Unified Theory of Acceptance and Use of Technology applied

Unified Theory of Acceptance and Use of Technology applied to mobile Augmented Reality

Applications in The Netherlands for retail purposes

Name: Ghislaine Heidman

Student number: 10813543

Thesis supervisor: Dhr. Dr. Y.B. Altayligil

Study: MSc Business Administration

Track: Digital Business

Institute: University of Amsterdam

Date of submission: 22.06.2018

Version: Final Version

CONSUMER ACCEPTANCE IN MOBILE AUGMENTED REALITY APPLICATIONS

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Statement of originality

This document is written by Ghislaine Heidman who declares to take full responsibility for

the contents of this document. I declare that the text and the work presented in this document

is original and that no sources other than those mentioned in the text and its references have

been used in creating it. The Faculty of Economics and Business is responsible solely for the

supervision of completion of the work, not for the contents.


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Abstract

New digital technologies continuously emerge that could give retailers opportunities to

increase sales. One of these emerging digital technologies is augmented reality (AR), which

has been available on mobile devices since 2017. Thus, it is not surprising that retailers are

acting on these new possibilities and have started developing their own mobile AR-based

applications. However, there is limited research on the acceptance of mobile AR apps in

retail. Therefore, this study investigates a revised Unified Theory of Acceptance and Use of

Technology (UTAUT2) model in the context of mobile AR retail applications and provides an

understanding of the different drivers that influence the behavioural intention to use mobile

AR apps in the retail industry, specifically for the Dutch customer. Furthermore, the

moderating effect of personal innovativeness in the domain of information technology is

investigated. The data was collected via an online survey (n = 179). The proposed model was

analysed via a hierarchical linear regression model, a stepwise regression model and process

analysis. From the 13 hypotheses, 4 were found to be statistically significant. The results

indicate that personal innovativeness in the domain of information technology does not

moderate the relationship between the drivers and the behavioural intention to use AR apps in

retail. However, personal innovativeness in the domain of information technology is a driver

of the behavioural intention to use mobile AR apps in retail. More drivers that influence the

behavioural intention to use AR apps in retail are performance expectancy, social influence

and hedonic motivation. The findings of this study provide new insights for both researchers

and retailers. For retailers, these insights indicate they should aim to develop the AR-based

applications according to consumer’s hedonic motivation, convince the customer of the apps’

benefits and focus on early adaptors to drives m-commerce sales.

Key words: Augmented Reality, Mobile Applications, UTAUT2, Technology

Acceptance, Consumer Acceptance, Retail


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Table of Contents

Abstract 3

1. Introduction 8

1.1 Problem Statement 9

1.2 Research Contributions & Research Questions 10

1.3 Thesis Outline 11

2. Literature Review 12

2.1 Technology 12

2.1.1 Augmented Reality 12

2.1.2 Virtual Reality 13

2.1.3 Retail Segments Using Mobile AR Applications 13

2.2 Technology Acceptance Models 15

2.2.1 Technology Acceptance Model 15

2.2.2 UTAUT: Unified Theory of Acceptance and Use of Technology 16

2.2.3 UTAUT2: Unified Theory of Acceptance and Use of Technology 2 17

2.3 Personal Innovativeness in the domain of IT 18

2.3.1 Personal Innovativeness in the domain of IT as Moderator 19

2.3.2 Personal Innovativeness in the domain of IT as a predictor of UTAUT2 20

2.4 Conceptual Framework 20

2.5 Hypotheses 22

3. Data & Method 26

3.1 Methodology 26

3.2 Data collection and sample 27

4. Results 30

4.1 Preliminary steps 30

4.2 Reliability and Validity 30


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4.2.1 Principal Components Analysis 31

4.2.2 Convergent validity 34

4.2.3 Correlation Matrix 34

4.3 Analyses 37

4.3.1 Hierarchical Regression Model 37

4.3.2 Stepwise Linear Regression Model 39

4.3.2 Moderation effect 42

4.3.3 One-way ANOVA 44

4.4 Hypotheses testing 47

5. Discussion 49

5.1 Findings Confirm UTAUT 2 49

5.2 Role of Personal Innovativeness in the Domain of IT 50

5.3 Retail segments 51

5.4 Contribution to the theory 51

5.5 Managerial implications 52

5.6 Limitations and future research 53

6. Conclusion 54

References 55

Appendices 60


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List of Tables

Table 1. Retail segments using Augmented Reality for M-commerce 14

Table 2. UTAUT2 survey items (Venkatesh et al., 2012) 27

Table 3. Educational Level 29

Table 4. Age Groups 29

Table 5. Reliability, Cronbach’s Alpha 31

Table 6. KMO and Bartlett’s Test 32

Table 7. Factor Loading PCA 33

Table 8. Mean, Standard Deviation and Correlations 36

Table 9. Hierarchical Regression Model of Behavioral Intention 39

Table 10. Stepwise regression – Model Summary 40

Table 11. Stepwise regression – Coefficients 41

Table 12. Process moderation effect of PIIT on performance expectancy 43

Table 13. Process moderation effect of PIIT on effort expectancy 43

Table 14. Process moderation effect of PIIT on social influence 43

Table 15. Process moderation effect of PIIT on hedonic motivation 44

Table 16. Process moderation effect of PIIT on habit 44

Table 17. One-way ANOVA Retail segments 45

Table 18. One-way ANOVA Gender 46

Table 19. One-way ANOVA Education 46

Table 20. One-way ANOVA Age 47


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List of Figures

Figure 1. Technology Acceptance Model 15

Figure 2. Unified Theory of Acceptance and Use of Technology 17

Figure 3. Unified Theory of Acceptance and Use of Technology 2 18

Figure 4. Personal Innovativeness in the domain of IT 19

Figure 5. Conceptual Model Behavioral Intention to Use Augmented Reality 21

Figure 6. Histogram model 5 42

Figure 7. Path Coefficients and significance level 48

Abbreviations

AR: Augmented Reality

VR: Virtual Reality

Apps: Applications

PIIT: Personal Innovativeness in de domain of IT

PE: Performance Expectancy

EE: Effort Expectancy

SI: Social Influence

HM: Hedonic Motivation

FC: Facilitating Conditions

H: Habit

BI: Behavioral Intention to Use

UTAUT(2): Unified Theory of Acceptance and Use of Technology (2)

TAM: Technology Acceptance Model


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1. Introduction

Pokémon Go, the app that went viral in 2016, had more daily active users than Tinder and its

average daily usage time was higher than for Whatsapp (Schwartz, 2016). The app allows

mobile users to interact with their surroundings via their smartphone camera. Favourite

Pokémon can be caught with Pokéballs, and people can use their physical locations to obtain

credits in the virtual map. The app combines a live view of the physical world with virtual

reality elements, called augmented reality (AR) (Azuma, Behringer, Julier & Macintyre,

2001). Pokémon brought AR to the masses, although the technology had existed for decades.

The success of the Pokémon Go app can indicate to businesses consumers’ eagerness to

embrace AR in the near future (Chen, 2017). In the world of mobile commerce (m-commerce)

especially, the question marketers should ask themselves is ‘Will augmented reality shape the

future in the m-commerce environment?’

In m-commerce, one of the main obstacles consumers face is determining whether a

product or service is right for them. Often, consumers do not know whether a product or

service is in the same style, the same colour and size in reality compared to what they have

seen online. Augmented reality gives consumers the opportunity to test and see the product in

different settings, such as at home or even on the consumers’ body and face via AR mirror

software. Furthermore, AR can be used to give a consumer additional information (online on

a smartphone) when looking at the physical product via the phone’s mobile screen (Chen,

2017). For example, the Yummly app, a search engine for food recipes, is now equipped with

an AR function that allows users to point a phone’s camera over the items users have in their

kitchens. Yummly will recommend recipes that include the ingredients a user already has at

home (Yummly, 2018). To conclude, AR could offer consumers confidence that eventually

could motivate a purchase decision for a certain product or service (Chen, 2017).


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Retailers are increasingly implementing AR within their mobile applications to market

their products or services (Chen, 2017). For instance, IKEA gives consumers the opportunity

to envision IKEA furniture in a physical environment, such as their living room (IKEA,

2017). The application allows consumers to see the different colours and styles of many

varieties of furniture, and consumers could, therefore, make a more confident purchase

decision. With this application, IKEA distinguishes itself from other furniture retailers. Not

only products can be envisioned with AR, but there are also applications that allow consumers

to envision a service that could be delivered. Examples of these are tattoo apps, measure kit

apps and the app called Modiface. The Modiface application comes with a new sort of AR:

the AR mirror software (Modiface, 2018). Mostly used by beauty brands, this software

enables consumers to look at a mirror in a store or at the in-front camera of their smartphones

and see a product on the consumers’ body, face or hair (Modiface, 2018). Hairdressers could

use this type of apps to allow consumers to first see the end result before they start cutting or

dyeing hair.

The AR mirror software is also used by retailers such as Ray-Ban to gives consumers

the opportunity to see different styles of sunglasses on their faces (Ray Ban, 2018). In

addition, the Tattoo app called Inkhunter allows consumers to see different tattoo designs and

sizes on their bodies before they are inked (Inkhunter, 2018).

1.1 Problem Statement

The continuous rise of m-commerce is inevitable and unstoppable according to data (Ismail,

2018). Therefore, AR is at the start of being introduced to m-commerce, and it would be

beneficial for retailers to make AR a significant component of their marketing and sales.

Ultimately, this integration will give the retailers the opportunity to stay ahead of their

competitors.


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However, before incorporating AR within a business, retailers should be aware of the

factors influencing consumers’ acceptance of this new technology used in marketing.

Therefore, this study aims to measure consumers’ acceptance for augmented reality retail

applications by extending the UTAUT2 model with a new moderator, personal innovativeness

in the domain of IT. Furthermore this study aims to determine whether these factors are

different for different retail segments.

1.2 Research Contributions & Research Questions

Prior research is limited to the acceptance of AR technology in general and has not

empirically tested AR acceptance in retail applications. Therefore, this study will contribute

by specifically measuring the factors that influence the Dutch consumers’ acceptance of AR

used in retail applications, that will be extended by a new factor/moderator, personal

innovativeness in the domain of IT. In addition, no previous research measures to what extent

there are differences between retail segments. Therefore, this research examines four large

retail segments and determines whether these drivers differ per segment: Beauty and

Cosmetics, Food, Home Décor and Fashion items. These four segments were chosen based on

existing AR mobile apps that aim to sell products or services (Kolo, 2018; Sheehan, 2018).

The factors that will influence consumers’ acceptance should help marketers to design

augmented reality applications more efficiently in the world of m-commerce.

Furthermore, most prior studies are limited to one particular media environment,

computers, because mobile phones have only recently become compatible with the AR

function. This is a limitation in literature because AR-based applications are used on mobile

phones. For example, in September 2017, Apple launched its ARKit, which is only

compatible with the iOS11 or higher and the Samsung S9 with Bixby Vision AR function.

Therefore, this research focuses specifically on customer acceptance on mobile devices, as


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gathering data about customer acceptance is now possible due to Snapchat filters, the high

usage of the Pokémon Go app and other AR related apps.

Despite available data, general literature about factors influencing the Dutch

consumers’ acceptance of AR retail applications seems to be missing; therefore, the following

research question was formulated:

‘What are the drivers that influence consumers’ behavioural intention to use mobile

augmented reality applications in retail and how does personal innovativeness

influence these relationships?’

1.3 Thesis Outline

The remainder of this paper is organized as follows. The second chapter elaborates on the

literature found on the following topics: augmented reality, virtual reality and retail segments

using mobile AR applications. Furthermore, the technology acceptance theories technology

acceptance model, the Unified Theory of Acceptance and Use of Technology (UTAUT) and

UTAUT2 models from Venkatesh, and the Personal Innovativeness construct from Agarwal

and Prasad are discussed (1998). In Chapter 3, the research model and the hypotheses are

presented. Furthermore, the modified UTAUT2 model is explained in detail. The fourth

chapter discuss the data and method that is used. The fifth chapter, Results, elaborates on the

gathered data and consist of the preliminary steps, data analyses, hypotheses testing and an

empirical section. In Chapter 6, the discussion is presented, followed by a more explicit

conclusion in Chapter 7, which finishes with the limitations and future research of this study.


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2. Literature Review

In the first section of this chapter, the literature on the main subject is discussed to develop a

background for this study. The first section is divided into three sub-sections: Augmented

Reality, Virtual Reality and Retail Segments Using Mobile AR Applications. In the second

section, three acceptance models that explain consumer’s acceptance of new technologies are

discussed. In the third section, the personal innovativeness model in the domain of IT is

presented. Followed by the fourth section where the theoretical framework is presented by

showing the conceptual model of this study. The fifth section includes the hypotheses that are

tested.

2.1 Technology

2.1.1 Augmented Reality

New digital technologies constantly emerge; one of these is AR. Augmented reality is likely

to have an impact on marketing in the near future (Kannan, 2017), although the impact has

not yet been scientifically proved. Augmented reality is a new method of visual informatics

and is defined by Azuma et al. (2001) as ‘a system that supplements the real world with

virtual (computer generated) objects that appear to coexist in the same space as the real

world’. In the age of e- and m-commerce, showcasing products are becoming increasingly

more important. Augmented Reality creates new possibilities for content delivery to

consumers, as the technology allows for online customers to fully inspect a product before

they decide to purchase it. Furthermore, the fast developments in the mobile industry have

brought AR experiences to mobile devices. Prior studies have shown that augmented reality-

based marketing applications are a more persuasive tool for consumers than the traditional

web-based product presentation (Yim, Chu & Sauer, 2017).


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2.1.2 Virtual Reality

It is essential to understand the differences between augmented reality and virtual reality (VR)

to better understand the strength of AR and realize how it could be beneficial for businesses.

‘Virtual Reality is a three-dimensional computer-generated environment designed for and

controlled by a person’s physical presence; it is a disruption between the physical and virtual

world’ (Dictionary, 2018). