Virtual test-driving: The impact of simulated products on purchase

intention.

Abstract

This paper studies a number of key determinants of users’ experience and engagement

when driving a simulated car model, the outcome of this engagement in relation to enjoyment

and satisfaction and the role of user satisfaction in purchasing the actual product. We test a

holistic model using an experimental quantitative approach. Our analysis suggests that

hedonic experience may create higher levels of engagement among users of the simulated car.

Enjoyment and engagement were found to positively influence user satisfaction while driving

the simulated car. In turn, user satisfaction with the simulated car was found to positively

influence purchasing intention for the actual car. Our work has shown how a simulation

based on widely available technologies can provide a foundation for the development of a

relationship between a user and the simulated product. Consequently, our research findings

have significant theoretical and practical implications beyond the auto-manufacturing

industry, as experiencing simulated products can play an important role in the context of

electronic commerce. This is especially true given the increasingly important role 'experience'

plays in electronic marketing.

Keywords: user experience, product simulation, product authenticity, enjoyment,

engagement, purchase intention, electronic commerce

Virtual test-driving: The impact of simulated products on purchase

intention

1 Introduction

The online retail experience is typically confined within the narrow two-dimensional

boundaries of the web browser. Users get to browse products and read about their

characteristics on web pages that often feature still images and occasionally videos.

Arguably, although the way users experience and interact with products online has not

changed fundamentally since the early days of the World Wide Web, recent advances in

technologies can not simply play a key role in influencing in-store consumer behaviour when

it comes to searching, choosing and comparing products, but also when it comes to

interacting with them (Pantano and Naccarato, 2010). Alternative options such as that of

metaverse retailing (Bourlakis et al., 2009; Domina et al., 2012) may have offered a glimpse

of what is potentially possible, but experiencing real products online is still a phenomenon in

its infancy. Still, with the advent of new technologies that promise higher authenticity and

immersion, such as 3D viewing, product simulations promise to become more realistic and

their application in marketing and promotion are likely to become more valuable. For users

who want to experience products online and not just collect information about them,

simulations could facilitate and inform their decision making, while for retailers online

simulations could provide an additional means for engaging and influencing consumer

behaviour.

The research objective of this study is to identify the key determinants of users’

experience and engagement when driving a simulated car model. User experience as a

consequence of a user’s internal state, the characteristics of the designed system and the

context or the environment within which the interaction occurs are about technology that

fulfils more than just instrumental needs in a way that acknowledges its use as a subjective,

situated, complex and dynamic encounter (Hassenzahl and Tractinsky, 2006). This is why in

this paper we are interested not only in the human-computer interaction aspects of the

simulation, but also in how these may lead to affecting purchase intention. Consequently, we

also study the outcome of this engagement in relation to enjoyment and satisfaction and the

role of user satisfaction in purchasing the actual product. This is not to say that an online

simulation may necessarily replace the real experience (i.e. the test drive of the real car) and

suffice to lead to a purchase, even though it may be technologically possible to simulate a car

realistically. In fact, one could think of flight simulators that are good enough for pilots to

learn how to fly commercial jets. However, these are very complex and expensive approaches

to addressing this challenge. Our exploratory work considers a practical and financially

viable alternative based on widely available game consoles. If driving a virtual car, even on a

game console, is sufficient to positively influence customer purchase intentions, then this can

potentially pave the way for robust, authentic and purpose-built simulations to be created. By

using relatively inexpensive components to build a “simulator” the study demonstrates in

practice the potential that such an approach could have.

In the following section, the paper outlines the relevant theory underpinning our

theoretical framework. The methodology that was adopted will then be discussed and the

results presented and commented on. Our overall findings will be discussed in the context of

the literature, before concluding by outlining future research avenues.

2 Literature Review & Conceptual Framework

A simulation can help users engage better with products; it improves their learning

processes and enriches their overall experience (Algharabat and Dennis, 2010a, b). This

emanates from a “3D virtual experience that should be an authentic presentation of the direct

(offline) experience” and where “virtual objects presented in 3D in a computer-mediated

environment are perceived as actual objects in a sensory way” (Algharabat and Dennis,

2010a, p.17). The locus of customer value is shifting from product to service to experience

and interaction (Prahalad and Ramaswamy, 2003). The marketing implications from the

above are numerous. Hedonic and affective factors such as user engagement and enjoyment

significantly influence consumer behaviours (Zaman et al., 2010). For example, the work of

Algharabat and Dennis (2010a) provides evidence for the importance of 3D applications for

customer attraction within the online retail context. A simulation could also improve the

overall user experience, acting as a critical enabler of the online relationship between the user

and the product. In turn, this relationship could provide the platform for a possible purchase

of the real / physical product, assuming that the user has had an engaging and enjoyable

experience with the simulated product and was fully satisfied. Simulation experience, user

engagement, enjoyment and satisfaction and finally purchase intention represent the key

elements of our framework (Figure 1) that we will be testing in this paper. Firstly, we study

the determinants of the user experience while undertaking the simulation and how these

affect engagement. Then, based on optimal flow theory we examine how attentional

involvement and engagement affect enjoyment and satisfaction. Finally, we examine how

satisfaction affects purchase intention.

