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Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions
Journal Pre-proof Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions
Junghun Kim, Hyunchan Seung, Jongsu Lee, Joongha Ahn PII:
S0140-9883(20)30005-0
DOI:
https://doi.org/10.1016/j.eneco.2020.104666
Reference:
ENEECO 104666
To appear in:
Energy Economics
Received date:
28 March 2017
Revised date:
28 September 2019
Accepted date:
31 December 2019
Please cite this article as: J. Kim, H. Seung, J. Lee, et al., Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions, Energy Economics(2020), https://doi.org/10.1016/ j.eneco.2020.104666
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© 2020 Published by Elsevier.
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Asymmetric preference and loss aversion for Electric Vehicles: The reference-dependent choice model capturing different preference directions
Junghun Kima , Hyunchan Seunga , Jongsu Lee a , Joongha Ahnb*
Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
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Technology Management, Economics and Policy Program, Seoul National University, 1,
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a
E- mail: [email protected] (Junghun Kim), [email protected] (Hyunchan Seung),
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b*
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[email protected] (Jongsu Lee).
Corresponding author: Samsung Economic Research Institute, 4, Seocho-daero 74-gil,
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E-mail: [email protected].
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Seocho-gu, Seoul, 06620, South Korea.
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Abstract Greenhouse emissions from internal combustion engines have become a serious social issue. As of 2019, most vehicles in the South Korean car market have internal combustion engines. Although the South Korean government is adopting and implementing various policies to facilitate the penetration of battery electric vehicles (EVs) in the car market, its efforts have been in vain. Several studies on EV strategies have analyzed consumer
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preference for EVs using discrete choice experiments with virtual alternatives. However, they do not consider that consumers choose a new product based on the properties of products
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they already own; this aspect has a significant effect on consumer preference and has several
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implications. Therefore, this study uses a discrete choice model with a reference point to investigate asymmetric consumer preferences for the major attributes of EVs and analyzes the
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related loss aversion parameters. The results are then used to conduct a sensitivity analysis
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for fuel efficiency and infrastructure attribute to explore which factor is more influential to
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consumers` EVs choice probability.
Keywords: alternative fuel vehicles; conjoint analysis; discrete choice model; reference point; reference-dependent preference; loss aversion parameter.
JEL codes: C11, C18, D11, D81, O33, Q42
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1. Introduction Despite countries` continuous efforts to reduce greenhouse gas (GHG) emissions through regulations, the GHG emission of vehicles with internal combustion engines is becoming a serious social issue. More than 95% of the vehicles worldwide use gasoline and diesel as fuel, accounting for more than 50% of crude oil use (Andersen et al., 2009). In 2013, the share of CO 2 emissions produced by the transportation sector for South Korea, the United
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States, and the European Union was 16%, 33%, and 25%, respectively (World Bank, 2016). With the international community`s call for stronger measures to reduce GHG emissions, the
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vehicle industry instituted several changes (Hoyer, 2008; Kyoto Protocol, 1997; Paris
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Agreement, 2015; Rio Summit, 1992). This growing concern led to active research to develop
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vehicles that use alternative energy sources such as hydrogen, methanol, ethanol, and electricity (Sierzchula et al., 2012; Byun et al., 2018; Choi et al., 2018). Among these, electric
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and hybrid vehicles that use electricity as an energy source are gaining ground in the vehicle
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industry due to consistent R&D investments and growing social attention (Skerlos and
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Winebrake, 2010; Byun et al., 2018; Choi et al., 2018). By 2020, South Korea aims to reduce GHG emissions in the transportation sector, including commercial and passenger vehicles, by 34%. To achieve this objective, the South Korean government introduced various policies (MOTIE, 2018). Specifically, the government has been concentrating its R&D investments on expanding the per-charge driving distance of battery electric vehicles (EVs). In addition, the government planned to provide a subsidy to reduce consumers` financial burden to buy EVs. Further, to increase the convenience of EVs to users, by 2020, the number of express charging facilities is expected to increase by seven times the number available in 2014 (177 facilities) to eradicate the societal factors that hinder the penetration of EVs. Electric vehicles are expected to play an important role in future
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vehicle demand not only because of role in alleviating environmental problems but also because of its superior fuel economy to traditional vehicles. (Choi et al., 2018; Byun et al., 2018). However, EV market diffusion is progressing considerably slower than the rate predicted by research organizations, for both technological and social reasons 1 . Specifically, as of June 2019, about 10 years after the first sale of EVs, the cumulative number of EVs
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registered is 72,814, which is only 0.31% of total number of registered vehicles. In this regard, the reasons for the slow diffusion are as follows. From a technological perspective,
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there is a lack of infrastructure, such as convenient charging stations, and the driving distance
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per charge is limited (Egbue and Long, 2012; Wikström, et al., 2014; Donateo et al., 2015).
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From a social perspective, the lack of awareness of new technologies, risk-aversion tendencies, and the high price of EVs are factors that negatively affect consumers`
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perceptions (Diamond, 2009). Nevertheless, enhanced technology has dramatically decreased
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the cost of batteries and increased the per-charge driving distance. Therefore, the market
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share of EVs is expected to increase in the future (Hidrue et al., 2011; Choi et al., 2018; Byun et al., 2018). In addition to the related technology and social factors, another important factor of the EVs market is that EVs are mainly focused on ordinary passenger car such as sedan type. Accordingly, the influence of the availability of SUV (RV) electric vehicles on the consumer choice should also be discussed carefully. Given the influence of consumer choice on the spread of EVs, the focus of this study, it is necessary to analyze consumer preferences to facilitate faster market diffusion. As for
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The technical aspect is composed of functional attributes corresponding to technology and specification attributes of electric vehicles and factors influencing functional attributes. The social aspect is composed of non-functional attributes such as brand, price, consumers` perceptions and attitudes towards EVs (Kim et al., 2020).
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other economic goods, consumers choose EVs by considering various attributes and purchase the one that maximizes their utility. Because consumer data for innovative products introduced in the market are not readily available, researchers can use product attributes for virtual alternatives to analyze consumer preferences (Train, 2009). In this study, aspects of various alternatives for EVs, such as price, fuel efficiency, maintenance cost, maximum speed, and accessibility to charging stations, are suggested to consumers, who then choose the best
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options as per their preference. However, if the suggested alternatives are substitutable goods, consumers cannot choose the new product solely based on the suggested alternative; that is,
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consumers make a purchasing decision by comparing the attributes of the new product with
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products they already own (Tversky and Kahneman, 1991). Psychologists have discovered that relative product attributes based on a reference point rather than absolute product
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attributes play an important role in determining consumer preference (Carson and Groves,
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2007; Hardie et al., 1993; Tversky and Kahneman, 1991). In addition, the referencedependent utility functions are known to have better explanatory power than the standard
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utility functions in terms of consumer behaviour (Bateman et al., 2009). Accordingly, various studies used the reference-dependent choice model to analyze the consumer preferences for services and products including alternative fuel vehicles in the early stages of the market. (Hardie et al., 1993; Hess et al., 2012; Masiero & Hensher, 2010; Mabit & Fosgerau, 2011; Mabit et al., 2015). However, these previous studies only applied reference-dependent choice model to attributes such as price and time that consumers` preference direction is same, limiting the use of reference-dependent choice model. Specifically, when analyzing the attributes that preference directions are different using the existing reference-dependent choice model, the loss aversion parameters and the willingness to pay derived from the model may be misleading and contradictory (Kim et al., 2016; Kim et al., 2018). In this regard, the car type is an important attribute that influences the consumer`s
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decision making, consumers` preference directions for the car type are different. Therefore, in order to analyze consumer`s asymmetric preferences on attributes such as fuel type and car type, it is necessary to apply the reference-dependent choice model that captures the consumer`s preference heterogeneity and consider the relative level of the alternative attribute and the reference point (Kim et. al., 2016; Kim et al., 2018). However, to the best of our knowledge, there are no studies analyzing asymmetric preferences for attributes that
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consumers` preference directions are different, such as car type, using a reference point effect. Unlike other studies on consumer preference for EVs, this study performs a
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consumer utility analysis and examine reference-dependent preferences for EVs, by setting
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certain attributes of consumers` owned vehicles as the reference point. In particular, by using
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the reference-dependent choice model that can analyze not only attributes with the same preference direction but also the different preference directions, consumers` behaviors are
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captured in a higher dimension, better describing their preferences for EVs. Also, this study
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conduct a sensitivity analysis for fuel efficiency and infrastructure attribute to explore which
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factor is more influential to consumers` EVs choice probability. The remainder of this paper is organized as follows. Section 2 reviews the literature on consumer preference for vehicles with alternative fuel sources. Section 3 presents the discrete choice experiment and the analytical model used in this study. Section 4 discusses the data for EVs used and the model`s estimation results. Section 5 reviews this study`s results and find which attribute is more important for consumers` EVs choice probability.
2. Literature review Since there are certain limitations in obtaining the revealed preferences for products and services with low market diffusion, such as EVs and alternative fuel vehicles (AFVs),
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there are a few articles based on the revealed preferences only, one of which is Liu (2014). Liu (2014) analyzed consumer preference for hybrid vehicles. Unlike hybrid vehicles, sales and market share of EVs are very low. Therefore, this study used stated preferences to analyze consumer preferences (Layton, 2000; Roe et al., 1996). EVs began gaining attention following the oil shock in the 1970s. Since then, researchers have adopted discrete choice models based on consumer survey data to perform consumer preference analyses for EVs and
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vehicles with alternative fuel sources. Beggs et al. (1981) conducted a survey of consumers who intended to purchase EVs and compared the evaluation results for existing vehicles and
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EVs. The study confirmed heterogeneity between the two groups. Calfee (1985) used a
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questionnaire survey and probabilistic model to predict latent consumer demand for EVs and produced a distribution of consumer preferences. The results revealed a significant trade-off
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among various attributes for each consumer.
