Int. Journal of Business Science and Applied Management, Volume 5, Issue 3, 2010

3D Product authenticity model for online retail:

An invariance analysis

Raed Algharabat

Brunel Business School, Marketing Department, Brunel University

Elliot Jaques Building, Uxbridge, Middlesex, UB8 3PH, United Kingdom

Tel: +44 (0) 1895 266251

Email: raed.algharabat@brunel.ac.uk

Charles Dennis

Brunel Business School, Marketing Department, Brunel University

Elliot Jaques Building, Uxbridge, Middlesex, UB8 3PH, United Kingdom

Tel: +44 (0) 1895 265242

Email: charles.dennis@brunel.ac.uk

Abstract

This study investigates the effects of different levels of invariance analysis on three dimensional (3D)

product authenticity model (3DPAM) constructs in the e- retailing context. A hypothetical retailer

website presents a variety of laptops using 3D product visualisations. The proposed conceptual model

achieves acceptable fit and the hypothesised paths are all valid. We empirically investigate the

invariance across the subgroups to validate the results of our 3DPAM. We concluded that the 3D

product authenticity model construct was invariant for our sample across different gender, level of

education and study backgrounds. These findings suggested that all our subgroups conceptualised the

3DPAM similarly. Also the results show some non-invariance results for the structural and latent mean

models. The gender group posits a non-invariance latent mean model. Study backgrounds group reveals

a non-invariance result for the structural model. These findings allowed us to understand the 3DPAMs

validity in the e-retail context. Managerial implications are explained.

Keywords: 3D product authenticity, control, animated colours, value, behavioural intention, invariance

analyses

Raed Algharabat and Charles Dennis

1 INTRODUCTION

Scholars (e.g., Li et al., 2001, 2002, 2003) classify experiences, based on the interaction between a

product or an environment and an individual, into three types. First, direct experience permits

consumers to interact (e.g., physically) directly with a product. Second, indirect experience often

allows consumers to interact with second-hand source such as static visual pictures. Third, virtual

experience allows consumers to interact with three dimensional (3D) virtual models. According to

Steuer (1992, p.78) virtual reality (VR) is “a real or simulated environment in which a perceiver

experiences telepresence”. In contrast, virtual experience (VE) derives from VR and can be defined as

“psychological and emotional states that consumers undergo while interacting with a 3D environment”

(Li et al., 2001, p. 14). A 3D presentation enables consumers to interact with products, enriches their

learning processes, and creates a sense of being in a simulated real world. Furthermore, direct and

virtual experiences combine within VR, such that the latter enhances and enriches the overall

experience because consumers use almost all of their senses when interacting with a 3D product

visualisation (Klein, 2003; Li et al., 2001, 2002, 2003). Despite widespread discussions and various

definitions of VE, we notice that previous scholars, within the online retail context, consider the

notions of 3D telepresence as virtual substitutes for actual experience with the products. However, the

telepresence and presence constructs are not necessarily wholly appropriate concepts for marketers

since they represent a process of being mentally transported into other areas or being immersed into an

illusion environment. Such notions may not be particularly helpful for marketers and website designers

who are concerned with 3D product visualisation of real products. Instead, we propose the 3D product

authenticity construct, which refers to simulating a real product authentically online. We therefore first

discuss the notions of telepresence or presence in the immersive virtual reality (IVR) environment then

proceed to explain applications of non-immersive virtual realties (NIVR i.e., an online retailer context).

We also offer a new definition and measurement scale for the construct of 3D authenticity.

Furthermore, we introduce the 3D product authenticity model to replace the telepresence model in the

virtual reality environment. To validate our findings of the 3D product authenticity model, we

investigate the effects of different levels of invariance analysis, across gender, levels of education and

study backgrounds subgroups.

2 THEORETICAL BACKGROUND

2.1. 3D Product Visualisation in the Immersive and Non-Immersive VR

VR terminologies enter the vocabulary with the emergence of IVR devices, such as head-mounted

display, which allow users to interact with virtual environments and to visualise different objects (Suh

and Lee, 2005). As a result, the notions of telepresence or presence emerge. Notwithstanding, previous

literature in the IVR area has provided readers with different classifications and conceptualisations of

VR experience. For example, Steuer's (1992, p. 76) definition of VR focuses on human experience, not

technological hardware, and differentiates between two types of VE; presence and telepresence.

Whereas presence refers to “the experience of one’s physical environment; it refers not to one’s

surroundings as they exist in the physical world, but to the perception of those surroundings as

mediated by both automatic and controlled mental processes”, telepresence is “the experience of

presence in an environment by means of a communication medium”. In turn, Sheridan (1992)

distinguishes between virtual presence and telepresence, such that presence relates to the sense of being

in a computer-mediated environment, whereas telepresence indicates a sense of being in any real

remote location. However, Biocca (1992) defines VE (based on the telepresence construct) as users’

ability to be, psychologically, transported into another area. To that end, Biocca and Delaney (1995)

argue that the definition of virtual reality experience depends on technological hardware and software.

The authors define VE as perceptual immersion. This type of VE depends on sensory immersion in

virtual environments. To extend prior literature, Lombard and Ditton (1997) identify six taxonomies of

VE: social richness, realism, transportation, immersion, social actor within medium and medium as

social actor. Notwithstanding Lombard and Ditton’s (1997) classification, two types of presence are

identified in the NIVR area, concerning users interaction with e-retailers’ websites and products using

desktop or laptop computers (Suh and Lee, 2005). The first is telepresence, or the illusion of being in a

place far from the physical body (Biocca, 1997; Heeter, 1992). This conceptualisation of telepresence

relates to transporting a user, self, or place, to another place. The second form is telepresence in a

social sense, such that other beings exist in the VR world with whom users can interact (e.g., avatars).

