The Effect of Perceived Risk on the Purchase Intention of

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The Effect of Perceived Risk on the Purchase Intention of Alternative Fuel Vehicles

T Karunanayake��

Abstract

The diffusion of AFV’s is comparatively high in Sri Lanka, even though the

uncertainties and negative consequences of owning them are perceived

to be high. This contradicts the theory of perceived risk which postulates

that when customers perceive the negative consequences of purchasing,

goal driven purchase intention should weaken. Hence, the purpose of this

paper is to examine the effect of perceived risk on the relationships between

key purchasing determinants and purchase intention of alternative fuel

vehicles in Sri Lanka. This study examines this unexpected local consumer

behaviour through the lens of the Theory of Perceived Risk and the

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study employed structured questionnaires to gather primary data from

the sample utilizing a convenience sampling technique. It employed two-

step Partial Least Squares (PLS)-Structural Equation Modeling (SEM) to

analyze the data, and the analysis revealed that performance expectancy

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facilitating conditions (FC) are the key determinants of the purchase

intention of AFV customers in Sri Lanka. Furthermore, perceived risk was

found to moderate the relationships between these determinants and the

purchase intention of customers who plan to invest in AFVs.

Keywords: Alternative fuel vehicles, Perceived risk, Purchase intention,

UTAUT. PLS.

Mr. T Karunanayake is a Lecturer, Department of Marketing Management, Faculty of Commerce and

Management Studies, University of Kelaniya. E-mail: [email protected]

�� is a Senior Lecturer, Department of Management of Technology, Faculty of Business,

University of Moratuwa. E-mail: [email protected]

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Introduction

While people are entering into a new, technologically complex era, governing bodies

of countries and similar entities are creating an ongoing discussion on environmental

issues within their societies, so that consumers are beginning to see their lives being

impacted by the energy crisis and other environmental issues (Wang, Chen, & Chen,

2016). This situation is found to be common to many parts of the world (Karunanayake

& Wanninayake, 2015). Taking this situation into consideration, the government of

Sri Lanka also looks forward to encouraging green consumer practices by reducing

excise duty, and providing other subsidies or loans to its people to purchase eco-

friendly vehicles (Siripala, 2014). This encouragement has led some customers to make

purchases of alternative fuel vehicles (AFV) in Sri Lanka. In contrast to the Sri Lankan

context, many other countries are experiencing a low penetration rate in AFVs due to

unfavourable market conditions such as high price, limited charging points, limited

maintenance facilities and so on (European Automobile Manufacturers Association,

2015), which are common experiences of Sri Lankan consumers as well. In Sri Lanka,

however, statistics have revealed a relatively high penetration rate, even though the

automobile industry environment has unfavorable market conditions like a lack of

technological infrastructure and charging points, long refueling times and cycles,

inadequate maintenance and service centers, and range anxiety (Helmers & Marx,

2012; Siripala, 2014; Funke, Gnann, & Plötz, 2015). In a such a situation, the theory of

perceived risk postulates that when customers perceive the negative consequences of

purchasing, goal driven purchase intention should deteriorate. (Cox, 1967). Therefore,

in the Sri Lankan AFV context, a situation can be observed which contradicts the theory

of perceived risk. Hence, the purpose of this study was to examine the effect of perceived

risk on the relationships between key purchasing determinants of AFVs and purchase

intention related to AFVs in Sri Lanka.

In previous studies on consumer behaviour, perceived risk was explained as the

perceived uncertainty in a purchase situation. Referring to Sunitha, Justus, and

Ramesh, (2012), different types of risk situations have been recognized that can deter

consumers from decision making or delaying the purchase decision. In such situations,

consumers will engage in risk “tradeoff” behaviour. As Mitchell and Nygaard (1999)

point out, there are motives (purchasing determinants) that drive people to reach

their goals, and in doing so they invest time, money, and mental and physical energy.

Uncertainty lies in the outcome of this goal-orientated behaviour, which is known as

“failure,” and its consequences, known as possible “losses,” implying the existence of

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Justus & Ramesh, 2012). Hence, it can be concluded that the perceived risk factor has

an effect on the relationship between purchasing determinants and purchase intention

in this research context. Yet, this need-motivations-goals-risk-intention linkage has

not been fully explored in the literature (Mitchell & Harris, 2005). Since there is no

clear notion of the manner in which perceived risk has an impact on the relationship

between purchasing determinants and purchase intention in the extant literature, the

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said that, in line with the argument, the study attempts to address the question: Do

the relationships between purchasing determinants and purchase intention vary as the

customer experiences the negative consequences and uncertainties of purchasing?

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of Technology (UTAUT) (Venkatesh, Thong, & Xu, 2012), which was used as the

underpinning theory to identify the purchasing determinants of AFVs, as well as on

some theories of perceived risk, including the “Expected Utility Theory” (Mongin,

1998). These theories are employed in order to identify the construct of perceived risk

and its relationship to purchase intention.

As was discussed under the theory, UTAUT outlines seven constructs that play an

important role as direct antecedents of user acceptance and usage behaviour in terms

of technology. These seven variables directly affect behavioural intention towards a

given technological product. Bringing the notion of perceived risk to bear on purchase

intention, Stone and Gronhaung (1993) stated that perceived risk was a determining

factor of individuals’ intended and actual purchasing behaviour. Further, Cox (1967)

examined the cause of perceived risk, and proposed that consumers’ behaviour is

goal-driven, and that perceived risk arises when the consumer sees that the goal of

consumption may not be met by her/his behaviour. In drawing the connection between

UTAUT and perceived risk in this context, it can be seen that UTAUT explains how

people are carrying out subjective assessments of their actions and efforts in relation

to technology (Venkatesh, Thong, & Xu, 2012). Here, variances usually exist between

people’s expectations and the actual performance of the product. This implies a ‘risk’

situation because customers do not know the importance of this variance. If a so-

called technology is unable to deliver its expected performance, it will result in a loss

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Han 2008). Hence, the effect of perceived risk should be depicted in the antecedents

of the UTAUT construct when determining its impact on the relationship between

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purchasing behaviour in this context, this study contribute to the body of knowledge

which examines the instances when perceived risk makes an impact on the relationship

between purchasing determinants and purchase intention of alternative fuel vehicles.

