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AUGMENTING THE REALITY
Can AR Technology Entice Consumer
Engagement? A Quantitative Study
André Hellgren, Simon von Pongracz
Department of Business Administration
International Business Program & Civilekonomprogrammet
Degree Project, 30 Credits, Spring 2019
Supervisor: Tatbeeq Raza Ullah
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Acknowledgements
Before initiating the thesis, we would like to take the opportunity to thank a couple of
individuals who made this work possible. First and foremost, we would like to thank our
supervisor Tatbeeq Raza Ullah for his tireless support, patience and useful insights. His
time and effort have made a substantial impact on this work and for that we are very
grateful. Furthermore, we would like to take the opportunity to thank Tommy Eriksson at
Handla.io. Without Tommy and the ideas we gained from our meetings this thesis would
not be. As such, a very warm thank you and the best of luck in the future! It will be
interesting to follow the journey of Handla and the development of the AR industry.
There are others to thank as well, where our family and friends have been greatly helpful
and supportive throughout this process. Through ups and downs, these are the ones who
have always been there! Moreover, we owe a big thank you to the respondents of our
thesis which made it possible to draw conclusions and contribute with implications.
Finally, we would like to thank each other for this journey and what has been a
challenging and fun project.
A very big and warm thank you!
Umeå, 2019-05-24
_________________________ _________________________ André Hellgren Simon von Pongracz
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Abstract
Today, advances in the technological sector spurs invention toward new heights. What
can be achieved today was just decades ago science fiction. Recent years, augmented
reality has emerged which is explained by Kipper & Rampolla (2012, p. 1) as a
technology that combines the real world with virtual objects which creates a supplement
to reality. With its ability to strengthen the impressions of reality by weaving the physical
and the digital world together, enables it to be used in various settings.
The retail industry has been struggling as of late, with e-commerce flourishing on one
hand but contrastingly classic brick-and-mortar stores foreclosing by the thousands. Thus,
a technology that has the ability to combine these two channels would thus act as a
mitigating force enabling customers to virtually try on their clothes or make furniture
digitally appear in their living room. There are numerous possibilities with this
technology, given that it can be used in different industries as well with examples from
the marketing and gaming industries as the most prominent. What is evident is its ability
to interact and engage, making it a usable tool for many activities. Thus, through this
thesis we study if augmented reality can affect consumer engagement, and if so which
attributes of it has significant positive relationships with the dimensions of consumer
engagement.
In this thesis, we first provide a framework in which to measure augmented reality in
general settings quantitatively, through the use of attributes. These attributes consist of;
Interactivity, Playfulness (Escapism & Enjoyment), Service Excellence, Aesthetics, Ease
of Use and Perceived Usefulness. We then hypothesize the attributes relationship with
two dimensions of consumer engagement identified by Hollebeek et al. (2014); Affection
and Cognitive Processing. However, Ease of Use and Service Excellence were not tested
in this thesis, as a result of unsatisfactory loadings in the factor analysis.
Through an online survey, 79 useful responses were collected and used in testing the
hypotheses. Significant positive relationships were found for all tested attributes and
Affection, and further significant positive relationships were found between Aesthetics
and Perceived Usefulness with Cognitive Processing.
It is our belief that this thesis further develops and solidify the current work with
consumer engagement quantitively by validating the use of a known framework. Further,
it adds to the literature by adopting a general definition of the concept of consumer
engagement. This thesis also adds to quantitative work with augmented reality by creating
and using a framework in which to study the attributes of augmented reality in a general
setting, which has not been done previously. For practitioners, this thesis provides insight
into which attributes of augmented reality systems should be emphasized in order to
maximize consumer engagement.
The thesis ends in suggestions for future research, where we call upon further testing on
consumer engagement across different contexts with the use of Hollebeek et al.’s (2014)
framework. Such work could lead to a universally accepted quantitative scale for
measuring consumer engagement. Lastly, adopting the framework for augmented reality
presented in this thesis and applying it to further contexts could yield valuable results,
and further tests on Ease of Use and Service Excellence to validate their importance for
consumer engagement would be of utmost interest.
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Table of Contents 1. Introduction ................................................................................................................ 1
1.1 Problem Background .............................................................................................. 2
1.1.1 Augmented Reality (AR) ................................................................................. 3
1.1.2 Consumer Engagement .................................................................................... 5
1.2 Research Problem ................................................................................................... 6
1.3 Research Question .................................................................................................. 6
1.4 Research Purpose .................................................................................................... 6
2. Theoretical Framework ............................................................................................. 8
2.1 Technology Acceptance Model .............................................................................. 8
2.2 The Hype Cycle ...................................................................................................... 8
2.3 Literature Review of Consumer Engagement ...................................................... 10
2.4 ENTANGLE Framework ..................................................................................... 12
2.5 Augmented Reality Attributes .............................................................................. 14
2.5.1 Interactivity .................................................................................................... 16
2.5.2 Playfulness ..................................................................................................... 17
2.5.3 Service Excellence ......................................................................................... 17
2.5.4 Aesthetics ...................................................................................................... 18
2.5.5 Ease of Use .................................................................................................... 18
2.5.6 Perceived Usefulness ..................................................................................... 19
3. Conceptual Framework ........................................................................................... 20
3.1 Augmented Reality and Consumer Engagement .................................................. 20
3.2 Interactivity and Consumer Engagement ............................................................. 21
3.3 Playfulness and Consumer Engagement............................................................... 22
3.4 Service Excellence and Consumer Engagement .................................................. 23
3.5 Aesthetics and Consumer Engagement ................................................................ 24
3.6 Ease of Use and Consumer Engagement .............................................................. 25
3.7 Perceived Usefulness and Consumer Engagement ............................................... 25
4. Methodology .............................................................................................................. 28
4.1 Scientific Methodology ........................................................................................ 28
4.1.1 Pre-understanding .......................................................................................... 28
4.1.2 Research Approach ........................................................................................ 29
4.1.3 Research Philosophy ..................................................................................... 30
4.1.4 Literature Review .......................................................................................... 31
4.1.5 Source Criticism ............................................................................................ 32
4.2 Practical Methodology .......................................................................................... 33
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4.2.1 Ethical Issues ................................................................................................. 33
4.2.2 Population and Population Sample ................................................................ 34
4.2.3 Measurement ................................................................................................. 36
4.2.4 Online Survey ................................................................................................ 37
4.2.5 Survey Questions ........................................................................................... 38
4.2.6 Routing .......................................................................................................... 38
4.2.7 Pretesting Survey ........................................................................................... 39
4.3 Quantitative Data Analysis ................................................................................... 40
4.3.1 Factor Analysis .............................................................................................. 41
4.3.2 Cronbach’s Alpha .......................................................................................... 42
4.3.3 Composite Reliability .................................................................................... 42
4.3.4 AVE ............................................................................................................... 42
4.3.5 Regression Analysis ...................................................................................... 43
4.4 Quality Criteria ..................................................................................................... 45
5. Results ........................................................................................................................ 47
5.1 Survey Completion Rate ....................................................................................... 47
5.2 Demographic Results ............................................................................................ 47
5.2.1 Setting ............................................................................................................ 47
5.2.2 Regularity ...................................................................................................... 48
5.2.3 Age ................................................................................................................ 48
5.2.4 Gender ........................................................................................................... 49
5.2 Factor Analysis, Cronbach’s Alpha, Composite Reliability, AVE and Descriptive
Statistics ...................................................................................................................... 49
5.3 Discriminant Validity Assessment ....................................................................... 52
5.4 Single Linear Regression Results ......................................................................... 53
5.5 Multiple Linear Regression Results ..................................................................... 56
6. Analysis and Discussion ........................................................................................... 58
6.1 Discussion and Analytical Points of Departure .................................................... 58
6.2 Consumer Engagement ......................................................................................... 59
6.3 Control Variables .................................................................................................. 60
6.4 Analysis of Hypotheses - Single Linear Regression ............................................ 62
6.4.1 Interactivity .................................................................................................... 63
6.4.2 Playfulness Escapism and Playfulness Enjoyment ........................................ 63
6.4.3 Aesthetics ...................................................................................................... 64
6.4.4 Perceived Usefulness ..................................................................................... 65
6.4.5 Service Excellence and Ease of Use .............................................................. 66
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6.5 Analysis - Multiple Linear Regression ................................................................. 67
7. Conclusion and Recommendations ......................................................................... 69
7.1 Conclusion ............................................................................................................ 69
7.2 Theoretical Implications ....................................................................................... 70
7.3 Practical implications ........................................................................................... 70
7.4 Societal implications ............................................................................................ 71
7.5 Limitations ............................................................................................................ 71
7.6 Future research ..................................................................................................... 72
Sources: ......................................................................................................................... 75
Appendix 1. Factor Analysis Results in SPSS ............................................................ 86
Appendix 2. Constructs and Items .............................................................................. 87
Appendix 3. Online Survey .......................................................................................... 90
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1. Introduction This chapter introduces the reader to the background and information showing the thesis
points of departure and relevance. Both practical and theoretical relevance is discussed.
A limited overview of previous research and descriptions of concepts is provided,
followed by an exposition of the research purpose, research problem and research
question.
The world is becoming increasingly digital, with many advances recent years spurring
the evolvement to new heights. Some are calling this the fourth industrial revolution, as
the pace of current digital breakthroughs has no predecessors and because of the way
these breakthroughs mould and shape management, governance and entire systems of
production (Schwab, 2016). The technological advances have increased the customers’
expectations and shoppers today are increasingly preferring to use multiple channels
when shopping (Blázquez, 2014, p. 100). In a climate where retailers experience greater
competition and where it is a struggle to gain sustainable competitive advantages,
interactive digital technologies are thus becoming a crucial tool for retailers to sharpen
their edge of competitiveness. The retailing industry is facing many obstacles and in
Sweden 5000 stores has been forced to shut down recent years, partly due to the
increasing growth of online channels (Svensk Handel, 2018, p. 3). However, the majority
of the sales is still predicted to be conducted in the physical sphere, but e-commerce is
growing rapidly and is where the greatest future potential lies (Svensk Handel, 2018, p.
3). Thus, giving rise to huge opportunities for companies willing participate in the
development and seizing the opportunities that will arise by combining the physical with
the virtual world.
According to a survey-based study conducted by Fitzgerald et al. (2013, p. 2) a majority
of the participating managers believed that digital transformation would be an essential
part of their strategies in the subsequent years. However, nearly as many stated that the
pace of their development was going too slow (Fitzgerald et al., 2013, p. 2). Delving
further into that, Sender (2011, cited in Blázquez, 2014, p. 97) writes about how sluggish
the fashion industry was compared to others to fully adopt e-commerce, with one possible
reason explained to be the discrepancy in translating the physical environment to the
digital. Thus, highlighting the importance of making the shift to online channels a key
competitive advantage for any retailer. Balasubramanian et al. (2005) examines how
different factors, such as social interaction and experiential impacts, influence channel
choice and how consumers may use different channels across their decision-making
journey. Moreover, Blázquez (2014, p. 111) reports that online shopping is shaping the
future path of retail but emphasizes that online and offline channels should be seen as a
complement to one another rather than competing entities. It is further stated that retailers
must think of the multichannel experience holistically, the same way the consumer do,
where the journey begins before entering the store and continues after leaving it
(Blázquez, 2014, p. 111).
In line with previous paragraphs, the authors van Doorn et al. (2010, p. 253) highlights
the need for firms in today's business setting to look beyond customer repurchase
behaviour alone to sustain and nurture their customer base. The authors state that
consumer engagement behaviours, defined as “customer’s behavioural manifestations
that have a brand or firm focus, beyond purchase, resulting from motivational drivers”,
does just that - go beyond repurchase behaviour to create retention (van Doorn et al. 2010,
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p. 253, 254). Using technologies that entices these behaviours would thus be vital for
firms relying on e-commerce.
A relatively new technology, augmented reality (AR), weaves the physical and virtual
spectrums together (Huang & Liu, 2014, p. 82; Javornik, 2016, p. 252; Scholz & Smith,
2016, p. 150) and, depending on its design, is mainly hedonic or utilitarian (Javornik,
2016, p. 258, 259). It could therefore be used to help consumers in their shopping
experiences and retailers to gain an edge over its competition. Furthermore, it is a
technology which companies are increasingly investing in and in the coming years AR is
expected to get investments around $105 billion (Retail Perceptions, 2016). There is
however very limited empirical research on the matter of AR and its relation to consumer
engagement and experience (e.g. Poushneh & Vasquez-Parraga, 2017), creating a need to
further explore the area. In the problem background, these concepts will be further
explained and problematized, ending in area of research for our thesis.
1.1 Problem Background
When looking at European shopping patterns, 68% of internet users is shopping online
and as of 2018, Sweden was ranked second among the most advanced digital economies
in the EU (European Commission, 2019). Furthermore, 36% of global shoppers use their
smartphones to compare prices while in physical stores (Statista, 2019a). However, with
many customers increasingly using e-commerce and their phones when shopping, many
are still cautious about the online shopping experience with uncertainty about receiving
the goods and privacy and security concerns the major obstacles (European Commission,
2019). Moreover, according to the Yankee Group (cited in Mahoney, 2001) a common
reason why online users do not actually make a purchase when researching products is
the fact that the online channel prevents them from judging the quality of the good. Hence,
we believe that AR technologies such as virtual try-on and the interactivity that follows
has the possibility to fill that gap and provide great value to the online shopping
experience.
Childers et al. (2001) states the motivations for participating in an interactive online
shopping experience is of both utilitarian and hedonic nature. For the latter one,
enjoyment is central, and its attributes has more focus on aesthetics and design whereas
the former has focus on efficiency and functional attributes (Falk et al., 2010). Thus,
developers wanting to attract online consumers need to consider different layouts
depending on what type of experience they want to create (Ashraf et al., 2016, p. 71).
Furthermore, utilitarian aspects of a shopping experiences emphasize buying products in
a timely manner with as little exasperation as possible (Childers et al., 2001, p. 513).
Consequently, hedonic shoppers would naturally focus more on the enjoyment and the
playful aspects. Childers et al. (2001, p. 528) suggests that interactive shopping
experiences may in general engender more enjoyment than physical experiences, which
creates a need to discover what aspects of interactive technologies, such as AR, creates
the biggest engagement among consumers. Lastly, it has been found that AR has a
significant effect on hedonic qualities (Poushneh & Vasquez-Parraga, 2017, p. 234).
Recent years, AR has been increasingly adopted by various companies and popular apps
such as Snapchat, Instagram, and Facebook’s Messenger use AR with their popular face
filters. As such, AR is a technology employed in apps which are being used extensively
every day by many people, with Snapchat for instance reported to have 190 million users
every day as by the first quarter 2019 (Statista, 2019b). Furthermore, it has the possibility
to be used in other settings as well, with Google starting to implement AR through
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navigation in their app Google Maps (Schroeder, 2019). However, the widely recognition
and implementation of AR probably started with the immense success of Pokémon GO a
couple of summers ago, which to this date is the most successful mobile game ever
(GSMArena, 2017). Arguably, this game attracted users by engaging them fully in the
activity through a combination of the physical and the digital world. Positive outcomes
of this was more social interactivity among the users, as well as health benefits through
the increased mobility (Zach & Tussyadiah, 2017). Similar to this, Burger King had a
marketing campaign where users of their AR app got the chance to burn down rivalling
companies’ billboards in exchange for a free burger (O’Brien, 2019). By implementing
AR in ways as these examples thus enables the companies to engage the users and attract
new customers. As such, we believe that AR has a bright future ahead.
Connecting to the implementation and development of AR, it is a technology that has the
ability to be translated to numerous countries simultaneously as exemplified by Pokémon
GO, Snapchat and their likes. This implies that companies investing in AR and developing
it may be able to globally expand faster than companies in other sectors. This could
further imply that newly started companies in this sector would attract more born global
companies than other industries. Investments in the AR industry has been estimated to be
$105 billion by 2020 (Retail Perceptions, 2016), which further solidifies the notion that
AR can be immense in the near future. Moreover, while yet in its initial phases, there are
more growth to be made and spaces to compete about. AR and its current state can perhaps
be better understood with the help of the Blue Ocean Strategy, where focus should be put
on creating new market space which in turn would make the competitors insignificant
(Kim & Mauborgne, 2005, p. 106). By not contemplating too much about the possible
moves of competitors, more focus can be put on the unknown market space where more
lucrative possibilities exist (Kim & Mauborgne, 2005, p. 106). This in turn can turn into
competitive advantages which can create sustainable profits.
When competing in an industry, there are numerous factors to consider. One can consider
Porter’s Five Forces Model containing threats of new entrants, threat of substitutes,
bargaining power of suppliers and buyers, and rivalry (Porter, 2008, p. 27). Connecting
to AR, given its current phase the threats are not substantial where entry barriers are low,
and AR is arguably the substituting threat. Furthermore, continuing the words of Porter,
the profitability of a firm is dependent of the industry structure which is more important
the own capabilities (Clegg et al., 2017, p. 61). Thus, meaning that the very state of the
industry is also crucial to consider and not just the resources of the firm. With AR possibly
experiencing a surge in the coming years, the shape of the industry should meet the
requirements discussed by Porter. Hence, any firm that has already developed the
resources needed could have a competitive advantage as the entry barriers will rise in the
future.
1.1.1 Augmented Reality (AR)
The increasingly digital environment that encompasses the retailing industry raises the
importance of interactivity between customers and the stores. Augmented reality (AR)
interactive technology enables customers to virtually try on clothes and visualize other
goods in their own environment (Huang & Liu, 2014, p. 82-83). Kipper & Rampolla
(2012, p. 1) describe AR as a variation of a Virtual Environment (VE), or Virtual Reality
(VR), where virtual objects are combined with the real word, creating a new experience
for the user. Unlike VR, AR supplements reality, instead of completely replacing it
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(Kipper & Rampolla, 2012, p. 1). Furthermore, AR1 can be used as a multi-sensory
experience, combining all five senses, but is most commonly only used visually (Kipper
& Rampolla, 2012, p. 1). One defining trait of AR is that it is always interactive (Kipper
& Rampolla, 2012, p. 4). Scholz & Smith (2016 p. 153) states that AR consists of five
elements; AR content, users, targets, bystanders, and background. Likewise, many
researchers measure and view AR technology through its attributes. For instance,
Poushneh & Vasquez-Parraga (2017) measure the level of augmentation by studying the
level of interactivity and Huang & Liao (2015) among others measure AR through the
user-perceived usefulness and the user-perceived ease of use. From the customer’s
perspective, this kind of technology increases the value by adding convenience, speed
and entertainment to their experience (Huang & Liao, 2015, p. 270).
Kim & Forsythe (2008a, p. 45) further emphasizes the importance of interactivity and the
involvement that virtual try-on creates and that this enhances the entertainment value.
Pantano (2009, cited in Dacko, 2016, p. 243) continues previous author’s argumentations
and extends it by explaining the added value for the retailers that stems from the ability
to affect customer engagement and purchase decisions. Moreover, it is stated that mobile
augmented reality (MAR) applications has the ability to affect the decision-making
journey of the customer (Dacko, 2016, p. 245). Kent et al. (2015, cited in Dacko, 2016,
p. 245) adds that by adding immersive experiences for the customer, the retailers will be
able to increase the customer satisfaction which can increase the sales. Thus, the
dimension of entertainment and the value it has the potential to add, solidifies the
argument that AR can provide several benefits, for retailers and customers alike.
What the previous paragraphs has highlighted is that interactive technology has numerous
benefits both for the end user and the providers. What has been highlighted, is that many
write about the entertainment value that arises for anyone engaging in these technologies.
However, Dacko (2016, p. 254) found that while users appreciated the ability the mobile
AR applications have to entertain, the increased efficiency was considered more
important. Furthermore, the benefits provided by these applications is something they
would not receive in a normal shopping experience (Dacko, 2016, p. 254). Therefore,
making these technologies vital tools for retailers in order to increase consumer
engagement as well as providing them with complementary gadgets, supporting the
overall customer journey. Whilst the added entertainment should not be ignored,
increasing the efficiency could potentially be a competitive advantage in this day and age,
where time is increasingly becoming of the essence. Delving further into that, Kim &
Forsythe (2008a, p. 46) argue that virtual try-on in the retailing industry can aid customers
by giving product information similar to the physical experience, further solidifying the
efficiency and overall value this kind of technology can add.
In terms of interactive advertising, the authors Pavlou & Stewart (2000, p. 62) writes that
the general goals are similar to traditional advertising objectives, meaning that traditional
measures may stay effective and relevant even in the world of interactive media.
Furthermore, Pavlou & Stewart (2000, p. 62) writes that interactive advertising also may
1 There are a few different ways to refer to Augmented Reality, for instance, Huang & Liu (2014) and
Huang & Liao (2015) make use of the abbreviation ARIT (Augmented Reality Interactive Technology).
Other authors only make use of the term AR (e.g., Javornik, 2016; Poushneh & Vasquez-Parraga, 2017;
Scholz & Smith, 2016). To not overcomplicate the terminology, we will from now on only make use of the
abbreviation AR (Augmented Reality) throughout our thesis, except for when referring to other author’s
work, as it is the simplest and broadest form of the term.
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provide an increase in customers satisfaction and involvement, increase the quality and
efficiency of consumer decisions, and promote trust as well as produce a greater
advertisement quality and efficiency. Delving further into the outcomes of increased
interactivity, Dacko (2016, p. 254) found that users of mobile AR applications were
overall more satisfied with their purchases, were more likely to make an actual purchase,
and visited the store more frequently.
Thus, from a retailer’s perspective there are several implications for adopting these kind
of technologies - both from economical and value-adding perspectives. For this thesis and
its purpose, perhaps one of Dacko’s (2016, p. 254) more prominent findings was that
mobile AR applications were able to alter the customer behaviour by the benefits they
provide. Furthermore, by increasing the usage of these applications simultaneously
increased the retail valuation (Dacko, 2016, p. 254). Consequently, customers who
engage in this were found to be more satisfied and increase their visits to the stores by
engaging in activities once seen solely from an e-commerce point of view - solidifying
the notion of the versatility of combining both channels. Thus, by looking beyond the
horizon of entertainment, we believe that this technology has the potential to provide
useful benefits throughout the value chain. Lastly, we have found that there are scarce
and limited amounts of quantitative studies on the subject of augmented reality overall.
Especially if counting out work on technology acceptance, creating a need for further
empirical testing on how to quantitatively measure the technology.
1.1.2 Consumer Engagement
As mentioned in the introduction, consumer engagement behaviour impacts retention and
is a result from motivational drivers through greater consumer engagement (van Doorn et
al., 2010, p. 253, 254). The motivational drivers, or expressions as consequence from
consumer engagement, impacts both financial and reputational aspects of the firm (van
Doorn et al., 2010, p. 259). Besides impacting retention, the expressions are exemplified
as impacting cross buying, sales and transaction metrics, word-of-mouth, customer
recommendations and referrals, blog and web postings, knowledge-sharing, design and
development ideas, and product testing (van Doorn et al., 2014, p. 253, 260). The authors
emphasize that customer-made suggestions may lead to increased customer satisfaction
and lower prices, thus making the firm more efficient (van Doorn et al., 2010, p. 260).
Exploring the area further, Harmeling et al. (2016, p. 322) writes that customer
engagement initiatives can affect customer engagement long term and alter the core
offering through influencing existing customers cognitive bonds or creating new ones.
The value of increased customer engagement is not only gained through a possibly higher
retention rate as recall of product information and product imagery grows, but also by
enabling the use of customer owned resources, such as networks (Harmeling et al., 2016,
p. 314, 323). These may in turn lower the costs of firm initiatives at the same time as
reach grows, i.e. lower advertising costs as consumers indirectly or directly advertise
through their networks (Harmeling et al., 2016, p. 314, 323). Moreover, Haven et al.
(2007) writes in the Forrester that the use of consumer engagement provides brands with
a more holistic appreciation of their customers actions and that value is received through
actions people take to influence others and not only from transactions.
To gain a positive effect of the customer engagement behavioural expressions, naturally,
the consumer engagement needs to be positive. Correspondingly, negative consumer
engagement results in negative behavioural expressions where, for instance, disappointed
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customers might engage proactively in negative word-of-mouth to warn other customers
(van Doorn et al., 2010, p. 258). There is therefore a need for firms to be able to entice
positive engagement to control the engagement outcomes. The authors Scholz & Smith
(2016) explores AR’s possible use for gaining consumer engagement through their
framework, implicating that AR can be a tool of great use to control and maintain positive
engagement when used correctly. This notion, together with the vast potential positive
outcomes of positive consumer engagement, leads our interest to further explore Scholz
& Smith’s (2016) work and test which AR attributes can be used to create and control
positive consumer engagement. Furthermore, Calder et al. (2009, p. 321) writes that
practitioners and academics does not agree on “engagement” and what it is. Similarly,
Brodie et al. (2013, p. 107) writes that there are relatively few authors that have defined
the concept of consumer/customer engagement. We therefore conduct a literature review
in the theoretical framework to clarify the term.