PLEASE INSERT FIGURE 1

2.1 Determinants of users’ experience

There is a large amount of research that analyses the enjoyment of individuals when they

use computers and especially the interactive challenges they may experience from any

computer games and similar online activities. Liu and Shrum (2002) formulate a three

classifications of this interactive experience: user-machine interaction, user-user interaction

and user-message interaction. In this paper, we focus on the first classification, showing

computer systems being responsive to users’ actions (for further analysis of interactivity, see

McMillan and Hwang, 2002). More specifically, the concept of tele-presence is relevant to

our study as it refers to the experience produced by a computer-mediated environment (Fiore

et al., 2005b), which depends on how closely this experience simulates the consumer’s real-

world interaction with a product (Shih, 1998). Interactivity is a critical characteristic for

enabling tele-presence (Steuer, 1992), with Vrechopoulos et al. (2009) supporting the latter

statement for virtual reality / virtual environment applications, such as the three dimensional

(3D) simulated car model we will be testing in this paper. In this context, 3D does not refer to

the three dimensional viewing that viewers may experience in cinemas, but to interacting

with an object in a three dimensional world. For tele-presence, Fiore et al. (2005b) suggest

the following three key determinants: firstly “the ability to control the relationship of one’s

sensors to the stimulus”, secondly “the ability to modify the stimulus” in order to provide a

more vivid and stimulating experience and lastly “the extent to which online sensory

information approximates the real world stimulus”. Sheridan (1992) suggests that virtual

model technology will foster all three determinants. Fiore et al. (2005b) found that the

provision of real world experiences for a product within an online application had a positive

impact on consumers as they feel they make a better decision and get a more rewarding

experience. They noted that for products that are rich in sensory information, require various

kinds of inspection and can be modified during usage, a higher degree of the three

determinants of tele-presence could be a key advantage. In terms of these determinants the

ability to control is a powerful sense and this is the key reason why individuals are captivated

by computer-based activities (Ghani and Deshpande, 1994; Song and Zinkhan, 2008). Klein

(2003) found that user control led to a stronger attitude towards the products under

examination. “The ability to modify the stimulus” determinant refers to changes and

modifications to elements of products and services offered which result in a positive user

reaction.

Fiore et al. (2004) note that mass customisation technology used in apparel products has

created a positive, unique and stimulating experience for consumers, as they have an input

during the design of these products. Co-creation of value can have not just marketing and

customer relationship management implications (Payne et al., 2009; Payne et al., 2008;

Prahalad and Ramaswamy, 2000), but also inform innovation (Sawhney et al., 2005), as users

can effectively feed back their ideas via the simulation. Prahalad and Ramaswamy (2004)

argue that co-creation is about the joint creation of value by the company and the customer,

by creating an experience environment in which consumers can have active dialogue and co-

construct personalised experiences. These consumers are seeking variety, they are curious

and risk takers and are more interested in the new stimuli that new technological

environments offer (Fiore et al., 2005a). Simulated products can potentially offer such

customisation opportunities before the user begins the simulation. Shih (1998) also

hypothesised that vividness provides hedonic pleasure, which will increase time spent

(online) and increase possible repeat visits. “The extent to which online sensory information

approximates the real world stimulus” determinant refers to the extent to which the online

environment can resemble the real environment for products and services used online.

Relatively small differences in online presentation, e.g. whether a product is communicated

via a static or moving image, can make a clear difference in the build-up of attachment

(Ashman and Vazquez, 2012). Fiore et al. (2005b) report that interactivity may reduce any

negative effects resulting from the inability to experience the real product, as virtual model

technologies can convincingly imitate the authentic product, while Klein (2003, p.42) stresses

the point that “as the degree of tele-presence increases, the more similar the mediated

experience will be to a direct product experience”. Interactivity can take various forms, such

as customisation of the information presented, image manipulation via the use of various

authentic graphics and colours, three dimensional virtual tours and entertaining contests and

games to name a few, which create a unique and authentic experience for the user (Fiore et

al., 2005a). This is the case in our paper, where an authentic experience emanating from the

use of the 3D car model is expected to generate higher levels of engagement. Given the

nature of our work, for engagement we adopt the definition in Hutchins et al. (1985), which

considers direct engagement as taking place when a user experiences direct interaction with

the objects in a domain, leading to a feeling of involvement directly with a world of objects.

As far as authenticity is concerned, there are three important points to emphasise. Firstly,

authenticity does not mean a perfect reproduction of reality, which may be neither practical

nor desirable (Gonçalves et al., 2010). Instead, as has been suggested in the context of

learning games, the key concern is the level of authenticity the game requires in order to have

an accurate match of what users can expect in the real world with what they need to learn

(Gonçalves et al., 2010). Secondly, authenticity and realism do not necessarily match,

although as might be expected the more authentic and realistic a simulation is the closer it

will be to the real product. Finally, tourism literature, which has seen an emerging body of

work dedicated to authenticity, has placed it in the context of two major ideologies: “the

constructivist approach (mirroring tourists’ desire for a specific authentic ingredient,

sharing their vision, or selling their perceptions) and the essentialist approach (using already

set standards) to describe, derive, or create authenticity” (Chhabra, 2005, p.64). Given that

authenticity has become an important dimension of brand identity as marketing managers

seek to create stronger brands (Alexander, 2009), how users translate these two extremes can

effectively set their expectations of the simulation's outcome.

Based on the above analysis, the following hypotheses are constructed:

H1: Sufficient level of control will create higher levels of engagement among users of the

simulated car.

H2a: Vivid colours will create higher levels of engagement among users of the simulated

car.

H2b: Vivid graphics will create higher levels of engagement among users of the

simulated car.

H3: Product authenticity will create higher levels of engagement among users of the

simulated car.

Bhattacherjee (2001) noted that consumers’ expectations are often coloured by their first-

hand experience. In this paper, the first hand user experience stems from driving the

simulated car and by interacting with it. This experience will be influenced by both utilitarian

(instrumental) and hedonic (experiential) values (Holbrook and Hirschman, 1982) and many

researchers in the past have shown that consumer attitude is inherently bi-dimensional,

relying on both sets of values (see for example Batra and Ahtola, 1991). Utilitarian values

can help users make a better, more informed and rational decision for evaluating the physical

car, with Fiore et al. (2005b) suggesting that utilitarian value could also be in the form of

saving time / effort and minimising risk. Hedonic dimensions include factors such as colour,

graphics, animation, and other design elements that either implicitly or explicitly cause an

affective state of pleasure (Coursaris et al., 2008). Hedonic values will emanate from fun,

enjoyment, entertainment and excitement when driving the 3D car model, while the overall