The rest of the paper is structured as follows: In the following section, the existing

literature related to purchase intention, perceived risk and determining factors of

purchase are reviewed, and the hypotheses are formulated. Next, the methodology

used to conduct the study, including the measurement items chosen for the survey, are

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the implications are discussed. Finally, limitations and suggestions for further research

are presented, together with the concluding remarks.

Literature review

Alternative fuel vehicles

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in full or in part by alternative fuels such as biogas and electricity (Jansson, 2011).

Electric vehicles such as plug-in hybrid electric vehicles (PHEVs), battery electric

vehicles (BEVs), hybrid electric vehicles (HEVs) and extended-range battery electric

vehicles (E-REVs) come with varied technologies. The wider introduction of HEVs

to the US and Japanese markets started at the beginning of the current century, and

continued from the late 2006’s until now (Jansson, 2011). These technological advances

hold implications for consumer behaviour and thereby for the type of research that

is necessary in order to further the understanding of consumer behaviour (Jansson,

2011).

Some of the best-known forms of AFV’s which are in use today are hybrid electric

vehicles. A hybrid electric vehicle has an internal combustion engine (ICE) with an

added electric power train connected to an electric motor driven by a battery. This

battery is charged by regaining the energy that can be lost during braking or idle driving

in a consistent phase. Hence, all the HEV’s energy is mainly generated through fossil

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Skippon, & Kinnear, 2013). A plug-in hybrid electric vehicle (PHEV) is an evolution of

the HEV, with a much improved battery capacity and a plug-in electric charger which

enables the battery to be charged from the electricity grid (Egbue & Long, 2012). A

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PHEV is driven by electricity or by an ICE (Internal Combustion Engine), but in general,

it has a short all-electric range. An extended-range electric vehicle (E-REV), similar to

a PHEV, is driven by a battery that can be charged from an electric charging outlet, and

it consists of a fuel tank as well, which gives the driver an extended driving distance

between charging outlets. A battery electric vehicle (BEV) consists of an all-electric drive

train powered by a large capacity battery which can be recharged from the electricity

grid (Proff & Kilian, 2012). As for the statistics, the distance of driving on electricity is

usually longer in BEVs than in PHEVs, as electricity is the only power source of BEVs.

The main challenge for researchers and practitioners is to understand consumer

behaviour towards AFV’s (Proff & Kilian, 2012). AFV technologies, which are disruptive

innovations in the automobile industry, make different behavioural demands on

consumers. For example, to run on electricity with an AFV that is a PHEV, E-REV or

BEV, drivers are required to connect the car to a power grid and charge the battery

while it is not in the driving mode, and this requires a certain degree of planning for

their next drive (Axsen, TyreeHageman, & Lentz, 2012). Another situation is where the

driver experiences range anxiety. This anxiety is related to the perceived limited driving

distance of electric batteries against the perceived range needed to be driven in day to

day life. This anxiety is also impacted by the charging cycles of batteries, and the limited

number of charging stations when compared to fuel stations (Sovacool & Hirsh, 2009).

Purchase intention

The main underlying concept of consumer behaviour is the purchase intention, which, in

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mindset with which to make a transaction with the seller (Heyvaert, Coosemans, Mierlo,

& Macharis, 2015). As was discussed by Dodds, Monroe, and Grewal (1991), purchase

intention takes into consideration the instances when a customer has a drive towards

attempting to purchase a product or a service. For marketers, purchase intention is a

very helpful statistic as their forecasted consumer behaviour is mainly dependent on

the purchase intentions of their target customers. Counting and predicting consumer

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it continues to change owing to unknown and uncertain factors; therefore, measuring

consumer behaviour under different conditions is most likely to be a challenging task at

any given time (Rizwan, Qayyum, Qadeer & Javed, 2014).

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In addition, Gogoi (2013) mentions that purchase intention is an effective tool with

which to arrive at an understanding about the customer buying process. He has also

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external motives and drives during the buying process (Gogoi, 2013). In line with that

explanation, researchers have come up with six stages in the mindsets of consumers

that occur when they make up their minds to buy the product. These are awareness,

knowledge, interest, preference, persuasion and purchase (Gogoi, 2013).

Perceived risk

The concept of risk is referred to as early as the 1920s (Knight, 1921). The role

of perceived risk in the business arena has been actively researched in consumer

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psychological risk. The individual’s uncertainty about the performance of the product is

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may not have been fully tested, and thus, may not function in a proper and reliable

manner (Wiedmann, Hennigs, & Astrid, 2007). In this regard, uncertainty related to

the long-term functionality of the AFV battery might be a functional risk. In particular,

the battery capacity could drain over time, which clearly makes an impact on the AFV’s

driving range. Additionally, recharging time might increase when the battery has been

used for some time (Turrentine & Kurani, 2007). It should also be noted that an adequate

charging station network is crucial to facilitate the AFV’s functional constraints, such as

its driving range. Unfortunately, most areas in Sri Lanka lack this proper infrastructure.

It should be noted that this condition could be one fundamental adoption barrier.

Additionally, this perceived risk becomes a vicious cycle (Turrentine & Kurani, 2007).

Without charging stations, many consumers are not willing to purchase AFV’s. However,

without having a proper market base of AFV’s, no Sri Lankan supplier would be willing

to invest in a charging station business. How soon this vicious cycle will be circumvented

is still uncertain, and, therefore, this represents probable functional risk.

Social risk refers to consumers’ uncertainty about their social environments’ (e.g.

reference groups) approval of the adoption (Golob, Bunch, & Brownstone, 1997).