Authors such as Heeter (1992) and Lombard and Ditton (1997) empirically test this concept, and

McGoldrick and colleagues (2008) emphasise the avatar’s role in enhancing virtual personal shopper

capabilities. Moreover, to identify the main determinants of VE within IVR, researchers follow

Int. Journal of Business Science and Applied Management / Business-and-Management.org

interactivity and vividness theories. For example, previous scholars (Biocca & Delany, 1995; Heeter,

1992; Lombard & Ditton, 1997; Sheridan, 1992; Steuer, 1992) assert that interactivity and vividness

may represent the main antecedents of virtual reality experience. Interactivity appears particularly of

interest since the appearance of new communication channels such as the World Wide Web, for which

it represents a critical concept and primary advantage (Rafaeli & Sudweeks, 1997). Considerable

research investigates and empirically tests the construct, but there is little agreement on the definition

or operationalisation of the interactivity construct (e.g., Ariely, 2000; Klein, 2003; Liu & Shrum, 2002;

McMillan & Hwang, 2002). For example, Steuer (1992) classifies it into three elements: speed,

mapping and range. Rafaeli and Sudweeks (1997) argue interactivity relates to the communication

process, and Ariely (2000) defines it on the basis of the control construct (the narrowest definition).

Rowley (2008) focuses on information interactivity. Still other scholars (e.g., Lui & Shrum, 2002;

McMillan & Hwang, 2002) argue that definitions of interactivity cannot be restricted to messages,

human interactions or communications but rather should include multidimensional aspects. Thus speed,

responsiveness and communications represent the main elements to define and measure interactivity

construct. In contrast, vividness, according to Steuer (1992, p. 81) is “the way in which an environment

presents information to the senses”. Steuer explains that vividness is stimulus driven and depends

completely on the technical characteristics of a medium. In turn, it represents a product of two

important variables: sensory breadth, and sensory depth. Most scholars use this definition of vividness.

To that end, Lee (2004) revises all the previous definitions of telepresence or presence and argues

that none of the previous definitions could be used to tap the concept of using virtual environment to

reflect consumers’ virtual experience. The author posits two ways for an experience to become a

virtual. First, using “Para-authentic objects” in which the users interact with objects in which they can

find in real life aspects such as clothing. Secondly, using “Artificial objects”, which simulates objects

that do not exists in real life. On that basis, we claim that using the notions of 3D telepresence or

presence and their definitions to define VE neither help marketers and e-retailers to understand the

effect of 3D product visualisation on consumers’ VE, nor suit the online retail context. Because (i)

these notions represent a process of being mentally transported into other areas or being immersed into

an illusion environment, such notions often reflect negative meanings such as immersion, delusion and

transportation (Lee, 2004); (ii) presence and telepresence measurement scales, were originally built

upon external devices, such as head-mounted display, which are not used in online retailers’ 3D virtual

model; and (iii) the lack of agreement upon the antecedents of telepresence and presence (interactivity

and vividness) often complicates measuring the 3D product visualisation VE, and (iv) these notions

measure VE based on different technologies (see Table 1). For example, to measure VE, Shih (1998)

proposes a conceptual framework. Coyle and Thorson (2001) focus on videocassette movies. Klein

(2003) employs a simple technology such as Authorware © 3.0 and 4.0, and Hopkins et al. (2004)

investigate websites VE. Moreover, we notice that only few of the previous studies focused on the use

of 3D product visualisation to measure VE (see Table 1). For instance, Li et al. (2001, 2002, 2003) and

Fiore et al. (2005a) measured VE using 3D product visualisation. Unfortunately, both studies measured

it based on the telepresence construct. Based on the above gaps, we claim that a 3D virtual experience

should be an authentic representation of the direct (offline) experience. The concept of 3D authenticity

of the product visualisation implies that ability of the 3D to simulate the product experience in bricks-

and-clicks contexts. We felt that it is important to measure how consumers, within the online retail

context, could imagine that 3D presented products. Particularly, we introduced our new construct,

namely, 3D product authenticity to reflect customers’ virtual experience, where customers can feel the

authenticity of the 3D products.

Raed Algharabat and Charles Dennis

Table 1: Previous research on online VR using 3D telepresence

3D Product Authenticity (3DPA) Construct

None of the previous definitions of telepresence or presence that use 3D virtual models

realistically taps consumers’ virtual experiences. A 3D virtual experience should be an authentic

representation of the direct (offline) experience. We therefore propose a new notion that relates to the

simulation of online products and virtual experience, namely, the authenticity of the 3D product

visualisation. Telepresence and presence are not particularly well suited to the online retail context,

because they reflect illusion and transportation to other places. In contrast, the concept of 3D

authenticity of the product visualisation implies the ability to simulate the product virtual experience in

bricks-and-clicks contexts. We propose the following definition of perceived 3D product authenticity in

a computer-mediated environment: 3D Product Authenticity (3DPA) is a psychological state in which

virtual objects presented in 3D in a computer-mediated environment are perceived as actual objects in

a sensory way. Furthermore, we identify users’ ability to control the content and form of the 3D flash

(interactivity), animated colours (vividness) and 3D authenticity as the main elements of the 3D virtual

experience. Moreover, we define control and animated colours as the main antecedences of 3D

authenticity.

