Environmental History

Exploring Drivers of Innovative Technology Adoption Intention: The Case of Plug-In Vehicles

Saba Siddiki School of Public and Environmental Affairs, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana

Jerome Dumortier School of Public and Environmental Affairs, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana

Cali Curley School of Public and Environmental Affairs, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana

John D. Graham School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana

Sanya Carley School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana

Rachel M. Krause School of Public Affairs & Administration, University of Kansas, Lawrence, Kansas

Abstract

How individuals respond to innovative technologies depends on how motivated they are by an array of internal and external factors and the informational and cost barriers they face. To better understand technology adoption decision making we (i) assess changes in intent to purchase plug-in vehicles in response to reductions in their price and (ii) identify motivators that incline new car buyers toward plug- ins under status quo and reduced vehicle cost scenarios. We find that individuals already inclined toward alternative vehicles have a higher interest in plug-ins under a reduced-cost scenario than individuals who favor conventional vehicles. We also find that individuals who shift their vehicle preference from conventional gasoline to plug-in vehicles are motivated by material factors and fears relating to the innovative technology, whereas those shifting preferences between less to more innovative technologies are likely to be motivated by a mix of material and nonmaterial factors.

KEY WORDS: alternatively fueled vehicles, stated preference, survey, logistic regression, innovation, technology adoption, plug-in vehicles, hybrid vehicles

Introduction

Whether and how individuals respond to innovative technologies depends on how motivated they are by an array of internal and external factors as well as the

informational and cost barriers they face. Governments employ various strategies

to reduce cost barriers for technologies where widespread adoption of such aligns

with broader policy goals. One of the most commonly applied strategies to facilitate

technology development is policy-directed investments that subsidize the cost of

Review of Policy Research, Volume 32, Number 6 (2015) 10.1111/ropr.12147 VC 2015 Policy Studies Organization. All rights reserved.

649

bs_bs_banner

research and development. When applied to appropriate supply (e.g., “science

push”) and demand side (e.g., “demand pull”) conditions, it is expected that subsi-

dies can facilitate a reduction in the costs of innovative technologies making their

widespread adoption more feasible for consumers. This policy strategy to encour-

age technology uptake is grounded in a particular decision-making logic consistent

with the rational choice perspective; namely, that consumer interest in products

increases as their price goes down.

While this logic is generally validated by rational choice theorists, its explanatory

scope is too narrow to account for the complex motivations that underlie individual

decision making about innovative technology adoption under various cost condi-

tions. Decision-making theories, often housed in fields like public administration

and related disciplines, which explicitly consider the various intrinsic and extrinsic

motivations that inform human behavior alongside price, are better suited for clari-

fying this relationship (Ryan & Deci, 2006). In our discussion of these theories, we

acknowledge as a starting premise that subsidies are alone insufficient to alter tech-

nology price, but may enable price reduction when both suppliers and consumers

have adequate interest and resources to facilitate development and/or adoption of

a policy-supported technology. Many decision-making theories do not clarify this

relationship but offer still valuable expectations about motivations in rule-based

contexts.

Some of the decision-making theories we consider in this article focus on how pol-

icy strategies alter decision-making conditions and outcomes. Wilson and Dowlata-

badi (2007), for example, suggest that policy strategies, such as subsidies and

incentives, can primarily serve to alter external decision-making parameters which

then, in turn, temper the effect of psychological factors (e.g., fears, perceptions of

need, and so forth) that are most logically proximate to behavioral intent and action

(Wilson & Dowlatabadi, 2007). Sharp (1978) argues that decision making in policy

contexts is motivated by a suite of material (e.g., financial), solidary (e.g., identity

building), and expressive (e.g., altruistic) motivations and the relative effect of these

different motivations changes as decision-making parameters are altered. Even

more, the altering of certain parameters through policy instruments (e.g., costliness

of behavior) can enable the expression of otherwise subdued motivations.

These and related theories provide an appropriate foundation for assessing the

motivational characteristics of individuals that persuade or dissuade them from

considering innovative technologies like plug-in vehicles. In the specific context of

plug-in vehicles, previous research on the barriers to plug-in vehicle adoption con-

firms that individuals consider more than price. They also have concerns about

plug-in vehicles’ limited driving range, long recharging time, inadequate recharg-

ing infrastructure, safety, and resale potential (Carley, Krause, Lane, & Graham,

2013; Deloitte, 2010; Egbue & Long, 2012; Ernst & Young, 2010). If applied in the

right context, policy strategies may influence the salience of such fears in individu-

als’ decision-making calculi by altering the incentives of behavioral action/inaction.

Further, there may be systematic differences across subcomponents of the popula-

tion in the specific ways in which incentives influence their decision making. Thus,

it is important to consider decision-making motivations relating to innovative tech-

nology adoption in ways that account for differences among consumer

subpopulations.

650 Saba Siddiki et al.

Along these lines, this article applies policy and individual decision-making

perspectives to examine the following research questions: (1) How does con-

sumer intent to purchase plug-in vehicles change in response to price reduc-

tions? (2) What material, personal, and social factors predict changes in

consumer intent to purchase a plug-in vehicle under a reduced-cost scenario?

Overall, our objectives are to understand how consumer demand for plug-in

vehicles might change under a technological breakthrough scenario that results

in lower plug-in vehicle price as well as to gain insight about the characteristics

associated with individuals who become willing to purchase a plug-in vehicle

under a reduced-cost scenario, holding their other advantages and disadvantages

constant.

Public Investment in the Plug-In Industry

Alternatively fueled vehicles such as plug-in vehicles (plug-in hybrid and battery

electric) have attracted considerable attention from policy makers. Government

interest in alternatively fueled vehicles is based largely on the promise they hold in

addressing major policy challenges such as reducing greenhouse gas and other tail-

pipe emissions as well as curbing dependence on insecure foreign sources of

energy (Egbue & Long, 2012). These promises have helped motivate the ambitious

quest for electrification of the transport sector (Sandalow, 2009). They also form

the basis of the U.S. Department of Energy’s (U.S. DOE) near-term goal to see one

million plug-in vehicles on the road as soon as possible—an initiative now called

“EV Everywhere” (Johnson, 2013a, 2013b). Many other countries around the

world have also established national goals for the emerging plug-in vehicle industry

(Lane et al., 2013). Currently, one of the largest barriers to the widespread adop-

tion of electric vehicles is their cost (Carley et al., 2013; Healey, 2011; National

Research Council, 2014; Nixon & Saphores, 2011). Relative to their gasoline-

powered counterparts, plug-in vehicles typically have a cost premium of $10,000–

$20,000 (Dumortier et al., 2015; National Research Council, 2013; White, 2012).

The difference in cost is largely attributed to expensive battery components that

support plug-ins. To help alleviate some of the cost burden to consumers and

thereby facilitate broader adoption of plug-ins, the U.S. government has invested

heavily in policies to support the development of plug-in technology. A portion of

this investment has been specifically targeted at improving battery technologies to

reduce their overall cost.