Perceived disapproval or social isolation has a negative effect on purchase intention.

Taking this into consideration, Golob, Bunch, and Brownstone, (1997) pointed out that

cars can project a certain image of their owners. In the Sri Lankan consumer behavioural

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context, in the process of consumption stereotyping, other people interpret a vehicle’s

image and draw conclusions about its holder (Ligas, 2000). A Sri Lankan consumer

might hesitate to buy an AFV, because she/he may worry that other people would

perceive her/him as being too progressive, too cheap or a green geek (Karunanayake &

Wanninayake, 2015). In most cases, post purchase dissonance is very likely to be high

when the customer feels that the purchase decision has not been accepted or valued

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the reference group and taking a risk will be challenged with the perceived social risk

factor. The more the target customer depends and relies on social networking, the more

she/he will be likely to be exposed to social risk (Karunanayake & Wanninayake, 2015).

Hauser and Urban (1979), citing the Neumann–Morgenstern utility function, stated

that the extension of the theory of consumer preferences includes the part of the theory

of consumer behaviour that deals with risk perception and variance. This was taken

into the consideration by Neumann and Morgenstern in the Theory of Games and

Economic Behaviour (1944), and they came up with the expected utility hypothesis.

It was emphasized here that when a consumer has a choice of items, the most correct

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derived from the choice made. The expected value is the accumulation of the products

of the various utilities and their corresponding probabilities. The consumer is supposed

to rank the outcomes in terms of preference, but it must be noted here that the expected

values will be conditioned by their probability of occurrence, where the probability

could lead to not meeting the expectation, implying risks (Hauser & Urban, 1979).

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Venkatesh, Morris and Davis (2003) empirically tested the variables in eight different

models that deal with users’ technology acceptance, including the technology acceptance

model (Davis, 1989) and diffusion of innovation (Rogers, 1983). Obtaining insights from

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Technology by using four determinants of acceptance/use and four moderating factors.

UTAUT focuses on four constructs that play an important role as direct antecedents of

user acceptance and usage behaviour with regard to technology. These are performance

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foundation of UTAUT, a new model has been developed that can be applied in the

context of consumer technologies. This is known as UTAUT2 proposed by Venkatesh,

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Thong, and Xu, (2012). Three new determinants of behavioural intention have been

added to the model already used by UTAUT, and these are hedonic motivation, price

value, and habit.

The purpose of formulating UTAUT was to integrate the fragmented theory and research

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technology were evaluated and compared to select empirical similarities across these

models, in order to formulate UTAUT. According to the conclusions of Venkatesh, Thong,

and Xu, (2012), the following argument was presented; the stronger the determinants,

the stronger becomes the purchase intention in a given context.

As the UTAUT model was generated from the experiences of previous technology

adoption theories, UTAUT is a relatively comprehensive model. As is presented in

the discussion of Venkatesh, Thong, and Xu, (2012), the power of explanation of the

model (R2) is around 70 percent. With such a model explanation, and broad coverage

in explaining technology adoption behaviour, the UTAUT model had a better power of

explanation than other theories, and became a better choice for researchers in the area

of customer behaviour related to technological products. It has been assumed within

the framework of UTAUT that it focuses on the way in which determinants of intention

and behaviour evolve over time. Venkatesh, Thong, and Xu, (2012) also highlight the

role and importance of contextual analysis when developing strategies for technology

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as ‘intrinsic motivation’ or the possible negative consequences of the purchase intention

towards using a particular technological innovation.

Thus, UTAUT constructs deal with goal achievement in technological adaptation related

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new technology may bring, compared with what has already been achieved by other

means; the construct of intention focuses on the consequences of behaviour, which

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conditions, price value, habits and hedonic motivations that relate to individual goals.

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AFV, which is a high involvement product category, there is a need to revisit the validity

of the two antecedents that have been used in the UTAUT2 model in order to strive for

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used as an antecedent, as far as the current context and technology is concerned, this

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for experimental purposes, fun, and excitement like games, smart phones, and luxury

watches. (Strahilevitz & Myers, 1998). In contrast to hedonic values, utilitarian values

have more relevance to this context, as automobile purchase intention deals with the

cognitive perspective of attitude and its emphasis on the economic and monetary

values of the purchase decision. The utilitarian aspects of an automobile might include

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Grohmann, 2003). Further, automobile purchase decisions are always cited as high

involvement purchase decisions. Financial risk and the personal relevance of the

purchase decision dictate that the automobile buyer put an extra effort into the choice

before making a decision, together with paying a great deal of attention to the utilitarian

values of the product (Abramson & Desai 1993). Once further analysis was made, it

was found that UTAUT takes an approach that emphasizes the importance of certain

utilitarian values embedded in the constructs in the model, for example performance

expectancy (Venkatesh, Thong & Xu, 2012). Therefore, it can be established that hedonic

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current study context. The second antecedent which needs to be taken into account

when revisiting the validity of the model, is habit. Joshi and Rahman (2015) pointed

out in their paper that consumer habits did not impose an important barrier to green

purchase behaviour, especially because, in the current research context, ecofriendly

vehicles can be considered synonymous to AFV’s. Several other studies have reported

a negative relation between consumer habits and green purchase behaviour, and state

that consumers were more prone to follow their habitual consumption patterns when

buying low involvement products such as FMCG products and other grocery items.

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is expected in this research context, as far as the use of technology is concerned. All

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model in the current research context pertaining to the type of technology considered.

Hypotheses and conceptual model

Relationship between performance expectancy of AFVs and purchase intention

Venkatesh, Morris, Davis, and Davis, (2003) referred to “performance expectancy” as

the degree to which the user expects that using the technological product will help him

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which is contributed by behavioural theories, has a direct relation to perceived

usefulness in TAM.