3 RESEARCH MODEL

We demonstrate our research model in Figure 1. Our model is testing the relationships between

control, animated colours, 3D product authenticity, hedonic and utilitarian value and behavioural

intention. As the objective of our study is 3D product authenticity model’s measurement equivalence,

the focus of our model is concentrated on whether gender, education levels and study backgrounds

affect participants’ responses to our 3D product authenticity model.

Study

Sample

Stimuli

Virtual experience

measurement

Virtual experience

antecedents

Invariance

analysis

Shih

(1998)

Conceptual

paper

N/A

Conceptual

Vividness (breadth and

depth) and interactivity

(speed and control)

N/A

Coyle

and

Thorson

(2001)

Students

Videocassette

movies.

Blues music CDs.

Women’s golf

clothing and

equipment.

Hot sauces.

Transporting into

another place; being

there.

Vividness (breadth and

depth) and interactivity

(speed and control)

N/A

Li et al.

(2001)

Students

3D products:

Bed, ring, watch,

laptop computer.

Illusion and

Immersion

Virtual experience is

vivid, involving, active,

affective and

psychological states

N/A

Li et al.

(2002)

Students

3D/2D

bed, ring, watch,

laptop

advertisements

Presence: based on

physical engagement,

naturalness, and

negative effects.

Interactivity and media

richness

N/A

Li et al.

(2003)

Students

3D/2D product

type: wristwatch,

bedding material

and laptops

Telepresence and

virtual affordance

Interactivity and media

richness

N/A

Klein

(2003)

Non-

students

Authorware © 3.0

and 4.0

Study = 1, Wine

Study = 2, Face

cream

Telepresence:

transporting into

another area

User control and media

richness (full-motion

video and audio)

N/A

Hopkins

et al.

(2004)

students

Website for the

National Arbor Day

Foundation

Telepresence: being

there

Vividness (media

richness)

N/A

Fiore et

al.

(2005a)

Students

Clothing (3D

virtual model)

Telepresence: being

there

Interactivity and

vividness

N/A

Int. Journal of Business Science and Applied Management / Business-and-Management.org

3.1 3D Product Authenticity Antecedents and Definitions

We use the control construct to represent interactivity in an online retail context. Ariely’s (2000)

definition of control refers to users’ abilities to customise and choose Web site contents to achieve their

goals. We focus more on consumers’ ability to control and easily interact with the 3D virtual model.

Therefore, we define control as users’ abilities to customise and choose the contents of the virtual

model (i.e., 3D product visualisation), rotate, and zoom in or out on the product in the virtual model

and the ability of the virtual model (3D) to respond to participants’ orders properly. In turn, we

hypothesise:

: A high level of control of 3D product visualization increases 3D authenticity.

Furthermore, 3D vividness should facilitate virtual experience by providing more sensory depth

and breadth (Li et al., 2002, 2003). High-quality online animations enhance perceived reality of the 3D

products (e.g., Fortin and Dholakia, 2005; Klein, 2003; Shih, 1998). Specifically, we consider

vividness of the visual imagery, such that consumers can see online products with different colours

(skins) just as they would see them in person. Media richness may lead to a real (authentic) experience,

according to research on online shopping (Algharabat and Dennis, 2009a; Klein, 2003; Schlosser,

2003). Moreover, consumers’ ability to change the animation (colours) of the 3D product might help

them sense control over the product. We therefore hypothesise:

: A high level of 3D animated colours increases perceived 3D authenticity.

: A high level of 3D animated colours increases control.

3.2 Effects of 3D products Authenticity on Utilitarian and Hedonic Value

To identify the main consequences of using authentic 3D product visualisations, and to explain

cognitive and emotional experiences that consumers might have from navigating an authentic 3D

product visualisation, we follow the hedonic and utilitarian value theories (based on Babin et al., 1994;

Fiore et al., 2005a). Scholars (e.g., Fiore and Jin, 2003; Fiore et al., 2005a; Kim et al., 2007; Klein,

2003; Li et al., 2001, 2002, 2003; Suh and Chang 2006) explain the importance of using 3D product

visualisations in enhancing consumers’ understanding of product attributes, features and characteristics.

3D visualisation increases consumers’ involvement and encourages them to seek more information

about the products (Fiore et al., 2005a). Suh and Lee (2005) posit a positive relationship between

higher levels of 3D product visualisation and seeking more information about the products’

characteristics and features. Suh and Chang’s (2006) empirical research of the influence of 3D product

visualisation and product knowledge reveals a positive relationship between 3D and perceived product

knowledge. Using 3D product visualisation helps consumers to imagine how a product may look and it

gives them more details about the products’ characteristics (Fortin and Dholakia, 2005; Klein, 2003;

Shih, 1998). Therefore, we hypothesise:

: 3D authenticity in a retailer website will positively affect website use for utilitarian value.

Scholars (Fiore et al., 2005b; Kim and Forsythe, 2007; Lee et al., 2006; Schlosser, 2003) report

the importance of 3D product visualisation in enhancing the experiential aspects of a virtual shopping.

The above researchers find that the ability of 3D product visualisation to produce hedonic values for

shoppers is greater than its ability to produce utilitarian values. Fiore et al. (2005b) assert that 3D

virtual model produces hedonic value, which is highly correlated with consumers’ emotional pleasure

and arousal variables. Fiore et al. (2005a) posit the importance of virtual models in boosting hedonic

value (enjoyment). Fiore et al. (2005a) also report the importance of 3D virtual model technology in

producing more hedonic value. Many scholars in the communication field (e.g., Heeter, 1992; Lombard

and Ditton, 1997; Song et al., 2007) report the importance of enjoyment as a consequence of using 3D.