Estimates report that the U.S. government has invested $2.4 billion in recent

years in electric battery production facilities and nearly $80 million annually for

electric battery research and development (Canis, 2013, p. 2). This investment

comes at a time when manufacturers are increasingly developing plug-in vehicle

technology voluntarily to respond to market demand (e.g., in the case of Tesla) or

as a result of regulatory mandate (e.g., manufacturers subject to California’s Zero

Emission Vehicle [ZEV] policy must produce and distribute a certain number of

ZEVs in California and other states that have adopted California’s policy). Based

on these programs and cooperative efforts in the private sector, the U.S. DOE has

established a target of reducing the cost of lithium-ion batteries used in plug-in

Exploring Drivers of Innovative Technology Adoption Intention 651

vehicles to $125/kWh by 2022, down from $1,000/kWh in 2008 and $500/kWh in

2012 (Canis, 2013, p. 14).

Coupling Policy and Individual Decision-Making Strategies

There are a wide array of policies that governments can use to stimulate the devel-

opment and market penetration of innovative technologies, although their effec-

tiveness in doing so is shaped by the market contexts in which they are applied.

Example policies include regulations that require industries to meet certain techno-

logical standards, performance benchmarks, research and development grants,

and subsidies (Jung, Krutilla, & Boyd, 1996; Weimer & Vining, 2005). Such policy

instruments vary in their coerciveness: regulations mandate certain activities while

subsidies incentivize them. Some argue that incentive-based policy instruments are

particularly useful for facilitating the growth of nascent industries, whereas more

coercive policies may be most beneficial in mature industries (Milliman & Prince,

1989). This may be particularly true in cases where nascent industries are in direct

competition with mature industries. For example, Veugelers (2012) argues that

policy subsidies in the form of research and development grants are especially ben-

eficial for clean technologies to gain a foothold in markets dominated by more pol-

luting technologies.

The logic undergirding incentive-based policies is rooted in rational choice eco-

nomics and theories of utility maximization, wherein individuals are expected to

react strongly to price signals. Put simply, if the price of a product or activity is

diminished, the pool of potential consumers of that product or performers of the

activity is expected to increase in size. While economic research proves this axiom to

be generally true, behavioral economics and social psychologists have uncovered an

array of mitigating factors that reflect the normative and otherwise subjective per-

ceptions/motivations that influence decision making in ways that are often not con-

sistent with purely rational choice models of the individual (Tversky & Kahnemann,

1981; Wilson & Dowlatabadi, 2007).

One way to frame an analysis of such mitigating factors is to first assess decision

making in relation to behavioral constraints. Two key barriers to making decisions

are information and money. Decision-making theory suggests ways that the pres-

ence or absence of these barriers tempers the impact of psychological factors that

may shape the likelihood of behavior—in this case, adoption for individuals

(Wilson & Dowlatabadi, 2007). Our research approach builds off of Sharp’s (1978)

research. Sharp (1978) argues that individual decision making is informed by

expressive (e.g., altruistic), solidary (e.g., identity building), and material (e.g.,

financial) motivations. Expressive motivations draw upon an underlying motive of

altruistic nature. In the case of the plug-in vehicle this motivation guides the subset

of the population who purchase a new car for the environmental benefits. Solidary

motivations relate to feelings of identity building resulting from the engagement in

a particular behavior. In the case of plug-ins, the individual is motivated to adopt

because of the potential to build an identity and signal that they are part of a small

group of the population that belongs to the alternative vehicle club. This individual

is primarily motivated to adopt a plug-in because of the potential to build a group

652 Saba Siddiki et al.

identity. Included in this motivational concept is a peer pressure dimension. Indi-

viduals crave acceptance by a group of likeminded peers and therefore plug-in

ownership sends a very clear signal about their belonging to a social identity. The

third motivation is material, or financial.

Sharp’s perspective on individual decision making is not in contrast with that of

the diffusion literature. Diffusion scholars point to general innovation adoption tra-

jectories that model patterns of new product adoption over time (Rogers, 1962).

From a temporal perspective, according to innovation and diffusion models, cumu-

lative adoption of an innovative product typically follows an S-shaped trajectory:

adoption is low immediately following the introduction of the product, starts to

pick up as early adopters take interest in it, rises quickly as the early and late major-

ities adopt the product, and then begins to plateau.

Diffusion scholars have established that adopters at different points on the trajec-

tory are motivated by different factors. For example, early adopters tend to be moti-

vated by personal beliefs and intrigue in innovation; this is consistent with the idea of

adopting given expressive (altruistic) or solidary (identity building) motivations.

Later adopters are typically motivated by material or tangible (e.g., financial) benefits

(Janssen & Jager, 2009). More generally, diffusion scholars argue that the decision

process associated with innovation adoption is influenced by a set of prior conditions

(e.g., social norms), characteristics of the adopter (e.g., personal demographics, sub-

jective perceptions of risk, or uncertainty relating to particular innovation), charac-

teristics of the innovation itself, and feedback regarding the innovation through

social network and other communication channels (Wilson & Dowlatabadi, 2007, p.

177). The prior conditions exist before the individual makes a decision about adop-

tion and include the primary barriers of information and budget constraints that

prohibit individuals from opting into the innovation adoption.

In this article, we seek to expand an understanding of how the reduction in the cost

of an innovative technology can facilitate the relative effect of material and nonmate-

rial motivations. We expect that when individuals are priced out of markets their reve-

lation of other motivations is limited. However, when cost constraints are removed,

individuals reveal other types of material and nonmaterial motivations. In presenting

this proposition, we suggest that individual motivations (material, solidary, and

expressive) exist in individuals regardless of where they adopt along the S-shaped tra-

jectory and that these individuals have dormant motivations for innovation adoption

until they are provided information or are priced into the market. An individual who

exercises their option to join a market for innovation adoption after being priced into

the market is not solely acting on a material (monetary) incentive, but rather overcom-

ing the barrier to adoption allows for those incentives to be realized.

Methods

Data Collection

Data for this research were collected during the Fall of 2013 via the Qualtrics

online survey platform from a sample of 3,199 respondents across 32 metropolitan

areas in the United States.1 The chosen metropolitan areas represent those

Exploring Drivers of Innovative Technology Adoption Intention 653

identified by major plug-in vehicle manufacturers as “roll-out” cities, or those

which manufacturers have targeted for initial deployment of their plug-in offer-

ings. The survey research community has found that online surveys, when

designed and implemented properly, are comparable or superior to random digit

dial telephone methods on the key criteria of interest (e.g., measurement error and

the time and cost of data acquisition) (Chang & Krosnick, 2009; Yeager et al.,

2010).

A set of screening criteria were used to ensure that the survey was completed by

a relevant sample of potential consumers. Namely, to participate in the study,

potential respondents had to meet the following four criteria: (i) be 18 years of age

or older, (ii) have a valid driver’s license, (iii) intend to purchase or lease a new

vehicle within the next two years, and (iv) intend to purchase either a small/mid-

sized vehicle (e.g., Honda Civic, Chevrolet Malibu) or SUV/cross-over vehicle (e.g.,

Ford Escape, Toyota RAV4). Questions corresponding to each of these four screen-

ing criteria were presented at the outset of the survey. The third criterion was

important because it is adults who purchase a new vehicle in the foreseeable future

who will determine the near-term fate of plug-in vehicle technologies. The last

screening criterion filtered out individuals interested in purchasing or leasing a

large luxury sedan or large SUV, van, or pick-up, for which there are limited to no

plug-in vehicle offerings on the market (Guilford, 2013). In the question associated

with the last screening criterion, respondents were asked which type of vehicle

(small/midsize or SUV/cross-over) is closest to what they would actually consider

purchasing or leasing. The stated choice of vehicle was used to tailor the set of

questions that each respondent was presented in order to better reflect what they

would actually purchase and, thus, make the survey more realistic.

