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SIGNIFICANCE OF A SHORT-TERM TEST DRIVE FOR PROSPECTIVE PLUG-IN ELECTRIC VEHICLE CONSUMERS: AN EXPLORATORY STUDY

Alina Dini (Corresponding author)

Queensland University of Technology 2 George Street, Brisbane, Queensland, Australia

P: +61 420 796 009 F: +61 7 3138 1170

alina.dini@qut.edu.au

Simon Washington Professor and ASTRA Chair

Queensland University of Technology 2 George Street, Brisbane, Queensland, Australia

P: + 61 7 3138 9990 simon.washington@qut.edu.au

Word Count: 5412 plus 6 figures and tables (1500) = 6912 word-equivalent

Proceedings of the 95th Annual Meeting of the Transportation Research Board in Washington DC, January

2016, and forthcoming in Transportation Research Record

November 15, 2015

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ABSTRACT

The test drive is a well-known step in car buying. In the emerging plug-in electric vehicle (PEV) market, however, the influence of a pre-purchase test drive on a consumer’s inclination to purchase is unknown. Policy makers and industry participants both are eager to understand what factors motivate vehicle consumers at the point-of-sale. A number of researchers have used choice models to shed light on consumer perceptions of PEVs, and others have investigated consumer change in disposition toward a PEV over the course of a trial, wherein test driving a PEV may take place over a number of consecutive days, weeks or months. However, there is little written on the impact of a short-term test drive - a typical experience at dealerships or public “ride-and-drive” events. The impact of a typical test drive, often measured in minutes of driving, is not well understood. This paper first presents a synthesis of the literature on the effect of PEV test drives as they relate to consumer disposition toward PEVs. An analysis of data obtained from an Australian case study whereby attitudinal and stated preference data were collected pre- and post- test drive at public “ride-and-drive” event held Brisbane, Queensland in March 2014 using a custom-designed iPad application. Motorists’ perceptions and choice preferences around PEVs were captured, revealing the relative importance of their experience behind the wheel. Using the Australian context as a case-study, this paper presents an exploratory study of consumers’ stated preferences toward PEVs both before and after a short test drive.

Keywords: Plug-in electric vehicle, Consumer Demand, Awareness, Discrete choice, Test drive, PEV Adoption

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INTRODUCTION

In today’s interconnected world, future transport scenarios include a range of vehicle technologies. It is well recognized that multiple modes or technology pathways will be required to meet a range of goals in coming years, such as CO2 abatement and energy independence. Currently, the marketplace is undergoing transition to adopt emerging vehicle technologies and business models. Plug-in electric vehicles (PEVs) – specifically any vehicles which derive some portion of electric fuel from an external source (e.g. the electricity grid) – are expected to be a key participant in that multi-modal future.

However, as a relatively new vehicle technology with limited application, the adoption of plug-in electric vehicles has been limited, with a reported 665,000 (equivalent to 0.08 percent) PEVs in the global passenger vehicle fleet since the end of 2014 (1). While countries such as the United States, the Netherlands and Norway are seeing market penetration equal to or greater than 1% of the fleet, adoption rates are lower than anticipated, especially in markets such as Australia where government policy does not exist and/or industry initiative is unable to catalyze interest (2). Lower than expected adoption rates can hinder policy goals and impede market development, therefore clarity around barriers to adoption of PEVs are pivotal to accelerating market growth. Barriers to adoption of PEVs vary based on jurisdiction, however two key barriers appear to exist across jurisdictions, irrespective of policy drivers: [1] a lack of mainstream awareness of PEV technology (3) and [2] lack of consumer confidence in both PEV technology and products (4) (5).

Using Australia as a case study for this research, the authors investigate the importance of a test drive in determining a prospective buyer’s disposition toward PEV technology. Experiencing a test drive is a common part of the new car buyer’s decision process and with new technologies, such as PEVs, its importance is even more critical to assess value-in-use and build consumer confidence (6). For years, PEV trials have been held in Germany, the United States, and Denmark, among many others, examining a consumer’s attitude toward PEVs over an extended period of time. Despite extensive trial of PEVs around the world, and conclusion that extended exposure to the technology increases consumer familiarity with and confidence in it (7), the literature lacks understanding of the consumer’s disposition of PEVs after a test drive experience that occurs when one trials a new car. U.S. PEV advocacy organisation Plug-in America (8) reported on improved consumer disposition toward PEVs after a test drive at a public “ride and drive” event, but like other industry reports, did not apply statistical methods. The value of a typical test drive, often measured in minutes, is not understood. As the PEV market evolves and adoption targets are delayed, policy makers and industry participants are both eager to understand how to increase adoption of these vehicles and maximize return on investment (9).