Consumers use 3D product visualisation to have more fun, enjoyment and entertainment (Kim and

Forsythe, 2007). Such sources of fun or enjoyment come from consumers’ ability to rotate, and zoom

in or out on the product (Fiore et al., 2005a), seeing different animated coloured pictorial images that

may enhance their mental pleasure when using 3D sites.

: 3D authenticity in a retailer website will positively affect website use for hedonic value.

Raed Algharabat and Charles Dennis

3.3 Effects of 3D Product Authenticity, Utilitarian and Hedonic Value on Behavioural

Intention

The role of 3D product visualisation in enhancing behavioural intentions appears well supported;

3D utilitarian and hedonic values improve willingness to purchase from an online retailer (Fiore et al.,

2005a, 2005b), intention to buy (Schlosser, 2003) and purchase intentions (Li et al., 2001; 2003).

Moreover, 3D realism improves users’ beliefs and attitudes towards an online store (Klein, 2003).

Therefore,

: The relationship between 3D authenticity and behavioural intention is positive.

: The relationship between utilitarian value and behavioural intention is positive.

: The relationship between hedonic value and behavioural intention is positive.

Figure 1: Conceptual framework (source: the authors)

4 METHODS

4.1 Stimuli

A retailer’s website with one stimulus was custom-designed for this study. The stimulus was

illustrated in 3D product visualisation sites in which participants can see, the focal product, laptops

from different angles; they can rotate it and zoom it in or out. The 3D stimulus is designed to help

consumers to imagine the product in appropriate and relevant ways and it enhances consumers’ virtual

experiences (Li et al., 2001). Moreover, we decide to use the 3D stimulus which users can control

(content and form) and see from different colours to bridge the gaps in measuring VE using the 3D

product visualisation. Previous scholars measure VE based on movies, simple technology but not 3D

product visualisation. Moreover, those who use the 3D product visualisation measure it based on the

telepresence construct (see Table 1).

4.2 Interface Design

We designed one stimulus, a 3D flash (site), for testing the proposed hypotheses. The site allows

participants to control the content and form of the 3D flash. For example, participants can zoom in or

out on the product, rotate it and can see different parts of the product when clicking on it. The 3D flash

permits participants to change the colour of the laptop and see it with animated colours. Also the flash

allows participants to get actual and perceived information about the laptop features and attributes.

Moreover, our site enhance participants’ fun and enjoyment values by enabling them to control (i.e., to

zoom in or out on and rotate), to change the colour of the laptop and to see more information about the

product (see Appendix A). In designing this interface, we consider a comprehensive site to visualise an

electrical online retailer to surpass actual experience. Moreover, this study adds more features and

cases to the ones that might be found in real sites. For example, none of the national sites that sell

laptops (e.g., Sony and Dell, to the best of the authors’ knowledge) has a flash combining both 3D and

information about laptops. The website we created for this study was not previously known to users,

nor did users have any knowledge of the fictitious brands on the site. Thus, we eliminated any impact

Control

Animated

colours

Authenticity

Utilitarian

value

Hedonic

value

Behavioural

intention

H1a

H1b

H3a

H3b

H4a

H4b

H3c

Int. Journal of Business Science and Applied Management / Business-and-Management.org

of previous experiences or attitudes (Fiore et al., 2005a). The site offers a wide variety of laptops,

similar to those that many college-aged women and men currently buy and use. Therefore, site provides

a suitable context for the present sample.

4.3 Participants

Student samples are well suited to online shopping research (e.g., Balabanis & Reynolds, 2001;

Fiore et al., 2005a; Kim et al., 2007; Li et al., 2002, 2003), because they are computer literate and have

few problems using new technology. Students also are likely consumers of electrical goods (Jahng et

al., 2000). We employed a sample of 312 students to perform this study. The sample was gender

balanced, consisting of 48% women and 52% men, and 90% of the sample ranged from 18 to 30 years

of age. Approximately 90% reported having had prior online shopping experience.

4.4 Instrument

Participants were informed that this study pertained to consumers’ evaluations of an electrical

retailer’s Web site. The questionnaire contained five-point Likert-type scales, anchored by “strongly

disagree” and “strongly agree”.

To measure the control construct, we developed a five-item scale that centres on users’ ability to

rotate and zoom in or out the virtual model based on Liu and Shrum’s (2002); McMillan and Hwang’s

(2002) and Song and Zinkhan’s (2008) studies. To measure animated colours, we developed a four-

item animated colour scale based on Fiore and colleagues (2005a), Klein’s (2003), Steuer’s (1992)

studies. The items tap how closely the simulated sensory information reflects the real product. We

could not find an existing scale to measure 3D product authenticity so we developed a new five-item

scale. We submitted the items to evaluations by academics (lecturers in online retailing and Ph.D.

students); these respondents considered the items relevant for measuring the authenticity construct. We

followed Churchill’s (1979) procedures for developing a marketing construct scale and adopted

Christodoulides and colleagues (2006) procedures for developing a scale for the online context. Each

item began with “After surfing the 3D sites”, and then obtained responses to the following: “3D creates

a product experience similar to the one I would have when shopping in a store”, “3D let me feel like if I

am holding a real laptop and rotating it” (i.e. virtual affordance), “3D let me feel like I am dealing with

a salesman who is responding to my orders”, “3D let me see the laptop as if it was a real one”, and

“Being able to zoom in/out and rotate the laptop let me visualise how the laptop might look in an

offline retailer”. To measure hedonic values, we adopted a modified version of Babin and colleagues

(1994) scale. We based the study on 4 of the 11 items. To measure utilitarian values, we adopted a

modified version of Fiore and colleagues (2005a) scale. To measure Behavioural intention, we used a

modified version of Fiore and colleagues (2005a) scale. See Table 2 for the purified items.