The survey asked respondents a variety of questions relating to their familiarity

with and perceptions of plug-in vehicles, travel behavior, vehicle ownership, and

demographics. It also provided information about conventional gasoline, conven-

tional hybrid, plug-in hybrid, and battery electric vehicles, to ensure that all partici-

pants had the same basic information about these vehicle technologies. Surveys

administered to participants were identical in all respects except that the information

they received regarding vehicles differed based on (i) respondents’ self-selection into

a preferred vehicle size group in response to the fourth screening criteria described

above (i.e., SUV/cross-over group or small/mid-sized sedan group) and (ii) respond-

ents’ subsequent random assignment by Qualtrics into one of three subgroups

(within the broader vehicle size group respondents expressed interest in) according

to which they received different types of vehicle cost information. Differences in the

cost information provided to respondents in each of these subgroups are described

in more detail below. We provide a graphical summary of our survey research design

in Figure 1.

We used the U.S. Environmental Protection Agency’s (EPA) fuel economy labels

as a design template to present different types of cost information to individuals in

the various subgroups. One group received all of the information normally

included on the EPA’s fuel economy label relevant to a particular vehicle type (gaso-

line, hybrid, plug-in, hybrid, electric), with the exception of 5-year fuel expendi-

tures and total cost of ownership. The second group received the 5-year fuel

expenditure information in addition to all of the information shown to the first

654 Saba Siddiki et al.

group. The third group had all of the same information as provided on group 2

labels, in addition to total cost of ownership. We used the fuel economy labels as

the design template upon which to present cost information, as these labels are

what potential car buyers actually see in dealer showrooms. Examples of the labels

shown to participants are included in Figure 2 (the complete set of labels used in

the survey can be found as an Appendix).

To ensure that respondents could correctly interpret the information provided

on the labels, we included a key with descriptions of all of the acronyms and techni-

cal language displayed thereon (e.g., MPG, miles per gallon; CO2, carbon dioxide;

GHG, greenhouse gas; and so forth). The order of the presentation of labels was

randomized to avoid any anchoring effects. During the development phase of the

survey instrument, cognitive interviews were conducted with a small sample of

individuals to identify the accessibility and clarity of information and concepts pro-

vided on the labels.

Each survey respondent received two sets of labels containing vehicle cost infor-

mation, each set corresponding to one of two price scenarios. The cost information

provided on the first set of labels assumed current vehicle technology costs—

scenario 1. The cost information on the second set of labels assumed a 50%

reduction in battery technology costs for the plug-in hybrid and the battery electric

vehicle (not the gasoline or conventional hybrid), thus impacting overall vehicle

purchasing and operating costs—scenario 2.

Respondents were first presented the scenario 1 labels, and then immediately

asked the following question: “Considering what you know about cars and the

information provided on the labels above, will your next vehicle purchase or lease

be a conventional gasoline, conventional hybrid, plug-in hybrid, or plug-in electric

vehicle?” Respondents were then presented with the second set of labels with the

following preamble: “Assume technological progress leads to a 50% reduction in

the cost associated with batteries. This would lead to a reduction in the price of

plug-in hybrid and plug-in electric vehicles. The labels below reflect the vehicle

price changes resulting from this technological breakthrough.” They were then

immediately asked the same question as was posed following the presentation of

the first set of labels. The presentation of the labels in this manner is consistent

Pool of respondents in 32 U.S. metropolitan areas

(n=3,199)

SUV/Cross-over Group

Self-selection based on screening question

Small/Mid-Sized Group

Sub- Group 1

Sub- Group 2

Sub- Group 3

Sub- Group 2

Sub- Group 1

Sub- Group 3

Random assignment Random assignment

Figure 1. Graphical Summary of Research Design

Exploring Drivers of Innovative Technology Adoption Intention 655

with stated preference survey methodologies, whereby researchers are interested

in ascertaining the sensitivity of participant responses to variations in the informa-

tion relating to a particular choice set (Newell & Siikamaki, 2013).

Our two-price willingness-to-pay format is similar to the double-bounded dichoto-

mous choice format in contingent valuation (Boardman, Greenberg, Vining, &

Weimer, 2006; Hanemann, Loomis, & Kanninen, 1991), except that we focus on

respondents who reject a good at a high price and explore whether they can be

enticed to purchase at a lower price (i.e., we do not raise the price for respondents

who purchase at the initially high price). There is a chance that offering the same

respondent two prices will introduce bias, as the respondent may think that the

lower price is an indication that price is negotiable and therefore refuse (dishonestly)

at the second price, seeking to obtain an even lower price. We counteract this possi-

bility by framing the second price as the result of an unexpected technological break-

through. Our presurvey cognitive interviews suggested that respondents took this

context at face value. We considered the possibility of executing a between-subject

design (with price varied between subjects), but feared that respondents would per-

ceive the breakthrough prices as implausibly low, without first exposing them to cur-

rent prices. If there is downward bias at the second price, then our figures for the

take-up of plug-in vehicles at breakthrough prices are underestimated (Watson &

Ryan, 2004).

In Table 1, we provide brief descriptions of the parameters used in calculating

the cost information that was provided on the labels shown to the different sub-

groups. For costs associated with vehicle components, we relied on an incremental

cost calculation approach that produced aggregated vehicle costs reflecting the pri-

ces of vehicle technology (e.g., battery pack, motor, electrical equipment, and so

forth). This helped us control for manufacturer-imposed costs that do not necessarily

Figure 2. Survey Labels with Vehicle Cost Information

656 Saba Siddiki et al.

reflect the true vehicle technology costs and may be more volatile over time (Al-Alawi

& Bradley, 2013). For example, current prices for plug-in vehicles reflect near-term

marketing considerations and may not be sustainable in the long run.

Data Analysis

Our analysis focused specifically on ascertaining (i) differences in consumers’ vehi-

cle preferences between the first and second price scenarios as portrayed on the

two sets of labels presented to survey respondents (i.e., the type of vehicle they

intend to purchase based on price information presented on labels embedded in

the survey) and (ii) characteristics of individuals who switched from indicating an

intent to purchase a conventional gasoline or conventional hybrid vehicle under

the first price scenario and a plug-in vehicle (plug-in hybrid or plug-in electric)

under the second price scenario. Ultimately, the objectives of this two-part analysis

were to see how consumer demand for plug-in vehicles changes under a reduced-

price scenario and the types of factors that motivate individuals who change their

vehicle choice after a breakthrough in battery technology.

To identify characteristics of individuals that predict movement toward plug-in

vehicles under the breakthrough scenario, we performed logistic regression analy-

ses. The dependent variable was binary, wherein respondents were labeled as those

who did not switch their vehicle preferences between the two vehicle price scenar-

ios (0) and those who did switch their preferences (1). A set of variables were then

identified as possible predictors of belonging in the “switcher” group as consistent

with relevant scholarship. Two separate models were estimated: one model for

respondents who indicated a preference for gasoline vehicles under the first price

Table 1. Cost Calculation Parameters (Dumortier et al., 2015)

Cost Metric Calculation Parameters

Purchase Price Vehicle purchase price from Al-Alawi and Bradley (2013)

adjusted to 2013 $U.S. dollars based on Consumer Price Index (CPI). We chose a PHEV that has a 40-mile all-electric

range (PHEV40) and a BEV that has a 100-mile all-electric

range (BEV100).