In previous research in this context, performance expectancy is referred to as the

perceived advantages of the product, and focuses on ‘driving performance, safety,

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reduction of the ‘carbon foot print’ in the current research context. Nordhoff, Arem and

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fact that customers expect AFVs, in general, to lead to the reduction of accidents by 70

percent, decrease emissions by 64 percent, reduce fuel consumption by 72 percent, and

reduce travel time by 57 percent. Having said that, Kockelman, Bansal, and Singh (2016)

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mechanical and technical system failures, interactions/compatibility with conventional

vehicles, and affordability in terms of social status. Therefore, customers will be more

likely to utilize AFVs if these vehicles help them accomplish their personal performance

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H1: Performance expectancy of an AFV has a positive impact on the purchase intention of an AFV.

Relationship between effort expectancy and purchase intention of AFVs

Effort expectancy is “the degree of ease associated with the use of a system” (Venkatesh,

Morris & Devis, 2003). Dimensions of effort expectancy include perceived ease of

use from the Technology Acceptance Model (TAM) and complexity from the Model of

Personal Computer Use (MPCU). The logical implication of this study relating to AFVs

is related to the accessibility of these vehicles to people and the degree of effort needed

to use this sustainable technology. According to Wang and Wang (2010), the technology

or product needs to be potentially effortless in its usage, and thus, the more technical

and complex the system, the lower will be the potential of adoption. Therefore, in the

present research context, effort expectancy will refer to how easily people get used to

an AFV for effortless usage of its systems and driving requirements. In a similar study,

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the early stages of customer buying behaviour, when there are obstacles to be overcome,

and that it later becomes overshadowed by instrumentality concerns.

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and behavioural intention in their empirical study, emphasizing that there could be

other moderating effects as well on the relationship. Hence, the following hypothesis

is advanced;

H2: Effort expectancy towards AFVs has a positive impact on the purchase intention of AFVs.

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perceives the importance of whether others believe he or she should use the technological

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norms from the Theory of Reasoned Action (TRA) and the TAM extension, social factors

from the MPCU, and images from the Diffusion of Innovation (DOI) theory. Social

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relation to the use of AFVs (Venkatesh, Morris & Devis, 2003).

As Jeon, Yoo and Choi (2012) have mentioned, there are three basic social motivational

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to the adoption of AFVs by transcending self-interest and promoting the green life

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using AFVs as a means to be innovative or to project a green personality and social

status to others; and the third is a subjective norm which is to perform innovatively and

pro-environmentally in order to comply with others’ wishes.

As Jeon, Yoo and Choi (2012) point out, these factors have been proven to affect

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is a very strong determinant of intention and behaviour for products and services that

are symbolic and used in the public context, such as AFVs have been used, in order

to garner social status for an individual. Altruism has an effect on intention and has

been proven to be an independent measure of attitudes, mainly for pro-environmental

products such as transportation and food-related choices. The subjective norm has

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Karunanayake and Wanninayake (2015) have proven empirically that there is a

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context, people refer to those whom they get along well with, for insights into their

purchase decisions. Therefore, taking into consideration these social factors and their

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following hypothesis is advanced;

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Relationship between facilitating conditions and purchase intention of AFVs

Facilitating conditions is an individual’s perception about infrastructures or technical

support included in, and developed for using a technology or system (Venkatesh et

al., 2003). In the context of AFVs, it can be understood as the availability of batteries,

learning tools or maintenance, charging infrastructures at home and on the roads, or

after sales service. This relationship is adopted from the Use of Technology Theory

(UTT) along with UTAUT (Khazaei & Khazaei 2016).

As per the view of Pedersen, Tsang and Wooding (2012), the limited availability of

recharging points and service/ maintenance stations is the most crucial barrier to the

wider adoption of AFV’s. Pedersen, Tsang and Wooding (2012), citing Melaina and

Bremson (2008), described this context as a “three-way” bind among key stakeholders:

consumers are reluctant to buy automobiles that cannot be easily refueled, automobile

manufacturers are not willing to produce vehicles that are not purchased, and fuel and

service providers are reluctant to provide maintenance and services for vehicles that

do not exist. As was pointed out by Pedersen, Tsang and Wooding (2012), AFV owners

need to have access to reliable power outlets for overnight recharging. In general, the

importance of facilitating conditions is manifest, as most potential EV consumers are

limited by the charging facilities in their own homes (Pedersen, Tsang, & Wooding,

2012). Hence, the following hypothesis is advanced;

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H4: Facilitating conditions have a positive impact on the purchase intention of AFVs.

Relationship between price value and purchase intention of AFVs

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(Venkatesh et al., 2003). Heyvaert, Coosemans, Mierlo, and Macharis (2015) pointed

out in their study that none of their respondents experienced the high purchase price

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of price in the purchase decision was separated and analyzed through other related

antecedents; the attention consumers give to prices, the usage of price information, and

the tendency to compare and contrast prices very often (Karunanyake & Wanninayake

2015; Degirmenci & Breitner 2017). Degirmenci and Breitner (2017) have also revealed

that consumers are, very often, either unable to or don’t want to carry out careful

economic calculations of their car purchases. Turrentine and Kurani (2007), who have

conducted in-depth interviews in the USA, revealed that none of the respondents made

a quantitative assessment of the present value of future fuel savings as part of their

decision making process in relation to AFVs, in order to assign and compare value

against price. In such a context, the relatively high purchase cost of AFVs is considered

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H5: Price value has a positive impact on the purchase intention of AFVs.

The moderating effect of perceived risk on the purchase intention of AFVs

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form between predictor and criterion variables (Mitchell, 1989). Perceived risk has

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situations can be those where the probabilities of outcomes are not recognized and the

outcome is known or unknown (Im, Kim & Han 2008). In the Utility Theory put forward

by Neumann and Morgenster,(1944) value is understood to be the various utilities

and their corresponding probabilities of occurrence. As is mentioned in that study, the

consumer is supposed to rank the outcomes in terms of preference before she/he makes

up her/his mind to purchase a utility, and hence, in such a situation, the expected value

will vary in accordance with its probability of occurrence. This probability could even

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lead to the utility not meeting the expectation, implying a risk situation. Therefore, risk

can have a moderating effect on the utility, which results from the consumption decision

of the customer. As Tuu, Olsen, and Linh, (2011), as cited in Dowling and Staelin, (1994)

state, risk very often refers to losses and future uncertain consequences, where it can

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customers perceive high levels of risk, their buying intention may not be stable, along

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Bauer (1960), cited in Tuu, Olsen, and Linh, (2011). Therefore, this implies that the goal

driven purchase intention generated by the customer can be weakened or strengthened

by perceived risk.