5 RESULTS

5.1 Measurement Model for the 3D Product Authenticity Model

We evaluated the measurement and structural equation models using AMOS 16. The measurement

model includes 23 indicators, and we provide its results in Table 2, including the standardised factor

loading, standard error (S.E), critical ratios (C.R), composite reliability, squared multiple correlation

and average variance extracted (AVE) for each construct. The standardised factor loadings (λ) are all

greater than .61. The composite reliabilities for control (.80), animated colours, (.782), 3D authenticity

(.86), utilitarian (.85), hedonic (.86) and behavioural intention (.88) are acceptable (Hair et al., 2006).

Moreover, average variance extracted by each construct exceeds the minimum value recommended by

Hair et al. (2006), (i.e., exceeds .5).

Raed Algharabat and Charles Dennis

Table 2: Measurement model results for hypothetical 3DPAM.

Construct

Indicator

Standard-

ised factor

loading ( λ)

S.E

C.R

Average

Variance

extracted

Squared

multiple

correlation

Composite

reliability

η1 (Control)

- I felt that I could choose freely

what I wanted to see

- I felt that I had a lot of control

over the content of the laptop’s

options (i.e. angles and

information)

- I felt it was easy to rotate the

laptop the way I wanted.

- I felt I could control the laptop

movements.

.78

.71

.61

0.077

0.076

0.071

12.097

10.009

8.916

0.50

0.602

0.508

0.503

0.369

0.80

η2 (Animated colours)

-There are lots of colours on 3D

laptop websites.

- Colours brightness of the 3D

laptop let me visualize how the

real laptop might look.

- The laptop illustrated by 3D

was very colourful

.79

.71

.61

0.067

0.064

11.391

10.099

.502

0.631

0.499

0.375

0.78

η3 (3D Authenticity)

- 3D Creates a product

experience similar to the one I

would have when shopping in a

store.

- 3D Let me feel like if I am

holding a real laptop and rotating

it (i.e. virtual affordance)

- 3D Let me feel like I am

dealing with a salesman who is

responding to my orders.

- 3D let me see the laptop as if it

was a real one.

.77

.79

.81

.74

0.078

0.076

14.093

14.581

13.293

.608

0.598

0.628

0.656

0.550

0.86

η4 (Hedonic value)

- Would be like an escape.

- Would be truly enjoyable

- Would be enjoyable for its own

sake, not just for the items I may

have purchase.

- Would let me enjoy being

immersed in an existing new

product.

.64

.77

.88

.79

0.105

0.128

0.144

12.752

11.987

11.123

0.59

0.411

0.589

0.722

0.618

.86

η5(Utilitarian value)

- Help me make a better decision

about the product.

- help me buy the right product.

- Aid me in evaluating the laptop

items.

- Help me in finding what I am

looking for

.80

.92

.69

.61

0.079

0.067

0.066

16.179

12.481

11.002

.582

0.637

0.844

0.475

0.375

0.85

η6 (Behavioural intention)

- After seeing the web site, how

likely is it that you would buy a

laptop from this online store.

- I would be willing to purchase a

laptop through this online store.

- I intend to buy a laptop from

this online store.

- I would be willing to

recommend this online retailer to

my friends.

.81

.82

.72

0.061

0.075

0.059

16.151

15.323

13.160

0.631

.88

Int. Journal of Business Science and Applied Management / Business-and-Management.org

5.2 Structural Equation Model for the 3D Product Authenticity Model

The hypothesised model achieves a chi-square of 350.225 (df = 219), with a goodness-of-fit index

(GFI) of .911, comparative fit index (CFI) of .965, root mean square residual (RMR) of .038 and root

mean square error of approximation (RMSEA) of .044, normed fit index (NFI) of .912, relative fit

index (RFI) of .9, incremental fit index (IFI) of .965, and χ

/df = 1.599. These results indicate a good

fit of the data to the model (Byrne, 2001; Hair et al., 2006). Furthermore, the structural equation model

confirms that control and animated colours have significant positive effects on 3D authenticity (H

t =

2.098; H

t = 7.951). Moreover, animated colour exhibits a significant positive effect on control (H

= 7.888). Finally, as we hypothesized, 3D authenticity, hedonic and utilitarian values have positive

effects on behavioural intention (H

: 2.465, H4

: t = 2.216, H

: t= 2.454). Table 3 reports estimates,

standardised estimates, and critical ratio for each hypothesized path. All the hypothesized paths are

supported (p < .05).

