UNDERSTANDING THE DIGITAL FUTURE - DIVA

UNDERSTANDING THE DIGITAL FUTURE
 – APPLYING THE DECOMPOSED THEORY OF PLANNED
 BEHAVIOUR TO THE GENERATION Y’S ONLINE FASHION
 PURCHASE INTENTION WHILE CREATING AND USING A
 CUSTOMISED AVATAR

 Thesis for One-Year Master, 15 ECTS
 Textile Management

 Eva Lancere De Kam
 Jacqueline Diefenbach

 2020.18.02

Title: Understanding the Digital Future - Applying the Decomposed Theory
 of Planned Behaviour to the Generation Y’s Online Fashion Purchase
 Intention while Creating and Using a Customised Avatar

Publication Year: 2020

Supervisor: Professor Daniel Ekwall

Abstract
Purpose - The purpose of this master thesis is to research the Generation Y’s online purchase
intention for fashion items while creating and using a customised avatar. Overall, the objective
is to create a better understanding of this technology’s potential, formulate managerial
implications for fashion businesses and strengthen business viability.

Design/Methodology/Approach - The research approach of this study is deductive, whereby
hypotheses derive from the Decomposed Theory of Planned Behaviour. After secondary data
is reviewed, a single quantitative data collection is applied, thus following a mono-method.
This primary data is gathered virtually through a self-administered online questionnaire. A total
number of 205 qualified responses from the Generation Y are statistically analysed using a
structural equation modelling. This descriptive research design is chosen to conduct the
relationships between the latent variables and the behavioural intention.

Findings - The empirical findings reveal, that the attitude, subjective norm and perceived
behavioural control significantly and positively influence the Generation Y’s online purchase
intention to create and use a customised avatar. While the attitude, with the behavioural belief
of perceived usefulness specifically, shows the strongest influence on the behavioural
intention, the research sample also sees a fit to all technology facilitating conditions, affecting
the perceived behavioural control. In comparison to this, the subjective norm influences the
behavioural intention in a weaker manner, whereby the research sample is influenced more
by external than interpersonal factors.

Implications - To enlarge the Generation Y’s online fashion purchase intention while creating
and using a customised avatar, fashion marketers are advised to highlight and improve the
usefulness of the technology. Fashion businesses are recommended to implement interactive
digital platforms, by employing influencer marketing, in order to endorse and promote the
brand awareness in regard to the technology.

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Originality/Value - This master thesis addresses the online purchase intention for fashion
items while creating and using a customised avatar from a commercial perspective. Where
prior literature findings lack the link to managerial implications, this study examines the
Generation Y’s behavioural intention towards this technology. The Generation Y has an
immense and increasing purchasing power, which is accompanied with technical skills, thus
making them crucial for the market success of online fashion businesses. Therefore, the
authors examine the technology's commercial potential and encompass the whole fashion
industry.

Keywords - Virtual fitting, virtual fashion, virtual avatar, customised avatar, Theory of Planned
Behaviour, Decomposed Theory of Planned Behaviour, Generation Y, online buying
behaviour.

Acknowledgements
We would like to thank our supervisor, Professor Daniel Ekwall, for his valuable advice during
our research. Also, we would like to sincerely acknowledge the experts, who affirmed us in
using the software program AMOS: Professor Vijay Kumar and Mariela Acuña Mora.

As without the participants of our online questionnaire, the research could not have been
successfully conducted, we would like to thank all respondents for taking their time and filling
out the survey.

We would also like to express our gratitude to our parents, siblings and friends for
uncompromisingly supporting and encouraging us throughout our years of studying, including
throughout the process of conducting this research.

Finally, we are both beyond grateful about the possibility and experience to have written this
master thesis together. We do not take the smooth course of action for granted and sincerely
want to thank each other and, regardless of the outcome, cheers to an enriched friendship!

Conflicts of Interest
The authors declare no conflict of interest.

 __________________________ __________________________
 Eva Lancere De Kam Jacqueline Diefenbach

 Borås, 7th of June 2020

 III

Table of Content
List of Tables ...................................................................................................................... VI
List of Figures .................................................................................................................... VI
List of Abbreviations ........................................................................................................ VII
1. Introduction ..................................................................................................................... 1
 1.1 Background and Problem Identification ....................................................................... 1
 1.2 Research Gap ............................................................................................................. 1
 1.3 Research Relevance ................................................................................................... 2
 1.4 Research Purpose ....................................................................................................... 3
 1.5 Research Outline ......................................................................................................... 3
2. Literature Review ............................................................................................................ 4
 2.1 Disruptive and Emerging Technologies ....................................................................... 4
 2.2 Fashion as a Social Phenomenon ............................................................................... 6
 2.3 Virtual Fashion............................................................................................................. 6
 2.3.1 Virtual Prototyping and Fitting ............................................................................... 7
 2.3.2 Visual Simulation................................................................................................... 8
 2.3.3 Haptic Simulation .................................................................................................. 9
 2.4 Customised Avatars..................................................................................................... 9
 2.4.1 Personalised Avatars .......................................................................................... 10
 2.4.2 Scanatars ............................................................................................................ 10
 2.4.3 Key Features....................................................................................................... 11
 2.5 Understanding Information Technology Usage .......................................................... 12
 2.5.1 Background Factors: Generation Y ..................................................................... 13
 2.5.2 Behavioural Beliefs and Attitude .......................................................................... 15
 2.5.3 Normative Beliefs and Subjective Norm .............................................................. 17
 2.5.4 Control Beliefs and Perceived Behavioural Control ............................................. 18
 2.5.5 Behavioural Intention and Behaviour ................................................................... 20
 2.6 Theoretical Framework and Hypotheses.................................................................... 20
3. Methodology .................................................................................................................. 23
 3.1 Research Method ...................................................................................................... 23
 3.1.1 Phase I: Determination of the Research Problem ................................................ 24
 3.1.2 Phase II: Development of the Research Design .................................................. 24
 3.1.3 Phase III: Execution of the Research Design ...................................................... 33
 3.1.4 Phase IV: Communication of the Results ............................................................ 34
 3.2 Research Quality ....................................................................................................... 34