CONSUMER DEMAND FOR PEVs Demand for plug-in electric vehicles depends on a many considerations, but for the sake of this study, focus will remain on consumer attributes including (a) socio-economic/demographic characteristics and (b) attitude toward PEV technology. Consumer “attitude” or “perception” toward PEVs has been covered extensively in the literature, with consumer interest evolving over time. An early approach to consumer disposition toward electric vehicle technologies is discussed by UC Davis researchers relating to hybrid vehicles in the 1990s (10). Later, (11) expand their understanding of hybrids motorists, underscoring the importance of personal identity when buying cars, uncovering a link between vehicle ownership and use and personal and social perception. Revealed in these and similar works, expected PEV early adopters are well documented and are typically (although not universally) characterized as possessing the following qualities (12) (13) (14):

• Male, typically of mid-age

• Well-educated

• Of significantly high income

• Home-owner, generally with at least two vehicles parking in covered garages

• Technology-savvy, often active in the politics and interests of the environment and/or energy However, beyond characterization of the likeliest PEV customers per socio-economic persona or attitude toward PEVs, study of practical considerations relating to PEV adoption have also been considered in-depth, namely the economics of PEV purchase and ownership (15), the impact of PEVs on household energy consumption (16), PEV utility (often relating to vehicle range and at fit to consumer’s driving behavior) (17) and frequently of late, recharging functionality (18) (19).

Knowledge and awareness of PEVs is also a critical variable in considering consumer demand for PEVs. Study of consumer knowledge can vary by jurisdiction and technology maturity. To date, many surveys have been conducted to assess consumers pre-disposition toward PEVs in various jurisdictions including Norway (20), Hong Kong (21), Germany (22) (23), and the U.S. (24)

Influence of Real-World Test Drives Through study of PEVs and consumer interactions, a synthesis of the relevant literature indicates some links between a consumer’s knowledge of PEVs and the amount of time they have been exposed to the technology first-hand. In some cases, knowledge is acquired from information acquisition over time, in others, through real-

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world experience, but in most cases, through both. Succinctly presented by (25) as part of the Mini-E study, this information acquisition is described as a “lifestyle learning process” where in knowledge achievement for PEV owners occurs in stages, with repeated exposure. In nearly all past studies reviewed, increased exposure to PEVs cultivates incremental familiarity and favorability toward the technology among users, noting consumers and their experiences are heterogeneous. A representation of this hierarchical relationship is shown in Figure 1.

FIGURE 1 Hierarchical knowledge and confidence acquisition based on period of time a consumer

spends familiarizing himself/herself with PEVs

PEV ownership offers the highest attainable level of PEV knowledge among vehicle consumers, with PEV owners often serving as outward-facing advocates and sharing their experiences in their social networks, facilitating market growth (26). This group of consumers report little question of PEV technology capability or durability and express high levels of satisfaction in driving experience. Similarly, non-PEV consumers who participate in long term trials (6 months of greater) reveal generally positive disposition toward PEVs and “possess moderate purchase intentions” (27).

Among the earliest year-long modern-day field trials of electric vehicles involved retrofitted Mini-E. In the case of Berlin (7), a central outcome suggested that participants revealed a “high acceptance and purchase intention” and in fact, 97 percent of participants reported a desire to drive a PEV again in the future after the initial three months. A similar trial of the Mini-E, conducted in the Los Angeles and greater New York City/New Jersey metropolitan regions reported that upon trial conclusion, approximately 75 percent of participants expressed greater interest in owning an electric vehicle (28).

Shorter term trial participants, those enduring 3 months or less of PEV experience find their pre-conceptions about range and travel behaviour is adjusted and their personal opinion toward environmental sustainability becomes more attune. After three months with an EV in the UK, consumers expressed a preference for the refuelling characteristics of an electric vehicle compared to a petrol vehicle (19).