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Discussions of EVs commercialization followed these two studies. Many studies
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examined consumer preferences based on the various attributes of EVs. In particular, they not
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only examine the part worth of each attribute but also analyze the willingness to pay for EVs (Hidrue et al., 2011; Liu, 2014), market share (Brownstone et al., 1996, 2000; Ahn et al., 2008), GHG emission effect according to the introduction of alternative fuel vehicles (Ahn et al., 2008), government policy (Shin et al., 2012), the effects of tax incentives (Liu, 2014) and the expected revenue of companies manufacturing EVs (Hong et al., 2012). Table 1 summarizes the attributes of vehicles with alternative fuel sources adopted in analyses and methodologies since 2000. Previous studies have adopted linear-additive utility maximization assumptions that differ by how consumers choose a product. However, when making a product purchase decision, consumers not only consider the attributes of a given product, but are also affected by the gain and loss from the reference they established for the product, a fact confirmed by prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman,
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1991). Accordingly, Mabit & Fosgerau (2011) analyzed the Danish consumers` preferences for vehicles by using the reference-dependent choice model to predict the demand for alternative fuel vehicles. Specifically, the choice experiment was first carried out with binary choices, 6 attributes (purchase price, annual cost, operation range, refueling frequency, acceleration time, and service dummy) for each alternative categorized by different fuel types
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were constructed with linear or dummy variables. Further, fuel types are conventional, hydrogen hybrid, bio-diesel, and electric. Mabit & Fosgerau (2011) referred to vehicles other
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than internal combustion engines to alternative fuel vehicles. The internal combustion engine
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vehicles were defined as the reference vehicles. In particular, the study considered the high
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vehicle registration tax in Denmark, and focused on analyzing the price of the vehicle. That is, the relative price of the alternative fuel vehicles compared with the purchase price of the
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reference vehicles was set as the attribute level of the purchase price. The result of their
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analysis indicated that the reference dependence had a significant effect on the vehicle choice
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of consumers, and emphasized the importance of controlling the reference dependence. However, unlike the studies that considered the reference-dependent preference based on the discrete choice model, the loss aversion parameter for price attribute was not derived. Mabit et al. (2015) also empirically analyzed how reference-dependent preferences and attitudes, the main theory explaining non-rational behaviour, influence the consumers` vehicle choice. The questionnaires, number of respondents, and the attributes of the vehicles were conducted similarly to the Mabit & Fosgerau (2011) study. Unlike the Mabit & Fosgerau (2011) study, Mabit et al. (2015) study used four models to examine the degree of improvement of the reference-dependent choice model. The results of the analysis revealed that the explanatory power of the model considering the reference-dependent preferences was
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significantly better than the models that did not. The studies of Mabit & Fosgerau (2011) and Mabit et al. (2015) are significant in that they found the importance of reference-dependent preferences in consumers` vehicle choice. However, rather than reflecting the individuals` reference points such as attribute levels of vehicles they own, they set the attribute levels of the internal combustion engine vehicles as reference points. That is, they assumed that the reference point for every consumer was equal, which limited the degree of improvement of
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the model. In addition, rather than allowing the different preference directions of consumer, they only applied the basic methodology of considering the simple relative difference among
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attribute levels.
Methodology
No. of choice set/attributes/ levels
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Study
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Data used
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Table 1. Studies on consumer preference for attributes of EVs
Brownstone et al., 2000
SP/RP
Joint SP/RP Mixed logit
2/13/4
Potoglou and Kanaroglou, 2007
SP
Nested logit
8/9/4
Ahn et al., 2008
SP
MDCEV
4/6/4*
Hidrue et al., 2011
SP
Latent class model
1/4/4*
Attributes Price, range, home refueling time, home refueling cost, service station refueling time, service station refueling cost, service station availability, acceleration, top speed, tailpipe emission, vehicle size, body type, luggage space Fuel type, purchase price, annual fuel cost, annual maintenance cost, fuel availability, acceleration, incentives, pollution level Fuel type, body type, maintenance cost, engine displacement, fuel efficiency, fuel price Driving range, charging time, fuel cost saving, pollution reduction, performance
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Pr
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Fuel type, fuel cost, purchase Shin et al., 2012 SP MDCEV NA/5/4* price, maintenance cost, accessibility of fueling station Fuel type, fuel cost, price of Hong et al., 2012 SP Mixed logit NA/5/4* vehicle, maintenance fee, accessibility Vehicle year, make, model, Liu, 2014 RP Mixed logit NA/7/NA** horsepower, fuel efficiency, size, fuel type (hybrid or not) Minimum guaranteed driving range, required plug-in time per day, annual cash back Latent class payment, price relative to Parsons et al., 2014 SP 2/6/4* model preferred gasoline vehicle, driving range on full battery, time, acceleration, pollution, fuel cost Purchase price, weekly fuel Latent class cost, electric-driving range, Axsen et al., 2015 SP 6/5/4* model home recharge access, recharge time CO2 emissions, number of Mixed logit charging stations, fuel refilling Byun et al., 2018 SP 4/6/3* model time, car maintenance cost, car purchase price, fuel type Fuel type & operation method, Mixed logit availability of charging Choi et al., 2018 SP 8/4/4* model facilities, fuel cost, vehicle price Note: SP, stated preference; RP, revealed preference; MDCEV, multiple discrete-continuous extreme value * The level varies by attribute. Please see the references for detailed descriptions. ** The level relies on the vehicle model owned by the household. Please see the references for detailed descriptions.
3. Methodology 3.1. Experiment design This study used a discrete choice model to analyze consumer preferences for vehicles. A wide range of fields use discrete choice models, which provides a virtual environment in
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which consumers choose virtual alternatives that are not sold in the market (Green and Srinivasan, 1978). EVs have gradually increased their market share. However, sales of EVs remain at a nascent stage with a low market share. Therefore, market data to identify consumer preferences are limited. In this case, a discrete choice model, which presents consumers with alternatives, including the attributes of EVs similar to a real choice environment, is suitable for analyzing consumer preferences for EVs.
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Studies should use less than eight attributes for a discrete choice model (Phelps and Shanteau, 1978). Previous studies on consumer preference for EVs highlight six key
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attributes (Ahn et al., 2008; Axsen et al., 2015; Brownstone et al., 2000; Byun et al, 2018;
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Choi et al., 2018; Hidrue et al., 2011; Hong et al., 2012; Parsons et al., 2014; Potoglou and
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Kanaroglou, 2007; Shin et al., 2012). Table 2 summarizes each attribute and its corresponding level. In the cases bellow, the number of possible alternatives by combining six vehicle
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attributes and levels is 576 (4 × 2 × 3 × 3 × 4 × 2 = 576). Since it is not possible to analyze all
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alternatives, this study used an orthogonality test to create 12 choice cards, which were then
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grouped as three sets, each containing four cards. Therefore, the respondents choose one alternative card among a set of different alternative cards that provides the highest utility. Table 3 shows a sample choice set of discrete choice experiment used in this study.
Table 2. Attributes and levels used for discrete choice experiments Attribute
Description
Fuel type
Energy source
Vehicle type
Vehicle type
Level Gasoline Diesel Hybrid (gasoline + electricity) Electricity (battery) SUV (Sport-utility vehicle) Ordinary passenger car
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Fuel cost (Fuel efficiency)
Cost for 1 km driving distance (KRW)
Refuel/Recharge accessibility (%)
Assumes the number of gas stations for ordinary cars is 100, the share of gas/charging stations where cars can obtain refuel/recharge services Vehicle purchase cost excluding insurance and tax
Vehicle price
Reinforcing driving safety, sharing smart tool services, internet connectivity, etc. Note: All prices in KRW; USD 1= KRW 1,197.5 (September 24, 2019).
KRW 25 million KRW 30 million KRW 35 million KRW 40 million Provided Not provided
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Smart car options
KRW 200/km (10 km/1 L) KRW 100/km (20 km/1 L) KRW 50/km (40 km/1 L) 100% 80% 50%
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Smart car options Choice
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Type B Diesel SUV(RV) 40 km/1L 80% KRW 40 million Provided Type B
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Vehicle price
Type A Gasoline Sedan 40 km/1L 50% KRW 25 million Not provided Type A
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Attribute Fuel type Vehicle type Fuel efficiency Recharge accessibility
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Table 3. Sample choice set of discrete choice experiments
Type C Hybrid SUV(RV) 20 km/1L 50% KRW 40 million Provided Type C
Type D Electricity Sedan 20 km/1L 100% KRW 35 million Not provided Type D
3.2. Model Specification
This study used a discrete choice model and a reference-dependent model. Similar studies could analyze only the attributes associated with a same preference direction, for example, cost and time (Hess et al., 2008; Masiero and Hensher, 2010). However, Kim et al. (2016) enhanced the methodology so that a reference point can reflect attributes with a different preference directions, such as the operating system in a smart phone. However, it is also necessary to categorize attributes in the model for reflecting both a same and different preference directions. For vehicles, in particular, product attributes such as fuel efficiency and
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accessibility to charging stations have a same preference direction and significantly affect consumer choice. Therefore, this study developed a discrete choice model that reflects a reference point for products with both a same and different preference directions More specifically, attributes that preference directions are different require a two-step analysis (Kim et al., 2016). In the first step, a mixed logit model that can reflect consumer heterogeneity was used to analyze the attribute preference direction of each consumer. The
j is as follows (McFadden, 1973; Train, 2009).
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gained by consumer n from alternative
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mixed logit model is based on random utility theory. In a random utility model, utility U nj
(1)
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U nj Vnj nj n' x j nj
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Here, individual consumer utility can be divided into an observable deterministic term and an uncertain stochastic term. The deterministic term is expressed as a product of vector
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x 'j x j1 , x j 2 ,..., x jK , which represents the level of K attributes composing the alternative
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' j , and the coefficient vector n , which denotes the value consumer n assigns to each
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attribute of the alternative. In other words, the deterministic term represents the explained part, for example, product attributes. A researcher using mixed logit model can assume the distribution of the coefficients. In this study, the coefficients are assumed to follow a normal or log-normal distribution. Further, the discrete choice model can assume various forms depending on the assumptions of the distribution of the stochastic terms. In this study, the stochastic term is assumed to follow an independent and identically distributed (i.i.d.) type I extreme value distribution. The second step combines a discrete choice model and reference-dependent utility function. In the reference-dependent utility function, the value function v is defined in terms of relative differences in reference points ( r ), and not the absolute levels of product attributes
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(Dellavigna, 2009; Kahneman and Tversky, 1979). Here, the function can be defined depending on the sign of the difference between the reference point and attribute level. This can be expressed as follows:
xr vx | r | xr |
if x r if x r
,
(2)
where is a loss aversion parameter. In this expression, there is an assumption that the
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greater the consumer gain from the attribute level, the more valuable the product will be. Thus, if is greater than 1, the loss has a more significant effect on consumer preferences
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than the gain. Selection of a reference point for each respondent is an important matter to find
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reference dependence and loss aversion (Hardie et al., 1993; Hess et al., 2012). The reference
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point can be set based on past experience, current status and future expectations. The most widely used reference point is the current state (Hess et al., 2012), which is considered as the
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most realistic reference point in the actual consumer`s choice situation (Kim et al., 2016).