Im, Kim and Han, (2008) created a model depicting that perceived risk does have a

moderating effect on both perceived usefulness and perceived ease of use, which

are the key determining factors of purchase intention in the Technology Acceptance

Model (TAM) and on effort expectancy and performance expectancy, which are the

determining factors in the UTAUT. This model further elaborates on the fact that when

perceived risk changes, the effects of perceived usefulness and perceived ease of use on

behaviour intention change as well. Customers who perceive a higher risk in adopting

the technology will be affected by how easily it can be used (Im, Kim, & Han, 2008;

Li and Huan, 2009). Making a link between perceived risk and purchase intention,

Yee and San (2011) have proven empirically that the perceived risk factors do have a

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are one of the relatively more expensive assets, most consumers look forward to their

automobiles lasting a long time. Hence, they will face uncertainty if they have made a

wrong decision that results in poor performance, poor self-image and insecurity, which

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risk (Sunitha et al., 2012). Therefore, customers will tend to purchase automobiles that

promise value for money.

Schmiege, Bryan and Klein (2009), by conducting an empirical research study,

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of Planned Behaviour. In contrast, Cameron and Reeve, (2006); Klein, Geaghan, and

MacDonald, (2007) revealed that greater risk perceptions do not necessarily result

in decreased behavioural intentions. In this context, there are two schools of thought

that have emerged with regard to risk, namely, the motivational hypothesis and the

accuracy hypothesis (Brewer, Weinstein, Cuite, & Herrington, 2004). The motivational

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perspective is expressed by a positive relationship between risk and behaviour, whereby

greater perceptions of risk facilitate behavioral promotion. The accuracy hypothesis is

expressed by a negative relationship between risk and behaviour and depends on the

notion that higher levels of action result in reduced risk perceptions.

In the literature, it has been empirically proven that psychological risk, physical risk,

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risk when purchasing an automobile (Sunitha et al., 2012). However, the validity of the

phenomenon has not been tested in the context of new technological innovations like

AFVs. Furthermore, perceived risk as an antecedent did not clearly explain the model,

whereas the impact of perceived risk is clear in terms of an AFV’s purchase intention. As

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role in its impact on the purchase intention of the target customer. However, there has

not been a clear notion of the impact of the perceived risk factor on purchase intention

of AFVs, and it has not yet been empirically proved in the Sri Lankan context. Hence, the

following hypotheses were advanced;

H6: Perceived risk moderates the effect of Performance Expectancy (PE) on the purchase intention of AFVs.

H7: Perceived risk moderates the effect of Effort Expectancy (EE) on the purchase intention of AFVs.

H8�� intention of AFVs.

H9: Perceived risk moderates the effect of Facilitating Conditions (FC) on the purchase intention of AFVs.

H10: Perceived risk moderates the effect of Price Value (PV) on the purchase intention of AFVs.

- 82 -

Figure 1: The conceptual framework

In this context, this study has proposed a complete model for measuring the factors that

��"��$��"��$��'��*��

purchasing determinants and purchase intention. Hence, the above depicted model was

developed.

Methodology

Participants and procedures

In this study, hypotheses were developed, and the independent and dependent variables

based on a selected representative sample were measured. The research approach used

was a “deductive approach” (Akbayrak, 2000). The current study was also descriptive,

and attempted to arrive at a conclusion while dealing with numbers, statistics and

�� +�� [��"�� %'�� >� ��

2008).

In the present study, all the car owners or those prospective car buyers who plan to

purchase in the near future, were considered as the population. When it is necessary to

receive unbiased responses, it is very important to target customers who are familiar

with the context of the research; hence, to have an effective result responses were

collected from an audience which was familiar with AFVs as well as conventional vehicles.

Since past experiences with alternative fuel vehicles would have biased the responses,

�� [�� [�� [��

belonged to customers who did not have any experience related to AFVs. Therefore,

- 83 -

due to the above mentioned issue as well as time constraints and the complexity of the

pool of respondents, the convenience sampling technique was employed. In the current

research study, 350 responses were collected through surveys collected from executive

level employees in the Western Province, and after the process of data cleaning, 319

responses were chosen for analysis.

Measures

The UTAUT model is one of the most important models in behavioural psychology

�� %�� *�� %�$�� %��

individuals’ intentions and actual purchases of a product (Riga & Thatcher, 2015).

With the given provision, items for the purchasing determinants were adapted from

the UTAUT scale used by Venkatesh, Thong, and Xu. (2012) and made to suit AFVs.

To measure purchase intention, the UTAUT model has deployed 5 constructs, namely,

��%��

price value.

