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Linköping University SE-581 83 Linköping, Sweden +46 13-28 10 00, www.liu.se
Linköping University | Department of Management and Engineering
Master’s Thesis, 30 credits | MSc Business Administration - Strategy and Management in International Organizations
Spring 2020 | ISRN-nummer: LIU-IEI-FIL-A--20/03422--SE
Transition Risk on a Consumer’s Journey
Influencing Concepts towards the occurrence of
Transition Risk on a Consumer’s Journey on
Virtual Reality Shopping
Keariam Gebremichael
Saadul Islam Khan
Supervisor: Andrea Fried
English title:
Transition Risk on a Consumer’s Journey
Influencing Concepts towards the occurrence of Transition Risk on a Consumer’s
Purchase Journey through Virtual Reality Shopping
Authors:
Keariam Gebremichael and Saadul Islam Khan
Advisor:
Andrea Fried
Publication type:
Master’s Thesis in Business Administration
Strategy and Management in International Organizations
Advanced level, 30 credits
Spring semester 2020
ISRN-number: LIU-IEI-FIL-A--20/03422—SE
Linköping University
Department of Management and Engineering (IEI)
www.liu.se
i
ABSTRACT
Title Transition Risk on a Consumer’s Journey – Influencing concepts towards
the occurrence of Transition Risk on a Consumer’s Journey in Virtual
Reality Shopping
Authors Keariam Gebremichael and Saadul Islam Khan
Supervisor Andrea Fried
Date May 25th, 2020
Background Retailing through Virtual Reality (VR) is faced with a dilemma of
potential customers using the VR to look for products online, but
somehow do not make a purchase online and prefer to visit the physical
stores instead. This phenomenon is referred as Transition Risk.
Aim To develop an understanding regarding the concepts and factors that
influence the occurrence of transition risk by using UTAUT2 framework.
Identify those concepts and thus be able to assist retailers in diminishing
the transition risk gap.
Methodology Is a quantitative study that involves an experiment followed by a
questionnaire as the research instrument. The data was analyzed through
regression analysis by using SmartPLS 3.0 as the data analysis tool for
SEM. An exploratory research design for the cross-sectional study of a
small sample of 45 people experimented.
Findings Findings of the research suggest that transition risk has a direct relation
with the UTAUT2 constructs: performance expectancy, effort
expectancy, facilitating conditions, social influence, hedonic motivation,
and habit of the consumer. Moreover, absence of familiarity with VR
retailing, social influence and consumer’s habit of web-rooming and retail
therapy are significant contributors towards transition risk. Furthermore,
UTAUT2 framework can also be used to identify reason for no usage
and/or abandoning of use technology.
Keywords
Virtual Reality, Virtual Environment, Transition risk, VR Retailing, UTAUT2, Digital
Platforms
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ACKNOWLEDGEMENTS
First off, we would like to thank our thesis advisor Andrea Fried and Josefine Rasmussen for
guiding us and sharing their knowledge extensively and repeatedly. They made us better
students and writers in addition to helping us figure a way out when we got stuck, as we did a
few times. We thank you for not only encouraging us but also giving us a nudge now and then
when we needed it. In addition, we appreciate the dynamic duo Amal and Jaheda for the
meticulous comments that helped us improve our thesis, time and time again while also lending
a hand plus a brain to pick whenever we needed it, the realization of this paper came because
of all help we got from you all.
In addition to the immediate people involved in our thesis, we cannot pass without mentioning
all the teachers who were involved in our previous courses, we know what we know because
of your efforts to instill knowledge in us that we can and should use going forward, in our
future endeavors. Furthermore, all the friends and colleagues that helped us in ideas,
proofreading and finding holes in our writings in addition to those who participated in our
experiments, not only for your time but also for the experience of watching you all get amazed
while in the virtual world, you have made us smile in the real world.
We are forever grateful to our families who have supported us in ways we cannot repay but
hope to make you proud by proving that your sacrifices and efforts were worth it. To friends
that stuck by our sides, for the good, the bad and in between, we appreciate you and may we
never forget our bond as we grow wiser. And finally, to the Almighty that made all this possible
in mysterious ways, may we follow the footsteps you have laid for us as you have bigger and
better plans for us, forever thankful for what you have done for us.
May 25th, 2020
iii
“If you change the way you look at things, the things you look at change.”
Wayne Dyer
iv
I. Table of Contents 1. Introduction ........................................................................................................................ 1
1.1 VR Environment .............................................................................................................. 2
1.2 VR Platform ..................................................................................................................... 3
1.3 Research Gap & Motivation ............................................................................................. 4
1.3.1 Research Gap ............................................................................................................. 5
1.3.2 Importance ................................................................................................................. 5
1.4 Research Contribution ...................................................................................................... 6
1.4.1 Practical Contribution ................................................................................................ 7
1.4.2 Theoretical Contribution............................................................................................ 7
2. Literature Background ........................................................................................................ 8
2.1 Virtual Reality .................................................................................................................. 8
2.1.1 E-commerce ............................................................................................................... 8
2.1.2 Transition to Digital/Online Marketing ................................................................... 10
2.1.3 Changing Consumer Behavior................................................................................. 11
2.1.4 Conceptualizing Virtual Reality .............................................................................. 12
2.1.5 Business Application ............................................................................................... 14
2.1.6 VR in Retailing ........................................................................................................ 14
2.2 Consumer Journey .......................................................................................................... 15
2.2.1 Channels for Consumer Journey .............................................................................. 16
2.2.2 Consumer experience of VR .................................................................................... 18
2.3 Cognitive Load ............................................................................................................... 19
2.4 Channel preference for expected customer satisfaction ................................................. 21
2.5 Drivers for offline purchase ........................................................................................... 21
2.6 Theoretical Framework .................................................................................................. 22
2.6.1 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) .................... 23
2.6.2 Research Model & Hypothesis .................................................................................... 25
2.6.2.1 Performance Expectancy ...................................................................................... 26
2.6.2.2 Effort Expectancy ................................................................................................. 26
2.6.2.3 Facilitating Conditions ......................................................................................... 27
2.3.2.4 Social Influence .................................................................................................... 27
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2.6.2.5 Hedonic Motivations ............................................................................................ 28
2.6.2.6 Habit Schema ........................................................................................................ 28
3. Methodology ........................................................................................................................ 30
3.1 Research Approach ........................................................................................................ 30
3.2 Research Design ............................................................................................................. 31
3.3 Research Strategy ........................................................................................................... 32
3.4 Experiment Design ......................................................................................................... 33
3.5 Sample Selection ............................................................................................................ 36
3.6 Data Collection ............................................................................................................... 37
3.7 Data Analysis ................................................................................................................. 40
3.8 Literature Review in Research Process .......................................................................... 41
3.9 Reliability & Validity ..................................................................................................... 42
3.9.1 Reliability ................................................................................................................ 42
3.9.2 Internal Consistency ................................................................................................ 42
4.0 Results & Analysis ............................................................................................................. 44
4.1 Demographic Distribution .............................................................................................. 44
4.2 Construct Reliability and Validity.................................................................................. 45
4.2.1 Cronbach’s Alpha .................................................................................................... 45
4.2.2 Composite Reliability .............................................................................................. 46
4.2.3 Dillon-Goldstein’s rho ............................................................................................. 47
4.2.4 Average Variance Extracted (AVE) ........................................................................ 47
4.3 Structural Model ............................................................................................................. 48
4.3.1. Structural Model Testing ........................................................................................ 48
4.3.2 Standardized Factor Loadings ................................................................................. 49
4.3.3 Path Coefficient ....................................................................................................... 50
4.4 Hypothesis Testing ......................................................................................................... 51
4.4.1 T Statistics ............................................................................................................... 51
4.4.2 Performance Expectancy ......................................................................................... 52
4.4.3 Effort Expectancy .................................................................................................... 52
4.4.4 Facilitating Conditions ............................................................................................ 53
4.4.5 Social Influence ....................................................................................................... 53
4.4.6 Hedonic Motivations ............................................................................................... 53
4.4.7 Habit Schema ........................................................................................................... 54
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5.0 Discussion & Findings ....................................................................................................... 55
5.1 Performance Expectancy ................................................................................................ 55
5.2 Effort Expectancy ........................................................................................................... 57
5.3 Facilitating Conditions ................................................................................................... 59
5.4 Social Influence .............................................................................................................. 60
5.5 Hedonic Motivations ...................................................................................................... 61
5.6 Habit Schema ................................................................................................................. 62
6.0 Conclusion ......................................................................................................................... 64
6.1 Answering the Question ................................................................................................. 64
6.2 Theoretical Contribution ................................................................................................ 66
6.3 Practical Implications of Findings.................................................................................. 66
6.4 Limitations ..................................................................................................................... 67
6.5 Future research ............................................................................................................... 67
Appendix I: Original UTAUT2 Model .................................................................................... 69
Appendix II: Construct Specification and Items Description .................................................. 70
Appendix III: Theoretical Model ............................................................................................. 71
Appendix IV: Path Coefficient ................................................................................................ 72
Appendix V: Construct Reliability & Validity ........................................................................ 72
References ................................................................................ Error! Bookmark not defined.
vii
II. List of Figures
Fig. 1 The impact of VR through the Consumer journey stages Farah et al.(2019)... 02
Fig. 2 Components that make VR experience (Lee & Chung, 2008)……………… 11
Fig. 3 Types of Consumers (Veen & Ossenbruggen, 2015)………….. 15
Fig. 4 Research Model and Hypotheses relation…………………………………….. 26
Fig. 5 Methodology of the research…………………………………………………. 27
III. List of Tables
Tab. 1 Constructs used in UTAUT2………………………………………………... 24
Tab. 2 Items, Constructs and Labels……………….………………………...…….. 37
Tab. 3 Detail of Data Coding……………………………………………………….. 38
Tab. 4 Demographic Statistics……………………………………………………… 44
Tab. 5 Cronbach’s alpha for all individual latent variables………………………… 46
Tab. 6 Composite reliability values for individual latent variables……………........ 46
Tab. 7 Rho_A values for individual latent variables……………………………...... 47
Tab. 8 AVE values for individual latent variables…………………………….….... 47
Tab. 9 Specifications of constructs and relevant loadings………………………….. 49
Tab. 10 Path coefficients before and after deletion of low performing items……….. 51
Tab. 11 t-values for latent variable…………………………………………………... 52
Tab. 12 Statistical Results for Proposed Hypotheses………………………………... 54
Tab. 1 Constructs used in UTAUT2………………………………………………... 24
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List of Abbreviations
VR Virtual Reality
AR Augmented Reality
VE Virtual Environment
3D 3 Dimensional
2D 2 Dimensional
TR Transition Risk
PE Performance Expectancy
EE Effort Expectancy
FC Facilitating Conditions
SI Social Influence
HM Hedonic Motivations
HB Habit
UTAUT 2 Unified Theory of Acceptance & Use of Technology
SEM Structural Equational Modeling
AVE Average Variance Extracted
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INFLUENCING CONCEPTS TOWARDS THE OCCURRENCE OF
TRANSITION RISK ON A CONSUMER’S JOURNEY THROUGH
VIRTUAL REALITY SHOPPING
1. Introduction
The business environment has become highly intense and the increasing trend of online
purchases have become a valuable attraction for global consumers, therefore, state-of-the-art
web-based technologies have been employed by online stores and companies to match this
competition (Shu & Lee, 2005). One of the ways they have chosen to separate themselves from
the herd and by means also disrupting the industry is by using Virtual Reality as an aid to
shopping.
According to Farah et al. (2019), Virtual Reality compliments the consumers experience across
the different journey stages, starting from Awareness, Consideration, Engagement, Purchase
and finally Loyalty. As can be seen from the figure below, where the first part or blue line
indicates the observed behaviors of consumers when they are on VR, this shows that the
effectiveness of VR is highest at the engagement stage and rises until that point and then it
decreases in effectiveness on its way to purchase stage, while leveling out on loyalty. When
we come to in-store journey of consumers which is marked by the purple line, it is shown there
is an increase starting from Awareness to Consideration then to Engagement, but the highest
point is achieved on Purchase, after which the in store traffic dials down to even out at Loyalty.
With these two different mediums of consumer journey stages, there is vast difference formed
between the two lines, one is Expectation Gap and the other Transition Risk. The expectation
gap is the occurrence where there is a significant difference between how consumers behave
in-store versus while using VR device for shopping, where the engagement is higher on VR.
Following the engagement from those using VR, the peak declines due to the consumers need
to have an encounter with the product and the physical store, this infliction point is called
Transition Risk, and which occurs at the purchase stage.
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Figure 1: The impact of VR through the Consumer journey stages. (Farah et al., 2019)
Among the existing industrial sectors, retail industry is one of the fastest growing around the
globe. The ever-changing marketing situation of retail industry and consumer trends has made
it difficult for traditional marketing ideas to further sustain their historic competitive advantage
in this sector (Zhu & Gao, 2019). This change is due to recent improvements in life standards
of people and the subsequent shift in demand for consumer goods, as societies have developed
globally (Su, 2016).
Hence, it is simple to presume that there have been significant alterations in the consumption
psychology and need of consumers. Technological evolutions over time have supported the
growing need of consumers and have facilitated various industries. Among these technologies
a highly vibrant advancement is the use of VR. According to Li et al. (2001), VR is a
technology that provides the users with an interactive software generated environment which
appears to be highly realistic.
1.1 VR Environment
VR is a decades old concepts, though, the applied researches made since the 1990’s, this
technology has considerably evolved Loureiro et al. (2019), and now there are multiple
business applications and business dimensions which offer VR interactions. The business
dimensions are vast such as in tourism (Abergel et al., 2016; Jeng et al., 2017; Yeh et al., 2017),
retailing (Evans & Wurster, 1999; Krasonikolakis et al., 2014), real estate (Farshid et.al, 2018),
education and training (Abboudi et al., 2013; Farshid et al., 2018) in addition to medical
3
procedure trainings and surgery simulations (Abboudi et al., 2012; Slater & Sanchez, 2016).
This is done by creating an environment which provides a simulation of the real environment
and thus facilitates the process of learning and training.
The retail industry has also seen the use of VR technology to play a key role in its development
(Bonetti et al., 2018). Mainly due to the ability of VR to create an environment which is like
the real world, this is done by creating software generated simulation. VR has been successful
to simulate real case scenarios in the field of medical surgery Abboudi et al. (2012), where the
replication of surgery environment provides trainees (users) a clear depiction of the real
environment. Loureiro et al. (2019), has proposed that any type of simulated environment can
be designed for the users, which are both efficient and cost effective. These simulations are
used by marketers to create an environment which can harness the individual psychological
reactions comparable to that of physical environments (Peperkorn et al., 2015).
Thus, retailers have used this technology to gain consumer’s attraction, reaction and use VR as
a marketing tool (Loureiro et al., 2019). These reactions are owed to consumer telepresence
within a virtual environment. Steuer (1993), has defined telepresence to be the mediated
perception of an environment to attract consumers. Therefore, in contrast to presence in a
physical environment telepresence occurred as a substitute in a software-generated
environment.
1.2 VR Platform
Virtual reality VR is a software-generated environment where the user is not only able to
navigate but also could interact with the environment which could trigger real-time simulation
of the user’s senses (Guttentag, 2010). However, the use of VR in retail industry required a
viable medium and platform to connect sellers with buyers and this platform was provided by
internet. There is an ever-increasing diversity in informational environments provided by
internet and the use of internet-accessible devices. In this context Mosteller et al. (2014), has
emphasized to capitalize on the growing adoption of internet accessible devices by consumers,
which can change their perception towards their online shopping needs.
Studies have shown that the use of VR marketing has a positive effect on consumer’s intension
to buy (Verhagen et al., 2014). However, Loureiro et al. (2019), suggest that even though VR
is influencing marketing decisions and business methods there is still a need to examine the
4
use of VR technologies and its business application. This may also be due to the difference in
between the environments of virtual and physical settings for consumers (Roo & Hachet, 2017).
