Consumer purchase intentions for flexible-fuel and hybrid-electric vehicles

Consumer purchase intentions for flexible-fuel and hybrid-electric vehicles

Transportation Research Part D 18 (2013) 9–15 Contents lists available at SciVerse ScienceDirect Transportation Research Part D journal homepage: ww...

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Transportation Research Part D 18 (2013) 9–15

Contents lists available at SciVerse ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Consumer purchase intentions for flexible-fuel and hybrid-electric vehicles Xiaogu Li a, Christopher D. Clark b,⇑, Kimberly L. Jensen b, Steven T. Yen b, Burton C. English b a b

Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, USA Department of Agricultural and Resource Economics, University of Tennessee, Knoxville, USA

a r t i c l e

i n f o

Keywords: Alternative-fuel vehicles Flexible-fuel vehicles Hybrid-electric vehicles Automobile purchase expectations

a b s t r a c t This study explores the factors that influence consumer likelihood of purchasing two different types of alternative-fuel vehicles – flexible fuel and hybrid-electric. Data for the study come from an online survey of US automobile owners. Results suggest that concerns about energy security, the environment, and the availability of alternative fuels, along with demographic factors, have significant effects on consumer purchase expectations for alternative-fuel vehicles. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Heavy reliance on automobiles in the US raises a variety of environmental concerns, including tailpipe emissions of greenhouse gases (GHGs). One option for reducing these emissions is the use of alternative-fuel vehicles (AFVs), such as flexiblefuel (FFV) or hybrid-electric vehicles (HEVs). Despite policies designed to promote the purchase of AFVs and renewable fuel production and sales, AFVs still constitute a relatively small share of vehicles in operation or new automobile sales; there are only about nine million FFVs and two million HEVs in operation in the US. Here we consider consumer preferences for AFVs by analyzing the results of a national online survey of adult automobile owners that examines how consumer demographics, socioeconomic status, driving patterns, and attitudes affect the self-reported likelihood of purchasing FFVs and HEVs. While previous work has often focused on preferences for AFVs in general, we also examine interactions between preferences for HEVs and FFVs. 2. Methodology Data for the empirical analysis were collected through an online survey hosted by Knowledge NetworksÒ (KN) (2009) during January and February of 2009. The sample was drawn from an online research panel maintained by KN that is designed to be representative of the US population. KN recruits panel members by telephone using random-digit dialing and, for greater effectiveness, by address-based sampling methods. If needed, panel members are provided with free access to the Internet and a laptop computer. A sample of 2851 respondents age 18 or older was randomly selected from KN’s online panel, and of these 1909 responded before the survey was closed to further responses. Of responders, 182 were screened out because either their households did not currently own or lease at least one automobile or the household automobile that they drove most often did not have a gasoline or hybrid-electric engine. Of the remainder, 1516 provided a complete enough set of responses to be used in the analysis. Respondents were asked about the automobile they currently own, their driving patterns, and their future purchase intentions for AFVs. The survey also included a number of attitudinal and behavioral questions on a variety of topics related ⇑ Corresponding author. E-mail address: [email protected] (C.D. Clark). 1361-9209/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2012.08.001

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X. Li et al. / Transportation Research Part D 18 (2013) 9–15

to ethanol production and consumption, including fuel security, the food vs. fuel debate, and concern about climate change and the environment. In addition, respondents were asked whether they were members of an environmental organization and where they generally obtained information on environmental issues. Responses to the survey questions were supplemented with demographic information from a panel member profile maintained by KN. The definitions of, and summary statistics for, variables used are shown in Table 1. Questions about attitudes towards global climate change employed Likert scale responses. Factor analysis was used to analyze responses to these three questions to construct an index of concern about global climate change (GCC). A Chronbach’s alpha test of reliability was performed to test the index and indicates the predicted index is reliable. A survey weight designed to compensate for non-response was calculated by comparing respondent demographics with benchmark demographics (gender, age, race/ethnicity, education, Census Region, metropolitan area, and internet access) from the Current Population Survey. The weight was calculated with an iterative proportional fitting procedure. The distribution of the calculated weights was examined to identify and, if needed, trim outliers at the extreme upper and lower tails of the weight distribution. The post-stratified and trimmed weights were then scaled to the sum of the total sample size. All results are weighted with the resulting weights.