3D virtual experiences engage consumers further and entice them to purchase relevant

products (Fiore et al., 2005a). Kim and Forsythe (2007) focused on the role of hedonic

dimensions when using product virtualisation technologies in online apparel shopping. These

technologies are popular in the fashion industry and include 3D rotation views and virtual

fitting of the clothes; hence, it is not surprising that few studies in the past examined relevant

issues in relation to an apparel shopping experience (see also Song et al., 2007 or Pantano and

Laria, 2012). Kim and Forsythe’s (2007) major contribution to the literature is the finding

that consumers’ attitudes rely on the utilitarian or hedonic nature of the actual technology

involved. Therefore, in their study, they showed that when consumers use a technology that is

designed to provide hedonic benefits (e.g. entertainment), then they will be more inclined to

have hedonic motivations than utilitarian ones (Kim and Forsythe, 2007). For the latter, a

technology-oriented perspective that treats shopping media as cold information systems,

rather than immersive, hedonic environments, is likely to be fundamentally misguided,

especially for products with strong hedonic attributes (Childers et al., 2001). Ideally, a

shopping experience should provide a good balance between the two sets of values,

depending on the product in question.

Based on the above discussion we conclude that both hedonic and utilitarian experience

values could result in improving user engagement and attentional involvement and we

formulate the following hypotheses:

H4: Utilitarian experience values will create higher levels of engagement among users of

the simulated car.

H5: Hedonic experience values will create higher levels of engagement among users of

the simulated car.

2.2 Process for generating users’ experience outcome

The theory of optimal flow can be used to study individual experiences when using

computers and the influential factors in those experiences (Ghani, 1995). Flow theory is the

critical conceptual link in our framework as it suggests that user experience, engagement and

enjoyment could be determining factors in satisfaction. Csikszentmihalyi (1990, p.4)

pioneered this theory, defining flow as “the state in which people are so intensely involved in

an activity that nothing else seems to matter; the experience itself is so enjoyable that people

will do it even at great cost, for the sheer sake of doing it”. Elements of flow may be divided

into two categories (Bridges and Florsheim, 2008). The first category develops out of a need

for a utilitarian process and outcome, while the second one includes such hedonic flow

elements as tele-presence, time distortion, arousal, and challenge, which represent escaping

from real life into the online environment, where the users perceive themselves as more

socially adept than in reality. Enjoyment provides hedonic value through consumption

experience (Holbrook and Hirschman, 1982) and although both utilitarian and hedonic values

are important elements resulting in higher product usage (Voss et al., 2003), in the

entertainment context, consumers may prefer hedonic over utilitarian alternatives (Dhar and

Wertenbroch, 2000). Perceived quality, and enjoyment, are significantly associated with

hedonic values (Benlian et al., 2010; Nah et al., 2011). When it comes to the impact of flow

on utilitarian and hedonic values, it has been shown that flow positively influences the

hedonic value of consumers' online shopping experience, but does not influence their

utilitarian value (Senecal et al., 2002). Csikszentmihalyi (1990) also suggested that the

perceived challenge of this activity is a determinant of the experience the individual has.

Empirical evidence suggests that flow and presence, as discussed earlier, are distinct

constructs, the first referring to the sensation of being involved in the action, the latter

referring to the sensation of being there (Weibel and Wissmath, 2011). Two key elements of

flow theory are the total concentration / attentional involvement / engagement in an activity

and the enjoyment stemming from that activity (Ghani and Deshpande, 1994). Enjoyment in

our context relates to the extent to which the activity of driving the simulated car is perceived

to be enjoyable (Davis et al., 1992). Based on the above, it is clear that engagement and

enjoyment are interrelated concepts. Engagement has been studied under various perspectives

and settings. For example, Turkay and Adinolf (2010) analysed flow theory in virtual worlds

(massively multiplayer online games) and demonstrated how specific variables such as the

customisation of game design features could have a positive impact towards user

engagement. Similarly, D’Alba et al. (2011) stressed how engagement can motivate students

to learn successfully in virtual learning environments. Mollen and Wilson (2010, p.923) shed

further light on the role of engagement in relation to consumer experience and following a

comprehensive analysis they attempted to illustrate it by providing the following definition:

“It (engagement) is characterised by the dimensions of dynamic and sustained cognitive

processing and the satisfying of instrumental value (utility and relevance) and experiential

value (emotional congruence with the narrative schema encountered in computer-mediated

entities)”. Building on the previous discussion, we suggest that attentional involvement /

engagement could be a key prerequisite for the emergence of enjoyment (positive experience)

and for the positive evaluation of a service (satisfaction), i.e. driving the simulated car in this

paper. In many technological applications (e.g. mobile entertainment services such as video

and music), users are required to engage if they are to make the most of the experience and to

get a fruitful and joyful experience, such as enjoyment (Oulasvirta et al., 2005). Vorderer

(1992) distinguished two levels of engagement: low and high engagement. In our case, an

individual watching a driving demonstration of a simulated car by another user and from a

distant perspective could be a low engagement situation. However, if that individual is

actively driving the car and is emotionally and cognitively engaged then this presents a high

engagement situation. The low engagement situation is normally associated with utilitarian

values, whilst the high engagement one that could result in enjoyment is associated with

hedonic values. Consequently, engagement could act as an antecedent to enjoyment.

Based on the previous analysis, we hypothesise:

H6: Engagement will positively influence the enjoyment a user derives from driving a

simulated car.

Affective factors such as engagement and enjoyment have been shown to influence

information system usage and user satisfaction. For example, Beaudry & Pinsonneault (2010)

and Loiacono & Djamasbi (2010) demonstrated the importance of emotion and mood on

information technology usage. User engagement (Jiang et al., 2010) and emotional enjoyment

due to one’s tele-presence in the virtual space (Suh et al. 2011) are significant factors in user

satisfaction. In an experimental design with school students, Van Vugt et al. (2006)

developed a model in which they aimed to link user engagement with satisfaction. Their

perspective was that issues such as user involvement (e.g. empathy, sympathy), as well as

distance (e.g. antipathy, boredom), are key dimensions of engagement and user experience.

