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
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 ShoppingAuthors
Keariam Gebremichael and Saadul Islam KhanSupervisor
Andrea FriedDate
May 25th, 2020Background
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
ii
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.
iii
“If you change the way you look at things, the things you look at change.”
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
v 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
vi
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………... 24Tab. 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
viii
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 HabitUTAUT 2 Unified Theory of Acceptance & Use of Technology
SEM Structural Equational Modeling
1
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.
2
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
5
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.
7 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.
8
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 E-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
11
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.
24
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