Fuel Expenditure and Savings Assume that vehicles travel 15,000 miles/year over 10 years. Gasoline and electricity prices at beginning of year are

assumed to be $3.50/gallon and $0.12/kilowatt hour, respec-

tively, with an average annual increase of 0.8% in real gaso-

line prices and 0.3% in real electricity prices (EIA, 2013). Assume 55% city driving and 45% highway driving.

Annual fuel expenditures based on a multi-day utility factor

that calculates a weighted average of the percentage of miles

that a vehicle is expected to be operated in charge-depleting mode over a specified time period.

Total Monthly Cost of Ownership Dollar value for purchase price depreciated over 10 years (loga-

rithmic depreciation of the car with a residual value of 15%

over 10 years [Huang et al., 2011]), along with fuel, financ- ing, maintenance, insurance, registration costs over same

period. We assume a 6% sales tax, $2,000 cost for charging

station of PHEV and BEVs, and $7,500 point-of-sale tax

credit.* For financing, we assume a down payment of 10%, a loan period of 60 months, and an interest rate of 5%.

*Not current practice, but the point-of-sale tax credit has been proposed by the Obama administration. Mainte-

nance and insurance costs based on values used by Al-Alawi and Bradley (2013).

Exploring Drivers of Innovative Technology Adoption Intention 657

scenario and one model for respondents who indicated a preference for conven-

tional hybrid vehicles under the first price scenario. Separating the models in this

manner allowed us to pick up on important variation among characteristics of the

switchers who express an initial inclination toward conventionally or alternatively

fueled vehicles.

The selection of predictor variables was conducted in accordance with Sharp’s

(1978) decision-making theory and the extant literature on diffusion of innova-

tions, highlighting the kinds of variables that are likely to be most salient in affect-

ing consumers’ decisions about the adoption of new high-tech products. Together,

these variables generally relate to material, expressive, and solidary motivations;

characteristics of the adopter (e.g., personal demographics, subjective perceptions

of risk or uncertainty relating to plug-in vehicles); characteristics of the plug-in

vehicles (e.g., vehicle features); and feedback regarding plug-ins through social

network and other communication channels (Wilson & Dowlatabadi, 2007, p. 177).

We also drew from previous consumer-oriented studies of plug-in vehicles (Carley

et al., 2013; Caulfield, Farrell, & McMahon, 2010; Krause, Carley, Lane, &

Graham, 2013; Nixon & Saphores, 2011; Zhang, Gensler, & Garcia, 2011). The

variables selected for our analysis are grouped into the following categories: perso-

nal demographics and vehicle characteristics, perceptions of plug-in vehicle bene-

fits, perceptions of plug-in vehicle concerns, plug-in vehicle component exposure,

and preferred vehicle features. A description of the specific variables included in

the analysis that relate to each of these categories is provided in Table 2. In addition

to theoretically and practically relevant variables, we also controlled for whether

respondents expressed interest in an SUV for their next lease or purchase and

what type of vehicle cost information they received (i.e., their subgroup

assignment).

Results

Results from our descriptive and logistic regression analyses of survey responses

are presented below as they relate to our two research questions.

How Does Consumer Intent to Purchase Plug-In Vehicles Change in Response to

Reductions in Their Price?

Differences in vehicle rankings between the two price scenarios were analyzed by

counting the number of people that indicated (i) an intent to purchase a conven-

tional gasoline vehicle under both price scenarios, (ii) an intent to purchase a con-

ventional gasoline vehicle under the first price scenario and a plug-in vehicle

under the second, (iii) an intent to purchase a conventional hybrid vehicle under

both scenarios, and (iv) an intent to purchase a conventional hybrid vehicle under

the first price scenario and a plug-in vehicle under the second.

Table 3 provides a breakdown of the numbers of individuals belonging to each

of the four categories of interest. In the “gasoline group” (n 5 1042), we see that

865 or 83% of respondents indicated an intent to purchase a conventional gasoline

vehicle under both price scenarios and 177 or 17% of respondents indicated an

658 Saba Siddiki et al.

intent to purchase a conventional gasoline vehicle under the first price scenario

and a plug-in vehicle under the breakthrough battery scenario. In the “hybrid

group” (n 5 895), 540 or 60% of respondents indicated an intent to purchase a con- ventional hybrid under both price scenarios and 355 or 40% indicated an intent to

Table 2. Variable Definitions

Variable Name Variable Description Variable Scale

Respondent Classification SUV Respondent plans to purchase or lease a SUV

within the next 2 years 0 5 small or midsize car and

1 5 SUV

Information group Respondent membership in vehicle cost informa-

tion group 1, 2, or 3

1 5 group1, 2 5 group 2,

3 5 group 3

Personal Demographics and Vehicle Characteristics

Income Annual household income 1 5 under $15,000 to

10 5 above $250,000

Number of cars Number of cars household owns or leases 1 5 0 to 11 5 10 or more Monthly gas cost Household gasoline expenditure in a typical

month

1 5 $0–74 to 7 5 above $600

Long trips Number of long trips (250–499 miles) respond-

ent has taken in the last year

1 5 0 trips to 3 5 more than

five trips Perception of Plug-In

Benefits Peer-motivated environmental

benefit

Agreement with: “Owning a plug-in will demon-

strate to others that I care about the environment.”

1 5 strongly disagree and

5 5 strongly agree

Self-motivated environmental

benefit

Agreement with statement: “Changing from a

gasoline-powered vehicle to a plug-in vehicle

will lessen my impact on the environment.”

1 5 strongly disagree and

5 5 strongly agree

Innovation benefit Agreement with statement: “Plug-in vehicles are

at the cutting edge of transportation

technology.”

1 5 strongly disagree and

5 5 strongly agree

Maintenance cost benefit Agreement with statement: “A plug-in vehicle will save me money on maintenance costs.”

1 5 strongly disagree and 5 5 strongly agree

Fuel cost benefit Agreement with statement: “A plug-in will save

me fuel expenditures because electricity is

cheaper than gasoline.”

1 5 strongly disagree and

5 5 strongly agree

Perception of Plug-In Concerns Range concern Agreement with statement: “I am concerned

about the all-electric driving range in plug-in

vehicles.”

1 5 strongly disagree and 5 5 strongly agree

Resale concern Agreement with statement: “I am concerned

about the resale value of plug-in vehicles.”

1 5 strongly disagree and

5 5 strongly agree

Peer perceptions concern Agreement with statement: “I am concerned

with others in my community will think of me if I drive a plug-in vehicle.”

1 5 strongly disagree and

5 5 strongly agree

Safety concern Agreement with statement: “I am concerned

about the safety of plug-in vehicles.”

1 5 strongly disagree and

5 5 strongly agree

Plug-In Component Exposure Level 2 Charger Respondent is aware of Level 2 charging stations

in his/her community

1 5 yes and 2 5 no

Preferred Vehicle Features MPG feature Importance of vehicle feature to respondent:

Fuel economy (MPG) 1 5 very unimportant and

5 5 very important

Cargo space feature Importance of vehicle feature to respondent:

Cargo space

1 5 very unimportant and

5 5 very important

Safety rating feature Importance of vehicle feature to respondent: Safety rating

1 5 very unimportant and 5 5 very important

Manufacturer reputation feature Importance of vehicle feature to respondent:

Reputation of manufacturer

1 5 very unimportant and

5 5 very important

Dealer services feature Importance of vehicle feature to respondent: Services offered by nearby dealer (e.g., main-

tenance, repair)

1 5 very unimportant and 5 5 very important

Exploring Drivers of Innovative Technology Adoption Intention 659

purchase a conventional hybrid under the first price scenario and a plug-in under

the second.