The differences can be that of temperature, odor, texture, or people that make virtual
environment different from physical or offline stores (Loureiro & Roschk, 2014; Roschk et al.,
2017). However, Krasonikolakis et al. (2014), found that features such as security and privacy
make virtual environments more favorable to some consumers.
Meanwhile, as we mention the difference in the virtual and physical environments, it is
imperative to mention that due to the change in the environments, there is a significant
difference across the Consumer journey for both settings (Nam & Kannan, 2020). Lemon &
Verhoef (2016), have referred to consumer journey as the experience which a consumer has
contact with the firm through different touch points in multiple channels and media during the
process of purchase. Therefore, with respect to VR, Li et al. (2002), proposes that adding VR
along the purchase funnel can have a positive impact to stimulate consumers’ experience.
Moreover, VR has reinvented the retail experience by providing an immersive experience for
consumers into visually appealing dimensions and is a direct attempt to stimulate the purchase
process (Suddaby et al., 2017). Digital marketers who market their products and service
electronically on the internet are using VR environment to match changing shopping needs of
their consumers. A few examples can be taken of McDonalds happy Goggles1, NYX Cosmetics
+ Samsung World’s First VR Makeup Tutorial Launch Event2, Coca Cola Virtual reality
campaigns3.
1.3 Research Gap & Motivation
As e-commerce is witnessed to manifest a more rapid growth than the traditional modes of
commerce (US Census Bureau, 2019), retailers are struggling to maintain a balance between
their online vs offline sales strategy, because there are different extents to which consumers
drive value from both retail channels (Soysal et al., 2019). The example can be taken of DVD,
music and bookstores where online channels have substantially reduced the value and utility.
Soysal et al. (2019), mentions consumers have different levels of satisfaction and value with
their interaction on an online and offline store, therefore, some retailers consider it necessary
________________________________________________________________________________________________________________
1. http://www.happygoggles.se/en/ 2. https://laguestlist.com/nyx-cosmetics-samsung-worlds-first-vr-makeup-tutorial-launch-event/
3. https://vimeo.com/149889854
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to have physical stores in order to offer a higher level of satisfaction. Hence, there is a need to
find a match for consumer satisfaction of online retailing with that of physical stores.
1.3.1 Research Gap
Study conducted by Farah et al. (2019), on consumer experience, shopping journey and
physical retailing, has identified the decline in VR effectiveness during the purchase stage of
consumer journey in shopping, which they have referred to as transition risk. However, their
research did not explicitly focus on why transition risk occurred but rather the existence of it,
and this is the gap identified and will be researched in this study. The authors of this research
will focus on the why transition risk occurs and what the contributing factors are. This will be
done by attacking the question with concepts from marketing literature on consumerism,
consumer behavior, decision making, risk and personality.
When we refer to earlier researches into VR, they had focused on VR acting as a stimuli for
consumers’ experience (Bigné et al., 2016; Verhagen et al., 2014; Yeh et al., 2017) and how it
has introduced concepts such as consumers’ virtual attachment, engagement and identity
(Grewal et al., 2017; Koles & Nagy, 2012; Nagy & Koles, 2014) as well as consumers’
purchase behavior (Krasonikolakis et al., 2014; Rizzi et al., 2019). However, to the authors’
existing knowledge, the concept of transition risk has not been a focus of any other research
yet, and there is no research to identify the causing concepts for it.
This research will investigate the influencing concepts towards transition risk and discuss on
how they pave the way for the occurrence of transition risk during a consumer’s shopping
journey, using VR as purchase mechanism. This change of shopping channel made the authors
of this research paper question why transition risk occurred. This research will describe the
research gap and find out why transition risk occurs and the underlying causes for it. So, our
research question is
What are the influencing concepts towards the occurrence of Transition risk on a
Consumer’s journey through Virtual Reality shopping?
1.3.2 Importance
To answer the research question, on has to understood that VR technology is effective for the
user until the purchase stage and that consumers are satisfied with the usefulness of it (Farah
6
et al., 2019). However, the usefulness of VR has a sudden decline at the purchase stage and
therefore, the search for value is sought elsewhere, at the physical store. Because of this change
in medium of shopping, there is an increase in traffic at a physical store than online, which is
rendering the usefulness of VR less effective. This decreases the service potential VR brings
to the people and the business as well in terms of connecting these two parties, while also
decreasing potential income that can be realized (Farah et al., 2019). Apart from that it is also
weakening the online channel for consumer integration, which is a cheaper and faster way for
businesses to attain customers when compared to traditional offline marketing. Looking
through a consumers’ lens, satisfaction is an outcome of consumer expectation and perceived
value. These concepts along with the concepts of prevailing conditions, influencing factors for
decision making, habit of consumers and the happiness they draw from the product or service
are addressed in UTAUT2 framework. Therefore, to address the question, this research will
use a theoretical framework based on the Unified Theory of Acceptance and Use of Technology
2 (UTAUT2) (Venkatesh et al., 2012).
Additionally, supporting concepts are used by the authors to develop the hypotheses with the
intent of a person to purchase with VR, for investigating transition risk are cognition Fan et al.
(2020), channel preference for an expected customer satisfaction Hult et al. (2019), drivers for
offline purchase such as human interaction and risk reduction Laroche et al. (2005), and
personal character towards shopping (Veen & Ossenbruggen, 2015). The authors focused on
these concepts as they can give explanations to the motivations and behavior of customers from
a psychological viewpoint and can add towards the development of the hypothesis besides the
model that will be used, UTAUT2.
1.4 Research Contribution
Farah et al. (2019), suggest that the VR effectiveness curve shows a sudden decline at the
purchase stage during the consumer journey. On the contrary, in-store traffic curve shows a
rise at this stage. In response to the growing need for online retailing it is imperative for online
retailers to breach this gap and so the contribution of this research will be two dimensional, i.e.
practical contribution and theoretical contribution.
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1.4.1 Practical Contribution
The practical contribution of this research is to develop an understanding for retailers for why
Transition Risk occurs and gives helpful insights for digital marketers to be more effective in
their business development, retention and subsequent growth. This research will highlight
concepts which lead to transition risk and will provide recommendations which can assist in
reducing it. The research is supposed to contribute to helping retailers match the changing
demands of consumers by eliminating the highlighted concepts that harness transition risk.
Therefore, to have consumers who are satisfied with the shopping experience.
1.4.2 Theoretical Contribution
Moreover, previous research has used UTAUT2 framework to understand technology adoption
and use Ain et al. (2015), behavioral intention to use technology (Lima & Baudier, 2017). This
research will be the first of its type to the authors knowledge in the attempt to use UTAUT2
framework to understand the elements associated with the non-usage/abandoning of use of
technology during a consumer journey. The theoretical contribution of this research is to use
UTAUT2 framework to understand the concepts that hinder the usage of technology. This
research further opens new dimensions for developing and understanding regarding the
concepts due to which a technology usage is abandoned at a certain stage during an ongoing
consumer journey. In this research the technology is taken in context of the increasing digital
innovations in the field retail marketing and business.
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2. Literature Background
This section will shed light on the theoretical background for VR in marketing for online stores
and the transition risk during Consumer journey through VR shopping. This chapter has a
sequential design to address the reviewed literature in the attempt to answer the research
question. The chapter will briefly describe Virtual Reality and its connection with digital
marketing, Consumer behavior and its business application in respect of retailing. The chapter
then highlights the role of virtual reality in Consumer journey by highlighting the channels of
Consumer journey. Finally, this chapter will discuss in detail the theoretical framework in the
light of UTAUT2 to provide insights regarding the designing of hypotheses.
2.1 Virtual Reality
To ensure a superior and digitally interactive consumer experience, there is an increasing trend
for firms towards the use of immersive multimedia and computer-generated simulations like
Virtual Reality (VR) (Pallant et al., 2019). In addition to this, Gerewal et al. (2017), has
proposed that VR will turn out to be one of the core components that will drive interactions
between consumers and retailers in the future. Moreover, as the adoption and use of technology
is becoming increasingly common, it would be critical for firms to enhance consumer
experience by using newly developed technologies and interactive platforms.
2.1.1 E-commerce
The phenomenon of VR in marketing is driven by the channels and platforms which are used
to present computer generated simulations to consumers. In order to understand the role of VR
in digital marketing and developing consumer perception about purchase decisions, first there
is a need to understand the development of digital marketing platforms (Barnes, 2016). With
the ever-changing global business dynamics, if traditional retail marketing ideas do not counter
the varying and fierce market competition, the retail industry would not have been able to stand
(Zhu & Gao, 2019). However, this challenge to retail industry was addressed by the ease of use
of the internet and its direct impact on retail through e-commerce (Dennis et al., 2004). E-
commerce appeared as an electronic way of conducting commerce with the means to propose
business, sell, buy and/or exchange goods and services by using a computer, tablet or phone
with an active internet connection (McKay & Marshall, 2004).
9
E-commerce buying and selling could be done remotely, contrary to the cost inducive
conventional retail marketing strategy (Bucur, 2002). Examples of such can be seen in the
research of Stefan et al. (2017), where e-commerce is predicted to be the solution for survival
of the organization in retail business going forward into the future. Moreover, the development
in database technology and increasing ability to capture and analyze individual consumer data
has made marketing as an integral part of any progressive organization (Kumar, 2015).
Interestingly, there are various organizations that have been able to successfully merge e-
commerce into their business models, such as Amazon, Alibaba, eBay, Zalando, etc... They
have been able to adopt the e-commerce model in a highly advanced form and have proven
business stability and growth over the years. However, within this changing environment and
the fast growing of e-commerce enterprises, Zhu & Gao (2019), are of the view, that there are
still retailers which have not been able to integrate themselves to this transition. These retailers
believe that integration between offline and online is by itself a problem for them to be able to
achieve their e-commerce objective. In this case the proposition of these retailers cannot be
rejected outright, as the integration process has not matured fully. Several companies exist in
the retail industry who rely on traditional marketing modes, and therefore are unable to make
optimal use of available consumer data to better design their processes according to consumer
demands and psychology (Dong, 2018).
Considering the integration process as a major challenge for retailers to gradually shift towards
online business, the share of e-retailing is on a continuous rise. Sanders (2000), suggested that
e-commerce share will have a considerable rise in global economy and will account for 18%
of total exports. However, taking the example of companies like Apple Inc., they have
significantly increased their share of cross-border sales to a whopping 63% by using e-
commerce. This can be one area where VR can be used to acquire and engage more customers
as it has the ability to do so (Farah et al., 2019). As an outcome of increase in online sales the
mass availability of consumer data also provides companies with valuable source of
information which can be used for their benefit.
There is a plethora of available research about consumer demands and its evolving nature
which is owed to the evolving needs, wants and values of consumers (Noble & Schewe, 2003;
Schewe & Meredith, 1994). In addition to this, evolution of technology has also served
organizations to better utilize available resources and these resources are getting advanced as
10
time progresses. With the evolution of online sales, technological tools and advancements have
helped organizations acquire very valuable indictors in the form of processable and analyzable
data referred to as “big data” (Schönberger & Cukier, 2014). This advanced data collection
process is efficient in terms of the amount, speed of gathering and capturing a vast array of
varied data. All the data collection has been made possible by digitalization of data and its
availability through the internet and in the use of e-commerce. However, the use of collected
data and resultant formation of target consumer groups has also played its role in shaping the
technological advancement in the field of marketing (Loureiro et al., 2019).
This is one industry where VR can give benefit to both the users and the companies that develop
apps for their customers to make use of, as VR can be more immersive for the user in terms of
the experience of using the platform, which increases interest and creates engagement as
mentioned by (Farah et al., 2019). Since e-commerce is done online and the product listings
are not only local but international, there will be products people cannot find within their
physical location and proximity, this can be a push factor for customers to adapt to shopping
on VR while this integration can reduce the occurrence of transition risk as they wouldn’t have
a physical store to go to.
2.1.2 Transition to Digital/Online Marketing
While discussing the formation of target consumer groups, it is worthwhile to mention here
that with traditional marketing methods, the organization sends its marketing message to a large
population. Whereas, population contains every sector of the society, including the sector or
group, which the organizations seek to target and those that are not intended. According to
Bhor et al. (2018), this is the main flaw with traditional marketing methods and hence to
overcome this shortcoming marketers generated the idea to use the world’s largest and most
efficient platform (internet) and designed digital marketing which can now target select groups
of people based on interest.
In the context of developing marketing methodologies, data and information serves as a highly
critical and imperative assets for progress and survival of organizations (Zhu, 2018). Zhu
(2018), also suggests that consumer data provides enterprises with a foundation to analyze its
consumers and their demands as consumer data is collected through software, in the same way
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marketing is done through online/digital channels, known as “digital marketing,” by making
use of this data.
Alexander (2019), has referred to all marketing efforts which use an electronic device or the
internet as digital marketing while Mohite (2019), sees digital marketing as a technology that
can turn businesses by reaching out and catching the maximum number of consumers in a
digitally complicated world. Even though the digital marketing format is complicated and
different from conventional marketing methods, it is much more efficient for retailers to
segregate different consumers groups with the help of big data. So, it makes it much more
relevant for enterprises to reach out and to target groups while digitally marketing consumer
goods according to the needs and demands of consumers, therefore assist the organization to
position themselves in the market (Zhu & Goa, 2019). These advancements in technology and
progress in marketing methods have benefited users and businesses in many ways and
transition risk can become an inhibitor to this growth as it has the opposite effect over online
channels of purchasing and consumption, as it diverts consumers to offline stores (Farah et al.,
2019).
2.1.3 Changing Consumer Behavior
Research has revealed that digital marketing techniques can help enterprises use their
marketing campaigns in a way that can influence consumer behavior in multiple ways such as
purchase methods and brand favoritism, which can come in handy in relation towards VR
purchasing (Chen et al., 2012; Fang et al., 2015; Molitor et al., 2016). The ever-changing
technology and use of digital media platforms like social networks, are also affecting
consumer’s purchase behavior since people get influenced by their social circles (Ardura &
López, 2014). These changing purchase behaviors have led consumers to become increasingly
aware of the latest offerings that can provide the best utility, experience, and suit their
diversified choice. This has compelled companies to develop advanced technologies to provide
the best consumer experience. Therefore, the competitive environment has led enterprises to
develop tools that best serve consumer needs and lead to satisfied and loyal consumers. The
US retail industry is an example of such where rapid digitalization and increasing supremacy
of e-retailing is overwhelming the traditional retailers (Huang et al., 2019).
12
Similarly, global business environment has become highly intense and the increasing trend of
online purchases have become a valuable attraction for global consumers, therefore, state-of-
the-art web-based technologies have been employed by online stores to match this competition
(Shu & Lee, 2005). This is the current state with online stores being operated with websites
and apps on mobile and tablets, so VR shopping could be another addition to this line of
channels with the advantages of immersion, interactivity and presence which can give the
prospective consumer an experience unlike those previously listed mediums (Lee & Chung,
2008).
2.1.4 Conceptualizing Virtual Reality
Virtual reality (VR) is fairly old concept now, however, research regarding applied VR dates
back from the 1990s, (Milgram et al., 1994; Brooks, 1999; Slater & Wilbur, 1997; Steuer, 1992;
Wexelblat, 1993), and collectively agree that VR is a 3-dimensional software-generated
synthetic environment, it helps its users to get immersed into this artificially generated world.
It eases the users with a high quality and three-dimensional preview of an artificially created
but apparently realistic environment with enhanced levels of telepresence (Klein, 2003; Steuer,
1992).
The intention of using VR as a campaign method is so that consumers can have a better
experience that is emotional and immersing, which can nudge them towards buying. The
purpose of this process is to create revenue for the company and in the end have loyal
consumers, and not just one time buyers (Riva et al., 2007). This is done through the VR
experience that has three features, namely: Immersion, the feeling of being inside a digital
environment; Presence, the feeling of existence and lastly; Interactivity, the ability to engage
with objects and the surrounding environment in this case, the virtual environment (Lee &
Chung, 2008).
13
Figure 2: Components that make VR experience (Lee & Chung, 2008)
In the recent research conducted by Farshid et al. (2018), it is stated that VR provides a
complete digital view of the actual world with perceived presence of the user. This perceived
presence is supplemented by head mounted devices (HMD) commonly known as VR headset
to enhance the immersion of the user. This phenomenon of immersion and its growing extent
have been the driving force for the use of VR technology (Mills & Noyes, 1999). Mills &
Noyes (1999), has also categorized VR applications into segments which are based on the
extent of immersion supported. These categories are Immersive and Non-immersive VR,
which will be explained below, but the research of this paper will be on Immersive VR as the
research the authors of this paper used is from Immersive VR.
Immersive VR encloses the user of the system to be influenced by the surroundings that is
outside the real environment and hence the user is immersed in the virtual world while Non-
immersive VR, allows the user to be aware of the influences outside the VR system and does
not provide a complete feeling of presence, which is a desirable user attribute for full effect on
VR (Mills & Noyes, 1999). The core of this research is based on Farah et al. (2019) research
which was an examination of virtual reality at the intersection of consumer experience,
shopping journey and physical retailing with products that were purchased in an immersive
environment of VR.
Presence
Interactivity
VR Experience
Immersion
14
Farshid et al. (2018), emphasizes that with fully immersive VR, users can forget where they
are, hence there is a probability of experiencing VR sickness. Artificial motion can overwhelm
perceived motion and might cause disorientation, discomfort, headache, and nausea which
depict the influence of immersion of VR. Moreover, Loureiro et al. (2019), proposes that virtual
environments have rapidly thrived during the past decade and supply ample ground for
marketers to make use of these provoking latest technologies for commercial endeavors.
Statista (2020), has reported that the forecast for Augmented Reality (AR) and VR market size
worldwide, will increase from 10.5 billion USD in 2019 to 18.8 billion USD in 2020, thus
providing multiple opportunities and market domains to be explored for future business
endeavors.
2.1.5 Business Application
Since the decades old concepts and the applied researches made since the 1990’s, VR has
considerably evolved (Loureiro et al., 2019) and now there are multiple business applications
and business dimensions which offer VR interactions like tourism (Abergel et al., 2016; Jeng
et al., 2017; Yeh et al., 2017), retailing (Evans & Wurster, 1999; Krasonikolakis et al., 2014),
real estate (Farshid et.al, 2018), education and training (Abboudi et al., 2013; Farshid et al.,
2018). Moreover, retailing has seen a change in thinking, where a firm’s offerings are presented
through 3D rendering and the consumers can explore them at the comfort of their homes
(Farshid et al., 2018). AR and VR have manifested an effect on consumer decision making
process (Yim, et al., 2017; Wang, et al., 2015; Rose, et al., 2017) and various organizations are
trying to capitalize on the opportunity, hence are employing the use of VR and AR apps to
enhance consumer engagement for their products (Farah et al., 2019).
2.1.6 VR in Retailing
Kawada et al. (2019), have highlighted the impact of new tools and platforms, through which
access to information regarding available products and services is made convenient, to provide
consumers with the best experience. Mohite (2019), in his research has highlighted the use of
marketing tools and platforms like social media and applications, that are used by digital
marketing specialists to amplify the benefits of digital marketing and has emphasized that these
tools can help marketing specialists increase their marketing effectiveness and reduce costs
while increasing consumer value.
15
For the purpose of this research VR application through hardware such as Head Mounted
Device is taken as the core, however, the development of novel tools to enhance consumer
value through digital marketing is an ongoing process. This is because in the current era, people
are generally more exposed to digital and social media which provides opportunity for
marketers to make use of the trend, by shifting their emphasis towards increased usage of digital
marketing channels (Stephen, 2015). In addition to other advanced digital marketing platforms
and technologies, VR is also getting increasingly popular among digital marketers, hence the
high stake in transition risk.
2.2 Consumer Journey
Consumer journey in the words of Clark (2013), can be described as a description of consumer
experience where different touchpoints characterizes consumers interaction with a brand,
product, or service of interest. Different researchers have defined the consumer journey in their
own ways, and some of them will be discussed hereafter.
According to Clark (2013), a consumer’s journey can be categorized into 3 consecutive parts,
starting out with Consideration. This stage gets triggered by a stimulus, like an advertisement
or content from the company to the buyer. The second stage, Evaluation or Engagement, which
occurs if the prospective consumer follows through with the first contact and peruses the
product and or the services offered and gets engaged. After engagement has been pursued,
Purchase follows, where the consumer attains the product or service for a price, and this is the
stage Transition Risk occurs. According to Edelman (2010), these three stages in the consumer
journey can be classified as Awareness, Enjoyment and Bond building which finally leads to
Loyalty, respectively.
When we look at a different author’s perspective and refer to Zahy Bashir et al.(2018), the
consumer’s journey into buying an item or service starts with a need, a need to be satisfied
which triggers exploration or search over a platform. This platform can be online or offline and
is a medium for the satisfaction of a need, which has a long decision process. The final stage
of the consumer journey is need fulfillment, for the person and purchase for the business entity
unless the person becomes a loyal consumer, in which case that becomes the final journey.
This process can happen with just the intent and decision of the person without influence from
others, but in today’s world, it could also come from social influences such as social media
16
platforms, friends and family (Zahy Bashir et al., 2018). The key effectiveness of VR comes
from the phycological need to experience, as consumers can get elevated levels of engagement
while being excited with the experience (Farah et al., 2019). This should be a driving force for
purchase to occur but rather, the opposite effect has taken place which has created transition
risk, where the consumer journey changes to an offline/physical store. VR could be a platform
in addition to the previously mentioned concepts or it can be a stand-alone platform with a
sense of presence, that leads to sales conversions. The immersing nature of the virtual
experience, is like being in the real physical place but rather you shop from a distant place, and
that is one of the unique qualities VR has over other forms of online sales methods (Li et al.,
2002; Hoban & Bucklin, 2015).
2.2.1 Channels for Consumer Journey
According to Neslin & Shankar (2009), there are different channels consumers go through
when they seek to purchase; they can go through a single/linear channel, or in contrast they can
go through multiple channels which involves both online and offline. On the other side of the
coin, there are several reasons for companies to use multichannel strategies of communication
in order reach consumers, first one is cost efficiency and second is scale. By using these two,
businesses can reach multiple consumers within the distribution network. These methods of
communication from companies are stimulus to consumers, while at the same time feedback
loops for these companies as well.
According to the study by Verhoef et al. (2007), which focused on channel patterns, the
observation was that consumers had the pattern of online orientation as a channel to start their
purchase journey, but then followed by brick and mortar physical stores as a final touch point
while they made the purchase. This is by definition what transition risk is, but without the
involvement of VR. Since we are referring to VR in this paper, the preferred channels of
purchase by customers will be incorporated into the development of the questionnaire and the
hypothesis. It came to our understanding that different channels have their own characters that
satisfy varying sets of consumers’ needs, some of which are segmented into needs/characters.
Questions like where does that need come from, why is it different, to which a person can
simply answer personal choice but that does not give a definitive answer nor digs down into
the question as consumer needs are not just related to the product, but also to benefit and cost.
In addition, they trickle down to the risk associated with making a purchase in addition to the
17
time, effort and decision making process which involves pleasure or pain during and after the
fact (Broekhuizen et al., 2007; Kollmann et al., 2012).
The way consumers search and pick the channel they prefer to do their shopping, some research
papers shed light on habits, such that some people would go with the familiar route while others
would consider all other options before coming to a decision (Dholakia et al., 2010; Kollmann
et al., 2012). In addition to that, according to Veen & Ossenbruggen (2015), the concepts that
influence a consumer’s decision arise from two factors. One is Information and the other Risk,
in this case risk reduction and it indicates that a person’s character plays a big role within it.
1. Information seeking - Exploratory VS Goal Oriented
2. Risk consideration/Selecting the best option – Self-reliant Vs Advice-seeking
Within the four quadrants (Exploratory, Goal Oriented, Self-Reliant, Advice-seeking),
consumers are categorized into segments. Those who are self-reliant and goal oriented are
Convenience seekers, who know what they want without seeking advice from others. On the
other side of the quadrant, we have the self-reliant ones who are exploratory, who are
Information Seekers who are not influenced over their decision by others.
Figure 3: Types of Consumers (Veen & Ossenbruggen, 2015)
On the opposite side of self-reliant consumers, we have Advice-reliant ones, who seek advice
and incorporate it into their decision making. Within this section we have Reassurance seekers,
those who do not know what they want to purchase, so they explore and seek advice as well,
Exploratory
Self-reliant Advice- reliant
Goal Oriented
Source Fig 1. (van der Veen and van Ossenbruggen, 2015)
Information
seekers
Reassurance
seekers
Convenience
seekers Peace of mind
seekers
18
they are in the crosshairs between exploratory and advice seeking. Adjacent to them are the
peace of mind seekers, who are goal oriented and know what they want but they would like
advice as well, to be more certain about the purchase (Veen & Ossenbruggen, 2015). These
four quadrants of the type of shoppers are important to be mentioned because, the shopping
personality of people matters when we speak about transition risk, as some would be the
contributors for it. While transition risk is an occurrence that happens while using VR to make
a purchase, the type of person who is making that purchase with VR also makes a significant
difference towards the outcome and not just the VR experience or design of the application.
This will be incorporated in the hypothesis development as well under Habit Schema by
focusing on shoppers’ habit.
2.2.2 Consumer experience of VR
Consumer satisfaction is the result of the positive experiences minus the negative experiences
(Lemon & Verhoef, 2016). It is the closeness between consumers expectation and delivered
services. Companies can satisfy their consumers by paying attention to their interactions
starting from the minute details to the bigger ones. These interactions are what make or break
consumers intention to get the service or product of that organization or business entity (Lemon
& Verhoef, 2016). These interactions develop an emotional reaction or an impression, and so
they will be considered in the development of the hypothesis and questionnaire regarding
hedonic motivations.
This is achievable through different interactions at touchpoints, points in which the consumer
and the seller have contact which could be online or offline (Kumar et al., 2016). Some of
which could be clicking on advertisements, adding to cart, and checking out in the online
platform and when its offline, it could be coming into a store, contacting the sales person in
store, looking around and if all goes well, a purchase. These touchpoints can be either initiated
by the consumer when looking for online reviews or they can be firm initiated with content and
or promotions that are offered online to consumers (Kumar et al., 2016).
To summarize of what has been discussed in the previous sections, technology has been an aid
in the further development retail and commerce industry for quite some time now. This has led
to changes in marketing ideas and trends that come from advertisers or companies towards their
consumers’ attitudes on how they approach the unending need of consumers, all the while also
19
notably seeing that consumers also expect changes and welcome them as well. With the
applications of refined technology such as VR, retailers could give their consumers the feeling
of being in store without having to come in. That has made changes within the consumers
journey and channels the consumer will use, which are either online or offline platforms.
Consumers can have a singular/linear way of going through their journey or they can go across
multiple/(non-linear) channels and these experiences change the consumer’s perception and
sometimes need as well. These concepts all go hand in hand together as they are changes that
have effects on each other, where one change in the journey can be an influence towards the
occurrence of Transition Risk.
Moving forward, to understand what factors come into the decision-making process and/or
augment the occurrence of transition risk, the authors of this papers will look at supplemental
concepts to build on the main ones, that deal with intent to purchase, as mentioned previously
The main concepts of the thesis are the hypotheses that will be tested, which are 6 in number.
Prior to that, these supplemental concepts will help in the development of the hypotheses.
These concepts are studied by different researchers prior in different situations but apply to the
same conditions stated below. The supplemental concepts that are going to be used in the
formation of hypotheses to determine the intent of a person for purchase are: Cognition Fan et
al. (2020); Channel preference for an expected customer satisfaction Hult et al. (2019);
Drivers for offline purchase such as human interaction and risk reduction (Laroche et al.,
2005) and Personal character which affect a person’s shopping preference and nature (Veen
& Ossenbruggen, 2015).
The authors focused on these concepts as they can give some explanations to the motivations
and behavior of customers from a psychological viewpoint. Moreover, the factors that can alter
the intension to purchase, provide guidelines for why there is a sudden decrease in the use of
VR at the purchase stage of customer journey.
2.3 Cognitive Load
According to Fan et al. (2020), cognitive load is user’s extent of effort expensed to process
different amounts of information in order to develop knowledge and an understanding of a
multimedia channel. It is one factor that can contribute to a person’s intention towards making
purchase. There are two sources of cognitive load: Internal and External.
20
As described by Fan et al. (2020), internal cognitive load occurs due to the intrinsic difficulty
to understand what a person is processing or a material they look at. It could be the number of
components and interaction a person has with what they are facing like a website, manual,
instructions etc. On the other hand, external cognitive load comes from the external
environment which involves information, particularly like the way of design and presentation.
According to Harper et al. (2009), to reduce cognitive load, materials must be well-designed in
a way that they can match a person’s cognitive ability to process. This can be done by reducing
any unnecessary or ineffective procedures or information to be executed by the user. In other
words, if we are using it in the context of a website, the more complicated the site, the more
cognitive load it has on a user, the less motivated the person is to continue forward which
negatively affects their willingness to purchase (Jung et al., 2015).
Taking into consideration the characteristics of VR being immersive, detailed, with an overlay
of information and its 3D nature Poushneh & Parraga (2017), cognitive load can be reduced by
the utilization of these qualities to make a better and well-designed app or page. According to
the cognitive theory of multimedia learning, the qualities of VR and AR and their presentation
of information, reduce irrelevant or unnecessary cognitive processing as the visual environment
is life like, thereby making customers more comfortable when they shop (Zhao et al., 2017).
Therefore, one assumption is developed to support the forming of a hypothesis, can an
increase in cognitive load reduce a consumer’s intention to purchase and thus contribute to
transition risk.
According to Grohmann et al. (2007), physical interaction with a product (in this case VR’s
realistic capability) creates an emotional sense of pleasure and with the qualities of VR such as
the 3D representation Poushneh & Parraga (2017), which lessens the cognitive load. This is
also aided as the customer has a near real life representation of a product with the help of VR.
Hence is it possible that, a decrease in cognitive load can increase the emotional sense of
pleasure. Moreover, this assumption has another side which can be stated as, hedonic
motivations (fun, pleasant sensations) in the VR environment, improve a customer’s intent
to purchase.
21
2.4 Channel preference for expected customer satisfaction
Moving on from cognitive load to the perspective of customer satisfaction, according to Hult
et al. (2019), when customers make a purchase online one of the main qualities for their
satisfaction rating, is purchase (the product). Even more so, these customers are more
satisfaction sensitive for repurchase on online than they would be in an offline/physical-store
scenario (Shankar et al., 2003). On the other hand, in an offline or a physical store, the overall
process of a purchase and customer expectation are the most significant factors that contribute
to satisfaction of customers and not just the product (Fornell et al., 1996).
With the rise of e-commerce and options available online, consumers can choose between
making a purchase online on a variety of platforms and payment systems or they can choose to
purchase at a physical store (Hult et al., 2019). Consumers choose either one and the authors
will investigate what drives their intent to purchase, which will eventually lead to satisfaction
or complaint.
If we look at these two channels, they both have their pros and cons in comparison. While in
an online purchase, there is more convenience towards finding a product, browsing with shorter
time and in multiple places, price comparison and payment without having to be in a queue.
The utilitarian advantage takes the lion share with online stores and in contrast when we look
at the offline/physical stores, the hedonic aspects such as sensory and emotional connections
make greater impressions (Hult et al., 2019).
According to Johnson et al. (2003), customer satisfaction and loyalty are stronger over online
stores rather than physical or offline stores due to the cognitive lock-in effect. It is defined as
the amount of experience with a necessary product and the occurrence of usage errors while
trying to learn how to use the product, which will eventually build a connection. The person’s
choice will be affected/biased towards previous experience and product in the future.
2.5 Drivers for offline purchase
In addition to those, customers find much use in the convenience of online shopping but at the
discomfort of the uncertainty which can be in product, material or even delivery (Dai et al.,
2014). Hence, some people are identified as web-roomers, where they look at a product online
for information but go to physical stores to purchase. Therefore, web-roomers, can be one of
22
the contributing factors to the occurrence transition risk. So, an assumption is made,
webrooming as a habit can contribute to the increment of transition risk.
Furthermore, Rick et al. (2014), states that for people, retail is like a therapy. They often go to
physical shops to enjoy, relax, and socialize due to the physical environment. The environment
of shops has design aspects that impresses people, opportunity to browse without buying, some
stores have background music that soothing, and some people enjoy interacting with others and
getting service from customer care. Hence another relevant assumption is made for a formation
of a hypothesis, retail therapy as a habit decreases customers’ intention to make purchase
using VR.
2.6 Theoretical Framework
The theoretical framework in this thesis will be based on the Unified Theory of Acceptance &
Use of Technology 2 (UTAUT2), presented by Venkatesh et al. (2012). This section will develop
the reader’s understanding of UTAUT2 and explain the necessary foundations that UTAUT2
provides for determining the acceptance and use of technology. Moreover, this section will
provide an overview of the reasons due to which the selected framework is most appropriate
to answer the research question.
The two most used frameworks to analyze the acceptance and use of information technology
are the Technology Acceptance Model (TAM) (Davis, 1989), and the Unified Theory of
Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). It is imperative to
mention here that both TAM and UTAUT have witnessed gradual change. This change has
come in the shape of TAM 2 (Bagozzi, 2007) and TAM 3 (Venkatesh & Bala, 2008) for TAM
and UTAUT2 (Venkatesh et al., 2012) for UTAUT. These frameworks have been adopted by
researchers to study the impact of different the variables in the acceptance and use of
technology.
TAM model suggested by Davis (1989), discusses the constructs of Perceived Ease of Use and
Perceived Usefulness, which are the determinants of technology usage and acceptance. On the
other hand, Farah et al. (2019), suggests that users shopping through virtual reality devices,
manifest the acceptance and use of VR technology at the awareness, consideration, and
engagement stage within the consumer journey. However, usefulness of the technology
declines suddenly at the purchase stage of this journey. Therefore, it is important to understand
23
the concepts, due to which the usefulness of VR technology diminishes and thus leads to
transition risk. TAM suggests Perceived Usefulness and Perceived Ease of Use as the
determinant variables for the research model. However, there are other constructs like social
influence, hedonic motivations, facilitating conditions, and habits of technology users which
are not particularly accounted for in TAM. Therefore, in order to obtain a holistic view, this
research has adopted UTAUT2 framework to investigate the influencing concepts that account
for the occurrence of transition risk.
2.6.1 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)
The Unified Theory of Acceptance and Use of Technology model (UTAUT2), by Venkatesh
et al. (2012), has proven to be a useful method to explain the intentions to use technology by
its potential adopters (Lima & Baudier, 2017). UTAUT is a framework that provides the user
with the understanding of user’s intensions to use information system based on four different
constructs. These constructs are Performance Expectancy, Effort Expectancy, Social Influence
and Facilitating Conditions. However, UTAUT framework had limitations regarding
consumer effect, automaticity, and monetary cost (Ain et al., 2015). UTAUT was revised in
the later version in the shape of UTAUT2 by Venkatesh et al. (2012), with the addition of three
new dimensions: Hedonic Motivation, Price of acquiring the technological artefact and Habits
related to the use of technology.
UTAUT2 model has been adopted by the researchers to understand the adoption of
technologies, technological artefacts, and tool. Previous research shows the utilization of
several or all UTAUT2 constructs. Examples like, Ally and Gardiner (2012), have employed
UTAUT2 constructs of performance expectancy, effort expectancy, social influence,
facilitating conditions, hedonic motivation, habit, and price value to measure the user
acceptance of smart mobile devices. LaRose et al. (2012), measured the adoption of broadband
internet among inner-city residents, use of e-governance technology Krishnaraju et al. (2013),
web personalization (Vinodh & Mathew, 2012). Cohen et al. (2013), measured acceptance of
electronic prescribing by South African physicians, whereas Nikou & Bouwman (2013),
measured the role of habit and social influence in the adoption of mobile social network service
in China.
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This framework is beneficial to the research in a two-fold effect. Firstly because, it entails
constructs of performance expectancy, effort expectancy, social influence, facilitating
conditions, habit, and hedonic motivations. Secondly, the research by Davis (1989), validates
the variables of perceived usefulness and perceived ease of use, which had been hypothesized
by the researcher to be the fundamental determinants of user behavior for acceptance of
technology. These models provide reasoning for the use and acceptance of technology. The
current research is based on the users decline in usage of VR technology at the purchase stage,
hence, these frameworks will serve as guiding principles to understand the influencing concepts
towards the occurrence of transition risk.
Transition risk as highlighted by Farah et al. (2019), discussed the use of VR during the stages
of awareness, consideration, engagement stage and finally where a decline of use of VR at the
purchase stage occurs. Therefore, in this research the concepts that influence the behavioral
intention to use technology are employed to lead the research in the direction to determine
concepts that influence consumers to abandon this use during the purchase stage.
Table 1: Constructs used in UTAUT2
Subject Main Definition Reference
Performance
Expectancy
Degree to which using technology will
provide benefit to Consumer
Venkatesh et al. (2012)
Effort
Expectancy
the degree of ease associated with consumers’
use of technology
Venkatesh et al. (2012)
Social
Influence
the extent to which consumers perceive that
important others (e.g., family and friends)
believe they should use a particular
technology
Venkatesh et al. (2012)
Facilitating
Conditions
consumers’ perceptions of the resources and
support available to perform a behavior
Brown & Venkatesh
(2005); Venkatesh et al.
(2003)
Hedonic
Motivations
fun or pleasure derived from using a
technology
Brown & Venkatesh, 2005
Habit the extent to which people tend to perform
behaviors automatically because of learning”
Limayem et al. (2007)
The theory incorporates constructs like hedonic motivation of consumers and according to
Venkatesh et al. (2012), hedonic motivations are critical in determining consumers’ behavioral
intention. Therefore, the framework is likely to lead the research towards gathering of relevant
25
data. In addition to hedonic benefits, the utilitarian benefits are also the drivers of technology
use (Venkatesh et al., 2012). Thus, in this context the UTAUT2 model is appropriate with the
research objectives of this thesis.
Moreover, in addition to behavioral intension of consumers to use VR technology for online
purchases, facilitating conditions provide an environment for behavioral control and may
influence behavior directly (Venkatesh et al., 2012). The research studies consumer behavior
of those who use VR during their purchase journey. Therefore, considering the UTAUT2
model, it supports the facilitating condition construct that would determine the concepts
influencing towards transition risk considering the developed hypotheses.
2.6.2 Research Model & Hypothesis
The Unified theory of acceptance and use of technology (UTAUT) proposes that the employed
constructs: performance expectancy, social influence, facilitating conditions and hedonic
motivations have a direct and positive impact on the behavioral intension of technology users
(Venkatesh et al., 2012). However, the enhanced version UTAUT2 infers that behavioral
intensions have a direct, positive, and significant impact on technology use behavior.
Moreover, the constructs of facilitating conditions and habit schema of potential technology
user also have a direct and significant impact on use behavior of potential user (Venkatesh et
al., 2012).
The constructs in UTAUT2 framework suffice the need of this research study and are deemed
to be appropriate to investigate influencing concepts that lead to transition risk in the use of
VR while shopping. It is important to mention here that the price construct is neglected. This
is due to the reason that VR head mounted devices are manufactured by various manufacturers.
These devices are available to potential users from cheap prices, e.g. a cardboard VR device is
available at SEK 20 from Teknik Magasinet*. Therefore, it was not considered fruitful to
discuss the price factor as the device can be purchased easily and does not impact the user’s
consideration for price to use the technology.
The construct of social influence has been made part of this research as it is necessary to
identify the impact of social influence in the context of retailing through VR. As the authors of
this research are themselves driven by social influence on numerous occasions while making
decisions regarding purchase of various items. According to the understanding of the authors
26
of this research it was deemed interesting to identify the same phenomena with respect to VR
retailing in this research.
2.6.2.1 Performance Expectancy
Performance expectancy is defined by Venkatesh et al. (2012, p.159), as, “the degree to which
using technology will provide benefits to consumers in performing certain activities”.
Therefore, in technology usage, performance expectancy will be considered high if the
usefulness is high. Davis (1989), theorizes that usefulness has a direct impact on consumer’s
behavioral intention to accept and use technology. Hence, performance expectancy can be
referred as the user’s subjective probability, that the usage of a specific technology will
positively impact the efficiency and performance to execute a specific job. Previous studies,
by Ain et al. (2015), Lima & Baudier (2017), and Sumak et al. (2010), support the argument
that performance expectancy has a significant impact on the behavioral intention to accept and
use technology. Considering the findings of the mentioned research’s regarding the
significance of performance expectancy as a factor that can impact in changing the consumer’s
behavioral intention to use the technology. The same can also be analyzed in the case of VR
retailing and the associated transition risk. Therefore, the following hypothesis is proposed.
H1: Performance expectancy positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk.
2.6.2.2 Effort Expectancy
Effort expectancy is the degree to which potential users find it easy to use a technology
(Venkatesh et al., 2012). Effort expectancy is proposed to influence the behavioral intention of
potential users to use the technology. As in the case of Vinodh & Mathews (2012), there was
a significant relationship between effort expectancy and behavioral intention to use e-
governance. Therefore, in the context of this study, shopper’s perception regarding the ease of
use of VR shopping technology with minimal effort leads to their positive intention to purchase
through VR. This can be considered in context of the already available literature. However, in
the case of VR retailing this research has proposed the following hypothesis to analyze the
impact of effort expectancy on the consumers intention to make a purchase using VR and its
impact on transition risk
27
H2: Effort expectancy positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk.
2.6.2.3 Facilitating Conditions
This construct related to the Consumers’ perception of the availability of resources and support
required to shop using VR (Venkatesh et al., 2012). Lack of availability of technological
resources and platforms and the support necessary to perform the shopping activity could
hinder the shopper’s intention to purchase using VR. UTAUT2 proposes that facilitating
conditions have a direct influence on the actual use behavior. Therefore, in context of this study
facilitating conditions have an influence on the user’s decision to make a purchase using VR.
Therefore, in the given context, the lack of facilitating conditions can push the consumers to
deviate from the journey and thus end up not making a purchase i.e. transition risk. In accord
with this the research proposes the hypothesis:
H3: Facilitating Conditions positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk.
2.3.2.4 Social Influence
Venkatesh et al. (2012, p.159), has defined social influence as, “is the extent to which
consumers perceive that important others (e.g., family and friends) believe they should use a
particular technology”. It is the impact of the belief of others (friends and family) which
impacts individual’s intention to use technology (Ain et al., 2015). Previous research conducted
by Fidani & Idrizi (2012), regarding learning Management System, proposed that social
influence has a significant relationship with the student’s behavioral intention to use the
system. Similarly, in another research it was confirmed that employees are socially influenced
by other employees working in the same environment in their behavior to use e-government
services. Hence, the following hypothesis is proposed to understand the social influence on
consumers to make a purchase using VR and its subsequent impact on transition risk.
H4: Social Influence positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk.
28
2.6.2.5 Hedonic Motivations
This construct relates to the fun and pleasure derived by the user by using the technology
(Brown & Venkatesh, 2005; Venkatesh et al., 2012). Hedonic motivation is one of the key
predictors of the intention to use a technology, in the case of this research the intention to make
a purchase by VR. Hedonic motivation has been theorized as perceived enjoyment in the
research regarding information system and it has also been found to show a significant impact
on technology use (Venkatesh et al., 2012). Therefore, the fifth hypothesis is,
H5: Hedonic Motivation positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk.
2.6.2.6 Habit Schema
The habit of the users serves as an influencing factor to change the intention to use a technology
like facilitating conditions (Venkatesh et al., 2012). Moreover, habit of the consumer has a
direct influence on use behavior and people perform certain behaviors automatically, because
of the relevant learning they have made over a certain period. There have been several research
papers that propose habit to be a significant determinant in predicting consumer’s behavioral
intention towards the use and adoption of technology (Kim et al., 2005; Lim et al., 2007;
Venkatesh et al., 2012). In this research, the authors have identified habits like web-rooming
and retail therapy that can impact the tendency of consumers to make an actual purchase using
VR (Dai et al., 2014, Rick et al., 2014). Therefore, to understand the impact of habit on the use
of VR and the possibility of transition risk due to these habits, the 6th hypothesis is proposed
as,
H6: Habit Schema negatively influences the behavioral intention of the shopper to purchase
through VR, increasing transition risk.
29
H1
H2
H3
H4
H5
(-)
(+)
(+)
(+)
(+)
(+)
H6
Performance
Expectancy
Effort
Expectancy
Facilitating
Conditions
Social Influence
Transition Risk
Figure 4: Research Model and Hypotheses relation
Hedonic
Motivations
Habit Schema
30
3. Methodology
In this chapter the methodology used by the researchers to answer the research question will
be explained. The chapter will follow a step by step process as mentioned below, to explain the
adopted methodology of the research. The chapter will start by first explaining the research
approach, followed by the research design. Subsequently, the research strategy will be
presented including the methodological approach for selection of sample, data collection and
the process of developing questionnaire. Then, method of data analysis will be presented,
including the measures regarding reliability and validity.
Figure 5: Methodology of the research
3.1 Research Approach
The research approach used for this thesis comes from the theory provided by Venkatesh et al.
(2012) in the shape of Unified theory of acceptance and Use of Technology UTAUT2. The
authors have used literature to make assumptions in order to tackle the research question. These
assumptions based on the UTAUT2 framework provided by Venkatesh et al. (2012), were
leading grounds for making hypothesis, which needed to be tested quantitatively. By utilizing
this framework, the authors sought out data by running an experiment of which the results were
interpreted accordingly to answer the research question by using the UTAUT2 framework. This
is where it is vital to understand the relationship between theory and data as it becomes utterly
important (Saunders et al., 2009). The framework provided by Venkatesh et al. (2012), is
adjusted for the purpose of this research to match the requirements that can generate answers
to the research question. The approach of this thesis is to use already accepted tool for the
purpose of giving meaning to acquired data and findings, rather than developing a new
framework to measure consumer behavior.
Referring to the time horizon of this thesis, the collection of data was done within a short and
specific time frame due to limitations of time regarding testing and availability of respondents.
Research Approach
Research Design
Research Strategy
Experiment Design
Sample Selection
Data Collection
Data Analysis
31
Therefore, this study will be regarded as cross-sectional study. Saunders et al. (2016), has
suggested that cross-sectional study is a study that is concerned with the analysis of a certain
phenomenon at a given or specific point in time. In this study, the phenomenon under consideration
is the occurrence of transition risk. The user response to similar experiment may vary in future, if
the experience of the users regarding VR retailing enhances or the technology is able to cope up
with the existing factors that lead to transition risk. Therefore, the research is referred as cross-
sectional study. However, this research aims to respond to the research question and discuss the
findings based on the data collected. Moreover, this is a pilot study on a small scale
investigating the occurrence of transition risk which entails a relatively small sample size due
to the limitations of mobility, finance, and interactivity imposed by currently prevailing
pandemic which will be expressed in detail on limitations.
3.2 Research Design
This section will examine the research design of the thesis. In discussion of the design for the
thesis it is worthwhile to mention here that the research design forms the guiding pattern for
the execution of a research and leads till the analysis of collected data (Bell et al., 2019). This
thesis will follow an exploratory research design to pursue an answer for the research question.