Similar to the research on AR, we have found through our literature review that there are
scarce and limited research on quantitative measurements for consumer engagement.
Although there is substantial research on the concept, the lack of quantitative and
empirical work within the subject creates a great theoretical need to further develop and
test the existing scales. Moreover, although there is great theoretical progress on the
concept of consumer engagement, it still has an overwhelming amount of different
definitions based on applied area of research. As such, it is our belief that a thorough
objective review of the concept and adoption of a general definition would theoretically
progress consumer engagement.
Given this background, we believe that by investigating the factors of AR and how these
possibly can affect consumer engagement would be of great use for practitioners and
researchers within the field. This thesis and its implications would be numerous. By
discovering what features of AR will have the greatest effect on consumer engagement
can help developers and retailers by targeting the correct features when developing this
technology. Furthermore, and on a more general level, understanding the technology and
the foundations of it can be of great use for researchers, practitioners and users alike.
1.2 Research Problem Through the problem background, in which we describe the growing digital environment
and harsher competitive climate that demands companies to be active in both the physical
and digital setting, we can identify a need for further combination of the two and
strengthened customer relationships. Therefore, we will focus our thesis on examining
and discovering how AR can be used as the tool to weave the physical and digital settings
together, through which AR attributes should be emphasized to create more value in terms
of increased consumer engagement.
1.3 Research Question
Does Augmented Reality systems create Consumer Engagement? If so, which attributes
of an Augmented Reality system affect Consumer Engagement positively?
1.4 Research Purpose The purpose of our thesis is in practical use new ways to gain competitive advantages and
possible influence over the consumer decision process, as well as finding a technical
solution for combining the physical and virtual landscape. Furthermore, it will be an
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extended work on theoretical points regarding both augmented reality and consumer
engagement to further develop and test frameworks for quantitative empirical studies in
the area. Specific attributes will be chosen and used to explain AR, and their relationships
with consumer engagement will be tested. This is to investigate which specific attributes
entice greater consumer engagement, based on active users of AR currently residing in
Sweden.
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2. Theoretical Framework In the theoretical framework, we present earlier work on the subject of augmented reality
and consumer engagement and discuss their points in regard to this study. First, we
present the Technology Acceptance Model and the Hype Cycle before initiating a through
literature review on the subject of consumer engagement. Thereafter, we introduce the
ENTANGLE framework which will be a cornerstone throughout this thesis before
eventually presenting the various AR attributes which have been chosen for this thesis.
2.1 Technology Acceptance Model Within this area of research, a common model to use is the technology acceptance model
(TAM). It was first developed by Davis (1986) and helps to explain how new technology
is accepted and understood. Huang & Liao (2015, p. 270) integrates TAM with
experiential values to predict which factors affect the usage of AR. They found that TAM
can be used to predict the most valuable benefits to maintain customer relationships with
interactive technology (Huang & Liao, 2015, p. 273). Marangunić & Granić (2014, p. 81)
further explains TAM as a tool to understand which motivational factors are influencing
user’s behaviour regarding technology. Furthermore, through their extensive literature
review, they explain that Davis first hypothesized that motivation regarding behaviour
can be explained by the three factors: perceived ease of use, perceived usefulness, and
attitude toward using the technology (Marangunić & Granić, 2014, p. 85). The latter was
later removed and substituted with intention to use which could better mediate the other
factors (Marangunić & Granić, 2014, p. 85-86).
Adding to this, Kim & Forsythe (2008b, p. 901) extends TAM by including the concept
of sensory enabling technology (SET), examining what factors can predict attitudes
toward virtual try-on, 2D views and 3D rotations. They found that all SETs either had
strong hedonic roles, functional or both, and that these technologies increased the
entertainment value or reduced the product risk relating to information delivery (Kim &
Forsythe, 2008b, p. 901-902). Thus, with SETs being able to inform customers in a way
that can compete with physical retail and simultaneously adds entertainment makes them
a viable choice. TAM is therefore a useful model and will underpin our line of thought,
given that it can help predict how new technology can be accepted. With AR being in its
initial phases and not yet widely implemented, our reasoning is that TAM will be a
suitable model of reference for our thesis and a solid foundation for our subsequent
analyses. However, we are yet to know how the future will look and what will come
about. Nevertheless, TAM is well known within this field of research and is used
extensively with similar arguments as the ones used by us.
2.2 The Hype Cycle In order to better understand a technology and how it is predicted to be implemented,
numerous tools and analytics can be used. A very useful and highly eminent tool is the
Hype Cycle (see Figure 1) developed by Gartner, which contains different stages that can
help assess the industry and determine where a technology is in its life cycle (Fenn, 1995).
Furthermore, what makes this such a good aid is that it adds other dimensions, such as
reflections of human attitudes and knowledge measurements (Linden & Fenn, 2003, p.
6). However, it is important to remember that this merely covers the initial phases of the
whole life cycle (Linden & Fenn, 2003, p. 7), thus enabling a better understanding of
possible adoption rates and maturity and further which opportunities are ready to be
exploited (Gartner, n.d.). Moreover, being able to assess the current situation and get a
9
better prediction can, for instance, help reduce risks involved regarding potential
investments.
Figure 1. The Hype Cycle (Gartner, 2018).
Looking at Figure 1, there are five stages of the Hype Cycle where the first one is called
Technology Trigger. This occurs when the technology is introduced but generally no
products exist (Linden & Fenn, 2003, p. 7). Through increased awareness and hype
(Linden & Fenn, 2003, p. 7), it is followed by Peak of Inflated Expectations where
underdeveloped prototypes fails to meet the high expectations (Fenn, 2010, cited in
Stockinger, 2016, p. 62). This leads to negative publicity before entering the Trough of
Disillusionment, where negative hype extends and interest decreases while
simultaneously, and perhaps quite contradictory, trials continue and vendors improve the
product based on the feedback they receive (Linden & Fenn, 2003, p. 7). AR is currently
residing in this phase but is on its way towards the latter stages (Gartner, 2018). The
fourth and fifth stages are characterized by greater understanding and true acceptance
(Linden & Fenn, 2003, p. 8) and as the benefits are better understood, more companies
start to offer the technology and further develops it (Fenn & Raskino, 2008; Fenn 2010,
cited in Stockinger, 2016, p. 62). As it advances through the fifth stage and becomes more
implemented, the relating products and services evolves too meaning that the market
advances (Linden & Fenn, 2003, p. 8).
With AR being a relatively new technology, naturally the early adopters are the most
prominent users. In its current phase, these are the first ones to understand the benefits of
the technology and the first to adopt it (Linden & Fenn, 2003, p. 8). Furthermore, it has
been found that the people most aware of AR are individuals between 16 and 44 (Buckle,
2018). As such, by putting emphasis on this market segment could prove fruitful given
that these are more likely to fully grasp the benefits and usefulness of AR at this stage.
We further believe that the Hype Cycle can be very useful in order to understand the
industry of a technology. By understanding that AR is not yet widely accepted, knowing
how the coming stages usually occurs can become useful when planning for the future. It
will also be very fruitful for this thesis and its research question by adding an explanation
as to why certain technologies are accepted or not. By having this framework as a core
foundation will make it easier to comprehend the possible relationships between the AR
attributes and consumer engagement and further also how it is forecasted to evolve. This
is especially important given the infancy of AR and thus crucial when entering the latter
stages of its evolution. Moreover, by combining it with the insights gained from TAM, it
10
could potentially further increase the knowledge by understanding acceptance rates and
how trends generally occur and progress.
2.3 Literature Review of Consumer Engagement As stated in the problem background, there is a need to clarify the term of consumer
engagement (Brodie et al., 2013, p. 107; Calder et al., 2009, p. 321). In an effort to create
a general definition of consumer engagement, the authors Brodie et al. (2011, p. 260)
define it as “Customer engagement is a psychological state that occurs by virtue of
interactive, cocreative customer experiences with a focal agent/object (e.g., a brand) in
focal service relationships. [...] It is a multidimensional concept subject to a context-
and/or stakeholder-specific expression of relevant cognitive, emotional and/or
behavioural dimensions.”. The authors Chaffey & Ellis-Chadwick (2016, p. 308) describe
it as repeated interactions that strengthen the emotional, psychological or physical
investment a customer has in a brand and refers to a brand's long-term abilities of gaining
a customer’s attention regularly. Abdul-Ghani et al. (2011, p. 1061) refers to engagement
as “consumer commitment to an active relationship with a specific market offering.”
Vivek et al. (2012) defines consumer engagement as “the intensity of an individual’s
participation in and connection with an organization’s offerings and/ or organizational
activities, which either the customer or the organization initiate.” Hollebeek (2011, p.
740) make use of the term “customer brand engagement” (CBE)2, a term derived from
consumer engagement, and defines it as “the level of an individual customer’s
motivational, brand-related and context-dependent state of mind characterised by specific
levels of cognitive, emotional and behavioural activity in direct brand interactions.”. To
create theory on customer engagement marketing, Harmeling et al. (2016, p. 317) defines
it as” a firm’s deliberate effort to motivate, empower, and measure customer contributions
to marketing functions.” Hollebeek et al. (2014, p. 152) states that the concepts revolving
around customer engagement and brand engagement, though employing different concept
designations (names) may reflect a highly similar conceptual scope. Likewise, they are
viewed as the same concept in this study, however designated towards different areas.
Meaning that for instance consumer brand engagement and consumer engagement is
defined through the same conceptual scope.
As we can clearly see, there are numerous similar but different definitions used for the
concept consumer engagement. Thus, we have chosen to adopt the more general
definition of consumer engagement by Brodie et al. (2011, p. 260) to add to the literature
and move towards a universal definition of the concept.
“Customer engagement (CE) is a psychological state that occurs by virtue of
interactive, cocreative customer experiences with a focal agent/object (e.g., a brand) in
focal service relationships. [...] It is a multidimensional concept subject to a context-
and/or stakeholder-specific expression of relevant cognitive, emotional and/or
behavioral dimensions.” (Brodie et al., 2011. p. 260).
In the previous section, we clarify the term consumer engagement and adopt Brodie et
al.’s (2011, p. 260) definition of it. However, there is also a need to clarify the consumer
engagement construct and its use for our specific study, especially since there has been
limited previous quantitative empirical studies regarding consumer engagement. We have
identified this as a potential gap to the field and combined with the other constructs added
2 In this paper, we will refer to this simply as consumer engagement.
11
to this thesis, we believe that we can extend the topic consumer engagement and its usage.
Adding to the theoretical background encompassing this study, this construct can further
be added to, and explained by, different models, frameworks and theories which will be
clarified as we progress.
The authors Calder et al. (2009, p. 322, 325) explains online consumer engagement as a
scale consisting of eight different user experiences variables that together defines the level
of the consumer engagement construct. The variables are limited by being based on
experiences that provide indicators of engagement, rather than the actual engagement
(Calder et al., 2009, p. 324). However, likewise, the ENTANGLE framework by Scholz
& Smith (2016, p. 157) (see section 2.4) argues that experiences is a key variable in
creating consumer engagement within AR. Calder et al. (2009, p. 329, 330) report that
their variables both show favourable validity and reliability and that the experiences show
to examine both personal and social-interactive engagement, but calls for further testing.
Even so, we believe that the scale is limited to studies of explicitly online consumer
engagement. Hence, it was taken into consideration but will not be used, as our purpose
is to study a more holistic engagement; the bridge between the digital and physical
spectrum.
Investigating the area further, the authors Algesheimer et al. (2005) studied community
engagement through investigating brand communities in European car clubs. Like
previous authors, the engagement construct is limited, this time to consumer connection
with a specific media and its community (Algesheimer et al., 2005). Moreover, a
quantitative empirical investigation conducted by Rather (2018) on consumer
engagement and its relationship with customer loyalty, satisfaction, trust and
commitment, finds empirical evidence that consumer engagement has positive influence
on all factors. Here, ENTANGLE (see section 2.4) provides usable insights once more by
connecting it to commitment since enticing consumers by developing able relationships
with them could strengthen their commitment.
Moving further, Rather’s (2018) study was conducted on the hotel industry, using
consumer engagement as a unidimensional construct. We believe that this can be applied
to a retail-setting as well even though it is somewhat different to the hospitality industry,
as hotels, like retailers, are in the market of a business’s selling services and/or products
to consumers (B2C), enabling the use of the construct for a retail-setting study. A
unidimensional construct would be easy to use but could be lacking in terms of describing
the depth, as other authors have used multiple variables to describe consumer engagement
as a construct or scale (e.g. Calder et al., 2009; Hollebeek et al., 2014).
Hollebeek et al. (2014) conceptualize, scale, develop and validate constructs and variables
for consumer engagement in social media. The authors mention that further research
across multiple contexts and brands is required to further validate the construct
(Hollebeek et al., 2014, p. 161). The scale is based of research in a social-media context
(Hollebeek et al., 2014). There are many things that relate social media to interactive
technologies such as AR, for instance social media can be seen as an interactive process.
Moreover, the authors Hollebeek et al. (2014) use involvement as an antecedent to
consumer engagement. As such, although we understand the neighbouring dimensions of
these two constructs, they are not the same and consumer engagement is somewhat an
extension of involvement. The latter is described in the author’s context as “an
individual’s level of interest and personal relevance in relation to a focal object/decision
12
in terms of one’s basic values, goals and self-concept” (Mittal 1995; Zaichkowsky, 1985,
1994, cited by Hollebeek et al., 2014, p. 163). We believe that this “involvement”
described by a social media process can be generalized to the involvement in using any
interactive technology such as AR. This belief will be tested by adopting the models and
constructs used by Hollebeek et al. (2014) and applying them to an AR setting.
The construct consists of three dimensions, two attitudinal (cognitive processing,
affection) and one behavioural (activation) (Hollebeek et al., 2014, p. 160). Cognitive
processing can be described as a factor measuring how well a brand gets the user to think
about said brand, and not just the user-process (Hollebeek et al., 2014, p. 156). The
authors define it as “a consumer's level of brand-related thought processing and
elaboration in a particular consumer/brand interaction” (Hollebeek et al., 2014, p. 154).
The second attitudinal dimension describing the consumer engagement construct is
affection and is described as “a consumer's degree of positive brand-related affect in a
particular consumer/brand interaction” or the emotional dimension of consumer
engagement (Hollebeek et al., 2014, p. 154) Affection is like the dimensional name
suggests - a user's affection to the brand through using its product or service (Hollebeek
et al., 2014, p. 157). The behavioural dimension - activation, describes how well a user is
invested in the brand over other similar brands (Hollebeek et al., 2014, p. 157). The
authors have created two models describing consumer engagement, their differences,
usability and which model we will adopt is described and argued for below.
The first model describes involvement as a direct antecedent to all dimensions of
consumer engagement and is found useful in a context where there are many users and
possible data providers (Hollebeek et al., 2014). The second model see involvement as
direct antecedent to the attitudinal factors (processing, affection), and the attitudinal
factors as drivers for the behavioural factor (activation) (Hollebeek et al., 2014, p. 160).
The authors found significant value for both models but found their first model to be a
better fit to their data (Hollebeek et al., 2014, p. 160). Based on our limitations in our data
collection, lack of resources and limited respondents, we believe that their second model
will be of better use in our context and will therefore be adopted. Furthermore, the third
construct of consumer engagement, activation, was built upon three items that did not fit
our study given our research setting. Given how their questions were asked, that construct
would better suit a survey where a known brand was tested and compared to other brands. As such, we have chosen to exclude activation from our study given our usage of
Hollebeek et al.’s (2014, p. 160) second model where our attitudinal factors are drivers
for activation. Furthermore, given that we are only studying attitudinal factors in this
research, the behavioural factor from said authors can be deemed redundant. The specific
items and scales adopted can be viewed in Appendix 2.
2.4 ENTANGLE Framework The authors Scholz & Smith (2016, p. 150) have created a framework for designing
immersive experiences that maximize consumer engagement within the AR spectrum
based on analysis of over 50 AR marketing initiatives. Thus, meaning that with the help
of the authors we can more easily identify what factors of AR that can possibly have
greater effect on consumer engagement. The framework consists of eight steps and is
summed up with the acronym ENTANGLE (Scholz & Smith N., 2016, p. 157). However,
there is no known empirical testing on the framework, creating a need for further research
in the area.
13
• Experiences; the authors emphasize the need for AR initiatives to be driven by
consumer experience and not technology driven (Scholz & Smith, 2016, p. 157).
This is particularly important as technology driven initiatives stands the risk of
damaging brand image, waste resources and compromise future AR initiatives
through failing to connect to consumers and thus appearing gimmicky (Scholz &
Smith, 2016, p. 157). Validating this notion, Poushneh & Vasquez-Parraga (2017)
researched how user experience, satisfaction, and willingness to buy is affected in
an AR setting. Findings reveal that AR significantly affects retail UX positively,
and that AR enriched user experience results in higher user satisfaction and
willingness to buy (Poushneh & Vasquez-Parraga, 2017, p. 233).
• Nourishing engagement; Through greater interactivity, which can nourish the
user-brand engagement, companies should focus their resources on creating better
consumer engagement rather than creating flashy and expensive marketing
initiatives (Scholz & Smith, 2016, p. 158). To increase user-user engagement,
brands can enable content sharing outside of or within the augmented experience
(Scholz & Smith, 2016, p. 158).
• Target audiences; the authors emphasize the need for correct segmentation when
it comes to targeting the consumers, as those consumers can help create content
and diffuse the AR technologies (Scholz & Smith, 2016, p. 158). Further, correct
targeting can help the spread through positive word-of-mouth and should also be
designed in such a way that bystanders wants to participate (Scholz & Smith,
2016, p. 158).
• Aligning AR with marketing program; integrating the AR initiatives with the
marketing program can maximize the potential of both, as AR can provide
uniqueness to marketing communications and AR could need a push from
advertisement to encourage consumer use (Scholz & Smith, 2016, p. 158). Thus,
potentially making their relationship mutually beneficial.
• Neutralizing threats; With the possibilities of augmented reality, the authors also
describe threats. Examples of these can be the physical background not fitting
with the AR initiative, or how campaigns can be subverted by activists and
competitors. (Scholz & Smith, 2016, p. 159). This can be exemplified by Burger
King who encouraged their customers to virtually burn rivalling companies’
billboards for a free meal (O’Brien, 2019). Threats is not something we will
research in our thesis, even so, it could be influential on our results depending on
respondents’ previous experiences with AR. This is therefore something that
needs to be researched in future work regarding AR.
• Goals; As there are several ways to host an AR initiative, it is important to design
it in line with the companies’ objectives, for instance, high interactivity and
consumer freedom when trying to support a brand community, and public location
setting when reaching for greater awareness (Scholz & Smith, 2016, p. 159).
• Leveraging brand meanings; In line with integrating the marketing campaigns,
the AR initiative has to be consistent with the desired brand image (Scholz &
Smith, 2016, p. 159). Going outside this spectrum could potentially hurt the
14
company, causing confusion among the workers with the goals being diluted and
with the customers not recognizing them.
• Enticing consumers; By providing artefacts, meaning user actions within AR that
are observable to non-participating bystanders, the possibility to develop social
relationships and other positive qualities is enabled by the AR initiative (Scholz
& Smith, 2016, p. 159). Further, marketers should aim to entice the consumer to
try and re-visit the initiative, especially when consumers hold the trigger decision
to try it (Scholz & Smith, 2016, p. 159, 160).
Overall, the framework is somewhat straightforward. However, it provides valuable input
when it comes to structuring and planning an AR initiative and is highly relevant for our
study as it serves as an indicator for which AR characteristics will attract greater
consumer engagement. Especially since it is the only study to our knowledge connecting
AR with consumer engagement in an elaborate fashion. Therefore, the ENTANGLE
framework is deemed vital in the progression of this thesis, as it is the main scientific
work pointing us in the direction of if and how AR can affect consumer engagement. For
instance, the framework points towards that AR initiatives should be focused on the
consumer and brand as central, indicating that AR characteristics emphasizing consumer
satisfaction, ease of use, interactivity and brand recognition may be of most importance.
2.5 Augmented Reality Attributes Huang & Liu (2014) contributes to the field by investigating what factors yield the highest
experiential values in AR and found that from their chosen factors, narrative experience
had the highest values. Narrative experiences are said to consist of both chronology and
causality, meaning that the consumer experience develops over time and, in an AR-
setting, that each event is inter-correlated (Huang & Liu, 2014, p. 86, 87). Their findings
suggest that when designing an AR interactive technology, using a narrative perspective
and integrating more factors with it, creates more experiential value which can facilitate
a persuasive experience for the user (Huang & Liu, 2014, p. 102).
Narrative experiences thus shape the experiential value and helps design the AR
technology. Furthermore, narrative experiences had significant relationships with all their
dependent variables (Huang & Liu, 2014, p. 99). However, in our thesis we will use some
of their dependent variables connecting to AR, as our independent variables. The
reasoning for this is that our purpose is to explore the variables of AR and their relation
to consumer engagement, and not to explore why a strength of a variable is in place.
Moreover, we have decided to name these variables AR attributes, or characteristics of
AR, as they describe or define AR in some way.
In another study, Huang & Liao (2015) integrates TAM with experiential values and
investigates what factors can maintain the usage toward AR. They found that two of the
TAM constructs, perceived usefulness and ease of use, as well as service excellence,
aesthetics and playfulness could help maintain AR usage and affect adoption rates (Huang
& Liao, 2015, p. 287). Furthermore, they argue that by comprehending the value-adding
benefits of AR could aid retailers in designing technologies which can create consumer
engagement (Huang & Liao, 2015, p. 287). This goes in line with the thoughts of this
thesis, as we will investigate whether AR can help engage consumers by using AR as an
independent variable and testing its significance towards consumer engagement, the
dependent variable. However, the latter will not be derived from the construct “presence”,
15
which has been used by former studies (see Huang & Liao, 2015; Huang & Liu 2014).
TAM is, as previously stated, a tool used to explain why a new technology is adopted or
not. In our thesis we will explore a context where intention to use already is fulfilled,
given that our respondents will already have used AR, or use it for the sake of the study.
We will therefore exclude that construct. However, we believe that perceived usefulness
and ease of use are very helpful constructs for our thesis and can help predict AR usage,
which we in turn will hypothesize can affect consumer engagement. Thus, two of the
constructs from TAM will be used and TAM can be viewed as an antecedent to our thesis.
Continuing the matter, Huang & Liao (2015, p. 270) suggests that AR should be
considered a technology with persuasive effects, which goes beyond functional benefits
and can deliver experiential values. Explaining the concept of experiential values further,
Mathwick et al. (2001, p. 41) suggests that there are four dimensions of experiential
values; consumer return on investment, service excellence, aesthetics, and playfulness.
Huang & Liao (2015, p. 287) found that the latter three could positively affect the
adoption rates of AR and further be of utmost importance when maintaining the usage. In
our thesis, all these three will be connected to the construct of AR and help explain it.
Delving further into the matter and connecting to the retail industry, mobile AR
applications has been found to add experiential value by providing particular benefits
(Dacko et al., 2016, p. 254). Thus, AR can provide many benefits and add value on several
dimensions throughout the process.
Investigating the area even further, Javornik (2016) has created a research agenda for
studying the impact of AR and its media characteristics on consumer behaviour. In the
article, Javornik (2016) mentions that there is a need for further research on many topics
in the field. For instance, how different modalities in AR can yield different consumer
responses and how some of them are better suited for different goals. We will touch on
this by testing which attributes has most influence on consumer engagement.
Furthermore, the author asks if interactivity is more hedonic in consumer experience with
AR, or if it is made for utilitarian purposes (Javornik, 2016a p. 258). In this study, both
hedonic and utilitarian attributes will be added but not clearly defined. By addings these,
it is our belief that it can be discovered which attributes are most important when
developing the technology further.
The following sections, we will posit several attributes describing AR. These attributes
will not be technological, e.g. “the displayed figures are green”, but rather driven by
perceived user experiences, e.g. “I enjoy the colour display”. This is solidified by the
authors Scholz & Smith (2016, p. 157) as they argue that AR initiatives seeking to affect
consumer engagement should be driven by user experience rather than technology driven,
enabling us to measure the attributes effectiveness in reaching greater consumer
engagement. Furthermore, we believe that the chosen attributes can help to explain the
construct of AR used in our model, regardless of potential impact on consumer
engagement, which is the foundation of our thesis. By investigating these attributes in
detail, we want to add more knowledge before beginning our hypotheses section. The
attributes we have chosen are what we believe key features of AR and how users
apprehend the AR experience and are not exclusively of technological nature. Thus,
having a greater understanding of them can add a comprehension of what composes AR
and what the users perceive as the important variables.
16
2.5.1 Interactivity
Steuer (1992, p. 84) defines interactivity in communication media as “the extent to which
users can participate in modifying the form and context of a mediated environment in real
time”. At the most general level, feedback via sales reflects interactivity. Therefore,
interactivity can be seen as a characteristic of the consumer, as much as a characteristic
of the medium, since consumers can choose to respond or not. Likewise, AR is always
interactive (Kipper & Rampolla, 2012, p. 4). One way of interpreting interactivity is by
focusing on the presentation of different combinations of modalities, where new digital
breakthroughs makes it possible to combine more than ever through interactivity (Shin et
al., 2014, p. 1136).