The number of people who switched to a plug-in under the reduced-price sce-

nario is notably higher in the hybrid group. From the perspective of gasoline sav-

ings and environmental improvement, this finding is concerning because hybrid

vehicles have already accomplished a high degree of fuel savings (e.g., 50 MPG in

the case of the Toyota Prius) compared to a comparable gasoline vehicle (ranging

from 25 to 35 MPG cars of similar size to the Prius). Indeed, what has been termed

the “MPG illusion” may be inducing some consumers to be more impressed with a

movement from (say) 50 to 75 MPG than a movement from 25 to 50 MPG, even

though the move from 25 to 50 MPG saves much more gasoline (Larrick & Soll,

2008). From a decision-making standpoint, the greater rate of movement from the

hybrid group is not as surprising. Plug-in vehicles are more technologically proxi-

mate to conventional hybrids than they are to conventional gasoline vehicles and

thus movement toward plug-ins by respondents in the hybrid group may be seen

as a less drastic transition. It is also likely that people inclined toward hybrids are

already predisposed (e.g., by worldview or preference for innovative technologies)

or mentally primed to consider alternative vehicle technologies and the removal of

cost barriers facilitated decision making in favor of plug-ins (Carley et al., 2013;

Deloitte, 2012).

What Predicts Intent to Purchase a Plug-In Vehicle Under a Reduced-Cost Scenario?

While it is useful to describe how people switched or did not switch their vehicle

preferences between the two price scenarios, ultimately the more important ques-

tion for policy and marketing is who is more or less likely to switch. We captured

this information by conducting logistic regression analyses using our survey data.

Table 4 provides a summary of descriptive statistics relating to each of the predictor

variables included in the logistic regression models.

Tables 5 and 6 report the results of the logistic regression analyses. Because the

coefficients produced in logit models are difficult to interpret, odds ratios corre-

sponding to individual variables were calculated and translated into percentage

changes in odds, whereby a one-unit increase in the predictor variable is associated

with a certain percentage increase/decrease of belonging in the “switchers” group.

The model summaries in these tables indicate that both the gasoline and hybrid

models are significant at the 0.01 level. Examining the pattern of significant predic-

tor variables across the two models, the following relationships are evident. Indi-

viduals who intend to purchase a conventional gasoline vehicle under current

Table 3. Descriptive Summary: Number of Respondents Keeping or Changing Original Vehicle Choice

Gasoline to Gasoline Vehicle Gasoline to Plug-In Vehicle

Gasoline Group (n 5 1,042) 865 (83%) 177 (17%)

Hybrid to Hybrid Vehicle Hybrid to Plug-In Vehicle

Hybrid Group (n 5 895)

540 (60%) 355 (40%)

660 Saba Siddiki et al.

Table 4. Summary Statistics for Gasoline and Hybrid Group

Obs. Mean Std. Dev. Min. Max.

Variable Gas Hybrid Gas Hybrid Gas Hybrid Gas Hybrid Gas Hybrid

SUV 1047 901 0.44 0.50 0.49 0.50 0 0 1 1

Information group 1047 901 1.97 2.01 0.81 0.82 1 1 3 3

Income 1039 896 5.10 5.13 1.9 1.79 1 1 10 10

Number of cars 1005 843 2.87 2.94 0.98 1.06 1 1 10 11 Monthly gas cost 1046 901 2.64 2.74 1.29 1.21 1 1 7 7

Long trips 1024 884 1.63 1.71 0.58 0.59 1 1 3 3

Peer-motivated environmental

benefit

1046 901 3.50 3.92 1.05 0.87 1 1 5 5

Self-motivated environmental

benefit

1047 899 3.53 4.02 1.14 0.97 1 1 5 5

Innovation benefit 1046 899 3.41 3.92 1.07 0.88 1 1 5 5

Maintenance cost benefit 1043 900 2.92 3.35 1.02 0.97 1 1 5 5 Fuel cost benefit 1046 901 3.44 3.85 1.09 0.94 1 1 5 5

Range concern 1047 901 4.22 4.17 0.89 0.86 1 1 5 5

Resale concern 1043 900 3.98 3.75 0.95 0.97 1 1 5 5

Peer perceptions concern 1043 899 2.15 2.08 1.16 1.13 1 1 5 5 Safety concern 1045 896 3.64 3.42 1.07 1.09 1 1 5 5

Level 2 charger 1035 889 1.80 1.72 0.40 0.45 1 1 2 2

MPG feature 1046 899 4.40 4.61 0.73 0.61 1 2 5 5

Cargo space feature 1046 899 3.93 4.00 0.90 0.82 1 1 5 5 Safety rating feature 1046 896 4.39 4.56 0.80 0.65 1 1 5 5

Manufacturer reputation feature 1041 899 4.43 4.45 0.77 0.70 1 1 5 5

Dealer services feature 1045 901 3.98 4.02 0.94 0.92 1 1 5 5

Table 5. Logistic Regression Results: Gas Model

Model Estimates Coefficient Std. Error Odds Ratio

Percentage Change

in Odds (%)

Model Estimates and Model Summary

SUV 0.05 0.19 1.06 6

Information group 20.01 0.11 0.99 21

Income 0.03 0.05 1.03 3 Number of cars 0.10 0.09 1.11 11

Monthly gas cost 0.20*** 0.07 1.22 22

Long trips 20.22 0.17 0.80 220

Peer-motivated environmental benefit 0.11 0.13 1.12 12 Self-motivated environmental benefit 0.13 0.12 1.14 14

Innovation benefit 0.09 0.12 1.09 9

Maintenance cost benefit 0.26** 0.12 1.30 30

Fuel cost benefit 0.22* 0.12 1.24 24 Range concern 0.15 0.12 1.16 16

Resale concern 20.19* 0.11 0.83 217

Peer perceptions concern 20.24*** 0.09 0.79 221

Safety concern 20.30*** 0.10 0.74 226 Level 2 charger 20.12 0.23 0.89 211

MPG feature 0.38** 0.16 1.46 46

Cargo space feature 20.07 0.11 0.93 27

Safety rating feature 20.08 0.15 0.92 28 Manufacturer reputation feature 20.16 0.15 0.85 215

Dealer services feature 20.10 0.11 0.91 29

Constant 23.02*** 1.12 0.05 295

Model Summary Statistic P-Value Likelihood Ratio 114.07 .000

***p < .01; **p < .05; *p < .10.

Exploring Drivers of Innovative Technology Adoption Intention 661

vehicle price assumptions and a plug-in under reduced battery cost scenario are

motivated by financial and pragmatic factors such as their monthly gas expendi-

ture, the promise of reduced maintenance and fuel costs associated with plug-in

vehicles, and vehicle miles per gallon ratings. Conversely, intent to purchase a

plug-in vehicle under a reduced-price scenario is dissuaded by agreement with

popular concerns associated with plug-ins such as those relating to their resale

value, safety, and peer perceptions. Note that safety concerns are only a significant

predictor when specifically tied to plug-in vehicles, not as a general vehicle feature

of interest to consumers.