3. Estimation A bivariate probit model is used to jointly estimate the likelihood of choosing an FFV and/or an HEV. The variable representing the likelihood of choosing an FFV is created from responses to the question: ‘‘If similarly priced to other

Table 1 Summary statistics. Variable

Description

Mean

Dependent variables PFFV PHEV

Likely to purchase flexible-fuel vehicle (0, 1) Likely to purchase hybrid electric vehicle (0, 1)

Explanatory variables Demographics Age HSLess SomeColl College Income White Male Republican Child Rural NAC Corn Northeast South West

Respondent age Less than high school graduate (0, 1) Some college (0, 1) Bachelor’s degree or higher (0, 1) Household income ($1000) Race is white (0, 1) Gender is male (0, 1) Republican (reference is democrat/independent) (0, 1) At least one child under 18 in household (0, 1) Resides in rural area (0, 1) Resides in Clean Air Act non-attainment county (0, 1) Resides in county where corn production at or above national average (0,1) Resides in Northeast (Reference is Midwest) (0, 1) Resides in South (0, 1) Resides in West (0, 1)

48.090 0.088 0.285 0.329 31.270 0.822 0.513 0.482 0.257 0.222 0.483 0.202 0.175 0.371 0.209

Vehicle and driving characteristics CHEV CFFV Lease Public Carpool Miles Cheaper Next

Current vehicle is hybrid electric vehicle (0, 1) Current vehicle is flexible-fuel vehicle (0, 1) Current vehicle is leased (0, 1) Used public transportation in last year (0, 1) Shared vehicle with other people in last year (0, 1) Estimated miles driven in typical day (miles) How frequently go out of way to buy cheaper fuel (Categorical) (1, 4) Years before purchasing next automobile (Categorical) (1,5)

0.015 0.024 0.033 0.183 0.255 21.844 2.383 3.036

Beliefs GCC Food Security Expensive Unavailable

Concerned about global climate change (index) Believes farmland should be used for food not fuel (1, 5) Believes dependence on oil imports is threat to national security (1,5) Believes FFVs cost significantly more than other vehicles (1,5) Believes E85 not likely to be readily available in area in near future (1,5)

0.004 3.727 4.145 3.502 3.379

Environmental information Print Internet Fandf Child  Fandf

Obtains environmental information from newspaper/magazine (0,1) Obtains environmental information from internet (0, 1) Obtains environmental information friends and family (0,1) Interaction term of child and friends & family (0, 1)

0.592 0.450 0.294 0.082

0.759 0.289

X. Li et al. / Transportation Research Part D 18 (2013) 9–15

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automobiles, how likely is it that the next automobile you lease or purchase will be a Flex-Fuel Vehicle (E85 compatible)?’’ Responses include not at all likely, somewhat likely and very likely. The HEV variable is created from the extent to which respondents agreed (strongly disagree, somewhat disagree, neutral, somewhat agree, or strongly agree) with the following statement: ‘‘The next automobile I purchase or lease is likely to be a Gasoline/Electric Hybrid.’’ For this analysis we recode responses to the two questions into a pair of binary variables. Thus, respondents who were either somewhat or very likely to purchase an FFV were coded as FFV = 1, and zero otherwise. Likewise, respondents who either somewhat or strongly agreed that the next automobile they would purchase was likely to be a gasoline/electric hybrid were coded as HEV = 1, and 0 otherwise. The utility difference for individual i of purchasing each type of vehicle (vs. not purchasing) can be expressed as a latent variable yij , with j = 1 for FFV and j = 2 for HEV, such that (Greene, 2012)

yij ¼ x0i bj þ eij ;

j ¼ 1; 2

ð1Þ

where xi is a vector of observable characteristics of respondent i, bj is a conformable vector of parameters, and random disturbances (ei1, ei2) are distributed as bivariate normal with means (0, 0), variances (1, 1), and correlation q. Each pair of observed outcomes for (yi1, yi2) relates to the latent variables (yi1 ; yi2 ) such that

yij ¼1 if yij > 0

ð2Þ

¼0 if yij 6 0

ð3Þ

Maximum-likelihood estimation is based on the bivariate probabilities of the outcomes (yi1, yi2) for all i. To express these probabilities, we define dichotomous indicator variables jij = 2yij  1 such that jij = 1 if yij = 1 and jij = 1 if yij = 0 Then, the joint probability of an outcome for (yi1, yi2) is