Delving further into that, and highly interesting for this thesis, Sundar et al. (2011, p.
1479) found that variations in interactive modalities can affect user engagement through
the user experience. Song & Zinkhan (2008, p. 100) explains that most studies that
investigates the level of interactivity defines it “as the presence or absence of particular
features”. Thus, more features could be related to higher interactivity. However, Sundar
et al. (2011, p. 1482) conclude in their study that it is crucial to think about which
modalities that are added to interface and that the level of consumer engagement cannot
be measured simply by the quantity of interactions. Thus, the type of interaction and how
it is perceived is most important (Sundar et al., 2011, p. 1482).
Connecting to the previous paragraph, Song & Zinkhan (2008, p. 109) present the
interactivity theory and states that it views the quality of a message between the user and
a site as an antecedent of interactivity and their findings solidified that notion. It is
therefore important to equally consider the quantity and the quality of interaction
modalities and how to interact with customers. Findings like these can help answer
questions regarding how to best interact with customers and how to use it as a competitive
advantage. The convenience with classic retail regarding interaction and instant feedback
can thus get challenged through new technology and offer the users viable options, or at
the utopian visionary level - a way to combine the best features of both channels and
maximize the user experience.
Explaining interactivity further, Shin et al. (2014, p. 1136) adds that it moves the focus
on the receiving part when communicating from a broad and passive audience to the
active user, who selectively can choose to interact and what information they want to
receive. Contrary to previous studies which we have mentioned, they conclude their
findings that different interaction modalities do not differ that much for users (Shin et al.,
2014, p. 1151). However, given that they only include swiping, tapping, and similar
embodied interactions (Shin et al., 2014), raises the question what a highly interactive
technology such as AR can add to the topic. AR adds another dimension and thus expands
the horizon of what is possible regarding interaction between parties.
Furthermore, Shin et al. (2014, p. 1151) also found that user-engaged interaction can
enhance perceived interactivity and calls for further research to be done by looking at
different aspects of interaction techniques. We believe that AR can fill this gap and that
our research question therefore is highly relevant. Continuing, and connecting with one
of the factors of TAM, Pantano et al. (2017, p. 91) found in their research that interactivity
is one of the antecedents for Perceived Ease of Use. Given that AR is highly interactive
raises the question whether the ease of use could further increase the customer
engagement, which we will later hypothesize. They extend their findings by summarizing
17
that by focusing on technology characteristics can enhance the overall understanding of
AR in a retail setting (Pantano et al., 2017, p. 91).
Delving further into the matter, Huang & Liao (2017) provide some insights with their
research studying how AR can induce a higher state of flow. Furthermore, Huang & Liu
(2014) found that interactive websites engendered more positive attitudes and that users
remained in flow more easily, thus more involved. Flow is explained as the mental state
people reach when their productivity is at its highest and a deep level of satisfaction
emerges, i.e. “being in the zone” (Leadership & Flow, n.d.). This notion, we believe,
solidifies that AR can be seen as a highly interactive technology and has the ability to get
consumers more engaged. Adding to the ENTANGLE framework, managers should be
able to increase the consumer engagement through immersive experiences (Scholz &
Smith, 2016).
Lastly, the authors Poushneh & Vasquez-Parraga (2017, p. 230) use the variable
interactivity to reflect level of AR, which also has been identified by Javornik (2016).
Similarly, we will make use of interactivity as an attribute for describing the level of
augmented reality in this thesis.
2.5.2 Playfulness
Another attribute of AR that we have chosen is playfulness, which has been explained as
consisting of escapism and enjoyment by Mathwick et al. (2001, p. 43). The former is
explained by Huizinga (1955, cited by Mathwick et al., 2001, p. 44) as the ability to “get
away from it all” and the latter as the intrinsic enjoyment felt when engaging in activities
that has the ability to absorb the user (Mathwick et al., 2001, p. 44). Thus, these two are
quite similar and both consists of elements where the user gets so involved that they
become absorbed. Similar to the other attributes, playfulness is not of technological
nature. Rather, it explains a feeling following the usage of it. Mathwick (2001, p. 44)
explains that any activity that enable free engagement has some degree of playfulness in
it. Moreover, it is distinguished from aesthetic appeal by the active participation and the
exchange that proceeds (Mathwick et al., 2001, p. 44). Huang & Liu (2014, p. 83)
exemplifies IKEA’s work with interactive technologies, where users are able to virtually
fit furniture and other items from their catalogue into the simulation on their smartphones
and states that such technology enhances playfulness and convenience. Since then, the
technology has been refined with the app IKEA place where users conveniently can place
the chosen products virtually in their homes (IKEA, 2019). Furthermore, interactive
technologies have been found to create playful experiences when studying users of such
technologies in e-commerce settings, e.g. virtual try-on (Huang & Liao, 2017, p. 454).
Lastly, Huang & Liao (2017, p. 463) uses playfulness as one of the dimensions explaining
flow, which AR was said to generate. Therefore, we will make use of the construct
playfulness as one measurable characteristic for AR.
2.5.3 Service Excellence
Service excellence is explained by Huang & Liu (2014, p. 85) as reflection to providing
anticipated service and required information to consumers. Adding to this, Zeithaml
(1988, cited in Huang & Liu 2014, p. 85) states that the service providers ability to deliver
on its promise and the consumer appreciation is reflected in service excellence. This
means that service excellence as a quantitative attribute of AR measures how well the
technology provides the user with anticipated service and how the anticipated user
experience is perceived. Like playfulness, service excellence, is part of the dimensions
18
explaining flow (Huang & Liao, 2017, p. 463). Moreover, Ohlsson et al. (2013, p. 301)
found that the user’s willingness to share information is dependent on the perceived value
gained from the features requiring the information. As AR technologies many times are
information dependent, e.g. location specific and user device dependent (Scholz & Smith,
2016, p. 151), service excellence is a necessity for the general AR setting.
Moreover, service excellence creates reactive experiential value through an interactive
experience (Mathwick et al., 2001, p. 43, 48). Reactive experiential value is delivered to
the consumers by providing appreciation, visual attractions and visual sensory stimulation
(Huang & Liu, 2014, p. 85). Thus, an AR initiative involving high levels of SE could
result in greater experiential value, as it is an interactive experience. Thus, based on how
service excellence defines AR, we will make use of it as a quantitative attribute of AR in
this study.
2.5.4 Aesthetics
Another of our attributes within AR is aesthetics, which has been used extensively in
several studies more directly intertwined with AR (e.g. Huang & Liao, 2015; Huang &
Liu, 2014; Pantano et al., 2017; Poushneh & Vasquez-Parraga, 2017), but also at the more
general level in order to investigate its effect on the retail experience outside the physical
spectrum (Mathwick et al., 2002). The findings from Pantano et al. (2017, p. 91) suggest
that retailers should see the interactive element of AR as an enjoyable experience, where
aesthetic quality and interactivity is considered the most crucial variables to create an
overall positive participation. Huang & Liao (2015, p. 290) make up their construct of
aesthetics with three items, namely attractive display, liking the visual image, and
entertainment. Moreover, Huang & Liu (2014, p. 108) uses similar items for the same
construct with visual appeal and entertainment. The author Schmitt (1999, p. 61) states
that marketing through aesthetics may be used to motivate customers and add value to
products by differentiating companies and products. Lastly, aesthetics is said to be one of
the dimensions of interactive technologies and can enhance the realism of the experience
(Pantano et al., 2017, p. 85). These notions further validate the usefulness of aesthetics as
a quantitative attribute of AR. Thus, we will use aesthetics as another attribute defining
AR.
2.5.5 Ease of Use
As the name suggest, ease of use refers to "the degree to which a person believes that
using a particular system would be free of effort." (Davis, 1989, p. 320). The author Davis
(1989, p. 320) further claims that an application is more likely to be accepted by users if
its perceived to be easier to use than another, and all else is equal. As previously
mentioned, ease of use can affect adoption rates and helps maintain usage of AR (Huang
& Liao, 2015, p. 287), solidifying the claim by Davis (1989, p. 320). It is added that ease
of use is one of two most critical factors in encouraging consumers to using interactive
technology like AR (Huang & Liao, 2015, p. 273).
The authors Huang & Liao (2015, p. 287) further identifies that consumers have a stronger
preference for technology that are easy to use when they have a low level of cognitive
innovativeness. This notion does not mean that other consumers find ease of use
irrelevant, only that consumers with low cognitive innovativeness finds it more important.
However, it does speak of the ability of AR technology to add convenience to the
shopping experience. By enabling users to effortlessly switch among different websites,
e-commerce is already adding that factor (Ashraf et al., 2016, p. 69). Thus, ease of use as
19
an attribute of AR is not only necessary to entice and maintain usage from consumers,
but also to gain or retain competitiveness. Therefore, ease of use is adopted as one
defining quantitative attribute of AR.
In a study conducted on a similar technology, Lee & Chung (2008, p. 95) found that users
of a VR shopping mall, on average perceived it more convenient than an ordinary mall.
Furthermore, Bigham (2005, cited in Bulearca & Tamarjan, 2010, p. 242) found that
convenience was a core construct in increasing a purchase consideration. Thus, with ease
of use and convenience being neighbouring constructs, solidifies the argument for
including ease of use in our model given that we want to investigate if it can influence
consumer engagement.
Furthermore, Poushneh & Vasquez-Parraga (2017, p. 230) proves that the user experience
is reflected by, amongst others, an AR’s pragmatic quality, meaning utility and usability,
which ease of use also measure in some ways. Therefore, ease of use can be connected to
the ENTANGLE framework created by Scholz & Smith (2016, p. 157) as ease of use is
consumer experience driven over technology driven.
2.5.6 Perceived Usefulness
Connecting to AR, perceived usefulness is another dimension used in TAM (Davis, 1986;
Huang & Liao, 2015; Marangunić & Granić, 2014, p. 85), and refers to "the degree to
which a person believes that using a particular system would enhance his or her job
performance." (Davis, 1989, p. 320). Huang & Liao (2015, p. 273) further characterize
perceived usefulness as “how an individual think about the probability of improving
performance on tasks through use of a given technology.” Moreover, Davis (1989, p. 320)
adds that an existing positive use-performance relationship is believed to be in place by
the user when a system is high in perceived usefulness.
Like ease of use, perceived usefulness is identified to be the other most critical factor in
encouraging consumers to using interactive technology like AR (Huang & Liao, 2015, p.
273). Venkatesh & Davis (2000, p. 187) continues the linkages between the two
constructs by adding that the easier a system is to use simultaneously makes it more
useful, meaning that ease of use is influencing perceived usefulness. Furthermore, Davis
(1989) found that both perceived ease of use and perceived usefulness are determinants
of intention to use and user acceptance toward interactive technology. Therefore,
perceived usefulness not only measures how an AR system adds value to tasks but is also
critical in making users adopt AR, naturally making it one of the user-defined attributes
of AR.
Clearly, for anyone to accept a new technology, the usefulness needs to be adequately
distinguished. Similar to ease of use, perceived usefulness explores parts of an AR’s
pragmatic quality that is linked to increased user experience (Poushneh & Vasquez-
Parraga, 2017, p. 230). As such, perceived usefulness is directly linked to the
ENTANGLE framework (Scholz & Smith, 2016, p. 157) in the same way as ease of use,
described above. For all AR attributes, the specific items and scales adopted can be
viewed in Appendix 2.
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3. Conceptual Framework In this chapter the conceptual framework and the formulated hypotheses are presented.
Firstly, the conceptual framework, its antecedents and consequences are described.
Further two general hypotheses are stated and argued for. Lastly, individual hypotheses
for each attribute of augmented reality, derived from the general hypotheses, are
presented and argued for.
Figure 2. Conceptual Framework.
The use of “CBE” or Consumer Brand Engagement in the conceptual framework was
retained to stay true to the original work of Hollebeek et al. (2014, p. 160). The
relationship within CBE and CBE consequences are already proven to be significant and
will therefore not be examined (dotted line square) (Hollebeek et al., 2014, p. 160). The
relationship of AR attributes with cognitive processing and affection has not (full line
square), and their hypothesized relationship will be argued for and explained in coming
section.
3.1 Augmented Reality and Consumer Engagement As stated in the problem background, greater positive customer engagement can be
expressed through numerous positively impactful ways such as; retention, cross buying,
sales and transaction metrics, word-of-mouth, customer recommendations and referrals,
and others (van Doorn et al., 2014, p. 253, 260). As Scholz & Smith (2016, p. 157) suggest
by studying over 50 AR marketing initiatives, there is a relationship between AR and
consumer engagement, and AR initiatives can help facilitate this greater consumer
engagement through being designed in line with their ENTANGLE framework. As we
explain below in the individual attribute hypotheses arguments, the attributes
Interactivity, Playfulness, Service Excellence, Aesthetics, Ease of Use and Perceived
Usefulness, which we are using to measure AR, can all be connected to their framework
and should thus have a positive relationship with consumer engagement.
Furthermore, Hollebeek et al. (2014, p. 154) state that consumer engagement can be
measured by three dimensions consisting of cognitive processing, affection and
activation. “Involvement” was identified by Hollebeek et al. (2014, p. 157, 160) as
antecedent to the construct. AR reflects involvement in all characteristics as direct
consumer involvement is necessary to use and interact with it. This notion strengthens
our belief that all the identified AR attributes has a positive relationship with consumer
engagement and will thus be of great use for our thesis and its proposed research question.
Similarly, the authors Brodie et al. (2011, p. 259) state that the complexity of consumer
engagement is a result of its interactive and experiential nature, which is contained in AR.
21
Moreover, with AR being on its way towards the latter stages of the Hype Cycle (see
Figure 1), we believe that this will imply that peoples’ perceptions of its advantages will
increase in the coming years as it moves along the cycle. As mentioned, detailed
arguments on relations with each attribute will be presented individually. In line with the
conceptual framework and as an introduction to the upcoming hypotheses-model we
formulate the following comprehensive hypotheses:
H1: There is a positive relationship between AR Attributes and Cognitive Processing.
H2: There is a positive relationship between AR Attributes and Affection.
3.2 Interactivity and Consumer Engagement For technologies like AR, interactivity is ever present since users of it need to be involved
in order to engage. For instance, interactivity has been described as one of the most crucial
aspects for creating modern computer graphics, including AR (Chen et al., 2011, p. 164).
Moreover, both hedonic and utilitarian motives are said to underlie interactive technology
participation within an online shopping setting (Childers et al., 2001). Connecting to
aspects of both our dependent and independent variables, Brodie et al. (2011, p. 253)
suggests that theory based on interactivity and value co-creation can help explain the
conceptual roots of consumer engagement. Furthermore, it is stated that the concept of
consumer engagement is reflected by the users interactive and co-creative experiences
(Brodie et al., 2011, p. 264). Thus, by adding interactivity to our AR construct should
provide a solid foundation and add value when trying to prove the relationship between
the two constructs of AR and consumer engagement.
At the very general level, Hollebeek et al. (2014) found that involvement was an
antecedent to their construct of consumer engagement. Even though interactivity is
distinct from involvement (Steuer, 1992, p. 84), when interacting, a level of involvement
will always be present and these can thus be seen as neighbouring constructs. With that
said, we believe that interactivity also can demonstrate a relationship with consumer
engagement. van Noort et al. (2012, p. 229) among others, found that interactivity leads
to affective responses from the users and that cognitive responses increased with higher
levels of interactivity (van Noort et al., 2012, p. 229). In both cases, each variable was
mediated through flow (van Noort et al., 2012, p. 231). As been previously stated, AR
has been found to increase the state of flow and prolong its effect (Huang & Liao, 2017;
Huang & Liu, 2014).
Thus, there are many proven relationships between interactivity and consumer
engagement. Furthermore, it has been found that interactivity has increased both
cognitive and affective responses through higher flow (van Noort et al., 2012).
Accordingly, this thesis uses both cognitive processing and affection to explain the level
of consumer engagement. As a continuation on previous works we will investigate the
relationship between these constructs. With interactivity being one defining characteristic
of AR, we believe it is crucial to include in order to answer our research question and
22
conclude what characteristics are deemed most important in line with our purpose. With
this in mind, we posit the following hypotheses:
• H1a: There is a positive relationship between AR Interactivity and Cognitive
Processing.
• H2a: There is a positive relationship between AR interactivity and Affection.
3.3 Playfulness and Consumer Engagement Huang & Liao (2015, p. 287) found that, among other constructs, playfulness can affect
both the adoption rates of AR and maintain the usage. Likewise, the author Hakan (2011,
p. 397) find that playfulness in online shopping reduces the user perception of complexity,
and thus, such a setting would be more easily adopted. It was also found that as
interactivity increases, further increasing playfulness can affect the buying intention of
consumers (Huang & Liao, 2015, p. 287). Moreover, the authors discuss that by
understanding the benefits of playfulness allows retailers to design the best technologies
to engage the consumers (Huang & Liao, 2015, p. 287). It is therefore our belief that AR
technologies in general, and the construct playfulness in particular, can increase consumer
engagement. In this study measured by cognitive processing and affection.
Investigating playfulness further, it is explained to be enhanced when users have the
possibility to share personalized experiences on social networks (Huang & Liu, 2014).
Thus, greater playfulness is not only in line with the ENTANGLE framework through its
user experience emphasis (Scholz & Smith, 2016, p. 160). It also entices revisits and new
entries from consumers in line with the Enticing Consumers heading, resulting in greater
consumer engagement (Scholz & Smith, 2016, p. 160). Playfulness is also useful when
connected to the Hype Cycle, which adds dimensions connected to human attitudes
(Linden & Fenn, 2003, p. 6). By reflecting on the attitudes towards technology,
playfulness as an attribute of AR should be able to better explain how consumer
engagement is enticed.
Furthermore, playfulness, consisting of escapism and enjoyment, creates active
experiential value through an interactive experience (Mathwick et al., 2001, p. 43, 48).
Thus, an AR initiative involving high levels of playfulness could result in greater
experiential value. Harmeling et al. (2016, p. 313) find that long term consumer
engagement can be a result of both task-based and experiential-based initiatives.
Moreover, the authors state that firms can enhance the core offering or drive pleasurable
experiences outside the core transaction through experiential engagement initiatives
(Harmeling et al., 2016, p. 313). Further, they found a significant indirect relationship
between experiential engagement and consumer engagement (Harmeling et al., 2016, p.
313). As higher level of playfulness can create greater experiential value, it would mean
23
that the AR attribute playfulness also could create greater consumer engagement, in line
with Harmeling et al.’s (2016) work. Thus, the following hypotheses are formulated:
• H1b: There is a positive relationship between AR Playfulness and Cognitive
Processing.
• H2b: There is a positive relationship between AR Playfulness and Affection.
3.4 Service Excellence and Consumer Engagement As mentioned earlier, service excellence measures the consumer appreciation and how
well the service provider deliver on his or her promise (Huang & Liao, 2014, p. 85). It is
said that service excellence creates reactive experiential value through an interactive
experience (Mathwick et al., 2001, p. 43, 48). Reactive experiential value is delivered to
the consumers by providing appreciation, visual attractions and visual sensory stimulation
(Huang & Liu, 2014, p. 85). Thus, an AR initiative involving high levels of service
excellence could result in greater experiential value, as it is an interactive experience.
Thus, in a general setting in line with Harmeling et al.’s (2016) work, SE should positively
affect consumer engagement.
Moreover, the authors Padma & Wagenseil (2018, p. 432) makes several propositions on
antecedents and consequences of retail SE through extensive literature reviews. For
instance, the authors suggest that customer engagement, amongst others, is an antecedent
to service excellence as a stronger bond with the customer would enable co-creation,
innovation and enable predicting future anticipated customer needs, resulting in a better
service excellence design (Padma & Wagenseil, 2018, p. 427). Moreover, the authors
proposes customer commitment and brand love, amongst others, as consequences of
service excellence, where customer commitment “indicates the emotional bonding
between the retailer and customer, which is beyond the realms of loyalty and simple
repurchase intentions” (Padma & Wagenseil, 2018, p. 429), and brand love “is a blend of
intimacy and passion for a brand” (Carroll & Ahuvia, 2006, cited in Padma & Wagenseil,
2018, p. 429).
We feel the need to address the authors’ proposition that consumer engagement is an
antecedent to service excellence as we are studying a mirrored relationship. This could
be explained by, as mentioned in the problem background, the lack of a consistent
definition of consumer engagement in the marketing literature (Brodie et al., 2013, p. 107;
Calder et al., 2009, p. 321). This notion becomes evident here as we see brand love and
customer commitment as parts of consumer engagement and not individual constructs.
This suggests that service excellence and consumer engagement could have a
looped/circular relationship, where greater service excellence results in greater consumer
engagement and greater consumer engagement enables better service excellence.
24
However, how interesting this reciprocal relationship may be, this notion will not be
studied in this thesis but could be interesting for future work in the area.
Continuing the matter, brand love can be seen as a direct synonym for brand affection,
thus AR service excellence should have a positive relationship with the consumer
engagement dimension affection. Likewise, customer commitment is very similar to the
consumer engagement dimension cognitive processing as they both explain customers
recurring thoughts about a brand (Hollebeek et al., 2014, p. 154; Padma & Wagenseil,
2018, p. 429). Therefore, the following hypotheses are formulated:
• H1c: There is a positive relationship between AR Service Excellence and
Cognitive Processing.
• H2c: There is a positive relationship between AR Service Excellence and
Affection.
3.5 Aesthetics and Consumer Engagement As been mentioned earlier, AR based on narrative experiences was found to create
reactive experiential values and aesthetics is a reactive value (Huang & Liu, 2014).
Furthermore, Poushneh & Vasquez-Parraga, (2017, p. 231, 233) use aesthetics as one
characteristic of AR and found that there was a positive significant value between it and
user experience. As stated by Scholz & Smith (2016, p. 159), user experience driven AR
initiatives result in higher consumer engagement. Furthermore, aesthetic quality together
with interactivity is considered the most crucial variables to create an overall positive
participation (Pantano et al., 2017, p. 91). Without positive participation, there is no
positive involvement.
As such, aesthetics should at the lowest level enable greater consumer engagement, but
most likely also positively influence both cognitive processing and affection. Moreover,
with the Hype Cycle being able to measure knowledge and reflect attitudes towards a
technology (Linden & Fenn, 2003, p. 6), we strongly believe that aesthetic attributes of
AR are crucial to consider and therefore vital for our research. Therefore, we formulate
the following hypotheses:
• H1d: There is a positive relationship between AR Aesthetics and Cognitive
Processing.
• H2d: There is a positive relationship between AR Aesthetics and Affection.
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3.6 Ease of Use and Consumer Engagement As mentioned, ease of use has been proven to greatly affect intention to use and
maintained usage (Huang & Liao, p. 273, 287). Moreover, applications with greater ease
of use will be adopted over others with less in similar contexts (Davis, 1989, p. 320).
These notions indicate that ease of use, especially its comparative element, creates a
cognitive bond with the user and makes them think about the brand they are using - in a
positive or negative manner depending on the level of ease of use. Therefore, we believe
that ease of use has a strong positive relationship with the consumer engagement
dimension cognitive processing. The comparative element of ease of use would also be
able to impact the affection dimension, as it describes the consumer’s degree of positive
affect and in particular the consumer brand-interaction (Hollebeek et al., 2014, p. 154).
AR with great ease of use would as such be compared to other brand interactions
positively, resulting in greater affection. Therefore, we believe ease of use also has a
positive relationship with the consumer engagement dimension affection as well.
Furthermore, Poushneh & Vasquez-Parraga (2017, p. 230) proves that the user experience
is reflected by, amongst others, an AR’s pragmatic quality, meaning utility and usability,
which ease of use also measure in some ways. Therefore, ease of use can also be
connected to the ENTANGLE framework created by Scholz & Smith (2016, p. 157) as
ease of use is consumer experience driven over technology driven. Thus, we formulate
the following hypotheses:
• H1e: There is a positive relationship between AR Ease of Use and Cognitive
Processing.
• H2e: There is a positive relationship between AR Ease of Use and Affection.
3.7 Perceived Usefulness and Consumer Engagement With perceived usefulness being a neighbouring attribute to ease of use, and ease of use
sometimes even being viewed as an antecedent to perceived usefulness (Davis, 1989),
naturally, it should create similar effects. As mentioned in the attribute section; perceived
usefulness is identified to be the other most critical factor in encouraging consumers to
26
using interactive technology like AR (Huang & Liao, 2015, p. 273). In other words, it is
a determinant to intention to use and user acceptance towards interactive technology
(Davis, 1989). Thus, we believe that greater perceived usefulness engenders the user think
about the brand more positively, depending on the level of perceived usefulness. As such,
it has a positive relationship with the consumer engagement dimension cognitive
processing.