In contrast, the results in Table 6 show a different pattern of significant predictor

variables. These results suggest that individuals who intend to purchase a conven-

tional hybrid vehicle under current vehicle price assumptions and a plug-in under

a reduced battery cost scenario are persuaded toward plug-in vehicles by a mix of

nonmaterial and material factors. Nonmaterial motivations include wanting to

demonstrate to others they care about the environment and an interest in innova-

tive technologies. More material/practical factors include their monthly gas

expenditure, the number of long trips they take, and vehicle features. Similar to

the results observed in Table 5, vehicles’ miles per gallon rating is a positive and sig-

nificant predictor of movement toward plug-ins. However, when respondents value

cargo space and manufacturer reputation as vehicle features, they are less likely to

move toward plug-in vehicles under the second price scenario. Income was also a

significant predictor in the model, but in the negative direction: the higher one’s

income, the less likely s/he is to move toward plug-in vehicles. This is unsurprising

Table 6. Logistic Regression Results: Hybrid Model

Model Estimates Coefficient Std. Error Odds Ratio Percentage Change

in Odds (%)

Model Estimates and Model Summary SUV 0.10 0.16 1.11 11

Information group 20.02 0.10 0.98 22

Income 20.09* 0.05 0.91 29

Number of cars 0.08 0.08 1.08 8 Monthly gas cost 0.14** 0.07 1.15 15

Long trips 0.22* 0.13 1.24 24

Peer-motivated environmental benefit 0.24** 0.12 1.27 27

Self-motivated environmental benefit 20.02 0.11 0.98 22 Innovation benefit 0.19* 0.11 1.21 21

Maintenance cost benefit 0.22** 0.10 1.25 25

Fuel cost benefit 20.03 0.11 0.97 23

Range concern 20.14 0.10 0.87 213 Resale concern 20.07 0.09 0.93 27

Peer perceptions concern 0.02 0.08 1.02 2

Safety concern 0.10 0.08 1.10 10

Level 2 charger 20.15 0.18 0.86 214 MPG feature 0.45*** 0.15 1.57 57

Cargo space feature 20.27*** 0.10 0.76 224

Safety rating feature 20.13 0.14 0.88 212

Manufacturer reputation feature 20.24* 0.14 0.79 221 Dealer services feature 20.12 0.10 0.89 211

Constant 21.34 1.06 0.26 274

Model Summary Statistic P-Value Likelihood Ratio Chi-Squared 68.72 .000

***p < .01; **p < .05; *p < .10.

662 Saba Siddiki et al.

in the context of the other findings that demonstrate an association between finan-

cial prudence and interest in plug-in vehicles. People with high incomes may not

worry as much about the costs associated with owning and operating vehicles and

thus are less moved by the financial benefits associated with a technological break-

through. Another notable finding from Table 6 is that individuals in the hybrid

group are not significantly motivated—toward or against—plug-ins by popular

concerns (e.g., safety issues and diminished resale value) relating to them.

In terms of other predictors, we found that the subgroups in the survey design

(interest in a SUV/cross-over vehicle as opposed to a small/mid-size vehicle, and dif-

ferent cost information label designs) were not significantly associated with move-

ment toward plug-in vehicles under the breakthrough scenario. This finding

suggests that, while consumers in both the gasoline and hybrid groups are moti-

vated by financial aspects toward plug-ins under a reduced-price scenario, there is

not a particular type of financial information that is particularly salient (i.e., short-

term fuel costs, long-term fuel costs, aggregated purchase and operating costs, or

total monthly cost of ownership) to respondents. This finding differs from our pre-

vious analysis of current prices, where we found that providing monthly cost of

ownership information on the EPA label stimulated interest in plug-in vehicles

among respondents interested in small/mid-sized cars (Dumortier et al., 2014).

Discussion

In this research, we examine consumer interest in plug-in vehicles under two price

scenarios—one scenario based on current costs of vehicle production and one sce-

nario reflecting a breakthrough in battery technology that reduces the cost pre-

mium of producing plug-in vehicles by 50%. In doing so, our objective is to discern

the relative effect of different motivations in guiding individuals’ intent to purchase

an innovative technology as cost barriers are reduced. One reason for such a break-

through could be policy-directed investments, such as those made by the U.S. gov-

ernment in recent years that are specifically targeted at supporting the

development of the battery technologies used in plug-in vehicles. The innovation

that is hoped to result from these investments could lead to substantial reductions

in the overall price of plug-in vehicles because battery packs account for a large

portion of the cost of producing plug-in vehicles (National Research Council,

2013). As we argue throughout the article, policy subsidies can only lead to reduc-

tions in policy price if they are applied in the right market conditions—suppliers

must be able and willing to contribute to research and development of innovative

technologies and consumers must be able and willing to purchase them. The

absence of either in a marketplace could render a policy subsidy entirely

ineffectual.

This research has the ability to help engage and inform decision-making theory

as it applies to individuals and the incentives that underlie their intended behavior.

Individuals who intend to adopt a technological innovation are driven primarily by

solidary and material motivations. Here, the impact of solidary motivations appears

to act as a mechanism that slows some people’s intended adoption of this newer

technology because they may have invested in a group identity that stems from not

Exploring Drivers of Innovative Technology Adoption Intention 663

being a hybrid or EV owner. In other words, their participation may be somewhat

tempered by fears of unfavorable peer perception regarding new technology. How-

ever, participation for this subgroup is possible if the material motivation is strong

enough to overcome the negatives associated with making the intended change to

the newer technology. If the material motivation or financial incentive from making

the change is strong enough to offset the potential downsides of losing the solidary/

expressive incentive individuals will opt into the plug-in market.

We find that individuals who intend to make smaller changes to their purchasing

decision (hybrid to plug-in) are also driven by material and the solidary motivations

that stem from that change. However, individuals in this proximal decision space

seem to encounter positive benefits from the change associated with the solidary

incentives. In other words, this stated preference purchase decision is less con-

flicted for those with smaller proximal decisions. They derive additional benefit

from making the change from a hybrid to a plug-in given that they already have

technology knowledge, peer support, and have adjusted to some of the behavioral

changes necessary for owning a plug-in vehicle.

The finding that different motivations help to shape purchasing decisions for

innovative technologies helps to provide a more detailed assessment of how tech-

nology diffuses across individuals. This research has incorporated aspects of both

individual decision making and diffusion literature in order to provide a more

complete picture of the technology diffusion process. We suspect that individuals

are highly motivated through their peer groups and group identity (solidary moti-

vation) and their financial capital (material motivation). Contrary to stances in the

decision-making literature, this research suggests that after the barriers to deci-

sion making (information and cost constraints) are overcome, individuals can be

simultaneously impacted by these different forms of motivation when making a

decision.

While the link between policy instruments and technology price is ultimately

tempered by conditions of the markets in which instruments are applied, we think

it valuable to briefly comment here on the potential value of different policy strat-

egies in the case of plug-in vehicles. Previous research has shown that there is usu-

ally a bias between hypothetical and nonhypothetical choice experiments

(Heshner, 2010). In the case of vehicles, it is impossible to elicit consumers’ actual

behavior by presenting them vehicles at different prices. However, literature sug-

gests that incentives do matter in the purchase of conventional hybrids and plug-in

vehicles. Diamond (2009) shows that gasoline price is a significant contributor to

the sales of hybrid-electric vehicles but that monetary incentives such as tax credits

have an effect as well. Those effects are more important when received by the con-

sumer upfront as opposed to the end of the year. This is consistent with the

approach in our article where the cost reduction is assumed to be at the point of

sale. Gallagher and Muehlegger (2011) find a significant increase in hybrid vehicle

sales in the case of sales tax waivers. Jenn, Azevedo, and Ferreira (2013) find that

the Energy Policy Act of 2005 increased hybrid vehicles sales between 3% and 20%

depending on the model.

