Energy Policy 132 (2019) 736–743
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
Consumer intentions to purchase battery electric vehicles in Korea a
Jae Hun Kim , Gunwoo Lee , Ji Young Park , Jungyeol Hong , Juneyoung Park
Department of International Logistics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea Division for Climate Change and Sustainable Transport, The Korea Transport Institute, 370 Sicheong-daero, Sejong, 30147, Republic of Korea Department of Transportation Engineering, University of Seoul, 163 Seoulsirip-daero, Dongdaemun-gu, Seoul, 02504, Republic of Korea d Department of Transportation & Logistics Engineering, Hanyang University, 55 Hanyangdeahak-ro, Sangnok-gu, Ansan, 15588, Republic of Korea b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Battery electric vehicles Consumer intention Time of purchase Discrete choice model Korea
In Korea, the sales of battery electric vehicles (BEVs) have increased since their introduction to the market in 2010. However, considering the government's plan to introduce BEVs, current BEVs sales in Korea are still below targeted numbers. Among several reasons for this, consumers' intentions and desired time to purchase BEVs are the most important. This study identiﬁes the factors that aﬀect and inﬂuence these two reasons. We used survey data containing these two reasons as stated preferences and applied a binary choice model to estimate consumers' intentions to purchase BEVs and an ordered model to estimate the desired period for purchasing BEVs. The study's results show that prior experience driving BEVs and other additional factors—including number of household vehicles, educational level, and perception of government incentives and public parking beneﬁts—have a signiﬁcant eﬀect on consumers' intentions and desired time to purchase BEVs. Therefore, providing consumers with prior opportunities to drive BEVs is critical for BEVs' full market penetration in Korea.
1. Introduction In the global vehicle market, battery electric vehicles (BEVs) have been considered as new alternatives to gasoline-based vehicles, leading to an increase in BEV sales in many countries. From 2005 to 2017, global sales of new BEVs increased from 1,890 to 750,490 vehicles (International Energy Agency, 2018). China and the United States have the largest proportions of BEV sales: 468,000 and 104,490 vehicles in 2017, respectively. However, in terms of BEV market shares, these two countries show 1.8% and 0.6%, respectively, indicating that BEV vehicle markets in these countries require further development. Norway showed the highest market share for BEVs in 2017 (20.8%), although the number of new BEV sales (33,030 vehicles) was far below that of China or the United States. In line with this global trend, BEV sales in Korea have also increased enormously. Since 2010, when BEVs were ﬁrst introduced with 600 vehicles, the sales of new BEVs increased to approximately 13,300 vehicles in 2017. However, the market share for BEVs in Korea was 1.1% in 2017, revealing that BEVs have not yet eﬀectively penetrated the Korean vehicle market, unlike BEVs’ market penetration in other countries. Compared to conventional gasoline vehicles, BEVs produce zero emissions and reduced noise while driving and require smaller operating costs. Despite their higher vehicle price, shorter driving
range, and longer charging time, BEVs show great potential as new alternatives for transportation. Consequently, analyzing the factors that inﬂuence consumers' choice to purchase BEVs becomes necessary to create eﬃcient policies that will encourage BEV sales. Examining consumers' characteristics in terms of their intentions and desired time to purchase BEVs is important for policymakers, while understanding how consumers think about BEVs would greatly beneﬁt automobile companies. Numerous prior studies have focused on the factors that aﬀect consumers' purchase intentions and desired time to purchase BEVs, with survey data asking about these reasons as stated preferences. Regarding BEV purchase intentions, most studies have applied discrete choice models, such as binary and/or multinomial choice models (Ziegler, 2012; Junquera et al., 2016; Soltani-Sobh et al., 2017; Priessner et al., 2018), and the ordered choice model (Dumortier et al., 2015; Kim et al., 2015; Lin and Tan, 2017; Lane et al., 2018) to analyze the eﬀects of socio-economic factors (e.g., age, gender, household income, education level, occupation, and so on). These studies have shown that these socio-economic factors inﬂuence individuals' decision to purchase BEVs, both positively and negatively. For example, Kim et al. (2015) found that an individual's age positively aﬀects—while income negatively aﬀects—an individual's intention to purchase a BEV. Meanwhile, Egner and Trosvik (2018) found that an individual's income has a positive eﬀect on the
Corresponding author. E-mail addresses: [email protected]
(J.H. Kim), [email protected]
(G. Lee), [email protected]
(J.Y. Park), [email protected]
(J. Hong), [email protected]
(J. Park). https://doi.org/10.1016/j.enpol.2019.06.028 Received 15 January 2019; Received in revised form 9 June 2019; Accepted 15 June 2019 0301-4215/ © 2019 Published by Elsevier Ltd.