Their work showed that satisfaction within a specific computer / interface / 3D virtual

environment is the outcome of the process of getting involved with and keeping distant from

the character. More importantly, their extensive work in this study and subsequent ones (see

for example, Van Vugt et al., 2009) empirically confirmed that engagement strongly predicts

user satisfaction. According to Vorderer et al. (2004) enjoyment is a pleasant experiential

state. Other researchers (Fiore et al., 2005a; Fiore et al., 2005b; Webster, 1989) have argued

that a novelty within a specific technology (image interactivity) has led to enhanced pleasure,

experiential value and enjoyment. Overall, enjoyment is a key determinant of a user’s

experience that can positively influence the user evaluation of the product / service. This

positive influence could lead to satisfaction, which is viewed as critical for building and

retaining loyalty with long-term customers (Anderson and Sullivan, 1993).

Online interaction and experience should not be narrowly focused on the products and

services themselves, as the environment within which they take place plays an important role

too. Sylaiou et al. (2010), studying virtual museum experience, showed that entertainment in

general and enjoyment in particular are desirable attributes in virtual environments and a

significant positive correlation was found between enjoyment and satisfaction when

interacting with a technological system. In a commercial setting, Ha and Stoel (2012)

reported that while shopping online for apparel items, customers seek and value

atmospheric/experiential features such as those generating feelings of fun, excitement, or

pleasure and consider them as distinct benefits that influence their overall assessment of e-

stores. Similarly, Pantano and Servidio (2012), while studying virtual stores, found

consumers' perceived enjoyment to be a motivating factor for the store choice, as well as for

the quality of the shopping experience. Finally, end-user satisfaction is a major goal in every

user-system interaction as it presents a performance measurement of the system (Van Vugt et

al., 2009).

Based on the above, we propose the following hypotheses:

H7: Engagement will positively influence user satisfaction with driving a simulated car.

H8: Enjoyment will positively influence user satisfaction with driving a simulated car.

2.3 Outcome of users’ experience

Lastly, driving a simulated car may influence the purchasing decision with respect to the

actual product. In general, many offline and online studies in the past have shown the positive

influence of consumer satisfaction (and experience) towards purchasing a product. For

example, the offline study by Cronin et al. (2000) confirmed such an association. Also, an

online study by Hausman and Siekpe (2009) showed the positive association between

entertainment, enjoyment and satisfaction when a consumer is using a website and his/her

purchasing intentions to buy products from that website. Fiore et al. (2005a; 2005b) report

similar studies on purchasing products from websites and note that the web technology helps

consumers in making more informed decisions about purchasing products, including a better

evaluation of the available product range. This is due to the interactive nature of these

technologically-mediated environments, permitting customers to be entertained, be satisfied

enough to become a buyer of a product or even to become a regular web customer (see for

example Fiore and Jin, 2003; Li et al., 2001). These individuals could also be transmitting

positive word-of-mouth messages about their experience driving the simulated car,

notwithstanding that they could be positive about the actual product, the car, per se. Equally,

within a 3D context, Yang and Wu (2009) showed that satisfaction emanating from a 3D

mediated representation can increase e-shoppers’ buying behaviour and intentions. More

broadly it has been shown that past work on antecedents and behavioural outcomes (e.g.

customer retention, referral, purchase and repurchase intentions) of satisfaction in offline

settings applies to online settings too (Bansal et al., 2004). This is despite the fact that

satisfaction in an offline context is driven by provider-customer interaction during the service

encounter, whilst it becomes a non-personal interaction in the online context, where

satisfaction is driven by website characteristics and customer service features. Not

surprisingly, users have been found to regard technologically-mediated relationships as less

friendly and co-operative, but still more task-orientated than their human-to-human

counterparts (Keeling et al., 2013).

Based on the previous analysis, the following hypothesis is proposed:

H9: Satisfaction emanating from driving the simulated car will positively influence the

intention to purchase the actual car.

3 Methodology

3.1 Experimental setup

The research design is within-subject. A within-subject experiment was conducted to test

the model (Figure 1) proposed in this paper. The model has a total of 11 variables, or

constructs in the language of structural equation modelling. Bentler and Chou (1987) suggest

that the ratio of sample size to number of constructs should be at least 5:1, or 10:1 to be

optimal. Thus we aim at collecting an optimal sample size of around 110 data points. For the

setup an XBOX 360 with a Logitech Force Feedback wheel and pedals (i.e. the user can feel

the car’s reaction, e.g. by the wheel not steering as easily, to certain stimuli) was used. A 32

inch LCD screen was used to project the game. A relatively large screen was selected as

previous research suggests that projecting the digital gaming contents in a physically more

prominent form needs to be counted as one of the central concepts among all the contributing

factors of presence (Hou et al., 2012).We performed manipulations of the user experience

levels through two game versions of the same game and two interfaces. The two game

versions used were Forza Motorsport 2 and 3, which offer the opportunity to try a “good” vs.

a “better” simulation of the same product within the same environment. The two interfaces

were a wheel and a gamepad. With this 2 x 2 design, participants could experience different

levels for the six determinants of user experience: control, colour vividness, graphics

vividness, authenticity, hedonic and utilitarian values.

Participants were first briefed about the project, also completing their demographic

information, and were asked to assume they were interested in buying a Mini Cooper S. This

brand and model was selected because this was a car most users would probably be aware of

and because it was available in both game versions used to simulate it. Users were asked to

consider how realistic the car felt (both inside and outside), its behaviour, the environment,

the graphics, the weather etc. To achieve this they were asked to try driving the car using the

various available views (with the camera being inside the car, when available, outside and

behind the car, outside and above the car, in front of the car). They were also asked to drive

the car conservatively to start with, not exceeding 100Km/h. This was so that it would make

the driving more realistic and also because it would help them control the car better. The car's

driving supporting technology, such as ABS and traction systems, as well as assisted braking,

was on. Users were first asked to drive 3 laps in central London in order to familiarise

themselves with the 2 interfaces. Participants drove a Mazda MX-5 (using the ‘Project

Gotham Racing 4’ game) and not a Mini Cooper S, in order not to influence their perceptions

of the car when the actual drives were to be undertaken. Then, they were asked to play Forza

Motorsport 2 and 3 using the wheel and gamepad, driving the Mini Cooper S in central New

York (Screenshot 1). Each of the 4 drives (2 versions x 2 interfaces) lasted 10 minutes, which

should have provided users with sufficient time to get used to the environment and engage

with the simulation. Given that each run lasted about 10 minutes, each data collection session

lasted for about 1 ½ hours. After each drive, the same set of questions was answered. The

sequence orders of the four drives were assigned randomly. Each experiment was concluded

by asking participants if they were familiar with a particular car brand and model. If this was

available in the game’s car list, participants were asked to drive the familiar model with the

two interfaces and compare it to the experience of driving the real car.