Performance expectancy (PE), which is the level of an individual’s expectation that

�� O#� *�� "�� '�� *�� %�� %��

respondents were asked to express their expectations regarding the performance

��%��O#��%��%��%��*��!��"��"��

in my daily life’, ‘using alternative fuel vehicles helps me accomplish things more quickly

�� *��' ��%��O#��

was measured through 4 items such as ‘learning how to use alternative fuel vehicle

�� %�� *��%��*��%��"��

'��' ��*�%�� "�� %��%��*��

with 4 items which included ‘people whose opinions that I value prefer that I use an

alternative fuel vehicle’. In the present study, the availability of conditions that enable

customers to use AFVs was measured through the “facilitating conditions” variable,

which was captured with 4 measuring items, and in which one of the sample items was

�!��%��*��!��"��"��"��

�� '��*��"��"��'��*��

to as “price value” in the present study, was measured through 4 items. All the above

%��%��*��%��_��$��Q� ��]��

��_��

- 84 -

The concept of perceived risk, the moderating variable in the study, was measured

through a scale validated in the studies of Sunitha et al., (2012), and carries 15 items

in total, in which ‘I am not sure that local technicians can handle this technologically

superior AFV’, ‘I feel that this AFV does not have malfunctioning in its technical

equipment ‘were a few sample items. The dependent variable of the developed research

model, “purchase intention” was measured through four items which were validated in

the study of Khazaei and Khazaei, (2016) and measured on a 7 point Likert scale. Based

on the above measurements, the questionnaire was developed using a scale of 38 items

to collect data, to analyze the data, and to arrive at a conclusion regarding the impact

of perceived risk on the relationships between purchasing determinants and purchase

intention of AFVs in Sri Lanka.

��

In the present study, inferential statistics were used to test the hypotheses using

Structural Equation Modeling with the help of Partial Least Squares (PLS), since the

normality assumption was not adequately met in the data set of the present study. SEM

is a quantitative research technique that has been used to explain causal relationships of

constructs in research studies. As explained by Malhotra et al., (2007), the relationship

that has been tested through SEM is in accordance with the hypothesis developed in the

study. The present study was testing the extension that has been made to the existing

theory of UTAUT, and hence, PLS provides explicit hypothesis testing for factor analytical

problems (Stapleton, 1997).

Descriptive statistics for demographics

The sample group of this study consists of 75 percent males and 25 percent females.

As was reported by the Sri Lanka Department of Census and Statistics (2015), the total

number of driving licenses issued during the time period 2009 – 2015 was 45,200,

giving a composition of 23 percent female driving license holders. Therefore, it can

be concluded that the sample which was selected was very similar to the population

��

As for the designations of respondents, a higher representation can be seen from the

senior executive category, which was 34 percent, justifying the fact that most senior

executives can afford to purchase a vehicle in the current industrial environment under

- 85 -

current practices. Executive level representation was 27 percent in the sample, while 32

percent representation was generated from the managerial level. The senior managerial

level only accounted for 11 percent of the sample.

Reliability and validity measures

As was recommended by Henseler, Ringle, and Sinkovics, (2009), the present study

followed a two-step process to calculate and report the results of the PLS-SEM path.

This two-step process is (1) the assessment of the measurement model and (2) the

assessment of the structural model (Henseler et al., 2009). The two step assessment

of the measurement model has been performed based on the measurement of

individual item reliability, the measurement of internal consistency reliability, and the

measurement of convergent validity

In the present study, the reliability of the measures was calculated by the PLS-SEM

algorithm. As was pointed out by Fornell and Larcker (1981), in order to measure the

internal consistency reliability, the measurement indicator of the composite reliability

�� '�� _�� _�� "�� "��

(AVE) score should be .50 or more, and the square root of the AVE should be greater

than the correlation among the latent constructs. As for the above measures, reliability

��%��]_��^]^��'�"��"��%��%�%�

cut-off of .70 the values are satisfactory. Also, the values of the average variance extracted

are in the range of .531 to .714, and since each of the above should have a minimum cut-

�� '��

the measures used.

Validity refers to an instrument that in reality measures what it is expected to measure

(Neuman, 2014). In order to measure item validity, the outer loading of each construct’s

measure was referred, and the results are depicted in the Figure 02, path model. It can

be reported that outer loadings are in the range 0.595 to 0.904, where no outer loading

can be detected below the threshold point of 0.50 (Chin, 1998), and therefore it can

be concluded that there is individual item validity in the present study’s measurement

path model.

Construct validity could be measured through convergent validity and discriminant

validity (Neuman, 2014). Convergent validity is the situation where there is a positive

association of the construct with other measures of the same construct, whereas

- 86 -

discriminant validity illustrates the degree to which the construct does not show a

relationship with other measures that are similar to it (Neuman, 2014). The current

study generated convergent validity by examining the average variance extracted

of each latent construct. Discriminant validity was also generated by comparing the

correlations among the latent constructs and the square roots of the average variance

extracted (Fornell & Larcker, 1981).

��

EE FC PR PE PV PI SI

Effort expectancy 0.819

Facility conditions 0.440 ��

Perceived risk 0.044 0.302 0.565

Performance expectancy 0.767 0.437 0.215 0.803

Price value 0.478 0.181 0.038 0.388 0.757

Purchase intention 0.516 0.277 0.225 0.576 0.584 ��

�� 0.440 0.385 0.076 0.547 0.428 0.505 ��!��

Table 1 illustrates the comparison of correlations among the latent constructs with the

square roots of the average variance extracted. The square roots of the average variance

extracted were all greater than the correlations among the latent constructs, suggesting

��"�� O��>��$��]^�]��

�� "�� '�� *�� '�� %��

%��%�� %�� '��

discrepancy between the model-implied and the empirical correlation matrix could

be addressed. As PLS was run on a variance-based matrix, unlike AMOS, which is a

��"��'��%�� %��%��

quality of the model, has used the standardized root mean square residual (SRMR),

predictive relevance (Q2), the Stone-Geisser indicator, effect size (f2), and the normed

��O!��+��'��"��>�� `��

- 87 -

�� "�� #$��%&%��'()

Saturated model Estimated model

SRMR 0.076 0.076

NFI 0.872 0.872

The difference between the observed correlation and the model implied that the

correlation matrix is depicted by the Standardized Root Mean Square Residual (SRMR).

It also measures the average magnitude of the probable differences between observed

and expected correlations as an absolute measure. SRMR values less than 0.10 indicate

��'�� ̀ ��*�� %��

variance matrix platform is the Bentler-Bonett index or the NFI. The threshold value of

the NFI is 0.90, where values need to be close to 0.90 or above (Byrne, 2008). Therefore,

��'��`��*��'�"��%��'��'��%��

of NFI and SRMR.

