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

2020.18.02

Thesis for One-Year Master, 15 ECTS

Textile Management

Eva Lancere De Kam

Jacqueline Diefenbach

II

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.

III

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

IV

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

V

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

VI

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

VII

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

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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

5

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)

6

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).

7

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).

9

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

10

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

11

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

14

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

15

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

16

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.

17

H4: Among the Generation Y, the PE towards creating and using customised avatars for online

fashion purchases is positively related to the AT while using the technology for fashion purchases

online.

Thus, when a consumer holds a positive AT towards a particular behaviour, the favourable manner

results in a higher purchase intention (Fishbein & Ajzen, 2010; Mäntymäki et al., 2014; Taylor &

Todd, 1995a, 1995b). Few studies already investigated the positive relationship between the

Generation Y’s AT and purchase BI towards fashion (Jain et al., 2017; Khan et al., 2016; Valaei &

Nikhashemi, 2017). According to George (2004), as well as Loureiro and Breazeale (2016), the

online attitude is moreover positively related to the online purchase intention. Concluding, having

the behavioural beliefs PU, PEU, C and PE influencing the AT, the following hypothesis is

formulated.

H5: Among the Generation Y, the AT towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

2.5.3 Normative Beliefs and Subjective Norm

Normative beliefs result in a SN and describe beliefs of how referents and role models, such as

important groups or individuals in one's life, expect an individual to behave. Hence, it refers to

perceived social pressure to express, or to not express a behaviour. The SN also includes

subjective interpersonal beliefs since it depends on one's own perception of how relevant other

individuals’ opinions and expectations are for the individual intending to perform the behaviour.

Generally, the SN, which is likely to get approval of others about the behaviour in question,

indicates a higher social pressure and probability to engage in the implied behaviour (Ajzen, 1991;

Fishbein & Ajzen, 2010; Mäntymäki et al., 2014; Taylor & Todd, 1995a, 1995b). Generally, the

normative beliefs are influenced by Interpersonal Influences (II) and External Influences (EI).

II are defined through the perceived expectation of oneself, family and peers (Hsieh et al., 2008;

Mäntymäki et al., 2014) and the effect of the persuading power in the relationship between the

referents and role models to the individual performing the behaviour in question, as well as to

information stock in one’s own memory (Kotler & Keller, 2006). Hence, II are also referred to as

non-marketer stimuli (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.

18

H6: Among the Generation Y, the II towards creating and using customised avatars for online

fashion purchases is positively related to the SN while using the technology for fashion purchases

online.

The EI come from society and media influences (Mäntymäki et al., 2014), whereas they are known

as marketer stimuli (Kotler et al., 2016; Kotler & Armstrong, 2008; Kotler & Keller, 2006). These

are the drivers of the SN and disseminate awareness of a given innovation (Bhattacherjee, 2000;

Mäntymäki et al., 2014). According to Davis (1993), SN influences the consumers’ decision

making in a weaker manner in regard to their acceptance of new technologies. However, as online

shopping relies on a self-service in nature, Lim and Dubinsky (2005) state that, consumers

purchasing fashion online are less likely to seek approval from others. Yet, according to Parment

(2013), younger individuals, regardless of their cohort, are more worried about how they are

socially perceived in terms of their purchasing behaviour and hence, are more likely to seek the

approval of others. This is also supported by Schroeder (2006), who claim that young individuals

show commitment to their peers through wearing culturally accepted clothes. Based on this, the

following hypothesis is formulated.

H7: Among the Generation Y, the EI towards creating and using customised avatars for online

fashion purchases is positively related to the SN while using the technology for fashion purchases

online.

As described by Fishbein and Ajzen (2010), a SN which is likely to get approval by others about

a certain behaviour, indicates a higher BI. Young individuals are affected by the choices they make

in clothes as they are scrutinised by others in the society (Tan Tsu Wee, 1999). Considering

fashion as a social phenomenon (Fiske, 1990), the Generation Y is more likely to be influenced

by normative beliefs, wherethrough H8 is proposed.

H8: Among the Generation Y, the SN towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

2.5.4 Control Beliefs and Perceived Behavioural Control

Control beliefs refer to one's own perception of one's ability to perform the BI at ease or in difficulty.

Additionally, PBC reflects previous experiences and anticipated obstacles. Thereby, control

beliefs present the confidence and the perception of an individual to possess the knowledge and

skills for a certain behaviour. Additionally, the beliefs include the availability of tools, which are

necessary for the performance of the behaviour in question. Hence, the perceived behavioural

control does not only affect the BI but also, directly influences the behaviour through actual control

19

(Ajzen, 1991; Fishbein & Ajzen, 2010). The PBC factors, according to the DTPB, are driven by the

individual beliefs about one’s capacity to perform the behaviour in question (Ajzen, 1991; Fishbein

& Ajzen, 2010). As creating and using a customised avatar for online fashion purchases is a digital

technology, the degree of innovation and implementation varies. According to Taylor and Todd

(1995a; 1995b) and Mäntymäki et al. (2014), Self-Efficacy (SE), Technology Facilitating

Conditions (TFC) and Resource Facilitating Conditions (RFC) as control beliefs capture the

characteristic aspects of system controllability.

SE is proposed as a key determinant of the control beliefs (Hsieh et al., 2008; Mäntymäki et al.,

2014; Taylor & Todd, 1995b), and refers to an individual’s degree of self-confidence and ability to

perform an action. With the respect to the subject of this study and the reference of the Generation

Y’s to digital natives and technical enthusiasts (Ordun, 2015), it is anticipated that a higher level

of SE leads to a higher levels of PBC (Mäntymäki et al., 2014; Taylor & Todd, 1995a, 1995b).

Hence, H9 is formulated.

H9: Among the Generation Y, the SE towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

The TFC is related to technology compatibility characteristics, which may aid or constrain during

the creation and usage of a customised avatar for online fashion purchases (Taylor & Todd,

1995b). In regard to the matter of this research, the TFC especially includes actual control factors

(Ajzen & Fishbein, 2010), such as internet accessible devices. As technical compatibility is

ensured, the control belief directly assesses the PBC (Taylor & Todd, 1995b). Based on this, the

following hypothesis is established.

H10: Among the Generation Y, the TFC towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

Additionally, according to Taylor & Todd (1995b), the PBC can also be decomposed into the

control belief RFC, which relates to resource factors such as time and money, enabling the

compatibility of the technology. Essential for the technology in question is the presence of resource

facilitation that constitutes the usage and aids the PBC. Based on this, the following hypothesis is

developed.

H11: Among the Generation Y, the RFC towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

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Concluding, since the Generations Y is referred to as the generation of digital natives and

technology enthusiasts, who are likely to participate in online activities (Ordun, 2015), the

generation is considered to properly accept the technology of creating and using a customised

avatar. Based on this, it can be said that the Generation Y is likely to have higher control beliefs,

leading to a higher PBC. Hence, the following hypothesis derives.

H12: Among the Generation Y, the PBC towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

2.5.5 Behavioural Intention and Behaviour

Once the AT, SN, as well as the PBC are formed, they directly assess and guide the BI. The

intention is the central factor of the DTPB and represents an individual’s motivation to perform a

behaviour through a conscious decision. Generally, the stronger the intention, the more likely it is

for an individual to carry out a certain behaviour. The BI can thereby only find expression in the

behaviour, if the behaviour itself lies under a volitional control. Non-motivational factors include

skills, ability and the availability of opportunities and resources, such as time and money, and

represent the individual's’ actual control over the behaviour. In addition, environmental factors can

prevent the performance of the behaviour. However, if an individual has all basic requirements

and holds the necessary resources to perform the behaviour, one’s BI leads to the behaviour in

question (Ajzen, 1991; Fishbein & Ajzen, 2010).

2.6 Theoretical Framework and Hypotheses

As stated above, the theoretical framework is developed upon the DTPB (Ajzen, 1985; Ajzen,

1991; Fishbein & Azjen, 2010; Taylor and Todd, 1995a, 1995b; Mäntymäki et al., 2014), whereby

the authors hypothesised relationships between the variables in regard to creating and using a

customised avatar while purchasing fashion online. Figure 5 illustrates this framework, which

supports the empirical research through a visualisation.

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Figure 5. Theoretical Framework. Own representation, based on Ajzen (1985, 1991), Fishbein and Azjen (1980, 2010), Taylor and Todd (1995a, 1995b)

and Mäntymäki et al. (2014)

Following hypothesis address the variables of the behavioural beliefs and the AT.

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.

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.

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.

H4: Among the Generation Y, the PE towards creating and using customised avatars for online

fashion purchases is positively related to the AT while using the technology for fashion purchases

online.

H5: Among the Generation Y, the AT towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

22

The following hypotheses relate to the normative beliefs and the SN.

H6: Among the Generation Y, the II towards creating and using customised avatars for online

fashion purchases is positively related to the SN while using the technology for fashion purchases

online.

H7: Among the Generation Y, the EI towards creating and using customised avatars for online

fashion purchases is positively related to the SN while using the technology for fashion purchases

online.

H8: Among the Generation Y, the SN towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

The below stated hypotheses address the control beliefs and the PBC.

H9: Among the Generation Y, the SE towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

H10: Among the Generation Y, the TFC towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

H11: Among the Generation Y, the RFC towards creating and using customised avatars for online

fashion purchases is positively related to the PBC while using the technology for fashion

purchases online.

H12: Among the Generation Y, the PBC towards creating and using customised avatars for online

fashion purchases is positively related to the online fashion purchase BI while using the

technology.