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education tax—increased, while the amount of maximum reduction in local tax—such as acquisition tax—increased from 2017 to 2018 but then decreased in 2019. The total amount of maximum tax reduction for purchasing BEVs also increased up to USD5,200 from 2017 to 2018 and then decreased in 2019. Considering the rapid increase in BEV sales in Korea since 2011 and the national ﬁnancial burden that has resulted from incentives for purchasing BEVs, a gradual reduction in the amount of available incentives is inevitable in the long-term. Addition to tax incentives for purchasing BEVs, in order to provide charging infrastructure on roads, the Korea Energy Agency (www.kemco.or.kr) supports the installation of fast chargers for BEVs by providing subsidies of up to 50% for each charger's installation costs (subsidies cannot exceed approximately USD20,000 per charger). For individuals who purchase BEVs and wish to install chargers in their homes, subsidies for this can be provided. Even if an individual does not purchase a BEV, he/she can apply for subsidies to install public chargers. These eﬀorts have helped to increase BEV sales in Korea from 2011 to 2017, as shown in Table 2. In Korea, the BEV charging infrastructure has also expanded nationwide. Fig. 1 presents this increase, from 750 chargers in 2016 to 1,801 chargers in 2017. However, considering the government's plan to introduce BEVs and increase the number of charging stations, current BEV sales in Korea are still below the targeted sales numbers, as shown in Table 2. BEVs' relatively higher price, when compared to gasoline-based vehicles, could be one of the reasons for this. In Korea, the price of KIA Soul EV is currently about USD38,000, which is higher than that of KIA Soul gasoline model (about USD19,500) (www.kia.com/kr/). However, the most important reason behind insuﬃcient sales is consumers' intentions to purchase BEVs. Although several policies have been proposed to encourage the purchase of BEVs, people will not buy BEVs unless they intend to do so. In other words, if something stimulates people's intentions, more people are likely to purchase BEVs. In December 2018, the Korean Ministry of Trade, Industry, and Energy announced a plan to achieve total sales of BEVs of up to approximately 430,000 vehicles by 2022 (www.motie.go.kr). This amount is more than seven times the total sales of 56,000 in 2018. In order to achieve this goal, the required eﬀorts to enlarge the Korean BEV market must be identiﬁed; therefore, studying consumers' intentions to purchase BEVs is necessary. From this perspective, identifying the factors that aﬀect consumers' purchase intentions and desired time to purchase BEVs is important to help BEVs penetrate the Korean market.
adoption of BEVs. In addition to individuals' socio-economic factors, government policies such as incentives and individuals' prior experience with BEVs are also important potential factors inﬂuencing individuals' intentions to purchase BEVs, particularly since BEVs are currently being introduced to the market. Soltani-Sobh et al. (2017) analyzed the relationship between the market share of BEVs and several factors including government incentives. Habich-Sobiegalla et al. (2018) analyzed consumers' intentions to purchase BEVs to determine inﬂuential factors including prior experience with BEVs. These studies found that government incentives and individuals' prior BEV experience can be positive factors for inducing future BEV purchases. Regarding the timing of when consumers will purchase BEVs, Zhang et al. (2011) and Junquera et al. (2016) applied discrete choice models to determine the factors aﬀecting whether consumers typically intend to purchase BEVs in a short time. These studies found that a consumer's perception of BEVs' price and charging times (Junquera et al., 2016), education level, annual income, number of household vehicles, and opinions regarding incentives (Zhang et al., 2011) inﬂuence whether that consumer will purchase a BEV in a short amount of time. Most prior research related to modeling intentions to purchase BEVs has focused on applying binary or ordered choice models, while a smaller number of studies have examined the timing of when people intend to purchase BEVs through binary choice models. However, none of these studies has applied an ordered choice model to determine the factors that inﬂuence when people will purchase BEVs. Accordingly, this study's objective is to identify the factors that inﬂuence consumers' intentions to purchase BEVs through a binary choice model and to examine consumers' desired time to purchase BEVs through an ordered choice model. This study employs survey data collected by the Korea Transport Institute to identify factors inﬂuencing consumers’ intentions and desired time to purchase BEVs in Korea. The survey data include whether and when a respondent will purchase BEVs in the future, his/her socioeconomic characteristics, and prior experiences with and knowledge of BEVs. The remainder of this paper is organized as follows. Section 2 introduces the current status of BEVs in Korea. The proposed model is discussed in section 3. The data are described in section 4. Section 5 presents the study's estimation results. Finally, a summary of the study's ﬁndings and conclusions are provided in section 6. 