Pr½yi1 2 ð0; 1Þ; yi2 2 ð0; 1Þ ¼ U2



ji1 x0i b1 ; ji2 x0i b2 ; ji1 ji2 q

ð3Þ

where U2 is the bivariate standard normal cumulative distribution function. Upon estimation, marginal (discrete) effects of continuous (discrete) explanatory variables can be derived by differentiating (differencing) the probabilities of interest. For instance, the joint probability of (yi1 = 1, yi2 = 1) is U2 ðx0i b1 ; x0i b2 ; qÞ, the marginal probability of (yi1 = 1) is Uðx0i b1 Þ, and the conditional probability of (yi1 = 1|yi2 = 1) is U2 ðx0i b1 ; x0i b2 ; qÞ=Uðx0i b2 Þ, where U() is the cumulative distribution function of the unit normal. These expressions can also be used to predict probabilities of outcomes. 4. Results 4.1. Maximum-likelihood estimates of the bivariate probit model Maximum-likelihood estimation was conducted in STATA. The estimated parameter of correlation between probabilities of choosing an FFV and choosing an HEV, is statistically significant at the 1% level, and the likelihood-ratio and Lagrange multiplier tests also suggest significance of the error correlation, which justify estimation of the two equations as a system to improve statistical efficiency. This positive and significant correlation also indicates that unobserved factors have the same direction of effects on the likelihood of purchasing each type of vehicle. The results are reported in Table 2. A number of variables have similar effects on expectations of purchasing an FFV or an HEV and are significant in both equations. Respondents who went out of their way to save money on gasoline (Cheaper), were more concerned about GCC, believed that reducing dependence on foreign oil is important to national security (Security), and obtained environmental information from newspapers and magazines (Print), are all more likely to purchase either an FFV or an HEV. Several variables are significant in both equations but had different effects on the expectation of purchasing an FFV or an HEV. Rural residents are more likely to purchase an FFV but less likely to purchase an HEV. On the other hand, respondents who currently drive an HEV (CHEV), who believe that farmland should not be used for producing fuel (Food), and those who believe that E85 is not likely to be readily available in their areas in the near future (Unavailable), are more likely to expect to purchase an HEV but less likely to expect to purchase an FFV. Some variables are significant in one equation but not the other. Respondents who currently drive an FFV (CFFV), reside in a Clean Air Act non-attainment county (NAC), live in the South (South) or West (West) as opposed to the Midwest (Midwest), or do not expect to purchase an automobile in the near future (Next), are all more likely to expect to purchase an FFV. On the contrary, those with less than a high school education (HSLess), higher incomes (Income), lease their current vehicle (Lease), or believe that FFVs cost significantly more than other vehicles (Expensive) are all less likely to believe their next automobile purchase will be an FFV. On the other hand, respondents were less inclined to believe their next purchase will be an HEV if they are male (Male), of white race (White), have children (Child) a member of the Republican Party (Republican) or drive more miles in a typical day (Miles), but more inclined to believe they will purchase an HEV if they live in counties where corn production is above the national average (Corn), or obtain information about the environment from friends and family members and have at least one child in their household (Child  Fandf).

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X. Li et al. / Transportation Research Part D 18 (2013) 9–15 Table 2 Weighted maximum likelihood estimates of bivariate probit model.