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Accordingly, this study defines the status quo (current status) as a reference point (Hess et al.,
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2012). Therefore, the final utility equation in the second step that reflects a reference point in the attributes with a different preference direction can be described as follows (Kim et al., 2016).
1 U nj I {( nk1 0 & xk rk ) or ( nk 0 & xk rk )} nk | xk rk |
I{( nk1 0 & xk rk ) or ( nk1 0 & xk rk )} nk | xk rk | nj
(3)
On the other hand, as fuel efficiency and accessibility to a charging station have the same preference direction, it is easy to predict that each individual coefficient will be positive (or negative). This is consistent with the assumption in the first stage that the coefficient distribution of the attributes with same preference direction has a log normal for all consumers. Consequently, the final model for the second stage, in which attributes are
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categorized as having either a same preference direction (s.p.d) or different preference directions (d.p.d), can be described follows: 1 1 U nj I(k is d.p.d)[I{( nk 0 & x jk xrk ) or ( nk 0 & x jk xrk )} nk | x jk xrk | 1 1 I{( nk 0 & x jk xrk ) or( nk 0 & x jk xrk )} nk | x jk xrk |]
I(k is s.p.d){I( x jk xrk ) nk | x jk xrk | I( x jk xrk ) nk | x jk xrk |} nj
(4)
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where xrk , which is a reference point, is the level of the attribute k of the product currently owned by the consumer. For the attributes with different preference directions, if the sign of
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nk1 and ( x jk xrk ) is identical, a consumer prefers the attribute level of the alternative;
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otherwise, the consumer does not prefer the attribute level.
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A Bayesian inference method was used to estimate ( nk ), which is the assigned value
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of each attribute in the first and second stages. The Bayesian inference method adopts a
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priority assumption on assigned values and the posterior distribution of a likelihood function. The method can overcome the problem of producing a different maximum value based on an
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initial value. In addition, the consistency and efficiency of the estimation can be obtained under a more flexible condition and the estimation results can be interpreted from both a Bayesian and a classical perspective (Allenby and Rossi, 1999; Huber and Train, 2001). This study used Gibbs sampling for the Bayesian inference method to estimate the models.
4. Empirical Analysis 4.1. Survey Design and Data Description
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The survey was administered to 675 respondents (aged 20–59)2 living in Seoul and other larger cities,3 and individual interviews were conducted by Gallup South Korea. A purposive quota sampling method was used to select interviewees. Of the 675 respondents, 532 who currently owned a vehicle were selected to analyze for this study. Tables 4 and 5 summarize respondents` demographic data and information about the vehicles they own.
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Table 4. Respondents` demographic characteristics
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Group Male Female < 30 30–39 40–49 50–59 Seoul Busan Incheon Daegu Daejeon Gwangju Gyeonggi-do (new cities) Middle school or below High school College Graduate school No response Gasoline Diesel LPG Hybrid SUV
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Gender
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Pr
Age
Educational level
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Residential area
Type of vehicle owned Model of vehicle owned
No. of respondents (%) 253 (47.6%) 279 (52.4%) 135 (25.4%) 142 (26.7%) 157 (29.5%) 99 (18.6%) 209 (39.3%) 76 (14.3%) 59 (11.1%) 72 (13.5%) 38 (7.1%) 38 (7.1%) 40 (7.5%) 8 (1.5%) 184 (34.6%) 323 (60.7%) 13 (2.4%) 4 (0.8%) 367 (69.0%) 139 (26.1%) 24 (4.5%) 2 (0.4%) 155 (29.1%)
2
In South Korea, one must be 20 years or older to own a vehicle. Therefore, respondents` age range was limited to 20–59 years. 3
These include Gyeonggi-do (new town) and five metropolitan cities: Busan, Incheon, Daegu, Daejeon, and Gwangju.
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Ordinary passenger cars
377 (70.9%)
Table 5. The information of vehicles which respondents own Mean 12.616 11.436 18.205
Standard Deviation 11.257 2.813 8.565
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Items Average vehicle purchase price (million KRW) Per liter vehicle driving distance (km/L) Annual vehicle driving distance (1000 km)
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4.2. Estimation Results
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A standard Bayesian mixed logit model was used for the first-stage analysis.
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Equation (5) is the detailed utility function:
U nj n,diesel d j,diesel n,hybrid d j,hybrid n,electric d j,electric n,SUV d j,SUV
(5)
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n, fuel-effi x j, fuel-effi n,price x j,price n,accesibility x j,accesibility n,smart d j,smart + nj
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The value each consumer assigns to product attributes was estimated by assigning a
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conditional probability distribution according to the Markov chain Monte Carlo technique
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and repeated random sampling. Table 6 presents the estimated results, which indicate that respondents preferred gasoline vehicles over diesel vehicles. In addition, no significant results were obtained for the car type. This result implies that there are even distributions of respondents who prefer and who dislike SUV compared to ordinary cars. Therefore, it is appropriate to set the normal distribution for the coefficient of the car type attribute and examine the varying consumer preference directions based on the coefficient estimates. Since fuel efficiency, vehicle price, and accessibility to charging station were assumed to have a log-normal distribution, consumers preferred higher fuel efficiency, lower vehicle price, and easier accessibility. The coefficient of each attribute obtained from all respondents in the first stage was
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used with Equation (6) in the next stage of the analysis. Table 7 reports the results. Since consumers have different preferences toward the fuel type and vehicle type, it cannot be analyzed by the general reference-dependence model. Thus, I(k is d.p.d) in Equation (4) was used for the analysis. Also, as shown in [Table 4], there were 0 respondents with electric vehicles and only 2 respondents with hybrids, only allowing the analysis of reference point effect of gasoline and diesel for the fuel type attribute. Also, since the simulation analysis
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compared the utility of electric vehicles and gasoline vehicles, considering the reference point in fuel type attribute may harm the consistency of the study. As for the fuel efficiency
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attribute, consumer showed same preference direction, which allowed the use of I(k is s.p.d)
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in Equation (4) to analyze. Next, the charging station accessibility attribute was set at 100%
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for current accessibility to gas stations, while the attribute level for the alternatives was set below this threshold. Therefore, only the coefficients of non-preference direction in which
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accessibility decreases were estimated.
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U nj n,diesel d j,diesel n,hybrid d j,hybrid n,electric d j,electric 1 Pre I{( n,SUV 0 & x j,SUV xr,SUV ) or ( n1, SUV 0 & x j,SUV xr,SUV )} n,SUV | x j,SUV xr,SUV |
Non-Pre I{( n1, SUV 0 & x j,SUV xr,SUV ) or( n1, SUV 0 & x j , SUV xr , SUV )} n,SUV | x j,SUV xr,SUV |}
I( x j, fuel-effi xr, fuel-effi ) n,Prefuel-effi | x j, fuel-effi xr, fuel-effi |
I( x j, fuel-effi xr, fuel-effi ) n,Non-Pre fuel-effi | x j, fuel-effi xr, fuel-effi |}
Non-Pre I( x j,accesibility xr,accesibility ) n,accesibility | x j,accesibility xr,accesibility |}
n,price x j,price n,smart d j,smart nj (6)
Table 6. Estimation results for standard mixed logit model Attribute
Coding
Distributional
Mean (Std. D)
t
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assumption
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Fuel type (ref: Gasoline) Dummy Normal Diesel -0.754* (1.719) -1.714 Hybrid 0.284 (1.952) 1.608 EV -0.631 (2.310) -1.498 Car type (ref: Ordinary car) Dummy Normal SUV -0.047 (0.978) 0.358 Fuel efficiency (1km/1L) Linear Log-normal 0.164*** (0.720) -14.526 Charging station accessibility (10%) Linear Log-normal 0.255*** (0.500) -8.314 Vehicle price (KRW 10 million) Linear Log-normal -0.789*** (1.306) -3.055 Smart car option Dummy Normal 1.104*** (1.708) 3.441 Log-likelihood -1966.077 AIC (BIC) 3948.154 (3685.176) ***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level
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Table 7. Estimation results for mixed logit model reflecting reference-dependent preferences
Coding
Distributional assumption Normal
Mean (Std. D)
t
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Attribute and preference
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Fuel type (ref: Gasoline) Dummy Diesel -0.295 (2.274) -1.383 Hybrid 1.067*** (2.876) 3.486 EV -0.365 (2.931) -1.486 SUV Dummy Log-normal Preference direction 1.307*** (3.029) -3.216 Non-preference direction -2.825*** (3.002) 5.180 Fuel efficiency (1km/1L) Linear Log-normal Preference direction 0.325*** (1.572) -10.964 Non-preference direction -0.736*** (5.833) -10.090 Charging station accessibility (10%) Linear Log-normal Non-preference direction -0.726*** (4.260) -8.845 Vehicle price (KRW 10 million) Linear Log-normal -1.483*** (4.496) -3.213 Smart car option Dummy Normal 1.459*** (2.389) 5.201 Log-likelihood -1832.588 AIC (BIC) 3685.176 (3692.435) ***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level
If the reference-dependent preference has a significant impact on the consumer`s choices, the reference-dependent choice model should provide better explanatory power and model fit than the standard choice model (Hardie et al., 1993). Since the reference-dependent
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choice model requires more parameter estimation than the standard choice model, the model fit should not be examined by simply comparing the log likelihood values. Thus, AIC (Akaike`s Information Criterion) (Akaike, 1998) or BIC (Bayesian information criterion) (Schwarz, 1978) statistics were used to provide an accurate comparison on the degree of improvement of the model. As a result, the model considering the reference-dependent preference was found to have higher explanatory power (model fit) than the model that did
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not. Table 8 shows the test results for the presence of asymmetric preference for SUVs
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and fuel efficiency attributes. Since the T-test (Kim et al., 2016) and the asymptotic t-ratio
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test (Hess et al., 2008; Román & Martín, 2016) can be used to assess the significance of the
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difference between the preferred and non-preferred coefficients for the attribute, this study used the T-test to assess the asymmetric preferences. The results indicate that the negative
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effect on consumer utility was 2.264 times greater for a 1 km/L decrease than a 1 km/L
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increase in fuel efficiency. Asymmetric preferences also existed in SUV attributes, and the
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loss aversion parameter was 2.162. This result is consistent with findings in previous studies on reference-dependent effects (Hess et al., 2008; Masiero and Hensher, 2010; Tversky and Kahneman, 1991; Kim et al., 2016; Kim et al., 2018).
Table 8. Consumer preferences and loss aversion parameters Variables SUV Fuel efficiency
T-test statistics 22.390 7.394
Preference Asymmetry Asymmetry
Loss aversion parameter 2.162 2.264
Table 9 presents the results for preference or non-preference for an SUV. The presence of an asymmetric preference by car type has a significant implication in the South
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Korean vehicle market. As of 2019, manufacturers concentrate on EVs that are ordinary passenger cars as light, small, and middle type. In other words, they provide purchasing motivation for the 269 respondents (50.6%) who reported non-preference for an SUV. However, for consumers who prefer SUVs, electric ordinary passenger cars have reduced utility. Therefore, manufacturers should consider setting the marketing strategy of electric SUVs, and the government can offer subsidies for such production to help accelerate their
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market diffusion.
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Own SUV 83 (15.6%) 72 (13.5%) 155 (29.1%)
Do not own SUV 180 (33.8%) 197 (37.0%) 377 (70.9%)
Total 263 (49.4%) 269 (50.6%) 532 (100.0%)
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Preference for SUV Preferred Not preferred Total
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Table 9. Car types owned by respondents and preference for SUVs
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4.3. Sensitivity analysis: Fuel efficiency and accessibility to charging stations
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Incentives are crucial factors for diffusion of Hybrid vehicles (Jenn et al., 2013). However, in the case of EVs, subsidies are not an important factor (Hirte and Tscharaktschiew, 2013), while fuel efficiency and infrastructure construction are more important factors, because to personal the biggest advantage of EVs is fuel costs and the biggest disadvantage of EVs is charging problem. Therefore, this study performed sensitivity analysis for fuel efficiency and infrastructure construction to examine which factor is more influential to consumers` EVs choice probability. This sensitivity analysis was performed based on the above estimation results. Currently, gasoline vehicles have a predominant market share in the passenger vehicle market. Although there was not statistical significance, the coefficients revealed that
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consumers prefer gasoline vehicle alternatives over electric ones. This indicates the necessity of a strategy to increase the consumer utility of owning EVs compared to owning gasoline vehicles to aid EV market diffusion. Accordingly, electric and gasoline vehicle alternatives were compared and analyzed. In particular, a change in consumers` net utility for EVs was analyzed as the levels of certain attributes of EVs alternatives varied, which is described in Equation (7) (Allenby and Ginter, 1995):
j is an alternative for reference. In this study, the
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where j * is an alternative for change and
(7)
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U nj* U nj* U nj 0 ,
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j * alternative is an EV and j alternative is a gasoline vehicle. In addition, the utility of
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EVs changes as the level of certain attributes varies, which results in a change to the ratio of respondents with positive net utility for EVs ( U nj* ) among all respondents. This result can be
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used to indirectly predict the choice probability of EVs compared to gasoline vehicles.
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Assuming that the attributes other than the six attributes in this study are identical, the biggest
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difference between gasoline vehicles and EVs is fuel efficiency and accessibility to charging stations. Therefore, this study used these attributes for a sensitivity analysis. In 2016, the number of EVs charging stations in South Korea is about 3% of the total number of gas stations. Considering that the number of new electric vehicles registered in 2017 was about 15,000, which accounted for about 3% of the new gasoline, the sensitivity analysis of this study proved to be valid. In addition, if this number of charging station stands, in Figure 1, the ratio of positive net utility for EVs utility will be about 30%, even when the battery performance increases to 100 km/L. This is close to the current government`s target (i.e., EVs should comprise 30% of new vehicles), although it is not easy to implement the policy due to the technical limitations of developing a battery that is three times as fuel
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efficient as that in current EVs. The aim of Korean government for EVs diffusion will be achieved if accessibility to EV charging station is 80%, although there will not be improvement for fuel efficiency than the present.
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80
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60 50
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30 20
0 20
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3
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10
60
Fuel efficiency 30km/L Fuel efficiency 40km/L
Fuel efficiency 50km/L Fuel efficiency 60km/L Fuel efficiency 70km/L Fuel efficiency 80km/L
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The ratio of the positive net utility of EVs compared to gasoline vehicles (%)
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Fuel efficiency 90km/L Fuel efficiency 100km/L
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The ratio of EV charging station compared to gas station (%)
[Fig. 1] Changes in the positive net utility ratio of EVs due to changes in EVs according to variations in fuel efficiency and ease of access to charging stations
The result in Figure 1 indicates that when charging station accessibility rises from 3% (current condition in South Korea) to 80%, the positive net utility ratio of EVs increases at almost the same rate (slope). However, the slope sharply increases in the 80–100% range. This seems to occur because consumers` reference point for charging station accessibility is set at 100%. In other words, it is consistent with prospect theory, in that the utility of an incremental reduction in a non-preference unit reaches its maximum at the reference point and gradually declines thereafter (Masiero and Hensher, 2010; Tversky and Kahneman, 1991).
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Thus, the point at which the slope of the marginal utility begins to decelerate is crucial in determining the lower limit when examining product characteristics related to the trade-off. The South Korean government aims to increase the number of EV charging stations to 25% of total gas stations by 2020. Therefore, it would be wise to invest heavily in improving accessibility to charging stations, even if the government cannot maintain a subsidy for EVs. However, if this is not a feasible option, then concentrating on charging stations in areas with
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high consumer interest in EVs would help accelerate market diffusion.
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5. Conclusions
Consumers tend to purchase new products based on the properties of products they
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currently own. Therefore, reflecting a reference-dependent utility function in a discrete choice model enables an analysis of the asymmetric preferences of various product attributes. It can
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also be useful in developing successful market diffusion strategies for a new product.
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However, few studies on consumer preferences for alternative fuel vehicles reflect reference-
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dependent effects on existing discrete choice experiments and models. Therefore, this study classified consumer preferences for vehicles in terms of a same or different preference directions and performed the analysis using the attributes of vehicles that consumers currently own as a reference point. The results indicate that the value of the loss aversion parameter by car type, which may reflect a different preference directions, fuel efficiency, which may represent a same preference direction, was greater than one. Since SUV had the high loss aversion parameter (2.162), car type may have a key role in consumers` purchasing decision. Therefore, though EVs have several advantages over other fuel type vehicles, those resembling general vehicles will considerably reduce the utility of consumers who own and prefer SUVs. This has
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implication because many countries, including South Korea, are intensively producing EVs that are small, compact, or sedans. Therefore, governments can help significantly increase market diffusion by extending support to the production of SUV EVs. These results can provide major implications for the hydrogen car market, which has been in the spotlight in South Korea recently. In the case of hydrogen cars, SUVs were first introduced to the market due to battery size as opposed to EVs, and there are still no plans to release hydrogen cars in
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the form of ordinary passenger cars such as small cars and midsize cars. As a result, it was evident that if a consumer already owns a sedan, prefers a sedan, and his loss aversion
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parameter for the car type is high, the choice probability of an SUV hydrogen car will be very
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low. Therefore, in order to avoid limited preference and achieve the government`s goal of diffusing eco-friendly cars, it is essential to release the various models of eco-friendly
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vehicles for the consumers.
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The sensitivity analysis results suggest that the government should concentrate on a
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policy that increases the number of EV charging stations to facilitate market diffusion.
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Although the number of charging stations for EVs cannot be on par with gas stations, establishing an infrastructure for a sufficient number of EV charging stations will help dramatically increase the market penetrate rate of EVs. As demonstrated in this study, EV charging station availability is determined by government policy and is a factor that cannot be controlled at the supplier and consumer level. In other words, continuous and consistent policy implementation plays an important role in the diffusion of innovative products like EVs. For example, in the case of EVs, various factors such as power generation, infrastructure, product qualities, and government subsidies significantly affect consumer utility. However, these factors rely heavily on government policy, so it is necessary for governments to establish and execute consistent policy to facilitate EV market diffusion.
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This study finds the presence of asymmetric preferences when consumers choose a new product. Meanwhile, such behavior is largely influenced by products consumers already own and it affects EV market diffusion. The present discussion began by addressing whether product attributes should be accepted at a relative rather than an absolute level. However, to capture the level of relativity, it is necessary to consider consumer habits as well. Unlike internal combustion EVs, EVs have dramatically different fuel efficiency levels depending on
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the environment and the manner in which they are driven. For example, EV`s fuel efficiency (km/kWh) varies by not only the usage of the air conditioner or heater, but also driving habits.
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Therefore, although consumers may accept the absolute level of product attributes in the early
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stages of product diffusion, during the mainstream stage, they are more likely to accept product attributes in terms of their driving habits, which is critical for diffusion. Thus, further
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study is needed in this context to help identify technologies and the policy implications
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required for the successful diffusion of EVs. In addition, future studies should consider the rebound effect because declined fuel costs increase the frequency and time of vehicle use
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(Stapleton et al., 2016; Lu et al., 2017).