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3. Methodology

3.1 Research Method

Setting up a market research involves multiple steps. For this study, Mooi’s et al. (2018) market

research process is combined with Köksal’s (2018) process, whereby the research passes the

following four phases: Determination of the Research Problem, Development of the Research

Design, Execution of the Research Design and Communication of the Results. This market

research approach is illustrated in Figure 6 and is explained subsequently.

Figure 6. Methodological Framework.

Own representation, based on (Köksal, 2018; Mooi et al., 2018)

24

3.1.1 Phase I: Determination of the Research Problem

Step 1: Determination and Clarification of Information Needs

To determine and clarify information needs, the literature review summarises the reviewed

secondary data for the relevant topic. Besides using the search engine Google Scholar, the

authors also access the library of the University of Borås, Primo and DiVA, and utilise the provided

databases, such as Scopus and Web of Science. The table in Appendix A overviews the search

keywords (virtual fitting, virtual fashion, virtual avatar, customised avatar, Theory of Planned

Behaviour, Decomposed Theory of Planned Behaviour, Generation Y, online buying behaviour)

in regard to their number of results. To collect literature, the primary sources of research studies

are investigated, whereby especially the latest findings are considered. In total, this thesis

incorporates data from 85 scientific journal articles, 23 books, five book sections, seven reports,

five theses, four web pages, one blog post and four presentations. By linking virtual fitting with a

customised avatar to the online buying behaviour of the Generation Y, the research gap is outlined

in Chapter 1.2. The research gap detects a somewhat defined problem since the latent variables

are already known but the relationships are not determined yet (Köksal, 2018; Mooi et al., 2018).

Step 2: Redefinition of the Decision Problem as a Research Problem

In order to fill the research gap, the decision problem is redefined as a research problem, where

through the research question is formulated in Chapter 1.2. Upon theoretical findings, a theoretical

framework, based on the DTPB (Taylor and Todd, 1995a, 1995b; Mäntymäki et al., 2014), is

developed in Chapter 2.6, from which hypotheses derive from. Thus, this study follows a deductive

approach.

Step 3: Establishment of the Research Objectives and Determination of the Information

Value

The research objective is to collect empirical data in order to identify the Generation Y’s online

purchase BI for fashion while creating and using a customised avatar. This allows the statement

of managerial implications (Chapter 6.2.3) for fashion businesses, which is of relevance and yields

high value, as mentioned in Chapter 1.3.

3.1.2 Phase II: Development of the Research Design

Step 4: Determination and Evaluation of the Research Design and Data Sources

A descriptive research design is chosen for the matter of this master thesis. It investigates the

somewhat defined problem and conducts the relationships between the salient beliefs, the AT,

SN, PBC as well as BI. In particular, this research design enables the data collection and the

creation of data structures between the variables (Bryman & Bell, 2015; Köksal, 2018; Kline, 2011;

25

Tarka, 2017). While the salient beliefs, the AT, SN and PBC are the latent and independent

variables, BI is stated as the observed and dependent variable in relation to creating and using a

customised avatar while purchasing fashion online.

After the first part of the data collection, the review of secondary data, the second part of the data

collection refers to primary data, which relates to the information gathered for the specific research

problem (Hox & Boeije, 2005). Therefore, this master thesis uses a single quantitative data

collection, whereby it follows a mono-method (Bryman & Bell, 2015). According to Saunders et al.

(2016), quantitative data is based on meanings deriving from numbers and is commonly used

when collecting samples, analysing greater sets of data and measuring variables.

A self-administered online questionnaire is chosen as the research technique. This technique is

supportive since it enables to reach many individuals of the population of interest in a time and

cost-efficient way. It is also suitable because the questionnaire allows participants to show their

opinions and feelings (Bryman & Bell, 2015) towards creating and using customised avatars while

purchasing fashion online. Since the online questionnaire does not require assistance and ensures

anonymity of the respondents, it is beneficial for gaining valid and reliable answers.

Step 5: Determination of the Sample Plan and Sample Size

The self-administered online questionnaire targets the Generation Y as the population of interest.

Deciding upon the Generation Y, the Generational Cohort Theory is followed. This theory claims

to group populations into generation cohorts on the basis of the years of birth, whereby a

generational cohort is referred to as “a consumer segment that uses an individual's coming-of-age

year as a proxy to postulate his or her value priorities developed through life experiences during

his or her formative years, which may persist throughout that person’s lifetime” (Jackson et al.,

2011, p. 2). The population of interest is appropriate for this study as it is known as the generation

of digital natives and technology enthusiasts, whose motivation to participate in online activities

has become common (Ladhari et al., 2019; IMRG, 2020; Ordun, 2015). Due to the fact, that not

all members of the population are given equally accessible chances to participate in the online

questionnaire, the sampling plan counts as a non-probability sampling. The method selected is a

convenience sampling, as the population is based on the researchers’ convenience (Saunders et

al., 2016). According to Roscoe (1975), a sample size between 30 and 500 respondents is

appropriate for most studies to ensure validity and reliability. As a sample size of 200 or fewer

may be erratic for the analysis applied (Kline, 2011; Tarka, 2017), this study aims to gather a

minimum of 200 responses.

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Step 6: Determination of the Measurement Issues and Scales

The self-administered online questionnaire is constructed based on the proposed theoretical

framework, hinged upon the DTPB. It consists of 44 items in total and is divided into four main

parts, which are described in the following.

Initially, the background of the questionnaire is stated in order to familiarise the respondents with

the topic and task. After an image and definition of a customised avatar is given, the first item aims

to confirm the respondents’ correct understanding. Therefore, a nominal scale is used.

The second part of the self-administered online questionnaire inquires the participants’ opinion

about the latent construct variables within the theoretical framework of the DTPB in regard to

creating and using a customised avatar while purchasing fashion online. To ensure the

effectiveness of answers (Fishbein & Ajzen, 2010), participants are asked to consider a six-month

timeframe for the BI. Whereby four items are formulated for the control belief of RFC, three items

are designed for each of the remaining construct variables within the theoretical framework,

resulting in 40 items in total. Using multiple indicators for one framework variable, additionally

ensures the reliability of the data (Kline, 2011). All of these are developed from previous studies

on similar topics (Ajzen, 1991; Bhattacherjee, 2000; Fishbein & Ajzen, 2010; Hsieh et al., 2008;

Mäntymäki et al., 2014; Taylor & Todd, 1995a, 1995b), wherethrough reliability and validity is

ensured, as specified in Chapter 3.2. Moreover, all items are self-directed, closed-ended and

individually tailored to the research question and hypotheses. A seven-point bipolar adjective

Likert scale is employed as suggested by Fishbein and Ajzen (2010), which indicates the extent

of belief for a given statement. As the scale includes a neutral midpoint, respondents can rate the

question according to their personal opinion (Backhaus et al., 2016; Fishbein & Ajzen, 2010). The

following table overviews the metric scale format.

27

Table 1. Seven-Point Likert Scale. Own representation, based on (Backhaus et al., 2016; Fishbein & Ajzen, 2010)

(1) (2) (3) (4) (5) (6) (7)

Strongly disagree

Disagree More or less disagree

Neutral More or less agree

Agree Strongly agree

Extremely bad

Bad More or less bad

Neutral More or less good

Good Extremely good

Extremely unpleasant

Unpleasant More or less unpleasant

Neutral More or less pleasant

Pleasant Extremely pleasant

Extremely boring

Boring More or less boring

Neutral More or less interesting

Interesting Extremely interesting

Never true Rarely true Sometimes but infrequently true

Neutral Sometimes true

Usually true Always true

To specify the buying behaviour of the Generation Y more closely, the authors add one more item

regarding the respondents’ past online buying behaviour in general, adapted from Hsu et al.

(2006). This third part of the online questionnaire also uses the metric scale format of the seven-

point Likert scale.

Furthermore, the online questionnaire queries the participants’ demographic profile in order to

ensure the participants belonging to the generational cohort of the Generation Y. It also allows the

researchers to vary across subgroups and further state managerial implications for the subject

applied. In addition, demographics enhance the generalisability of the sample size and the

external validity of the study (Bryman & Bell, 2011). Whereas the age of the respondents is queried

with an open-ended question, the gender is investigated using a nominal measurement scale.

Hence, this fourth and last part of the online questionnaire comprises two demographic items.

In order to check all items according their comprehensibility and adequateness of scales, a pilot

study is conducted. To ensure a representative sample, the pre-test is composed of ten test

participants, belonging to the population of interest (Fishbein & Ajzen, 2010; Bryman & Bell, 2015).

In consideration of their feedback, face validity could be improved. An answering time of

approximately five minutes is recorded according to the participants of the pilot study. Google

Forms is used to develop the online questionnaire “Creating and Using a Customised Avatar while

Purchasing Fashion Online” in English. The following Table 2 overviews the final

operationalisation of the questionnaire.

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Table 2. Operationalisation of the Online Questionnaire.