These studies as well as the findings from this research suggest that less coer-

cive policies (i.e., subsidies instead of direct regulations) can be an effective policy

strategy given that the plug-in industry is still relatively nascent and

664 Saba Siddiki et al.

manufacturers are increasingly engaging in plug-in and research and develop-

ment in response to economic opportunity or complementary regulatory man-

dates (Jung et al., 1996; Milliman & Prince, 1989). However, less coercive

strategies may not be very effective with owners of the least fuel-efficient vehicles,

and they may be more difficult to persuade. From a different perspective, less

coercive policies can foster innovation in the private sector, such that individual

firms can invest in the production of new technologies, make some sales to early

adopters, while working overtime to curb the cost of producing the innovation. At

the early stages of commercialization, a period of market testing and product

refinement may precede the point when the new technology can be offered at a

reasonable price to consumers.

To achieve mass commercialization, plug-in vehicles may require the assistance

of more coercive policy strategies such as the sales mandates under California’s

Zero Emission Vehicle program or the highly stringent miles per gallon perform-

ance requirements under the federal Corporate Average Fuel (CAF �E) economy

program. Without the coercive strategies, the penetration of plug-in vehicles may

not occur beyond selected niche markets. Before coercive strategies are adopted, a

careful cost-benefit analysis of options is appropriate.

No study is without limitations. A primary limitation of stated preference studies

concerns uncertainty about the validity of participants’ responses. For example, a

gap between stated preference and revealed preference was found in the case of

conventional hybrids where survey respondents who declared a preference for

hybrids did not always go on to purchase one (Popiel, 2011). More specifically, our

two-price research design may introduce some bias at the second price; a between-

subject design with varying prices could shed light on the nature and extent of any

bias. Despite the limitations of stated preference surveys, they still afford the valua-

ble opportunity to discern potential consumer preferences in relation to choice sets

alongside other personal perceptions and demographics. Indeed, our findings

offer important insight about the sensitivity of consumer preferences for plug-in

vehicles under alternative price scenarios and individual characteristics that temper

their decision making. A second limitation of our study is that we only focus on the

effects of reduced costs for plug-ins and not other factors that may motivate poten-

tial buyers of these vehicles such as informational campaigns, changes in gasoline

prices, changes in purchase incentives (e.g., tax credits), and so forth. Indeed, our

future research should aim to incorporate an analysis of these factors in shaping

consumers’ intent to purchase plug-in vehicles.

Conclusions and Policy Implications

Recent efforts by the U.S. government indicate an increasing level of commitment

to the development of the plug-in industry. This commitment to plug-in vehicle

development and adoption has been linked to the furtherance of broader salient

national energy policy goals, including improving the competitive position of lead-

ing nations in plug-in vehicle technology innovation, enhancing energy security by

reducing dependence on foreign oil, cutting fuel costs for families and businesses,

and reducing greenhouse gas emissions (U.S. Department of Energy, 2014).

Exploring Drivers of Innovative Technology Adoption Intention 665

Recognizing that the cost of plug-in vehicles and supporting technologies repre-

sents a key challenge in large-scale plug-in adoption, recent policy efforts have

been largely directed at reducing the cost of plug-in technology itself (e.g., through

grants for battery production facilities, research, and development) or subsidizing

consumers’ plug-in purchasing costs (e.g., through tax incentives for new plug-in

buyers and time-of-sale rebate proposals). Overall, the policy strategies pursued at

this point are noncoercive in nature and focus on catalyzing consumer uptake of

plug-in vehicles by reducing their cost. Reflecting this policy objective, in 2012,

President Barack Obama announced that the United States would actively seek to

become the first country in the world to produce plug-in electric vehicles that are

as affordable as traditional gasoline powered vehicles within the next decade (U.S.

Department of Energy, 2014).

Ultimately, what this research suggests is that contextually appropriate policy

strategies that focus on reducing the price of plug-ins appear promising. However,

alongside price there are several individual and technology-specific factors that

persuade and/or dissuade movement toward plug-ins even under reduced-cost sce-

narios that are best addressed by other stakeholders in the plug-in industry. Our

results thus reinforce previous findings that public policy efforts aimed at promot-

ing new technologies must be complemented by effective bottom-up strategies

(Ewing & Sarigollu, 2000) that emphasize education of new technologies or their

various material and nonmaterial benefits. This is particularly relevant in the case

of the plug-in industry wherein public policy makers, utility companies, and vari-

ous private actors representing different parts of the vehicle supply chain (battery

suppliers, vehicle manufacturers, car dealers) must cooperate to ensure that suffi-

cient resources are in place to support plug-ins and their associated infrastructures.

Our analysis makes timely advancements in our understanding of how policy can

be used to incentivize technological innovation and adoption in the energy domain.

A logical next step is to assess the interplay between bottom-up strategies and public

policy incentives (e.g., high occupancy vehicle lane access, reduced vehicle registra-

tion costs, tax credits), specifically targeting the subset of consumers who may be pre-

disposed to consider purchasing or leasing various kinds of plug-in vehicles.

Note

1 Austin, Boston, Bridgeport, Chicago, Dallas, Denver, Detroit, Houston, Indianapolis, Los Angeles,

Nashville, New York, Orlando, Phoenix, Portland, Raleigh, Richmond, Sacramento, San Diego,

San Francisco, Seattle, Sonoma County, Tucson, Washington, El Paso, Charlotte, Philadelphia, Bal-

timore, Jacksonville, Memphis, San Antonio, and Atlanta.

About the Authors

Saba Siddiki is an Assistant Professor in the School of Public and Environmental Affairs, Indiana University-Purdue University Indianapolis.

Jerome Dumortier is an Assistant Professor in the School of Public and Environmental Affairs, Indiana University-Purdue University Indianapolis.

Cali Curley is an Assistant Professor in the School of Public and Environmental Affairs, Indiana University-Purdue University Indianapolis.

666 Saba Siddiki et al.

John D. Graham is Dean of the School of Public and Environmental Affairs, Indiana University.

Sanya Carley is an Associate Professor in the School of Public and Environmental Affairs, Indiana University.

Rachel M. Krause is an Assistant Professor in the School of Public Affairs & Administration, The University of Kansas.

References

Al-Alawi, B. M., & Bradley, T. H. (2013). Total cost of ownership, payback, and consumer preference model-

ing of plug-in hybrid electric vehicles. Applied Energy, 103, 488–506.

Boardman, A. E., Greenberg, D. H., Vining, A. R., & Weimer, D. L. (2006). Cost-benefit analysis: Concepts and

practice (3rd ed., pp. 373–374). New York: Prentice Hall.

Canis, B. (2013). Battery manufacturing for hybrid and electric vehicles: Policy issues. Congressional Research

Service Report. Washington, DC: Congressional Research Service.

Carley, S., Krause, R. M., Lane, B. W., & Graham, J. D. (2013). Intent to purchase a plug-in electric vehicle:

A survey of early impressions in large U.S. cities. Transportation Research Part D, 18, 39–45.