Consumers who had access to an electric vehicle overnight in the UK in 2010 had mixed reviews on the technology, however their disposition to purchasing one in future was not considered (29). Sensitivities around context can affect inclination to purchase. For example, when asked if consumers would consider a PEV as their next car based on a particular maximum range, or whether it is used as a primary or secondary vehicle, responses varied (30). Perception that the PEV market is still a “work in progress” and not as evolved as traditional petrol vehicles is likely to prohibit rapid mainstreaming (5).

The experience of prospective PEV consumers (who are not trial participants) remains under-reported in the literature, aside from recent work by (26). Some data have been reported via informal industry studies, undertaken in the U.S. by not-for-profit organizations hosting ride-and-drive events. For example, data

Minimal

Somewhat

informed

Moderate

High

AdvocatePEV Owner

Long Trial Participant

Short Trial Participant

Expected PEV Customer

Average Prospective New

Car Buyer

PEV Knowledge Level Consumer Category

PEV owners

Deep understanding and

acceptance of technology; well-

educated on PEVs; often de-facto

industry liaison or product

advocate.

e.g. Overnight trial or test drive

Prospective consumers whose

characteristic match “early PEV

adopters”

Uninformed consumer who have

~15 minute test drive

3 months to 1 year trial

participants

Thoroughly knowledgeable on

product benefits and barriers; most

often harboring favorable opinion of

PEVs.

Comfortable with general overview of

technology; positive enough to trial.

Familiar with and somewhat knowledgeable of PEVs,

and generally favorable attitude.

Actively exploring “new car” for purchase.

May befamiliar with PEVs.

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collected from a ride-and-drive event held in the Gold Coast, Queensland in 2012 states that 11 of 15 participants who test drove an electric vehicle for the first time were more inclined to buy one in the future (31). Additionally, Plug-in America released a report in April 2015 detailing some findings coming from public events offering public test drives such as an improved opinion toward PEVs by 79 percent of event participants after having experienced a test drive.

EXPERIMENTAL DESIGN A stated preference (SP) experiment was designed and administered to participants before and after test drives at a single-day ride-and-drive event, coordinated by Queensland University of Technology, the Royal Automotive Club of Queensland (RACQ) and Brisbane’s Sustainability Agency, Citysmart. The event was held on a Sunday at South Bank, a high-traffic pedestrian-friendly parkland just outside the city of Brisbane. Event participants were self-selected, having heard about the event via prior media solicitations and/or through day-of signage. The intent of the SP survey was to illicit trade-offs of attributes of PEVs compared to petrol cars, and to assess the impact of the test drive itself on choice probabilities.

FIGURE 2 Flow of discrete choice set

Utilizing a purpose-built application (app) installed on a series of iPad Mini devices, participants completed a survey which achieved three tasks, similar in design to (32): [1] collected demographic data, [2] measured the respondent’s familiarity with PEVs by testing their knowledge through a quiz, and [3] administered a discrete choice survey based on hypothetical scenarios. The first discrete choice survey with 3 choice sets was administered prior to a 15-minute test drive in an all-electric vehicle, and the second choice set was administered immediately after completion of the test drive. The demographic data collection and quiz were both administered prior to the test drive. Twenty-four participants completed the survey resulting in a total 576 unique variables. Thus, the variability in individuals was obtained from the sample of 24 people, whereas the variability within individuals and across choice tasks was obtained by asking individuals to complete 6 difference choice tasks. While both variability between and within individuals is important, we recognise that the sample of individuals is smaller than we would have preferred—and hence describe the analysis as ‘exploratory’ in nature.

At the outset of the two-part discrete choice survey, respondents selected the ideal price range of their next vehicle, ranging by $10,000 increments that started at “Less than $10,000” to “More than $50,000”. Based on the range selected by the respondent, the median price (e.g. $25,000 if the $20,000-$30,000 range) was then

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used as a pivot point to determine the “price” presented in the discrete choice set, offering a range of scenarios beyond current market conditions as seen in (33). Five pivot prices were created around the selected price, with a value ranging as 50 percent, 75 percent, 90 percent, 110 percent or 125 percent of the original median price chosen. Respondents were also asked to select their ideal vehicle body shape, choosing from a range of 9 options (hatch, sedan, SUV, coupe, ute/truck, van, convertible, people mover, wagon). Figure 2 presents the design flow of this survey.