Part Theoretical Framework Variables

Items Scale Hypothesis References

I / I1: Do you understand what a customised avatar is? > Yes / No / Not sure

Nominal /

II PU PU1: The creation and usage of a customised avatar while purchasing fashion online is beneficial to me. > Strongly disagree / Strongly agree PU2: Creating and using a customised avatar improves my fashion purchases online. > Strongly disagree / Strongly agree PU3: The advantages of creating and using a customised avatar while purchasing fashion online outweigh the disadvantages. > Strongly disagree / Strongly agree

Metric H1 Taylor & Todd (1995a; 1995b)

PEU PEU1: The web shop’s instructions for creating and using a customised avatar while purchasing fashion online are easy to follow. > Strongly disagree / Strongly agree PEU2: It is easy to learn how to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree PEU3: It is easy to operate the equipment of how to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H2 Taylor & Todd (1995a; 1995b)

C C1: Creating and using a customised avatar while purchasing fashion online fits well with the way I shop. > Strongly disagree / Strongly agree

Metric H3 Taylor & Todd (1995a; 1995b)

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C2: Creating and using a customised avatar while purchasing fashion online fits into my shopping style. > Strongly disagree / Strongly agree C3: The setup of creating and using a customised avatar while purchasing fashion online is compatible with the way I shop. > Strongly disagree / Strongly agree

PE PE1: It is enjoyable to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree PE2: It is fun to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree PE3: It is entertaining to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H4 Taylor & Todd (1995a; 1995b), Mäntymäki et al. (2014)

AT Creating and using a customised avatar while purchasing fashion online is AT1: > Extremely bad / Extremely good AT2: > Extremely unpleasant / Extremely pleasant AT3: > Extremely boring / Extremely interesting

Metric H5 Ajzen (1991), Fishbein & Ajzen (2010), Taylor & Todd (1995a; 1995b)

II II1: My family thinks I should create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree II2: My friends think I should create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree II3: People I communicate with most often think I should create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H6 Ajzen (1991), Bhattacherjee (2000), Mäntymäki et al. (2014), Taylor & Todd (1995a; 1995b)

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EI EI1: I feel pressure from media and commercials to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree EI2: I feel encouraged by media and commercials to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree EI3: I feel persuaded by media and commercials to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H7 Bhattacherjee (2000), Mäntymäki et al. (2014)

SN SN1: People who are important to me create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree SN2: People who influence me think I should create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree SN3: People who are important to me think I should create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H8 Ajzen (1991), Bhattacherjee (2000), Ajzen & Fishbein (2010), Mäntymäki et al. (2014), Taylor & Todd (1995a; 1995b)

SE SE1: I feel comfortable creating and using a customised avatar while purchasing fashion online on my own. > Strongly disagree / Strongly agree SE2: I could easily create and use a customised avatar while purchasing fashion online on my own. > Strongly disagree / Strongly agree SE3: I feel comfortable creating and using a customised avatar while purchasing fashion online even though there is no one around me to tell how to create and use it. > Strongly disagree / Strongly agree

Metric H9 Mäntymäki et al. (2014), Taylor & Todd (1995a; 1995b)

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TFC TFC1: The equipment (e.g. internet accessible devices) for creating and using a customised avatar while purchasing fashion online is compatible with the other equipment I use. > Strongly disagree / Strongly agree TFC2: Creating and using a customised avatar while purchasing fashion online is compatible with the web shop/brand I shop from. > Strongly disagree / Strongly agree TFC3: I do not have trouble using the equipment when creating and using a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H10 Taylor & Todd (1995a; 1995b)

RFC RFC1: There is enough equipment (e.g. internet accessible devices) for everyone to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree RFC2: I am able to use the equipment (e.g. internet accessible devices) for creating and using a customised avatar while purchasing fashion online when I need it. > Strongly disagree / Strongly agree RFC3: There are web shops/brands for everyone to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree RFC4: I am able to use web shops/brands to create and use a customised avatar while purchasing fashion online when I need it. > Strongly disagree / Strongly agree

Metric H11 Taylor & Todd (1995a; 1995b)

PBC PBC1: I can create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree PBC2: I know how to create and use a customised avatar while purchasing fashion online. > Strongly disagree / Strongly agree

Metric H12 Ajzen (1991), Taylor & Todd (1995a; 1995b), Mäntymäki et al. (2014)

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PBC3: Creating and using a customised avatar while purchasing fashion online is entirely within my control. > Strongly disagree / Strongly agree

BI BI1: I intend to create and use a customised avatar while purchasing fashion online within the next 6 months. > Never true / Always true BI2: I am willing to create and use a customised avatar while purchasing fashion online within the next 6 months. > Never true / Always true BI3: I plan to create and use a customised avatar while purchasing fashion online within the next 6 months. > Never true / Always true

Metric H5, H8, H12

Ajzen (1991), Fishbein & Ajzen (2010), Taylor & Todd (1995a;1995b)

III / P1: In the past 6 months, I have purchased fashion online. > Never true / Always true

Metric / Hsu et al. (2006)

IV Background Factors / Demogra phics

D1: What is your year of birth? > Open question D2: What gender do you identify with? > Female / Male / Other

Nominal / Fishbein & Ajzen (2010)

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3.1.3 Phase III: Execution of the Research Design

Step 7: Data Collection and Data Process

In order to test the hypotheses, primary data is obtained via the self-administered online

questionnaire. To reach many members of the Generation Y in a fast and cost-efficient way,

respondents were gathered virtually. Therefore, the link of the online questionnaire was distributed

to the population of interest through WhatsApp messages. In addition, the link was posted in

Facebook groups (“Thesis/Survey Questionnaire Filling Group”, “Dissertation Survey Exchange -

Share Your Research Study, Find Participants”, “Research Participation - Dissertation, Thesis,

PhD, Survey Sharing”, “Survey sharing 2020” and “Dissertation Survey Exchange”) and shared

through Facebook (“Novitas”, “Eva de Kam” and “Jacky Diefenbach”) and Instagram

(“studentliviboras”) profiles. Next to this, the researchers actively approached respondents in the

University of Borås, Sweden, in order to draw awareness to the link shared via the mentioned

social media channels.

The questionnaire link was active from the 2nd until the 14th of May, 2020. Subsequently to the

data collection, the gathered results were processed through Google Forms and Microsoft Excel.

As all items were required to submit the online questionnaire, no data is missed, and 227

completed answers were collected. However, eleven responses needed to be excluded due to

invalid generational cohorts, six responses were discarded due to the missing understanding

about a customised avatar and five responses could not be considered due to an unusual

answering pattern, such as a straight liner. After the screening process, a total of 205 qualified

responses were noted, which yield an effective rate of 90.3 percent. The research sample is further

examined in Chapter 3.3.

Step 8: Data Analysis

After the data of the online questionnaire is processed, it is analysed descriptively through

Microsoft Excel and the International Business Management Corporation Statistical Package for

the Social Science (IBM SPSS) for Windows version 26. Thereby, the descriptive statistics include

the mean (M), standard deviation (SD), mode and percentiles to investigate the data and its

distribution of each variable. Structural Equation Modelling (SEM) is used for the statistical

hypotheses’ testing, whereby, the statistical dataset is imported to the IBM SPSS Analysis of a

Moment Structures (AMOS) for Windows version 26. SEM is appropriate for this research as it

conducts a precise, complex and multidimensional analysis of empirical gathered data, while

taking aspects of the examined reality and theoretical constructs into account. Moreover,

conducting a SEM enables the researchers to analyse a set of interrelated items in a systematic

and comprehensive approach by examining the relationships among the multiple latent and the

34

dependent variable simultaneously (Anderson & Gerbing, 1988; Kline, 2011; Tarka, 2017). To

answer the research question, the authors apply the confirmatory factor analysis (CFA), whereby

the standardised factor loadings and the levels of significance support or reject the hypotheses.

Step 9: Transformation of Data Structures into Information

Subsequently to the data analysis, the findings are discussed, and cross checked with the data of

the existing theoretical literature. To conclude, the authors answer the opposed master thesis

research question entailing the Generation Y’s beliefs and BI of creating and using a customised

avatar while purchasing fashion online.

3.1.4 Phase IV: Communication of the Results

Step 10: Preparation and Presentation of the Final Report

Ultimately, this study will be submitted on June 7th, 2020, and presented to the examiner and the

researchers’ opponents at the University of Borås, Sweden.

3.2 Research Quality

Since the reliability, validity and ethics of a study is especially important for a social quantitative

data analysis, this matter is addressed in the paragraphs below. Although reliability and validity

can be differentiated analytically, they are related as validity presumes reliability (Bryman & Bell,

2011).

3.2.1 Reliability

Reliability refers to the consistency of reproducing the measurements (Bryman & Bell, 2011;

Kumar, 2019). The internal consistency of the data is tested with the use of the statistical analysis

Cronbach’s alpha in IBM SPSS Statistics 26. According to Bryman and Bell (2011), “its use has

grown as a result of its incorporation into computer software for quantitative data analysis” (p.

159). Cronbach alpha calculates the average of all possible split-half reliability coefficients, thus

testing internal reliability of the measurement items (Table 5). This is used to measure the strength

of consistency of the DTPB latent variables, thus the data for the hypotheses 1 to 12. Alpha

coefficients range in value between 0 and 1, whereas the higher the alpha coefficient of a

respective construct, the higher the internal consistency and the more reliable the results. A

Cronbach’s alpha of 0.7 indicates an acceptable reliability coefficient, whereas an alpha coefficient

below 0.7 indicates possible problems with the online questionnaire (Cronbach, 1951).

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3.2.2 Validity

Validity refers to whether or not a measurement item actually measures a certain concept. To

gauge the validity of a measure, the following is considered for internal validity: content, face,

construct and replicability (Bryman & Bell, 2011; Fornell & Larcker, 1981; Hooper et al., 2007; Hu

& Bentler, 1999; Jöreskog & Sörbom, 1993; Kumar, 2019). The statistical analyses to assess

these are performed with IBM SPSS AMOS 26.