2. Current status of BEVs in Korea In Korea, BEVs were introduced to the vehicle market in 2010. In order to support BEVs’ penetration of the market, the Korean Ministry of Environment (ME) proposed annual plans to introduce BEVs, setting targeted annual sales numbers for BEVs. The ME also announced a BEV incentive program in 2011 and has provided incentives such as subsidies and tax exemptions to encourage consumers to purchase BEVs. The nationwide subsidy initially oﬀered approximately USD13,636 for BEVs, but this amount will decrease between 2014 to 2020 (icct, 2016). In 2019, the ME currently provides an USD8,000 nationwide subsidy for BEV sedan purchases. In addition, major cities including Seoul, Busan, Jeju, and other metropolitan cities in Korea are providing additional subsidies ranging from approximately USD4,000 to USD9,000 (www.ev.or.kr). For example, in 2019, Jeju is oﬀering additional subsidies worth about USD4,200 to achieve a carbon-free region by 2030. Nationwide incentives for purchasing BEVs have also been provided in the form of tax exemptions for individual consumption tax (5% of vehicle base price) and education tax (30% of vehicle base price). Local exemptions for acquisition tax have also been provided (total amount including 10% of vehicle base price, the amount of individual consumption tax, and education tax). Table 1 shows tax exemption incentives from 2017 to 2019, which are oﬀered as tax reductions for purchasing BEVs in Korea. In Table 1, the amount of maximum tax reduction in nationwide tax—such as individual consumption tax and
3. Methodology Understanding whether a consumer will consider purchasing a BEV requires discrete choice models including the discrete indicators of the consideration to purchase BEVs and the desired time to purchase BEVs. Additionally, a binary probit model is applied for intentions to purchase BEVs while an ordered probit model is used for desired time to purchase BEVs. For each individual i=1, 2, …, N, the models can be expressed as follows:
Ui∗ = x i′ β + εi
where Ui∗ is the net utility, which signiﬁes the probability of considering the purchase of a BEV (for a binary probit) or the probability of purchasing a BEV within the desired time by an individual i (for an ordered probit). β is a vector of parameters, x i′ is a vector of dependent variables for an individual (i), and εi is a stochastic term. Equation (1) is equivalent to a probit model:
yi∗ = x i′ β + εi
For a binary probit model, yi indicates intention to purchase BEVs, where the value is 1 if an individual (i) considers purchasing BEVs; the value is otherwise 0. This can be expressed as follows: 737
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Table 1 Tax exemption incentives for purchasing BEVs in Korea. Area
Individual Consumption (a) Education (b) Acquisition
Max. tax reduction for BEV Year of 2017
Year of 2018
Year of 2019
5% of vehicle base price
30% of base price 10% of base price + (a) + (b)
600,000 KRW 1,400,000 KRW 4,000,000 KRW
900,000 KRW 2,000,000 KRW 5,900,000 KRW
900,000 KRW 1,400,000 KRW 5,300,000 KRW
*KRW(South Korean Won) and average currency rate: 1 USD= 1,122 KRW (January 2019). Source: www.ev.or.kr and www.bok.or.kr.
years (yi =1), and after 10 years (yi =0). The log likelihood function for an ordered probit model is as follows:
Table 2 BEV targeted and actual sales in Korea.
Targeted EV sales (Cumulative) Actual EV sales (Cumulative)
Prob[yi = j|x i] = [F (μj − β′x i ) − F (μj − 1 − β′x i )] > 0, j = 0,1, …J
= 0 otherwise
− F (β′x i )] + yi lnF (β′x i ) ∑ (1 − y)ln[1 i i=1
The parameters of the log likelihood function in equation (6) are estimated through maximum likelihood estimation.
The log likelihood function for a binary probit model is as follows (Greene and Hensher, 2010):
lnL(β|X , y ) =
where mij takes the value of 1 if an individual (i) chooses category j; the value is otherwise 0. In addition, F (μj − β′x i ) and F (μj − 1 − β′x i ) indicate cumulative forms of the normal distributions, which make the following equation:
Source: Lee (2014) for targeted EV sales, and www.ev.or.kr for actual EV sales.
