinants of accepting wireless mobile data services in China. Inf. Manag. 45, 52e64. Lu, Y., Cao, Y., Wang, B., Yang, S., 2011. A study on factors that affect users' behavioral intention to transfer usage from the offline to the online channel. In: Comput. Hum. Behav., Current Research Topics in Cognitive Load TheoryThird International Cognitive Load Theory Conference, vol. 27, pp. 355e364. Luarn, P., Lin, H.H., 2005. Toward an understanding of the behavioral intention to use mobile banking. Comput. Hum. Behav. 21, 873e891. Meuter, M.L., Bitner, M.J., Ostrom, A.L., Brown, S.W., 2005. Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. J. Mark. 69, 61e83. Mock, P., Yang, Z., 2014. Driving electrification: a global comparison of fiscal incentive policy for electric vehicles. Int. Counc. Clean Transp. ICCT 15, 1e40. Mostafa, M.M., 2007. Gender differences in Egyptian consumers' green purchase behaviour: the effects of environmental knowledge, concern and attitude. Int. J. Consum. Stud. 31, 220e229. Müller-Seitz, G., Dautzenberg, K., Creusen, U., Stromereder, C., 2009. Customer acceptance of RFID technology: evidence from the German electronic retail sector. J. Retail. Consum. Serv. 16, 31e39. Noppers, E.H., Keizer, K., Bolderdijk, J.W., Steg, L., 2014. The adoption of sustainable innovations: driven by symbolic and environmental motives. Global Environ. Change 25, 52e62. Noppers, E.H., Keizer, K., Milovanovic, M., Steg, L., 2016. The importance of instrumental, symbolic, and environmental attributes for the adoption of smart energy systems. Energy Pol. 98, 12e18. Nunnally, J., Bernstein, I., 1978. Psychometric Theory. McGraw-Hill, New York. Oliver, J., E. Rosen, D., 2010. Applying the environmental propensity framework: a segmented approach to hybrid electric vehicle marketing strategies. J. Market. Theor. Pract. 18, 377e393. Ozaki, R., Sevastyanova, K., 2011. Going hybrid: an analysis of consumer purchase motivations. Energy Pol. 39, 2217e2227.

1069

€ Ozbek, V., Alnıaçık, Ü., Koc, F., Akkılıç, M.E., Kas¸, E., 2014. The impact of personality on technology acceptance: a study on smart phone users. Proced. Soc. Behav. Sci. 150, 541e551. Pagiaslis, A., Krontalis, A.K., 2014. Green consumption behavior antecedents: environmental concern, knowledge, and beliefs. Psychol. Mark. 31, 335e348. Parveen, F., Sulaiman, A., 2008. Technology complexity, personal innovativeness and intention to use wireless internet using mobile devices in Malaysia. Int. Rev. Bus. Res. Pap. 4, 1e10. Peter, J.P., Tarpey, L.X., 1975. A comparative analysis of three consumer decision strategies. J. Consum. Res. 2, 29e37. € tz, P., Schneider, U., Globisch, J., Dütschke, E., 2014. Who will buy electric vehiPlo cles? Identifying early adopters in Germany. Transp. Res. Part Policy Pract. 67, 96e109. Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879. Prakash, N., Kapoor, R., Kapoor, A., Malik, Y., 2014. Gender Preferences for alternative energy transport with focus on electric vehicle. J. Soc. Sci. 10, 114. Rakhi, T., Mala, S., 2014. Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Res. Electron. Netw. Appl. Policy 24, 369e392. Rezvani, Z., Jansson, J., Bodin, J., 2015. Advances in consumer electric vehicle adoption research: a review and research agenda. Transp. Res. Part Transp. Environ. 34, 122e136. Rogers, E.M., Shoemaker, F.F., 1971. Communication of Innovations. Free Press, New York. Sang, Y.-N., Bekhet, H.A., 2015. Modelling electric vehicle usage intentions: an empirical study in Malaysia. J. Clean. Prod. 92, 75e83. Schuitema, G., Anable, J., Skippon, S., Kinnear, N., 2013. The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transp. Res. Part Policy Pract. Psychol. Sustain. Travel Behav. 48, 39e49. Schultz, P.W., 2000. New environmental theories: empathizing with nature: the effects of perspective taking on concern for environmental issues. J. Soc. Issues 56, 391e406. She, Z.-Y., Sun, Qing, Ma, J.-J., Xie, B.-C., 2017. What are the barriers to widespread adoption of battery electric vehicles? A survey of public perception in Tianjin, China. Transp. Pol. 56, 29e40. Shimp, T.A., Bearden, W.O., 1982. Warranty and other extrinsic cue effects on consumers' risk perceptions. J. Consum. Res. 9, 38e46. Siegrist, M., 2000. The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk Anal. 20, 195e204. Sierzchula, W., Bakker, S., Maat, K., van Wee, B., 2014. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Pol. 68, 183e194. Sinnappan, P., Abd Rahman, A., 2011. Antecedents of green purchasing behavior among Malaysian consumers. Int. Bus. Manag. 5, 129e139. Song, J., 2014. Understanding the adoption of mobile innovation in China. Comput. Hum. Behav. 38, 339e348. Straughan, R.D., Roberts, J.A., 1999. Environmental segmentation alternatives: a look at green consumer behavior in the new millennium. J. Consum. Mark. 16, 558e575. Tan, M., Teo, T.S.H., 2000. Factors influencing the adoption of Internet banking. J. AIS 1, 1e42. Wold, M.F., Ølness, S., 2016. An Empirical Analysis of Drivers for Electric Vehicle Adoption : Evidence from Norway 2010-2014. Yang, S., Lu, Y., Gupta, S., Cao, Y., Zhang, R., 2012. Mobile payment services adoption across time: an empirical study of the effects of behavioral beliefs, social influences, and personal traits. Comput. Hum. Behav. 28, 129e142. Yang, S., Wang, B., Lu, Y., 2016. Exploring the dual outcomes of mobile social networking service enjoyment: the roles of social self-efficacy and habit. Comput. Hum. Behav. 64, 486e496. Yeung, S.P.-M., 2004. Teaching approaches in geography and students' environmental attitudes. Environmentalist 24, 101e117.