Furthermore, we believe that perceived usefulness generates affection through its
comparative element. As stated by Huang & Liao (2015, p. 273) perceived usefulness
measures how well a given technology improves performance on tasks based on an
individual's perceptions. Thus, an AR with greater perceived usefulness would create
affection through making tasks easier, and through being superior to other options.
Furthermore, the latter stages of the Hype Cycle are characterized by acceptance and a
greater understanding of the usages of the technology (Linden & Fenn, 2003, p. 8). Thus,
with AR moving towards these stages we believe that perceived usefulness will be able
to create consumer engagement.
Similar to ease of use, perceived usefulness explores parts of an AR’s pragmatic quality
that is linked to increased user experience (Poushneh & Vasquez-Parraga, 2017, p. 230).
Thus, the attribute is consumer experience driven rather than technology driven, which is
also in line with the ENTANGLE framework and the arguments made by the authors
(Scholz & Smith, 2016, p. 157). Moreover, as has been stressed throughout this chapter,
the Hype Cycle adds a reflective dimension towards technology attitudes (Linden & Fenn,
2003, p. 6). As such, by implementing this will shine additional light on this concept and
help explain attitudes towards certain attributes of AR and further help us understand how
playfulness can create consumer engagement. As a result, we formulate the following
hypotheses:
• H1f: There is a positive relationship between AR Perceived Usefulness and
Cognitive Processing.
• H2f: There is a positive relationship between AR Perceived Usefulness and
Affection.
27
Figure 3. Aggregated Hypotheses.
28
4. Methodology In this chapter the scientific and practical methodology is presented. The chapter is
introduced with the pre-understanding followed by the research approach and research
philosophy, in which we present the scientific foundations for our research. Further
information regarding our literature review and source criticism is presented.
Afterwards, the practical methodology is presented, introduced with discussion regarding
the ethical issues regarding our type of research. The chapter is ended with information
regarding our foundation for data collection and analysis.
4.1 Scientific Methodology
4.1.1 Pre-understanding In order to receive insights of the background of this thesis, it is useful to acknowledge
the authors’ pre-understanding and previous experiences. These will naturally affect the
direction of the study given the general way our previous experiences tend to shape future
outcomes. As Marzano (2004) explains, background knowledge is one of the most crucial
factors when learning new about new information. Consequently, both our academic and
professional careers influence our knowledge and thus the study and how we interpret its
findings. Therefore, these will be further explained in more detail in this section.
Both authors are studying the 8th and final semester of Civilekonomprogrammet at Umeå
university, where André is specialized towards business development and
internationalization and Simon marketing. Although different areas, the two fields are
somewhat overlapping and both were initiated with a strategic course, where our gained
insights will somewhat help us during the forthcoming chapters of this thesis. Moreover,
much like the authors’ mixed areas of expertise, this thesis is to some extents a combined
work with features of both entrepreneurship and marketing.
Regarding professional careers, both authors have been working in various sectors before
and during our academic careers which gives us a broader understanding of different areas
outside of university. Our previous experiences did somewhat shape our understanding
of AR. However, and more importantly, after our thorough literature review and
meticulous research, the insights gained helped us think about how AR could be used and
help the industries we have previously worked in. Thus, our pre-understanding has also
worked the other way around where ideas and knowledge from work and this thesis has
been cross-fertilizing each other.
To gain further insights how this thesis came about and how our previous knowledge and
contacts helped shape it, the very origins of it need to be carefully explained. When
studying the last module of business development and internationalization, André came
into contact with Tommy Eriksson, working at a local company called Handla.io trying
to combine the physical and digital world through the usage of AR. Throughout the
module André and fellow students were consulting Tommy and came up with different
solutions for their business model. This collaboration was the spark of this thesis and how
the initial idea of it came about. With Simon entering the picture, the authors set up a
meeting with Tommy where we discussed different approaches to this thesis and where
Tommy explained what areas were of particular interest of Handla. After a thorough
literature review, the authors found their gap and an area that simultaneously were of
great interest for Handla.
29
4.1.2 Research Approach
In order to fully grasp how this thesis came about and how the purpose and hypotheses
was developed, the reasoning underpinning them needs to be fully comprehensive.
Regarding how the theory was developed, two different approaches were considered. On
a basic level, the research either moves from the general to the particular or the other way
around; from the specific to the general (Collis & Hussey, 2014, p. 7). The former is
called deductive research, where a theoretical structure is developed and later tested by
the empirical observations we make (Collis & Hussey, 2014, p. 7). In research following
this reasoning, the rule and the explanation are considered premises from which the
observation is derived (Mantere & Ketokivi, 2013, p. 71).
Continuing on the aforementioned movement of a research, in the latter case the research
is inductive where observations of empirical reality guides the development of theory
(Collis & Hussey, 2014, p. 7). Ketokivi & Mantere (2010, p. 316) further states that
inductive research always generalizes from data. For this thesis, we argue that deductive
research is more fitting given that we want to make specific conclusions made from
general observations. Moreover, the very structure of our theories is being tested through
the observations made from our survey, which is exactly how Collis & Hussey (2014, p.
7) explains the implementation of such research. Furthermore, our hypotheses are
grounded on previous research and existing models. Consequently, inductive research
induces general inferences from particular instances (Collis & Hussey, 2014, p. 7),
meaning that it does not fit the execution of this research.
Regarding other approaches considered, at the very general level this thesis will try to
apply characteristics from descriptive research. This means that we will try to describe a
certain phenomenon as it exists and gather information from our given problem (Collis
& Hussey, 2014, p. 4). Kelley et al. (2003, p. 261) explains descriptive research as a type
of enquiry aiming to observe and collect information about a certain phenomenon. Collis
& Hussey (2014, p. 4) further explains that this goes beyond exploratory research where
conclusions more often merely are suggestions for future research. The aim of this thesis
is to answer the questions relating to the problems we have identified and to be more
conclusive with our observations, therefore making descriptive research more fitting.
However, parts of an exploratory research design are also contained in our work and
future suggestions will naturally be given. The purpose of an exploratory study is to
clarify the understanding of a problem and to assess a phenomenon in new light through,
for instance, a literature review (Saunders et al., 2000, p. 97). Collis & Hussey (2014, p.
4) further states that exploratory research often is conducted when little or no previous
research exists. This is somewhat applicable for this thesis given that the chosen variables
and their relationship has previously, to our knowledge, had limited amount of research -
if any. However, exploratory research is more commonly associated with qualitative
surveys (Kwadwo Antwi & Hamza, 2015, p. 220), emphasizing our logic of using
exploratory sparsely.
Moreover, given that we study relationships between AR attributes and consumer
engagement, makes the study somewhat explanatory, as we try to explain the
relationships between different variables (Saunders et al., 2000, p. 98). Explanatory
research has been described by Collis & Hussey (2014, p. 5) as an extension of descriptive
research by further trying to explain how the phenomenon being analysed is happening.
30
The aforementioned reasons suit this thesis adequately given our purpose and our quest
of establishing relationships between AR characteristics and consumer engagement and
understanding which are most important. Collis & Hussey (2014, p. 8) further explains
that a research can have different methods given its process and can thus be described in
numerous ways. With these arguments in mind, this thesis will have its core in deductive
research but use elements of other research types as well, making it somewhat a
combinatorial work with characteristics from several scientific methods.
4.1.3 Research Philosophy
When writing a thesis, it is important to consider different approaches. Ontology becomes
relevant when discussing issues regarding the nature of reality and is concerned with the
very essence of social entities (Bryman, 2012, p. 32; Collis & Hussey, 2014, p. 47).
Bryman (2012, p. 32) differentiates the two major ontological approaches on whether
social entities are constructed through the perceptions of the interacting social actors, or
if they are objective in the sense that they have a reality regardless of external actors. The
latter one, objectivism, suggests that social reality is an external phenomenon beyond the
actor’s influence (Bryman, 2012, p. 32). Contrary to this assumption, constructivism lies
on the other side of the spectra with emphasis on the social actors which are shaping social
reality through social interaction (Bryman, 2012, p. 33).
Given our thesis, we have identified objectivism as the most fitting approach regarding
ontology. Constructivism and its focus on social interaction as the main construct in social
reality (Bryman 2012, p. 32), albeit intriguing, will not be as suitable for our purpose.
Although interaction will be central and of great importance as one of the attributes of
our independent variable regarding AR, the focus on interactivity is between the
consumer and the provider of the technology itself - not necessarily between two social
actors. With arguments to be made that this could be of importance as well, objectivism
is argued to be more adequate for this thesis.
As previously mentioned, Bryman (2012, p. 32) explains objectivism as a setting where
social reality exists independently of the social actors interacting within it. In later works,
it is further explained as a phenomenon consisting of facts beyond our reach which cannot
be affected (Bryman, 2018, p. 58). Connecting to this thesis, this fits our purpose and the
nature of our research. Furthermore, the ontological assumptions of positivists are
explained by Collis & Hussey (2014, p. 47) that social reality is objective and external to
the researcher, which further strengthens our incentives for our chosen ontological stance.
Positivism and our line of thought will be further delved into in the subsequent section
regarding epistemology.
Connecting back to objectivism, this research will use material for constructing our
theories that are existing independently from the social reality and use answers from
social actors independent from the external environment they are residing in. Thus, the
availability of these regardless of the social reality further strengthens the choice of
objectivism as our ontological stance. Although the virtual and augmented reality in
which the users will reside when using the technology needed for answering our survey,
we argue that this is different from the social reality in which they exist and therefore
justifies our chosen ontology.
Depending on the chosen approaches for the thesis, various methods can be used.
However, some methods are better suited for different kinds of approaches and it is
31
important to use the appropriate one for the purpose of the thesis (Collis & Hussey, 2014,
p. 2). After considering the ontology, the paradigm of which our thesis is guided through
was considered (Collis & Hussey, p. 43). The major ones are positivism and
interpretivism, and depending on which, different philosophical assumptions can be made
(Collis & Hussey, 2014, p. 46). Given our purpose and the methods we have chosen, we
have identified the approach we believe are best fitting for this thesis.
Epistemology involves questions regarding what is accepted as valid knowledge (Collis
& Hussey, 2014, p. 47). This in turn poses other questions, which Collis & Hussey (2014,
p. 47) begins with the very relationship between the researcher and what is being
researched, or what is known as explained by Kwadwo Antwi & Hamza (2015, p. 219).
The latter authors further explain that positivists view social reality as something
“measurable using properties which are independent of the researcher and instruments”
and conclude that “knowledge is objective and quantifiable” (Kwadwo Antwi & Hamza,
2015, p. 218). As a positivist, it is important to preserve an independent viewpoint and
throughout the research remain unbiased (Collis & Hussey, 2014, p. 47).
With the aforementioned notions in mind, having a positivist epistemological stance
naturally aligns with a quantitative survey given that the knowledge obtained will be
measured and quantified. On the other side of the spectra, interpretivists aim to evaluate
theories and use a qualitative methodology (Kwadwo Antwi & Hamza, 2015, p. 219,
220), with no intention of statistically analysing the data (Collis & Hussey, 2014, p. 52).
Contrary to this, our thesis aims to generate theories from our hypotheses and propose
conclusions derived from our observations. Lastly, positivist tries to explain certain
behaviours through data (Kwadwo Antwi & Hamza, 2015, p. 219) which is exactly our
aim - measuring which variables entices the highest amount of consumer engagement.
4.1.4 Literature Review
During the course of this thesis, extensive literature search and reviews have been
conducted which is vital for any type of research project (Boote & Beile, 2005, p. 3; Booth
et al., 2012, p. 1; Machi & McEvoy, 2009, p. 7). The general purpose of the literature
review is to gain more knowledge or skills in the area of research (Machi & McEvoy,
2009, p. 1). Therefore, an advanced literature review has been applied in this study in
order to gain comprehensive knowledge about the field. A literature review is described
as selecting a research interest and research topic and then reviewing the literature,
leading to a research thesis (Machi & McEvoy, 2009, p. 3). Further research is then
proposed, resulting in the research project which ultimately determines the research
findings and conclusions (Machi & McEvoy, 2009, p. 3).
In this study, several research fields have been combined with research in technology
acceptance, technology readiness, relationship marketing and customer behaviour all
been very vital. Of utmost importance and most extensively researched has been the
literature review regarding consumer engagement and augmented reality in any setting.
The literature in respective field have been found using the Umeå University database,
that grants access to a great number of scientific articles through other scientific
databases. Examples of these other databases are Emerald Journals, EBSCO, Springer
Journals, JSTOR and ScienceDirect (Elsevier). In specific instances, Google Scholar has
been used when no access to full text, online or printed, could be gained through Umeå
University’s database. Google Scholar was also used to ensure no relevant research in the
32
field of augmented reality had been missed in the literature search using the Umeå
University database.
To find the relevant literature keywords were identified, used and combined. As the
literature was found, it was categorized and saved in Google Drive for easy access. First
and foremost, umbrella style keywords that had been carefully considered such as
Augmented Reality, Mobile Augmented Reality, Virtual try-on, Consumer/Customer
Engagement, Engagement, Technology Acceptance and Consumer/Customer Experience
were used to gain an overview and background of the existing literature. Further into the
search references in already found scientific articles was specifically targeted. Before a
clear purpose had been defined within the thesis, keywords like Sustainability,
Environmental Concern, Environmentalism and CSR were used as we had initially
thought of an additional “sustainability” approach for the thesis. They were however to
little use when the study’s direction was specified to only research AR and consumer
engagement. It was found that too many variables and constructs in the model only made
it too complex and therefore resulted in the exclusion of sustainability. Likewise, the
study was first solely intended towards a retail setting. However, this was later removed
due to the state of AR and the phase it is residing in (see Figure 1), were it was deemed
to narrow only to focus on a specific setting.
Before finishing a literature review, it needs to be edited a couple of times to ensure that
all vital elements are included before moving further (Rennision & Hart, 2018, p. 87).
When reading on the topic and the same sources started reoccurring, we tried to
summarize their findings and made sure we had the key considerations included. Later,
when the extensive and thorough literature review was done, we identified some gaps
within the literature from which we started do develop our research gap. These were then
discussed between the authors, as well as with external parties such as supervisors,
Tommy at Handla, and through seminars. When aligning our gap with the insights gained
from our meetings, the purpose of the thesis was easier to clearly formulate. Although
existing before the gap was formulated, by completing the gap enabled us to write the
purpose in more detail than before which were setting the theme for the rest of the thesis
and the subsequent chapters.
4.1.5 Source Criticism
Foundations of source criticisms is explained by Thurén (2013, p. 7,8) as fairly simple
and consisting of four criteria’s; authenticity, timeliness, independency and tendency
freedom. First, authenticity refers to that the source is what it states it is and do not
counterfeit nor fabricate (Thurén, 2013, p. 17). To ensure authenticity we have at the
highest level possible used peer-reviewed articles for the theories and background
presented, meaning that they have been expertly revised before publication. In many
cases, these have also been found in well-respected journals. Secondly, timeliness refers
to the aging of observed data, meaning that more time between observation of something
and the presentation of it should lead to more concern regarding its veracity (Thurén.
2013, p. 7).
Furthermore, Thurén (2013 p. 7) states that as the information searched for become more
specific, the source’s simultaneity needs to be greater. Likewise, the literature on AR can
be viewed through the lens of the Hype Cycle on AR (see section 2.2). As the technology
evolves, intentions to use and attitudes change with it. This does not mean that the sources
results were wrong at time of presentation, but could however mean that older research
33
findings are not necessarily true for AR today given what stage it is residing in.
Nevertheless, the methods and theories of research are still relevant and since there are
scarce publication on the topic of AR and consumer behaviour, most, if not all sources
known to us, have been revised. However, due to the aforementioned issues, we have
tried to be selective when it comes to older sources and actively been trying to find the
newest articles possible. Especially given that AR is such a new phenomenon, implicating
that older articles in some instances can become irrelevant.
Connecting back to the criteria’s - thirdly, independency refers to the source capability to
stand on its own, not being dependent on other sources and referred through second hand
sources (Thurén, 2013, p. 8). This was ensured through usage of the first hand source as
reference in every possible way. However, in a few cases the first hand source was not
available to us in the databases we have access to. In those cases, second hand referencing
was used. However, this was limited to information that was not deemed vital in the
fundamentals of the theories used. Fourth and lastly, tendency freedom refers to the
source being free from giving a false image of reality as a result of personal, economic,
political or other individual gains through altering data (Thurén, 2013, p. 8). As
previously mentioned, this was ensured by using peer-reviewed articles from well-
respected journals. Furthermore, several different articles and authors have been used to
describe the concepts and theories. We have also considered how established the articles
are and the authors consistency in the field of research.
While conducting our literature review, we often came across the same authors in several
articles, being cited in numerous instances. This was for us an indication of the authors’
validity and credibility within the given field which further helped us greatly when
choosing which articles were relevant and not. Authors such as Hollebeek, Huang & Liao,
Huang & Liu, Kim & Forsythe were typical examples of such authors and were
subsequently widely used in our inaugural chapters. Being referred to extensively,
strengthens the logic of using them and increases the trustworthiness of the thesis. By
targeting the most prominent authors within a given field demonstrates at the very least
general knowledge and provides a solid foundation for the rest of the thesis.
4.2 Practical Methodology
4.2.1 Ethical Issues
When conducting a survey, certain information should be provided to individuals before
they start responding (Saunders et al., 2000, p. 135). This was considered when
constructing the survey and ethical guidelines proposed by ICC/ESOMAR (The Internal
Chamber of Commerce/European Society for Opinion and Marketing Research) was
followed. This means that no personal data was collected, the purpose was clearly stated,
and ethical behaviour was followed in line with ICC/ESOMAR (2016, p. 4). For instance,
researchers should take special care when conducting research that involves children,
young people or vulnerable individuals (ICC/ESOMAR, 2016, p. 4). As such we have
clearly stated that participants are required to be at least 18 years of age to participate in
the survey. Moreover, ethical guidelines and rules stated or proposed by Vetenskapsrådet
(2017), SATORI (2017), Codex (2019) and ALLEA (All European Academics, 2017) are
followed.
Delving further into the matter, respondents remained anonymous throughout the survey.
This was ensured by not collecting any personal data such as name or birthdate in line
with ICC/ESOMAR (2016, p. 4). Furthermore, we did not ask of any details regarding
34
their whereabouts such as their current city of residence. However, there was an option
for respondents to register their email address for access to the thesis once completed,
which could link answers to an individual. Therefore, all email address responses were
separated from the data before analysis to retain the anonymity. All data was further
handled confidentially, meaning that only the authors handled the data and could not be
used by anyone else.
Buchanan & Hvizdak (2009, p. 43) present results from a study conducted on Human
Research Ethic Committees in the US and cites responses claiming that tools such as
SurveyMonkey delete confidential information that can identify respondents. It is further
proposed that engines such as SurveyMonkey help maintain the anonymity of the
participants in contrast to surveys conducted via e-mail (Buchanan & Hvizdak, 2009, p.
43). For this thesis, the anonymity of the respondents and the confidentiality of the data
was stated clearly in the introduction of the survey. Furthermore, participation was
completely optional and emphasized by stating that respondents could close the survey at
any point. Our response rates reflect this, where roughly 71% of those who answered the
background questions on the first page completed the survey in its entirety and 29% did
not. Lastly, the purpose of the thesis and the intended use of the data was clearly stated,
as well as how the data will be shared to others in the future in line with (ESOMAR,
2016).
Other ethical dilemmas arise when conducting a survey and is not exclusively dealing
with issues of anonymity. One of the control questions was regarding the respondent’s
gender. When asking about a respondent’s gender there are several things to consider.
The first question that arise is the very issue if gender is relevant or not? RFSL (2016)
reports that many asks the question regarding gender routinely and without considering
the answers for the analysis. For this thesis, we included that answer because we were
curious if it was any major differences between the genders. Moreover, when including
questions regarding gender it is important to consider what constitutes as gender. RFSL
(2016) further states that the word gender has numerous meanings and can mean anything
from legal gender to which gender you identify yourself with. Given that there are several
ways of identifying yourself and not just as a man or a woman (RFSL, 2016), we wanted
to include several alternatives so that everyone can have a chance of feeling included and
not discriminated.
4.2.2 Population and Population Sample
Entities whose characteristics are being recorded in research are called cases (Kent, 2007,
p. 227). These cases make up the population of which the research is conducted and there
is a need to define this population in order to avoid ambiguities (Kent, 2007, p. 227). In
the research purpose we mentioned the targeted population as individuals currently
residing in Sweden that have used augmented reality. Thus, the true population size is
rather unknown as little is known about the amounts of augmented reality users. As such,
a sample of the population will be researched. However, a probability sample, where
respondents are chosen randomly from the population is not entirely possible as we do
not know the true population (Kent, 2007, p. 231).
When conducting quantitative research, random sampling is the most commonly used
method which allows the researchers to generalize from their findings (Kelley et al., 2003,
p. 264). Kelley et al. (2003, p. 264) further explains that this sampling method entails that
every member of a given population will have the same chance of being included. Alas,
this was not clearly fulfilled for this thesis and was thus having some characteristics of
35
non-random sampling fulfilled as well. By not fully accomplish all requirements entails
a sampling error. These are always present when writing a thesis, but can be influenced
by the sampling method of choice (Kelley et al., 2003, p. 264). Thus, this is one limitation
of the generalizability of this thesis. However, as previously mentioned, the population
itself was not the most crucial part for us when setting the direction for the research - but
rather investigating what AR characteristics are deemed important for any given user
within our specified population.
The sample is further limited, or defined, by the limited resources and time connected
with our thesis (Kent, 2007, p. 229). Therefore, a somewhat purposive sample base was
used with the goal of achieving a representative sample. Purposive samples are generated
when the researchers select which cases could be used for the study or is important for
the study (Kent. 2007, p. 230). Similarly, we had to make a judgement on where to reach
respondents and collect our data. Representative samples on the other hand are chosen as
to be a representation of the population, primarily for quantitative analysis (Kent, 2007.
p. 231). This was done through Swedish Facebook groups with enthusiasm for AR, and
others, where cases were as representative as possible, but due to the limitations in some
cases may be purposive.
Explaining the aforementioned issues more closely, the population was reached through
Facebook, LinkedIn, and personal messages. The hope was to reach our sample size goal
by sharing the survey and with the possibility of respondents sharing it further. We
therefore counted on the effects of snowball sampling, in which respondents passes the
survey to other individuals potentially having the sought for characteristics (Biernacki &
Waldorf, 1981). A potential issue by only using social media and the internet is that the
population becomes narrower and somewhat selective to individuals only possessing such
technology. Furthermore, snowball sampling is known for being limited to the social
network of the respondents (Biernacki & Waldorf, 1981, p. 160). Although we recognize
the issues this implies, we believe we somewhat mitigate that issue since our survey only
aims to investigate certain factors of AR and thus only users of such technology are
relevant for this thesis. Thus, meaning that the demographics are not as important, but
rather the feelings towards the technology.
Going more into detail regarding how the respondents were reached, by approaching
admins of several technologically themed groups, we were able to share the survey with
their members. The reasoning behind that was that technologically minded people to a
larger extent probably has heard about AR and thus more likely used it more commonly.
Furthermore, by sharing the survey within our own networks, the survey received more
wind in its sail through likes and shares from our friends. This inevitably enabled the
survey to reach a total of 104 responses, out of which 79 was included for our results
which will be further delved into in the subsequent chapter. However, it is useful to
mention that it is not solely the amount of missing data that is crucial to consider, but also
issues regarding the pattern of the missing data (Schlomer et al., 2010, p. 2). Schlomer et
al. (2010, p. 2) continues that notion by explaining that if the pattern is considered non-
random could potentially result in a bias.
Regarding the size of the sample, Sudman (1982, p. 180) states that there are several
approaches to determine how big the sample should be. One approach is to adopt the
amount other researchers have used in similar contexts (Sudman, 1982, p. 180). By
comparing with earlier theses at the same level and arguments proposed in section 4.3 we
decided to adopt this approach as well. This resulted in a goal of at least 70 respondents
36
from the sample, which we reached with 79 useful responses. However, it is important to
consider that smaller samples can have detrimental effects on the survey. Since
correlations can differ depending on the sample size, the reliability of the factor analysis
can be questioned the smaller the sample (Field, 2009, p. 645). However, if the sample is
not big enough, there are other measurements to consider.
Limitations do arise due to our choices and we are aware of alternative solutions that
could have been utilized. Instead of trusting the effects of snowball sampling, we could
have tried to find e-mail lists with students or other populations and send out the survey.
This could have enabled us to be more clinical with our reminders and reaching more
respondents. However, given our approach, alternatives to reminders was possible due to
the sharing of our survey in different social medias as well as different channels within
those networks. Furthermore, personal messages with reminders have been sent out in
some occasions which meant that we gained a couple of additional responses. There are
other alternatives as well, such as physically asking people on the street if they could
answer the survey. This was considered to do in association with one of Handla’s events,
which would have enabled us to reach more users of AR, but was dismissed due to
conflicting schedules and too little time.