Table 3: Summary of results of structural model estimation

Standardised regression paths (β)

Estimate

S.E

C.R

Hypothesis

Animated colours → Control

.539

.068

7.888

***

Supported

Control → 3D Authenticity

.165

.079

2.098

.036

Supported

Animated colours → 3D Authenticity

.672

.085

7.951

***

Supported

3D Authenticity → Utilitarian

.470

.055

8.567

***

Supported

3D Authenticity → Behavioural intention

.229

.093

2.465

.014

Supported

3D Authenticity → Hedonic

.483

.054

8.875

***

Supported

Utilitarian → Behavioural intention

.211

.086

2.454

.014

Supported

Hedonic → Behavioural intention

..274

.124

2.216

.027

Supported

6 INVARIANCE ANALYSIS

We use the invariance analyses to determine the effects of gender, education levels and study

backgrounds and their relationships in our conceptual framework. We start with conducting a

measurement invariance analysis (measurement weight) for gender, education levels and study

backgrounds to determine whether, for example, the males and females groups would use the same

pattern in measuring the observed items. If the result is invariant, then the data of each group is suitable

for further analysis (i.e., structural invariance analysis). However, if the two groups understood the

items in different ways (i.e., non-invariance), then, we identify the source of the non-invariance. To do

so, we identify the observed item(s) that caused the non-invariance. If the result of the measurement

model is invariance, then, we go to the next step. However, if the results still non-invariant, then, we

stop the analysis.

Secondly, after having the insignificant results in the measurement model, we conduct the

invariance structural model analysis to determine if gender, education levels and study background

groups have invariance or non-invariance results in perceiving the relationships between the

unobserved constructs. To conduct this analysis, we follow two steps; (i) if the members of any group

(e.g., the males and females groups) perceive the relationships between the constructs similarly (i.e.,

invariance), then, we move to the third step (i.e., latent mean invariance analysis), (ii) however, if the

members of any group perceive the relationships between the constructs differently (i.e., non-

invariance), then we determine the source of the non-invariance. Moreover, if the structural model

analyses are non-invariance, we calculate the un-standardised direct, indirect and total effects. Thirdly,

we conduct the latent mean invariance analyses among latent constructs to determine if the groups have

perceived each construct similarly (invariance) or differently (non-invariance). In all the three previous

steps, we report ∆χ

and ∆df and fit indices (TLI, CFI and RMSEA) models for the comparison

purposes.

6.1. Invariance Analysis Results

The invariance analyses provide a better understanding of our conceptual model and its constructs

invariance validity. Following a series of invariance analyses, we could conclude that our conceptual

framework was invariant of measurement loading, structural loading and latent mean across gender,

Raed Algharabat and Charles Dennis

education level and study background. The following explains the invariance analysis and it reports the

non-invariance models.

Gender

We classify the participants into two groups according to their gender (i.e., males or females). The

measurement model results (Table 4) reveal insignificant differences between the males and females

groups regarding the measurement and structural models. However, result shows a significant

difference in the mean model. The females group is higher (.179) than the males group in perceiving

the behavioural intention construct (Table 5).

Table 4: Results of factorial invariance analysis for gender: assuming model unconstrained to be

correct.

Model

∆ χ

∆df

CFI

RAMSE

Measurement model

.404

635.786

455

17.761

.952

0.036

Structural model

.082

649.793

463

14.007

.952

0.036

Structural mean model

.019

650.619

464

15.136

.946

.950

Table 5: Means: (male-Measurement weight)

Construct (gender mean 312)

Estimate

S.E

C.R

Control

-.138

.088

-1.562

.118

Animation

.069

.994

.320

Authenticity

.016

.097

.168

.867

Hedonic

.071

.065

1.092

.275

Utilitarian

.048

.055

.875

.382

Behavioural intention

.179

.069

2.581

.010

Education Level

The second invariance analysis classifies participants into two groups according to the

participants’ educational levels (undergraduates and postgraduates groups). The measurement model,

structural model and latent mean model results reveal invariance differences (i.e., insignificant

differences) between the undergraduates and postgraduates groups (Table 6).

Table 6: Results of factorial, structural and mean invariance analysis for education: assuming

model unconstrained to be correct.

Model

∆ χ

∆df

CFI

RAMSE

Measurement model

.562

649.828

455

15.466

.949

0.37

Structural model

.240

660.190

463

10.363

.943

.948

Structural mean model

.072

656.679

464

11.575

.945

.949

Participants’ Study Backgrounds

The third invariance analysis classifies the participants into two groups according to the

participants’ study backgrounds (Business-Social and Maths-IT-Engineering groups). The

measurement model and mean model results (Table 7) reveal insignificant differences between the

Business-Social studies and the Maths-IT-Engineering studies backgrounds. However, structural model

results reveal non-invariance (significant) differences between the Business-Social studies and the

Maths-IT-Engineering studies groups in determining the relationships between the proposed constructs

(Table 8). The relationships between 3D product authenticity→ hedonic, and hedonic → behavioural

intention (BI) are the source of this non-invariance. In other words, both groups perceive the

importance of the hedonic values differently.

Table 7: Results of factorial, structural and mean invariance analysis for background: assuming

model unconstrained to be correct

Model

∆ χ

∆df

CFI

RAMSE

Measurement model

.221

675.953

455

21.115

.943

0.040

Structural model

.010

696.033

463

20.080

.934

.939

Structural mean model

.664

681.002

464

4.094

.938

.944

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Table 8: Results of path coefficient invariance analysis for study background.

Model

∆ χ

∆df

TLI

CFI

RAMSE

Animation →Authenticity

Control→ Authenticity

3D Authenticity→ Hedonic

3D Authenticity →Utilitarian

Utilitarian →BI

Hedonic → BI

Animation → Control

3D Authenticity →BI

.221

.589

.002**

.128

.295

.048*

.326

.419

654.963

655.131

664.788

657.156

655.934

658.745

655.804

655.493

439

441

.125

.292

9.950

2.317

1.096

3.906

.966

.654

.935

.932

.935

.934

.935

.944

.941

.943

.944

.943

.944

.040

.041

.040

* p < 0.05; ** p <0.01.