Content validity

The authors deduce hypotheses based upon the DTPB, which are relevant to the research topic

(Chapter 2.5). By using a well-established framework from literature and selecting relevant

measurement variables for the study, the content validity of the research is ensured (Ajzen, 1991;

Bhattacherjee, 2000; Fishbein & Ajzen, 2010; Hsu et al., 2006; Mäntymäki et al., 2014; Taylor &

Todd, 1995). The constructs and measurement items used are psychological, therefore, Table 2

visualises the measurement items applied with references. As these studies reflected validity of

content, this study ensures content validity. Next to this, to assure measurement validity, a

multiple-indicator in the form of a seven-point Likert scale per measurement item is applied. By

asking multiple questions that aim for the same DTPB latent variable, the authors also get access

to a wider range of aspects of the theory.

Face validity

Prior to distributing the online questionnaire, a pilot study is conducted. In addition, the

measurement items are adapted to feedback from a professional researcher and peers to improve

the measurement items, leading to face validity.

Construct validity

Construct validity entails how well a measurement item supports the theory behind a certain

research and if the measures behave appropriately as the theory states the measure should within

the construct (Bryman & Bell, 2011; Graziano & Raulin, 2014). According to Messick (1998),

“construct validity becomes the unifying force that makes validity a coherent unitary concept and

validation a unified process for evaluating evidence of the adequacy and appropriateness of

interpretations and actions based on test scores” (p. 39). Thus, construct validity assures the value

of the conclusions, inferences or propositions made from the analysed dataset. This study ensures

construct validity through a CFA, using a maximum likelihood (ML) estimator to examine the

overall scores, which is evaluated through goodness of fit and factorial validity analyses (Jöreskog

& Sörbom, 1993; Kline, 2011).

36

To assess the adequate model fit, a goodness of fit analysis is employed. The chosen analysis

statistical tests are the chi-square statistic divided by the degrees of freedom (CMIN/df), the

comparative fit index (CFI) and the root mean square error of approximation (RMSEA). Table 6

summarizes the tests for the goodness of fit. The chi-square (𝜒²) index statistic is used to check

the relationship between the variables. By employing this test, the goodness of fit relation between

the covariance matrix of the data set and the covariance matrix of the DTPB structure can be

analysed (Fornell & Larcker, 1981). Values closer to 0.00 have a smaller difference between the

items and imply a better fit. To overcome errors concerning the sample size, this study also reports

the CMIN/df, which is the chi-square divided by the degree of freedom (𝜒²/df). The threshold level

of 1.00 < 𝜒²/df < 3.00 is followed to indicated a good model fit (Ahmed & Ward, 2016; Hair et al.,

2010). The CFI analysis the model’s fit by examining the discrepancy between the data and the

hypothesised theory. This test modifies issues concerning possible sample size issues inherent

in the chi-square test. CFI values range from 0.00 to 1.00, with greater values indicating a better

fit. Preferred is a CFI of more than 0.90 to ensure that miss-specified models are not deemed

acceptable. The RMSEA reviews sample size issues by analysing the discrepancy between the

hypothesised model and the optimally selected parameter estimates and the population

covariance matrix. Values for RMSEA range from 0.00 to 1.00, with the lower values indicating a

better fit. A value of less than 0.08 is preferred (Hooper et al., 2008; Kline, 2011).

According to Bryman and Bell (2011), the validity of a measure should be tested by comparing it

to measures of the same concept developed by using other methods. The factorial validity is

assessed through the standardised factor loadings, in order to evaluate the hypothesised

relationships. Here, the latent and observed variables are examined through the loadings of the

multiple measurement items. Factorial validity of the study is ensured when the standardised

factor loadings are over threshold of 0.50 and ideally 0.70 or higher (Fornell & Larcker, 1981;

Kline, 2011) (Table 7).

Replicability

According to Bryman and Bell (2011), “it is often regarded as important that the researcher spells

out clearly his or her procedures so that they can be replicated by others, even if the research

does not end up being replicated” (p. 165). Therefore, to minimise the researchers’ biases and

ambiguities concerning the study and optimise transparency and replicability of the study, an in

depth research process explanation of the research method (Chapter 3.1) is written.

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3.2.3 Research Ethics

Ethical guidelines can vary from researcher to research area. Notwithstanding the research

method, from the authors’ point of view, everyone involved in the study has to understand the

research ethics, including participants and the readers. Since this research collects primary data

from human participation through the online questionnaire, research ethics must also be taken

into account to minimise potential ethical issues that may rise during the empirical data collection

(Bryman & Bell, 2011; Ekwall, 2019). Based on Diener’s and Crandall’s (1978) four main ethical

principles in business research; harm to participants, lack of informed consent, invasion of privacy

and deception, are used. The following are considered for the research ethics; data collection,

informed consent, incentives, sensitive information, risk of harm and maintaining confidentiality.

Concerning the harm of the participants, the authors incorporate confidentiality of the data through

anonymous responding for the online questionnaire. Prior to participating in the online

questionnaire, the study takes precautions with implementing an informant consent, which informs

the respondents about the research study and the self-administered online questionnaire. By

doing so, the researchers ensure the privacy of the respondents. Finally, deception is minimised

by adopting a transparent research method, purpose and informed consent. The authors need the

respondents to understand the research in order to gain a comprehensive understanding of the

Generation Y’s BI to create and use a customised avatar while purchasing fashion online.

Therefore, both omission (passive deception) and commission (active deception), would not

benefit the research (Bryman & Bell, 2011; Diener & Crandall, 1978; Ekwall, 2019).

3.3 Research Sample

The online questionnaire composes a total of 205 valid responses, whereas the sample has a

population size (N) of 205. Table 3 overviews the research sample more specifically in order to

understand the characteristics of the participants. Respondents are distinguished between the

year of birth and gender. Additionally, the table indicates whether in the last six months, the

questionnaire respondents generally purchased fashion online.

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Table 3. Research Sample Characteristics.

Characteristics Items Sectioning N=205 N (%)

Year of birth D1 1981-2000 205 100.0

Gender D2 Female 138 67.3

Male 67 32.7

Other 0 0.0

Online fashion purchase in the last six months P1 Never 40 19.5

Almost never 12 5.9

Once in a while 9 4.4

Neutral 9 4.4

Most of the time 37 18.0

Almost always 48 23.4

Always 50 24.4

All 205 respondents are born between 1981 and 2000 and hence, belong to the Generation Y.

The average year of birth is 1994, where through the research sample has an average age of 25.5

years. Female respondents outweigh the number of male respondents. Whereas 67.3 percent of

females participated, only 32.7 percent of males completed the survey. Regarding the question

P1, 19.5 percent of respondents stated that within the last six months, they have not bought any

fashion items online. Whereby the majority (80.5 percent) has bought online fashion apparel in

the last six months, 24.4 percent of all participants even state always having bought fashion

apparel online.

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4. Research Analysis

4.1 Descriptive Analysis

Table 4 states the descriptive statistics of the measurement items. The table includes the mean,

mode, standard deviation and percentiles of the first and third quartile, as well as the 95th per

measurement item.

Table 4. Descriptive Statistics of the Online Questionnaire.

Construct Variables

Items

Mean

Mode

Standard Deviation

Percentiles

25 75 95

Perceived Usefulness (PU) 5.341 1.352

PU1 5.341 6 1.343 5 6 7

PU2 5.361 6 1.309 5 6 7

PU3 4.990 6 1.404 4 6 7

Perceived Ease of Use (PEU) 5.034 1.187

PEU1 4.961 6 1.192 4 6 7

PEU2 5.083 6 1.191 4 6 7

PEU3 5.059 5 1.178 4 6 7

Compatibility (C) 4.613 1.667

C1 4.600 6 1.728 4 6 7

C2 4.556 5 1.678 3.5 6 7

C3 4.683 6 1.594 4 6 7

Perceived Enjoyment (PE) 5.283 1.497

PE1 5.190 6 1.504 4 6 7

PE2 5.361 6 1.484 4.5 6 7

PE3 5.298 6 1.503 4 6 7

Interpersonal Influence (II) 3.304 1.712

II1 3.049 4 1.647 1 4 6

II2 3.488 4 1.708 2 5 6

II3 3.376 4 1.780 1 4 6

External Influence (EI) 2.649 1.579

40

EI1 2.332 1 1.481 1 3 5

EI2 2.927 1 1.718 1 4 6

EI3 2.688 1 2.688 1 4 6

Self-Efficacy (SE) 5.028 1.552

SE1 4.912 6 1.609 4 6 7

SE2 5.093 6 1.500 4 6 7

SE3 5.078 6 1.545 4 6 7

Technology Facilitating Conditions (TFC) 4.702 1.594

TFC1 5.000 6 1.550 4 6 7

TFC2 4.263 4 1.737 3 6 7

TFC3 4.844 5 1.493 4 6 7

Resource Facilitating Conditions (RFC) 4.566 1.649

RFC1 4.469 6 1.690 4 6 7

RFC2 5.151 6 1.585 4 6 7

RFC3 3.995 4 1.691 3 5 6.7

RFC4 4.468 4 1.632 4 6 7

Attitude (AT) 5.537 1.182

AT1 5.537 6 1.165 5 6 7

AT2 5.346 6 1.197 5 6 7

AT3 5.732 7 1.185 5 7 7

Subjective Norm (SN) 2.668 1.702

SN1 2.498 1 1.635 1 4 6

SN2 2.717 1 1.709 1 4 6

SN3 2.790 1 1.763 1 4 6

Perceived Behavioural Control (PBC) 4.441 1.691

PBC1 4.473 5 1.705 4 6 7

PBC2 4.176 6 1.782 3 6 6

PBC3 4.673 4 1.586 4 6 7

Behavioural Intention (BI) 4.062 1.742

BI1 3.693 4 1.714 2 5 6

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BI2 4.556 5 1.797 3 6 7

BI3 3.937 5 1.715 3 5 6

Purchase Behaviour (P) P1 4.634 7 2.229 2 6 7

Concerning the behavioural belief PU, data indicates that the usefulness of creating and using a

customised avatar for online purchases is perceived as beneficial and has the ability to improve

one’s online fashion purchases. Overall, the PU of the technology scores a mean of 5.341

(SD=1.352), indicating that the research sample agrees more or less with this perception.