yi = 1 if Ui∗ > 0
∑ ∑ mij log [F (μj − β′xi) − F (μj−1 − β′xi)]
4. Survey data (4)
For this study, we used survey data collected by the Korea Transport Institute in 2017. To collect the dataset, the survey was conducted online, meaning that most respondents were likely to be relatively familiar with technology. Moreover, to analyze potential customers in the vehicle market (i.e., a population group that is most likely to purchase BEVs), the survey was conducted with people aged at least 18 years old and who drive private vehicles at least once a week. The survey collected answers from a total of 924 people from all Korean regions. The survey questionnaire was composed of (1) questions regarding respondents' intentions and desired time to purchase BEVs as well as prior experiences with and knowledge of BEVs; (2) questions on respondents' perceptions of BEVs' characteristics, incentives, and beneﬁts; and (3) questions on the respondents' characteristics. Therefore, the survey data includes socio-economic characteristics such as age, gender, and educational level; information regarding participants' intentions and desired time to purchase BEVs; and information regarding participants' prior experiences with and knowledge of BEVs. Among the survey questions, two key questions associated with consumers' intentions and
where X is the vector of dependent variables and y is the vector of yi . For an individual's desired time to purchase a BEV, this study applies an ordered probit model considering ordinary dependent variables. Generally, an ordered probit model is expressed as follows (Greene and Hensher, 2010):
yi = 0 if Ui∗ ≤ μ0 (=0)
= 1 if μ0 < Ui∗ ≤ μ1
= 2 if μ1 < Ui∗ ≤ μ 2 ......
= J if μJ − 1 < Ui∗ where μj (j=0,1,2, …, J) is a threshold point, which is estimated with β . The respondents choose a category indicating their desired time to purchase BEVs in the future. Therefore, the dependent variables can be categorized as follows: in 3 years (yi =3), in 3–5 years (yi =2), in 5–10
Fig. 1. Number of installed BEV chargers in Korea (Year of 2016-2018). Source: www.me.go.kr. 738
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desired time to purchase BEVs are: ‘Q1. When you purchase a vehicle in the future, will you consider purchasing a BEV?’ and ‘Q2. When will you purchase a BEV in the future?’ Some respondents may not have answered these questions because they.
respondents are signiﬁcant predictors of consumers’ intentions to purchase BEVs. 5.2. Desired time to purchase BEVs
• Do not know (for Q1), or • Will not purchase (for Q2).
The estimation results of desired time to purchase BEVs using the ordered probit model are summarized in Table 6. The coeﬃcients for AGE, BEV_EXP, and BEV_KNOW are positive and statistically signiﬁcant, implying that older respondents who have already experienced and know BEVs well are likely to purchase BEVs earlier. The coeﬃcient for GENDER is positive, revealing that female respondents are likely to purchase BEVs earlier than males. Given that the coeﬃcients for INCENTIVE and PUBLIC_PARKING are positive and statistically signiﬁcant, it can be said that respondents who consider government incentives and public parking beneﬁts for BEVs important are likely to purchase BEVs in a short timeframe. In addition, the coeﬃcient for EDU is negative, which means that respondents with lower educational levels are not likely to purchase BEVs in a short timeframe. For marginal eﬀects, as Table 7 shows, BEV_EXP has the greatest eﬀect on the timing of BEV purchasing, followed by BEV_KNOW, PUBLIC_PARKING, INCENTIVE, and AGE. However, coeﬃcients for these variables are negative in Level 0 and Level 1, while the coeﬃcient for EDU is positive. On the other hand, coeﬃcients for variables (except for EDU) are positive in Levels 2 and 3. This implies that prior driving experience with and good knowledge of BEVs, BEV beneﬁts (government incentives and public parking beneﬁts), and gender and educational level of respondents are signiﬁcant predictors of consumers’ desired time to purchase BEVs.
After excluding these cases, a total of 779 dataset samples were used in the analysis. Table 3 shows the deﬁnitions and sample statistics of variables that we used in the dataset. Respondents' average age group was 30-40. Regarding BEVs, only 16.9% of respondents had experienced BEVs before, 24.6% of respondents knew BEVs well or very well, and 72.4% of respondents said that they would consider purchasing BEVs. In addition, 84.6% of respondents consider BEVs’ monetary incentives (i.e., subsidies and tax rebates) as an important factor, while only 9.9% consider public parking beneﬁts for BEVs (i.e., priority spaces and free parking in public parking lots) as their main reason for purchasing BEVs1. The correlations among all variables are shown in Table 4. The correlation between RESPONSE_INTENT and RESPONSE_TIME is the highest (=0.639). Except for these, the correlation between BEV_KNOW and BEV_EXP has a high value (=0.251). Other values do not show high correlations among each other, although some of the values are statistically signiﬁcant. 5. Analysis results All data were analyzed using discrete choice models in Limdep 11 (Greene, 2016). The results of our estimations are shown in Tables 5–7. For the selection process, RESPONSE_INTENT was considered as a binary dependent variable while BEV_EXP, BEV_KNOW, AGE, GENDER, EDU, INCENTIVE, and PUBLIC_PARKING were considered as exploratory variables in the binary probit model. For an ordered dependent variable, RESPONSE_TIME was considered as an ordinary dependent variable while BEV_EXP, BEV_KNOW, AGE, GENDER, EDU, INCENTIVE, and PUBLIC_PARKING were considered as exploratory variables.