The total amount of potential respondents and the response rate is not possible to specify
accurately as a result of our method. As we have mentioned, the survey was shared on
several social media’s, in different groups and networks. As such the total potential
respondents could have been up to more than 2000 individuals. However, due to for
instance Facebook’s algorithms on individual preferences and individuals’ content
followage, a large number of potential respondents may never have seen the survey from
the beginning. The respondents also had to have been online on those social media’s close
to the date when the survey was posted. As such, an accurate calculation on specific
response rate is not possible given that we chose not to send out the survey personally via
for instance e-mail.
4.2.3 Measurement
All items in this survey is measured by either 5- or 7-point Likert scale depending on
previous authors’ use of measurement. Likert scales are based on getting respondents to
indicate their degree of agreement or disagreement with a series of statements about the
object or focus of the attitude (Kent. 2007, p. 135). In order to be consistent throughout
the survey, adopting all items into a 5-point scale was considered as it was suggested by
participants in the pretesting of the survey. This suggestion was however rejected as we
wanted to stay true to previous authors and enable comparison with their results.
Furthermore, by having two types of scales, we argue that it would more likely allow the
respondents to remain in flow while answering given that the survey was quite heavy.
The main concern with the Likert scale is said to be single dimensionality, which is
making sure that all the items would measure the same thing (Kent. 2007, p.135). In our
thesis, this should already be somewhat prevented, since all items are taken from previous
studies that have validated them and their reliability of measurement. However, factor
analysis, a number of reliability measures, and discriminant validity analysis was used to
ensure it in the result section.
As we used two different scales in the questionnaire, both 5-point and 7-point, they had
to be converted into the same scale before analysis. This was done using the following
mathematical formula in SPSS to translate the 5-point items into 7-point (IBM, n.d.):
37
y = 1.5 * x - 0.5
Where y = the new item value and x = old item value. Consequently, the formula works
in such a way that a response that was previously 1 on the 5-point scale is still 1 on the 7-
point scale and a value of 5 results in 7, etc.
Furthermore, some researchers do include negatively worded questions in order to get the
respondents more contemplative regarding the questions (Barnette, 2000, p. 362). This
was considered for this thesis but discarded for two major reasons. First, given that we
used previous authors’ work, recoding the questions would mean that we create our own
question which would ensue extensive pre-testing which we had already ruled out.
Secondly, and perhaps more important, constructing reversed items usually aggravates
the accuracy of the results without the extensive usage of negations and further entices a
risk of not receiving as thoughtful answers (Krosnick & Presser 2010, p. 277).
4.2.4 Online Survey
The data for this thesis was collected exclusively in a web-based survey. Saunders et al.
(2000, p. 282) explains that the attributes of the respondents when conducting an online
survey should be liberal individuals with easy access to internet, which this survey
certainly fulfils. Another crucial reason for choosing this type of survey is that the size of
the sample can severely increase due to geographical dispersion (Saunders et al., 2000, p.
282). Consequently, this was one of the reasons for using this type of survey and
considered a convenient way of collecting data rather quickly. However, as Buchanan &
Hvizdak (2009, p. 37) points out - although online surveys are easy to use, it is important
to anchor the choice of survey based on methodological choices and not just convenience.
With our epistemological and ontological considerations previously mentioned, solidifies
the argument of choosing an online survey - not merely considering the convenient
factors.
Moving further, Kent (2007, p. 193-194) writes about several advantages with online
surveys and mentions speed, coverage, anonymity, convenience, control and cost as the
standout favourable reasons. The latter did not really affect our choice of survey because
the other alternatives where associated with similar costs, which was close to non-
existent. As previously mentioned, convenience was one reason for choosing this type of
survey, given that respondents can choose to answer it as it pleases them (Kent, 2007, p.
193). Furthermore, speed, coverage and anonymity were utmost important factors with
the former two providing easy access and instant responses with the possibility of
covering a large area (Kent, 2007, p. 193). Naturally, the latter is equally important
meaning that respondents can be sure that their information is safe and being able to
answer it within the private space of their choice (Kent, 2007, p. 193). Lastly, control was
another key factor given that it enables us to manage the survey conveniently and monitor
the response rates (Kent, 2007, p. 194).
While there are several advantages with this type of survey, there are some setbacks with
choosing it as well. These are mostly concerned with issues regarding non-observation
errors, namely coverage, sampling and non-response according to Fricker et al. (2005, p.
372). However, since then internet usage has increased which eradicates some of these
problems regarding coverage. What can be said though is that while effective and a
relatively quick way of reaching potential respondents, it does mean that a bit of control
diminishes regarding sampling and response rates. Nevertheless, this survey was aiming
to investigate what certain factors potential users of AR prefer in our given setting which
we believe has been fulfilled to a decent extent.
38
The survey was created, and responses collected through SurveyMonkey, a paid product
for survey-creation and quantitative data collection. The cheapest version is adequate and
enables the researchers to collect and analyse data through various tools and has more
choices regarding design than other similar programs. However, the program was
somewhat limited in terms of exporting the data where the most sought after features is
only included in the paid versions. As such, other programs like Google Survey is
recommended if the study should be replicated by researchers with similar resources as
ourselves.
4.2.5 Survey Questions
All the items used in the survey was gathered from previous authors’ research. The ones
regarding AR attributes was naturally adopted from work within the field of augmented
reality (see Appendix 2). Likewise, the items regarding consumer engagement was
adopted after extensive literature review on the field of consumer engagement (see
Appendix 2). However, there were a few items that had to be reconstructed before used
in our survey. Specifically, the items regarding perceived usefulness which were
reconstructed in such a way that they now would ask about perceived usefulness in a
general setting, instead of a specific setting as they had been used previously. As the items
were reconstructed, pretesting was conducted to ensure the reliability of the survey before
initiating it (see section 4.2.7).
Several control questions were asked to respondents. Some out of need, for instance age,
in terms of limiting respondents to be in line with ethical research (see section 4.2.1).
Other questions that were asked were “How often do you use AR” and respondents had
several options from daily to less than a few times a month. No option regarding “never”
was available, and if a respondent chose to continue without answering the question an
error message emerged with the statement “This survey targets those who have used AR
at least once, if you have not used AR we ask you to not participate.”. This was to ensure
that the only respondents in the population of augmented reality users was targeted.
Likewise, the question “In what setting do you usually use AR?” was asked with options
identified as the most general settings, and one “other, please specify:” option. No option
for “I never use AR” was stated, instead we made use of the same error message as before.
Lastly the identified gender of the respondents was asked.
4.2.6 Routing
The routing of the items was chosen with care. The aim of the routing was to make the
survey as smooth as possible for respondents at the same time as responses would reflect
the stated question. This was especially important to us as respondents were expected to
be very limited due to the niched subject. There were arguments to have all the items in
a clear order, with the items reflecting one attribute consistently after one another. This
strategy was expected to result in less time spent for the respondent. However, this was
not adopted as we suspected respondents would grade the items based of the first one
reflecting the same variable, instead of thinking thoroughly about each statement. This
was emphasized further after the pre-test as respondents found some items to be very
similar to one another. Instead, the items were put in somewhat of a random order, where
those that was easiest to comprehend would be asked first. By randomizing the order in
which the questions are asked and not grouping items from the same variable could ensue
reliability results closer to the reality (Wilson & Lankton 2012, p. 3). Calculations based
on grouped items are explained to give higher values (Wilson & Lankton, 2012, p. 3),
which would not accurately represent the views of our sample. Thus, a randomization of
39
the questions was done with items measuring the same variable more spread out than
before.
This was somewhat limited due to the use of both 5-point and 7-point Likert scales as we
suspected that the unstructured use of different scales would cause confusion and changes
in the scale could possibly be overlooked by respondents. As such, the 7-point scale items
were structured to come after all items with a 5-point scale and a note emphasizing the
change was used. Some of the 7-point scale items, especially the ones measuring
engagement, included the more complicated items. This made them better suited to be
asked last, making the strategy functional. However, this strategy was not flawless, as
some respondents commented on the survey being long and requiring high level of mental
effort, which possibly caused some respondents to not complete the survey and those who
completed it had to spend more time in it.
4.2.7 Pretesting Survey
Before ultimately sending out the final survey, pretesting should be done which will
inform the researchers if any of the questions are incomprehensive and if the respondents
understand the meaning of the questions in the same way (Kelley et al., 2003, p. 263). A
need to conduct a pre-test on the survey can arise to ensure that respondents understand
the items and does not find the survey too difficult, at the same time as eliminating
possible errors (Kent, 2007, p. 154; Saunders et al. 2000, p. 305). Pilot-testing has also
been identified as critical for successful research (Kent, 2007, p. 154; Saunders et al.,
2000, p. 306). There are three main types of pilot studies; testing to check language and
the range of likely opinions, testing to see how the questionnaire works, and testing to
obtain approximate results (Kent, 2007, p. 154).
To obtain content validity Mitchell (1996) states that the researcher may ask for the
opinions and comments from expert individuals. For our thesis, this was done through
several supervisions, were the foundation was created and language errors was identified.
Furthermore, face validity is said to have a similar meaning, but is generally conducted
on non-experts (Mitchell, 1996). As such, the survey was also tested on a group of 10
people, consisting of friends and family, that still could be viewed as representatives of
the population; augmented reality users in Sweden. By conducting a survey on people
close within our own social networks allowed us to quickly gain vital answers before
proceeding with the finished survey. Nevertheless, we have previously mentioned that we
did not want to use our own items with the main argument that it would require another
and more extensive pre-test. However, pretesting the survey increases the credibility.
The main goal of the pilot was to check if the survey was clear and understandable and at
the same time to test if the altered items still measured what was intended. Thus, the pilot
was mainly a test to identify possible errors and see if the questionnaire worked
adequately. The most recurring comment we received was on the similarity on some of
the items. This is something that was not changed, as the items are taken from previous
authors and therefore will undergo as little alteration as possible. Furthermore, the items
are meant to measure the same concept, thus some level of similarity will occur. However,
some changes were made and at the initial stages some of the questions had to be
reworded in order to fit our setting.
One error was identified in the control variable age, where we had listed “18-25 and 25-
34” making two options available for those at the age of 25. This was changed
immediately. In the pilot, one item describing the attribute playfulness and its escapism
40
element: “When I use AR, I get so involved that I forget everything else” was found to
get the same value across all respondents namely “Strongly disagree”. All of the pre-test
respondents also had a hard time understanding the question. As such, the item was
removed entirely from the survey to avoid ambiguities. Moreover, another item in the
escapism spectrum was clarified after comments from respondents. “Using an AR system
‘gets me away from it all’” was to be fair, quite inexplicit, and at least one of the
respondents did not understand that question. Thus, we added an explanatory sentence
after it with an example so that the respondent could get the true meaning of the question.
4.3 Quantitative Data Analysis As previously stated, using a survey and analysing the data through quantitative methods
fits our ontological and epistemological stances. The collected data was initially analysed
with the use of SPSS, a statistical program which both of us authors have used before and
was therefore comfortable with. Furthermore, the computer labs on campus provides free
access to this program which made it a convenient choice as well. However, due to
inconclusive results and difficulties with the latter part of the tests we had to rethink that
strategy. By advice from our supervisor we changed program to SmartPLS, which
provided full use of their program for 30 days, and moreover is free to use for analyses
with less than 100 samples. As such analyses on the results were done through PLS-SEM
(partial least squares structural equation modelling) which was easier to use and enabled
us to receive several results simultaneously, without having to execute numerous
calculations. Nevertheless, SPSS was still used to compute and gather some data, as well
as checking criterions for linear regression. However, the main calculations were
conducted in SmartPLS.
Going more into detail, in order to test for significance SmartPLS uses bootstrapping
which is a resampling method which results in estimations of the standard error of
regression paths (Garson, 2016, p. 17). This method enables the creation of subsamples
with randomly drawn observations from the original dataset, which is repeated until a
large number of samples have been created (SmartPLS, n.d.). With our sample being
relatively small, this method and its calculations thus come in handy and makes our
sample appear larger. Moreover, the usage of SmartPLS is also documented from some
of our sources used for the hypotheses which further strengthens the argument of using
that program.
Poushneh & Vasquez-Parraga (2017, p. 233) explains that SmartPLS is very formidable
when working with smaller sample sizes, which is also what this thesis is dealing with.
Hair et al. (2017, p. 18-22) further solidifies the claim that PLS-SEM (using SmartPLS)
is especially good for small sample sizes. In our case it is stated that a minimum number
of 70 observations is needed to achieve statistical power of 80%, with a 5% probability
error, for detecting R2 values of at least 0.25 (Hair et al., 2017, p. 22). This amount is
derived from analysing the maximum amount of “connections” or “proposed
relationships” to a latent variable in the model (Hair et al., 2017, p. 20). As will be
reported further in the result section, we have a usable sample size of 79, which based on
the guidelines of PLS-SEM on our structural model is deemed sufficient for reliable
results. Therefore, the usage of this program is further strengthened. However, there is no
well identified global optimization criterion for PLS path models, and as a result each
part of the model needs to be validated (Leisch & Monecke, 2012, p. 18).
41
4.3.1 Factor Analysis
In his book The Essentials of Factor Analysis, Child (2006, p. 1) states that factor analysis
is conducted to test or confirm generalizations, making it very suitable for quantitative
research. For us to ensure item validity, an exploratory factor analysis, or EFA, was
conducted through SPSS to identify the underlying items to the variables and their
correlations and intercorrelations. In this thesis, we are using the threshold limit of 0.6 for
outer loadings, as scores of composite reliability between 0.6 and 0.7 has been deemed
sufficient by Nunnally & Bernstein (1994, cited in Hair et al., 2011, p. 145). When the
test had been done, factor analysis through SmartPLS was conducted as well and got
similar results. EFA can be explained as an “orderly simplification of interrelated
measures” and enables the identification of the underlying factor structure (Suhr, n.d., p.
2). Hayton et al. (2004, p. 192) explain that factor analyses help establish what factors
should be retained and calls this decision as one of the most crucial when managing EFA.
Moreover, by eliminating some items can facilitate the achievement of improving the
construct quality and thus getting better results (Pantano et al., 2017, p. 88).
Crucially, EFA further facilitates the establishment of the amount of latent constructs and
the structure of the factors involved (Suhr, n.d., p. 2), which at the given stage of the
research is helpful before computing regression analyses. It has further been explained as
a method with the aim of discovering structures in the given variables (Child, 2006, p. 8).
This was one of the main logics for using this method in this thesis. Contrary to this
method is confirmatory factor analysis (CFA) which verifies the factor structure of
already established variables (Suhr, n.d., p. 1). Furthermore, this method is usable when
there is a solid foundation to make strong assumptions about the existence of common
factors and enough basis to specify a priori model (Fabrigar et al., 1999, p. 277, 283).
However, EFA was deemed more fitting for this thesis.
There are however some limitations with using EFA that needs to be considered. Suhr
(n.d., p. 3) explains that although correlations can explain certain relationships between
variables, causal inferences cannot be made by solely considering correlations.
Connecting back to our aforementioned problems with the sample size, Suhr (n.d., p. 3)
states that a larger sample means a higher correlation. It is further exemplified that a
minimum of 100 observations or 5 times the number of items is required to ensure reliable
results (Suhr, n.d., p. 3). Given our number of items, our sample would thus not suffice.
However, according to Mundfrom et al. (2005) there is often little empirical evidence
behind sample size suggestions. Even so, the results of the factor analysis were analysed
with care, as it would most likely not be perfectly accurate. As mentioned before, we used
both SPSS and SmartPLS and then compared the two to gain optimal results. Using this
method, we believe that the limitations presented by the low amount of observations had
been mitigated to the best extent possible.
Moving further, there is also limitations to measuring unidimensionality through factor
analysis in two and three item measures (Ping Jr., 2004, p. 128), and only using two items
to identify an underlying construct has been recognized as problematic (Eisinga et al.,
2012, p. 1). This is a common situation for researchers, where items with low loadings
have to be removed ultimately resulting in small number scales, e.g. 2 item scales (Eisinga
et al., 2012, p. 2). However, a factor with only two items may be retained if the items are
relatively uncorrelated with other variables and the items are highly correlated with each
other (Worthington & Whittaker. 2006, p. 821). In this study, one construct (service
excellence) consisted of 2 items from the start and was later removed (see results) as a
result of low correlation derived from the factor analysis. Furthermore, playfulness was
42
divided into two separate constructs consisting of 2 item scales as a result of the factor
analysis. However, they were chosen to be retained due to high loadings and low
intercorrelation with other factors in line with the authors Worthington & Whittaker
(2006, p. 821) argument.
4.3.2 Cronbach’s Alpha
“Any research based on measurement must be concerned with the accuracy or
dependability or, as we usually call it, reliability of measurement” (Cronbach, 1951, p.
297). Cronbach (1951, p. 297) states that the preferred method for ensuring reliability of
measurement is to conduct two independent measurements and compare them. Meaning
that all the respondents gets to answer the survey or questionnaire again after some time.
In practice, opportunities for remeasurement is limited due to, for instance, time, and if
such opportunities occur scientists prefer conducting additional tests over retesting
(Cronbach, 1951, p. 297). Likewise, retesting was not possible in our situation, as all the
respondents was kept anonymous. Furthermore, it would be very time consuming to make
respondents repeat the survey if it was possible, and if that was a requirement many
potential respondents could have been scared off.
In social and organizational sciences, which business administration fall under, the most
widely used measure of reliability is Cronbach’s (1951) alpha (Bonett & Wright, 2015,
p. 3). It is considered in a range from 0 to 1 (Cronbach 1951). “values of less than 0.6 are
usually viewed as unsatisfactory [...] and increasing reliability beyond 0.8 is unnecessary
because at that level correlations are attenuated very little by measurement of error.”
(Nunnally, 1967, cited in Mitchell, 1996). As such, we have adopted a lower limit of 0.6
for Cronbach’s alpha in this study.
4.3.3 Composite Reliability
As an alternative to the test explained above, composite reliability is deemed as a
prominent option given the tendency of Cronbach’s alpha to over- or underestimate scale
reliability (Garson, 2016, p. 63). On the other hand, this test more commonly results in
higher estimates and has the same cut off as the other tests ranging from 0 to 1 (Garson,
2016, p. 63). The threshold value for this test is 0.7 according to Hair et al. (2011, p. 145),
which is further endorsed by Henseler et al. (2012, p. 269) Our motivation for including
this test was because it can paint a broader picture than what solely relying on Cronbach
would ensue. Furthermore, we argue that the more tests we use can further strengthen our
reliability and determine that our results are sufficient on several dimensions.
Additionally, we experienced some insufficient results after our initial calculations which
did warrant further tests. Thus, by supplementing with composite reliability enabled us to
keep more variables. This will be further explained in the forthcoming chapter.
4.3.4 AVE
Another test included is the average variance extracted (AVE) which is used to measure
convergent validity (Henseler et al., 2012, p. 269). Moreover, according to Hair et al.
(2011, p. 146) stated that values above 0.5 is accepted and this will suffice for adequate
levels of convergent validity. This further implicates that more than half of the indicator’s
variance is being explained by the latent variables (Hair et al., 2011, p. 146). For us and
our thesis, this indicates that if our AVE calculations results in numbers above 0.5, the
constructs can explain more than half of the variance of the items. Supplemented with the
other tests we are conducting, this can mitigate some of the reliability lost with a smaller
sample. Therefore, this proved to be very useful for our purposes.
43
4.3.5 Regression Analysis
Regression analysis stems from the mathematical method least squares (Hair et al., 2010,
p. 163). Its intended use is to predict a dependent variable with one or more independent
variables (Hair et al., 2010, p. 162). The purpose of regression analysis can more
specifically be described as predicting a future unknown value with specified values of
the independent variables, at the same time as determining the relationship between the
independent variables and the dependent variable (Faraway, 2014, p. 8). Each variable is
given a correlation coefficient that tells how strong a relationship is between the
independent and the dependent variable and how much of the dependent variable that is
explained by the independent variable (Hair et al., 2010, p. 163; Pallant, 2005, p. 145).
The correlation coefficient can have a value between -1 and 1, where a value between 0
and 1 means that there is a positive correlation and a value between -1 and 0 means a
negative correlation (Saunders et al., 2012, p. 521). The further a correlation coefficient
is from 0, the stronger the correlation, as a value of 0 means that there is no correlation
between the variables (Saunders et al., 2012, p. 521).
When a single independent variable is measured against a dependent variable, it is called
simple linear regression (Faraway, 2014, p. 8; Hair et al., 2010, p. 162). However, in
reality, there are few examples of dependent variables being fully explained by only one
independent variable. In the cases where there are more than one independent variable
explaining a dependent variable, one must use multiple linear regression (Hair et al., 2010,
p. 161; James et al., 2013, p. 71). In multiple linear regression, each of the independent
variables is weighed and then the coefficient of determination (R2) is given (Hair et al.,
2010, p. 161; Pallant, 2005, p. 145). The independent variables together make a regression
model that determines the dependent variable (Hair et al., 2010, p. 162).
The mathematical formula for the simple linear regression is as follows (James et al.,
2013, p. 61): 𝑌 ≈ β0 +β1, …
and the formula for multiple linear regression is as follows (James et al., 2013, p. 71): 𝑌
= β0 + β1𝑋1 + β2𝑋2 + … + β𝑝𝑋𝑝+ 𝜀
Xn represents the different variables and 𝛽n the quantity of the relationship between an
independent variable and dependent variable, which can be described as the standardized
effect on Y when X is increased by one unit and all the other variables are kept constant
(James et al., 2013, p. 71). 𝛽0 represents the intercept and 𝜀 the random error (Long, 1997,
p. 11).
In this study the independent variables consist of the attributes of AR, e.g. interactivity,
aesthetics, etc., that is hypothesized to explain the dependent variables, cognitive
processing & affection, positively. Both single linear regression and multiple regression
was conducted in this study. The single linear regression was conducted on all the
hypothesized relationships between the independent and dependent variables in isolation
using SmartPLS. Meaning that they were tested one by one. As such, the hypotheses were
accepted or rejected depending on the significant values of the single linear regression
analyses. This was conducted as the hypotheses only touched upon the individual
independent variables influence on the individual dependent variables in isolation,
without regard for the other factors. However, as single linear regression does not show
regard to other factors (James et al., 2013, p. 71), multiple linear regression was also
conducted with all the latent variables (both dependent and independent variables) and
control variables (gender, setting, regularity) for optimal combination of factors (Hair et
44
al., 2010, p. 10). The reasoning behind this is that multiple linear regression could be
viewed as more useful in practical terms as the independent variables does not affect the
independent variables in isolation in reality. Further, assumptions of the most prominent
attributes (independent variables) can be made through the multiple regression analysis
enabling us to answer the dimensions of our research question effectively.
For a regression analysis to work correctly, a number of assumptions of the data is made,
which should be solidified before the analysis is conducted. The assumptions can to some
extent be analysed through scatterplots of the residuals in relation to the dependent
variable (Pallant, 2005, s. 143), and the relation to the independent variable when a simple
linear regression analysis is made (Hair et al., 2010, p. 183). A residual is the difference
between an observed value and a predicted value for the variable (Hair et al., 2010, p.
183). The following assumptions are made:
Firstly, the residuals are normally distributed (Hair et al., 2010, p. 182; Pallant, 2005,
p.143). The first assumption was studied through histograms in SmartPLS in line with
Hair et al. (2010, p. 185). SPSS was also used to study normal P-Plots, where the
assumption is fulfilled it the residuals follows a diagonal straight line (Hair et al., 2010,
p. 185). This was also made as Hair et al. (2010, p. 185) states that this is a better strategy
than through studying histograms.
Secondly, the residuals show a linear correlation (Hair et al., 2010, p. 182; Pallant, 2005,
p.143). The second assumption was also ensured by studying the data through SPSS, with
the help of residual plots, where the residuals should show a linear correlation and not,
e.g., a curved correlation (Hair et al., 2010, p. 183).
Thirdly, the residuals show constant variance (Hair et al., 2010, p. 182; Pallant, 2005,
p.143). The third assumption was also studied through residual plots, but here the
residuals was studied in relation to the predicted value instead of the dependent variable
(Hair et al., 2010, p. 185).
A fourth assumption is also made. Hair et al. (2010) state that the residuals should be
independent of each other. This was not studied in this thesis as it touches upon effects
that one observation affects another. Instead assumption of non-collinearity proposed by
Saunders et al. (2012, p. 524-525) was conducted by studying the the Fornell & Larcker
criterion and variance inflation factor (VIF) values in SmartPLS in line with (Hair et al.,
2010, p. 200-201). The Fornell & Larcker criterion measures discriminant validity, i.e.
whether concepts that are supposed to be unrelated actually are (Hamid et al., 2017, p. 1).