However, the measurement item PU3 indicates a slightly lower agreement to the advantage of the

technology, having a mean score of 4.990 (SD=1.404). In total, 95 percent of the participants

strongly agree with the PU of the technology.

The overall mean score for the construct variable PEU is 5.034 (SD=1.187). Hence, data gathered

shows that respondents more or less agree that the web shops’ instructions to create and use a

customised avatar for online fashion purchases and the equipment operate the technology seems

easy to learn and follow, as per measurement items PEU1, PEU2 and PEU3. Yet, the mode for

measurement items PEU1 and PEU2 is 6, stressing an agreement upon PEU concerning following

web shop’s instructions and learning how to create and use a customised avatar for online fashion

purchases.

Overall, the construct variable C of the technology is more or less agreed upon with a mean score

of 4.613 (SD=1.667). Respondents more or less agree with the compatibility of the technology

suiting into the way the respondents shop (C1), fitting into the respondents’ shopping style (C2)

as well as the setup being compatible (C3). The mode for C2 supports this, whereas modes for

C1 and C3 are 6 (agree), underlining the respondents’ opinion of the technology being compatible.

The behavioural belief construct variable PE scores a mean of 5.283 (SD=1.497), which highlights

that creating and using a customised avatar for online fashion purchases is more or less agreed

upon to be enjoyable (PE1), fun (PE2) and entertaining (PE3). Even though this construct variable

mean scores not too high and the mode for all three measurement items is 6 (agree), it should be

stressed that 95 percent of the respondents strongly agree to the technology’s PE.

Data gathered and analysed from the normative belief II shows for measurement item II1 a mean

score of 3.049 (SD=1.647), stating the more or less disagreement of the respondents’ family

influence to create and use a customised avatar while purchasing fashion online. Even though

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respondents also state a more or less disagreement towards being influenced by friends (II2) or

people they communicate with (II3), the mean of those measurement items are higher than

compared to family (II1). Data also shows that, 95 percent of the respondents agree with their

families’ and friends’ opinion about them using the technology. Generally, data indicates that the

construct variable II is rather low, scoring a mean of 3.304 (SD=1.712).

In comparison to II, the normative belief EI is perceived even lower as respondents are on average

disagreeing with feeling pressure (EI1) and more or less disagreeing being encouraged (EI2) from

or persuaded (EI3) by media and commercials to create and use a customised avatar while

purchasing fashion online. This is supported by 95 percent of the participants. The construct

variable EI scores an overall mean of 2.649 (SD=1.579), with a mode of 1 (strongly disagree) for

all measurement items.

The control belief SE scores relatively high, with a mean score of 5.028 (SD=1.552) for the

variable, which indicates respondents agreeing on this belief more or less. Explicitly, data shows

that 95 percent of the respondents strongly agree with their SE towards the technology. Also, a

mode of 6 (agree) for all measurement items, indicates that most respondents feel comfortable

(SE1) to create and use a customised avatar while purchasing fashion online easily (SE2), even

on their own (SE3).

Data shows a mean score of 4.702 (SD=1.594), expressing that participants more or less agree

upon the control belief TFC. However, data of the measurement item TFC1 concerning the

compatibility of the technology’s equipment with the equipment used is strongly agreed upon by

95 percent the respondents, as well as having no trouble using the equipment (TFC3).

The construct variable RFC is perceived as neutral (25 percent) to strongly agree (95 percent) by

respondents, with a mean score of 4.566 (SD=1.649). Analysing measurement item RFC3, the

mean score of 3.995 (SD=1.691) underlines the rather neutral opinion of respondents towards

having enough resources in the form of web shops and brands or both supplying the technology.

Besides this, where measurement items RFC3 and RFC4 score a neutral response regarding the

mode of 4, the mode for measurement item RFC1 and RFC2 is 6 (agree). Nevertheless, percentile

data shows that 95 percent of the respondents strongly agree to measurement items RFC1, RFC2

and RFC4.

AT scores an overall mean of 5.537 (SD=1.182), which indicates that the research sample shows

a positive attitude towards the technology. Thereby, this construct variable scores the lowest SD

within the model. In specific, 95 percent of the participants rate creating and using a customised

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avatar while purchasing fashion online as extremely good (AT1), extremely pleasant (AT2) and

extremely interesting (AT3). The sample thereby stresses the interesting matter of the technology

(AT3) as the most important one with a mode of 7 (extremely interesting).

With an overall mean of 2.668 (SD=1.702), the respondents more or less disagree on being

influenced by SN when creating and using a customised avatar while purchasing fashion online.

However, a large SD within construct variable SN is visible. This is supported by the data, showing

that 95 percent of the sample agrees on being influenced by others, while 25 percent strongly

disagrees. Even though the sample more or less neglects that people who are important to them

use the technology (SN1) with a mean of 2.498 (SD=1.635), they show a higher confirmation that

people who influence them (SN2) and people who are important to them (SN3) think they should

make use of this technology.

Regarding the variable PBC, participants more or less agree on being in control with an overall

mean of 4.441 (SD=1.691). While 95 percent of the research sample agrees on having the

knowledge (PBC2) about creating and using an avatar, 95 percent even strongly agrees on being

able to use the technology (PBC1) and having this entirely within their control (PBC3).

The BI data shows that respondents are neutral towards their intention (BI1) and plan (BI3) to

create and use a customised avatar. The construct variable scores a mean of 4.062 (SD=1.742).

The broad SD for all measurement items indicate that respondents BI ranges from rarely true to

usually true. However, according to 95 percent of the sample, their willingness to use the

technology within the next six months (BI2) is higher.

Reflecting on the data gathered concerning the respondents online fashion purchase behaviour

within the last six months, measurement item P1 indicates that the respondents purchases fashion

online. This is supported by the mode for the measurement item P1, which is 7 (always true).

However, the purchase behaviour scores a mean of 4.634 (SD=2.229), which indicates that

participants sometimes purchase fashion online. With the SD being this broad, 25 percent of the

respondents almost never purchased fashion online, whereas 95 percent always do.

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4.2 Statistical Analysis

4.2.1 Reliability Analysis

To examine the degree of internal reliability and consistency between the whole construct as well

as the construct variables, Cronbach’s alpha (>0.70) is applied. As Table 5 shows, the overall

construct is internally consistent with a Cronbach’s alpha of 0.951. Additionally, all measurement

constructs hold acceptable alpha values between 0.787 and 0.941, indicating high internal

consistency, where through reliability of the data is ensured.

Table 5. Cronbach's Alpha.

Construct Variables Included Items Cronbach’s Alpha >0.70

Perceived Usefulness (PU) PU1, PU2, PU3 0.825

Perceived Ease of Use (PEU) PEU1, PEU2, PEU3 0.852

Compatibility (C) C1, C2, C3 0.941

Perceived Enjoyment (PE) PE1, PE2, PE3 0.931

Interpersonal Influence (II) II1, II2, II3 0.935

External Influence (EI) EI1, EI2, EI3 0.885

Self-Efficacy (SE) SE1, SE2, SE3 0.824

Technology Facilitating Conditions (TFC) TFC1, TFC2, TFC3 0.787

Resource Facilitating Conditions (RFC) RFC1, RFC2, RFC3, RFC4 0.821

Attitude (AT) AT1, AT2, AT3 0.855

Subjective Norms (SN) SN1, SN2, SN3 0.911

Perceived Behavioural Control (PBC) PBC1, PBC2, PBC3 0.805

Behavioural Intention (BI) BI1, BI2, BI3 0.898

Overall scale PU1, PU2, PU3, PEU1, PEU2, PEU3, C1, C2, C3, PE1, PE2, PE3, II1, II2, II3, EI1, EI2, EI3, SE1, SE2, SE3, TFC1, TFC2, TFC3, RFC1, RFC2, RFC3, RFC4, AT1, AT2, AT3, SN1, SN2, SN3, PBC1, PBC2, PBC3, BI1, BI2, BI3

0.951

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4.2.2 Validity Analysis

The aim of the CFA is to test the DTPB structure in accordance to the measurement items used

to evaluate the model fit. Whereas the CFI almost reaches the acceptable threshold with 0.862,

the CMIN/df and RMSEA (df: 732; 𝜒²: 1648.691, p<0.0001; CMIN/df 2.252; RMSEA: 0.078) yield

acceptable values above the thresholds. Thus, the model has an adequate fit and construct validity

is confirmed. Table 6 overviews the model fit statistics.

Table 6. Model Fit Statistics in Confirmatory Factor Analysis.

Fit Index Value

Chi-square / Degrees of Freedom (CMIN/df) <3.00 2.252

Comparative Fit Index (CFI) >0.90 0.862

Root Mean Square Error of Approximation (RMSEA) <0.08 0.078

In regard to the factorial validity, the standardised factor loadings are shown in Table 7. The values

vary between 0.598 and 0.956, apart from the SN factor loading, which has a score of 0.480

p<0.0001. Hence, SN is not exceeding the threshold of 0.50, where through factorial validity is not

met. As the ideal factorial threshold of 0.70 is not reached for PEU (0.528 p<0.0001) and PBC

(0.598 p<0.0001), the relationship between PEU and AT, as well as between PBC and BI is

mediocre.