5.3. Testing multicollinearity and endogeneity In addition, multicollinearity among variables and endogeneity of models are tested for checking robustness of models. First, in order to check a multicollinearity issue among variables, a variance inﬂation factor (VIF) test is performed. A VIF is calculated through equation (8) (Greene, 2012).
1 1 − Rk2
is obtained when the kth variable is regressed on the rewhere maining variables. The optimal value for VIF is 1.0, which occurs when the Rk2 is zero (i.e., no correlation between kth variable and the remaining variables). For a VIF value, however, there is no consensus on what value should be focused. Some anonymous authors suggest that VIF values larger than 10 are problematic, while others suggest 30 or 40 as a benchmark value. In summary, it is obvious that variables with large VIF values should be focused as indicators of multicollinearity issues. A result of the VIF test for all variables in Table 3 except for RESPONSE_INTENT and RESPONSE_TIME, which are used as dependent variables, is shown as Table 8. In Table 8, VIF values of all variables except for constant are around 1. Consequently, it can be said that the variables have no collinearity issue. Please see Greene (2012) for more details on the VIF test. Next, an endogeneity check is performed. In general, endogeneity occurs when a model's error terms are related to dependent variables. The equations of a model with an endogenous variable are shown as equation (9) (Greene, 2016).
5.1. Consumers’ intentions to purchase BEVs The estimation results of consumers' intentions to purchase BEVs using a binary choice model are summarized in Table 5. The coeﬃcient estimates for AGE, BEV_EXP, and BEV_KNOW are positive and statistically signiﬁcant. This implies that older respondents who have experienced and know BEVs well are more likely to consider purchasing BEVs. The coeﬃcient for GENDER is positive, implying that female respondents are more likely to purchase BEVs than males. Given that the coeﬃcients for INCENTIVE and PUBLIC_PARKING are positive and statistically signiﬁcant, it can be said that respondents' perceptions of government incentives and public parking beneﬁts for BEVs play important roles in respondents' intentions to purchase BEVs. Because the coeﬃcient for EDU is negative, a lower educational level is not likely to increase respondents’ consideration to purchase BEVs. In terms of marginal eﬀects, the coeﬃcients for BEV_EXP and BEV_KNOW show the largest values and are statistically signiﬁcant, followed by PUBLIC_PARKING, EDU, INCENTIVE, and GENDER. The coeﬃcient for AGE shows the smallest value, although it is still statistically signiﬁcant. The marginal eﬀect results imply that prior driving experience with and good knowledge of BEVs, BEV beneﬁts (government incentives and public parking beneﬁts), and gender and educational level of
y1∗ = β′x + αy2 , y1 = 1 [y1∗ > 0] y2 = γ ′z + u
1 ρσ ⎞ ⎤ ⎡ 0 (ε , u) ∼ N ⎢ ⎜⎛ ⎟⎞, ⎜⎛ ⎟ 0 ρσ σ2 ⎠⎥ ⎦ ⎣⎝ ⎠ ⎝
In general, incentives for using BEVs may include parking beneﬁts, but in this study, we diﬀerentiate these beneﬁts into monetary incentives (INCENTIVE) and incentives of accessibility (PUBLIC_PARKING).
where y2 is an endogenous variable and z is an instrumental variable, 739
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Table 3 Summary of survey data. Types
Binary dependent variable Ordinary dependent variable
Whether respondents consider purchasing BEVs (consider = 1, don't consider = 0) Respondents' desired time to purchase BEVs (0–3): Level 3: In 3 years Level 2: In 3–5 years Level 1: 5–10 years Level 0: After 10 years Prior experience with BEVs (yes = 1, no = 0) How well respondent knows BEVs 0: Not at all 1: A few 2: Well 3: Very well Respondents' age (2= 20s, 3=30s, 4=40s, 5=50s, 6=60s) Respondents' gender (male = 0, female = 1) None 1 2 or more Respondents' educational level (high school or below = 1, upper = 0) Respondents' perception of government incentives for BEVs Important Moderate Not important Don't know Whether respondent considers public parking beneﬁts the main reason for purchasing BEVs (yes = 1, no = 0)
AGE GENDER HHVEH
0.186 0.285 0.316 0.213 0.169 0.099 0.655 0.223 0.023 3.565 0.440 0.585 0.343 0.072 0.173 0.846 0.100 0.021 0.033 0.099
Table 4 Correlation analysis of variables (N=779).