PLEASE INSERT SCREENSHOT 1

3.2 Measurements

All measurement scales are adapted from the existing literature (see Table 1). We

operationalised the measurement instrument in the same way as previous research. The

questionnaire consists of 38 items to assess the ten constructs: control, colour vividness,

graphics vividness, product authenticity, hedonic values, utilitarian values, engagement,

enjoyment, satisfaction, and purchase intention. All items are on a 7-point Likert scale.

Control consists of four items such as “I felt that I could control the car speed easily” and is

adapted from Liu and Shrum (2002), McMillan and Hwang (2002) and Song and Zinkhan

(2008). Colour vividness has three items such as “There are lots of colours in the game” and

is adapted from Fiore et al. (2005b), Klein (2003) and Steuer (1992). Graphics vividness is

measured by three items such as “The car and the outside environment illustrated by 3D

graphics was very real” and is adapted from Fiore et al. (2005b), Klein (2003) Steuer (1992).

Measuring product authenticity is based on four items such as “3D lets me feel as if I am

driving a real car” and is adapted from Algharabat and Dennis (2010a). The hedonic value

construct consists of four items like “Would be truly enjoyable” and is adapted from (Babin et

al., 1994). For measuring the utilitarian value we have four items like “Aid me in evaluating

the car model” and is adapted from Fiore et al. (2005b).The engagement construct consists of

four items such as “I am deeply engrossed” and is mainly adapted and modified from Ghani

(1995). Enjoyment, having four items such as “The game is interesting”, is operationalised

with modification from Ghani (1995). The satisfaction construct, comprising four items such

as “I am pleased with the experience in watching the video clip”, is adapted from

(Bhattacherjee, 2001). The purchase intention construct is measured by four items such as

“The driving experience in the game would increase my intention to buy the car model I

played with” and is adapted from Fiore et al. (2005b).

PLEASE INSERT TABLE 1

3.3 Sampling and participant information

In our experiments, we collected a total of 144 data points, which are sufficient for our

analysis (Bentler and Chou, 1987). One sixth of our subjects were women. The average age

was 26. All of them were students in higher education who had a driving license, with 22

owning or having previously owned a car. The average experience of playing video games

was 12 years, while the average experience of playing car games was 7.7 years. The average

subject plays video games and car games a few times per month and a few times per quarter

respectively. None of them had played the particular car games used in the experiment

before. Their average experience on a 7-point scale (1:the worst, 7:the best) of using a wheel

and gamepad to play car games were 3.42 and 4.15 respectively.

3.4 Statistical Analysis Approach

In this research, we use a 2x2 design to create experiment sessions with enough

variations in user experience through two controllable factors: game versions and control

interfaces. The research model is a structural model, lending itself naturally to structural

equation modelling analysis. Thus we applied this class of techniques in testing the

hypotheses. Before analyzing the data to search for support of the research model and its

associated hypotheses, standard procedures of assessing the quality of the data collected are

followed. The measurement instrument (i.e. the questionnaire) and data collected are assessed

in terms of convergent and discriminant validity, composite reliability, and risk of having

common factor bias using Harman’s single-factor test.

4 Results

We follow the general structure for validity analysis proposed by Henseler et al. (2008).

The psychometric properties of the scales, descriptive statistics, and correlations are shown in

Tables 2 and 3. To test the reliability and validity of the measurement constructs, we

computed the average per participant to obtain the final score of each measurement item. This

averaging is calculated only for reliability testing of the measurement instrument. In

subsequent analysis, raw data collected from the four rounds was used to take advantage of

the within subject experiment design. Except for that of Uti4 (the fourth item of utilitarian

value construct), PI1 and PI2 (the first and second items of the purchase intention construct),

all standardised factor loadings are significant and above 0.70 (Fornell and Bookstein, 1982),

indicating excellent convergent validity. While Uti4, PI1 and PI2 are potential candidates for

removal, as they have standardised factor loadings of .49, .64, and .43 respectively, we

looked further into the composite reliabilities of the constructs. Composite reliabilities

exceeded the 0.70 cut-off point (Boudreau et al., 2001). Given these excellent composite

reliabilities and also further support from discriminate validity, as discussed below, we

decided to keep all question items for measurement.

PLEASE INSERT TABLE 2

Discriminant validity was assessed by comparing the average variances extracted (AVEs)

with squared correlations among latent variables (Fornell and Larcker, 1981). The AVEs are

all above the recommended 0.50 level (Hair et al., 1998), suggesting that more than half of

the variance observed in the items was accounted for by their hypothesised constructs. All

squared correlations were lower than the corresponding AVEs, thus supporting discriminant

validity. Considering that a few pairs of the constructs have high correlations with values

higher than 0.7 we further examined the possible common method variance by Harman’s

single-factor test. Exploratory factor analysis with all items entered did not support a

common method factor that explained the majority of the variance.

To test our hypotheses, the conceptual framework was tested empirically as a single

research model, using the partial least square (PLS) approach of the Structural Equation

Modelling (SEM) technique. PLS based SEM analysis does not generate any overall

goodness-of-fit indices similar to the way covariance-based SEM does (Hu and Bentler,

1999). The logic of multiple regression was followed to understand the overall model fit. The

empirical results are summarised in figures 2 to 4. The results are presented in three separate

figures for the purposes of clarity only. Our whole conceptual model was empirically tested

using SEM, implemented as a single research model. The numbers above the arrows are the

path coefficients and the numbers in the circle are the R-squared figures. The R-squared for

the engagement, enjoyment, satisfaction, and purchase intention constructs are 39%, 55%,

39% and 31% respectively. An R-squared figure of 67% could be considered “substantial”,

35% could be considered as “moderate”, and 19% as “weak” (Chin, 1998). Our reported R-

squared figures mainly range between substantial and moderate. A good level of variation in

the four constructs was explained with our conceptual framework. The statistics show that the

model fit is very good, supporting the claim that the conceptual framework is empirically

valid.

PLEASE INSERT FIGURE 2

We searched for support for the hypotheses through the significances of the path

coefficients with a one-tailed t-test. From figure 2, the path coefficients for H1, H2a, H3, and

H4 are not statistically significant. Therefore hypotheses H1, H2a, H3, and H4 are not

supported. The path coefficient for H2b is .219, which is significant at the 5% level and

consequently H2b is supported. Graphics vividness influences the participant engagement

level positively and significantly. The path coefficient for H5 is .360, which is significant at

the 1% level. Thus, H5 is supported. Hedonic value has a significant and positive impact on

the consequent participant engagement level.

Figure 3 shows the empirical results for the remaining part of our conceptual framework.

All path coefficients are significant, providing support for the three underlying hypotheses.

The path coefficient for H6 is .394, which is significant at the 1% level. Consequently, H6 is

supported. Enjoyment will positively influence the participant satisfaction level with the

gaming experience. The path coefficient for H7 is .743, which is significant at the 1% level

and H7 is supported. The participant engagement level, which represents the quality of the

gaming experience, will significantly and positively affect the perceived enjoyment level of

the participant. The path coefficient for H8 is .272, which is significant at the 5% level, and

consequently H8 is supported. The engagement level experienced by the participant will have

a significant positive impact on the corresponding satisfaction level.

PLEASE INSERT FIGURE 3

Finally, as figure 4 shows, the path coefficient for H9 is .558, which is significant at the

1% level and hence H9 is supported. The participant satisfaction level will influence the

corresponding purchase intention for the underlying product positively and significantly.

PLEASE INSERT FIGURE 4

5 Discussion

5.1 Determinants of users’ experience

In our simulation of the test driving we chose to examine three key dimensions in relation

to the product in question, namely controlling the car and what the car looked like (colours

and graphics) and how realistic it appeared. Hypothesis 1 was not supported by our empirical

work, indicating that the level of control is not critical for creating higher engagement among

the simulated car users. This is an interesting result as previous research (Fiore et al., 2005b;

Klein, 2003; Sheridan, 1992; Song and Zinkhan, 2008) has stressed the pivotal influence of

control in users’ captivation by computer activities and the positive attitude and experience

created for users. Although this control may have been of great relevance to test driving a car,

given that as most modern console games have sufficiently good control and navigation

attributes, users may not perceive it as being of significant importance. Similar reasoning can

be provided to justify the outcome of hypothesis 2a (not supported), where we found that

vivid colours do not create higher levels of engagement. Although the relevant literature (see

for example Fiore et al., 2005a; Fiore et al., 2005b; Klein, 2003; Steuer, 1992) highlights the

key role of vivid colours in computer user engagement, we believe that the advanced and

sophisticated use of colours within modern simulations makes users anticipate very colourful

products which will visualise the actual environment, setting and the real product (car) too.

Hypothesis 2b was supported, though, and confirms findings suggested in the relevant

literature (Fiore et al., 2005a; Fiore et al., 2005b; Klein, 2003; Steuer, 1992) on the key role

of vivid graphics in computer applications. However, product authenticity was not found to

be a critical determinant in user engagement (hypothesis 3 was not supported) and this

finding provides an original and quite different perspective from the current literature

(Algharabat and Dennis, 2010a, b). In our car simulation, product authenticity seems to be a

well-designed and fully-integrated component that presents the offline experience in a real

(authentic) manner, with users appearing to anticipate this.

The next two hypotheses (4 and 5) provide contrasting outcomes as hypothesis 4,

referring to utilitarian values was not supported, whilst hypothesis 5, referring to the hedonic

values, was supported by our empirical work. In the past, researchers have noted the

influential role of both utilitarian and hedonic values in online user experience and

engagement (Batra and Ahtola, 1991; Bhattacherjee, 2001). Normally, utilitarian experience

values are expected to engage users with computer applications and Fiore et al. (2005a) argue

that they will support users’ purchase decision-making when it comes to various physical

products (including expensive, non-frequently purchased products such as cars). Our findings

indicate that users are carried away by hedonic experience values, with aspects such as fun

and enjoyment contributing largely to further user engagement. This finding relates strongly

to the outcome of Hypothesis 2b (supported), where we noted that vivid graphics create

higher levels of engagement amongst users. It also confirms the views proposed by Shih

(1998), who stresses the role of vividness for getting hedonic pleasure, and by Dhar and

Wertenbroch (2000), who note that in the entertainment context consumers may prefer

hedonic over utilitarian values. In summary, given that hypotheses 3 and 4 (authenticity and

utilitarian values) were not supported, but hypotheses 2b and 5 (vivid graphics and hedonic

values) were supported, one could suggest that users expect the simulation to provide a fun

and enjoyable experience, instead of a factual authentic one that replicates the exact product

characteristics.

5.2 Users’ experiences and their impact on purchase intention

Hypotheses 6, 7, 8, 9 were fully supported. Starting with hypothesis 6, engagement is

linked to enjoyment, with our findings suggesting that it provides extra motivation for users

to interact with the simulated car. This supports previous work on the role of motivation in

relation to engagement (D’Alba et al., 2011). Engagement was also found to influence the

enjoyment of a simulated car user positively (hypothesis 7), confirming past studies (e.g.