Utilizing an orthogonal design, randomly selected attributes were assigned across three additional categories: Fuel Cost per Week, Range (per ‘tank’), and Service (times per year). Figures for fuel cost per week were extracted from local electricity retailer, Origin Energy’s online electric vehicle cost calculator. Considering the standard rates of electricity in Queensland ($0.14 per kWh on an off-peak tariff) and liquid fuels ($1.53 per litre of petrol and $1.52 per litre of diesel) at the time of design, fuel costs were assessed on a weekly basis using L/100km (or Wh/100km) fuel economy statistics offered by the Australian Green Vehicle Guide and an assumed 15,000 kilometres per anum (or 288 km per week), the private vehicle operating average in Australia. Four options for fuel cost per week for each technology were created based on the baseline fuel cost per week for each technology and then three variants: 10 percent of the base cost per week, 20 percent of the base cost per week, and 150 percent of the base cost per week. Using the same vehicle technology assumptions as were used in determining “fuel cost per week” variable, the “range” variable was calculated per technology. Again, the baseline figure was presented as well as three variants: 10 percent of the base per tank, 20 percent of the base per tank, and 150 percent of the base per tank. Servicing was presented in number of services per vehicle per year, starting at one and ranging to four times per year. Base vehicles were a MY2014 Mazda 3 for both petrol and diesel technologies, a MY2012 Nissan Leaf for the all-electric and a MY2014 Toyota Prius for the hybrid.

As depicted in Figure 3, upon selection of ideal purchase price and vehicle body shape respondents were presented with a choice set offering four vehicle technologies (i.e. petrol, diesel, hybrid and electric) in their selected body, with four unique variables per fuel type representing four different attributes discussed above.

FIGURE 3: Example representation of the app-based discrete choice set presented to event participants

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Table 1 presents a summary of the measured variables. The event was not designed to target a specific sample, but rather to appeal to any to all members of the public who may or may not be prospective new car buyers or plug-in vehicle aficionados. No deference was given to a single age, education, income or gender demographic. The event was publicly advertised, but not broadly and the “test drive” aspect of the event was deliberately not the sole aim of the event (a diverse range of vehicles were displayed by a local motoring organization), in an effort to attract a diverse group of parties not a specific sample. The final sample of drivers was less than anticipated, and thus the analysis is contingent upon a relatively small sample of individuals and should be treated with caution.

TABLE 1 Summary of Measured Variables

Demographic data

Min. Mean Max.

Age 18-24 31-40 65+

Gender Male (0) or Female (1)

Average Household Income (AUD, annual) Less than $50,000 n/a $125,000 or more

Education level (highest completed) Primary School n/a Graduate School

Average kilometres driven daily none 52 250

Household Size (no. of persons) 1 2.5 5+

Familiarity Test (no. correct of 3) 0 1.3 3

Discrete Choice Set

Vehicle Technology Petrol, Diesel, Hybrid, Electric

Vehicle Type hatch, sedan, SUV, coupe, ute/truck, van, convertible, people

mover, wagon

Purchase Amount (of vehicle) $2,500 $25,694 $68,750

Fuel Cost (total per week) $4.77 $30.00 $104.31

Range (kilometres, per full 'tank') 87.5 859 2308

Servicing (times per year) 1 2 4

RESULTS Respondents participated in an SP experiment, providing stated preferences for vehicles among petrol, hybrid, electric, and diesel options. Discrete choice models were fit to the data, including multinomial (MNL) and mixed logit models (34). A variety of random parameters (mixed logit) specifications were tested, with no evidence favouring a random parameters specification. As a result an MNL specification was chosen to best explain and capture the choice behaviour resulting from the SP experiment. The model estimates are corrected for repeated observations across respondents both before and after the test drive. Table 2 shows the MNL estimation results, where petrol vehicles represent the base case for making relative utility and preference comparisons. This model was obtained after extensively testing model specifications and variable effects.

TABLE Table 3 shows the estimated elasticities arising from the MNL model. An artefact of the independence of irrelative alternatives (IIA) property in assigning equal probabilities across remaining alternatives is evident in the table.