AGE BEV_EXP BEV_KNOW INCENTIVE PUBLIC_PARKING RESPONSE_INTENT RESPONSE_TIME EDU GENDER
Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed)
-.020 .577 1
-.021 .567 .251b .000 1
.061 .091 -.048 .181 .045 .210 1
-.086a .016 .022 .533 -.001 .977 -.161b .000 1
.104b .004 .141b .000 .174b .000 .129b .000 .051 .159 1
.132b .000 .179b .000 .174b .000 .109b .002 .075a .037 .639b .000 1
.099b .006 -.116b .001 -.033 .357 -.119b .001 -.027 .458 -.112b .002 -.086a .016 1
-.016 .665 -.035 .325 -.144b .000 .010 .784 .018 .613 .085a .018 .088a .014 .045 .212 1
-.020 .577 -.021 .567 .061 .091 -.086a .016 .104b .004 .132b .000 .099b .006 -.016 .665
.251b .000 -.048 .181 .022 .533 .141b .000 .179b .000 -.116b .001 -.035 .325
.045 .210 -.001 .977 .174b .000 .174b .000 -.033 .357 -.144b .000
-.161b .000 .129b .000 .109b .002 -.119b .001 .010 .784
.051 .159 .075a .037 -.027 .458 .018 .613
.639b .000 -.112b .002 .085a .018
-.086a .016 .088a .014
Correlation is signiﬁcant at the 0.05 level (2-tailed). Correlation is signiﬁcant at the 0.01 level (2-tailed).
Table 5 Estimated results of consumers’ intention to purchase BEVs. Model
Constant AGE GENDER BEV_EXP BEV_KNOW INCENTIVE PUBLIC_PARKING EDU Log likelihood Chi-Squared Pr(> Chi-squared)
Table 6 Estimated results of consumers’ desired time to purchase BEVs.
-1.574 0.156 0.331 0.476 0.403 0.351 0.411 -0.340 -419.6275 78.61061 0.00000
-4.303*** 3.425*** 3.229*** 3.040*** 4.508*** 3.249*** 2.225** -2.639***
-0.243 0.050 0.105 0.136 0.129 0.113 0.117 -0.116
-12.675*** 3.436*** 3.294*** 3.529*** 4.550*** 3.248*** 2.588*** -2.507**
Constant AGE GENDER BEV_EXP BEV_KNOW INCENTIVE PUBLIC_PARKING EDU μ1 μ2 Log likelihood Chi-Squared Pr(> Chi-squared)
** and *** indicate statistical signiﬁcance at the 5% and 1% levels, respectively.
-0.988 0.154 0.273 0.436 0.274 0.273 0.365 -0.210 0.934 1.803 -1018.690 87.95692 0.00000
-3.319*** 4.402*** 3.463*** 4.062*** 4.215*** 3.017*** 2.767*** -2.007** 22.230*** 33.231***
** and *** indicate statistical signiﬁcance at the 5% and 1% levels, respectively.
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and y2 and z are independent variables for y1. With an endogenous variable y2 , an estimation of model based on y1 and (x, y2 ) will not consistently estimate (β , α ) because the correlation between y2 and ε is induced by the correlation between u and ε . In other words, y2 is inﬂuenced by z. For example, if a respondent's intention to purchase BEVs is inﬂuenced by his/her prior experience with BEVs (BEV_EXP), his/her education level (EDU) can become an unobserved eﬀect that aﬀects their experience. In this case, BEV_EXP would be an endogenous variable and EDU would be an instrumental variable. In this study, an endogeneity of the binary probit model's is tested through Limdep. BEV_EXP is used as an endogenous variable and EDU is used as an instrumental variable. The result is shown as Table 9. In Table 9, a standard deviation of regression disturbances is statistically signiﬁcant (z-values=6.452), but a correlation between probit and regression disturbances (Rho) has no signiﬁcance (z-values=0.000). Consequently, it can be said that there is no endogeneity issue within the model.