�� %�� 2 values) values are one of the criteria which are

used to assess the quality of the structural model in PLS-SEM (Henseler et al., 2009).

��%��%��%��

variable’s variance that is explained by its predictor variables (Hair et al., 2012). As for

the generated R2 values, they could be categorized in levels such as weak, moderate, or

substantial explanation of variance by predictor variables, by looking at the threshold

values of 0.25, 0.50 and 0.75 (Hair et al., 2012) . As Falk and Miller (1992) have pointed

out, the minimum acceptable level of R squared is 0.10. In the current study, the R

squared value is 0.525, which means that the measure of the proportion of purchase

intentions of alternative fuel vehicles that is explained by predictor constructs is at a

moderate level.

Cohen’s Indicator (f2) is calculated and performed by the inclusion and exclusion of

model constructs (one at a time). It illustrates how the measured variance explains

each exogenous variable in the model. Values of 0.02, 0.15 and 0.35 are considered

small, medium, and large explanations, respectively (Hair et al., 2012). As the analysis

��"��*��$��`��_��

�� *��%��%��`��"��'��

��%�� %�� "��"��

on purchase intention.

- 88 -

In addition to the measurement of effect sizes and the predictor’s explanatory power

which uses R2 values as a criterion for predictive accuracy, Hair et al., (2013) have

recommended that researchers assess Stone-Geisser’s Q2 value. This has also been

��%�� %��Q��Q�� [��

Structural Modeling (Richter et al., 2016). A research model with Q2 value(s) greater

than zero is considered to have predictive relevance (Henseler et al., 2009). As has been

depicted in the statistics, all the items and variables listed under Q2 are greater than 0,

��%��'��'��"��"��`�

Structural model

Assessment of direct relationships

After ensuring that the construct measurement indicators were reliable and valid, the

next step was to generate the structural model results. Figure 02 and Table 3 depict the

results generated through the PLS algorithm.

(�� *��#� �

- 89 -

Table 3: The Result of the structural model for direct relationships

H Relationship + P Values � ��#H

1��Q��! 0.266** 0.000 Supported

H2

��Q��! 0.159** 0.014 Supported

H3

�!�Q��! 0.243** 0.000 Supported

H4

O��Q��! 0.084* 0.0890 Supported

H5

�#�Q��! 0.212** 0.000 Supported

Note: ��]��"��"��

O��%�� '�� '��O��*��'�� "��

�� *�� "�� ^�� *�� *��

�� ^�� "�� +�*�"�� %�� *��

compared to the other predictors, it does not have comparative prominence in the

%�� "�� '�� `��

�� ]_�� ]�� !� � ��`�� #� � ��`]`�� !� ��

�� '�� +�� '�� %��

hypothesis is supported by making a direct impact on the purchase intention of AFV’s.

O��%��"��%��%��' �

�� "��"��%�'��%��

Assessment of moderating effects

The current study has used the product indicator approach, using PLS-SEM to detect

and estimate the strength of the moderator, perceived risk, on the relationships

between purchasing determinants and purchase intention of alternative fuel vehicles

(Jörg Henseler & Chin, 2010). Among the four approaches used in PLS, this study has

chosen the orthogonalizing approach, as it is known to generate the best point accurate

estimates for the interaction effect as well as for the single effects. As was pointed out by

Henseler and Chin (2010), it has a high predictive accuracy, as most of the studies using

PLS path models are done for predication purposes, like customer satisfaction indexes

and many technology acceptance studies (Henseler & Chin, 2010).

- 90 -

Table 4: The result of the structural model for direct relationships

H Moderating effect

+ P Values � ��#

H6

��Q��! -0.093** 0.020 Supported

H7

��Q��! -0.169** 0.000 Supported

H8

�!��Q��! -0.159** 0.000 Supported

H9

O��Q��! -0.146** 0.002 Supported

H10

�#��Q��! 0.151** 0.001 Supported

Note: ��"��

As Table 4 depicts, perceived risk weakens the relationships between purchasing

determinants and purchase intention of AFVs, except for the relationship between price

value and purchase intention, where the results reveal that perceived risk strengthens

the relationship between price value and purchase intention of AFVs. Yet, the moderation

role of perceived risk has been established under each relationship in the current study

pertaining to the context of AFVs.

��#

The main purpose of this study was to review the effect of perceived risk on the

relationships between purchasing determinants and purchase intention of alternative

fuel vehicles in Sri Lanka. The said review is based on the theoretical rationale and

��"��%��*��%��*��

��'�� $��*��*��

empirical evidence was presented to support the arguments. This study developed

hypotheses derived from the theories of perceived risk and UTAUT in order to examine

how perceived risk has an effect on the relationships between purchasing determinants

and purchase intention of alternative fuel vehicles.

The current study has three main objectives; to identify the key purchasing determinants

of AFVs; to examine their impact on purchase intention; and to measure the effect of

perceived risk on the relationships between purchasing determinants and purchase

intention of AFV’s. In terms of measuring the relationships and effects in line with the

developed hypotheses, this study employed Partial Least Squares (PLS)-Structural

Equation Modeling.

- 91 -

��"�� *�� %��

Theory of the Acceptance and Use of Technology (UTAUT) (Venkatesh, Thong, & Xu,

`�]`�� *�� "�� %�� *�� "�� %��

�� "��!��

��*��"��"��"��*��

purchase intention of AFVs.

+�*�"��%��"��*��O��

�� *�� '�� %�� "��'�� %�� %��

making purchase decisions related to AFVs, the current study has not found it to be

�� %�� "�� $� *�� %��

to facilitating conditions in the current research context, the study argues that the high

penetration of AFVs does exist, as the risk related to facilitating conditions may not have

been perceived by customers.