 IV

3.2.1 Reliability ............................................................................................................ 34
 3.2.2 Validity ................................................................................................................ 35
 3.2.3 Research Ethics .................................................................................................. 37
 3.3 Research Sample ...................................................................................................... 37
4. Research Analysis ........................................................................................................ 39
 4.1 Descriptive Analysis................................................................................................... 39
 4.2 Statistical Analysis ..................................................................................................... 44
 4.2.1 Reliability Analysis .............................................................................................. 44
 4.2.2 Validity Analysis .................................................................................................. 45
 4.2.3 Hypotheses Analysis ........................................................................................... 48
5. Discussion ..................................................................................................................... 50
6. Conclusion and Future Research Directions .............................................................. 55
 6.1 Conclusion................................................................................................................. 55
 6.2 Future Research Directions ....................................................................................... 56
 6.2.1 Research Limitations........................................................................................... 56
 6.2.2 Theoretical Implications....................................................................................... 57
 6.2.3 Managerial Implications....................................................................................... 58
Reference List ................................................................................................................... 59
Appendix ........................................................................................................................... 71

 V

List of Tables
Table 1. Seven-Point Likert Scale. ...................................................................................... 27
Table 2. Operationalisation of the Online Questionnaire. .................................................... 28
Table 3. Research Sample Characteristics. ........................................................................ 38
Table 4. Descriptive Statistics of the Online Questionnaire. ................................................ 39
Table 5. Cronbach's Alpha. ................................................................................................. 44
Table 6. Model Fit Statistics in Confirmatory Factor Analysis. ............................................. 45
Table 7. Standardised Factor Loadings. .............................................................................. 45
Table 8. Hypotheses Results. ............................................................................................. 49

List of Figures
Figure 1. Gartner Hype Curve. .............................................................................................. 5
Figure 2. 3D Virtual Prototyping and Fitting. .......................................................................... 7
Figure 3. Scanatar Process. ................................................................................................ 11
Figure 4. Decomposed Theory of Planned Behaviour. ........................................................ 13
Figure 5. Theoretical Framework......................................................................................... 21
Figure 6. Methodological Framework. ................................................................................. 23
Figure 7. Result Hypothesised Theoretical Framework. ...................................................... 48

 VI

List of Abbreviations
2D Two-dimensional
3D Three-dimensional
AMOS Analysis of a Moment Structures
AT Attitude
BI Behavioural Intention
C Compatibility
CAD Computer Aided Design
CAM Computer Aided Manufacturing
CFA Confirmatory Factor Analysis
CFI Comparative Fit Index
CMIN/df Chi-square statistic divided by the Degrees of Freedom
DTPB Decomposed Theory of Planned Behaviour
EI External Influences
IBM SPSS International Business Management Corporation Statistical Package for the
 Social Science
II Interpersonal Influences
M Mean
ML Maximum Likelihood
N Population Size
PBC Perceived Behavioural Control
PE Perceived Enjoyment
PEU Perceived Ease of Use
PU Perceived Usefulness
RFC Resource Facilitating Conditions
RMSEA Root Mean Square Error of Approximation
SD Standard deviation
SE Self-Efficacy
SEM Structural Equation Modelling
SN Subjective Norm
TAM Technology Acceptance Model
TFC Technology Facilitating Conditions
TPB Theory of Planned Behaviour
TRA Theory of Reasoned Action
VR Virtual Reality

 VII

1. Introduction
1.1 Background and Problem Identification
The Covid-19 pandemic stresses the importance of digitalisation. Due to the closing of physical
stores and social distancing, digital channels are emphasised more than ever. Since the start of
the pandemic, fashion businesses have faced a 27 to 30 percent contraction in revenues. With
the digital escalation, this priority is visible across the entire value chain for businesses to scale
up and strengthen their capabilities. To cope with new regulations, reduce the pandemic’s
devastating effect and adjust to economic and market changes, fashion businesses need to
implement new technologies, such as virtual reality (VR), digital avatars and assistants into their
value chain to future-proof their business models (BoF & McKinsey & Company, 2020; Gartner
Group, 2019). Technology thereby describes a strategy for many businesses to grow and move
forward (Amed et al., 2017; Amed & Mellery-Pratt, 2017; Diamandis, 2016; O’Leary, 2008). It is
the conveyor of innovation, innovation being a new way to do something (Cie, 2011).

Since the early 2000s, there has been a significant aesthetic and technical development of diverse
textiles and virtual garments (Kalbaska et al., 2019). Virtual human bodies and clothing are widely
used in multiple scenarios, such as in online fashion retail (Guan et al., 2013). Clothing is the
largest single product sector in most countries for online shopping (Cullinane et al., 2017).
However, looking at digital retail environments, on average 25 percent of all clothing purchases
are returned, increasing up to 50 percent for high fashion items (Cullinane et al., 2017; Daanen &
Psikuta, 2018; IMRG, 2020). According to the IMRG report (2020), the most common issues faced
by customers when receiving and fitting online purchased clothes are poor fit, an uncomfortable
feeling when wearing the item and a surprise of the colour. Especially younger consumers tend to
return fashion items more frequently (IMRG, 2020), which results in high return rates and
dissatisfaction about their online shopping experience (Cordier et al., 2003b).