This method compares the square root of the average variance extracted (AVE) with the
correlation of the latent variables (Hamid et al., 2017, p. 3). For desired results, e.g. good
discriminant validity, a latent variable should explain the variance of its own indicator
better than that of other latent variables (Hamid et al., 2017, p. 3). The VIF quantifies the
severity of multicollinearity, no formal cut-off value or method exists to determine when
a VIF is too large, even so, typical suggestions for a cut-off point are 5 or 10 (Craney &
Surles, 2002, p. 393). However, Pennsylvania state university (PennState, 2018) suggest
that all VIF values above 4 warrants further investigation. As such investigation would
be conducted if any variable surpassed 4 and immediate cut-off of at values greater than
4 was adopted before analysing. As such, all the assumptions, except the residual
independency, was studied for both the single linear regression and the multiple linear
regression.
45
In the result section, we will amongst other results present the standardized betas (β).
They are derived from the corresponding path coefficients and estimate path relationships
for the structural model (Hair et al., 2017, p. 202). Moreover, we will also present the t-
values. The t-values can be used to determine the significance level (Hair et al., 2017, p.
134). The path coefficient is deemed significantly different from zero at a level of 5% (p
=0.05; two-tailed test) when the size of an empirical t-value is above 1.96 (Hair et al.,
2017, p. 134). As such, the critical t-value in this thesis is 1.96 in line with Hair et al.,
(2017, p. 171). Likewise, we will also present the P-values. The P-values correspond to
the probability of rejecting the null hypothesis (Hair et al., 2017, p. 172). When a P-value
is below 0.05 the relationship can be seen as significant at the 5% level (Hair et al., 2017,
p. 172). All the tests will be used using a two-tailed test. One tailed-tests measures one
end area of the distribution, and two-tailed both end areas (Kock, 2015, p. 6). The main
reasoning behind using two-tailed tests is that one tailed tests are more likely to yield
distorted results and estimate the P-values wrongly (Kock, 2015, p. 7). Further, the use of
a two-tailed test is based on the prior knowledge incorporated into the hypotheses, in our
work two-tailed test is more fitting in line with Kock (2015, p. 5).
4.4 Quality Criteria When conducting research, there are different measurements to consider in order to
ensure that the quality of the work is sufficient. In quantitative research, it has been found
that validity and reliability both have substantial support for inclusion (Bryman et al.,
2008, p. 274). Furthermore, generalizability and replicability are simultaneously
important for the extension and ability to reproduce the findings respectively (Bryman &
Bell, 2017, p. 180, 181). Thus, by focusing on these we will briefly explain their
significance for this thesis and how these contribute.
Validity concerns questions whether or not the indicators designed to measure a variable
truly measures what they are supposed to (Bryman & Bell, 2017, p. 176). However, when
discussing validity, the authors Cook & Campbell (1979, p. 37) states that one should
always make use of the modifier “approximately”, as one can never know what is true
only what has not yet been ruled out as false. In this study, validity has been fulfilled
through the numerous tests and analyses we have conducted and our findings mostly
support that the indicators did indeed measure its intended areas. There are many ways to
measure the validity (Bryman & Bell, 2017, p. 176), and we will present internal, external
and construct validity.
The former concerns whether the research can accurately establish a causal relationship
between two variables (Saunders et al., 2016, p. 203). This means that the questionnaire
should measure what it is intended to (Saunders et al., 2016, p. 451), which has been
achieved by the usage of factor analyses and single and multiple regression analyses
respectively. Indeed, the questionnaire did measure its intended areas and the relationship
was demonstrated to some extent for all our variables.
Moving further, external validity concerns the generalizability of the findings to other
settings or groups (Saunders et al., 2016, p. 204). The purpose of the thesis was to research
Swedish inhabitants, meaning that its generalizability outside Sweden is therefore limited.
However, it is applicable to test in other settings and we believe that we have proposed a
relevant ground for future research. Regarding construct validity, this regards the extent
to which you can deduce relevant hypotheses from a theory (Bryman & Bell, 2017, p.
176). Connecting to this thesis, we did use theories such as ENTANGLE to aid our
46
hypotheses and more importantly anchored them in previous work from authors found
through our literature review. Furthermore, construct validity can be related to the
question “How well can I generalise from this set of questions to the construct?”
(Saunders et al., 2016, p. 451), which was also checked by factor analysis.
Reliability was ensured through transparency, our thorough methodology (see Chapter
4), and tests such as Cronbach’s Alpha which tests for internal consistency (Saunders et
al., 2016, p. 451). More thoroughly explained, reliability concerns questions regarding
replication and consistency (Saunders et al., 2016, p. 202), meaning that the survey should
produce persistent findings if it were reproduced under different circumstance (Saunders
et al., 2016, p. 451). If a study is not possible to reproduce implies that it cannot be
validated (Bryman & Bell, 2017, p. 181). Furthermore, transparency has been argued as
an important factor to enable replication of a study as well as more critical assessment
(Dale, 2006). As previously stated, we believe that this study can and should be
reproduced to ensure validity but also to investigate our findings further. Moreover, the
decisions made should be clear as these will affect the analysis and without knowledge
of these make replication a very challenging endeavour (Dale, 2006, p. 146).
Consequently, we have consciously tried to be as transparent as possible to make
replicability smoother as well as the very ethicality of the question.
47
5. Results This chapter presents the results from the collected data, analysed using SmartPLS and
SPSS. The chapter is introduced with a description of survey completion rate and
demographic results. Following, a presentation of the results from the factor analysis
(ldg), Cronbach’s alpha, composite reliability and AVE analysis is presented. Further,
the discriminant validity and VIF values are ensured and analysed. Afterwards, a
presentation of the single linear regression is conducted, and the hypothesized
relationships accepted or rejected. Lastly, the multiple linear regression is conducted to
see which attribute(s) could be viewed as most important.
5.1 Survey Completion Rate
Figure 4. Survey completion rate.
A total of 104 individual responses were
collected for this survey. However, as
illustrated by the chart, 29% of
respondents only completed the first
page then chose to exit the survey. Out of
the 81 respondents that completed the
survey in its entirety, 2 responses had to
be deleted. 1 because the respondent had
stated “I do not use AR” under the
question “In what setting do you usually
use AR?” the 2nd response was removed
because it was handed in after the end
date of the survey when results was
already gathered for calculations. No
data on respondents who did not fully complete the first page was collected, meaning that
there is potentially a larger amount of incomplete responses than accounted for.
5.2 Demographic Results In the following section the demographic results are presented and briefly discussed.
5.2.1 Setting
Figure 5. Setting.
After doing our literature review and
prior to conducting our survey, we had
strong indications that the majority of
people probably have not used AR for
shopping as much as they have in other
channels. Connecting to the Hype Cycle
(see Figure 1), it is clear that AR still is
in its initial phases before widespread
recognition and implementation.
However, with its potential and the fact
that most people in the targeted ages uses
AR quite regularly, we thought that we
might still be able to receive conclusive
responses usable for our purpose. As can
be seen in Figure 5, a vast majority of the
48
respondents use AR through social media with 69.2% of the responses. This is contrasted
to 20.2% through games and 7.7 in retail. The aforementioned numbers were very much
in line with our projections, with perhaps the exception of the somewhat high numbers
for games. However, with Pokémon GO’s immense success a couple of years ago, these
numbers could perhaps be quite suspected as well. In the following presentation of the
results, this data is presented as “Setting”.
5.2.2 Regularity
Figure 6. Regularity.
As illustrated by the chart to the left,
24% of the respondents use AR daily and
nearly 30% a few times a week.
Moreover, 10.6% use AR about once a
week resulting in roughly 64% of the
respondents using AR weekly or more
and roughly 36% use it a few times a
month or less. As such, although a
considerate part of the respondents only
uses it quite moderately, an even greater
majority use it at least once a week. This
indicates some of our initial thoughts,
namely that most people use AR quite
extensively. However, we also believed that most do not necessarily realize that that they
are using AR technology which was why we included a thorough introduction
exemplifying AR utilization. In the following presentation of the results, this data is
presented as “Regularity”.
5.2.3 Age
Figure 7. Age.
As expected, an overwhelming majority
of respondents (roughly 95%) are
residing in the younger sphere, between
18 and 34. More concretely, consisting
of 52% between the ages of 18-24 and
43% between 25 and 34. Therefore, this
study can be viewed as limited to
researching the population sample of
people aged 18-34. If assumptions
regarding age and user- AR attribute
importance is made, this study cannot be
applied to people included in the age
group of 35 and over. As such, age will
not be applied further in the tests, since it
only reflects one age dimension. However, age and other demographics was never crucial
areas for us to consider since we are more interested in what characteristics can enhance
consumer engagement. Therefore, using the technology was enough. Further research on
the matter will be suggested later in the forthcoming chapters in which these matters will
be more closely discussed.
49
5.2.4 Gender
Figure 8. Gender.
Most of the respondents identify as male.
Even so, as it is not an overwhelming
majority it can be considered
representative for both male and
females. The distribution could simply
be a result of the number of individuals
reached when sharing the survey. For
instance, the survey was shared on
engineering related Facebooks groups,
where most of the members were male.
One respondent has chosen “other” as
identified gender. However, this
response was deleted as the respondent
had stated “I do not use AR”. As such, the answers were not deemed representative given
the respondents lack of experience using AR - a prerequisite for participating in the
survey. Furthermore, answering all the questions relating to AR usage without having
used the technology would imply severely incorrect answers and misrepresentative. In
the following presentation of the results, this data is presented as “Gender”. In the coming
section, the results from the factor analysis and reliability measures are presented.
5.2 Factor Analysis, Cronbach’s Alpha, Composite Reliability, AVE and
Descriptive Statistics In this section, the results from the factor analysis, Cronbach’s alpha, composite
reliability and the average variance extracted (AVE) is presented and discussed. Further,
the descriptive statistics are provided and briefly discussed.
Table 1. Constructs & Indicators, factor analysis, reliability, AVE, mean and Std. Dev.
Construct Indicator Ldg α CR AVE Mean Std.Dev
Affection I feel very positive when I
use [AR brand]
.901* .871 .913 .725 4.30 1.50
Using [AR brand] makes
me happy
.850* 3.98 1.32
I feel good when using
[AR brand]
.903* 4,20 1.51
I am proud to use [AR
brand]
.741* 3.49 1.60
Cognitive
Processing
Using [AR brand] get me
to think about [AR brand]
.688* .624 .773 .539 4.44 1.78
I think about [AR brand] a
lot when I am using it
.590* 3.63 1.65
Using [AR brand]
stimulates me to learn
more about [AR brand]
.892* 3.37 1.50
50
Interactivity AR provides a variety of
ways for viewing product
image
.738* .799 .870 .626 5.54 1.24
AR provides personalized
product
.802* 4.50 1.53
AR allows the user to
adjust the product
.777* 5.48 1.18
AR shows dynamic
product images
.844* 5.29 1.33
Aesthetics The way AR system
display its products is
attractive
.870* .743 .853 .661 4.95 1.38
AR systems are
aesthetically pleasing
.804* 4.82 1.42
I like the way AR system’s
site look
.760* 4.72 1.38
Perceived
Usefulness
AR improves my
productivity
.774* .780 .810 .687 4.95 1.59
AR improves my
effectiveness
.759* 3.87 1.42
AR is helpful for my
activity
.792* 4.06 1.56
AR improves my ability to
complete my activity
.794* 3.50 1.59
Playfulness
Enjoyment
I enjoy using AR for the
sake of it, not just for the
items I may have
purchased
.627* .599 .795 .669 5.71 1.55
I use AR systems for the
pure enjoyment of it
.972* 4.44 1.82
Playfulness
Escapism
Using an AR system “gets
me away from it all”
.717* .647 .830 .713 3.18 1.86
Using an AR system
makes me feel like I am in
another world
.955* 2.59 1.58
Ease of Use - - - - -
Service
Excellence
- - - - -
51
*p < 0.05
Ldg: Outer loadings; α = Cronbach’s alpha; CR: Composite Reliability; AVE: average
variance extracted.
As can be seen in Table 1, we have included our constructs and their supplementing items
as well as several tests with their results. These tests are mentioned more specifically in
Chapter 4 and have all their merits of being included. Going more into detail, the tests
included in Table 1 are in the following order: Outer loadings, or factor analysis (ldg),
Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE).
The factor analysis was conducted for every item whereas the latter tests more generally
measure the variables as a whole. As such, the values presented in the first column for
every construct is the values for the whole construct, not merely the first item.
Delving further into the calculations, a factor analysis was conducted on a model
containing all the latent variables. As previously stated, all items in the survey was
originally included. However, 13 items had to be completely removed due to either
unacceptable loadings or not showing unidimensionality under their latent variable. Tests
were made in order to strengthen them, only to make the others worse. The constructs did
not measure what they were supposed to and with their items too scattered, they did not
appear to originate from the same construct. As such, Ease of Use and Service Excellence
had to be excluded from our subsequent analysis meaning that two full constructs were
removed. The removal of Ease of Use was especially surprising, as it was initially
believed after conducting the pre-test that this would have one of the highest values.
As a result of the factor analysis, Playfulness was divided into two separate
constructs/latent variables instead of one. This decision was made as its dimensions
Escapism and Enjoyment did not load onto the same latent variable. Again, this was
contrasting our expectations based on previous findings. Two item constructs are as stated
in the method chapter not optimal and can be problematic (Eisinga et al., 2013, p. 637).
However, as it was not possible for the constructs to contain more items unless the study
was redone and they showed relatively high loadings (.717 and .955 for escapism and
.627 and .972 for enjoyment) as well as not cross-loading onto other factors, they were
retained as separate constructs.
After conducting the necessary tests and removing the low loading or cross loading items,
the final draft consisted of seven latent variables/constructs and 22 items that had been
identified. All loadings exceeded the limit of 0.6 (see section 4.3.1) except one item under
Cognitive Processing. However, it was chosen to be retained as it was close to the limit
of 0.6 with 0.590. Furthermore, the values for α, CR and AVE all worsened if it was
removed and we wanted to avoid as many two item constructs as possible as 2 item
constructs can be problematic (Eisinga et al., 2012, p. 637). The rest of the indicators
showed sufficient or great results, both when using SmartPls and SPSS (see Appendix 1).
Lastly, all outer loadings showed significant values of p<0.001 except Playfulness
Enjoyment, which had a significant level of p<0.02, which was deemed sufficient.
Moving further with the subsequent tests included in Table 1, all of the constructs had an
α over the limit of 0.6 (see section 4.3.2) except for Enjoyment. This construct had a value
of 0.599, which arguably is close enough to 0.6 to warrant an inclusion. However, the
reason for keeping it was also because of its sufficient values in CR and AVE and we
argue that these more than enough argues for its involvement. Moreover, looking at the
52
other tests it becomes clear that the latter two were quite undramatic with great results
throughout. All the constructs had great values in CR, all above 0.7. Lastly, the AVE was
above the limit of 0.5 for all constructs further ensuring their reliability.
The mean and standard deviation (Std. Dev) for the items (indicators) remaining after the
factor analysis is provided. The mean and standard deviation provided is calculated after
the 5-point Likert scale items were transformed into the 7-point scale (See section 4.6.3).
In this data we can see that most of the mean values are around 4, the middle point of the
7-point Likert scale. Furthermore, the mean for interactivity as expected slightly higher
(around 5) solidifying the notion by Kipper & Rampolla, (2012, p. 4) that AR always is
interactive. Lastly, the mean for affection and cognitive processing indicates that the
participants in the survey was some form of engaged in the brands they used.
The importance of these tests become more evident when considering the forthcoming
analyses. What these tests has shown us, is what items and constructs to keep. Thus,
signifying their importance and making their inclusion more evident. With the results
gained, it becomes clear which items to include for testing the hypotheses which the
upcoming section regarding regression analysis will delve further into. Before the
regression analyses is conducted the discriminant validity assessment and VIF values will
be presented and corrections will or will not be made depending on the results.
5.3 Discriminant Validity Assessment In this section we present the results from the discriminant validity assessment gained
from analysing the Fornell-Larcker criterion and VIF-values.
Table 2. Discriminant validity assessment (Fornell-Larcker criterion).
To ensure discriminant validity we firstly checked the Fornell-Larcker criterion (see
Table 2 above). It confirms discriminant validity across all latent variables, as the square
root of each construct's AVE (i.e., diagonal elements) are greater than its highest
correlation (off-diagonal elements) with any other latent variable (Hamid et al., 2017, p.
3). Furthermore, the variance inflation factor, or VIF, was analysed to determine any
possible multicollinearity issues. In this study, the highest VIF value was 3.35 for
affection as shown in Table 3 below, the rest of the variables and indicators showed very
low values (< 2), suggesting that multicollinearity was not a reason for concern in line
with our previously stated maximum value 4 (see section 4.3.5).
53
Table 3. Outer & Inner VIF
Outer VIF Inner VIF Affection CP
Aest1 1.992 Aesthetics 1.806 1.806
Aest2 1.849 Affection
Aest3 1.25 CP
Affect1 3.35 Enjoyment 1.428 1.428
Affect2 3.18 Escapism 1.606 1.606
Affect3 2.909 Gender 1.145 1.145
Affect4 1.556 Interactivity 1.705 1.705
CP1 1.301 Regularity 1.154 1.154
CP2 1.249 Setting 1.24 1.24
CP3 1.18 Usefulness 1.455 1.455
Regularity 1
Interact1 1.434
Interact2 1.712
Interact5 1.587
Interact6 1.953
Male 1
PUse1 1.617
PUse2 1.762
PUse3 1.657
PUse4 1.62
PlayEnjo1 1.224
PlayEnjo2 1.224
PlayEscap1 1.297
PlayEscap2 1.297
SocialMedia 1
5.4 Single Linear Regression Results As the outer loadings (factor analysis), reliability and discriminant validity has been
ensured the coming section will present the results from the single linear regression,
depending on the results our hypotheses will be accepted or rejected. Afterwards a
presentation of the multiple linear regression will be conducted.
Table 4. Single Linear Regression Results.
The results were generated using a bootstrap of 5000 samples. (Two-tailed)
Construct R2 R2-adjusted β t-value P-value
Cognitive
Processing
.089 .077
Interactivity
.298 1.866 .062
Affection .189 .178
Interactivity
.417 4.283 .000***
Cognitive
Processing
.139 .128
54
Playfulness
Escapism
.372 1.761 .078
Affection .123 .111
Playfulness
Escapism
.350 3.755 .000***
Cognitive
Processing
.099 .087
Playfulness
Enjoyment
.314 1.128 .260
Affection .152 .141
Playfulness
Enjoyment
.390 4.933 .000***
Cognitive
Processing
.113 .101
Aesthetics
.336 2.032 .042*
Affection .175 .164
Aesthetics
.418 4.921 .000***
Cognitive
Processing
.252 .243
Perceived
Usefulness
.502 6.941 .000***
Affection .256 .246
Perceived
Usefulness
.506 7.646 .000***
***p<0.001 **p < .01 *p < .05 (Two-tailed)
The tested hypothesized relationship is presented with the dependent variable above the
independent variable.
The table above contains our single linear regression analysis with the results from each
independent variable and the respective dependent variable. We will later provide a
multiple regression analysis as well, where our variables are tested for significance
altogether. All our independent variables are being tested one by one in isolation towards
our dependent variables, in line with our hypotheses. Some independent variables have
significant positive relationship with Affection, some with Cognitive Processing, whereas
some with both constructs.
Generally, all constructs have relatively low values both for R2 and the adjusted R2. This
implies that our independent variables cannot fully explain Consumer Engagement which
further means that there are other factors to consider that also can affect our dependent
variable. Perceived Usefulness towards Cognitive Processing had the highest values for
both R2 and R2 adjusted followed by Perceived Usefulness towards Affection. This
suggests that when predicting the variance of Consumer Engagement, Perceived
Usefulness could possibly be the most important of our independent variables.
Moreover, the β values were relatively high across the tests and positive for all
independent variables towards the dependent variables. As such, all independent
variables have a positive relationship with the dependent variables. However, in order see
if we can accept or reject our hypotheses, we also need to see if the t-values are significant.
55
This will be done by examining the P-values, as the β does not indicate a significant
relationship. Therefore, looking at the P-values and starting from the top, Interactivity
(0.001), Escapism (0.002), and Enjoyment (0.001) were all found to have significance
towards Affection and not Cognitive Processing. Moving further, Aesthetics and
Perceived Usefulness show significance towards both constructs encompassing
Consumer Engagement. Thus, there were no variables with significance only towards
Cognitive Processing. The independent variables showing significant P-values and
positive β values towards the dependent variables have then fulfilled their hypothesized
relationship. The acceptance or rejection of a hypothesized relationship derived from
Table 4 can be viewed in Table 5 below.
Table 5. Hypotheses.
H1a H2a
There is a positive relationship between AR Interactivity and Cognitive
Processing.
There is a positive relationship between AR interactivity and Affection.
Rejected Accepted
H1b H2b
There is a positive relationship between AR Playfulness and Cognitive Processing.
There is a positive relationship between AR Playfulness and Affection.
Changed3
Changed3
H1b3
H2b3
H3b3
H4b3
There is a positive relationship between AR Playfulness Escapism and Cognitive
Processing.
There is a positive relationship between AR Playfulness Escapism and Affection.
There is a positive relationship between AR Playfulness Enjoyment and Cognitive
Processing.
There is a positive relationship between AR Playfulness Enjoyment and Affection.
Rejected
Accepted Rejected
Accepted
H1c
H2c
There is a positive relationship between AR Service Excellence and Cognitive
Processing. There is a positive relationship between AR Service Excellence and Affection.
Not tested4
Not tested4
H1d
H2d
There is a positive relationship between AR Aesthetics and Cognitive Processing.
There is a positive relationship between AR Aesthetics and Affection.
Accepted Accepted
H1e
H2e
There is a positive relationship between AR Ease of Use and Cognitive Processing.
There is a positive relationship between AR Ease of Use and Affection.
Not tested4
Not tested4
H1f
H2f
There is a positive relationship between AR Perceived Usefulness and Cognitive
Processing. There is a positive relationship between AR Perceived Usefulness and Affection.
Accepted Accepted
Looking at Table 5 above, 4 of the hypotheses was not tested at all. These were the ones
failing to succeed when doing the factor analysis, i.e. Service Excellence and Ease of Use.
3 Playfulness was as previously stated divided into two separate constructs following the factor analysis,
Escapism and Enjoyment. As such, the old playfulness hypotheses are divided into four new. 4 The hypothesized relationship was not tested as a result of the items not loading onto the same variable
or unacceptable loadings in the factor analysis.
56
It can also be seen that 2 of the hypotheses had to be divided into 4, where Playfulness
was divided into Escapism and Enjoyment. This has been more thoroughly explained in
a previous section. Moreover, 2 out of these 4 was accepted and the rest rejected at a
p<0.05. In total, 7 hypotheses were accepted, 3 was rejected, 4 not tested and 2 changed.
Thus, most of the hypotheses being tested was significant and found to be true. The only
AR constructs where both hypotheses were found significant towards both consumer
engagement constructs was Aesthetics and Perceived Usefulness. For the remaining ones,
i.e. Interactivity, Escapism, and Enjoyment, only the former was significant towards
Cognitive Processing whereas the latter was significant towards Affection.
5.5 Multiple Linear Regression Results Beyond testing the hypothesized relationships in isolation with the control variables, their
relationships were also tested in SmartPLS using a multiple regression model with all of
the latent and control variables included at once, as SmartPLS allows it. As mentioned in
section 4.3.5 multiple linear regression is conducted as well as single linear regression as
in reality the independent variables affect each other and ultimately their relationship with
the dependent variables (Hair et al., 2010, p. 10). The nature of the proposed hypotheses
does not regard for these possible effects, but the test is conducted in order to gain
arguably more practical insight into what attribute possibly is of greatest importance for
the dimensions of consumer engagement. In Table 6 below, the dependent variables
(affection and cognitive processing) have been separated to make the results easier to
view, they were however tested together.
Affection has an R2-adjusted of .348 meaning that the combined attributes of AR and the
control variables explain roughly 35% of the affection in consumer engagement. The
independent variable with the highest positive influence on affection is Playfulness
Enjoyment (β = .297), followed by Perceived Usefulness (β = .292). Meaning that more
Playfulness Enjoyment and Perceived Usefulness in AR, the more consumer engagement
affection will come as a result. Both these variables show significance t-values at the 5%
level (p < .05). Meaning that Playfulness Enjoyment and Perceived Usefulness could be
viewed as the most important attributes of augmented reality when trying to entice greater
consumer engagement affection. Interactivity also shows fairly high influence in this
study (β = .207). However, it does not have a significant influencing relationship (p >
.05). As such, it is not considered as one of the most important attributes for affection.
The rest of the independent variables and control variables does not show high
influencing values on affection (low β) and are non-significant (p > .05).