Table 9 shows the results of un-standardised indirect, direct and total effects- estimates for the

Maths-IT-Engineering studies background group and the Business-Social studies background group.

Table 9: Results of un-standardised indirect, direct and total effects- estimates

Predictor

variables

Behavioural intention toward

the online retailer

Indirect

effects

Direct

effects

Total

effects

Animated

colours

.221

------

.221

Control

0.029

------

.029

3D Authenticity

.075

.230

.306

Utilitarian value

------

.169

Hedonic value

------

.029

.34

Predictor

variables

Behavioural intention toward

the online retailer

Indirect

effects

Direct

effects

Total

effects

Animated

colours

.433

------

.433

Control

0.224

------

.224

3D Authenticity

.156

.419

.574

Utilitarian value

------

.069

Hedonic value

------

.225

.34

Un-standardised indirect, direct and total effects- estimates

6 DISCUSSION

This research aims to measure 3D product visualisation virtual experience, to provide a validated

conceptual model that integrates different constructs and to clarify the theoretical problems of using

different measurement of the 3D virtual experience. Moreover, this research provides invariance

analysis to determine the main moderators within our model. Our survey validates the hypothesised

model, and the model findings confirm that animated colours and control are the main determinants of

3D authenticity (VE). Moreover, we find that the authenticity of the 3D model, hedonic and utilitarian

values are the main determinants of users’ behavioural intention. We follow a series of invariance

analyses to confirm our results across gender, education levels and study background. Results show

that our 3D product authenticity model is invariant in respect of measurement model. Furthermore, we

find invariance results regarding the structural model across gender and education level. However, the

non-invariance results appear well in the mean model (across gender) and the structural model (across

study background). The difference (non-invariance) in the latent mean between males and females

groups suggests that females tend to accept the idea of buying from our fictitious e-retailer more than

the males group does. This result supports Tversky and Morrison’s (2002) findings regarding the

ability of the animated graphics to increase females’ comprehension and learning. Moreover, the ability

of the 3D flashes to enhance users’ understanding of the laptops’ features especially when using

animations makes Females’ ability to make purchase decisions (based on non-verbal cues) easier

(Dennis et al., 1999) than men.

Raed Algharabat and Charles Dennis

The non-invariance (significant) differences between Business-Social group and Maths-IT-

Engineering groups clearly come in the relationships between the proposed constructs (i.e., the

structural model). The 3D authenticity→ hedonic and the hedonic→ behavioural intention relationships

are the source of the coefficients non-invariance. In other words, both groups perceive the importance

of the hedonic values and the behavioural intention differently. That is, Maths-IT- Engineering group

tend to accept that the 3D authenticity and the novelty of the 3D flash increases the level of fun and

entertainment. On the other hand, Maths-IT- Engineering group does not accept that the high level of

entrainment may end with a positive behavioural intention towards the online retailer. In regards to the

un-standardised effects, students with the Maths-IT- Engineering backgrounds perceive the total effects

of the 3D authenticity construct on the behavioural intention (.574) more than the Business-Social

backgrounds (.306) do. This could be justified due to the Maths-IT- Engineering group ability to

understand and criticize the novelty of the 3D more than the Business-Social backgrounds. However,

the Business-Social background group perceives the total effects of the utilitarian values (.169) on

behavioural intention more than the Maths-IT- Engineering group does. On the other hand, the Maths-

IT- Engineering group perceives the total effects of the hedonic values (.225) on behavioural intention

more than the human-studies group (.029) does. In contrast to the Maths-IT- Engineering group who

perceives the direct effect of the hedonic values (.225) on behavioural intention more than utilitarian

values (.069), the Business-Social studies group perceives the direct effect of the utilitarian values on

behavioural intention (.169) more than the hedonic values (.029). These results could be explained as

follows. First, Raijas (2002) finds that the experienced people know what they are looking for.

Moreover, these results support the findings of Dennis and King (2009) and Dholakia and Chiang

(2003) regarding shopping styles. In other words, when shopping for technical and expensive products

shoppers who are Empathisers turn to become Systemisers and vise verse. Second, in comparison to the

Business-Social group, the Maths-IT- Engineering group bought on average more laptops online (M

Maths-IT-

Engineering

= 1.33, M

Business-Social

= 1.3) than the Business-Social group did. The Business-Social

group are more interested in a laptop features and characteristics than entertainment features. The

animation construct had the strongest indirect effect (.221, 433 respectively) in both groups. However,

the indirect effect of the control construct in the Maths-IT- Engineering group (.244) is greater than the

indirect effect of control on the Business-Social studies group (.029). Finally, in both groups, the 3D

authenticity construct has the strongest direct effect and total effects.

7 CONCLUSION AND CONTRIBUTION

From a theoretical standpoint, our results contribute to the existing literature in several ways. First,

previous research on VE has focused on three elements to surpass the offline (direct) experience;

interactivity, vividness and 3D telepresence. However, we claimed that the notion of 3D telepresence

reflects negative meanings. Instead we propose the notion of 3D authenticity to reflect the 3D virtual

experience. Second, to solve the lack of agreement regarding defining and measuring the interactivity

and vividness constructs. We narrowed the operationalisations of 3D authenticity antecedents to control

and animated colours to reflect a real authentic VE. In line with other online retail researchers who

investigated the influence of using 3D product visualisation on VE (Li et al., 2001, 2002, 2003), we

find that marketers should focus on specific aspects of interactivity and vividness (rather than on the

abstract constructs) when defining 3D virtual experience. For example, when it comes to 3D virtual

models, we prefer focusing on the narrowest, most relevant aspects of interactivity (i.e., control).