1.2 Research Gap
Increased digital fashion purchases, implying physical fitting is impossible, make the technology
of virtual fitting with a customised avatar increasingly valuable (Daanen & Psikuta, 2018; Hu et al.,
2017). Hence, the creation and usage of customised avatars in the fashion industry has
accumulated research regarding this topic. However, previous studies approach the science from
a technical perspective (Cichoka et al., 2007; Guan et al., 2013; Magnenat-Thalmann et al., 2007)
and the literature lacks the link to managerial implications through research into the commercial
potential of the specific technology. This is supported by Flosdorff’s et al. (2019) study about VR

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in the fashion industry, which recommends further research “to get insights from more diverse
perspectives” (p. 62) in order to encompass the whole fashion industry.

Previous studies investigated the Generation Y’s online behaviour, such as interaction with
brands, social media consumption and online purchases (Bento et al., 2018; Hall et al., 2017), or
examined online and offline attitudes and behaviours compared to other generational cohorts
(Jackson et al., 2011; Parment, 2013; Soares et al., 2017). However, only a few studies
researched the Generation Y’s online buying behaviour for fashion (Bento et al., 2018; Ladhari et
al., 2019; Sethi et al., 2018). According to the knowledge of the authors of this master thesis, no
study has thereby addressed this concern in regard to customised avatars, or in relation to the
Decomposed Theory of Planned Behaviour (DTPB), or both. Conforming to Taylor and Todd
(1995b), by applying the DTPB, specific salient beliefs of individuals can be identified. Fishbein
and Ajzen (2010) support this approach by stating that “relatively few studies have looked at
background variables in relation to [...] behavior-relevant beliefs” (p. 252). To bridge this literature
gap, the following research question is developed:

 Based on the Decomposed Theory of Planned Behaviour, which variables positively
 influence the Generation Y’s intention of creating and using a customised avatar
 while purchasing fashion online?

1.3 Research Relevance
With the increase of digitalisation, many fashion businesses are working on advanced product
visualisation technologies to provide sensory input in the online shopping environment. Virtual
fitting with a customised avatar can be a solution to growing online customer demands and the
pressure of businesses to stay relevant in the volatile fashion industry. As the technology has
become widely available, its popularity amongst customers and development in the online fashion
industry is increasing (Hauswiesner et al., 2013). Customised avatars can be used for online
strategies to enable digital fitting before purchasing fashion. By doing so, fashion businesses aim
to reduce product realisation risk and enhance customers’ interactive virtual shopping experience
(Daanen & Psikuta, 2018; Flosdorff et al., 2019; Guan et al., 2013; Kim & Forsythe, 2008).
Thereby, customers can easily fit garments on their customised avatars without physically wearing
them, and receive tailored advice during their online purchase (Cordier et al., 2003a). For
businesses, this can lead to a stronger customer-business relationship and increased brand
loyalty. Adding interactive technologies, such as virtual fitting with a customised avatar, has the
potential to offer personalised customer service and improve online conversion rates. This
stresses the importance of customer acceptance towards this technology and contributes to

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maximising online sales revenue while decreasing online returns (Flosdorff et al., 2019; Hirt, 2012;
IMRG, 2014; Kim & Forsythe, 2008; Kite-Powell, 2011; Whittaker, 2014).

Incorporating the customer point of view is also of high relevance since online shopping in the
fashion industry is growing (IMRG, 2020). Moreover, the Generation Y has an immense
purchasing power, which is paralleled with technical skills, and presents a crucial market for the
success of online retailers (Ladhari et al., 2019). Analysing the Generation Y’s buying behaviour,
particularly in regard to online fashion purchases while creating and using a customised avatar,
provides valuable psychological insights for both, theoretical research and fashion businesses
aiming to learn about the Generation Y’s beliefs. Especially in comparison to other generational
cohorts, it has been proven that the Generation Y has different motivations for online fashion
shopping (Bento et al., 2018; Ladhari et al., 2019; Sethi et al., 2018). Given the value of the
generation for fashion marketers (IMRG, 2020; Ladhari et al., 2020), aiming to target the
Generation Y, or offering the creation and usage of customised avatars, or both, it is vital to get a
comprehensive understanding of the generation’s online purchasing behaviour.

1.4 Research Purpose
The purpose of this research is to gather empirical data concerning the Generation Y’s behavioural
intention in regard to creating and using a customised avatar. In specific, the generation’s
influence of psychological factors towards the behavioural intention of this technology is
investigated based on the DTPB. Thereby, the aim is not to develop the best statistical model
performance, but to assess the real-world dataset’s fit to the theoretical framework. Overall, the
objective is to create a better understanding of the commercial potential of creating and using a
customised avatar for online fashion purchases, formulate managerial implications for fashion
businesses, and thus strengthen business viability.

1.5 Research Outline
Chapter 2 presents a concise literature review about the technology and the Generation Y’s online
purchase behaviour corresponding to the research topic, whereupon the theoretical framework is
developed. In the methodology section, Chapter 3, the research process including data collection
procedure and measure is described. Additionally, the research quality and sample are introduced.
The gathered data are analysed in Chapter 4, where after, in Chapter 5, findings are discussed
and critically reflected upon in relation to the developed hypotheses and literature review of this
study. Lastly, in Chapter 6, the research question is answered, whereby, with respect to the
limitations of this study, theoretical and managerial implications are proposed.

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2. Literature Review
2.1 Disruptive and Emerging Technologies
According to Diamandis (2016), one of the fundamental things that makes the planet a healthier,
safer, enjoyable, more effective and better educated, is technology. The coming era of prosperity
is powered by a new technology: computational power. In our modern world, growth is stimulated
through this power. Thus, when computing is growing, other computationally driven technologies
are developing alongside it. Networked sensors, robotics, 3D printing, synthetic biology, virtual
and augmented reality as well as artificial intelligence are expanding (Gartner Group, 2018) and
the converging effects of integrating these trends build new business models and future
innovations that one cannot yet imagine (Stamatoula & Kirke, 2019).