The model shown in Table 2 depicts the best fitting model of all models tested, including random parameters specifications which failed to yield any significant random parameters. The model shows statistically significant effects (at 10% level of significance chosen because of the relatively small sample of respondents at the test drive event) with one exception; the TestDrive effect in the EV indirect utility function (from here on shortened to utility function) is at best marginally significant.

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TABLE 2: Multinomial Logit Estimation Results for Vehicle Choice Stated Preference Survey on Test

Drive Event

MNL: Choices = Electric Vehicle (EV), Hybrid, Diesel, and Petrol Fuelled Vehicles N = 143 Log-Likelihood Function at convergence-138.30451 Log-Likelihood Function constants only -165.4796 Likelihood ratio chi-square 54.35023, 8 degrees of freedom, p < 0.0001 Akaike’s Information Criterion 298.6 R-Square 16.42% Adjusted R-Square 14.22%

Choice Coefficient

Estimate

Standard

Error

Z value P value

Utility Function for Hybrid Vehicles

Alternative Specific Constant 1.59 0.49 3.24 0.0012

Gender 1.93 0.79 2.45 0.0143

Average kilometres driven daily -0.007 0.004 -1.75 0.0796

Utility Function for Electric Vehicles

Alternative Specific Constant 2.09 0.95 2.20 0.0276

Range 0.005 0.002 2.75 0.0060

Gender 1.93 0.79 2.45 0.0143

Purchase Price -0.03 0.016 -1.91 0.0567

Average kilometres driven daily -0.015 0.005 -3.24 0.0012

Score on familiarity test -0.873 0.334 -2.61 0.0090

Test drive indicator 0.430 0.388 1.11 0.2676

Household size (persons) 0.380 0.187 2.03 0.0421

Utility Function for Diesel Vehicles

Alternative Specific Constant 0.588 0.394 1.49 0.1361

Testing the effect of TestDrive on choice was a primary motivation of this study, hence it is included in

the model. It had a p-value of 26%, Z value of 1.11, and thus is marginally significant at best. In a larger sample this effect may become significant, but replication with a larger sample is needed to further test the effect of the test drive. If significant, the test drive increases the probability of choosing an electric vehicle by about 10% on average, while reducing the probabilities of choosing the other fuel types by 11% each. This effect needs additional testing and was not statistically significant in this study, although the sample size was quite limited. The 11% reduction in purchase probabilities of the remaining fuel types is an artefact of the MNL model—whereas a random parameters specification would allow for precise estimates of decreased choice probabilities for Diesel, Hybrids, and Petrol vehicles respectively. Again, a larger sample may also support a random parameters specification.

The effect of Gender was significant in both the Hybrid and EV utility functions as a generic effect for these two fuel types, with females more likely to choose a Hybrid or Electric vehicle compared to males. Females were 28% more likely to choose an EV relative to petrol, whereas females were 39% more likely to

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purchase a Hybrid vehicle relative to petrol. The effect of average kilometres driven daily had a negative impact on the probability of choosing EV

and Hybrid vehicles. For a 1% increase in average kilometres driven daily, respondents were 0.54% less likely to choose an EV and 0.24% less likely to choose a Hybrid. In other words kilometres driven has twice the impact on the disutility on EV purchase compared to Hybrid purchase. This result is expected for EVs due to range anxiety issues, but perhaps not for Hybrid vehicles, where significant fuel cost savings could accrue for vehicles driven more. It is possible that this effect is masking an underlying unobserved effect of say vehicle size or ‘fit for purpose’ issues that the Hybrid vehicle might suggest to respondents.

Unsurprisingly and consistent with other studies the purchase price of EVs, which is considerably higher than petrol cars of similar size and functionality in Australia, had negative effect on the probability of choosing an EV. Respondents were asked about purchase price, not about ongoing operational cost or total cost of ownership, where the EV tends to out-perform the petrol option (35).

An increase in household size resulted in a 46% increase in likelihood of EV purchase. While this effect is also not well-understood contextually, it could be attributed to promise offered by EV to reduce operational costs due to lower cost of electricity per kilometre relative to petrol. This effect could also potentially be explained by the age of the respondent, with middle-aged respondents more likely to families and thus have larger households.