Acknowledgment:
The authors are grateful to Professor Yoonmo Koo (Seoul National University) and Professor Jungwoo Shin (Kyunghee University) who designed discrete choice experiment used in this study. Further, thank you Stephen Youngjun Park for conducting a proof reading to improve both language and organization quality. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Highlights Analyzes consumer preferences for EVs using a discrete choice model
Improve the discrete choice model with reference-dependent utility function
Finds asymmetric preferences in vehicle attributes due to status quo
Performs sensitivity analysis to examine EV choice probability
Proposes improving accessibility to EV charging stations in the short term
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Junghun Kim, Hyunchan Seung, Jongsu Lee, Joongha Ahn PII:
S0140-9883(20)30005-0
DOI:
https://doi.org/10.1016/j.eneco.2020.104666
Reference:
ENEECO 104666
To appear in:
Energy Economics
Received date:
28 March 2017
Revised date:
28 September 2019
Accepted date:
31 December 2019
Please cite this article as: J. Kim, H. Seung, J. Lee, et al., Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions, Energy Economics(2020), https://doi.org/10.1016/ j.eneco.2020.104666
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© 2020 Published by Elsevier.
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Asymmetric preference and loss aversion for Electric Vehicles: The reference-dependent choice model capturing different preference directions
Junghun Kima , Hyunchan Seunga , Jongsu Lee a , Joongha Ahnb*
Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
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Technology Management, Economics and Policy Program, Seoul National University, 1,
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E- mail: [email protected] (Junghun Kim), [email protected] (Hyunchan Seung),
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b*
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[email protected] (Jongsu Lee).
Corresponding author: Samsung Economic Research Institute, 4, Seocho-daero 74-gil,
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E-mail: [email protected].
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Seocho-gu, Seoul, 06620, South Korea.
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Abstract Greenhouse emissions from internal combustion engines have become a serious social issue. As of 2019, most vehicles in the South Korean car market have internal combustion engines. Although the South Korean government is adopting and implementing various policies to facilitate the penetration of battery electric vehicles (EVs) in the car market, its efforts have been in vain. Several studies on EV strategies have analyzed consumer
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preference for EVs using discrete choice experiments with virtual alternatives. However, they do not consider that consumers choose a new product based on the properties of products
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they already own; this aspect has a significant effect on consumer preference and has several
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implications. Therefore, this study uses a discrete choice model with a reference point to investigate asymmetric consumer preferences for the major attributes of EVs and analyzes the
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related loss aversion parameters. The results are then used to conduct a sensitivity analysis
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for fuel efficiency and infrastructure attribute to explore which factor is more influential to
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consumers` EVs choice probability.
Keywords: alternative fuel vehicles; conjoint analysis; discrete choice model; reference point; reference-dependent preference; loss aversion parameter.
JEL codes: C11, C18, D11, D81, O33, Q42
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1. Introduction Despite countries` continuous efforts to reduce greenhouse gas (GHG) emissions through regulations, the GHG emission of vehicles with internal combustion engines is becoming a serious social issue. More than 95% of the vehicles worldwide use gasoline and diesel as fuel, accounting for more than 50% of crude oil use (Andersen et al., 2009). In 2013, the share of CO 2 emissions produced by the transportation sector for South Korea, the United
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States, and the European Union was 16%, 33%, and 25%, respectively (World Bank, 2016). With the international community`s call for stronger measures to reduce GHG emissions, the
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vehicle industry instituted several changes (Hoyer, 2008; Kyoto Protocol, 1997; Paris
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Agreement, 2015; Rio Summit, 1992). This growing concern led to active research to develop
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vehicles that use alternative energy sources such as hydrogen, methanol, ethanol, and electricity (Sierzchula et al., 2012; Byun et al., 2018; Choi et al., 2018). Among these, electric
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and hybrid vehicles that use electricity as an energy source are gaining ground in the vehicle
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industry due to consistent R&D investments and growing social attention (Skerlos and
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Winebrake, 2010; Byun et al., 2018; Choi et al., 2018). By 2020, South Korea aims to reduce GHG emissions in the transportation sector, including commercial and passenger vehicles, by 34%. To achieve this objective, the South Korean government introduced various policies (MOTIE, 2018). Specifically, the government has been concentrating its R&D investments on expanding the per-charge driving distance of battery electric vehicles (EVs). In addition, the government planned to provide a subsidy to reduce consumers` financial burden to buy EVs. Further, to increase the convenience of EVs to users, by 2020, the number of express charging facilities is expected to increase by seven times the number available in 2014 (177 facilities) to eradicate the societal factors that hinder the penetration of EVs. Electric vehicles are expected to play an important role in future
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vehicle demand not only because of role in alleviating environmental problems but also because of its superior fuel economy to traditional vehicles. (Choi et al., 2018; Byun et al., 2018). However, EV market diffusion is progressing considerably slower than the rate predicted by research organizations, for both technological and social reasons 1 . Specifically, as of June 2019, about 10 years after the first sale of EVs, the cumulative number of EVs
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registered is 72,814, which is only 0.31% of total number of registered vehicles. In this regard, the reasons for the slow diffusion are as follows. From a technological perspective,
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there is a lack of infrastructure, such as convenient charging stations, and the driving distance
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per charge is limited (Egbue and Long, 2012; Wikström, et al., 2014; Donateo et al., 2015).
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From a social perspective, the lack of awareness of new technologies, risk-aversion tendencies, and the high price of EVs are factors that negatively affect consumers`
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perceptions (Diamond, 2009). Nevertheless, enhanced technology has dramatically decreased
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the cost of batteries and increased the per-charge driving distance. Therefore, the market
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share of EVs is expected to increase in the future (Hidrue et al., 2011; Choi et al., 2018; Byun et al., 2018). In addition to the related technology and social factors, another important factor of the EVs market is that EVs are mainly focused on ordinary passenger car such as sedan type. Accordingly, the influence of the availability of SUV (RV) electric vehicles on the consumer choice should also be discussed carefully. Given the influence of consumer choice on the spread of EVs, the focus of this study, it is necessary to analyze consumer preferences to facilitate faster market diffusion. As for
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The technical aspect is composed of functional attributes corresponding to technology and specification attributes of electric vehicles and factors influencing functional attributes. The social aspect is composed of non-functional attributes such as brand, price, consumers` perceptions and attitudes towards EVs (Kim et al., 2020).
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other economic goods, consumers choose EVs by considering various attributes and purchase the one that maximizes their utility. Because consumer data for innovative products introduced in the market are not readily available, researchers can use product attributes for virtual alternatives to analyze consumer preferences (Train, 2009). In this study, aspects of various alternatives for EVs, such as price, fuel efficiency, maintenance cost, maximum speed, and accessibility to charging stations, are suggested to consumers, who then choose the best
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options as per their preference. However, if the suggested alternatives are substitutable goods, consumers cannot choose the new product solely based on the suggested alternative; that is,
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consumers make a purchasing decision by comparing the attributes of the new product with
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products they already own (Tversky and Kahneman, 1991). Psychologists have discovered that relative product attributes based on a reference point rather than absolute product
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attributes play an important role in determining consumer preference (Carson and Groves,
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2007; Hardie et al., 1993; Tversky and Kahneman, 1991). In addition, the referencedependent utility functions are known to have better explanatory power than the standard
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utility functions in terms of consumer behaviour (Bateman et al., 2009). Accordingly, various studies used the reference-dependent choice model to analyze the consumer preferences for services and products including alternative fuel vehicles in the early stages of the market. (Hardie et al., 1993; Hess et al., 2012; Masiero & Hensher, 2010; Mabit & Fosgerau, 2011; Mabit et al., 2015). However, these previous studies only applied reference-dependent choice model to attributes such as price and time that consumers` preference direction is same, limiting the use of reference-dependent choice model. Specifically, when analyzing the attributes that preference directions are different using the existing reference-dependent choice model, the loss aversion parameters and the willingness to pay derived from the model may be misleading and contradictory (Kim et al., 2016; Kim et al., 2018). In this regard, the car type is an important attribute that influences the consumer`s
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decision making, consumers` preference directions for the car type are different. Therefore, in order to analyze consumer`s asymmetric preferences on attributes such as fuel type and car type, it is necessary to apply the reference-dependent choice model that captures the consumer`s preference heterogeneity and consider the relative level of the alternative attribute and the reference point (Kim et. al., 2016; Kim et al., 2018). However, to the best of our knowledge, there are no studies analyzing asymmetric preferences for attributes that
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consumers` preference directions are different, such as car type, using a reference point effect. Unlike other studies on consumer preference for EVs, this study performs a
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consumer utility analysis and examine reference-dependent preferences for EVs, by setting
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certain attributes of consumers` owned vehicles as the reference point. In particular, by using
Pr
the reference-dependent choice model that can analyze not only attributes with the same preference direction but also the different preference directions, consumers` behaviors are
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captured in a higher dimension, better describing their preferences for EVs. Also, this study
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conduct a sensitivity analysis for fuel efficiency and infrastructure attribute to explore which
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factor is more influential to consumers` EVs choice probability. The remainder of this paper is organized as follows. Section 2 reviews the literature on consumer preference for vehicles with alternative fuel sources. Section 3 presents the discrete choice experiment and the analytical model used in this study. Section 4 discusses the data for EVs used and the model`s estimation results. Section 5 reviews this study`s results and find which attribute is more important for consumers` EVs choice probability.
2. Literature review Since there are certain limitations in obtaining the revealed preferences for products and services with low market diffusion, such as EVs and alternative fuel vehicles (AFVs),
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there are a few articles based on the revealed preferences only, one of which is Liu (2014). Liu (2014) analyzed consumer preference for hybrid vehicles. Unlike hybrid vehicles, sales and market share of EVs are very low. Therefore, this study used stated preferences to analyze consumer preferences (Layton, 2000; Roe et al., 1996). EVs began gaining attention following the oil shock in the 1970s. Since then, researchers have adopted discrete choice models based on consumer survey data to perform consumer preference analyses for EVs and
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vehicles with alternative fuel sources. Beggs et al. (1981) conducted a survey of consumers who intended to purchase EVs and compared the evaluation results for existing vehicles and
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EVs. The study confirmed heterogeneity between the two groups. Calfee (1985) used a
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questionnaire survey and probabilistic model to predict latent consumer demand for EVs and produced a distribution of consumer preferences. The results revealed a significant trade-off
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among various attributes for each consumer.
