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.

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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

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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.

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• 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.

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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

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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.

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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.

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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

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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.

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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.

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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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