In striving towards achieving the third objective, this study argued that as the UTAUT

explains people’s subjective assessments of actions and efforts in making a purchase

decision (Venkatesh, Thong, & Xu, 2012), the uncertainty and negative consequences

��'��"��%��'��

Hence, those construct should be moderated by perceived risk. As was found by the

current study, perceived risk moderates the relationship between each purchasing

determinant and purchase intention of AFVs. However, it could be observed that the

impact created on the relationship between price value and purchase intention was a

positive effect, in contrast to the predictions of the theory of perceived risk, where the

uncertainty and the negative consequences relating to the purchase decision should

hinder or deter the purchase intention (Cox, 1967). According to Brewer, Weinstein,

Cuite,and Herrington (2004), the reason for this result could be validated through the

motivational perspective of risk, whereby greater perceptions of risk actually facilitate

behavioural promotion. In the current research context this could be due to the fact

that the price perceptions of customers of AFVs will be based on mainly nonmonetary

and other indirect costs, and hence, that customers were looking at prices in line with

��%��"��=�%��`�]��¡"� ��>��`�]_��

Unlike traditional customer behaviour with regard to gasoline automobiles, people

*��$�%��"��'��*��"��

��*�� *� �� $�� O#� ��¡"� � �� `�]_��

can be argued that the existence of risk enhances the importance of considering the

��'�'��"��%��*��

- 92 -

intention. Moreover, when the tradeoff between quality and perceived risks decreases,

��*��*�� ¡"� ��`�]_��!��

a context, some researchers have found that there is an impact of perceived risk on the

trust in and quality of the product, when assessing the purchase intention (Kim, 2015;

Williams & Noyes, 2007).

Based on the above arguments and in line with the motivational perspective of perceived

risk, which is expressed by a positive relationship between risk and behaviour, it has

been proven that greater perceptions of risk facilitate behavioural promotion (Brewer

��`�� '��

study reveal a different form of the impact of perceived risk on the purchase intention

towards AFVs, and not that they contradict the risk theory per se.

Theoretical implications

This study intends to explain the instances where perceived risk has an impact on the

relationships between purchase intention and purchasing determinants in the context

of alternative fuel vehicles. As perceived risk theory emphasizes, individuals’ purchase

decisions are goal driven, and when individuals perceive the negative consequences of

their decisions related to goals, the intention to purchase the product or service may

be delayed, or they might even be deterred from purchasing (Cox, 1967). According

��%��%�� %��

making process, whether to purchase an AFV or not, entails risks (Gorman et al., 1993).

However, there are not many insights provided by existing theories to understand

customer decision making related to the need-motivation-risk-intention linkage. Given

the existing knowledge on this complex situation, this study contributes to the existing

�� ' ��

it explains how people in Sri Lanka make a subjective assessment of their effort and

expectations in terms of their purchasing decisions related to AFVs, and in such a

situation, it explains how risk has an effect on the relationship between expectation

(purchasing determinants) and purchase intention. Previous studies and theories do

not shed light on customer behaviour in this context.

- 93 -

Managerial implications

In most contexts, consumers are looking for risk reduction strategies to make

their lives more comfortable with high involvement purchases, or to minimize the

perceived risk until it is below their level of acceptable risk (Chu & Li, 2008). Simply

put, customers want to understand more about their purchase item and to know

how to resolve the uncertainties within the transaction process. In such a situation,

what can be derived from this study is the manner in which companies can employ

risk relievers in accordance with the strength of the impact created by perceived risk

on the decision making process. This revelation has several implications as far as

the managerial perspective is concerned. When a company adopts a technology that

consists of perceived risk factors, it needs to emphasize ‘ease of use.’ However, when

users perceive a low risk, the company has to focus on communicating the ‘usefulness’

of the technology, which enables it to reach its customers effectively. In doing so, an

automobile company could call on its customers to test drive its vehicles, which would

be a good opening move for customers to get a feel for the vehicles, generating interest

in the vehicles, arousing desire and resulting in a purchase This will entice a customer

from just having an interest in a product to making a purchase. Concurrently, companies

have to have adequate service centers and infrastructure which will result in a positive

buzz for their product in the long run, since the degree of impact of current purchasing

determinants on purchase intention as customer experience evolves.

Limitations and directions for future research

This research can only be generalized to the Sri Lankan alternative fuel vehicle context.

¢��*�� %�� '��"�� %� � �� "��

�� !�� '�� %�� *��

AFV technology in different ways than Sri Lankan customers do, and hence a broader

understanding of consumer behaviour dynamics can be obtained by employing a mixed

method or a qualitative research approach. Moreover, as this study employed the

��"��%��[��%� ��'��|�'��

Opening an avenue for a future research study, the study suggests an examination of

the concepts of perceived worry, risk tolerance and perceived risk, to see whether

they have the same impact on purchase intention or not. There is ample room to

examine and differentiate the concepts of risk tolerance and perceived risk related to

purchase intention in the consumer behaviour behavioural context. This has also been

- 94 -

�� %��"��$�

on the relationship between facilitating conditions and purchase intention has not

been comparatively strong. This can also be explored by employing a mixed method

to examine the psychological aspects of purchase intention. As this study is primarily

based on reasoning related to the effect of perceived risk on purchase intention utilizing

�� %�� "�� $� �� '�� "��

other theoretical models as well. Commercially, the impact of branding and customer

perceived brand values would lead to the reduction of perceived risks in selected

contexts, and therefore, future research can be undertaken to measure brand image

and perceived brand values in order to investigate whether they change the impact of

perceived risk on purchase intention or not.

Conclusion

The study presents and validates performance expectancy, effort expectancy, social

�� "�� %��

intention of customers of AFVs in Sri Lanka. However, though it is cited in previous

studies that facilitating conditions is the most important factor in the automobile

research context relating to AFVs, this study concluded that the impact of facilitating

conditions on the purchase intention of AFVs in Sri Lanka was comparatively low. This

was attributed to perceived risk, which was treated as a moderating variable of this

�� "��$��

purchase of AFVs, especially in Sri Lanka.

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