* **

Variable

FFV

HEV

Constant Age/10 Age2/1000
0.694* 0.238* 0.222* 0.755*** 0.074 0.161 0.044*** 0.042 0.044 0.024 0.161 0.161* 0.285*** 0.183 0.092 0.351*** 0.254** 0.538* 1.097** 0.399* 0.163 0.084 0.009 0.082* 0.132*** 0.346*** 0.146*** 0.275*** 0.184*** 0.097** 0.195** 0.038 0.171 0.251

2.325*** 0.099 0.085 0.101 0.076 0.102 0.003 0.140* 0.146* 0.255*** 0.185* 0.193** 0.067 0.231** 0.107 0.107 0.109 1.041*** 0.023 0.260 0.117 0.010 0.058*** 0.119*** 0.016 0.288*** 0.101*** 0.116*** 0.005 0.100*** 0.186** 0.220*** 0.022 0.566***

Error correlation (q) Log likelihood

0.224*** 1518.740

P < 0.10. P < 0.05. P < 0.01.

***

4.2. Predicted probabilities and marginal effects Table 3 presents predicted marginal, joint, and conditional probabilities. These are calculated for each individual and then averaged over the sample; the estimates are fairly precise with low standard errors, calculated by the delta method. On average, respondents are nearly 75% likely to believe that it is either likely or highly likely that their next automobile purchase will be an FFV and 29.1% likely to believe that it will be an HEV. They are 24.6% likely to believe that it will be both. They are 82.4% likely to believe that it will an FFV given that they believe it will be an HEV, and 31.7% likely to believe that it will be an HEV given that they believe it will be an FFV. Marginal effects of the explanatory variables on the probabilities of these five possible outcomes are provided in Table 4. These marginal effects are also calculated for each individual and then averaged over the sample. In general, marginal effects measure the effects of changes in explanatory variables on changes in probabilities of each possible outcome, holding all other variables constant. Table 3 Predicted probabilities.

a

Probability

Estimatea

FFV = 1 HEV = 1 FFV = 1 & HEV = 1 FFV = 1 | HEV = 1 HEV = 1 | FFV = 1

0.748 0.291 0.246 0.824 0.317

All predicted probabilities are significantly different from 0 at the 1% level of significance.

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X. Li et al. / Transportation Research Part D 18 (2013) 9–15 Table 4 Marginal effects of explanatory variables on probabilities. Probability of Variable

FFV = 1

HEV = 1

FFV = 1 & HEV = 1

FFV = 1 |HEV = 1

HEV = 1|FFV = 1

Age/10
0.008 0.233*** 0.020 0.043 0.012*** 0.011 0.012 0.006 0.026 0.042* 0.076*** 0.047 0.024 0.091*** 0.065* 0.162* 0.199*** 0.117* 0.043 0.022 0.002 0.022* 0.035*** 0.093*** 0.039*** 0.074*** 0.049*** 0.026** 0.053** 0.010 0.029

0.006 0.031 0.023 0.031 0.001 0.043 0.044* 0.077*** 0.001 0.056** 0.020 0.071* 0.033 0.032 0.033 0.351*** 0.007* 0.073 0.036 0.003 0.017*** 0.036*** 0.005 0.086*** 0.030*** 0.035*** 0.001 0.030*** 0.056** 0.067*** 0.037

0.004 0.038 0.023 0.035 0.002 0.037* 0.038** 0.060*** 0.006 0.038 0.033 0.069** 0.032 0.046 0.042 0.173** 0.031 0.079* 0.039 0.002 0.013*** 0.034*** 0.012 0.090*** 0.016* 0.044*** 0.012 0.018** 0.056*** 0.056*** 0.037*

0.007 0.215*** 0.014 0.031 0.010*** 0.004 0.004 0.015 0.023 0.042** 0.061*** 0.031 0.016 0.072*** 0.050* 0.193** 0.148*** 0.090 0.031 0.019 0.004 0.014 0.029*** 0.066*** 0.037** 0.057*** 0.041*** 0.026*** 0.037** 0.000 0.020

0.007 0.059 0.022 0.028 0.002 0.044* 0.045* 0.082*** 0.003 0.064** 0.013 0.069* 0.032 0.024 0.028 0.378*** 0.027 0.066 0.033 0.006 0.018*** 0.035** 0.001 0.081*** 0.036*** 0.029** 0.004 0.034*** 0.053** 0.069*** 0.035