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Discussions of EVs commercialization followed these two studies. Many studies
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examined consumer preferences based on the various attributes of EVs. In particular, they not
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only examine the part worth of each attribute but also analyze the willingness to pay for EVs (Hidrue et al., 2011; Liu, 2014), market share (Brownstone et al., 1996, 2000; Ahn et al., 2008), GHG emission effect according to the introduction of alternative fuel vehicles (Ahn et al., 2008), government policy (Shin et al., 2012), the effects of tax incentives (Liu, 2014) and the expected revenue of companies manufacturing EVs (Hong et al., 2012). Table 1 summarizes the attributes of vehicles with alternative fuel sources adopted in analyses and methodologies since 2000. Previous studies have adopted linear-additive utility maximization assumptions that differ by how consumers choose a product. However, when making a product purchase decision, consumers not only consider the attributes of a given product, but are also affected by the gain and loss from the reference they established for the product, a fact confirmed by prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman,
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1991). Accordingly, Mabit & Fosgerau (2011) analyzed the Danish consumers` preferences for vehicles by using the reference-dependent choice model to predict the demand for alternative fuel vehicles. Specifically, the choice experiment was first carried out with binary choices, 6 attributes (purchase price, annual cost, operation range, refueling frequency, acceleration time, and service dummy) for each alternative categorized by different fuel types
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were constructed with linear or dummy variables. Further, fuel types are conventional, hydrogen hybrid, bio-diesel, and electric. Mabit & Fosgerau (2011) referred to vehicles other
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than internal combustion engines to alternative fuel vehicles. The internal combustion engine
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vehicles were defined as the reference vehicles. In particular, the study considered the high
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vehicle registration tax in Denmark, and focused on analyzing the price of the vehicle. That is, the relative price of the alternative fuel vehicles compared with the purchase price of the
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reference vehicles was set as the attribute level of the purchase price. The result of their
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analysis indicated that the reference dependence had a significant effect on the vehicle choice
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of consumers, and emphasized the importance of controlling the reference dependence. However, unlike the studies that considered the reference-dependent preference based on the discrete choice model, the loss aversion parameter for price attribute was not derived. Mabit et al. (2015) also empirically analyzed how reference-dependent preferences and attitudes, the main theory explaining non-rational behaviour, influence the consumers` vehicle choice. The questionnaires, number of respondents, and the attributes of the vehicles were conducted similarly to the Mabit & Fosgerau (2011) study. Unlike the Mabit & Fosgerau (2011) study, Mabit et al. (2015) study used four models to examine the degree of improvement of the reference-dependent choice model. The results of the analysis revealed that the explanatory power of the model considering the reference-dependent preferences was
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significantly better than the models that did not. The studies of Mabit & Fosgerau (2011) and Mabit et al. (2015) are significant in that they found the importance of reference-dependent preferences in consumers` vehicle choice. However, rather than reflecting the individuals` reference points such as attribute levels of vehicles they own, they set the attribute levels of the internal combustion engine vehicles as reference points. That is, they assumed that the reference point for every consumer was equal, which limited the degree of improvement of
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the model. In addition, rather than allowing the different preference directions of consumer, they only applied the basic methodology of considering the simple relative difference among
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attribute levels.
Methodology
No. of choice set/attributes/ levels
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Study
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Data used
Pr
Table 1. Studies on consumer preference for attributes of EVs
Brownstone et al., 2000
SP/RP
Joint SP/RP Mixed logit
2/13/4
Potoglou and Kanaroglou, 2007
SP
Nested logit
8/9/4
Ahn et al., 2008
SP
MDCEV
4/6/4*
Hidrue et al., 2011
SP
Latent class model
1/4/4*
Attributes Price, range, home refueling time, home refueling cost, service station refueling time, service station refueling cost, service station availability, acceleration, top speed, tailpipe emission, vehicle size, body type, luggage space Fuel type, purchase price, annual fuel cost, annual maintenance cost, fuel availability, acceleration, incentives, pollution level Fuel type, body type, maintenance cost, engine displacement, fuel efficiency, fuel price Driving range, charging time, fuel cost saving, pollution reduction, performance
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Pr
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Fuel type, fuel cost, purchase Shin et al., 2012 SP MDCEV NA/5/4* price, maintenance cost, accessibility of fueling station Fuel type, fuel cost, price of Hong et al., 2012 SP Mixed logit NA/5/4* vehicle, maintenance fee, accessibility Vehicle year, make, model, Liu, 2014 RP Mixed logit NA/7/NA** horsepower, fuel efficiency, size, fuel type (hybrid or not) Minimum guaranteed driving range, required plug-in time per day, annual cash back Latent class payment, price relative to Parsons et al., 2014 SP 2/6/4* model preferred gasoline vehicle, driving range on full battery, time, acceleration, pollution, fuel cost Purchase price, weekly fuel Latent class cost, electric-driving range, Axsen et al., 2015 SP 6/5/4* model home recharge access, recharge time CO2 emissions, number of Mixed logit charging stations, fuel refilling Byun et al., 2018 SP 4/6/3* model time, car maintenance cost, car purchase price, fuel type Fuel type & operation method, Mixed logit availability of charging Choi et al., 2018 SP 8/4/4* model facilities, fuel cost, vehicle price Note: SP, stated preference; RP, revealed preference; MDCEV, multiple discrete-continuous extreme value * The level varies by attribute. Please see the references for detailed descriptions. ** The level relies on the vehicle model owned by the household. Please see the references for detailed descriptions.
3. Methodology 3.1. Experiment design This study used a discrete choice model to analyze consumer preferences for vehicles. A wide range of fields use discrete choice models, which provides a virtual environment in
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which consumers choose virtual alternatives that are not sold in the market (Green and Srinivasan, 1978). EVs have gradually increased their market share. However, sales of EVs remain at a nascent stage with a low market share. Therefore, market data to identify consumer preferences are limited. In this case, a discrete choice model, which presents consumers with alternatives, including the attributes of EVs similar to a real choice environment, is suitable for analyzing consumer preferences for EVs.
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Studies should use less than eight attributes for a discrete choice model (Phelps and Shanteau, 1978). Previous studies on consumer preference for EVs highlight six key
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attributes (Ahn et al., 2008; Axsen et al., 2015; Brownstone et al., 2000; Byun et al, 2018;
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Choi et al., 2018; Hidrue et al., 2011; Hong et al., 2012; Parsons et al., 2014; Potoglou and
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Kanaroglou, 2007; Shin et al., 2012). Table 2 summarizes each attribute and its corresponding level. In the cases bellow, the number of possible alternatives by combining six vehicle
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attributes and levels is 576 (4 × 2 × 3 × 3 × 4 × 2 = 576). Since it is not possible to analyze all
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alternatives, this study used an orthogonality test to create 12 choice cards, which were then
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grouped as three sets, each containing four cards. Therefore, the respondents choose one alternative card among a set of different alternative cards that provides the highest utility. Table 3 shows a sample choice set of discrete choice experiment used in this study.
Table 2. Attributes and levels used for discrete choice experiments Attribute
Description
Fuel type
Energy source
Vehicle type
Vehicle type
Level Gasoline Diesel Hybrid (gasoline + electricity) Electricity (battery) SUV (Sport-utility vehicle) Ordinary passenger car
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Fuel cost (Fuel efficiency)
Cost for 1 km driving distance (KRW)
Refuel/Recharge accessibility (%)
Assumes the number of gas stations for ordinary cars is 100, the share of gas/charging stations where cars can obtain refuel/recharge services Vehicle purchase cost excluding insurance and tax
Vehicle price
Reinforcing driving safety, sharing smart tool services, internet connectivity, etc. Note: All prices in KRW; USD 1= KRW 1,197.5 (September 24, 2019).
KRW 25 million KRW 30 million KRW 35 million KRW 40 million Provided Not provided
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Smart car options
KRW 200/km (10 km/1 L) KRW 100/km (20 km/1 L) KRW 50/km (40 km/1 L) 100% 80% 50%
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Smart car options Choice
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Type B Diesel SUV(RV) 40 km/1L 80% KRW 40 million Provided Type B
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Vehicle price
Type A Gasoline Sedan 40 km/1L 50% KRW 25 million Not provided Type A
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Attribute Fuel type Vehicle type Fuel efficiency Recharge accessibility
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Table 3. Sample choice set of discrete choice experiments
Type C Hybrid SUV(RV) 20 km/1L 50% KRW 40 million Provided Type C
Type D Electricity Sedan 20 km/1L 100% KRW 35 million Not provided Type D
3.2. Model Specification
This study used a discrete choice model and a reference-dependent model. Similar studies could analyze only the attributes associated with a same preference direction, for example, cost and time (Hess et al., 2008; Masiero and Hensher, 2010). However, Kim et al. (2016) enhanced the methodology so that a reference point can reflect attributes with a different preference directions, such as the operating system in a smart phone. However, it is also necessary to categorize attributes in the model for reflecting both a same and different preference directions. For vehicles, in particular, product attributes such as fuel efficiency and
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accessibility to charging stations have a same preference direction and significantly affect consumer choice. Therefore, this study developed a discrete choice model that reflects a reference point for products with both a same and different preference directions More specifically, attributes that preference directions are different require a two-step analysis (Kim et al., 2016). In the first step, a mixed logit model that can reflect consumer heterogeneity was used to analyze the attribute preference direction of each consumer. The
j is as follows (McFadden, 1973; Train, 2009).
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gained by consumer n from alternative
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mixed logit model is based on random utility theory. In a random utility model, utility U nj
(1)
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U nj Vnj nj n' x j nj
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Here, individual consumer utility can be divided into an observable deterministic term and an uncertain stochastic term. The deterministic term is expressed as a product of vector
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x 'j x j1 , x j 2 ,..., x jK , which represents the level of K attributes composing the alternative
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' j , and the coefficient vector n , which denotes the value consumer n assigns to each
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attribute of the alternative. In other words, the deterministic term represents the explained part, for example, product attributes. A researcher using mixed logit model can assume the distribution of the coefficients. In this study, the coefficients are assumed to follow a normal or log-normal distribution. Further, the discrete choice model can assume various forms depending on the assumptions of the distribution of the stochastic terms. In this study, the stochastic term is assumed to follow an independent and identically distributed (i.i.d.) type I extreme value distribution. The second step combines a discrete choice model and reference-dependent utility function. In the reference-dependent utility function, the value function v is defined in terms of relative differences in reference points ( r ), and not the absolute levels of product attributes
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(Dellavigna, 2009; Kahneman and Tversky, 1979). Here, the function can be defined depending on the sign of the difference between the reference point and attribute level. This can be expressed as follows:
xr vx | r | xr |
if x r if x r
,
(2)
where is a loss aversion parameter. In this expression, there is an assumption that the
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greater the consumer gain from the attribute level, the more valuable the product will be. Thus, if is greater than 1, the loss has a more significant effect on consumer preferences
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than the gain. Selection of a reference point for each respondent is an important matter to find
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reference dependence and loss aversion (Hardie et al., 1993; Hess et al., 2012). The reference
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point can be set based on past experience, current status and future expectations. The most widely used reference point is the current state (Hess et al., 2012), which is considered as the
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most realistic reference point in the actual consumer`s choice situation (Kim et al., 2016).