Vorderer et al., 2004; Ghani,1995). Our finding is in alignment with previous work by Van

Vugt et al. (2009; 2006), who found a similar result for other experiments and settings

involving 3D virtual environments. It is worth noting that driving a simulated car is normally

perceived as a high engagement situation, which is associated with hedonic values (Vorderer

et al., 2004). As far as hypothesis 8 is concerned, enjoyment was found to be positively

associated with user satisfaction, when driving a simulated car. Similar results have been

reported in the literature (for instance Vorderer et al. (2004), especially in the theory of

optimal flow, which connects user experience, engagement and enjoyment with satisfaction

(Ghani, 1995)). This also confirms previous work by Anderson and Sullivan (Anderson and

Sullivan, 1993) and Sylaiou et al. (2010) in relation to virtual experiences. Lastly, our results

supported hypothesis 9 and highlighted the positive association between satisfaction (when

driving a simulation car) and the intention to purchase the actual car. Satisfaction was linked

to an intention to purchase the actual car. This agrees with previous studies in both offline

and online contexts (Cronin et al., 2000; Fiore et al., 2005a; Fiore et al., 2005b; Hausman and

Siekpe, 2009). More importantly, this association was confirmed within a 3D context, where

fewer studies currently exist (see for example Yang and Wu, 2009). This extends previous

studies (e.g. Fiore et al. 2005a; 2005b) that have shown the satisfaction-intention to purchase

association only when purchasing products from websites. More importantly, this finding

supports the proposed framework and suggests a process that could be a supporting tool for

companies aiming to develop and implement marketing strategies.

6 Conclusion

Previous research on both hedonic and utilitarian shopping value has focused much effort

on the antecedents of shopping value, with very little emphasis on the outcomes of shopping

value (Jones et al., 2006). We have followed a similar approach in this paper, aiming to study

how simulation characteristics and hedonic and utilitarian values may affect engagement and

enjoyment, and in turn satisfaction and purchase intention. Our work has shown how a

simulation can provide a foundation for the development of a strong relationship between a

user and the simulated product, a car model in this case. More importantly, it has shown that

a possible outcome is the purchasing of the real product by the user, with this having major

marketing implications.

We anticipate the current work to be very helpful to marketing managers and

practitioners as they could add an extra weapon to their marketing arsenal. These applications

provide an inexpensive and fast medium for testing user reactions and companies willing to

employ these applications could embark on a more strategic-oriented, integrated and cross-

functional marketing activity with these users. Major automotive manufacturers are already

using similar applications and approaches for their car products and users can experience

these cars on their mobile phones or desktops or even their own simulators (Audi, 2011; Law,

2011; Mobile Marketer, 2009). However, these applications enable users to experience a

product without direct inspection and contact and this requires managers to understand the

applications in detail.

Considering that the majority of our experiment participants were students, future work

could examine other user groups. This is of special importance if we take into account the

fact that the participants in our experiment had a good average experience of playing video

games in general and car games in particular. Hence, these were relatively experienced users

who were familiar with console and computer games and they are bound to anticipate good

navigation and control, vivid colours and authenticity in these applications (hypotheses 1, 2a,

3). Future work conducted amongst less-experienced users and other user groups could

provide different results. Given the objectives of the simulation and that users typically may

have their first encounter via the simulation, first-impressions could potentially extend the

framework. Similar work could take place for other products. There are numerous products

sharing similar characteristics (e.g. motorbikes, helicopters etc.) where the use of simulations

may be appropriate. This is of particular interest for electronic retailers, who can offer user a

more interactive way to experience products online before committing themselves to a

purchase. For example, a clothing e-retailer could offer an application that enables users to

create a virtual representation of themselves that has their physical characteristics and try a

range of clothes on the avatars to explore different options. A purpose-built simulator could

study users’ perceptions under more realistic conditions. Since previous research has shown

that user experience may differ in two different game playing situations (laboratory and

home) (Takatalo et al., 2011), future studies could consider repeating the experiment within a

residential environment, which would make for a more natural setting. Finally, it may be of

value to compare a real test drive with a simulated one and compare the customer’s views

when it comes to the actual product and the simulated one.

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Figure 1: Integrating Product and User Experience

Colour

Vividness

Graphics

Vividness

3D

Authenticity

Engagement

ControlHedonic Value

Utilitarian

Value

Simulation

Experience

Enjoyment

SatisfactionPurchase

Intention

Figure 2: Determinants of User Experiences

Colour Vividness

Graphics Vividness

3D Authenticity

Engagement

R2 = .39

Control

Hedonic Value

Utilitarian Value

H1: ns

H2a: ns

H2b: .219**

H3: ns

H4: ns

H5: .360***

Note: *: significant at 10% level, **: significant at 5% level, ***: significant at 1% level, ns: not

significant

Figure 3: Process for Building / Generating the User Experience Outcome

Engagement

Enjoyment

R2 = .55

Satisfaction

R2 = .39

H7: .743 ***

H6: .394 *** H8: .272 **

Note: *: significant at 10% level, **: significant at 5% level, ***: significant at 1% level, ns: not

significant

Figure 4: Outcome / Impact of Users’ Experience

SatisfactionPurchase Intention

R2= .31

H9: .558 ***

Note: ***: significant at 1% level

Screenshot 1: Screenshot of the car’s interior

Table 1: Measurement Scales

Construct Item Statement Source

Control

CON1 I felt that I could control the car speed easily (Liu and Shrum, 2002;

McMillan and Hwang,

2002; Song and

Zinkhan, 2008)

CON2 I felt that I had a lot of control over the driving experience

CON3 I felt it was easy to rotate the wheel, and drive in the direction

I wanted.

CON4 I felt I could control the movements of the car.

Colour

Vividness

COL1 There are lots of colours in the game. (Fiore et al., 2005b;

Klein, 2003; Steuer,

1992) COL2 Colour brightness of the car (both inside and outside), and the

outside environment, let me visualise how the real car might

look.