*

P < 0.10. P < 0.05. *** P < 0.01. **

Following, Nixon and Saphores (2011) individuals with higher incomes could be hypothesized to have higher opportunity costs of time and, thus, less likely to purchase an FFV as the lower energy content of E85 implies longer travel times due to more frequent refueling. The marginal effects of Income support this hypothesis and support the findings of Qian and Soopramanien (2011). For a $10,000 increase in income, respondents are 1.2% less likely to believe their next automobile will be an FFV and 1.0% less likely to believe that it will be an FFV given that they believe it will be an HEV. Thus, preferences for FFVs appear to be inversely related to household income. Respondents with less than a high school education are 23.3% less likely to expect to purchase an FFV in the future, a finding in line with Geiver (2010). White respondents are 3.7% (4.4%) less likely to expect to purchase an FFV (HEV) given that it will be an HEV (FFV). Males are 4.4% less likely to believe that their next automobile purchase will be an HEV; 3.8% less likely to believe that it will be both an FFV and an HEV; and 4.5% less likely to believe that it will be an HEV, given that they believe it will be an FFV. This result suggests that female buyers may be a target market for AFVs, in concurrence with findings by Phoenix Marketing International (2007). Republicans are 7.7% less likely to believe that their next automobile purchase will be an HEV and 6.0% less likely to believe that it will be both an FFV and an HEV. Rural residents are 4.2% more likely to believe that their next automobile will be an FFV conditional or unconditional on buying an HEV; they are less likely to believe that it will be an HEV, conditional (6.4%) and unconditional (5.6%) on buying an FFV. Residents of Clean Air Act non-attainment counties (NAC) are 7.6% more likely to believe their next purchase will be an FFV. Respondents who reside in a county that produces more than the average amount of corn (Corn) are 7.1% more likely to believe that their next automobile purchase will be an HEV. Respondents who reside in the South and West are, respectively, 9.1% and 6.5% more likely to believe that their next automobile will be an FFV than residents in the Midwest. Current drivers of HEVs (CHEV) are 35.1% more likely to believe that their next automobile will be an HEV and 16.2% less likely to believe it will be an FFV, while current drivers of FFVs (CFFV) are 19.9% more likely to believe their next automobile will be an FFV. In addition, current drivers of HEVs are 17.3% more likely to believe their next automobile will be an FFV and an HEV. These results suggest that consumers who are already an FFV or HEV driver are more likely to consider purchasing that type of AFV in the future, but, interestingly, not any more likely to consider purchasing the other type of AFV.