Understanding the relationship and difference between disruptive and emerging technologies is
important to discuss the notions between these two, and to position the virtual fitting technology’s
future potential. Li’s et al. (2018) bibliometric study reflects on the relationships between disruptive
and emerging technologies, as these terms are frequently used in literatures, but one must first
understand these concepts. A disruptive technology can be defined as “a technology that changes
the bases of competition by changing the performance metrics along which firms compete” (Bower
& Christenson, 1995, p. 286). This is further supported by Millar et al. (2018), who defines a
disruptive technology as a “change that makes previous products, services and/or processes
ineffective. The implication is therefore one of discontinuity - previous technologies and/or ways
of working are no longer viable” (p. 254). Emerging technologies are defined by Li et al. (2018) as
“a concept that targets various characteristics, including the potentially dramatic impact a new
technology has on the socio-economic system, significant uncertainties, and novel features” (p.
286). While both, disruptive and emerging technologies, involve a degree of innovation and rapid
development, emerging technologies may be capable of being a revolution, but it may also fail or
become a generic technology (Li et al., 2018; Millar et al., 2018; Stamatoula & Kirke, 2019).

The research and consultancy company Gartner Group developed the Gartner Hype Curve in
1995 to help their business clients evaluate technologies, especially regarding the information
systems (Gartner Group, 2018). Gartner’s work dominates the practical side of technology,
however, academics have given it limited attention (O’Leary, 2008). The Gartner Hype Curve is
supported by Diamandis (2016), to understand disruptive technologies, and it is used to describe
a typical development of an emerging technology towards its eventual market (Fenn, 2007). In
practice, the Gartner Hype Curve intents to help companies determine when to invest in a
technology. Moreover, the Gartner Hype Curve helps companies to see beyond the hype and

 4

evaluate how many companies are utilizing a technology (O’Leary, 2008). Figure 1 demonstrates
the Gartner Hype Curve and its five stages of technology acceptance. These are technology
trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment and plateau
of productivity. The technology trigger is defined by Fenn (2007) as the moment of “breakthrough,
public demonstration, product launch or other events generate significant press and industry
interests” (p. 4). Hereafter, the peak of inflated expectations entails the stage where over-
enthusiasm and unrealistic projections, combined with well-publicised activities by technology
leaders show successful results, but more failures. The technology slides into a phase of
disillusionment, where “the technology does not live up to its over inflated expectations” (Fenn,
2007, p. 4) and rapidly loses popularity. After focused experimentation, an increased diverse range
of organisations discover the technology’s applicability, risks and benefits, and slowly commercial
methodologies and tools ease the development process. Finally, reaching the plateau of
productivity, which is described by Fenn (2007) as where “the real-world benefits of the technology
are demonstrated and accepted. Growing numbers of organizations feel comfortable with the
reduced levels of risk, and the rapid growth phase of adoption begins” (p. 4).

Figure 1. Gartner Hype Curve.
Own representation based on (Fenn, 2007; Gartner, 2018)

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2.2 Fashion as a Social Phenomenon
Fashion is a social phenomenon and has communicational power with the capacity to construct a
social environment, as well as to form social settings through collective intelligence. Comparing
fashion, thus clothing, to other consumer products, one can state that fashion represents a visual
expression of an individual’s identity (Fiske, 1990). Eco (1972) declared that in a social setting
one can ‘‘speak through its clothes’’ (p. 59). Hence, fashion is a nonverbal form of communication
and clothing may be treated as being in some way analogous to spoken or written language
(Barnard, 2002). Yet, social risk for this type of product might occur due to fashion’s low
semanticity. Due to evolving meanings over time, denotation is overpowered by connotation,
which is very personal (Wittrock, 2020). Based on Eco’s (1972) metaphor, one can argue that
fashion - clothes as an outfit - can be assembled into sentences, in much the same way as words
are assembled into sentences (Lurie, 1992). Thus, fashion product groups need to be
differentiated. For instance, generic pieces of clothing are often worn as an inner layer, which
cannot be seen by others and are therefore less sensitive to social risk (Parment, 2013).
Hernández (2018) adds that, social factors affecting one’s clothing comfort is related to one’s
personal experience and how others respond to the individual. Psychological comfort is attained
when the wearer has the feeling of wearing the appropriate clothes for an occasion (Sontag, 1985).
Based on this, clothes that are more likely to be seen by others are more likely to have higher
return rates, since the external social factor tends to influence the purchase intention and actual
purchase behaviour, as well as the post buying behaviour (Mäntymäki et al., 2014). Owing to its
inherent tension between tradition and creativity, fashion in the contemporary world has a key role
in understanding individual and collective societal behaviour, both offline and online (Kalbaska et
al., 2019).

2.3 Virtual Fashion
With the increasing development of smart textiles and computational technology, the fashion
industry attempts to combine aesthetics and style with functional technology, intersecting different
areas such as design, science and technology. Two-dimensional (2D) and three-dimensional (3D)
tools are used to combine digitalised techniques with traditional analogue working methods
(Kalbaska et al., 2019). The fashion industry has been using 2D Computer Aided Design (CAD)
and Computer Aided Manufacturing (CAM) systems to help designers simplify their work for many
years. In the late 1980s, the computer graphics community became interested in clothing
simulation and work has since flourished in this field opening up a new path for the textile industry
(Weil, 1986). As a result, the industry has started to look into 3D features (Fontana et al., 2005).