Barney & Strauss (1967), argue that there is no limit for an exploration to be qualitative or
quantitative. Hence, exploratory research can either be qualitative or quantitative. However, it
is important to mention that the basic phenomena of exploratory research are to investigate a
problem that is yet to be studies or has not been thoroughly investigated earlier. It thus provides,
a better understanding of an existing problem. Therefore, considering the current agenda of the
research, the phenomena of transition risk has not been studied thoroughly by any of the
previous research. A reason for this is because of the novelty of the concept of transition risk
itself and that VR in retailing is still an emerging technology. The current research explores the
concepts and factors that cause transition risk. The authors of this research have performed
literature review in the context of the virtual reality and causes of in-store traffic, which is
necessary in the context of exploratory research. Strauss & Corbin (1998) argue that in
literature can serve as a cornerstone for exploratory research and provide relevant insights for
exploration. This was also true with our research as literature provides us with the concepts for
which we used a statistical model to find if the explored concepts have a relationship or not.
32
Therefore, this can also be referred to as regression study as it involves modeling and analyzing
several variables to answer the research question. A set of hypotheses have been formulated
and will be subsequently tested through the analysis of data (Goeman & Solari, 2011). As
suggested by Tukey (1980) the hypotheses designed for this research are based on open minded
observations and study of literature. Therefore, in this research, the hypothesis design
procedure was mild and open to all assumptions in the beginning, flexible in terms where the
hypotheses were formulated, prioritized and then followed up for another level of ranking on
the basis of the experiment.
3.3 Research Strategy
This research is focused to answer a question which is novel in its nature, regarding transition
risk at the purchase stage of consumer journey. This research has adopted a quantitative
research strategy and quantitative data has been obtained during the research process. Firstly,
during the initial research it was observed that due to the newness of the concept there is a
niche that uses VR for retailing, and it was difficult to find those users from whom we can
acquire qualitative data. Moreover, the data acquired from those limited number of users cannot
be generalized. Thus, for the generalization of our findings a quantitative research would was
required, as Jick (1979) suggest that quantitative research can contribute a greater confidence
in the generalizability of the research.
Secondly, the research question dealt with the technology use behavior and after the study of
literature we were convinced that UTAUT2 framework possesses all the prerequisites that can
help us answer the research question. This is because it is a widely used and accepted
framework for technology use behavior studies. It is pertinent to mention here that UTAUT2
framework has been used by researchers for quantitative study and none of the previous studies
used this framework for qualitative studies. As it is designed for quantitative studies.
Thirdly, as the research entails technology use behavior. Therefore, it was deemed necessary
to use a quantitative approach as it is a quick and effective method to collect data regarding
user behavior. It was also a quick way to collect user data regarding the concepts like cognitive
load, retail therapy, web-rooming, social influence, hedonic motivations in the research.
Moreover, the research is regarding a technological domain which has high practical
implications. Therefore, the quantitative research strategy agrees to the argument that
33
quantitative methods can make important contributions to fieldwork (Seiber, 1973; Jick, 1979).
In addition, the quantitative methods provided us the path towards finding the concepts that
lead to transition risk with the help of hypotheses developed for this purpose through the
UTAUT2 framework. Quantitative data allowed the researchers to analyze the response data
and record the outcomes for investigating transition risk.
3.4 Experiment Design
The experiment is designed to obtain quantitative data regarding user experience of VR. The
experiment was designed to obtain quantitative data regarding the user purchase behavior while
using VR. During this research, the experiment was rather a difficult task yet necessary one to
perform. Performing the experiment was necessary due to the reason that people do not have
hands-on experience of VR retailing in general. This claim was supported by the fact that none
of the people from the study sample had any experience of VR retailing prior to the experiment.
Therefore, a survey with such a sample would not have supported to answer the research
question. Therefore, as the experiment was performed with a sample who did not have past
experience with VR retailing, this resulted in minimal biasness (if not zero), towards the use of
VR in retailing. Therefore, for the experience of the users an experiment was designed so that
they can make their judgements based on their experience with VR. The data obtained from
this experiment was then analyzed to look for the causes of user’s deviation from VR usage
while shopping.
The experiment involves the use of a head mounted device (HMD). There are two devices used
during the experiment. One of the devices used is manufactured by Spectra Optics Industries
and the product is Spectra Optics VR – 100, a VR head mounted device. The other device used
in the experiment is a cardboard VR headset, which is a product from SAAB, named as VR
360⸰*. To perform the experiment, an Android Operating System cell phone application was
used. This application is available on Google Play with the name VR Supermarket Cardboard,
which was installed and used on the cell phones to conduct the experiments. VR Supermarket
Cardboard application is developed by Vivente Rosell. The authors of also acquired
permission from the original developer regarding the use of this application for research and
study purposes. This is mainly done in regards with the ethical considerations in order to
maintain a higher level of integrity of the research. The application is developed in Unity. Unity
is a 3D graphic modeling technology and is mainly used in simulation development.
34
The respondents in the experiment were required to mount the device on their heads and
perform shopping in a virtual environment provided by the application. The users virtually
went through the shopping mall within the application. They walked their trolly in the virtual
supermarket environment in order to virtually purchase items of their choice. The users after
completing their virtual shopping journey were then provided with a questionnaire based on
their virtual retailing experience. These questions were based on constructs from the theoretical
framework which provided qualitative data for analysis.
First Screen upon start of application
Shopping Mall View
35
Selected Products for Purchase
Teleport View
Checking Out Screen
36
3.5 Sample Selection
The target population of this research are students studying at Linköping University, at campus
Valla. As proposed by Venkatesh et al. (2012) younger people show a higher tendency towards
novelty and innovativeness. This is relevant because majority of the students are young and as
proposed by Venkatesh et al. (2012) will show a tendency towards the innovativeness and
technology. Moreover, as the study targets Linkoping University students because it is
observed that a large proportion of them are aware of new technological gadgets, which can be
said about most university students in Sweden, as the country is also known for its
innovativeness. It is also contributed by the interaction of student with numerous innovative
companies at on campus job fairs. Therefore, it is expected that the study will have respondents
who will be similar in nature in terms of their attitude towards new technology. As the study
has an experimental design in which respondents will mount the VR device on their head, it
required a face to face interaction to make the experiment happen. While considering the small
sample size we refer to Roscoe (1975) who has proposed that the sample size should be from
a minimum of 30 and less than 500. Considering Roscoe (1975) the sample size is close to the
absolute minimum range provided by Roscoe (1975) and greater than the minimum
requirement. However, Halim & Ishak (2014) have suggested that for a simple experiment
research it is possible to do a successful research with a sample size from 10 to 20 in size.
Therefore, it can be said that the current sample size can serve the purpose of this research.
The respondents were contacted through social media, in groups for students of which the
authors were members of. A text message was circulated to acquire the consent of willing
respondents so that the experiment could be performed. A total of 110 students from different
departments of the university were contacted. However, it is necessary to mention here that,
due to the current prevailing pandemic, many students have moved back to their hometowns to
make use of the distant learning mode. Moreover, due to the current pandemic and WHO’s
instructions for maintaining social distancing and avoiding contact, students were reluctant to
wear the head mounted device and therefore, did not responded to the call. Therefore, out of
the 110 students contacted, only 45 students took part in the experiment and thus consisting of
the sample. Hence, being the reason for small sample size of n=45.
37
3.6 Data Collection
Jick (1979) has mentioned that quantitative data can take the form of multiple scales or indices
focused on the same constructs. Similarly, in this research, an instrument was adopted from a
research conducted by Lima & Baudier (2017) and Ain et al. (2015) using UTAUT2
framework. Moreover, necessary amendments in regards with technology were made to the
instrument to suit the purpose of this research and to answer the research question. The data
was collected by means of questionnaire to be filled by the respondents of the experiment, but
since their eyes were covered by the VR device, the researchers asked the questions and filled
out their response. The data was collected on a linear 5-point Likert scale. The data acquiring
questions were grouped based on the constructs employed from the UTAUT2 framework. A
comprehensive detail of the research instrument is provided in below and in Appendix I.
Table 2: Items, Constructs and Labels
Variable Label Indicator
Performance
Expectancy
PE 1 I find Virtual Reality (VR) useful as a tool to think about convenient shopping.
PE2 Shopping through VR increases my chances of purchasing things online.
PE3 Using VR helps me to complete my shopping more quickly.
Effort
Expectancy
EE1 Interaction with VR shopping is very clear and highly understandable to me.
EE2 I did not find any stress while shopping through VR.
EE3 It is very easy for me to become quickly skillful to shop using VR.
EE4 It is easy to find the exact product while shopping through VR.
Facilitating
Conditions
FC1 It is easy to find a VR device to shop using VR.
FC2 Checking out after shopping through VR was a convenient process.
FC3 Products on the virtual shelf looked real and therefore it was easy to choose a product.
FC4 I can easily seek help from friends while shopping through VR.
FC5 Consumer support was easy to avail of while shopping through VR.
Social
Influence
SI1 People who are important to me think that I should do my shopping using VR.
SI2 People who influence my behavior think that I should shop through VR.
SI3 People whose opinions that I value prefer that I shop through VR.
Hedonic
Motivations
HM1 Navigating through the shopping mall was fun and pleasing for me.
HM2 I am pleased to find a sufficient description of the products while shopping through VR.
HM3 Shopping experience through VR got me lightened and relaxed.
Habit HB1 I never look for a product online to buy that from a physical store.
HB2 I go to shopping malls as a regular practice.
HB3 I never visit retail stores for fun and to socialize.
Transition
Risk
TR1 I do not intend to purchase through VR if these are standard grocery items.
TR2 I will purchase expensive and customized items while shopping through VR.
38
A total of 110 students were contacted. These students were contacted through students
WhatsApp groups and student community groups of which the authors are a part of. These
groups consist of students from different departments of the university, including males and
females. These students were invited to perform virtual retail shopping by using a VR head
mounted device. They were provided with a questionnaire, but it was filled by the researchers,
as they had the head mounted devices on their faces. To get real time answers based on their
experience, the researchers helped with filling out the answers, as a delayed response after the
experiment by the respondents can affect their responses. The questionnaire was only presented
to the students which took part in the experiment as it was not relevant to provide questionnaire
to those who have not experienced the virtual retailing experience with the same application
(i.e. Supermarket VR Cardboard) used for the experiment.
The respondents were required to answer a total of 23 questions which related to the constructs
used for the research. All 23 questions were mandatory to be answered as they were used to
identify the respondent’s intention to make a purchase using VR. The questions helped in
highlighting a user’s intention to make a purchase using VR retailing, it was necessary to ask
questions that can assist the authors to identify the users behavior with respect to the 6
constructs mentioned in the theoretical framework. The respondents were asked to respond to
the questions on a scale of 1 to 5, where 1 represented Strongly Agree, 2 = Agree, 3 = Neutral,
4 = Disagree and 5 = Strongly Disagree. Table ___ provides description of the coding
performed against the responses.
Table 3: Detail of Data Coding
Gender Age Marital Status Educational Qualification
Strongly
Disagree = 5
Female = 1 Younger than
18
1 Single or Never Married 1 Doctorate 1
Disagree = 4 Male = 2 19 to 24 2 Married 2 Masters 2
Neutral = 3 Prefer Not to
say = 3
25 to 34 3 Widowed 3 Bachelors 3
Agree = 2 35 to 45 4 Divorced /Separated 4 High School / Diploma 4
Strongly Agree =
1
46 Above 5
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The questions from PE1 to PE3 provided data regarding the user’s viewpoint if VR shopping
will offer to them with enhanced benefits. These benefits are provided to the users as, 1) retail
shopping is made convenient by VR to be done at the comfort of your home but in a
supermarket environment, 2) shopping conveniently at home through VR can benefit the user
in increasing the volume of online purchasing than at a physical store while having no
dependency on weather and similar external mobility influencers, 3) VR shopping in
supermarket can save shopping time for the users as being in a virtual environment.
Questions from EE1 to EE4 provide data regarding the user’s viewpoint if shopping through
VR will offer to them with the ease of use to that they can continue using VR till they make a
purchase. Anything that hinders the ease of use might deviate consumers to proceed further
with using VR, and thus can result in transition risk. The data collected in this regard is
measured for the ease in interaction and if there is stress, that can hinder the efficiency of VR.
It also provides data for the ease in navigation through the shopping environment and the ease
with which the user thinks that they will be able to use the VR application.
Questions regarding facilitating conditions FC1 To FC5 gathered data regarding the resources
and support available to the users so that they can continue their shopping experience and make
a purchase. The availability of these conditions can support users to consider VR retailing as a
viable shopping experience and instead of leaving the shopping journey due their
unavailability.
Data regarding the construct Social Influence was obtained from SI1 to SI3. It provided data
regarding the influence of important people to the users, which can influence them to shop
using VR. As user behavior can be influenced by people important to potential users, therefore,
this data is relevant to know the influence that significant others make in the purchasing
behavior of users.
Similarly, data regarding hedonic motivations of the respondents was collected through their
responses to HM1 to HM3. This data is necessary and relevant as Brown & Venkatesh (2005)
suggest that, the use of technology is directly influenced by hedonic motivation of the users.
Therefore, if the users do not find any pleasure and fun while shopping through VR, then the
chances of transition risk would be higher, as users will lose the motivation to continue towards
purchase stage.
40
Moreover, as mentioned in the last hypothesis, data regarding the habits of the users who were
web-roomers and fond of retail therapy was obtained through HB1 to HB3. This provided us
with the data of people who are web-roomers where they use the online channels of retail to
get information but choose to go to physical store instead and thus contribute to transition risk.
Data was also collected for people who take retail as a therapy and have a habit to visit physical
stores for the sake of enjoyment. The data provided us with information and showed that they
have the tendency to buy from a physical store as well, leading to transition risk.
Furthermore, data was also collected from the respondents in regards to their shopping behavior
towards standard grocery items (e.g. dairy products, packaged foods products and everyday
use items that are similar elsewhere) from their trusted brands and for customized products like
clothes, jewelry, shoes etc. This data indicated the respondents were against the purchase of
customized items through VR, providing information regarding the products for which there
can be a higher transition risk and the lack of intention to purchase.
3.7 Data Analysis
A SEM (Structural Equational Model) analysis was deemed most suitable and thus employed
in this research to understand the relationship between various components of the technology
usage and acceptance with transition risk. The method of using SEM as the research’s
analytical tool is beneficial in two folds. First it is beneficial to test the research’s conceptual
model, secondly, it allows the researchers to isolate the research from possible observational
errors (Chin, 1998).
The analysis of quantitative data collected by means of survey is analyzed with the help of
SmartPLS 3.0. The use of SmartPLS software to analyze the quantitative data was helpful in
order to check whether the hypotheses developed for this research were supported or not, and
whether the connections are either positive or negative. At first, we categorized the questions
into groups, these groups were formed based on the hypotheses and questions (instrument
items), in relevance with the constructs of the SEM. These questions were then coded into a
.csv file. The data was transported into SmartPLS software for analysis. Clusters of questions
were organized in SmartPLS on the same pattern relevant to the hypotheses for analysis.
Subsequently, a model was built, and a simulation was run by calculating the PLS algorithm
which gives us various measures to test the validity, reliability, and the structural equational
41
model. Using the outcome of that we further calculated with the bootstrap option on Smart PLS
software. The calculation with bootstrap method provided us with the sign and numerical
values of t-statistics, which have different benchmark levels for each of the measure under
review. Bootstrap for each latent variable was perform individually. By using these, we could
conclude to either validate or not the hypotheses.
3.8 Literature Review in Research Process
In this section we will discuss the process and division of literature review for the purpose of
this research. The literature review had been segmented into three parts. The first part relates
to deepening the knowledge and developing an understanding regarding Virtual Reality (VR).
The review of this literature was necessary to identify the platforms which supported the idea
of massively expanding digital marketing business and VR. This systematic understanding led
to creating awareness of digital marketing functions. This allowed the researchers to dig deeper
into the business applications of VR and its evolution in context of retailing.
The second part of the literature review was performed to recognize possible applications of
VR in retail and how this technology can be utilized to help consumers and retailers. The
literature review highlighted the influence of VR in retailing. During this phase, the researchers
were exposed to the concepts of consumer experience, consumer journey, stages of consumer
journey, consumer behavior. During this phase, transition risk was identified and thus
developed the research question.
The third part of review of literature was performed to formulate hypotheses to answer the
research question. Therefore, literature regarding Consumer behavior, cognitive load, hedonic
motivations, and habit schema were reviewed. These concepts led to the formulation of
research hypotheses. To operationalize these concepts a theoretical framework was required to
translate feasible constructs to identify concepts influencing towards transition risk. The
theoretical framework most feasible to the research was identified to be Unified Theory of
Acceptance and Use of Technology 2 (UTAUT2) Venkatesh et al. (2012). The given model is
modified to suit the purpose of the research.
42
3.9 Reliability & Validity
To ensure the authenticity of the research and the readers confidence in the process of
conducting this research, several measures were adopted and applied. These measures ensured
the validity and reliability of this research and to build the trustworthiness regarding the quality
of the study (Merriam & Tisdell, 2015). The validity of a quantitative research lies with the
consistency in the measurements applied through the research, whereas validity is concerned
with the accuracy of these measurements during the research (O'Dwyer & Bernauer, 2013).
The measures taken to conform to the research reliability and validity are provided in this
section.
3.9.1 Reliability
Reliability is a measure which guides the consistency of the study in way that if the study is
repeated within similar circumstances it would again provide the same results as obtained from
the current study (Bryman & Cramer, 2011). However, the reliability was ensured by
presenting the findings of the study in accordance with the data obtained (Lincoln & Guba,
1985).
The reliability can be further segregated into external and internal reliability. One way to check
for the external reliability is through the test-retest process. It is a process where the study is
reiterated with the same sample. As due to the prevailing pandemic crisis, it was firstly quite
difficult to convince respondents for the experiment and then to call them again for retesting
was practically not possible with the same sample. It was also not possible to retest with the
exact same sample as due to the non-availability of some respondents who had gone to their
hometown after the provision of distant based learning.
3.9.2 Internal Consistency
Another measure to confirm the reliability is to measure the internal consistency of the data.
The internal consistency in this research is ensured by analysing the results related to the
measurement of internal consistency. There are several measures that have been adopted the
measure the internal reliability of the constructs and the structural model. These measures are
thoroughly discussed in chapter 4 (Results and Analysis), however, an overview is provided
herewith for a glance.
43
To measure the internal reliability of a quantitative research there are various scales to which
can be adopted. Initial measures were taken to test the internal validity and reliability if the
constructs and the internal consistency of the research instrument. For this purpose, the authors
adopted a widely and comprehensively used reliability coefficient of Cronbach’s Alpha,
(Cronbach, 1951). The analysis of the measure provided the results that all values were under
the acceptable range.
Details for the test for composite reliability are provided in chapter 4, to confirm the linear
relationship between the constructs and to comply with the adopted reliability measure the
values were under the acceptable range of above 0.7 (Werts et al., 1974). This provided
additional strength to the reliability measures and the internal consistency.
Moreover, another measure to determine the internal reliability and consistency, is adopted in
the form of Average Variance Extracted (AVE) (see 4.2.4). AVE is a measure for convergent
validity to the extent to which the variables are related and internally consistent. This measure
controls the inter-connectedness of variables. The test of these values provided that according
to Fornell & Larcker (1981) all values were acceptable.
During the analysis of the results it was found that there were some standardized factor loadings
that were below the benchmark value of 0.5 (Hair et al., 2010). These values to some extent
can adversely impact the validity of the model, therefore, these were removed in order to
enhance the validity of the constructs.
Moreover, the validity is also ensured by the testing of the Structural Equational Model (SEM).
The parameters for such testing are provided by analyzing the R2 values, which provided the
extent of change in dependent variable from an occurring change in the independent variable.
The value (0.606) obtained after the test was closest to substantial as recommended by Chin
(1998). Therefore, in this research the authors have tried their best to maintain reliability and
validity of the research.
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4.0 Results & Analysis
The analysis of data in this research was performed through confirmatory factor analysis
(CFA) to evaluate the measurement model fit. Structural equational modeling (SEM) was
employed to evaluate the hypothesized relationships. This chapter will discuss the results from
the structural equational model (SEM) developed from the UTAUT framework. This chapter is
presented in four different part. The first part discusses the demographic distribution, the
second part contains results for reliability and validity. The third part provides results for
testing the structural model and the fourth part test the proposed hypotheses.
4.1 Demographic Distribution
The details of the respondent’s characteristics for this research is presented in table 2. An
analysis of the respondent’s profile depicts that there is a high majority of male respondents
(73%) as compared to the female respondents (27%). Majority of these respondents belonged
to the age group of 24 to 35 (86.66%) and most of them were single or never married (87%).
In context of the respondent’s educational qualification, 53% of them held master’s
qualification.
Demographics Statistics
Gender
Female 26%
Male 73%
Age
19 - 24 6.7%
24 – 35 86.7%
35 – 45 6.7%
Marital Status
Single or Never Married 87%
Married 13%
Educational Qualification
Bachelors 47%
Masters 53%
Table 4. Demographic Statistics
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4.2 Construct Reliability and Validity
The reliability and validity of the tests and questionnaires used by researchers is a highly
important concern for researchers in marking the accuracy of assessments and evaluations
(Mohsen et al., 2012). Moreover, validity and reliability are the most essential elements for the
purpose of evaluation of the instrument employed by the researchers. In this research multiple
forms of reliability and validity measures have been used to confirm the validity of the
measurement instrument in use. These forms of reliability are Cronbach’s Alpha, Composite
Reliability and Average Variance Extracted (AVE).
4.2.1 Cronbach’s Alpha
The first method to assess the reliability of the research is based on Cronbach alpha. The
Cronbach’s Alpha reliability scale is one of the most famous and widely used reliability
coefficient, (Mohsen et al., 2012). Cronbach Alpha was developed by Lee Cronbach (1951) to
provide a measure of internal consistency of a test or scale and is expressed in the number range
between 0 to 1. Internal consistency for the current quantitative research is a highly imperative
subject for the authors of this research.
The benchmark value for Cronbach’s Alpha is suggested to be 0.70 Churchill (1979) and is
considered reliable till the value is 1. Therefore, the data collected from the research was
subject to calculation of Cronbach’s Alpha. The initial calculation was done on a holistic scale
for all the data which was used in the Structural Equational Modeling (SEM). The Cronbach’s
Alpha for the overall research instrument was calculated to be “0.949”. This calculation was
performed by transporting the data into excel format. The data was analyzed with the Excel
data analysis option of Anova: Two factor Analysis. Thus, the overall reliability was
authenticated as the value was above 0.7.
Moreover, reliability and internal consistent of the research instrument is also tested with
SmartPLS 3.0, the data analysis software used for this research. The results showed that four
out of 6 constructs had a Cronbach’s Alpha ranging from 0.774 to 0.949. However, the results
for two variables resulted in a low value. This low value is due to small sample size, low
number of questions or poor interrelations between the items of the research instrument
(Mohsen et al., 2012). Therefore, after the removal of one item i.e. PE2 from the latent variable
Performance expectancy and two items i.e. EE2 & EE4 from latent variable effort expectancy
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the minimum threshold value of 0.7 was achieved. Hence, for all latent variables, the individual
Cronbach’s Alpha ranged from 0.774 to 0.962. The individual values are presented below in
table 3 for all latent variables.
Latent Variable Value for Cronbach’s Alpha
Effort Expectancy 0.900
Performance Expectancy 0.871
Facilitating Conditions 0.949
Social Influence 0.774
Hedonic Motivations 0.877
Habit Schema 0.962
Table 5: Cronbach’s Alpha for all Individual Latent Variables
4.2.2 Composite Reliability
The second method to determine the accuracy of assessment of evaluations was conducted
through composite reliability test. Composite reliability test is used to confirm the linear
relationships between the constructs. Similar to Cronbach’s Alpha, the value for Composite
reliability is also recommended and the acceptable threshold value is 0.7 and above (Werts et
al., 1974). The results from composite reliability testing from SmartPLS showed that the value
for all latent variable fell within acceptable range i.e. from 0.865 to 0.981. Therefore, the
reliability and validity of the measurement instrument and assessment evaluations was further
authenticated by the values resulting from composite reliability testing with SmartPLS 3.0. The
detail of individual construct composite reliability can be found in table 4.
Latent Variable Value for Composite reliability
Effort Expectancy 0.952
Performance Expectancy 0.939
Facilitating Conditions 0.962
Social Influence 0.865
Hedonic Motivations 0.923
Habit Schema 0.981
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Table 6: Composite Reliability Values for Individual Latent Variables
4.2.3 Dillon-Goldstein’s rho
Another method employed to ascertain the reliability of structural equational modeling is
through Dillon-Goldstein’s rho. Chin (1998) has recommended Dillon-Goldstein’s rho to be
an even better reliability measure than Cronbach’s Alpha in structural equational modelling.
This is because Dillon-Goldstein’s rho is calculated based on the loadings rather than the
correlations between the variables to be observed. The recommended value for Dillon-
Goldstein’s rho by Chin (1998) is 0.7, which is comparable to other measures used for this
purpose. It is to be noted that items like PE2 from the latent variable Performance expectancy,
two items i.e. EE2 & EE4 from latent variable effort expectancy and one item from latent
variable Habit Schema HB2 were removed from the Structural Equational Model as the factor
loadings were below than satisfaction level.
The testing results show that rho_A values fall between 0.795 to 0.973 for the latent variables
used for structural equational modelling in this research. The detail is provided in table 5.
Latent Variable Rho_A Value
Effort Expectancy 0.904
Performance Expectancy 0.881
Facilitating Conditions 0.973
Social Influence 0.795
Hedonic Motivations 0.923
Habit Schema 0.968
Table 7: Rho_A Values for Individual Latent Variables
4.2.4 Average Variance Extracted (AVE)
The constructs validity was judged based on convergent and discriminant validity evaluation.
The Average Variance Extracted (AVE) values were determinants to assess the convergent
validity. The values calculated by SmartPLS 3.0 showed that these were within the range of
0.551 to 0.963. These values are higher than the suggested threshold of greater than 0.5 (Fornell
& Larcker, 1981). The table 6 provides a glance of the individual rho_A values, whereas Table
7 provides an assessment of discriminant validity based on Fornell-Larcker Criterion.
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Latent Variable Average Variance Extracted (AVE)
Effort Expectancy 0.909
Performance Expectancy 0.885
Facilitating Conditions 0.835
Social Influence 0.681
Hedonic Motivations 0.801
Habit Schema 0.963
Table 8: AVE Values for Individual Latent Variables
Results of above-mentioned different evaluations techniques confirmed the validity and
reliability of our measuring instrument as well as the research’s outer model.
4.3 Structural Model
The results and analysis of the values relevant to the structural model are discussed in this
section. The structural model is one of the key elements in the development of this research.
The characteristics and results of the model are presented to develop the analysis afterwards.
4.3.1. Structural Model Testing
The inner model was estimated by analyzing the R2 and f2 values. Chin (1998) has suggested
that the R2 of the model can be considered substantial, moderate, and low at 0.7, 0.33 and 0.19,
respectively. R2 explains the percentage of variance of the dependent variable by any change
in the independent variable in the structural equational model. Thus, to obtain the value for R2
for the model PLS algorithm run was executed in SmartPLS 3.0. The results from the execution
of PLS algorithm function provided an R2 value of 0.606. This value is significantly higher
from the moderate value of 0.33 and very close to the substantial position of 0.7. This represents
that there is a significant combined effect of the independent variables on the dependent
variable in the structural equational model.
Apart from R2 value another parameter to analyze the inner model estimations is f2 value. Cohen
(1988) suggested that the f2 has a minimal effect on R2 if the value is at 0.02. This effect is
considered moderate if the value is at 0.15 and taken as high if the value stands at 0.35. In the
current model the f2 value ranges from 0.009 of effort expectancy to 0.044 of construct social
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influence. Therefore, results from the f2 value reveal that the size effect of f2 at the resultant
values indicate a small effect on R2.
4.3.2 Standardized Factor Loadings
The structural equational model has been developed and tested with the help of SmartPLS 3.0.
The standardized loadings are obtained after allocating the indicators to their respective
constructs within the SmartPLS 3.0 model. The indicators in this context are the questions
formulated in the measurement instrument and their obtained values from the Likert scale
employed to scale the responses. These indicators were developed in consideration with the
constructs of UTAUT2 model, Venkatesh et al. (2012) used in the current research while
developing the model. Each of the indicators represent a question from the measuring
instrument. The benchmark value for factor loadings is suggested to be 0.5 (Hair et al., 2010).
During the analysis of the measurement model it was observed that the factor loadings for
performance expectancy (PE2 = 0.304) and 02 indicators for effort expectancy (EE2 = 0.175
and EE4 = 0.269) were considerably very low. Moreover, the factor loading for one of the
items from the construct habit schema was also removed as it had a factor loading of 0.469,
which is lesser than the suggested value. In the SEM process it was observed that the
considerably low factor loadings of these indicators have an adverse effect on the path
coefficients and constructs validity. Hence, it was productive to remove these indicators from
the original structural equational model to gain a higher precision. The removal of these items
increased the validity of the constructs, performance expectancy, effort expectancy and habit
schema. The remaining standardized factor loadings for the present 17 items from the
constructs was higher than the benchmark value of 0.5. The factor loadings for these items
were distributed between 0.759 and 0.983. The details of distribution are provided in table 8.
Variable Label Loading
Performance Expectancy PE1 0.933
PE2 X
PE3 0.949
Effort Expectancy EE1 0.950
EE2 X
EE3 X
EE4 0.957
Social Influence SI1 0.821
SI2 0.805
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SI3 0.848
Facilitating Conditions FC1 0.954
FC2 0.953
FC3 0.759
FC4 0.922
FC5 0.965
Hedonic Motivations HM1 0.910
HM2 0.958
HM3 0.811
Habit Schema HB1 0.983
HB2 X
HB3 0.980
Table 9: Specifications of Constructs and Relevant Loadings
“X” in the table represents the items which have been deleted due to low standardized factor
loading. Results reveal that standardized factor loading other than the deleted factor loading
are distributed in a very fine consistent range from 0.759 to 0.965 for the items relevant to the
constructs of the model.
4.3.3 Path Coefficient
Consistency in the estimation for correlations between latent variables as well as the derived
coefficients can only be obtained if viable reliability measures are confirmed through tested
reliability coefficients (Dijkstra & Henseler, 2015). Hence, to present the results regarding the
path coefficients, this research has presented results of validity coefficients as its first
preference. After providing sufficient evidence of the reliability tests of this research, here
follows the results for SEM path coefficients. The results from the execution of PLS Algorithm
function in SmartPLS 3.0 demonstrate that path coefficients do not show a large difference
before and after the deletion of items with low factor loadings. However, there is one exception
where of Effort Expectancy where the path coefficient has increased to a tune double of the
value before deletion. As suggested by Ain et al. (2015) that items with low factor loading can
affect the validity of the construct. Considering the benchmark value of 0.5, Hair et al. (2010)
two of the items from the construct of Effort Expectancy had to be removed. The total number
of items from this construct were 4, thus upon removal of 2 items there was a large change in
the path coefficient value for Effort Expectancy. In addition to this there is a relatively higher
change observed in the construct Facilitating Conditions. This is because this construct had 5
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items, due to which there was already a greater coverage of the construct’s theoretical domain.
All the existing factor loadings of this construct fell under acceptable range. Whereas in other
constructs some of the items were deleted afterwards. Therefore, the path coefficient for
construct Facilitating Conditions was further strengthened in this process.
Moreover, all path coefficients have a value greater than the benchmark value of 0.200 as
suggested by (Lohmoller, 1989). Results and values for relative Path Coefficients is presented
in Table 9.