Caulfield, B., Farrell, S., & McMahon, B. (2010). Examining individuals’ preferences for hybrid-electric and

alternatively fueled vehicles. Transport Policy, 17(6), 381–387.

Chang, L., & Krosnick, J. A. (2009). National surveys via RDD telephone interviewing versus the internet:

Comparing sample representativeness and response quality. Public Opinion Quarterly, 73(4), 641–678.

Deloitte Consulting LLP. (2010). Gaining traction: A customer view of electric vehicle mass adoption in the

U.S. automotive market. New York: Deloitte Global Services.

Diamond, D. (2009). The impact of government incentives for hybrid-electric vehicles: Evidence from US

states. Energy Policy, 37, 972–983.

Dumortier, J., Siddiki, S., Carley, S., Cisney, J., Krause, R. M., Lane, B. W., et al. (2015). Effects of providing

total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehi-

cle. Transportation Research: Part A, 72, 71–86.

Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer

attitudes and perceptions. Energy Policy, 48, 717–729.

Energy Information Administration (EIA). (2012). Annual energy outlook 2013 with projections to 2040. Tech.

Rep. DOE/EIA-0383(2013). Washington, DC: U.S. Energy Information Administration.

Ernst & Young. (2010). Gauging interest for PHEVs and EVs in selected markets. London: Ernst & Young.

Ewing, G., & Sarigollu, E. (2000). Assessing consumer preferences for clean-fuel vehicles: A discrete choice

experiment. Journal of Public Policy & Marketing, 19(1), 106–118.

Gallagher, K. S., & Muehlegger, E. (2011). Giving green to get green? Incentives and consumer adoption of

hybrid vehicle technology. Journal of. Environmental Economics and Management, 61(1), 1–15.

Guilford, D. (2013, March 25). EVs: The next wave. Automotive News, p. 4.

Hanemann, M., Loomis, J., & Kanninen, B. (1991). Statistical efficiency of double-bounded dichotomous

choice contingent valuation. American Journal of Agricultural Economics, 73(4), 1255–1263.

Healey, J. R. (2011, May 25). Americans say ‘no’ to electrics, despite high gas prices. USA Today.

Heshner, D. A. (2010). Hypothetical bias, choice experiments and willingness to pay. Transportation Research

Part B: Methodological, 44(6), 735–752.

Huang, S., Hodge, B.-M., Taheripour, F., Pekny, J. F., Reklaitis, G. V., & Tyner, W. E. (2011). The effects of

electricity pricing on PHEV competitiveness. Energy Policy, 39, 1552–1561.

Janssen, M. A., & Jager, W. (2002). Stimulating diffusion of green products: Co-evolution between firms and

consumers. Journal of Evolutionary Economics, 12, 283–306.

Jenn, A., Azevedo, I. L., & Ferreira, P. (2013). The impact of federal incentives on the adoption of hybrid

electric vehicles in the United States. Energy Economics, 40, 936–942.

Johnson, J. (2013a, April 22). Energy: Renewable energy set for boost. Chemical and Engineering News, pp.

29–30.

Johnson, J. (2013b, August 26). $36 million awarded for battery R&D. Chemical and Engineering News, p. 20.

Jung, C., Krutilla, K., & Boyd, R. (1996). Incentives for advanced pollution abatement technology at the

industry level: An evaluation of policy alternatives. Journal of Environmental Economics and Management,

20(1), 95–111.

Krause, R. M., Carley, S., Lane, B. W., & Graham, J. D. (2013). Perception and reality: Public knowledge of

plug-in electric vehicles in 21 cities. Energy Policy, 63, 433–444.

Lane, B., Messer, N., Hartman, D., Carley, S., Krause, R., & Graham, J. (2013). Government promotion of

the electric car: Risk management or industrial policy? European Journal of Risk Regulation, 2, 227–245.

Exploring Drivers of Innovative Technology Adoption Intention 667

Larrick, R. P., & Soll, J. B. (2008). The MPG illusion case. Science. 320, 1593–1594.

Milliman, S. R., & Prince, R. (1989). Firm incentives to promote technological change in pollution control.

Journal of Environmental Economics and Management, 17(3), 247–265.

National Research Council. (2013). Review of the research program of the U.S. drive partnership. Washington, DC:

National Academy Press.

Newell, R. G., & Siikamaki, J. V. (2013). Nudging energy efficiency behavior: The role of information labels. NBER

Working Paper 19224. Cambridge, MA: National Bureau of Economic Research.

Nixon, H., & Saphores, J. D. (2011). Understanding household preferences for alternative-fueled vehicle technologies.

Research Report 10-11. San Jose, CA: Mineta Transportation Institute.

Popiel, S. (2011, February 9–11). From nozzle to plug: Achieving zero emissions. SAE 2011 Hybrid Vehicle

Symposium. Anaheim, CA.

Rogers, E. M. (1962). Diffusion of innovations. New York: The Free Press.

Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: Does psychology

need choice, self-determination, and will? Journal of Personality, 74(6), 1557–1586.

Sandalow, D. B. (Ed.). (2009). Plug-in electric vehicles: What role for Washington? Washington, DC: Brookings

Institution.

Sharp, E. B. (1978). Citizen organizations in policing issues and crime prevention: Incentives for participa-

tion. Nonprofit and Voluntary Sector Quarterly, 7(1-2), 45–58.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211,

453–458.

U.S. Department of Energy. (2014). EV everywhere grand challenge: DOE’s 10-year vision for plug-in elec-

tric vehicles. Retrieved from http://energy.gov/eere/vehicles/ev-everywhere-grand-challenge-does-10-

year-vision-plug-electric-vehicles, Accessed October 1, 2014.

Veugelers, R. (2012). Which policy instruments to induce clean innovating? Research Policy, 41(10), 1770–

1778.

Watson, V., & Ryan, M. (2004). Testing for inconsistencies in double bounded dichotomous choice contingent valuation

studies. Paris, France: CES/Health Economics Study Group.

Weimer, D. L., & Vining, A. R. (2005). Policy analysis: Concepts and practice (4th ed.). Upper Saddle River, NJ:

Pearson Publishers.

White, J. B. (2012, April 26). Engineers cast wary eye on role of electric cars. Wall Street Journal, p. B4.

Wilson, C., & Dowlatabadi, H. (2007). Models of decision making and residential energy use. Energy Policy,

32, 169–203.

Yeager, D. S., Krosnick, J. A., Chang, L. C., Javitz, H. S., Levendusky, M. S., Simpser, A., et al. (2010). Com-

paring the accuracy of RDD telephone surveys and internet surveys conducted with probability and

non-probability samples. Public Opinion Quarterly, 75(4), 709–747.

Zhang, T., Gensler, S., & Garcia, R. (2011). A study of the diffusion of alternative fuel vehicles: An agent

based modeling approach. Journal of Product Innovation Management, 28(2), 152–168.

668 Saba Siddiki et al.

Appendix: Labels Shown to Respondents in Survey

Exploring Drivers of Innovative Technology Adoption Intention 669

670 Saba Siddiki et al.

Exploring Drivers of Innovative Technology Adoption Intention 671

672 Saba Siddiki et al.

Exploring Drivers of Innovative Technology Adoption Intention 673

674 Saba Siddiki et al.

Copyright of Review of Policy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.