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Virtual fashion is an interplay of digital technology and fashion. Unlike any previous visualisation
tool, a virtual environment in cyberspace provides one a platform where innovative ideas can be
shared at any time, on any scale as a 3D shape. The virtual world is run on user-generated content
(Flosdorff et al., 2019). Terzopoulos et al. (1987) were the first to use 3D models for garment
simulation. Thereafter, Baraff and Witkin (1998) proposed an implicit manner to compute the
garment simulation in real-time. This has elevated the popularity to integrate the equations of
motion in the garment simulation (Meng et al., 2010). An online made-to-measure system was
presented by Cordier et al. (2003) allowing the virtual fitting of garments match according to a
customer’s body measurements. Different colours can be used to illustrate the stretched and
compressed zones, displaying the stretching-stress curves of the chosen garment. With this,
customers are able to predict if the garment fits on the 3D avatar (Cichoka et al., 2007).

2.3.1 Virtual Prototyping and Fitting
Figure 2 exemplifies the typical 3D virtual fitting solution. A process of developing or transforming
2D patterns into 3D virtual patterns and simulate them on a 3D model. Traditionally, all garment
pattern pieces are created as 2D pattern designs, using CAD/CAM software, such as Lectra,
Gerber or Optitex. Hence, the material properties of the garment are essential, as the mechanical
properties of the fabric, such as elasticity, details and embellishments affect the overall
performance of the garment (Boonbrahm et al., 2015; Hu et al., 2017). Thereafter, the 2D pattern
pieces are exported to a virtual 3D CAD/CAM simulation software (Daanen & Psikuta, 2018;
D’Apuzzo, 2009; Hu et al., 2017; Protopsaltou et al., 2002). These patterns are then stitched
together and lifted to 3D, where a physics-based gravitational force field simulation is used to
generate the final garment shape (Hu et al., 2017; Volino & Magnenat-Thalmann, 2000). To
ensure the most realistic clothing simulation and create a virtual clothing prototype, interactive
manipulation tools can be applied: moving, rotating, fixing and dragging. Those tools enable the
movement control and viewing point within the 3D environment.

Figure 2. 3D Virtual Prototyping and Fitting.
(D’Appuzo, 2009)

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Once the garment is virtually simulated on the 3D model, the dynamic behaviour of the shaped
garment can be analysed and additional features evaluated to determine the fit of the clothing
(D’Apuzzo, 2009; Hu et al., 2017). Virtual fitting thereby describes a systematic and objective way
to preview a garment in a 3D simulation before the garment has been physically seen, bought or
made (Hu et al., 2017). With this technology, customers can create their own virtual model based
on their measurements, facial characteristics, hair colour and body shape and it allows them to
effortlessly try on clothes on their digital avatar. Additionally, virtual fitting allows customers to
zoom in on product features, rotate and view the product from different angles and in a variety of
colours. Due to this, the technology can deliver product information similar to the information
obtained during a physical examination in a real-life buying process. By engaging with the
interactive technology, the customers’ value of entertainment enlarges and companies benefit
through an increased conversion rate (Flosdorff et al., 2019; Kim & Forsythe, 2008).

2.3.2 Visual Simulation

Visual virtual fitting simulations can be distinguished between 2D image and 3D model based
processes (Guan et al., 2013). Virtual fitting rooms currently available use 2D garment simulation,
created from CAD/CAM patterns or high-quality images. Virtual fitting rooms enable an overlay of
the virtual garment with a live video feed of a customer. The 3D garment is fitted on front of the
customer’s virtual avatar, displaying the garment digitally. However, due to its 2D image
properties, the downside is the non-rigid garment shape which is usually only attached on the front
side of the customer’s digital body. To achieve a more realistic garment simulation, 3D virtual
fitting along with physical interaction of the fabrics and the environment has been developed
(Boonbrahm et al., 2015). Markers are put on the digital 3D garment which need to be connected
to the 3D avatar. This technology allows customers to wear the markers in order to track motion
and reconstruct the garment on the customer’s 3D avatar. This approach depends on point
correspondences, for which image data is matched to the simulated clothing reconstruction.
Another option is a laser scanner or light dome, which can be used to virtually visualise clothing
in a 3D setting, eliminating both the obtainment of image data and usage of markers. However,
this technology contains an expensive hardware and no real-time processing can be performed
as the shape of the garment needs to be digitalised first (Hauswiesner et al., 2013).

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2.3.3 Haptic Simulation

Although the animation and rendering techniques employed in the textile simulation domain have
significantly improved over the past two decades, the ability to manipulate and alter virtual textiles
intuitively using ergonomic tools, has certainly been overlooked. Haptic display simulations use
computational systems and applications to provide a VR system that allow haptic interaction by
reproducing the sense of touch artificially (Magnenat-Thalmann et al., 2007). The palm of the hand
and the foot sole are particularly sensitive to the sensation of contact due to the density of
mechanoreceptors present in the glabrous skin (Culbertson et al., 2018). Information obtained by
touching with one's hands is defined by Lund (2015) as ‘‘critical for evaluating items that differ in
terms of texture, hardness, temperature and weight-related material properties” (p. 19), such as a
garment (Kalbaska et al., 2019; Peck & Childers, 2003). Haptics-based systems enable interaction
between humans and computers, exploiting kinesthetics and tactile procedures. These systems
are characterised by Culbertson et al. (2018) and based on the required interaction (graspable,
touchable, wearable, mid-air, contactless), as well as the used mechanisms (kinesthetic, vibration,
skin formation). Haptic technologies present a huge potential towards the textile and fashion
industry, providing customers with a touch evaluation option with this new level in digital
communication. Even though first technologies have been developed, it has not been
commercialised. However, the emerging trend has potential in the virtual fashion world and can
fill the tactile lacuna and narrow the gap between online, virtual experience of fashion and the
physical practice of dress in real life (Entwistle, 2015; Kalbaska et al., 2019; Shinkle, 2013).