Variables Path Coefficients before
deletion of items
Path Coefficients after
deletion of items
Performance Expectancy 0.257 0.228
Effort Expectancy 0.219 0.499
Facilitating Conditions 1.534 2.036
Social Influence 0.433 0.392
Hedonic Motivations 1.131 1.349
Habit Schema 0.375 0.373
Table 10: Path coefficients before and after deletion of low performing items
4.4 Hypothesis Testing
The hypotheses have been designed based on the Unified Theory of Acceptance and Use of
Technology 2 (UTAUT2). These hypotheses will be tested based on the following parameters:
path coefficients, p-value, and t-value. It is important to mention here that the suggested values
for these parameters is: path coefficients > 0.200, t-value > 1.96. It is to be mentioned here that
there is a limit in the number of decimals presented by the SmartPLS, therefore, these values
are presented as p<0.001 for the convenience of the readers.
4.4.1 T Statistics
T statistics is a measure used in several research to validate the assessments from the model
(Ain et al., 2015; Lima & Baudier, 2017; Joo et al., 2018). The higher the level of T-value the
greater is the evidence that Null hypothesis will be rejected. The recommended value for t-
value is recommended at > 1.96 (Lima & Baudier, 2017). However, Dahiru (2008) has
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presented the critical value of t statistics at > 2.11. The t-values for this research are presented
in table 10.
Table 11: t-values for latent variable
Latent Variable t-values
Performance Expectancy 5.037
Effort Expectancy 7.914
Facilitating Conditions 7.410
Social Influence 11.539
Hedonic Motivations 6.926
Habit Schema 9.358
4.4.2 Performance Expectancy
Performance Expectancy is the first construct of the Structural equational Model. Results reveal
that the p-value is <0.001 and significant. There is a limit to the number of decimals the Smart
PLS software to present, so it was not possible to show the extended number. Therefore, the
researchers will have to rely on the path coefficients and t-value to evaluate the hypothesis. As
it is already mentioned that the path coefficient for performance expectancy is 0.228. The value
is greater than the suggested value to 0.200. Moreover, the analysis of t-stats show that the t-
value is 5.037, which is also greater than the suggested t-value level (i.e. >1.96). Therefore, it
can be inferred that H1: “Performance expectancy positively influences the behavioral
intention of the shopper to purchase through VR, reducing transition risk” supported.
4.4.3 Effort Expectancy
Results show that the p-value for effort expectancy is very low and significant, thus it is
presented as p<0.001 in SmartPLS 3.0. However, the t-value is 7.914 which is significantly
higher than the suggested value of 1.96. Therefore, in addition to p-value the measure of path
coefficient and t-value will be considered. The path coefficient 0.499 and t-value suggest that
53
there is a significant relationship between Effort Expectancy and transition risk. Therefore,
based on given results it can be inferred that H2: “Effort expectancy positively influences the
behavioral intention of the shopper to purchase through VR, reducing transition risk” is
supported.
4.4.4 Facilitating Conditions
Facilitating conditions is the third latent variable under consideration. Results show that the t-
value for facilitating conditions is 7.410, higher than recommended figure whereas p<0.001.
The path coefficient for the construct facilitating conditions is 2.036. Hence, the result shows
that both, t-value, and path coefficients are higher than the suggested values. Therefore, based
on the results H3: “Facilitating Conditions positively influences the behavioral intention of
the shopper to purchase through VR, reducing transition risk” is supported.
4.4.5 Social Influence
Social Influence is another construct from UTAUT2. The results for social influence show that
for the said latent variable in the model, t-value stands at 11.539. In PLS-SME P-value for the
variable is p<0.001. The path coefficient for the variable stand at 0.392. Therefore, the H4:
“Social Influence positively influences the behavioral intention of the shopper to purchase
through VR, reducing transition risk” is supported. Based on the previously mentioned result
and thus it can be said that social influence has a positive and direct influence on the consumer’s
intention to make a purchase using VR.
4.4.6 Hedonic Motivations
Hedonic motivation has a significant impact on transition risk with t-value at 6.926 which is
significantly higher than the suggested value of 1.96. The path coefficient shows a significant
relation at 1.349, which is considerably higher than benchmark value of 0.200 with p-value
<0.001. Hence, H5: “Hedonic Motivation positively influences the behavioral intention of
the shopper to purchase through VR, reducing transition risk” is supported. Therefore, it can
be inferred that Hedonic Motivation has a significant impact on transition risk and influences
the behavior of the consumer to make a purchase using VR.
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4.4.7 Habit Schema
The last latent variable of the structural equational model is Habit Schema, and it should be
noted that this was the only hypothesis that has a negative relationship (is a push factor for the
occurrence of Transition Risk). Results from bootstrapping in SmartPLS 3.0 reveal that the t-
value is 9.358 which is significantly higher than the benchmark value. Moreover, as mentioned
earlier the path coefficients for Habit schema is 0.373, which is also higher than the suggested
value of 0.200 with level of significance p-value p<0.001. Therefore, H6: “Habit Schema
negatively influences the behavioral intention of the shopper to purchase through VR,
increasing transition risk” is supported. Based on the obtained results, it is confirmed that
Habit Schema has as negative influence on consumer’s intention to purchase through VR,
which increases the occurrence of transition risk.
The detail of hypotheses and the position of confirmation and is provided in table 11. The
letter X represents the confirmation/ support of the hypothesis and O represents the
invalidation.
Variable
Relation
Hypotheses Path
Coefficient
t-value Results
PE TR Performance expectancy positively influences the
behavioral intention of the shopper to purchase through
VR, reducing transition risk.
0.228 5.037 X
EE TR Effort expectancy positively influences the behavioral
intention of the shopper to purchase through VR,
reducing transition risk.
0.499 7.914 X
FC TR Facilitating Conditions positively influences the
behavioral intention of the shopper to purchase through
VR, reducing transition risk.
2.036 7.410 X
SI TR Social Influence positively influences the behavioral
intention of the shopper to purchase through VR,
reducing transition risk.
0.392 11.539 X
HM TR Hedonic Motivations positively influences the
behavioral intention of the shopper to purchase through
VR, reducing transition risk.
1.349 6.926 X
HS TR Habit Schema negatively influences the behavioral
intention of the shopper to purchase through VR,
increasing transition risk.
0.373 9.358 X
Table 12: Statistical Results for Proposed Hypotheses
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5.0 Discussion & Findings
This chapter will discuss the results and analyze the findings in relation to the literature, so
the reader can understand the causes of transition risk and thus to answer the research
questions. In addition, this chapter will discuss the empirical findings from the conducted
experiments during the research to support the arguments. Furthermore, these findings will
allow to present a more clear and targeted answer regarding the factors that lead to transition
risk.
5.1 Performance Expectancy
Venkatesh et al. (2012, p.159), have defined performance expectancy as “the degree to which
using technology will provide benefit to the consumers in performing certain activities”. The
same was tested during the experiments and from the subsequent responses obtained from the
measurement instrument. The results show that the performance expectancy has a direct and
significant impact on the intention of the shopper to use VR to make a purchase. Therefore,
considering these finding it can be inferred that the lower the degree of perceived benefits to
the consumers the lower are the chances to which consumers will use VR to make a purchase.
In this research this is the first reason that initiates transition risk is performance expectancy.
Consumer do not perceive any greater benefit to make a purchase while using VR and thus
visit offline stores, (Farah et al., 2019).
The benefit structure for respondents was integrated in the measurement instrument and used
in this research to focus on the perceived usefulness element of consumer use behavior. This
is termed as performance expectancy by Venkatesh et al. (2012) and the respondents were
asked regarding their convenience while using the VR head mounted set on their probability to
be able to use VR to shop online and their idea to perform all tasks quickly. Bowman et al.
(1999) proposed that VR can provide consumers with a variety of information that can be
perceived useful by the consumers. Contrary to this if the consumers do not find the
information useful enough and the virtual environment is not successful to convince them to
think that the information provided in the VR interface is helpful. The consumers may find this
factor to distract them from moving forward from the engagement stage to the purchase stage
and thus leading to transition risk.
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With the rise of e-commerce and options available online, consumers can choose between
making a purchase online on a variety of platforms and payment systems or they can choose to
purchase at a physical store (Hult et al., 2019). The psychology of choice designs multiple
factors that drive consumer’s intent to purchase from either of the two options. Looking into
the two channels, both have their pros and cons in comparison. While in an online purchase,
there is more convenience towards finding a product, browsing with shorter time and in
multiple places, price comparison and payment without having to be in a queue (Burke, 2002).
Moreover, Hsiao (2008) suggested that among other factors for consumers to make a choice
between online vs offline shopping is the time of delivery of items. Consumers might find VR
of low use due to same reason of the delivery of items at a delayed time. On the other hand,
during the experience of shopping at a physical store, consumers usually get the delivery of the
products at the time of purchase. Hence, there is no delay of delivery and the shoppers can
experience the product readily. The difference in the situations of the two shopping experiences
and the delivery of product at the same time as purchase at a physical store creates an enhanced
level of comfort. This comfort enhances the usefulness of shopping at a physical location rather
than through VR. This can become a reason to reduce consumer’s perception of usefulness of
VR usage, and to make a purchase using the technology. Thus, it leads the consumers to engage
fully into the VR experience but to make a purchase, the shoppers prefer to visit the physical
location of the store and get the product readily, increasing transition risk.
In addition to this, although consumers observe a higher level of usefulness in the convenience
of online shopping, but at the discomfort of uncertainty which can be in product or material
(Dai et al., 2014). This uncertainty will linger on until the time the product is delivered, and
the purchaser physically observes or experiences the product by using it. This can also be a
reason for the abandoning of use of VR in order to make a purchase while using VR, and thus,
the occurrence of transition risk.
Therefore, from the results of hypothesis and the discussion it is inferred that there are various
factors that diminish the usefulness of VR during the consumer journey. This shrinks the
performance expectancy of VR in the perception of the consumers and therefore, consumers
reach to the engagement stage while using VR, Farah et al. (2019) but end up not using the
technology in order to make a purchase.
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5.2 Effort Expectancy
The second construct under discussion is effort expectancy. Effort expectancy from the
theoretical framework of UTAUT2 can also be seen in terms of Davis et al. (1989), perceived
ease of use. Research has shown that effort expectancy increases the chances of potential users
to use technology (Venkatesh et al., 2012). The results from the current research reveal that
effort expectancy has a direct impact on the consumers intention to make a purchase using VR
and thus reducing transition risk.
Pantano & Servidio, (2012) suggest that consumer satisfaction derived from VR shopping is
highly affected and driven by the ease of use of the VR technology. The same was also observed
during the experiment process, where respondents with a hand on experience and a
considerable amount of previous exposure to virtual environment considered it less stressful to
navigate through the virtual environment as compared to those who did not have similar
amount of experience. Therefore, the higher the amount of stress the higher will be the amount
of effort that the consumer must give, to perform the task. This leads to a low level of
consumer’s perception for effort expectancy.
The stress factor is also discussed by Fan et al. (2020), refereeing to it as cognitive load.
Cognitive load is a user’s extent of effort expensed to process different amounts of information
in order to develop knowledge and understanding of a multimedia channel. It is one factor that
can contribute to a person’s intention towards making purchase. There are 2 sources of
cognitive load: Internal and External. As described by Fan et al. (2020), the internal cognitive
occurs due to the intrinsic difficulty to understand what the person is processing or a material
they look at. It could be the number of components and or interaction a person has with what
they are facing like a website, instruction, visualization or manual etc. On the other hand,
external cognitive load comes from the external environment which involves information like
a way of design, application, direction of use and presentation. According to Simon et al. (2009)
to reduce cognitive load, materials must be well-designed in a way that they can match a
person’s cognitive ability to process, this can be done by reducing any unnecessary or
ineffective procedures or information to be executed by the user. In other words if we are using
it in a context of a website, the more complicated the site and the more cognitive load it has on
a user, the less motivated the person is to continue forward, in this case their willingness to
purchase (Jung et al., 2015).
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Taking into consideration the characteristics of VR being Immersive, detailed, with an overlay
of information and its 3D nature Poushneh & Vasquez (2017), cognitive load can be reduced
by the utilization of these qualities. According to the cognitive theory of multimedia learning,
the qualities of VR & AR and their presentation of information, reduce irrelevant or
unnecessary cognitive processing as the visual environment is life like, thereby making
consumers more comfortable when they shop (Zhao et al., 2017).
It is imperative to discuss the stress factor from cognitive load because, the higher the level of
cognitive load the more will be the stress on consumers to process the information. It leads to
consumers making more effort to understand the retail system within the virtual environment.
This stress reduces the chances of consumer to proceed their journey using VR and thus
becomes a factor that contributes towards transition risk. Ballatine (2005) proposed that the
level of interactivity with the virtual environment and the level of usefulness of the information
provided by the virtual interface directly contribute towards user satisfaction.
Therefore, transition risk at the purchase stage can be a consequence of cognitive load that
results in low effort expectancy for the consumers. The results show that the effort expectancy
has a positive, direct, and significant impact on consumer’s intention to make a purchase using
VR. Therefore, a low level of effort expectancy can cause consumers to have more stress while
using VR retail applications and forces them to make more effort in order to operate them and
thus, cause transition risk.
In the given scenario of consumers confusion to adopt VR for making purchases, Howland
(2016) suggested that consumers are currently reluctant to use VR headsets to make purchases.
However, as the VR headset facility becomes more common and consumers develop more
interaction with the VR devices, consumers will get familiar with the VR retailing interfaces
and then it will be easier to get involved with the technology. This will help to reduce the
transition risk. More so, Howland (2016) proposed that it is evident that effort expectancy has
a direct impact on consumers intention to make a purchase through VR and thus supports the
validation of hypothesis 2. Therefore, stress and poor effort expectancy are contributing factors
for transition risk.
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5.3 Facilitating Conditions
As proposed by Venkatesh et al. (2012, p.159) “facilitating conditions refer to consumers’
perceptions of the resources and support available to perform a behavior”. The hypothesis
that facilitating conditions positively influence the behavioral intention of the shopper to
purchase through VR, reducing transition risk was supported by the results. In the measurement
instrument of this research the section of facilitating conditions was divided into two segments.
The two segments simultaneously contributed to generate knowledge towards two dimensions
of facilitating conditions, where it allowed the researchers to observe the facilitating conditions
in two folds.
One of the dimensions was to analyze the facilitating conditions from the perspective of
availability of resources also mentioned by (Venkatesh et al., 2012). This involved the
availability of resources to initiate and complete the shopping experience for VR retailing.
Therefore, this segment was focused on the process of shopping through VR and the physical
resources that make the shopping experience happen.
The second segment was based on the facilitating conditions that assist the consumers during
the retail shopping process. These facilitating conditions were consumer support and seeking
help from friends and family. The consumer support services are considered to be a crucial
factor in deciding the choice of the consumers. Lee & Chang (2008) have identified consumer
services as a salient dimension that determines the choice of shopping venue for the consumer.
The consumer services and support are a key factor which motivates the consumers to choose
between multiple options. This is basically due to the changing societal trend of moving
towards an experience economy (Pine & Gilmore, 1998).
Venkatesh et al., (2012), performed the study on the usage of mobile internet technology and
proposed that consumers who are exposed to low levels of facilitating conditions will show a
lower intention to use the technology. Similarly, this can also be applied in the case of transition
risk, where lower levels of facilitating conditions can drive the satisfaction level of the
consumers and hence can lead to non-usage of technology. Empirical evidence from the
experiment and the measurement instrument reveal that a large proportion of potential
consumer do not have a VR device. Thus, the lack of facilitating conditions influence the
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consumers perception to use VR negatively and hence the deviation from a smooth journey to
transition risk occurs.
Moreover, as Howland (2016) proposed regarding consumer’s reluctance to use VR for
retailing there is a greater degree that a lot of potential consumer are not familiar with the use
of VR for retailing. This was also evident from the experiment performed during the research.
In this context Notani (1998) proposed that users who are not familiar with the technology tend
to rely more on the facilitating conditions in order to develop their use behavior towards using
a certain technology. Therefore, if the facilitating conditions are low the same will be the
tendency to adopt the technology.
Laroche et al. (2005) suggested that one of the reasons for consumers preference for offline
shopping is the availability of human interaction, however VR retailing lacks human interaction
and consumer services and support, which is available in an offline store. Results from the
research reveal that consumers do not find any human interaction, support and service facility
while using VR. The absence of such help further diminishes the facilitating conditions and
thus result in increase of transition risk.