Table 7. Standardised Factor Loadings.

Factors Items Standardised Factor Loadings (>0.50)*

Perceived Usefulness (PU) 0.874

PU1 0.952

PU2 0.916

PU3 0.792

Perceived Ease of Use (PEU) 0.528

PEU1 0.760

PEU2 0.887

PEU3 0.838

Compatibility (C) 0.824

C1 0.942

C2 0.948

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C3 0.921

Perceived Enjoyment (PE) 0.848

PE1 0.923

PE2 0.956

PE3 0.906

Interpersonal Influence (II) 0.778

II1 0.912

II2 0.928

II3 0.900

External Influence (EI) 0.849

EI1 0.772

EI2 0.873

EI3 0.906

Self-Efficacy (SE) 0.798

SE1 0.678

SE2 0.829

SE3 0.893

Technology Facilitating Conditions (TFC) 0.920

TFC1 0.729

TFC2 0.663

TFC3 0.878

Resource Facilitating Conditions (RFC) 0.915

RFC1 0.716

RFC2 0.757

RFC3 0.638

RFC4 0.793

Attitude (AT) 0.765

AT1 0.903

AT2 0.864

AT3 0.776

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Subjective Norm (SN) 0.480

SN1 0.791

SN2 0.916

SN3 0.921

Perceived Behavioural Control (PBC) 0.598

PBC1 0.727

PBC2 0.731

PBC3 0.726

Behavioural Intention (BI)

BI1 0.707

BI2 0.791

BI3 0.918

*All factor loadings are significant at p<0.0001

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4.2.3 Hypotheses Analysis

To evaluate the hypothesised theoretical framework, this master thesis conducts a SEM using the

ML parameter. Whereas Table 7 lists the standardised factor loadings, which indicate strong

positive relations among the latent and observed variables, Figure 7 depicts the result of the

hypothesised theoretical framework.

Figure 7. Result Hypothesised Theoretical Framework. Own representation, based on the DTPB (Taylor & Todd, 1995b)

In regard to the standardised factor loadings, which are used to support or reject the hypotheses,

11 out of 12 exceed the validity threshold of 0.50, whereby nine even pass the ideal threshold of

0.70. All of those 11 standardised factor loadings are significant with a level of p<0.0001 and yield

positive values, where through the variables show a positive relation among each other. Hence,

those hypotheses are supported within the theoretical model of the DTPB and include H1, H2, H3,

H4, H5, H6, H7, H9, H10, H11 and H12. However, the standardised factor loading of H8 (0.480)

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does not meet the set threshold, which indicates that the relationship between SN and BI is less

strong compared to the other construct variables. Nonetheless, H8 also meets the significant level

of p<0.0001, supporting the hypothesis within the theoretical model of the DTPB. Table 8 gives

an overview of the hypotheses results in regard to the standardised factor loadings.

Table 8. Hypotheses Results.

Hypotheses Standardised Factor Loadings*

Results

H1 Perceived Usefulness (PU) ← Attitude (AT) 0.874 Supported

H2 Perceived Ease of Use ← Attitude (AT) 0.528 Supported

H3 Compatibility (C) ← Attitude (AT) 0.824 Supported

H4 Perceived Enjoyment ← Attitude (AT) 0.848 Supported

H5 Attitude (AT) ← Behavioural Intention (BI) 0.765 Supported

H6 Interpersonal Influence (II) ← Subjective Norm (SN) 0.778 Supported

H7 External Influence (EI) ← Subjective Norm (SN) 0.849 Supported

H8 Subjective Norm (SN) ← Behavioural Intention (BI) 0.480 Supported

H9 Self-Efficacy (SE) ← Perceived Behavioural Control (PBC) 0.798 Supported

H10 Technology Facilitating Conditions (TFC) ← Perceived Behavioural Control (PBC)

0.920 Supported

H11 Resource Facilitating Conditions (RFC) ← Perceived Behavioural Control (PBC)

0.915 Supported

H12 Perceived Behavioural Control (PBC) ← Behavioural Intention (BI)

0.598 Supported

*All standardised factor loadings are significant at p<0.0001

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5. Discussion

Behavioural Beliefs and Attitude

Even though PU does not have the highest internal consistency, it yields the highest mean

(M=5.341; SD=1.352) and highest standardised factor loading (0.874 p<0.0001) within the

behavioural beliefs. Thereby, the usefulness for online fashion purchases is strongly agreed upon

by 95 percent of the Generation Y respondents. In specific, the research sample agrees on the

technology being beneficial for themselves (PU1). The important matter of PU is in line with

previous investigated studies, clarifying that the Generation Y values the search for information

online (Bolton et al., 2013; Ladhari et al., 2019; Ordun, 2015; Parment, 2013). Thus, especially

technology connected characteristics, such as digitally instead of manually derived body

measurements, are beneficial for customers interested in purchasing made-to-measure clothing

(Daanen & Hong, 2007). According to Ordun (2015), the use of such a technology can thus help

the generation to manage their time more efficiently.

Besides the PU, the respondents also agree (M=5.283; SD=1.497) on the PE when creating and

using a customised avatar while purchasing fashion online with a standardised factor loading of

0.848 p<0.0001. This is supported by Schwarz et al. (2012), who state that users experience a

sense of pleasure and playfulness in virtual worlds. Especially in regard to other generational

cohorts, the Generation Y focuses on emotional and entertaining factors when interacting with

technological innovations (Bento et al., 2018; Butcher et al., 2017; Parment, 2013). This is also

supported by Flosdorff et al. (2019), as well as Kim and Forsythe (2008), who state that by

engaging with interactive technology, customers’ entertainment value increases. In addition,

based on the standardised factor loading of this attitudinal belief, PE highlights the Generation Y’s

motivation to participate in online activities (Bolton et al., 2013; Ladhari et al., 2019; Ordun, 2015;

Parment, 2013).

Whereas the C reaches a lower mean (M=4.613; SD=1.667) than the PEU (M=5.034; SD=1.187),

its standardised factor loading (0.824 p<0.0001) is higher than the one of PEU (0.528 p<0.0001).

In addition, one must acknowledge that the items targeting the construct variable C indicate the

highest internal reliability of Cronbach’s alpha coefficient being 0.941. Thereupon, one can state

that participants have the most consistent answers regarding the relation between C and the AT

to create and use a customised avatar while purchasing fashion online. One can state that, as 95

percent of the research sample strongly agrees on the technology’s PEU, the literature findings

are confirmed. Since the Generation Y is known as the generation of digital natives and technology

enthusiast, it is in the cohort’s nature to know how to operate (Ladhari et al., 2019, Ordun, 2015;

Parment, 2013) and quickly adapt to technological innovations (Valentine & Powers, 2013). In

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addition, based on the results, one can argue that the technology fits well with the online buying

behaviour of the participants (C1). This is also confirmed by the respondents, as the majority (80.5

percent) has purchased fashion online within the last six months. Moreover, 24.4 percent of all

participants even state always having bought fashion online, where through the C of creating and

using a customised avatar for online fashion purchases increases. Burkolter and Kluge (2011)

state that the younger the participants, the more the internet is used for information search and

the actual online purchase. In a critical light, however, the broad SD of measurement item P1

(M=4.634, SD=2.229) indicates that not all participants, even though they belong to the relatively

young generational cohort, shop online. Here, 19.5 percent of the research sample state not

having bought online fashion apparel. However, one can question this result as the measurement

item only entails the past behaviour in a time span of the previous six months.

Overall, one can state that the construct variable of AT has a positive relation towards the BI

(0.765 p<0.0001) of creating and using a customised avatar while purchasing fashion online. This

does not only support H5, it also confirms the findings of George (2004), as well as Loureiro and

Breazeale (2016). According to them, the online AT is positively related to the online purchase BI.

Moreover, the research finding is in line with Valaei and Nikhashemi (2017), stressing the positive

relation between the Generation Y’s AT and purchase BI towards fashion.

Normative Beliefs and Subjective Norm

It is noted that the mean of II (M=3.304; SD=1.712) is higher than the mean of EI (M=2.649;

SD=1.579), whereas one may argue that the Generation Y is more influenced by interpersonal

factors, such as family and friends. External factors, such as media and commercials, pressure,

encourage and persuade the research sample in a weaker manner. Those findings are in line with

previous investigated studies (Giovannini et al., 2015; Klein 2015; Ordun, 2015; Parment, 2013).

According to their outcomes, the Generation Y is highly exposed to social influence and even more

affected by their personal environment, especially by their peers, in contrast to other generational

cohorts. This is also supported by Barnard (2002), observing the growing trend of customised

avatars used by social media influencers. In addition, one can argue that the low mean of the

variable EI is also supported by Ström et al. (2014), as well as Valentine and Powers (2013), who

state that the generation senses word-of-mouth advertising more trustworthy than commercial

activities. Especially in comparison to older generations, the Generation Y takes a more sceptical

point of view towards advertisement, as they consider the online environment as private and

exclusive. Next to this, fashion can be seen as a social phenomenon with communicational power

and represents a visual expression of an individual’s identity (Fiske, 1990). Thus, social risk for

fashionable products might especially occur due to its’ low semanticity (Wittrock, 2020). According

to Giovannini et al. (2015), the Generation Y’s status motivation is influenced by the public self-

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consciousness and self-esteem. Hence, this might explain the Generation Y’s normative beliefs

in regard to the BI of creating and using a customised avatar while purchasing fashion online.