2.4 Customised Avatars

An avatar is an intangible virtual representation created by users to embody their identity,
character or alter ego and behave accordingly in the digital world (Ducheneaut et al., 2009;
Meadows, 2008). The avatar can be seen as a customised graphical illustration, which can be
represented either in dynamic 3D, such as in games or virtual worlds, or in static 2D, as an icon
or image (Belisle & Bodur, 2010; Holzwarth et al., 2006). Avatars are widely used on digital
platforms, such as websites, blogs, but also in role-playing games. They function as an integral
part of the digital chat and messaging system in the VR. Avatars can be moved and controlled
through a computer keyboard or mouse, or both. With the growing digitalisation trend, there are
grounds to belief that avatars can contain identity characteristics. Starting with individuals who are
insecure or oppressed in the real world, that view avatars as an opportunity to express their true
selves (Williams et al., 2010). Having this in mind, similar as to how clothes in the real world
convey information about ourselves to others (Barnard, 2002; Eco, 1972; Wittrock, 2020), the

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clothes customers choose for their avatar, may serve similar function. Based on the idea that
avatars can accurately reflect identity, individuals choose and prefer avatars perceived similar to
themselves (Nowak & Rauh, 2006, 2008). In addition, avatars can also be used for customers to
find the right size of garments, when purchasing fashion items online. Even though all avatars are
considered as customised, difference between personalised avatars (Chapter 2.4.1) via pre-set
features, from scanatars (Chapter 2.4.2) through the use of a 3D body scanning technology is
acknowledged.

2.4.1 Personalised Avatars

In the VR, the 3D avatar, which is controlled by the user, often has a customisable appearance
(Ducheneaut et al., 2009). Some platforms provide users with a selection of pre-set avatar settings
to choose form. However, more often, avatars in a VR are interactive characters, which can be
customised to the user’s likings. The possibility to selectively represent oneself highlights the
importance of first impressions, which in this case are through computer-mediated communication
(Fong & Mar, 2015).

2.4.2 Scanatars
A customer’s virtual representation can be created by using a 3D body scanner. The 3D model,
also known as a “scanatar”, is created with the help of a predefined human model through
measurements which can be obtained through 3D body scanning. 3D body scanning is about
capturing a real-world object or environment, by collecting data on its shape and appearance in
order to create a virtual representation (Lansard, 2020; van den Helder, 2016; Voellinger Griffey
& Ashdown, 2006). An increase in the use of 3D body scanners to derive body dimensions from
a human body, for example, to create made-to-measure clothing, is visible (Daanen & Hong,
2007). Looking at the 3D body scanning technology, the innovation lies in the process of deriving
body measurements. A new manner of obtaining these measurements is used. A 3D body scanner
derives within less time numerous body measurements, instead of manually deriving them.

When modelling a human body, data from the body scan is crucial to respect the human
morphology (Cichoka et al., 2007). During the scanning process (Figure 3), also known as
“Alignment” or “Registration”, the 3D body scanner measures different points from the subject’s
surface to attain the most precise data (Voellinger Griffey & Ashdown, 2006). All these points
collected, with each point having its own 3D coordinate, are called a “Point cloud” (Cichoka et al.,
2007; van den Helder, 2016). These points are analysed and can be filtered through interpolation

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of the human data. Thereafter, they are linked forming small triangles, through a process that is
called “Triangulation” (Daanen, 2014; van den Helder, 2016). In this phase, the scanatar
resembles a mesh pattern (Guerlain & Durand, 2006). The triangulated model closes, and it is
transformed into a “Polygon model” where depth is added. The polygon model is the final digital
3D model that represents the scanned real-world body. Depending on the application, the scanatar
can have various colours and textures (Lansard, 2020; Stapels et al., 1994; Voellinger Griffey &
Ashdown, 2006).

Figure 3. Scanatar Process.
(Daanen & Ter Haar, 2013)

2.4.3 Key Features

In order to understand the implementation of virtual fitting through a customised avatar, one must
research the key features necessary for optimum usage of the technology. 3D prototyping
software shows the interactive manipulations which are move, fix, drag, walk, zoom in and out.
These can be used to control the movements and viewing point of the simulated garment on the
avatar (Meng et al., 2010). The above-mentioned actions, can be transferred to a fashion
business’ online shop, where customers can interact with the avatar and virtually fit the garments.
Besides this, based on the concept that avatars can accurately reflect identity and individuals
choose and prefer avatars perceived similar to themselves (Nowak & Rauh, 2006, 2008), one
must also consider the appearance of the avatar. Thus, key features concerning the realistic
resemblance of the avatar to the customer are crucial. Guan et al. (2013) stress the importance
of the high quality visualisation of hair, as this is a key indicator for a realistic 3D avatar. The
standard techniques used for hair modelling imply physics-based simulations, which typically have
a high computational cost. Next to this, the human models need to include the kinematic (skeleton
and bones) and shape aspects (soft tissue, flesh and muscle) of a human being. The human body
has a kinematic tree, consisting of segmented body parts linked which are linked by joints.
Commonly, kinematic trees are used to model an articulated human pose for 3D avatars.
Geometric primitives are used, and focused on a segment of the body which display an optimum
articulated pose tracking (Guan et al., 2013).