Therefore, in the case of hypothesis 3 the study of literature support that due to non-availability
of appropriate facilitating conditions for retailing through VR and consumers not being familiar
with the process. The consumers deviate their reliance on VR and prefer to visit physical stores
and adopt the offline retailing channel. Thus, due to the absence of facilitating conditions,
transitions risk occurs. Therefore, low facilitating conditions are a contributing factor for
transition risk.
5.4 Social Influence
Social Influence is also found to be an important factor that contributes to transition risk. The
4th hypothesis: Social Influence positively influences the behavioral intention of the shopper to
purchase through VR, reducing transition risk is supported by the research. Social influence is
defined by Venkatesh et al. (2012, p.159) as “the extent to which consumers perceive that
important others (e.g., family and friends) believe they should use a particular technology”.
Social influence turned out to be a highly important construct to be included in the research.
This is mainly because none of the respondents during the research period confirmed that they
were influenced by anyone from their friends and family to use VR, for the purpose of retailing.
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Not only that there was no influencer from the family but also, they did not come across any
social influencer through electronic print or social media to influence people to use VR for
retailing. Therefore, there is a large gap in the influencer marketing strategy for retailers who
wish to transform their retailing towards VR. This gap allows consumers not to be influence in
order to make purchase using VR. Therefore, this absence can also be a contributor to transition
risk.
Moreover, absence of social influence as a contributor towards transition risk can also be
validated by the principle of social proof for influence and persuasion presented by Robert B,
Cialdini, in his book Influence – The Psychology of Persuasion. There is no social proof for
VR in retail, due to which there is no influence on consumers for considering making a
purchase using VR. Cialdini (2006) believes that a high majority of consumers are imitators
and hence are persuaded largely by the actions of others. The use of VR in retailing is not as
common as other channels of retailing due to which it fails to provide social proof to people
being largely engaged in retailing through VR. In the case of VR in retailing, results show that
there is no social proof that might influence the purchaser’s intention to use VR for retailing
by watching others doing the same. Therefore, with the results from the research and the
support of literature, it is highly plausible that the absence of social influence allows the
presence of transition risk.
5.5 Hedonic Motivations
Farah et al. (2019), proposed that VR is a fun and interactive approach to engage potential
consumers. Holbrook & Hirschman (1982) consider hedonic motivation as one of the key
indicators in consumer behavior research. Thus, an effort was made to understand if the lack
of hedonic motivation can deviate consumers from using the technology and in this case can
contribute to transition risk. The authors looked unto the research of Brown & Venkatesh
(2005) where they propose that hedonic motivations play an important role to determine the
use and acceptance of technology. If hedonic motivation is unable to create enjoyment and fun
for the consumers while using VR, then there is a high possibility that the consumers will not
welcome to further the use of VR and complete their purchase process.
According to Grohmann et al. (2007) the interaction with VR’s simulated environment creates
a sense of emotional pleasure due to VR’s ability of presenting 3D representations. This fun
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interaction and pleasure helps to reduce the cognitive load on the users as the information
provided through VR becomes easily understandable to the users (Poushneh & Vasquez,
2017). This load is also decreased in the cases where the 3D representation provides near real
life simulations which is easier for the user to get familiar. However, the VR retail application
used for the experiment was not considered very close to real life by the respondents. Therefore,
the respondents were reluctant to purchase new items that they had not purchased earlier.
Based on the results it is plausible to say that because of the not so real life like design of the
app and environment, which can be designed better, the consumer behavior deviated towards
have a negative intent in regards to their intent of purchase. This is mainly because the VR
retailing application (Supermarket) did not presented a highly realistic virtual environment.
Thus, minimizing the fun and emotional pleasure part for the consumers. This can be a reason
of why consumers did not complete the journey by making a purchase using VR.
5.6 Habit Schema
Habit schema is linked in this research as a contributing factor for transition risk. Research
indicates that habits are not driven by attitudes or intentions (Ji & Wood, 2007; Liu & Tam,
2010). Results from this research show that habit schema has a negative and direct influence
on the shopper’s intention to purchase through VR and thus can increase transition risk.
Therefore, it is important to know if there are certain habits that are associated with shoppers
that can deviate the regular course of a consumer’s journey. In the case of VR retailing these
habits can push consumers to visit physical stores instead of making a purchase using VR.
It is equivalently imperative to have literature to back up the arguments in support of the
hypothesis already validated by the research. Therefore, as mentioned earlier that there are
some consumers that find a higher degree of convenience while shopping online channels but
they are constantly faced with the discomfort of the uncertainty regarding the product or the
material of product in addition to the time of delivery (Dai et al., 2014). Hence, some people
are identified as web-roomers, where they look at a product online for information but go to
physical stores to purchase. Therefore, for web-roomers the perceived usefulness can be
affected by this habit.
Neal et al. (2009) and Wood & Neal (2009) have researched that as a habit is developed into a
consumer’s behavior, satisfaction will not be able to materially alter the behavior of the
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consumers. Similarly, in the case of VR retailing, if a VR user/potential consumer has a habit
of web-rooming then there is a high degree that they will look for a product through VR, but
then visit a physical store to buy the product. Additionally, in the context of habit, Rick et al.
(2014) states that for some consumers, retail is like a therapy. They often go to physical shops
to enjoy, relax, and socialize thanks to the physical environment. The environment of most
shops has some design aspects that capture people’s attention, multiple choices feeding the
need to browse for items, some have background music that soothes and also people like the
interaction with others or customer service. The same experience is not available in the same
way with the case of VR retailing. The VR retailing application that was used in the experiment
did not have the provision to include any other shopper within the shopping experience. Thus,
only the shopper can be present within the virtual environment to have the shopping experience.
This research also inquired with the respondents regarding their behavior for web-rooming and
retail therapy. In the case of web-roomers, there is a high probability that the shoppers use VR
at the consumer journey stages of awareness, consideration or engagement but they will then
visit the physical store to buy a product, thus contributing to transition risk as highlighted by
Farah et al. (2019). Considering the case of retail therapy, potential consumers with this habit
use VR to experience VR retailing, or for the purpose of fun but there is a high probability that
they will visit the physical store to serve their habit of retail therapy.
If consumers have a habit that influences them to visit physical stores, then there is a very low
probability that they will be able to make a purchase using VR. This phenomenon can be
assumed by the research of Neal et al. (2009) and Wood & Neal (2009), where a consumer had
a habit of eating pop corns while watching a movie at cinema. As an experiment the same
individual was provided with stale popcorns and the amount of popcorns consumed by the
individual remained the same. Similarly, if consumers who have the habit of web-rooming
and/or enjoy retail therapy, there is high chance that they will contribute to the inflation or
occurrence transition risk.
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6.0 Conclusion
This chapter will summarize the thesis with an emphasis to the answer the research question
considering the research findings and discussion. Moreover, the theoretical and practical
implications from this thesis are presented to enlighten the reader. This will be followed by our
suggestions for future research.
6.1 Answering the Question
The core objective of this thesis was to identify the core concepts that influence the occurrence
of transition risk. So, first we had to develop an understanding of consumer behavior in context
with the use of technology and in this case was retailing through VR. It was not to our surprise
that retailing through VR did not appear to be used as common as the other modes of online
shopping i.e. using a web browser on a computer or mobile device and making a purchase.
Moreover, the shopper’s level of comprehension for 2D online shopping, done through
computer and mobile devices is significantly greater than that of 3D shopping in a virtual
environment.
To understand the causes of occurrence of Transition risk, first there was a need to understand
the reasons why consumers accept, opt, and use new technologies. In this pursuit, the
theoretical framework of UTAUT2 was utilized. UTAUT2 framework was adopted as the
digging instrument in this research because it is a widely used and recognized tool to investigate
the acceptance and use of technology. As it was already established that the constructs used in
UTAUT2 have a significant impact on the acceptance and use of technology. Then it was
convenient for us to look for factors that disturb and disrupt the effectiveness and existence of
these concepts while consumers use VR for retailing. Thus, the six factors that were taken into
consideration from UTAUT2 were influencers that led to the occurrence of transition risk and
were helpful to answer the research question.
While analyzing the findings of the research we came across the highly important concept of
cognitive load. Previous researches by Dholakia et al. (2010) and Kollmann et al. (2012) have
analyzed that consumers would prefer to use shopping channels they are familiar with. One of
the reasons for this, is consumers draw a higher effort expectancy from the shopping channels
they are familiar with. However, another important reason for this is to avoid cognitive load.
65
Howland (2016) supports this argument of this research by affirming that, consumers are
reluctant to use VR for retailing. One reason for such preference is that consumers do not find
ease in processing a large volume of new information and thus rely on methods of shopping
already known to them.
Moreover, there are other utilitarian purposes for which customers prefer visiting physical
stores (Hult et al., 2019). One of the utilitarian needs is the readily availability of customer
support at a physical store, which was not present in the VR retail store environment, used for
the experiment. Therefore, potential customers may look for products in the VR retail store but
would prefer to visit the physical store’s location to satisfy their need for customer service and
support. This guided the research towards developing an understanding for the causes that
directly influences the occurrence of transition risk.
An interesting fact presented by the findings was that there is an absolute absence of social
influence and social proof for consumers to decide in favor of making a purchase using VR.
This is an extremely wide gap. Digital marketers and even conventional marketers pay a lot of
attention to influencer marketing. However, in the case of VR the findings of this research
claim that none of the respondents were influenced to make a purchase using VR. Consumers
find no social proof of retailing through VR and hence, bounce back from either of the three
stages prior to the purchase stage in consumer journey through VR. The absence of the concept
of social influence contributes a high degree towards the occurrence of transition risk.
Furthermore, the research identified a few consumer habits that have a direct impact on
behavior and contribute to the occurrence of transition risk. These habits were identified as
web-rooming and retail therapy. Web-rooming is a habit that has a direct relation as a
contributor and has an impact on transition risk. Webroomers look for the products online and
then buy products at an offline store, therefore, individuals with a habit of web-rooming will
continue to contribute to the occurrence of transition risk. Moreover, consumers who enjoy
retail therapy experience VR retailing, but because of the satisfaction that they draw from
visiting a physical store with their family or friends, they would be less likely to adapt VR
shopping as it doesn’t no have the feature of shopping with others for the moment.
Hence, through sensemaking and doing some amount of reverse engineering the research
question is answered. We mention reverse engineering because the UTAUT2 framework is
66
used for determining the factors that contribute to the acceptance and use of technology.
However, this research reversed the purpose of the framework and studied the factors which
led customers not to use the technology.
6.2 Theoretical Contribution
Farah et al. (2019), in their research introduced the term transition risk but did not provide any
evidence for measuring or noticing certain habits of the respondents. Therefore, this research
is a continuation to the work of (Farah et al., 2019). Moreover, this is a contribution to
understand the concepts that can explain one aspect of why consumers bounce back after
landing on a web page. The research and outcome of this paper is not only for VR retailing but
can also be helpful for other modes of online shopping as the concepts used were fundamental
in consumer behavior.
Another theoretical contribution is the reverse usage of the UTAUT2 framework. Researchers
have used this framework to analyze the relation of UTAUT2 constructs for the use and of
technology. To the contrary, this research has used UTAUT2 framework to determine reasons
and concepts that impede technology usage.
6.3 Practical Implications of Findings
On a practical level, this research can be highly useful for businesses who either want to change
towards or are in a transition towards 3D/ VR retailing. Moreover, for those businesses who
are already operating their VR retail stores, they can benefit from this research by incorporating
changes in their layout and environment to attract and retain more customers. In case if VR
retailers are able integrate the option of group shopping within the VR retailing application, we
believe that there is a higher chance for retailers to attract and retain customers who have a
habit of retail therapy.
It is also be feasible for retailers to introduce real time customer service and support within the
VR retail application. This can enable them to retain consumers who visit physical stores only
for such utilitarian reasons. However, there is a greater need for VR retailers to understand the
underlying benefits of influencer marketing in VR, in order to influence consumers to make
purchase using VR. This can contribute a lot in reducing the expectation and transition risk
gap.
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6.4 Limitations
Over the course of this research the first limitation is imposed by the prevailing global
pandemic. Firstly, due to this situation, limitations of mobility and contact were critical in
nature as a lot of respondents prefer to avoid any contact, with person and objects that were in
contact with other people which resulted in the existing sample size (see 3.5 Sample Selection
for varied literary opinions regarding sample sizes). Secondly, the sample size of this research
is skewed as it is concentrated of mostly students at Linköping University, Campus Valla. The
mean age of the sample size is of young people and thus, does not account the responses of
people above the ages of 34. Thirdly, financial constraints have set another limitation. Due to
limited amount of time and finance to perform the thesis this study is cross-sectional and
provides insight of respondents within a specific timeframe and without actual purchase in the
experiment but rather the intent of it. This study does not address the change in respondent’s
behavior over time regarding the use of VR shopping which could have been done in
longitudinal studies. The fourth limitations concern with the generalizability of this research’s
findings. This research is done in the city of Linköping and mostly with students at Linköping
University. Many of the students are well familiar with emerging technological advancement
and therefore, the same findings cannot be applied to people having different level of education
and or technological exposure.
Delimitation of this research is its focus on immersive VR and disregarding non-immersive
VR for the purposes of the experiment. Moreover, in the sampling process the research has
considered only the students at Linköping University.
6.5 Future research
The current study is limited only to the students at Linköping University. A lot of the students
are full time students with limited funds, and so there is a lot of potential in conducting future
research with a sample group who have jobs and or additional income for expenditures and
have the leverage to spend more than students. Moreover, due to the currently prevailing
pandemic a small sample size was managed in this research. Similar future studies can be
carried on a larger sample size to find the research generalizability in any specific region or
social class. The current research was cross-sectional in nature. Future studies can be done
longitudinally by iterating the experiment and analyzing the change in the behavior of the
68
respondents regarding the concepts mentioned as contributing factors to transition risk in this
study.
This research has not accounted for any of the moderators mentioned in the UTAUT2 model.
So, future research can incorporate moderators like experience with technology to notice the
change in the occurrence of transition risk.
69
Appendix I: Original UTAUT2 Model
70
Appendix II: Construct Specification and Items Description
Variable Label Indicator
Performance
Expectancy
PE 1 I find Virtual Reality (VR) useful as a tool to think about convenient
shopping.
PE2 Shopping through VR increases my chances of purchasing things online.
PE3 Using VR helps me to complete my shopping more quickly.
Effort
Expectancy
EE1 Interaction with VR shopping is very clear and highly understandable to
me.
EE2 I did not find any stress while shopping through VR.
EE3 It is very easy for me to become quickly skillful to shop using VR.
EE4 It is easy to find the exact product while shopping through VR.
Facilitating
Conditions
FC1 It is easy to find a VR device to shop using VR.
FC2 Checking out after shopping through VR was a convenient process.
FC3 Products on the virtual shelf looked real and therefore it was easy to choose
a product.
FC4 I can easily seek help from friends while shopping through VR.
FC5 Consumer support was easy to avail of while shopping through VR.
Social
Influence
SI1 People who are important to me think that I should do my shopping using
VR.
SI2 People who influence my behavior think that I should shop through VR.
SI3 People whose opinions that I value prefer that I shop through VR.
Hedonic
Motivations
HM1 Navigating through the shopping mall was fun and pleasing for me.
HM2 I am pleased to find a sufficient description of the products while shopping
through VR.
HM3 Shopping experience through VR got me lightened and relaxed.
Habit HB1 I never look for a product online to buy that from a physical store.
HB2 I go to shopping malls as a regular practice.
HB3 I never visit retail stores for fun and to socialize.
Transition
Risk
TR1 I do not intend to purchase through VR if these are standard grocery items.
TR2 I will purchase expensive and customized items while shopping through
VR.
71
Appendix III: Theoretical Model
72
Appendix IV: Path Coefficient
Appendix V: Construct Reliability & Validity
73
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