Cognitive processing has an R2-adjusted of .259 meaning that the combined attributes of
AR and the control variables explain roughly 26% of the cognitive processing in
consumer engagement. Perceived Usefulness has the greatest positive influence (β = .344)
at a significant level (p = .003) meaning that the greater the perceived usefulness, the
greater consumer engagement cognitive processing. Moreover, the control variable
setting shows great negative influence (β = -.296) at a significant level (p = .003). This
means that depending on what setting one performs their AR initiative (social media,
games, retailing) the results of cognitive processing may be higher or lower. In this study
social media was used for the control variable setting. Meaning that social media setting
has a significant negative impact on consumer engagement cognitive processing. None
of the other independent variables or control variables showed significant levels (p > .05).
As perceived usefulness has positive significant influence on both affection and cognitive
57
processing it can be viewed as the most important attribute of AR for enticing greater
consumer engagement in this study.
Table 6. Multiple linear regression.
The results were generated using a bootstrap of 5000 samples. (Two-tailed)
Construct R2 R2-adjusted β t-value P-value
Affection .415 .348
Interactivity .207 1.717 .086
Playfulness Escapism .035 .345 .730
Playfulness
Enjoyment
.297 2.385 .017*
Aesthetics .078 .757 .449
Perceived
Useufulness
.292 3.275 .001**
Gender -.126 1.148 .251
Regularity .082 .818 .413
Setting -.126 .1.348 .178
Construct R2 R2-adjusted β t-value P-value
CognitiveProcessing .335 .259
Interactivity .071 .436 .663
Playfulness Escapism -.002 .015 .988
Playfulness
Enjoyment
.158 1.235 .217
Aesthetics .065 .442 .658
Perceived
Useufulness
.344 3.003 .003**
Gender -.018 .169 .866
Regularity .066 .620 .535
Setting -.296 2.986 .003**
**p < .01 *p < .05
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6. Analysis and Discussion In this chapter the presented results are analysed and discussed further. Firstly, a brief
summary is presented and discussion regarding general findings are conducted. Further,
analysis and discussion regarding the results from the single linear regression is
presented. Lastly, analysis on the results from the multiple linear regression is conducted.
6.1 Discussion and Analytical Points of Departure Throughout this thesis, we have identified several AR characteristics and investigated
their significance towards consumer engagement. While conducting our literature review,
we discovered a gap within the chosen field and concluded that there was not anything
similar researched before. Although all the chosen AR characteristics had been researched
before, it was always in different settings and lacking a part with focus on what truly
engages the customer. With AR increasingly gaining popularity and with infinite areas of
usage we argue that there is a huge potential. Both in the technology itself, but also in
studying a topic that could possibly answer viable questions for anyone involved in the
industry. Furthermore, with our meetings with an industry professional we gained
additional insights which were useful when proposing the purpose and solidified our
notion of the path of the thesis.
Initially, the problem identified was the sluggishness associated with the retail industry
(Sender 2011, cited in Blázquez, 2014, p. 97), and how many stores have had to foreclose
in recent years due to the “threat” from internet retail among others (Svensk Handel, 2018,
p. 3). However, looking at the issue from a “glass half full” perspective enables you to
see it from a more optimistic point of view. With so many technological innovations
recent years, there are huge possibilities in combining several channels and allowing the
technology to aid. AR, as its name implies, is a technology that can strengthen the usage
of other devices when combined by augmenting the reality. This is something that
companies such as IKEA, H&M and Handla has identified and are therefore investing a
lot of efforts in. Nevertheless, as mentioned in previous chapters through the Hype Cycle,
AR is in its initial phases and yet to reach its fully implementation (Gartner, 2018).
When used effectively, AR truly has the ability to engage and engulf the user in the
activity. This was very palpable with the success of Pokémon GO a couple of years ago,
which was the most successful mobile AR game to date (Rauschnabel et al., 2017) and
the biggest mobile games ever (GSMArena, 2017). By adding a bit of nostalgia, as well
as combining the physical and the digital world, resulted in added value for anyone
engaged. This enabled users to experience a greater sense of social interactivity, increased
mobility, as well as increased physical activation (Zach & Tussyadiah, 2017). This in turn
raised the awareness and interest from other industries as well, such as the tourism
industry which perhaps surprisingly also gained from this (Zach & Tussyadiah, 2017).
Similarly, Burger King has also benefited from AR with their campaign where they
encouraged their customers to burn rivalling companies’ billboards for a free meal
(O’Brien, 2019).
What the aforementioned examples shows, is that when looking beyond the entertainment
value that AR can provide, it can be greatly beneficial in other areas as well. As such, we
wanted to investigate what AR constitutes of and what attributes are deemed most
important for the users. Furthermore, we were interested in examining what AR
characteristics is most successful in enticing customer engagement. In our Problem
Background, we cite numerous authors who have explained the entertainment value
59
stemming from AR (see Huang & Liao, 2015, p. 270; Kim & Forsythe, 2008a, p. 45;
Dacko, 2016, p. 254). Moreover, Dacko (2016, p. 254) found that the increased efficiency
was most valued from customers. Moreover, Scholz & Smith (2016, p. 150) created a
framework, ENTANGLE, enabling for a maximization of consumer engagement in an
AR setting. Therefore, with this background we hypothesized that AR had the ability to
add other values than just merely to entertain. This was further solidified through our
findings, where Perceived Usefulness was deemed most important of our AR
characteristics.
Reflecting on our findings further and connecting to our purpose and the questions
initially proposed, we believe that these have been answered to an adequate extent. With
the goal of shedding more light on whether AR can create consumer engagement, the
reality of our findings is that it is more complex than that. AR can create consumer
engagement and some characteristics are more important than others, but it is not so clear
cut that we can say without hesitation that AR in its isolation solely creates consume
engagement. Although it does engage and with the correct implementation can enhance
any experience, our low R-values suggest that there are other perspectives to consider as
well.
Low R-values are not exclusively a harmful occurrence (Minitab, 2013), but it does
generate some additional questions. It is proposed on Minitab (2013) that in some fields
of research, R-values are expected to be low and specifically exemplifies in areas where
human behaviour are to be predicted. This connects well to our research. While AR
potentially can be more easily predicted, consumer engagement is connected to
behaviours, therefore making it less predictable. Furthermore, our given characteristics
of AR are also not quite as measurable as processes, exemplified by Minitab (2013) as an
opposing factor. There are however still opportunities to draw conclusions from results
with low R-values, if the statistical predictors are significant (Minitab, 2013). While our
R-values were consistently low, we did have some significant results elsewhere resulting
in mitigating effects. These will be discussed in detail in the forthcoming sections of the
analysis.
6.2 Consumer Engagement The construct of consumer engagement was, as mentioned throughout the thesis,
measured by two dimensions; Affection and Cognitive Processing. Affection describes
the level or degree of a consumers’ brand-related affect and Cognitive Processing how
well a brand gets the user to think about the brand and not just the user-process (Hollebeek
et al., 2014, p. 154). Similar to the work of Hollebeek (2014, p. 156) with the construct,
affection received great values during the factor analysis and its reliability and validity
was further solidified through the many methods used in this thesis. Cognitive Processing
received slightly unfavoured loadings through the factor analysis, contrary to the work
by Hollebeek et al. (2014, p. 156) who received great values for the items underlying the
dimension. However, it was deemed acceptable as the reliability in this study was
solidified and further also the discriminant validity. As such, both the dimensions have
been found to be very useful in terms of measuring consumer engagement quantitatively
as the theory now have been applied to an additional setting; attributes of AR. This would
mean that the work by the authors Hollebeek et al. (2014) could possibly be applied to
many other settings and in the future possibly be generalized in terms of using a construct
for measuring consumer engagement and consumer brand engagement.
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Moving further, the two dimensions Affection and Cognitive Processing may be seen as
fairly different. Cognitive processing refers to the interest a user has in the brand and
interest in learning more about the brand, while affection is purely feelings associated
with using the brand. As such, it is not surprising that the two different dimensions show
significant relationships with different, more or fewer attributes of AR. From a practical
standpoint, a business must therefore assess whether their consumer engagement
initiative should aim to increase the dimension of Cognitive Processing or increase
affection mainly, as it is shown in this study that different attributes yield different results.
Interestingly, the attributes that showed significant positive relationship with Cognitive
Processing, also showed significant positive relationship with Affection, meaning that
affection could have a moderating effect on cognitive processing, at the very least in this
study. This was however not tested, but could be interesting when developing the concept
in the future.
Hollebeek et al. (2014) do not present the coefficient of determination (R2) values for
involvement on cognitive processing and affection in their study. In this study, our R2
values were relatively low. As stated, this means that the independent variables and
control, does not fully explain the dependent variables. Naturally, questions arise
regarding what would then be required to fully explain the construct? Consumer
engagement is quite complex, as it is encompassing attitudinal behavioural patterns.
However, it is possible that there could be one or more variables that are always present
as independent variables, no matter the setting. For instance, Hollebeek et al. (2014) make
use of involvement as the antecedent to consumer engagement. As such it could be argued
that involvement, but for AR, should have been an independent variable as well. In this
study, the purpose was however limited to exploring whether AR had any relationships
with engagement, not to fully explore the engagement. Moreover, our study found
perceived usefulness to explain the greatest amount in engagement. Therefore, perceived
usefulness, no matter the setting is possible to be a “set” or “definitive” independent
variable for studies quantitatively measuring consumer engagement in the future. Not
only because its great results in this study, but because perceived usefulness is also
applicable to most other settings exploring engagement, and since it is used as an
antecedent to the customer experience in other studies (e.g. Poushneh & Vasquez-
Parraga, 2017).
6.3 Control Variables Regarding the survey, the sample size and its completion rate, there are additional factors
to consider. As been mentioned before, the sample size was unfortunately quite low.
Naturally, this will affect the results where a larger sample generally means higher
correlation (Suhr, n.d., p. 3). Furthermore, factor analyses can also be less reliable with
smaller samples (Field, 2009, p. 645). However, as we discussed in chapter 4, there are
mitigating factors such as the correlation coefficient where our low R-values would
further be detrimental if it was not for our field of research (see section 6.1). Moreover,
as with any science, there are contradictory suggestions regarding sample size where
Mundfrom et al. (2005) states that there seldom is empirical evidence behind such
suggestions.
With all this stated, we would like to acknowledge the fact that a higher sample size
probably would have resulted in more significant results and the opportunity for us to
generalize more about the population. Moving further, from the respondents finishing the
first page there was a completion rate of 71% which we believe is an adequate amount.
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With the survey being quite heavy, with a lot of information as well as technical
abbreviations, we understand that some potential respondents quitted before finishing the
first page or even after it. Indeed, this is a limitation and paradoxically a must given that
AR is residing in its early phases according to the Hype Cycle (see Figure 1). With that
said, we argued that if people generally do not know exactly what a technology is about,
there is a need to explain it properly. Especially if you want them to answer your survey.
A possible limitation affecting the completion rate and possibly even the results is the
risk of respondents not reading the introduction. In that case, respondents would be much
more likely to not completely understand the questions and therefore give erratic answers.
This could also affect the completion rate if they were to jump straight into the control
variables without a specific context. Naturally, this could have been solved by adding
another control variable encouraging them to read the introduction. This was ignored as
we feared this could further reduce the response rate.
Going more into detail about the respondents most commonly used setting for AR,
unsurprisingly the vast majority uses it in social media, where the popular apps Snapchat,
Instagram, and Facebook’s Messenger implements AR and people use these quite
extensively. Connecting back to our introducing text in the survey, we deliberately
exemplified with these apps in order to raise the respondent’s attention and ensure them
that they most certainly have used AR at least a couple of times - and quite possibly more
often than that. A fifth of the respondents claimed that they most commonly use it in
games, where possibly a few Pokémon GO enthusiasts still reside among us. Given the
success of that game it was not very surprising, however 20% was quite high especially
considering the usage rate of Snapchat and similar apps.
In the results of the multiple linear regression, the AR setting was found to have
significant negative impact on the consumer engagement dimension Cognitive
Processing. As stated, social media was used to measure the impact of the setting,
meaning that social media has a direct negative impact. As such, when using AR in social
media, the dimension of Cognitive Processing is expected to receive lower values. This
could be explained by the intended use for augmented reality in social media, where user-
user interaction is stimulated over user-brand interaction.
Some of the questions asked in the survey were originally aimed towards users in a strict
retail setting, such as Huang & Liao (2015) and were accordingly adjusted to fit our
broader perspective. Naturally, this implies that we can only compare our findings to a
certain extent. However, we believe that our results can be viewed as an extension of their
findings. The only question included asking specifically towards a situation where an
item may have been purchased was regarding Playfulness Enjoyment from Huang & Liu
(2014). This question was included since it was hypothetically asked and to further not
fall short of the threshold of two questions per item as discussed in chapter 4 (see section
4.8.1). On a concluding note on the setting is that it would be interesting to see the results
of a similar study when AR has moved along the Hype Cycle towards more widespread
recognition. In a few years’ time, virtual try-on as explained by Kim & Forsythe (2008a,
p. 46) will perhaps be widely recognized, further implying that our study would be better
understood and with the ability to narrow its purpose towards retail.
Delving further into the control variables, the frequency of which the respondents use AR
was asked with differentiating results. Nearly 1 out of every 4 respondent uses it every
day, while close to 30% uses it a few times a week. Added with 10% using it once a week
62
implies that a majority of the respondents are very well acquainted with AR. This was in
line with our prediction. However, after discussing AR during meetings we had the
impression that people generally were not aware of the frequency of their AR usage - if
even aware of using it at all. Hence, we put great emphasis on explaining what the
technology is and in making the questions as comprehensible as possible without steering
the users in any direction by exemplifying too heavily. Lastly, nearly 36% of the
respondents only uses AR a few times a month or less, solidifying the notion that AR still
resides in its early stages of the Hype Cycle (Gartner, 2018).
The last control variables were questions regarding demographics, namely age and
gender. The former contained almost exclusively respondents residing in the two younger
spheres (18-24, 25-34), meaning that this thesis can only generalize about younger people
and not the population as a whole. Therefore, we excluded age in further testing since it
only was reflecting younger people. Indeed, this meant that measuring its impact on the
results would only reflect a small portion of the population. It can be good to mention
however that the awareness levels for AR has been reported to be significantly higher for
people between 16 and 44 (Buckle, 2018), implying that our results should not come as a
surprise. We believe that this market segment is a natural target for entrepreneurs
investing in AR. Similarly, for the developers behind the technology to have in mind
when considering which AR characteristic to pursue.
A reason for this skewed age distribution is probably reflected by our own social
networks. Naturally, with both of us residing within the age-span 18-34, most of our
friends and acquaintances are also in the same age. You could argue that through sharing
of the survey it had the potential to reach other spheres as well, but this has most likely
only resulted in a movement sideways on the age-span. Regarding gender, 2 out of 3
identified as males and the rests as females. Both were tested, but we did not find any
significance regarding gender and it is therefore not possible to generalize specifically
about gender and AR in our setting.
6.4 Analysis of Hypotheses - Single Linear Regression
Figure 9. Aggregated Single Linear Regression.
Figure 9 above represents all the results from the simple linear regression. Not to be
confused with the multiple linear regression. Dotted lines non-significant, full lines
significant. The values are the path coefficients and *p < 0.05
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6.4.1 Interactivity
It was stated in the inaugural chapters that AR is always interactive (Kipper & Rampolla,
2012, p. 4). Likewise, it has been used as a characteristic to describe AR in previous
quantitative studies, however at a limited amount (e.g. Huang & Liao, 2017; Javornik,
2016; Pantano et al., 2017; Poushneh & Vasquez-Parraga, 2017). An underlying gap
throughout this study is that AR has never been tested against consumer engagement
quantitatively. The closest for the attribute interactivity is Poushneh & Vasquez-Parraga’s
(2017) contribution on how AR can impact the customer experience. In their study,
significance was shown for interactivity as determinant for the level of AR and that the
higher level of AR ultimately increases the customer experience (Poushneh & Vasquez-
Parraga, 2017, p. 233). Similarly, Interactivity in this study has a significant positive
relationship with Affection, underlying the bigger construct of Consumer Engagement.
This means that higher interactivity in AR results in a higher “degree of consumers
positive brand-related affect in a particular consumer brand interaction” (Hollebeek et al.,
2014, p. 154). This is also in line with Brodie et al.’s (2011, p. 253) suggestion that
interactivity and value co-creation can help explain the conceptual roots of consumer
engagement and van Noort et al.’s (2012, p. 229) suggestion that higher interactivity leads
to greater affective responses.
However, Interactivity had a non-significant relationship with Cognitive Processing,
meaning that in this study interactivity in AR is not proven to improve how well a brand
gets the user to think about said brand (Hollebeek et al., 2014, p. 156). Controversially,
this is contrary to research by van Noort et al. (2012, p. 229) suggesting that greater
interactivity leads to greater cognitive responses. It is possible that this non-significance
is a result from the small sample (79) as the relationship were close to significant (p =
.063). However, more likely or perhaps in combination with the small sample, is that it
may also be that the respondents had experiences with AR systems that had not designed
their interactive elements in line with the ENTANGLE framework proposed by Scholz &
Smith (2016).
They proposed that engagement should be nourished through greater interactivity
meaning that companies should focus resources on enhancing engagement rather than
expensive marketing (Scholz & Smith, 2016, p. 158). For instance, for our respondents
the interactive elements may have been technology driven over user experience driven,
possibly resulting in less engagement (Scholz & Smith, 2016, p. 157). Another
explanation could be the stage of the Hype Cycle which AR currently resides in; Trough
of disillusionment, where negative hype extends and interest decreases (Linden & Fenn,
2003, p. 7), resulting in AR being less impacting on consumer engagement overall.
However, this argument may be weaker due to most of the hypotheses being accepted
anyway. Nevertheless, the result of Interactivity only having a positive relationship with
the Consumer Engagement dimension Affection and not Cognitive Processing is adopted
though the many possible explanations for it.
6.4.2 Playfulness Escapism and Playfulness Enjoyment
The use of Playfulness as an attribute defining AR has been used most prominently with
the TAM theory (Huang & Liao, 2015, p. 287), as a result of Playfulness being a defining
factor of previous studies adopting the theory outside of AR (e.g. Mathwick et al., 2001).
Furthermore, Playfulness in its entirety is described to affect both the adoption rates of
AR and maintain its usage (Huang & Liao, 2015, p. 287). AR has been suggested to add
experiential values (Huang & Liao, 2015, p. 270) and it has further been stated that one
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of four dimensions of such values are playfulness (Mathwick et al., 2001, p. 41). The
entertainment values of AR have been solidified by authors such as Huang & Liao (2015,
p. 270) and Kim & Forsythe (2008b, p. 901-902) and we believe that entertainment and
playfulness can cross-fertilize each other and pave the way for each other. Consequently,
both have crucial parts to play within the AR spectrum.
Huang & Liu (2014, p. 83) further states that AR adds playfulness through interactive
technologies and exemplifies with IKEA place which further adds convenience. Similar
to those findings, Hakan (2011, p. 397) found that playfulness in online shopping reduces
perceptions of complexity which makes it easier to adopt. From our findings it can be
noted that playfulness does have a part of AR and can to some extent further create
consumer engagement. From our factor analysis, playfulness had to be divided into two
separate constructs where Escapism and Enjoyment was created similar to how Matthew
et al. (2001, p. 46) measure Playfulness. Escapism is the dimension of playfulness which
describes how the user or customer temporarily “get away from it all” (Huizinga, 1955,
cited by Mathwick et al., 2001, p. 44). Enjoyment is the dimension of playfulness that
describes the intrinsic enjoyment felt by the user or customer when engaging in absorbing
activities (Mathwick et al., 2001, p. 44), meaning that it basically measures how enjoyable
an activity is.
There were further no significant results towards both Cognitive Processing and Affection
which means that we cannot conclude that neither escapism nor enjoyment affects
consumer engagement completely. Both variables showed significant positive
relationship with the consumer engagement dimension affection, and neither of them with
Cognitive Processing. As such, it can be concluded that the two dimensions gets the user
to perceive the brand more positively the more playfulness is provided. However,
Playfulness in this study does not get the user to think more about the brand neither
wanting to learn more about it. This could be explained by the nature of the construct
Playfulness. Naturally, the dimension enjoyment is targeted towards affection as the more
you enjoy something the more you would like it, i.e. show affection to it.
Contrary to this, just because you experience something as playful, it does not mean that
you want to learn more about it. From the pre-tests, some of the items stemming from
playfulness had to be modified due to inconclusive results and the fact that some of the
respondents had a hard time comprehending them. Many of our sources had found that
interactive technologies are able to create playful experiences when studying users of AR
in e-commerce (e.g. Huang & Liao, 2017, p. 454). From our study, it is clear that the
majority uses AR in social media or games, where retail was a clear minority. As such,
with so few using AR in this setting most often there can be a large portion of the
respondents not fully transmitting the importance of playfulness to other settings as well.
6.4.3 Aesthetics
Similar to interactivity, Poushneh & Vasquez-Parraga (2017, p. 231, 233) found AR
Aesthetics to have positive significant influence on the user experience. Comparably,
significant positive relationship between Aesthetics and both Consumer Engagement
affection and consumer engagement cognitive processing was found through the single
linear regression in this study. As hypothesized, the existing relationship could be
explained as a result of Aesthetics being user experience driven and thus likely to entice
Consumer Engagement (Scholz & Smith, 2016, p. 159).
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Pantano et al. (2017, p. 919) states that aesthetic quality together with interactivity are
considered the most crucial variables to create an overall positive participation. As such,
comparison between the two attributes is deemed fitting. The differences between
Interactivity and Aesthetics when it comes to their relationship with Cognitive Processing
may just be a result of Aesthetics being more prominently designed for the user
experience than the interactive processes in AR, rather than differences in their ability to
create positive participation.
Moreover, aesthetics has become increasingly acknowledged as a differentiator in the
marketplace and an important economic driver in terms of affecting and motivating
positive consumer experiences and behaviour (e.g. Hatch, 2012, p. 892; Schmitt, 1999,
p. 61). Looking at the results in our study, we can clearly see a connection with the
statement above, as the significant positive relationship between Aesthetics and
Consumer Engagement could be viewed as aesthetic has a positive relationship with
consumer behaviour. Our findings together with the findings from Poushneh & Vasquez-
Parraga’s (2017, p. 231, 233) on aesthetics influence on the consumer experience
solidifies the acknowledgement to be true for AR. For retailers, businesses and creators
of AR, this would mean that the aesthetics in their systems not only are used for example
the visual sensation but even more so as a great tool in enticing consumers to become
more engaged and improving their user experience. Hence, while much too much
emphasis should not be directed on the displaying of AR, having a solid foundation of
aesthetically pleasing visuals is nevertheless important.
6.4.4 Perceived Usefulness
Perceived Usefulness has, as described in the theoretical framework, proven to be of great
influence over the user technology acceptance and is a vital part of TAM (Davis, 1989;
Huang & Liao, 2015, p. 273). Moreover, it is deemed to be the most critical factor in
encouraging consumers to use interactive technology like AR by Huang & Liao (2015, p.
273). As such, we were not surprised by the highly significant results for Perceived
Usefulness positive relationship with both Affection and Cognitive Processing. It is
possible that its profound significance stems from the current position AR resides in the
Hype Cycle (see Figure 1). As negative hype and decreased interest is prominent during
its current phase (Linden & Fenn, 2003, p. 7; Gartner 2018), perceived usefulness could
become even more important for users to adopt the technology. As such, AR systems that
are adopted even though the hype is negative, could be viewed as outliers and have greater
values for the perceived usefulness than normal.” In this study both the mean and standard
deviation was very high for perceived usefulness, in line with previous argument.
As previously mentioned, perceived usefulness measures how well a given technology
improves performance on tasks based on an individual's perception (Huang & Liao, 2015,
p. 273). As hypothesized, increased perceived usefulness thus increase both the affection
and cognitive processing simply because it brings quality of life to the user and by
performing better than other alternatives, making the user consciously or subconsciously
preferring a brand or product. Moreover, Perceived Usefulness is completely user
experience driven, as it is the perceived usefulness of the user and not the actual
usefulness that matters. As such, in line with Scholz & Smith (2016, p. 157, 159), the
attribute naturally entices consumer engagement.
Connecting further to the ENTANGLE framework, AR experiences should be driven by
consumer experience and not technology, where experiences are considered a crucial
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variable in creating consumer engagement (Scholz & Smith, 2016, p. 157). Coupled with
our findings, we believe that it is vital for any developer of businesses interested in AR
to think of the perceived usefulness. This is further strengthened through our thesis by
our inclusion of more technology driven characteristics, such as Aesthetics, which did not
receive as significant results. It is therefore our suggestion that perceived usefulness
should be a vital part when developing the technology behind AR, since these are deemed
more important than ostentatious visuals. Indeed, no matter how aesthetically pleasing or
visually spectacular a technology appears, without a clear perception of the usefulness
any technology will be deemed useless.
There are opportunities arising from these facts. We believe that any entrepreneur
involved with the development of AR and willing to acknowledge these findings could
benefit. Choi & Shepherd (2004, p. 391) found that among other factors, it was more
probable for an entrepreneur to exploit an opportunity if they perceived to have more
knowledge of customer demand and if any enabling technologies are fully developed.