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2.5 Understanding Information Technology Usage
A variety of theoretical perspectives and research models have been developed to gain a better
understanding of the driving factors to use technology. One important research stream has
employed intention-based models, which consider the behavioural intention to predict technology
usage and, in turn, focus on identifying the determinants of the intention, such as attitudes, social
influences and facilitating conditions (Davis et al., 1989; Hartwick & Barki, 1994; Mathieson, 1991;
Taylor & Todd, 1995b). This research stream, entails social psychology models such as the
Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 2010), and the Theory of Planned Behaviour
(TPB) (Ajzen, 1985, 1991). Where after, the Technology Acceptance Model (TAM) has emerged
as a strong and parsimonious approach, which represents the antecedents of system usage
through beliefs and considers two factors: the perceived ease of use, and the perceived
usefulness of an innovative system (Davis, 1989, 1993; Davis et al., 1989). TAM is an adaption of
the TRA (Ajzen, 1991), whereupon the TPB (Fishbein & Ajzen, 2010) is developed. An even more
advanced intention-based model researching the Behavioural Intention (BI) is the DTPB (Taylor
& Todd, 1995b). Based on innovations characteristics literature, the DTPB explores the
dimensions of Attitude (AT), Subjective Norm (SN) and Perceived Behavioural Control (PBC) by
decomposing them into specific belief dimensions and adding additional factors, such as the
external social influence, perceived ability and control. By doing so, the DTPB identifies specific
salient beliefs that may influence the technology usage. This has proven to be key determinants
of behaviour (Ajzen, 1991), and provides a more complete understanding (Taylor & Todd, 1995b).

To address the research gap and the Generation Y creating and using a customised avatar while
purchasing fashion online behaviour, the DTPB (Ajzen, 1991; Bhattacherjee, 2000; Fishbein &
Ajzen, 2010; Hsieh et al., 2008; Hsu et al., 2006; Mäntymäki et al., 2014; Taylor & Todd, 1995a,
1995b) is applied as the theory to base this research on (Figure 4). It allows the authors to apply
a more comprehensive method of the theory-based decomposition of AT, SN and PBC over
unidimensional belief structures. Finally, the DTPB provides a consistent set of beliefs that can be
implemented across a number of different settings. This overcomes operationalisation issues
noted in relation to the conventional intention-based models (Mathieson, 1991; Taylor & Todd,
1995b).

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Figure 4. Decomposed Theory of Planned Behaviour.
Own representation, based on Ajzen (1985), Ajzen and Fishbein (1980, 2010), Davis (1989), Taylor and Todd (1995b) and
Mäntymäki et al. (2014)

2.5.1 Background Factors: Generation Y
It is assumed that human behaviourism results from background factors or beliefs, or both, which
individuals possess about the behaviour in question. Whereby background factors are unlimited,
they can explain an individual’s behaviour. However, a background factor can thereby only be
considered if it is reasonable to believe that individuals showing differences in that factor have
also been exposed to particular experiences. Hence, those individuals have formed other beliefs
influencing their behaviour. Thereby, their information origins from a variety of sources, such as
knowledge, media and interventions with the social environment (Taylor & Todd, 1995a, 1995b).
Individual differences, such as personality and mood, along with social and demographic
circumstances, do not only influence the individuals’ experience but also the sources they are
exposed to and the ways of interpreting the information. Due to this, individuals from different
social backgrounds are more likely to differ in their beliefs and behaviours (Fishbein & Ajzen,
2010).

Since this master thesis conducts a consumer research on the creation and usage of a customised
avatars while purchasing fashion online, background factors of the Generation Y, also known as
millennials (Ladhari et al., 2019), are considered more specifically. Whereas in literature no strict
consensus on the beginning and ending of the generation can be found, this study considers all
people born between the year 1981 and 2000 to the Generation Y (Ladhari et al., 2019; Sethi et
al., 2018; Soares et al., 2017). Regarding the research topic, this generation is appropriate to look
at, since it is also known as the generation of digital natives and technology enthusiasts. In
addition, the Generation Y has become a major force in the market with a high level of spending
power (Ladhari et al., 2019; Ordun, 2015; Parment, 2013). In specific, the generation is identified
as fashion obsessed since they spend two-thirds of their income on clothes (Kim, 2019), whereas

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they are consumption-oriented (Jackson et al., 2011, Ladhari et al., 2019). Having grown up in a
time characterised by many innovative technological advancements (Bento et al., 2018; Klein,
2015), the generation’s motivations to participate in online activities, such as searching for
information through digital channels, has become common in literature. Thus, online retailers
consider the Generation Y as one of the most important markets (Bolton et al., 2013; Ladhari et
al., 2019; Ordun, 2015; Parment, 2013). Members of the generation tend to be early adopters and
innovators, and not afraid to try new services and products (Jackson et al., 2011, Ladhari et al.,
2019). Moreover, they are highly exposed to social influence (Parment, 2013) and more affected
by the social environment in contrast to other generations (Ordun, 2015). This is also supported
by Giovannini et al. (2015), who state that the public self-consciousness and self-esteem
significantly influence the Generation Y’s status motivation. Klein (2015) adds, that the Generation
Y especially seeks approval from their peers through social media. Moreover, this generation is
also more likely to interact with brands and retailers that use social media platforms (Bolton et al.,
2013). Despite the Generation Y’s quick adaptation to technological innovations (Valentine &
Powers, 2013), which represents a source of information (Ordun, 2015), they rely more on external
influences and word-of-mouth while remaining apprehensive and untrusting to commercial
activities. In comparison to older generational cohorts, they take a more sceptical point of view
towards advertisement, as they consider the online environment as private and exclusive (Ström
et al., 2014; Valentine & Powers, 2013). According to Butcher et al. (2017), the Generation Y
further differs from other cohorts through the products and brands they purchase. Besides
focusing on brand image, product quality and affiliation motivation as a signification of brands,
they emphasis on emotional and entertaining factors as purchase criteria (Bento et al., 2018;
Butcher et al., 2017; Parment, 2013), especially through the interaction with technological
innovations. The use of technologies helps the Generation Y to manage their time more efficiently,
whereas they value customer services the most (Ordun, 2015). This is also supported by Soares
et al. (2017), who add that in comparison to older generations, members of the Generation Y are
most likely to complain about service failures or repurchases from the same provider once an item
is returned. In specific, this generation expects seamless return services and fast refunds for
online purchases. Especially the female members of the Generation Y order multiple products
online while already knowing before placing the order to return some or all of them. This practice,
so called bracketing, describes the high return rates that besides the expected sizes, shoppers
additionally order bigger or smaller ones to ensure the best choice of fit. As clothes come in
different styles and colour options, these elements also drive high return rates due to unwanted
items (IMRG, 2020). According to Klarna (2019), “for [the Generation Y] returns are a part of the
buying experience they can’t live without. The connected world they’ve grown up in means they
expect more from retailers – 88% of Millennial [...] shoppers think returns are now a normal part
of online shopping today” (p. 7). Therefore, a 59 percent of the Generation Y would never shop

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from a retailer, which does not offer free returns (Klarna, 2019). While most researchers found
age being related to a purchase behaviour, Burkolter and Kluge (2011) conduct a negative
relationship between the age and the online purchase. Nevertheless, according to their study, the
younger the participants, the more the internet is used for the information search and the actual
purchase (Burkolter & Kluge, 2011).

2.5.2 Behavioural Beliefs and Attitude
Behavioural beliefs develop from a favourable or unfavourable evaluation, as well as its outcome
and hence, result in the AT towards a behaviour. Thereby, behavioural beliefs can lead to a
positive or negative attitude towards the behaviour in question. The more positive the AT towards
a behaviour, the more favourable the AT and the stronger the BI to participate in this behaviour
(Fishbein & Ajzen, 2010). Based upon the DTPB, the behavioural beliefs to create and use avatars
for online fashion purchases are categorised in, Perceived Usefulness (PU), Perceived Ease of
Use (PEU), Compatibility (C) (Taylor & Todd, 1995b) and Perceived Enjoyment (PE) (Mäntymäki
et al., 2014).

PU entails the degree to which creating and using avatars for online fashion purchases enhances
the shoppers’ PU to perform the purchase behaviour. Hence, this behavioural belief focuses on
the relevance of creating and using a customised avatar for fashion purchases online. As the
garment can be virtually fitted on the shopper’s personalised avatar, the technology enables
shoppers to combine clothes, check multiple colour combinations and see how the garment
moves, adapt to personal colour preferences, and fits. Where after, the shopper performs the
purchasing behaviour, and the post-evaluation step of the buying process commence (Engel et
al., 1968). The shopper compares the extent to which the purchase decision is satisfying or not.
Therefore, customer satisfaction results when the customers’ expectations match the perceived
performance of the product. Due to this, the extend of the post-purchase satisfaction influences
future behaviour and purchases that can result in brand loyalty (Kotler et al., 2016; Kotler &
Armstrong, 2008; Kotler & Keller, 2006; Mäntymäki et al., 2014; Taylor & Todd, 1995b). Based on
this, the following hypothesis is formulated.
H1: Among the Generation Y, the PU towards creating and using customised avatars for online
fashion purchases is positively related to the AT while using the technology for fashion purchases
online.

Secondly, according to the DTPB, another key determinant of AT is the PEU of performing a
certain behaviour (Ajzen, 1991; Taylor & Todd, 1995a). Based on that, the authors incorporate
PEU as a belief, covering the degree to which one perceives using the system effort-free (Davis

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et al., 1989). Therefore, features, such as the ability to zoom in, out, turn and walk with the
customised avatar (Flosdorff et al., 2019; Hirt, 2012; IMRG, 2014; Kim & Forsythe, 2008; Kite-
Powell, 2011; Meng et al., 2010; Whittaker, 2014), as well as change clothes easily influence the
customer’s BI. Overall, online shopping allows customers a certain degree of control, through
maximising opportunities for online comparison as products are available 24 hours a day, 365
days a year. Also, customer service, such as telephone representatives, can support customer’s
questions and orders constantly and instantly (Lim & Dubinsky, 2005). PEU can be enhanced by
a customer support through guided instructions, a personal choice helper and a pocket rule to
measure, or webcam to scan, which affect one’s perceptions of ease (Hsieh et al., 2008;
Mathieson, 1991; Pavlou & Fygenson, 2006). However, as the Generation Y is referred to
technology enthusiast (Ordun, 2015), they are intended to have a more innovative mind set to
technology, such as creating and using an avatar for online fashion purchases, which makes the
PEU positive to perform the purchasing behaviour (Mäntymäki et al., 2014; Taylor & Todd, 1995b).
Based on this, the following hypothesis is formulated.
H2: Among the Generation Y, the PEU towards creating and using customised avatars for online
fashion purchases is positively related to the AT while using the technology for fashion purchases
online.

Thirdly, C describes the degree to which the technology matches to the potential user’s existing
values, past experiences and current needs (Hsieh et al., 2008; Rogers, 2003). With the increasing
advantages and compatibility of information technology, and its decreasing complexity, the AT
towards technology is expected to be more positive. Such an outcome is compatible with the
general distribution of literature on technologies (Taylor & Todd, 1995b). Based on this, the
following hypothesis is formulated.
H3: Among the Generation Y, the C towards creating and using customised avatars for online
fashion purchases is positively related to the AT while using the technology for fashion purchases
online.

Lastly, PE refers to the enjoyment of creating and using a customised avatar while purchasing
fashion online, which influences the BI. As this research focuses on creating and using a
customised avatar for online fashion purchases, it is important to understand the Generation Y’s
buying behaviour and thus, study the PE (Mäntymäki et al., 2014). Therefore, the behavioural
belief PE is added to Taylor’s and Todd’s (1995b) DTPB. Schwarz et al. (2012) study discusses
the enjoyment level of virtual worlds, where users experience a sense of pleasure and playfulness.
This could be translated to the use of virtual avatars as these are used in these virtual worlds to
engage with the technology’s system (Mäntymäki et al., 2014). Based on this, the following
hypothesis is formulated.

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