Nevertheless, looking at the standardised factor loadings, EI (0.849 p<0.0001) has a stronger

positive effect on the construct variable SN, than II (0.778 p<0.0001). One can argue that the

Generation Y’s external factors, such as media and commercials, have a stronger positive relation

to the construct variable SN, whereas it contradicts to previous literature findings. Nevertheless,

H7 is supported due to the positive relation of EI to the construct variable SN.

As the overall standardised factor loading of SN (0.480 p<0.0001) does not meet the set threshold

of 0.50, it indicates a weaker relationship between the latent construct variables SN and BI. Hence,

one can state that this variable does not influence BI as much as AT and PBC. Accordingly, one

should consider that fashion is a social phenomenon (Fiske, 1990), whereby especially young

individuals value their choice of clothing as important due to their feeling of being scrutinised by

others in the society (Tan Tsu Wee, 1999). However, data questions those literature findings.

Being critical, SN can therefore be seen as a problematic item. Nonetheless, the standardised

factor loading is positive and significant, where through H8 is supported.

Control Beliefs and Perceived Behavioural Control

Studies by Taylor and Todd (1995b), Hsieh et al. (2008) and Mäntymäki et al. (2014) claim that

SE has been long proposed as a key determinant of the control beliefs. Reflecting on the statistical

analysis, SE scores a factor loading of 0.798 (p<0.0001), thus confirming the positive relationship

with the latent construct variable PBC and stressing the research sample’s perception on their

ability to create and use a customised avatar while purchasing fashion online. In addition, 95

percent of the respondents strongly agree with their SE towards the technology. However, one

must acknowledge that SE scores the lowest factor loading within the PBC. Due to the fact that

the research sample rates the SE with a mean of 5.028 (SD=1.552), one can argue that

participants more or less agree on being self-confident regarding creating and using a customised

avatar when purchasing fashion online. Yet, compared to the control beliefs RFC and TFC, the

relationship between SE and PBC is mediocre. Nonetheless, according to Jackson et al. (2011)

and Ladhari et al. (2019), members of the Generation Y tend to be early adopters of technologies

and are not afraid to use new services and products. Additionally, Davis et al. (1989) mention the

ease of information technology, which can support the control belief SE in combination with the

Generation Y’s technically skilled characteristic. Based on this, one can argue that this might

positively affect the PBC.

Concerning the TFC, it can be acknowledged that this belief scores the highest standardised factor

loading (0.920 p<0.0001) of the latent variables within the DTPB, whereupon one can stress the

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positive perception of TFC among the Generation Y in regard to the technology. Specifically

arguing TFC3, the measurement item scores the highest factor loading (0.878 p<0.0001) within

this latent control belief, indicating that participants do not have trouble using the technology’s

equipment. Here, one must mention Ladhari et al. (2019), Ordun (2015), as well as Parment

(2013), as according to them, the Generation Y is also known as digital natives. Thus, it can be

expected that their perception on the creation and usage of a customised avatar while purchasing

fashion online has high technological facilitating conditions. Following Gartner’s Hype Curve

(2018), one can argue that virtual fitting is a sub trend of the VR technology. Even though the

technology has been present in the fashion industry since the late 1980s (Weil, 1986), virtual

fashion is still relatively new (Fontana et al. 2005) and one can place the technology in between

Gartner’s first step, technology trigger, and the second step, peak of inflated expectations.

According to Boonbrahm et al. (2015), to achieve a more realistic garment simulation, 3D virtual

fitting has been developed. Critically arguing upon the technology itself, haptic abilities are still

very minimal. However, according to Entwistle (2015), Kalbaska et al. (2019) and Shinkle (2013),

haptic technologies present a huge potential towards the textile and fashion industry, providing

customers with a touch evaluation option as a new level in digital communication. Connecting this

to the Gartner Hype Curve (2019), one may argue that after the technology has reached its peak

of success, the further development along with haptic technologies could speed up the process

towards a stable plateau of productivity.

The control belief RFC shows the second highest factor loading of the latent variables within the

DTPB, with a score of 0.915 p<0.0001, supporting H11 and confirming the positive effect of RFC

on PBC. According to Bento et al. (2018) and Klein (2015), the Generation Y has grown up in a

time characterised by many innovative technological advancements. Based on this, one should

mention their motivations to participate in online activities through digital channels and accuracy

of resource understanding, especially concerning online technologies, such as customised

avatars. Standardised factor loadings for measurement item RFC1 (0.716 p<0.0001), questioning

the availability of equipment, and RFC2 (0.757 p<0.0001) and RFC4 (0.793 p<0.0001), regarding

the Generation Y’s ability to use the equipment when they need it, score high. One could state

that the technology and the key features such as move, fix, drag, walk, zoom in and out (Meng et

al. 2010), done through a computer keyboard or mouse, or both (Williams et al. 2010), are self-

explanatory for the generational cohort. However, critically reflecting on the standardised factor

loadings of measurement item RFC3 (0.680 p<0.0001), one must point out that it scores the lowest

factor loading within the SEM, measuring the RFC of the availability of web shops or brands, or

both. Thereupon, one must highlight this weaker relationship between RFC3 and PBC, which

stresses the importance of resources in the form of web shops or brands necessary for the

technology to be used.

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Overall, one can state that the participants agree on being in control in regard to the system

controllability, as well as their own knowledge and skills of the technology’s characteristic aspects.

The PBC standardised factor loading of 0.598 p<0.0001 shows a significant and positive

relationship with BI, whereby H12 is supported. However, one must mention the rather low factor

loading in comparison to the other construct variable AT towards BI. According to Ström et al.

(2014) and Valentine and Powers (2013), the Generation Y considers the online environment as

private, which might be due to the risk of data security containing the personal information.

Behavioural Intention

The construct variable of BI shows internal consistency and reliability due a Cronbach’s alpha

coefficient of 0.898. Moreover, the mean of BI comprises a value of 4.062 (SD=1.742). This

indicates that the research sample holds a neutral opinion towards creating and using a

customised avatar while purchasing fashion online. Being critical, one may argue that this BI is

due to the fact that the deployment of the technology in online fashion retailers is rather scarce

and the degree of implementation rather low (Gartner, 2018). Nonetheless, 95 percent of

participants are willing (B2) to use the technology within the next six months, highlighting the

potential of customised avatars for online fashion retailers.

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6. Conclusion and Future Research Directions

6.1 Conclusion

This master thesis reviews which latent variables within the theoretical framework of the

Decomposed Theory of Planned Behaviour positively influence the Generation Y’s behavioural

intention of creating and using a customised avatar while purchasing fashion online, wherefore

the following research question should be answered:

“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?”

The research contributes to a greater psychological understanding of the Generation Y’s

behavioural purchase intention while using this technology. By doing so, the authors aimed at

further deepening the literature in the sphere of the commercial potential of customised avatars in

the fashion industry and fill the gap between the theoretical and managerial implications.

The applied theoretical framework provides preliminary evidence on internal consistency and

reliability. Moreover, based on the model fit indices of CMIN/df and RMSEA, all results yield valid

scores of the set thresholds, where through the theoretical framework has an adequate fit. Based

on the Decomposed Theory of Planned Behaviour, this study reveals the positive influence of all

latent variables (perceived usefulness, perceived ease of use, compatibility, perceived enjoyment,

attitude, interpersonal influence, external influence, subjective norm, self-efficacy, technology

facilitating conditions, resource facilitating conditions and perceived behavioural control) among

the Generation Y in regard to their intention of creating and using a customised avatar for online

fashion purchases. Thus, research findings indicate that all hypotheses are supported. However,

the Generation Y’s subjective norm shows to have a limited relationship towards the behavioural

intention. Notwithstanding this problematic relationship, the external influences are stronger than

the interpersonal influences to the subjective norm. This stresses the relevance of managerial

implications for fashion businesses, which aim to target the Generation Y, or offer the technology,

or both, to increase their influential power. Next to this, results underline the mediocre relationship

between the latent construct variable perceived behavioural control and dependent variable

behavioural intention. However, the relationship of the control beliefs technology facilitating

conditions and resource facilitating conditions towards perceived behavioural control are the

highest among the latent variables within the applied theoretical framework. Thus, this

accentuates the positive perception of the research sample in regard to the technology and

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resource facilitating conditions. Moreover, research findings imply that the latent construct variable

attitude has the strongest positive influence on the behavioural intention. In specific, the

technology’s usefulness is strongly agreed upon by the research sample and even has the

strongest relation towards the attitude. Hence, those insights into the positive psychological

perception suggest the technology’s commercial potential and create an opportunity for

managerial implications, which are further elaborated on in Chapter 6.2.3.

6.2 Future Research Directions

6.2.1 Research Limitations

This master thesis includes several limitations. Due to time and resource constraints, the sample

size is limited and amounts to a relatively small number of qualified responses, whereby external

validity is compromised. The usage of a non-probability sampling results in an arbitrary selection

of participants, where through it leads to a sample selection error. Moreover, the research sample

of the Generation Y shows variation and an unevenness due to a tendency towards more female

participants. Hence, this contributes to a sampling error. Since the sample is not perfectly

representative of the population from which it is drawn, this study is limited in its

representativeness and does not allow for generalisation.

Another limitation lies in the specific subject of this thesis. As only online fashion purchases are

included, findings cannot be adapted to creating and using a customised avatar while purchasing

fashion offline, such as through the use of a virtual mirror in a traditional brick and mortar store.