Table 7 Marginal eﬀects of variables. Coeﬃcient
AGE GENDER BEV_EXP BEV_KNOW INCENTIVE PUBLIC_PARKING EDU
-0.043 -0.075 -0.106 -0.076 -0.076 -0.089 0.062
-0.018 -0.034 -0.066 -0.033 -0.033 -0.056 0.021
0.022 0.038 0.047 0.040 0.039 0.040 -0.033
0.039 0.070 0.125 0.070 0.069 0.105 -0.050
Table 8 Results of variance inﬂation factor (VIF) test. VIF (1: optimal value) Constant AGE GENDER BEV_EXP BEV_KNOW INCENTIVE PARKING EDU
0.000 1.022 1.024 1.087 1.093 1.055 1.035 1.045
5.4. Discussion Both the binary probit model and ordered probit model estimations above indicate that a consumer's age, gender, educational level, prior experience with and knowledge of BEVs, and perception of government policies including incentives and public parking beneﬁts signiﬁcantly inﬂuence his/her intention to purchase BEVs in Korea. All of the above variables, except for educational level, have positive eﬀects on a consumer's intention to purchase BEVs. A comparative analysis of these ﬁndings with the results of other similar studies reveals a slight difference in the sign, as shown in Table 10. Habich-Sobiegalla et al. (2018) found multiple signs in each variable because the study analyzed consumers' intentions to purchase BEVs in three diﬀerent countries: China, Russia, and Brazil. In addition, Egner and Trosvik (2018) estimated an ordinary least squares model and generalized least squares model to analyze the adoption of BEVs; therefore, there are diﬀerent signs in consumers' perceptions of public parking. Regarding consumers' age, the eﬀects on intention to purchase BEVs were positive in Erdem et al. (2010) and speciﬁcally in China and Russia in Habich-Sobiegalla et al. (2018). These eﬀects were negative in Lin and Tan (2017) and speciﬁcally in Brazil in Habich-Sobiegalla et al. (2018). In terms of gender, most studies featured in Table 10 show positive signs, regardless of whether male or female has a value of 1. The cases of age and gender imply that the eﬀects of these two variables on consumers' intentions to purchase BEVs may diﬀer by country. Regarding consumers' prior experience with BEVs, Habich-Sobiegalla et al. (2018) found that it has a positive eﬀect, which is aligned with the present study's results. In the case of awareness of BEVs, Erdem et al.
Table 9 Result of endogeneity check with BEV_EXP and EDU. Coeﬃcient
Coeﬃcients in Probit Equation for RESPONSE_INTENT Constant -1.574 0.000 AGE 0.156 3.307 GENDER 0.331 3.238 BEV_EXP 0.476 0.000 BEV_KNOW 0.403 4.200 INCENTIVE 0.351 3.347 PUBLIC_PARKING 0.411 2.079 EDU -0.340 0.000 Coeﬃcients in Linear Regression for BEV_EXP Constant 0.189 1.947 EDU -0.115 -1.923 Standard Deviation of Regression Disturbances Sigma (w) 0.373 6.452 Correlation Between Probit and Regression Disturbances Rho (e, w) 0.000 0.000
P[|Z| > z]
1.0000 0.0009 0.0012 1.0000 0.0000 0.0008 0.0376 1.0000 0.0515 0.0545 0.0000 1.0000
Table 10 Comparing the ﬁndings of this study with other studies’ results (in the sign of coeﬃcients). This study
Erdem et al. (2010)
Habich-Sobiegalla et al. (2018)
Lin and Tan (2017)
Egner and Trosvik (2018)
Prior experience with BEVs Awareness of BEVs
Perception of incentives Perception of public parking beneﬁts
+ for China and Russia - for Brazil - for China + for Russia and Brazil (Female=1) + - for China + for Russia and Brazil N/A N/A
-(category, High school or below=1)
+ for China and Russia - for Brazil (continuous)
N/A + for ordinary least squares model - for generalized least squares model + (continuous)
Source: Korean Ministry of Environment (www.me.go.kr). 741
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vehicles simultaneously. In terms of the dataset used in this study, however, there is a potential response bias in respondents’ intentions to purchase BEVs. This bias might exist because 1) the survey was done online, and so most respondents are likely to be familiar with technology; and 2) in order to analyze potential customers in the vehicle market, the survey was conducted with people who were at least 18 years old and who drive vehicles at least once a week. Van de Ven and Van Praag (1981) stated that a sample selection model is one of the possible methods for dealing with response biases. In conclusion, this study provides ways to estimate a sample selection model to carry out this task in further research. Moreover, the survey data used in this study features some of the reasons why people hesitate to purchase BEVs, such as high vehicle price, poor charging infrastructure, and the vehicles' performance. However, these factors cannot be included in the models because they are not statistically signiﬁcant enough. Since both BEVs' drawbacks and advantages are important factors in terms of purchasing BEVs, these factors will have to be included in any analysis of consumers’ intentions or demands for BEVs in future research. Although the present study provides information regarding socio-economic characteristics and considerations of government incentives for BEVs, the survey data used in this study did not include other important information such as household incomes. Lastly, the survey data used in this study includes local regions where respondents live, but it does not include detailed information on local-based incentives such as subsidies. Therefore, this study was unable to analyze the inﬂuence of incentives based on geographical regions. Thus, recent survey data including additional factors and information should be applied to the present model in future research. In this study, the answer “will not (or never) purchase” in response to the question about desired time to purchase BEVs in the survey data was excluded in the analysis because it was ambiguous for considering when an individual will purchase a BEV. However, for the analysis of policies associated with BEVs, a negative answer could be important to identify and improve the factors that aﬀect that answer. Therefore, the negative answers should be considered in future research to address the drawbacks of these vehicles and propose solutions. This study mainly focused on identifying the factors that aﬀect people's intentions to purchase BEVs rather than forecasting the probability of intentions to purchase BEVs. If a model can be developed with a forecasting purpose—such as being able to predict the probability of purchasing BEVs—model validation for that model should be conducted. In conclusion, this study is expected to spark future studies on the market penetration of BEVs in Korea and beyond.