Thus, by knowing what characteristics of AR that are most valuable for the customers is
an opportunity in itself and could generate a competitive advantage by exploiting it.
According to research, investments in the AR industry is estimated as high as $105 billion
by 2020 (Retail Perceptions, 2016), which solidifies the notion of this thesis that AR can
be expected to have a huge impact in the future.
With large MNEs such as IKEA and H&M already a head of the curve may hint of their
continuous dominance within their sectors for years to come. Referring to the industry
life cycle (Johnson et al., 2017, p. 79), AR could be argued to reside in the early phases.
These phases are characterized by low rivalry, low entry barriers and high growth
(Johnson et al., 2017, p. 79), which means that smaller firms such as Handla are perhaps
making a very strategic decision in entering the market whilst it arguably still is seen as
a “blue ocean”, before it is too hard to enter it, i.e. a “red ocean”, in line with Kim &
Mauborgne (2005, p. 106). By focusing on the perceived usefulness of the technology
could thus help these companies investing time and effort on the most valuable
characteristics and thus possibly gain sustainable competitive advantages when the
market saturates, and the competition is fiercer.
6.4.5 Service Excellence and Ease of Use
Two of our original constructs had to be deleted after conducting our factor analysis and
these were as mentioned Service Excellence and Ease of Use. Much like the others, the
former had its merits of being included and was prominently referred to by several sources
such as Huang & Liao (2015), Mathwick et al. (2001), and Huang & Liu (2014). With
Service Excellence being described as a necessity for the general AR setting by Scholz &
Smith (2016, p. 151), we hypothesized that it would have a greater impact on consumer
engagement and that we would find a significant relationship between the two. However,
this was not possible to test which might have to do with the way the questions were
asked, e.g. “I think of AR as an expert in the product it offers”. The meaning could
perhaps be lost in translation given its wording, and/or due to the fact that people
generally do not consider AR as an expert - but merely as entertainment (Huang & Liao,
2015, p. 270) or as something that increases the efficiency of an activity (Dacko, 2016,
p. 254).
By continuing the thoughts of Zeithaml (1988, cited in Huang & Liu 2014, p. 85), it was
discussed in section 2.3.3 that Service Excellence could measure the anticipated service
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of AR and further how the anticipated user experience is perceived. Therefore, we
believed that this would be of greater importance for users of AR. Again, the problems
with the factor loadings could be because of the problems surrounding two item
constructs (Eisinga et al., 2012, p. 1), as discussed in section 4.8.1. However, it has been
tested before (see Huang & Liao, 2014) with significant results - although as a dependent
variable. The latter note notwithstanding, Huang & Liao (2014, p. 99) further tested
Aesthetics and Playfulness as well as dependent variables and both were included in our
work.
More surprisingly perhaps, was the omittance of Ease of Use. It was explained by
Marangunić & Granić (2014, p. 85) as one of the factors of the technology acceptance
model developed by Davis (1986). TAM is very prominently researched within this area
and numerous articles when conducting our literature review contained parts of this, such
as Kim & Forsythe (2008b, p. 901) and Huang & Liao (2015, p. 270). The former even
found that Ease of Use was one of the factors which could explain continuous usage of
AR and also affect the adoption of it (Huang & Liao, 2015, p. 287). Naturally, we believed
that by integrating parts of this within our AR variable would be a vital part towards
affecting Consumer Engagement, given that this arguably should increase the probability
of a sustainable relationship and enabling better adoption rates. Perceiving an app as easy
to use, should be an elementary part towards continuous usage. Furthermore, from the
ENTANGLE framework, we learned that AR initiatives should focus on user experience
driven attributes (Scholz & Smith, 2016, p. 158). We argued that this further implies that
AR characteristics with focus on ease of use among others should be central in terms of
enticing engagement.
As been proved in the former section, our most successful construct was another part of
TAM - Perceived Usefulness. Quite contrastingly, the other cornerstone of TAM was thus
not included given its low values in our factor analysis. With these two constructs
arguably being quite similar, it was a surprise that one of them had to be excluded. Huang
& Liao (2015, p. 284) has Ease of Use as an antecedent to Perceived Usefulness and was
therefore hypothesized as an equally important variable. However, the poor loading from
the former could perhaps stem from the way the questions were asked and their routing.
Although we tried to randomize the question order as much as possible, the items
connecting to Ease of Use were all in the beginning. Having questions connecting to how
easy it is to use, we argued was quite satisfactory given the introduction. It was our hope
that these two connected would let the respondents more easily think that AR was not as
foreign as perhaps first believed. With the results in hand, it is more presumable that the
heavy introduction probably made the beginning questions appear to be harder than they
were. Then, as the questionnaire progressed the questions appeared easier to comprehend
and thus to answer. Perhaps respondents disagreed too much with the first question
“Using AR is clear and understandable” as a result from the heavy and “hard to grasp”
introduction, and that this then set the tone for the forthcoming questions.
6.5 Analysis - Multiple Linear Regression As discussed in section 4.3.5 and section 5.5 in the multiple linear regression, we could
see the hypothesized relationships from a more practical perspective, as in reality the
attributes (independent variables), with others, would influence each other’s relationships
with consumer engagement (dependent variables). As such, conducting the multiple
linear regression would theoretically give a more practical overview of the hypothesized
relationships.
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In the previous segment, we presented individual arguments and reasons for each attribute
significant or non-significant relationship with consumer engagement. The arguments
apply in this section as well. However, as the results shown in the multiple linear
regression only Perceived Usefulness and Playfulness Enjoyment as attributes of AR
show significant relationships with one or both dimensions of consumer engagement.
Therefore, the importance of these attributes could be viewed as more important than the
others in terms of enticing greater consumer engagement. In such a case, perceived
usefulness would arguably be the single most important attribute for AR, given its
significance level towards both dimensions of consumer engagement whilst Playfulness
Enjoyment “only” had for affection. The interpretation of the results regarding
playfulness enjoyment, can as stated in the methodology chapter be controversial, since
it is a two-item construct. Furthermore, the construct did not have optimal factor loadings
in relativity to Perceived Usefulness.
The other attributes did not show significant relationships with any of the dimensions of
consumer engagement. One exception is the control variable Setting, which showed a
significant negative relationship with the dimension cognitive processing. The
relationship is described and discussed in detail in section 6.3.1 above.
Another defining result following the multiple linear regression is the increased value for
the coefficient of determination (R2) compared to the single linear regression. Naturally,
this was expected as there were more independent variables available for explaining the
variance in the dependent variables. However, the R2 (.415; .335) and R2-adjusted (.348;
.259) values were still relatively low for affection and cognitive processing respectively.
As discussed in previous section, this is not a reason for concern, it merely suggests that
there are other variables outside of the model describing the values for the dependent
variables as well. Which is to be expected for a complex behavioural subject as consumer
engagement.
Figure 10. Multiple Linear Regression Results.
Figure 10 above represents the results from the multiple linear regression. Dotted lines
non-significant, full lines significant. The values are the path coefficients and *p < 0.05
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7. Conclusion and Recommendations This chapter is initiated with a summarizing description of the study’s results in relation
to the research question and research purpose. Forthcoming, the theoretical and
practical implications are introduced, ending in societal implications. Lastly, the study’s
limitations and suggestions for future research are presented.
7.1 Conclusion The purpose of this study was to broaden the previous quantitative literature on
augmented reality and consumer engagement. Specifically, we wanted to discover if AR
could be used to gain influence over their consumers’ behaviour. Moreover, the purpose
was exploring which attributes of augmented reality systems entices greater consumer
engagement, and if certain attributes could be emphasized to promote a specific
behaviour. As such, we would be able to identify new possible ways for practitioners to
gain competitive advantages by being able to influence the consumer decision making
process. Further, the study was limited to AR users currently residing in Sweden. The
purpose of the study was defined by the study’s research question:
Does Augmented Reality systems entice Consumer Engagement? If so, which attributes
of an Augmented Reality system affect Consumer Engagement positively?
The attributes that were chosen for AR were based on previous quantitative work on AR
and were chosen primarily for how well they defined augmented reality as a concept,
regardless of setting or potential impact on consumer engagement. The construct of
consumer engagement was chosen through extensive literature review and consist of the
two-dimension affection and cognitive processing, both measuring consumer brand
engagement. The attributes were then hypothesized to have positive relationship with the
dimensions of consumer engagement based on previous studies findings with the
attributes, and relatable findings on consumer engagement.
Our quantitative single linear regression analysis found that all the tested attributes in
isolation show significant positive relationship with the consumer engagement dimension
affection. As such, augmented reality systems definitely entice consumer engagement.
Moreover, the attributes Perceived Usefulness and Aesthetics also showed significant
positive relationship with cognitive processing, influencing consumer engagement in its
entirety. This means that all the tested attributes Interactivity, Playfulness (Enjoyment &
Escapism), Aesthetics, & Perceived Usefulness affect consumer engagement positively,
or at the very least partially. Most of the findings is thus in line with similar previous
results in other authors studies. The attributes Service Excellence & Ease of Use were not
tested as a result of unacceptably low or non-existent loadings in the factor analysis.
Meaning that their relationships with consumer engagement are still empirically unknown
in an AR setting, given our findings.
When analysing the attributes in combination through the multiple linear regression
Playfulness Enjoyment and Perceived Usefulness were found to have significant positive
relationships. Playfulness Enjoyment exclusively with affection and Perceived Usefulness
with both affection and cognitive processing. This means that the attributes importance
is different when studied in isolation contra in unison. As such, Perceived Usefulness can
be viewed as the most important attribute for AR in terms of enticing complete customer
engagement.
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Lastly, the setting was found to have a significant negative relationship with cognitive
processing. Meaning that the impact the AR attributes has can be different depending on
which setting it resides in.
7.2 Theoretical Implications The quantitative empirical findings in this study contributes theoretically to both to
previous studies in AR and consumer engagement and in a new way by creating a
framework in which to quantitatively measure and analyse AR, through the use of
attributes. Both the areas of consumer engagement and AR has limited quantitative
research and AR’s relation to consumer engagement has never been studied quantitatively
to our knowledge.
All the attributes defining AR in this study has been used in previous studies regarding
AR (e.g. Huang & Liu, 2014; Huang & Liu, 2015; Poushneh & Vasquez-Parraga, 2017),
but never in complete unison as in this study. As such, we have created a framework in
which to study the general AR and its attributes quantitatively without the data collection
being coupled with the use of a practical setting (e.g. Poushneh & Vasquez-Parraga,
2017). Moreover, findings by Poushneh & Vasquez-Parraga (2017, p. 233) regarding the
user-importance of the attributes Aesthetics, Interactivity and Perceived Usefulness for
AR has been further solidified. The extensive findings by (Huang & Liu, 2015) regarding
technology acceptance has also been solidified, proving that perceived usefulness indeed
is of great importance.
Furthermore, consumer engagement has been researched quantitatively, however limited,
but there is still no absolute framework in how to measure it. Through extensive literature
review and ultimately choosing the best fitting theory amongst authors such as
Algesheimer et al. (2005), Calder et al. (2009), Hollebeek et al. (2014), Rather. (2018)
and others, the framework and theory by Hollebeek et al. (2014) was adopted. The use of
their constructs has answered their call of further validation through different online and
brand contexts (Hollebeek et al., 2014, p. 161) as it now has been tested in AR as well.
However, not completely as the consequences of consumer engagement was not
measured. As such, this study contributes to the theory on consumer engagement by
further validating the usefulness of the constructs describing consumer engagement
identified and created by Hollebeek et al. (2014). Ultimately this could lead to a general
use of their framework when measuring consumer engagement as it proves it to be
applicable to settings and contexts beyond strictly social media.
Lastly, the work by Scholz & Smith (2016) regarding how to entice consumer
engagement with AR has been proven very useful and applicable for this study. By
creating and arguing for the hypothesized relationships in line with their framework their
points have now been somewhat validated quantitatively. We say somewhat their
framework was not applied in its entirety. For instance, the importance of user-experience
driven attributes for AR was proven to show greater significance when enticing consumer
engagement, in line with Scholz & Smith (2016, p. 157, 159).
7.3 Practical implications The practical implications from our findings are numerous. With the knowledge gained
from this study entrepreneurs as well as developers could benefit given their implications.
It has been stated throughout this thesis that AR is currently residing in its early phases
both through the Hype Cycle (Gartner, 2018) as well as the Industry Life Cycle (Johnson
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et al., 2017, p. 79). Thus, anyone currently investing time, efforts and money towards
refining AR technology and its supplementing systems could potentially greatly benefit
by entering a Blue Ocean where much areas are yet to be discovered. It is true that much
of the future direction of AR is speculations, but they can also be considered educated
guesses.
Focusing on the attributes we discovered as most important, we believe that companies
that are already developing this technology could benefit. From this thesis perspective,
we tried to conclude from the users’ point of view which AR characteristics who was
most important. This in turn could enable their systems to become more user friendly and
more attractive. By recognizing this could enable companies to gain a competitive
advantage over their competitors. Moreover, we can see from the results in this study that
AR has a more prominent relationship with the consumer engagement dimension
affection. As such, it is possible that companies may want to use AR as a strategy for
enticing consumer affection towards their brand and another strategy in combination to
entice greater cognitive processing.
Depending on what direction AR will take in the future, there could potentially be
different attributes to consider depending on the route. When developing AR further,
some attributes may be deemed more important than others, which research such as our
own can help to determine. By determining what attributes are deemed most important
by the users could be of great usage when developing the technology further.
7.4 Societal implications The societal implications by this study is limited, as it is intended to measure user - brand
relationship. However, one potential harmful aspect of AR has been identified through
the literature review. This would be the potential use of AR as a tool to market oneself
over physical backgrounds intended for other use (Scholz & Smith, 2016, p. 159). This
has already been adopted by e.g. Burger King where the user gets to virtually born down
competitors marketing for a reward (O’Brien, 2019).
This study and its practical implications could therefore be used as an argument for
conducting such marketing ploys to entice greater consumer engagement towards
businesses own brands, at the same time as reducing it towards other brands. This would
mean that physical areas of society, intended for one use, could with AR be used for
something completely different, redefining the infrastructure. However, this is a long shot
as an implication strictly from this study and should be viewed more general to AR and
its area of use. But as the continuous positive outcomes are proven e.g. Poushneh &
Vasquez-Parraga (2017) on customer experience, Scholz & Smith’s (2016) work on
engagement, and now our work on engagement, this could come closer to reality.
7.5 Limitations Regarding limitations connecting to this thesis, there are a few to consider which to
different extent have affected the end results. As been mentioned, the sample was too
small even though there are mitigating factors as stated by Mundfrom et al. (2005).
However, it must be stated that more respondents would most likely equal better results
in line with (Field, 2009, p. 645; Suhr, n.d., p. 3). Therefore, we see this as the first and
biggest limitation to this study. We have already discussed different strategies when
sending out the survey that could have resulted in a higher sample. The implications of
this narrow sample are that it is purposive and thus not possible to completely generalize
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from. However, due to our use of SmartPLS, which is formidable when working with
smaller samples, in line with Poushneh & Vasquez-Parraga (2017, p. 233) and Hair et al.
(2017, p. 18-22) (see section 4.3), we believe that this is mitigated to a satisfying extent.
Our aim and hope were to pursue a quantitative study because of the ability to generalize
the results and apply them on a larger population and we believe that this is possible to
some extent. However, we do realize that it is somewhat controversial even though we
have taken the appropriate steps to fulfil this regarding our smaller sample size. In
hindsight we could have pursued another direction and perhaps ended up with more
representative results. By having a qualitative study perhaps in connection to an event
hosted by Handla or similar organizations, we could have interviewed people right after
them using AR where perhaps our questions could be more easily answered. But as stated,
the limitation of a small sample is deemed acceptable due to the steps taken in order to
mitigate the possible negative impact that comes with it.
Kelley et al. (2003, p. 264) further explains the limitations of non-random sampling which
was another limitation, connected with the sample size. Further limitations connect to the
questionnaire and the fact that our targeted audience was Swedish people whilst asking
the questions in English. This had its reasons in the fact that we did not want to alter the
questions too much with them losing their original meaning. In order to save time as well
as the authenticity of the questions, we chose not to translate the questionnaire.
Nevertheless, it is a limitation since some of the respondents may have struggled with the
survey given the language as well as the technological aspects which potentially could
make it harder to comprehend. Continuing with further limitations was the very ordering
of the questions. Wilson & Lankton (2012, p. 3) has explained the merits of randomizing
the question order with results closer to reality. However, as discussed in section 6.4.5,
the routing could quite possibly also have confused some of the respondents even though
it was designed as well as possible to do the opposite (see section 4.2.6).
Moreover, there is always the possibility of us missing some attribute that could be
equally or even more important than the attributes we chose to include. We did however
conduct a very thorough literature review and chose our attributes with care. As such, we
could have conducted a pre-test where we asked which AR attributes are most important.
Nevertheless, while their importance all has their significance in the literature, it could be
considered strictly subjective which was why we chose our method by selecting the ones
with most prominence within the literature. As a last note on this section, with all
respondents possibly not reading the whole introduction further limits the survey if they
were not aware of what AR was prior to participating. By adding another control variable
this could have been avoided by asking if they had a somewhat clear picture of what AR
really is and how it is being used. However, this was excluded since we already had a
similar limitation (see section 4.2.5).
7.6 Future research From our findings, other research could advantageously continue the work of this thesis
and elaborate some areas. First however, we must mention the construct Ease of Use
which had to be removed due to insignificant results and poor loadings in the factor
analysis. This was as explained a surprise given its prominence in previous work (see
Huang & Liao, 2015) and how we formulated a hypothesis based on that. Furthermore,
given its basis in TAM (Marangunić & Granić, 2014, p. 85), and similarity with perceived
usefulness we believe that further work needs to be done. It could be tested with new
items which could give better loadings and/or be tested in another setting as we still
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strongly believe that it would have similar impact as perceived usefulness on consumer
engagement. Furthermore, given that Service Excellence has been described as an
antecedent for AR (Scholz & Smith, 2016, p. 151), it would also be interesting to see how
it could affect consumer engagement. Thus, continuing this work but with alterations to
make these two characteristics work would be very interesting to see how much they
could affect.
Furthermore, applying the framework from this study on specific settings could yield very
useful and interesting results. As shown in this study, setting has a significant negative
relationship with cognitive processing. As such, the attributes may yield different results,
possibly more significant as this study’s “general setting” method most likely resulted in
the data being more scattered than a specific setting would yield. Such studies would also
be able to specifically identify the most important attributes for specific settings, as it is
possible that they differentiate. For instance, Poushneh & Parraga (2017) make use of a
practical setting in relation to the quantitative data collection, which could be applied to
the framework used in this study. Such a method would also most likely result in less
confusion regarding the items in the survey and yield more representative results. On a
concluding note on the setting is that it would be interesting to see the results of a similar
study when AR has moved along the Hype Cycle towards more widespread recognition.
In a few years’ time, virtual try-on as explained by Kim & Forsythe (2008a, p. 46) will
perhaps be widely recognized, further implying that our study would be better understood
and with the ability to narrow its purpose towards retail.
As mentioned throughout this thesis, there is no universally adopted scale, construct or
framework on how to measure consumer engagement in quantitative studies. In this study
the use of Hollebeek et al.’s (2014) work has been very useful, and further validated (see
section 7.2). To strive towards a universally accepted theory or method for measuring
consumer engagement, more settings needs to be applied to their research, and especially
more work on the consequences of consumer engagement, as they were not tested in this
study. In this thesis their second model describing consumer engagement was used, it
would be interesting to conduct a similar study as this one but use their first model when
describing consumer engagement to see if there are any further large differences between
the two, other than that the data fit for Hollebeek et al. (2014, p. 160) was better with the
first model. Further, we have discussed the potential of set or defined antecedents of
consumer engagement regardless of setting. We believe that a study conducted with the
purpose of finding such antecedents with the goal of reaching maximum value for the
coefficient of determination would empirically progress both the concept and theory of
consumer engagement. We suspect that perceived usefulness may be such an antecedent
to consumer engagement that could be applied no matter the setting.
Regarding the concept of consumer engagement, we also call for future work to adopt
same or similar definition as previous authors to continue to define the concept. As of
mentioned previously (see section 2.3), most of the work on consumer engagement base
their definition on previous authors work but add to or redefine the concept somehow.
The adoption of a universal definition would thus progress the use of consumer
engagement in both theoretical and practical work.
Throughout this thesis, we have referred to the Hype Cycle (see Figure 1) and the Industry
Life Cycle (Johnson et al., 2017, p. 79). As AR progresses in the future and moves along
these cycles, it will be interesting to see its implications for everyday life and how it
74
potentially can disrupt industries and give rise to new experiences. Through innovative
marketing, Burger King has showed that unconventional usages can help attract - and
engage - customers (O’Brien, 2019). With investments the coming year expected to be in
the hundreds of billions (Retail Perceptions, 2016), the possibilities are countless and in
the not so distant future perhaps AR has a greater role in retail and even in our homes.
From our findings, we believe that we have identified which attributes developers should
focus on in order to attract customers and engage the users towards a sustainable and
prolonged relationship.
75
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Appendix 1. Factor Analysis Results in SPSS
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Appendix 2. Constructs and Items
Background Questions:
In what setting do you usually use AR?
• Shopping/Social Media/Games/Other: Specify.
How often do you use some sort AR technology?
• Every day, a few times a week, About once a week, A few times a month, less
To which gender do you most identify? (RFSL, 2016).
• Male/Female/Prefer not to specify/Other (please specify)
What is your age?
• 18–24, 25–34, 35–44, 55–64, 65+
Aesthetics (Huang & Liu, 2014):
5-point Likert Scale (strongly disagree - strongly agree).
Original:
The way AR system display its products is attractive
AR systems are aesthetically pleasing
I like the way AR system’s site look
Adopted/Changed:
The way AR system display its products is attractive
AR systems are aesthetically pleasing
I like the way AR system’s site look
Playfulness (Huang & Liu, 2014):
5-point Likert Scale (strongly disagree - strongly agree).
Escapism:
Original:
Using an AR system “gets me away from it all”
Using an AR system makes me feel like I am in another world
Adopted/Changed:
Using an AR system “gets me away from it all”
Using an AR system makes me feel like I am in another world
Enjoyment:
Original:
I enjoy using AR for the sake of it, not just for the items I may have purchased
I use AR systems for the pure enjoyment of it
Adopted/Changed:
I enjoy using AR for the sake of it, not just for the items I may have purchased
I use AR systems for the pure enjoyment of it
Ease of Use (Huang & Liao, 2015):
5-point Likert scale (strongly disagree - strongly agree).
Original:
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Using this augmented-reality interactive technology (ARIT) is clear and understandable
Using this ARIT does not require a lot of mental effort
This ARIT is easy to use
I would find it easy to get this ARIT to do what I want it to do
Adopted/Changed:
Using AR is clear and understandable
Using AR does not require a lot of mental effort
AR is easy to use
I would find it easy to get the AR I use to do what I want it to
Perceived Usefulness (Huang & Liao, 2015):
5-point Likert scale (strongly disagree - strongly agree).
Original:
This ARIT improves my online shopping productivity
This ARIT enhances my effectiveness when shopping online
This ARIT is helpful in buying what I want online
This ARIT improves my shopping ability
Adopted/Changed:
AR improves my productivity
AR enhances the effectiveness of my activity
AR is helpful for my activity
AR improves my ability to complete my activity
Service excellence (Huang & Liao, 2014):
5-point Likert scale (strongly disagree - strongly agree).
Original:
When I think of AR system, I think of excellence
I think of AR system as an expert in the merchandise it offers
Adopted/Changed:
When I think of AR, I think of excellence
I think of AR as an expert in the product it offers
Interactivity (Poushneh & Vasquez-Parraga, 2017):
7-point Likert scale (strongly disagree - strongly agree).
Original:
The website provides a variety of ways for viewing product image
The website provides personalized product
The website is interactive
The website allows the user to interact with the products shown on the screen
The website allows the user to adjust the product
The website shows dynamic product images
Adopted/Changed:
AR provides a variety of ways for viewing product image
AR provides personalized product
AR is interactive
AR allows the user to interact with the products shown on the screen
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AR allows the user to adjust the product
AR shows dynamic product images
Consumer Engagement (Hollebeek et al. 2014):
7-point Likert scale (strongly disagree - strongly agree).
Cognitive processing:
Original:
Using [brand] get me to think about [brand]
I think about [brand] a lot when I am using it
Using [brand] stimulates me to learn more about [brand].
Adopted/Changed:
Using [AR brand] get me to think about [AR brand]
I think about [AR brand] a lot when I am using it
Using [AR brand] stimulates me to learn more about [AR brand].
Affection:
Original:
I feel very positive when I use [brand]
Using [brand] makes me happy
I feel good when using [brand]
I am proud to use [brand]
Adopted/Changed:
I feel very positive when I use [AR brand]
Using [AR brand] makes me happy
I feel good when using [AR brand]
I am proud to use [AR brand]
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Appendix 3. Online Survey
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