COL3 The car and the outside environment illustrated by 3D was

very colourful

Graphics

Vividness

GRA1 There are lots of graphics in the game. (Fiore et al., 2005b;

Klein, 2003; Steuer,

1992) GRA2

Graphics of the car (both inside and outside), and the outside

environment, let me visualise what the real car might look.

GRA3 The car and the outside environment illustrated by 3D

graphics was very real

3D

Authenticity

3DA1 3D creates an experience similar to the one I would have

when driving on a road. (Algharabat and Dennis,

2010a) 3DA2 3D lets me feel like as if I am driving a real car

3DA3 3D lets me feel like as if I am really test driving the car

3DA4 3D lets me see the car as if it was a real one

Hedonic

Value

HED1 Would be like an escape (Babin et al., 1994)

HED2 Would be truly enjoyable

HED3 Would be enjoyable for its own sake

HED4 Would let me enjoy being immersed in an exciting new

experience

Utilitarian

Value

UTI1 Help me make a better decision about the car model if I am to

consider buying it (Fiore et al., 2005b)

UTI2 Help me to find the right car model if I am considering

buying a car

UTI3 Aid me in evaluating the car model

UTI4 Help me in finding what I am looking for if I am considering

buying a car

Purchase

Intention

PI1 After playing the game with the car model, how likely is it

that you would consider to go test drive the real car model? (Fiore et al., 2005b)

PI2 The driving experience in the game would be helpful in

aiding me to make a purchase decision if I am considering

buying a car

PI3 The driving experience in the game would increase my

intention to buy the car model I played with

PI4 I would be willing to recommend to my friends to use the

driving experience in the game as a decision aid when

considering what car to test drive / buy

Satisfaction

SAT1 Very dissatisfied vs. Very Satisfied (Bhattacherjee, 2001)

SAT2 Very displeased vs. Very pleased

SAT3 Very frustrated vs. Very contented

SAT4 Terrible vs. Very delighted

Engagement#

EN1 I am deeply engrossed vs. Not deeply engrossed (Ghani, 1995)

EN2 I am absorbed intensely vs. Not absorbed intensely

EN3 My attention is focused vs. Not focused

EN4 I concentrate fully vs. Not fully

Perceived

Enjoyment#

PE1 Interesting vs. Uninteresting (Ghani, 1995)

PE2 Fun vs. Not fun

PE3 Exciting vs. Dull

PE4 Enjoyable vs. Not enjoyable

Table 2: Measurement Properties of Indicators

Construct Description Items Mean Standard

Deviation

Composite

Reliability

Standardised

Factor

Loading

Control

Key determinants

of interaction with

simulated product

(appearance and

behaviour)

CON1 5.45 1.37

.95

.86

CON2 4.92 1.53 .91

CON3 4.73 1.83 .93

CON4 4.91 1.60 .93

Colour

Vividness

COL1 5.46 1.28

.91

.88

COL2 4.99 1.43 .83

COL3 5.31 1.37 .93

Graphics

Vividness

GRA1 5.53 1.30

.89

.81

GRA2 4.82 1.53 .84

GRA3 4.63 1.72 .91

3D

Authenticity

How accurate is

the match of what

users can expect in

the real world with

what they need to

learn for the

simulated product?

3DA1 4.22 1.78

.97

.96

3DA2 4.02 1.72 .97

3DA3 3.94 1.72 .95

3DA4 3.87 1.69 .93

Hedonic

Value

Experiential

factors that

implicitly or

explicitly cause an

affective state of

pleasure

HED1 4.90 1.57

.91

.74

HED2 5.25 1.28 .93

HED3 5.16 1.24 .83

HED4 5.03 1.31 .89

Utilitarian

Value

Instrumental

values can help

users make a

better, more

informed and

rational decision

UTI1 3.66 1.62

.91

.95

UTI2 3.90 1.63 .95

UTI3 3.73 1.53 .94

UTI4 3.85 2.99 .49

Purchase

Intention

The intention to

purchase the

simulated product

(the car in this

case)

PI1 4.71 1.73

.84

.64

PI2 3.58 1.50 .43

PI3 3.71 1.67 .94

PI4 3.72 1.88 .92

Satisfaction

A performance

measurement of

the simulation

SAT1 5.31 1.09

.93

.87

SAT2 5.39 1.04 .91

SAT3 5.08 1.18 .86

SAT4 5.21 1.15 .89

Engagement#

Engagement takes

place during the

process of user’s

direct interaction

with an object,

leading to a feeling

of involvement

EN1 2.66 1.49

.93

.90

EN2 2.63 1.49 .87

EN3 2.19 1.28 .89

EN4 2.29 1.31 .87

Perceived

Enjoyment#

Enjoyment is a

perceived

consequence of the

activity of

interacting with

the simulated

product

PE1 2.31 1.36

.95

.91

PE2 2.26 1.38 .89

PE3 2.63 1.34 .88

PE4 2.31 1.40 .94

Table 3: Measurement Properties of Constructs

Mean S.D. Con Col Gra Aut Hed Uti PI Sat Eng Enj

Control 5.00 1.60 (.83)

Colour 5.25 1.37 .40 (.83)

Graphics 4.99 1.57 .33 .83 (.73)

Authenticity 4.01 1.73 .50 .69 .73 (.90)

Hedonic

Value

5.09 1.36 .44 .39 .35 .37 (.72)

Utilitarian

Value

3.78 2.03 .52 .54 .58 .79 .50 (.73)

Purchase

Intention

3.93 1.76 .48 .51 .58 .77 .41 .84 (.58)

Satisfaction 5.25 1.12 .57 .38 .37 .42 .64 .55 .56 (.78)

Engagement# 2.44 1.41 -.40 -.36 -.40 -.44 -.54 -.51 -.52 -.56 (.78)

Enjoyment# 2.38 1.38 -.43 -.22 -.22 -.35 -.58 -.38 -.39 -.60 .74 (.82)

Note: # - Measured in reverse scale. Negative sign with other construct not measured in reverse

scale implies a positive relationship. Off-diagonal values are correlations and on-diagonal values are

AVEs.