(2010) and Habich-Sobiegalla et al. (2018) found that it has a positive eﬀect. However, speciﬁcally in the case of China in Habich-Sobiegalla et al. (2018), awareness of BEVs had negative eﬀects. Regarding educational level, most studies featured in Table 10 used it as a continuous variable, and it showed positive eﬀects. This study considered educational level as a categorical variable, deﬁning high school or below as a value of 1. Consequently, although the eﬀects of consumers' age and gender on intentions to purchase BEVs diﬀer across studies, a conclusion can be drawn that a consumer with a higher educational level and prior experience with and knowledge of BEVs is more likely to purchase BEVs. 6. Conclusion and policy implications Although BEV infrastructure is poor, resulting in actual BEV sales that are still below the targeted amount, the number of BEVs in Korea has progressively increased. Accordingly, it is essential to create strategies to encourage more people to purchase BEVs. In particular, the estimation of consumers' intentions and desired time to purchase BEVs provides a useful method to analyze the factors that inﬂuence consumers. This study attempted to estimate consumers’ intentions and desired time to purchase BEVs using data from a survey of Korean residents. This study oﬀers signiﬁcant implications for potential strategies to encourage people to purchase BEVs in Korea. According to the estimation results, respondents with previous experiences of driving BEVs and good knowledge of BEVs are more likely to purchase BEVs in a short time. This ﬁnding is expected to be useful for automobile companies that sell BEVs. These companies need to establish promotion strategies such as providing BEV test drives for consumers. The study's estimation results also imply that respondents who consider government incentives and public parking beneﬁts for BEVs important are more likely to purchase BEVs. This ﬁnding is a signiﬁcant implication for the government to create policies to boost BEV sales. The government must also monitor whether policies for BEV incentives work well and concentrate on public parking policies for BEVs, which provide priority parking for BEVs. Based on the estimated results of the study's ordered probit model, the group of respondents who would consider purchasing BEVs after 5 years or later (Levels 1 and 0) may not actually consider purchasing BEVs, although they have the intention to purchase. Therefore, to encourage this group considering the purchase of BEVs, it is necessary to provide them with further opportunities to experience BEVs. Therefore, automobile companies must make greater eﬀorts to provide a more diverse range of BEV models and to sell BEVs at dealerships. In addition, the government must establish policies to encourage these eﬀorts, such as providing incentives to allow BEVs to use highways and free public parking, as well as providing subsidies and tax exemptions for those who purchase BEVs. The eﬀects of previous experiences with BEVs and BEV policies have been mentioned in prior studies (Mallette and Venkataramanan, 2010; Egbue and Long, 2012; Bjerkan et al., 2016; Lévay et al., 2017; Schmalfuβ et al., 2017). These studies have stated that government policies and prior experiences with BEVs have positive eﬀects on the purchase of BEVs. Therefore, prior experiences with BEVs and government policies for these vehicles are key factors in helping BEVs to penetrate Korean markets faster. The results in this study can be compared to the ﬁndings of Erdem et al. (2010). The results in Erdem et al. (2010) identiﬁed the factors that aﬀect individuals' intentions to pay for hybrid vehicles in Turkey. Although some factors are not statistically signiﬁcant, the results showed that awareness of hybrid vehicles, respondents’ educational level, and age group all had positive eﬀects on the willingness to pay for hybrid vehicles. These ﬁndings are similar to those in our study. This is the ﬁrst study that provides an estimation of consumers' intentions to purchase BEVs and desired time to purchase these types of
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