TABLE 3: MNL Estimated Elasticities of Impact of 1% Change in X on Choice Probabilities

Effect Petrol Electric

Vehicle

Diesel Hybrid

Gender on EV choice -0.3666 0.2818 -0.3666 -0.3666

Gender on Hybrid choice -0.2547 -0.2547 -0.2547 0.3936

Avg Daily Kilometres Driven on EV choice 0.2442 -0.5424 0.2442 0.2442

Avg. Daily Kilometres Driven on Hybrid choice

0.1133 0.1133 0.1133 -0.2478

EV Purchase Price on EV choice 0.3757 -0.4444 0.3757 0.3757

Familiarity Score on EV choice 0.5065 -0.6530 0.5065 0.5065

Test Drive on EV choice -0.1143 0.0993 -0.1143 -0.1143

Household Size on EV choice -0.5099 0.4674 -0.5099 -0.5099

Finally, the Familiarity Score yielded an unexpected result. As part of the survey, respondents were asked three questions of varied difficulty to assess their familiarity with PEV technology. These questions were selected at random from a list of 15 and reflected common attributes of PEVs. Results indicate that with every 1% increase in familiarity with PEVs (or the better respondents performed on the Familiarity Test), they were 0.65% less likely to consider one for their next purchase. This finding is counter-intuitive to our study, which at a qualitative level reveals that consumers, with greater exposure to PEVs, are generally more inclined to purchase one. DISCUSSION AND CONCLUSION

An exploratory study based on 24 individuals from Australian suggests that a short-term test drive in an electric vehicle affects a consumer’s attitude toward PEVs. Although the measured impact of a test drive in this model was not statistically significant, with an increase in sample size the model predicts that consumers are more likely to purchase EVs after a drive. Future work in this area should consider larger sample sizes and perhaps greater population diversity to verify whether the test drive indeed has the effect inferred from this study.

The exploratory model highlights some findings that require further investigation. Firstly, given that women are slightly more inclined to consider a PEV after a test drive, irrespective of their familiarity with the technology prior to the test drive, a larger sample of women should be studied, or perhaps a sample should be stratified by gender. Respondents with larger households were more likely to be future PEV customers in our study. Further data collection and analysis is required to study this outcome, particularly if it relates to

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household financial pressures and the opportunity for PEVs to reduce those costs over time. Range remains a perceived limitation of the PEVs relative to other vehicle technologies, but qualitative studies show that by increasing a consumer’s understanding of their actual driving behaviour as well as the continued expansion of public charging infrastructure can mitigate this effect. It may be interesting to test whether respondents’ perception of the effect of range on the attractiveness of EVs changes after a test drive and education by a qualified sales person—a model specification that could be tested on a larger sample.

In-depth understanding of how a traditional vehicle test-drive (such as those experience in a dealership or showroom) impacts a consumer’s disposition toward PEV technology. Compounding that experience is the impact it has on the consumer to seek out additional information or experience relating to PEV technology to determine if he or she should evaluate it further, or discard it from conservation and revert to a conventional vehicle option. Data presented in this model, although limited in representativeness and marginally significant, signals a potential shift in consumer disposition toward PEVs after a test drive. Even if small, that measurable shift in disposition highlights the importance of first-hand exposure to PEVs as part of a consumers purchase experience when considering PEVs for purchase. LIMITATIONS

In Australia, the plug-in electric vehicle market is relatively small, with 9 PEVs sold per every 100,000 Australian (compared to 99 PEVs sold for every 100,000 American). Additionally, vehicle availability is limited to large cities, making consumer access to PEV for test drive significantly lower than their access to conventional vehicle technologies. The result of this phenomenon is two-fold: PEVs are harder to come by for the sake of creating opportunities for test drive and consumers who test drive them tend to seek them out, resulting in a sample that is typically limited in numbers and self-selected. The sample size available for this study is perhaps the biggest limitation, rendering the results exploratory. Further work is needed to replicate these results on a larger sample and with perhaps less self-selection effects, perhaps by offering more frequent events targeting the general public. ACKNOWLEDGEMENTS The authors are grateful to the contributions of many for the support of this work including the AutoCRC for its thematic interests in Consumer Behaviour and its financials support of this research; the Royal Automotive Club of Queensland (RACQ) and Citysmart, for their in-kind and financial support of Future Drive; and to the Science and Engineering Faculty of QUT, including Associate Professor Dian Tjondronegoro and the Mobile Innovation Lab for their development of the Future Drive app.

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