Whereas Heeter (2000, p. 75) describes interactivity as “an overused and under defined concept”, we

posit that control represents a useful construct for 3D models in the online retail context. Moreover, in

support of previous research (Ariely, 2000; Coyle & Thorson, 2001) we narrow our conceptualisation

of control to consumers’ ability to control the content and form of the 3D flashes. In other words, users’

ability to zoom in or out, rotate and get more information about the product enhances their perceptions

of the authenticity of the 3D products. Furthermore, whereas prior research defines vividness according

to sensory breadth and depth, we argue that research might benefit from a tighter focus on specific

aspects of vividness through illustration, such as we have applied here. This result is in accordance with

Pimentel and Teixeira’s (1994, p. 146) study that asserts that visual stimuli are the main sensory cues in

producing virtual experiences.

Third, our use of invariance analyses gives this research a plus, since previous research has not

examined them in the context of 3D virtual experience. The invariance analyses led to another

contribution, which highlights the importance of this research’s conceptual framework applicability in

the e-retailing area. Following a series of invariance analyses, it could be concluded that our conceptual

framework is invariance of the measurement model, structural model and latent mean model across

gender, education level, and study background. However, the effect of 3D authenticity on hedonic and

the impact of hedonic on behavioural intention are moderated by study background. This result posits

Int. Journal of Business Science and Applied Management / Business-and-Management.org

that the study background is a significant moderator between the effect of 3D authenticity on hedonic

values, and the effect of hedonic value and behavioural intention. Marketers and website developers

should focus on this moderator when designing 3D product visualisation for the online retailer. Any 3D

flash should reflect more innovation in designing and it should reflect a state of enjoyment for students

with Maths-IT- Engineering backgrounds and Business-Social group. This conclusion posits that

overall all the subgroups conceptualise the constructs and variables (animated colours, control, 3D

authenticity, utilitarian, hedonic, and behavioural intention constructs) similarly. Also, this suggests

that our results have no obvious bias of gender, education level, and study background (Lai and Li,

2005).

8 MANAGERIAL IMPLICATIONS

E-retailers should pay more attention to 3D product authenticity antecedents, i.e., control and

animated colour when designing their 3D virtual models. Including real colours and flashes that

consumers can control easily will lead to more authentic online experiences. The direct and indirect

effects of animated colours and control constructs reveal the importance of these constructs within the

3D e-retail context. Any 3D flash should include the essential information that consumers seek rather

than just a pretty picture. For example, consumers should be able to click on any part of the 3D flash to

get access to information about it. Website developers should take advantage of technological

advancements to develop and update online retailers’ 3D flashes. Pechtl (2003) asserts a positive

relationship between perceived innovation attributes and online adoption behaviour. Algharabat and

Dennis (2009a) posit the importance of authentic 3D product to enhance users’ hedonic and utilitarian

values. Managers and Web sites designers should work together to ensure that the 3D product

visualisation provides customers with the complete and accurate information they need. In addition,

marketers should decide what information (or knowledge) to focus on before developing 3D flashes. It

should be accepted that developing 3D flashes is not a money-free issue. Nevertheless, many

companies have already claimed to have improved their sales as a result of designing and using 3D

flashes. For example, J.C. Penny, eBags and Wal-Mart claimed that their online sales have increased

10% to 50% after using rich media such as 3D flashes (Demery, 2003). Moreover, Demery (2006)

posits that the numbers of companies who are investing in 3D virtual models is increasing steadily

because these companies are seeing the potential of the technology for selling more products. Nantel

(2004) asserts that consumers shopping online for clothing are 26% more likely to purchase from the

sites that have 3D virtual model than from sites that have not. Moreover, Fiore (2008) posits that media

richness is an important way to differentiate retailers. Wagner (2000) asserts that online retailers with

3D product visualisations may reap benefits that extend beyond sales. For example, 3D increases site

stickiness: users will spend more time on the online retailer, which leads to more opportunities to learn

more about the products, interact with them, build trust and confidence. Finally, according to the Social

Issues Research Centre (SIRC, as cited in Herrod, 2007) study it is expected that “by 2020 virtual

commerce (v-commerce) will replace e-commerce” and the development of 3D virtual models (such as

3D virtual shopping malls) will be leading the whole industry by 2020.

9 LIMITATIONS AND FURTHER STUDIES

Although the generalisability of the results is limited by the student sample, and cannot be

generalised to all online consumer groups, we argue that students represent the shoppers of tomorrow

(Algharabat and Dennis, 2009b; Balabanis and Reynolds, 2001) and the research thus has prescient

value. Second, since this study has focused only on laptops, which we consider to be products that are

associated with more search or experience, it is unclear to what extent the results can be generalised

and applied to other online products. On the bases of our results, we recommend that website

developers should pay more attention to simulating 3D animation colours to reflect the real products

more authentically. Moreover, they should work to create an environment in which consumers sense

that they can feel the online products when they navigate the site. We recommend research efforts to

extend the generalisability of our findings to other contexts (e.g., clothing) and to non-student samples.

Further research may add and test other stimuli, for example by simulating real sounds to investigate

how auditory vividness may influence 3DPAM.

Raed Algharabat and Charles Dennis

APPENDIX A

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