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Respondents who lease their current vehicle (Lease) are less likely to believe that their next automobile will be an FFV, conditional (7.9%) and unconditional (11.7%) on it being an HEV. The number of miles driven on a daily basis has been shown to be an important determinant of consumer vehicle preferences (Bunch et al., 1993). In our findings, a 10 mile increase in the number of miles driven on a typical day (Miles) decreases the likelihood that a respondent believes that his or her next automobile will be an HEV by 1.7%. Respondents who often go out of their way to look for cheaper fuels (Cheaper), are 2.2% more likely to expect their next automobile to be an FFV, 3.6% more likely to expect it to be an HEV, and 3.4% more likely to consider both an FFV and an HEV. Hence, respondents who are fuel price conscious may consider fuel cost savings as an important attribute of AFVs. This finding concurs with that of Nixon and Saphores (2011). A single year increase in the expected time before purchasing or leasing a new automobile (Next), translates into a 3.5% increase in the likelihood that the respondent would expect to purchase an FFV. In line with Flamm (2009), Hidrue et al. (2011) and others, respondents who are more concerned about GCC, are more likely to expect to purchase a vehicle that is an FFV, an HEV, or both. Increased respondent agreement with the statement that farmland should be used to produce food and not fuel (Food) decreases the likelihood that the respondent will expect that his or her next automobile will be an FFV by 3.9% but increases the likelihood that the respondent will expect it to be an HEV by 3.0%. This result suggests that the segment of consumers who are concerned about food security are more likely to consider purchasing an HEV and less likely to consider purchasing an FFV. Respondents who more strongly agree with the statement that dependence on oil imports is a threat to US national security (Security) are 7.4% (3.5%) more likely to expect to purchase an FFV (HEV). Therefore, fuel security concerns are likely to have a positive effect on potential purchase of both FFVs and HEVs. Respondents who consider FFVs to be significantly more expensive than other vehicles (Expensive) are 4.9% less likely to expect to purchase an FFV. Respondents who expect that E85 is not likely to be available in their neighborhood in the near future (Unavailable), are 2.6% less likely to believe that their next automobile will be an FFV, but 3.0% more likely to believe it will be an HEV. Hence, fuel availability concerns are likely to negatively impact the likelihood of purchasing an FFV purchase but positively impact the likelihood of a future HEV purchase. Respondents who obtain environmental information by reading newspapers and magazines (Print) are 5.3% (5.6%) more likely to believe their next vehicle will be an FFV (HEV). Respondents who obtain environmental information from the internet (Internet) are 6.7% more likely to believe their automobile will be both an HEV and 5.6% more likely to believe it will be an FFV and an HEV. 5. Conclusion In general, consumers who go out of their way to buy cheaper gasoline, who are concerned about GCC or the effect of oil imports on national security, or who obtain environmental information from newspapers and magazines are more likely to purchase an FFV or HEV. Consumers are more likely to purchase an FFV if they currently drive an FFV, have more formal years of education, live in a Clean Air Act non-attainment county or the South or West (as opposed to the Midwest), or do not expect to purchase a new automobile in the near future. Consumers consider themselves less likely to purchase an FFV if they have higher incomes, currently lease their automobile or believe that FFVs cost significantly more than other vehicles. Consumers consider themselves more likely to purchase an HEV if they live in a county with an above-average amount of corn production, obtain environmental information from the internet, or have a child and obtain environmental information from their friends and family. Males, Republicans, and those driving more miles in a typical day are less likely to purchase an HEV. While others have found that greater educational attainment is positively associated with preferences for AFVs, our more detailed differentiation by vehicle type shows that it is positively associated with increased expectations of buying a FFV but not an HEV. Acknowledgements This research was funded in part by a grant from the US Department of Agriculture’s National Research Initiative Competitive Grants Program. Although the research described in this article has been funded wholly or in part by the US Department of Agriculture, it does not necessarily reflect the views of the Department and no official endorsement should be inferred. References Bunch, D.S., Bradley, M., Golob, T.F., Kitamura, R., Occhiuzzo, G.P., 1993. Demand for clean-fuel vehicles in California: a discrete-choice stated preference pilot project. Transportation Research Part A 27, 237–253. Flamm, B., 2009. The impacts of environmental knowledge and attitudes on vehicle ownership and use. Transportation Research Part D 14, 272–279. Geiver, L., 2010. Ford to Double US FFV Fleet. Ethanol Producer Magazine. May 5. (accessed 28.03.12). Greene, W.H., 2012. Econometric Analysis, seventh ed. Prentice Hall, Upper-Saddle River. Hidrue, M.K., Parsons, G.R., Kempton, W., Gardner, M.P., 2011. Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics 33, 686–705. Knowledge Networks, 2009. Field Report, Ethanol Conjoint Survey. February 6. Knowledge Networks, Menlo Park.

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Nixon, H., Saphores, J., 2011. Understanding Household Preferences for Alternative-Fuel Vehicle Technologies. MTI Report 10-11, Mineta Transportation Institute. San Jose State University. (accessed 21.03.12). Phoenix Marketing International, 2007. A Consumer Research Study of E85 Everywhere Minnesota Consumer Perception Survey for E85 & Flex-Fuel Vehicles. (accessed 21.03.12). Qian, L., Soopramanien, D., 2011. Heterogeneous consumer preferences for alternative fuel cars in China. Transportation Research Part D 16, 607–613.