Moreover, this study does not distinguish between online retail formats or purchased fashion

product categories, which leaves room for future research.

Even though the self-administered online questionnaire is built on the existing theoretical

framework of the Decomposed Theory Planned Behaviour, the belief constructs are

multidimensional and interdependent, which limits this study. The selection of a seven-point Likert

scale contributes to scale errors, as it is in human nature to choose the neutral midpoint in order

to avoid a decision conflict. Response errors might also lead to a bias of the findings through false

declarations or unconscious misinterpretations of the items. Moreover, respondents may have

overestimated their behavioural intention regarding fashion purchases, as fashion is a social

phenomenon, leading to socially desirable and self-presentation bias. Furthermore, there is a

limitation in monitoring the capacity, which means there is no certainty about the real individuals’

identity. Additionally, no differences are made between the Generation Y’s respondents having

and not having created and used a customised avatar while purchasing fashion online in the past.

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As participants are more confident in evaluating the items according to their own beliefs if they

have created and used an avatar before, this can contribute to biased responses. By taking the

current market dynamic into consideration, all measurement items could further be biased due to

Covid-19’s influence on the online fashion industry.

The quantitative data collection method is found as another limitation of this thesis. In specific,

using a mono-method approach is not sufficient in providing the full picture of the phenomenon.

Hence, the lack of the explanatory power warrants the need for future research.

6.2.2 Theoretical Implications

The findings as well as limitations of this research require further theoretical inquiries. Future

research is suggested due to the limited sample size, as well as the specified and uneven sample

of the Generation Y. Being able to access a larger sample size, as well as database to distinguish

among more socio-demographic characteristics or other generational cohorts might offer new

insights.

As the overall aim of this master thesis was not to find the perfect model fit but rather to research

to which degree the gathered data fits the theoretical framework of the Decomposed Theory

Planned Behaviour, the authors purposely neglected the adaptation of the dataset in order to

improve the subjective norm towards the behavioural intention. Nonetheless, the outcome of this

relationship suggests research to further examine the Generation Y’s interpersonal and external

influences in regard to creating and using a customised avatar while purchasing fashion online.

As an addition to the quantitative approach of this study, an in-depth examination through

qualitative research might improve the explanatory power of the theoretical framework. Hence,

the authors suggest gathering qualitative data in order to further specify the salient beliefs of the

generation.

As emerging technologies do not guarantee commercial success, future research attention is also

required due to the fact that this research does not include the actual online purchase of the

generation in regard to the technology. To comprise the complete model of the Decomposed

Theory Planned Behaviour, longitudinal research is recommended in order to validate the

behavioural intention towards the behaviour. Beyond this research scope, future research,

specifically focusing on the technology’s commercial potential, should also consider the offline

buying behaviour of the Generation Y within this context.

58

6.2.3 Managerial Implications

Creating and using a customised avatar for online fashion purchases can be seen as an emerging

trend of a new computational power through the lens of the Gartner Hype Curve. Therefore, a

better psychological understanding of the Generation Y, known for their increasing purchasing

power and technology affirmation, is crucial. Research findings highlight the positive perception of

the Generation Y respondents towards the technology and resource facilitating conditions, thus

underlining its great potential which can enhance businesses’ viability by adapting or implementing

such interactive technologies. The outcome of this master thesis draws attention to various

managerial implications to stimulate the Generation Y’s behavioural intention in creating and using

customised avatars while purchasing fashion online.

Based on the research findings of perceived usefulness being highly influential to the behavioural

intention, managers should consider the visualisation of the garment fit indicator. To inform the

customer about the garment fit, different colours can be used in order to illustrate the stretched

and compressed zones of the chosen item. This study additionally stresses the importance of

enjoyment towards the technology, which can be enhanced through interactive manipulation tools

or haptics-based systems, enabling the online customer to feel material textures and haptic effects

through a touchscreen. Furthermore, to enhance perceived ease of use of the technology, fashion

businesses can simplify the customised model by focusing on geometric primitives of smaller

human body parts, such as the torso for upper wear. By providing the same product information

through clothing simulation as in real-life buying processes, shopping experiences and post-

purchase satisfaction can be fulfilled.

The research sample of the Generation Y relies on marketer stimuli and is more likely to interact

with brands being active on social media channels. Thus, 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. This could result in an increased

influencing power and a higher online conversion rate. Furthermore, due to fashion being a social

phenomenon, managers should consider the social risk of sensitive fashion product groups in

order to decrease return rates. In specific, the implementation of this technology is beneficial for

fashion businesses offering outerwear, such as dresses and jackets, or made to-measure items.

Overall, fashion businesses adjusting to the digital trend, which is accelerated due to the Covid-

19 pandemic, and incorporating the emerging technology of creating and using a customised

avatar while purchasing fashion online, are guaranteed a future-proof business model that can

cope with market dynamics. This does not only cater to the Generation Y’s expectations, but also

creates a visible value across the entire chain to scale out and strengthen business’ capabilities.

59

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Appendix

A. Search Keywords

Search keyword Google Scholar Primo DiVA

Virtual fitting 743,000 140,171 9

Virtual fashion 1,410,000 264,147 21

Virtual avatar 168,000 24,887 8

Customized avatar 31,800 3,753 1

Customised avatar 2,800 3,735 0

Theory of Planned Behavior

1,970,000 746,264 7

Theory of Planned Behaviour

1,080,000 746,250 6

Decomposed Theory of Planned Behavior

95,500 26,840 0

Decomposed Theory of Planned Behaviour

55,100 25,659 0

Generation Y 5,780,000 2,728,556 32

Online buying behavior

1,010,000 129,949 17

Online buying behaviour

478,000 129,941 8

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B. Registration - MSc Textile Management Thesis

Project title* Consumer research on the online buying behaviour for fashion purchases using a

customised avatar

Summary (50 words)* This master thesis is a research project part aims to research consumers on the online buying behaviour for fashion purchases using a customized avatar. In specific, the research conducts a comparison between the buying behaviour of generation Z and

Y. The research project is interdisciplinary between technology and fashion business.

Report number (Obtained from school expedition)

Student name* Eva Lancere de Kam & Jacqueline Diefenbach

Programme* MSc Textile Management (one year)

E-mail* evadekam@live.nl & jacky.diefenbach@gmx.de

Proposed project starting date March 20th, 2020

Affiliation

Name of company, organization /

Company supervisor /

Address /

Phone, fax, e-mail /

Project Proposal* (400-500 words) including few relevant references and up to 5 keywords

This master thesis is a research project part aims to research consumers on the online buying behaviour for fashion purchases using a customized avatar. In specific, the research conducts a comparison between the buying behaviour of generation Z and

Y. The research project is interdisciplinary between technology and fashion business. As technology evolves, identifying market innovation opportunities and creating the right balance between the competitive trends, market influencers and evolving customers’ needs is necessary. Focusing on virtual fashion, especially generation Z and Y are appropriate to look at because

they are known as the generation of digital natives and technology enthusiasts. The main issue in the fashion industry is the sizing and fit of garments. 3D virtual fitting with a customized avatar can solve the fit issue that most online customers are dealing with. This is both relevant for business and customer. Important is to investigate

how this technology can create ease into customers online purchases, examine the acceptance rate, usefulness and participation readiness. The objective of this master thesis is do consumer research on the online buying behaviour for fashion purchases using a

customized avatar. Overall, the aim is to create a better understanding of the technology’s potential, strengthen business viability and formulate managerial implications for the fashion industry. Thereby, the following research question is created: What are key features for the generation Z and Y when creating and using an online customized avatar for fashion purchases? For this master thesis mainly a quantitative and abductive research approach will be used to gather literature concerning the

main topic of 3D virtual fitting with a customized avatar. Secondary data will be gathered through a profound desk research of the main literature, scientific articles, books, research journals, as well as reliable web databases within the theoretical fields (see keywords). Based on this, hypotheses will derive from the theoretical framework. As this topic will be researched from the

customers´ perspective, primary data will be gathered by developing an online questionnaire. Thereafter, the results of the online questionnaire will be matched with the theoretical conclusions and hypotheses will be revised. The discussion combines the findings of the theoretical framework and online questionnaire. Additionally, it argues the differences of the generations’

online buying behaviour of fashion purchases using a customized avatar. This will lead to a conclusion. The result should give a scientific proof about generation Z and Y online purchase behaviour while using a customized avatar. It is aimed to verify that the incorporating technology in business leads to a beneficial situation for companies and its customers. However, it can als o

be assumed to evidence an attitude-behaviour gap as customers´ participation readiness might lack. After deducting the study, the research assumes that this technology is more beneficial in the mid- to higher segments for more fashionable product categories such as suits, dresses and trousers.

The authors already agreed with Daniel Ekwall on supervising this thesis, especially in regard to the research method and the business perspective. In addition, Niina Hernandez will function as the contact person for the technical background of the research.

References:

Beck, M. (2018). I virtually try it … I want it ! virtual Fitting Room: A tool to increase online and offline exploratory behaviour,

patronage and purchase intentions. Mattila, H. (2016). Digital fashion—How and when? DIGITAL FASHION - HOW AND WHEN?, 6. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-11048

Meng, Y., Mok, P. Y., & Jin, X. (2010). Interactive virtual try-on clothing design systems. Computer-Aided Design, 42(4), 310–321. https://doi.org/10.1016/j.cad.2009.12.004 Keywords:

3D virtual fitting, Customized avatar, Digital customization, Online purchase behaviour.

*compulsory fields

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