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Accordingly, this study defines the status quo (current status) as a reference point (Hess et al.,
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2012). Therefore, the final utility equation in the second step that reflects a reference point in the attributes with a different preference direction can be described as follows (Kim et al., 2016).
1 U nj I {( nk1 0 & xk rk ) or ( nk 0 & xk rk )} nk | xk rk |
I{( nk1 0 & xk rk ) or ( nk1 0 & xk rk )} nk | xk rk | nj
(3)
On the other hand, as fuel efficiency and accessibility to a charging station have the same preference direction, it is easy to predict that each individual coefficient will be positive (or negative). This is consistent with the assumption in the first stage that the coefficient distribution of the attributes with same preference direction has a log normal for all consumers. Consequently, the final model for the second stage, in which attributes are
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categorized as having either a same preference direction (s.p.d) or different preference directions (d.p.d), can be described follows: 1 1 U nj I(k is d.p.d)[I{( nk 0 & x jk xrk ) or ( nk 0 & x jk xrk )} nk | x jk xrk | 1 1 I{( nk 0 & x jk xrk ) or( nk 0 & x jk xrk )} nk | x jk xrk |]
I(k is s.p.d){I( x jk xrk ) nk | x jk xrk | I( x jk xrk ) nk | x jk xrk |} nj
(4)
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where xrk , which is a reference point, is the level of the attribute k of the product currently owned by the consumer. For the attributes with different preference directions, if the sign of
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nk1 and ( x jk xrk ) is identical, a consumer prefers the attribute level of the alternative;
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otherwise, the consumer does not prefer the attribute level.
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A Bayesian inference method was used to estimate ( nk ), which is the assigned value
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of each attribute in the first and second stages. The Bayesian inference method adopts a
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priority assumption on assigned values and the posterior distribution of a likelihood function. The method can overcome the problem of producing a different maximum value based on an
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initial value. In addition, the consistency and efficiency of the estimation can be obtained under a more flexible condition and the estimation results can be interpreted from both a Bayesian and a classical perspective (Allenby and Rossi, 1999; Huber and Train, 2001). This study used Gibbs sampling for the Bayesian inference method to estimate the models.
4. Empirical Analysis 4.1. Survey Design and Data Description
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The survey was administered to 675 respondents (aged 20–59)2 living in Seoul and other larger cities,3 and individual interviews were conducted by Gallup South Korea. A purposive quota sampling method was used to select interviewees. Of the 675 respondents, 532 who currently owned a vehicle were selected to analyze for this study. Tables 4 and 5 summarize respondents` demographic data and information about the vehicles they own.
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Table 4. Respondents` demographic characteristics
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Group Male Female < 30 30–39 40–49 50–59 Seoul Busan Incheon Daegu Daejeon Gwangju Gyeonggi-do (new cities) Middle school or below High school College Graduate school No response Gasoline Diesel LPG Hybrid SUV
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Gender
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Pr
Age
Educational level
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Residential area
Type of vehicle owned Model of vehicle owned
No. of respondents (%) 253 (47.6%) 279 (52.4%) 135 (25.4%) 142 (26.7%) 157 (29.5%) 99 (18.6%) 209 (39.3%) 76 (14.3%) 59 (11.1%) 72 (13.5%) 38 (7.1%) 38 (7.1%) 40 (7.5%) 8 (1.5%) 184 (34.6%) 323 (60.7%) 13 (2.4%) 4 (0.8%) 367 (69.0%) 139 (26.1%) 24 (4.5%) 2 (0.4%) 155 (29.1%)
2
In South Korea, one must be 20 years or older to own a vehicle. Therefore, respondents` age range was limited to 20–59 years. 3
These include Gyeonggi-do (new town) and five metropolitan cities: Busan, Incheon, Daegu, Daejeon, and Gwangju.
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Ordinary passenger cars
377 (70.9%)
Table 5. The information of vehicles which respondents own Mean 12.616 11.436 18.205
Standard Deviation 11.257 2.813 8.565
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Items Average vehicle purchase price (million KRW) Per liter vehicle driving distance (km/L) Annual vehicle driving distance (1000 km)
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4.2. Estimation Results
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A standard Bayesian mixed logit model was used for the first-stage analysis.
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Equation (5) is the detailed utility function:
U nj n,diesel d j,diesel n,hybrid d j,hybrid n,electric d j,electric n,SUV d j,SUV
(5)
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n, fuel-effi x j, fuel-effi n,price x j,price n,accesibility x j,accesibility n,smart d j,smart + nj
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The value each consumer assigns to product attributes was estimated by assigning a
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conditional probability distribution according to the Markov chain Monte Carlo technique
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and repeated random sampling. Table 6 presents the estimated results, which indicate that respondents preferred gasoline vehicles over diesel vehicles. In addition, no significant results were obtained for the car type. This result implies that there are even distributions of respondents who prefer and who dislike SUV compared to ordinary cars. Therefore, it is appropriate to set the normal distribution for the coefficient of the car type attribute and examine the varying consumer preference directions based on the coefficient estimates. Since fuel efficiency, vehicle price, and accessibility to charging station were assumed to have a log-normal distribution, consumers preferred higher fuel efficiency, lower vehicle price, and easier accessibility. The coefficient of each attribute obtained from all respondents in the first stage was
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used with Equation (6) in the next stage of the analysis. Table 7 reports the results. Since consumers have different preferences toward the fuel type and vehicle type, it cannot be analyzed by the general reference-dependence model. Thus, I(k is d.p.d) in Equation (4) was used for the analysis. Also, as shown in [Table 4], there were 0 respondents with electric vehicles and only 2 respondents with hybrids, only allowing the analysis of reference point effect of gasoline and diesel for the fuel type attribute. Also, since the simulation analysis
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compared the utility of electric vehicles and gasoline vehicles, considering the reference point in fuel type attribute may harm the consistency of the study. As for the fuel efficiency
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attribute, consumer showed same preference direction, which allowed the use of I(k is s.p.d)
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in Equation (4) to analyze. Next, the charging station accessibility attribute was set at 100%
Pr
for current accessibility to gas stations, while the attribute level for the alternatives was set below this threshold. Therefore, only the coefficients of non-preference direction in which
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accessibility decreases were estimated.
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U nj n,diesel d j,diesel n,hybrid d j,hybrid n,electric d j,electric 1 Pre I{( n,SUV 0 & x j,SUV xr,SUV ) or ( n1, SUV 0 & x j,SUV xr,SUV )} n,SUV | x j,SUV xr,SUV |
Non-Pre I{( n1, SUV 0 & x j,SUV xr,SUV ) or( n1, SUV 0 & x j , SUV xr , SUV )} n,SUV | x j,SUV xr,SUV |}
I( x j, fuel-effi xr, fuel-effi ) n,Prefuel-effi | x j, fuel-effi xr, fuel-effi |
I( x j, fuel-effi xr, fuel-effi ) n,Non-Pre fuel-effi | x j, fuel-effi xr, fuel-effi |}
Non-Pre I( x j,accesibility xr,accesibility ) n,accesibility | x j,accesibility xr,accesibility |}
n,price x j,price n,smart d j,smart nj (6)
Table 6. Estimation results for standard mixed logit model Attribute
Coding
Distributional
Mean (Std. D)
t
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assumption
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Fuel type (ref: Gasoline) Dummy Normal Diesel -0.754* (1.719) -1.714 Hybrid 0.284 (1.952) 1.608 EV -0.631 (2.310) -1.498 Car type (ref: Ordinary car) Dummy Normal SUV -0.047 (0.978) 0.358 Fuel efficiency (1km/1L) Linear Log-normal 0.164*** (0.720) -14.526 Charging station accessibility (10%) Linear Log-normal 0.255*** (0.500) -8.314 Vehicle price (KRW 10 million) Linear Log-normal -0.789*** (1.306) -3.055 Smart car option Dummy Normal 1.104*** (1.708) 3.441 Log-likelihood -1966.077 AIC (BIC) 3948.154 (3685.176) ***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level
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Table 7. Estimation results for mixed logit model reflecting reference-dependent preferences
Coding
Distributional assumption Normal
Mean (Std. D)
t
Pr
Attribute and preference
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Fuel type (ref: Gasoline) Dummy Diesel -0.295 (2.274) -1.383 Hybrid 1.067*** (2.876) 3.486 EV -0.365 (2.931) -1.486 SUV Dummy Log-normal Preference direction 1.307*** (3.029) -3.216 Non-preference direction -2.825*** (3.002) 5.180 Fuel efficiency (1km/1L) Linear Log-normal Preference direction 0.325*** (1.572) -10.964 Non-preference direction -0.736*** (5.833) -10.090 Charging station accessibility (10%) Linear Log-normal Non-preference direction -0.726*** (4.260) -8.845 Vehicle price (KRW 10 million) Linear Log-normal -1.483*** (4.496) -3.213 Smart car option Dummy Normal 1.459*** (2.389) 5.201 Log-likelihood -1832.588 AIC (BIC) 3685.176 (3692.435) ***Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level
If the reference-dependent preference has a significant impact on the consumer`s choices, the reference-dependent choice model should provide better explanatory power and model fit than the standard choice model (Hardie et al., 1993). Since the reference-dependent
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choice model requires more parameter estimation than the standard choice model, the model fit should not be examined by simply comparing the log likelihood values. Thus, AIC (Akaike`s Information Criterion) (Akaike, 1998) or BIC (Bayesian information criterion) (Schwarz, 1978) statistics were used to provide an accurate comparison on the degree of improvement of the model. As a result, the model considering the reference-dependent preference was found to have higher explanatory power (model fit) than the model that did
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not. Table 8 shows the test results for the presence of asymmetric preference for SUVs
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and fuel efficiency attributes. Since the T-test (Kim et al., 2016) and the asymptotic t-ratio
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test (Hess et al., 2008; Román & Martín, 2016) can be used to assess the significance of the
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difference between the preferred and non-preferred coefficients for the attribute, this study used the T-test to assess the asymmetric preferences. The results indicate that the negative
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effect on consumer utility was 2.264 times greater for a 1 km/L decrease than a 1 km/L
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increase in fuel efficiency. Asymmetric preferences also existed in SUV attributes, and the
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loss aversion parameter was 2.162. This result is consistent with findings in previous studies on reference-dependent effects (Hess et al., 2008; Masiero and Hensher, 2010; Tversky and Kahneman, 1991; Kim et al., 2016; Kim et al., 2018).
Table 8. Consumer preferences and loss aversion parameters Variables SUV Fuel efficiency
T-test statistics 22.390 7.394
Preference Asymmetry Asymmetry
Loss aversion parameter 2.162 2.264
Table 9 presents the results for preference or non-preference for an SUV. The presence of an asymmetric preference by car type has a significant implication in the South
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Korean vehicle market. As of 2019, manufacturers concentrate on EVs that are ordinary passenger cars as light, small, and middle type. In other words, they provide purchasing motivation for the 269 respondents (50.6%) who reported non-preference for an SUV. However, for consumers who prefer SUVs, electric ordinary passenger cars have reduced utility. Therefore, manufacturers should consider setting the marketing strategy of electric SUVs, and the government can offer subsidies for such production to help accelerate their
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market diffusion.
Pr
Own SUV 83 (15.6%) 72 (13.5%) 155 (29.1%)
Do not own SUV 180 (33.8%) 197 (37.0%) 377 (70.9%)
Total 263 (49.4%) 269 (50.6%) 532 (100.0%)
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Preference for SUV Preferred Not preferred Total
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Table 9. Car types owned by respondents and preference for SUVs
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4.3. Sensitivity analysis: Fuel efficiency and accessibility to charging stations
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Incentives are crucial factors for diffusion of Hybrid vehicles (Jenn et al., 2013). However, in the case of EVs, subsidies are not an important factor (Hirte and Tscharaktschiew, 2013), while fuel efficiency and infrastructure construction are more important factors, because to personal the biggest advantage of EVs is fuel costs and the biggest disadvantage of EVs is charging problem. Therefore, this study performed sensitivity analysis for fuel efficiency and infrastructure construction to examine which factor is more influential to consumers` EVs choice probability. This sensitivity analysis was performed based on the above estimation results. Currently, gasoline vehicles have a predominant market share in the passenger vehicle market. Although there was not statistical significance, the coefficients revealed that
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consumers prefer gasoline vehicle alternatives over electric ones. This indicates the necessity of a strategy to increase the consumer utility of owning EVs compared to owning gasoline vehicles to aid EV market diffusion. Accordingly, electric and gasoline vehicle alternatives were compared and analyzed. In particular, a change in consumers` net utility for EVs was analyzed as the levels of certain attributes of EVs alternatives varied, which is described in Equation (7) (Allenby and Ginter, 1995):
j is an alternative for reference. In this study, the
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where j * is an alternative for change and
(7)
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U nj* U nj* U nj 0 ,
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j * alternative is an EV and j alternative is a gasoline vehicle. In addition, the utility of
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EVs changes as the level of certain attributes varies, which results in a change to the ratio of respondents with positive net utility for EVs ( U nj* ) among all respondents. This result can be
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used to indirectly predict the choice probability of EVs compared to gasoline vehicles.
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Assuming that the attributes other than the six attributes in this study are identical, the biggest
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difference between gasoline vehicles and EVs is fuel efficiency and accessibility to charging stations. Therefore, this study used these attributes for a sensitivity analysis. In 2016, the number of EVs charging stations in South Korea is about 3% of the total number of gas stations. Considering that the number of new electric vehicles registered in 2017 was about 15,000, which accounted for about 3% of the new gasoline, the sensitivity analysis of this study proved to be valid. In addition, if this number of charging station stands, in Figure 1, the ratio of positive net utility for EVs utility will be about 30%, even when the battery performance increases to 100 km/L. This is close to the current government`s target (i.e., EVs should comprise 30% of new vehicles), although it is not easy to implement the policy due to the technical limitations of developing a battery that is three times as fuel
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efficient as that in current EVs. The aim of Korean government for EVs diffusion will be achieved if accessibility to EV charging station is 80%, although there will not be improvement for fuel efficiency than the present.
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60 50
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Fuel efficiency 30km/L Fuel efficiency 40km/L
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The ratio of the positive net utility of EVs compared to gasoline vehicles (%)
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Fuel efficiency 90km/L Fuel efficiency 100km/L
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The ratio of EV charging station compared to gas station (%)
[Fig. 1] Changes in the positive net utility ratio of EVs due to changes in EVs according to variations in fuel efficiency and ease of access to charging stations
The result in Figure 1 indicates that when charging station accessibility rises from 3% (current condition in South Korea) to 80%, the positive net utility ratio of EVs increases at almost the same rate (slope). However, the slope sharply increases in the 80–100% range. This seems to occur because consumers` reference point for charging station accessibility is set at 100%. In other words, it is consistent with prospect theory, in that the utility of an incremental reduction in a non-preference unit reaches its maximum at the reference point and gradually declines thereafter (Masiero and Hensher, 2010; Tversky and Kahneman, 1991).
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Thus, the point at which the slope of the marginal utility begins to decelerate is crucial in determining the lower limit when examining product characteristics related to the trade-off. The South Korean government aims to increase the number of EV charging stations to 25% of total gas stations by 2020. Therefore, it would be wise to invest heavily in improving accessibility to charging stations, even if the government cannot maintain a subsidy for EVs. However, if this is not a feasible option, then concentrating on charging stations in areas with
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high consumer interest in EVs would help accelerate market diffusion.
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5. Conclusions
Consumers tend to purchase new products based on the properties of products they
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currently own. Therefore, reflecting a reference-dependent utility function in a discrete choice model enables an analysis of the asymmetric preferences of various product attributes. It can
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also be useful in developing successful market diffusion strategies for a new product.
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However, few studies on consumer preferences for alternative fuel vehicles reflect reference-
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dependent effects on existing discrete choice experiments and models. Therefore, this study classified consumer preferences for vehicles in terms of a same or different preference directions and performed the analysis using the attributes of vehicles that consumers currently own as a reference point. The results indicate that the value of the loss aversion parameter by car type, which may reflect a different preference directions, fuel efficiency, which may represent a same preference direction, was greater than one. Since SUV had the high loss aversion parameter (2.162), car type may have a key role in consumers` purchasing decision. Therefore, though EVs have several advantages over other fuel type vehicles, those resembling general vehicles will considerably reduce the utility of consumers who own and prefer SUVs. This has
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implication because many countries, including South Korea, are intensively producing EVs that are small, compact, or sedans. Therefore, governments can help significantly increase market diffusion by extending support to the production of SUV EVs. These results can provide major implications for the hydrogen car market, which has been in the spotlight in South Korea recently. In the case of hydrogen cars, SUVs were first introduced to the market due to battery size as opposed to EVs, and there are still no plans to release hydrogen cars in
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the form of ordinary passenger cars such as small cars and midsize cars. As a result, it was evident that if a consumer already owns a sedan, prefers a sedan, and his loss aversion
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parameter for the car type is high, the choice probability of an SUV hydrogen car will be very
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low. Therefore, in order to avoid limited preference and achieve the government`s goal of diffusing eco-friendly cars, it is essential to release the various models of eco-friendly
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vehicles for the consumers.
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The sensitivity analysis results suggest that the government should concentrate on a
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policy that increases the number of EV charging stations to facilitate market diffusion.
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Although the number of charging stations for EVs cannot be on par with gas stations, establishing an infrastructure for a sufficient number of EV charging stations will help dramatically increase the market penetrate rate of EVs. As demonstrated in this study, EV charging station availability is determined by government policy and is a factor that cannot be controlled at the supplier and consumer level. In other words, continuous and consistent policy implementation plays an important role in the diffusion of innovative products like EVs. For example, in the case of EVs, various factors such as power generation, infrastructure, product qualities, and government subsidies significantly affect consumer utility. However, these factors rely heavily on government policy, so it is necessary for governments to establish and execute consistent policy to facilitate EV market diffusion.
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This study finds the presence of asymmetric preferences when consumers choose a new product. Meanwhile, such behavior is largely influenced by products consumers already own and it affects EV market diffusion. The present discussion began by addressing whether product attributes should be accepted at a relative rather than an absolute level. However, to capture the level of relativity, it is necessary to consider consumer habits as well. Unlike internal combustion EVs, EVs have dramatically different fuel efficiency levels depending on
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the environment and the manner in which they are driven. For example, EV`s fuel efficiency (km/kWh) varies by not only the usage of the air conditioner or heater, but also driving habits.
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Therefore, although consumers may accept the absolute level of product attributes in the early
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stages of product diffusion, during the mainstream stage, they are more likely to accept product attributes in terms of their driving habits, which is critical for diffusion. Thus, further
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study is needed in this context to help identify technologies and the policy implications
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required for the successful diffusion of EVs. In addition, future studies should consider the rebound effect because declined fuel costs increase the frequency and time of vehicle use
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(Stapleton et al., 2016; Lu et al., 2017).
Acknowledgment:
The authors are grateful to Professor Yoonmo Koo (Seoul National University) and Professor Jungwoo Shin (Kyunghee University) who designed discrete choice experiment used in this study. Further, thank you Stephen Youngjun Park for conducting a proof reading to improve both language and organization quality. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Highlights Analyzes consumer preferences for EVs using a discrete choice model
Improve the discrete choice model with reference-dependent utility function
Finds asymmetric preferences in vehicle attributes due to status quo
Performs sensitivity analysis to examine EV choice probability
Proposes improving accessibility to EV charging stations in the short term
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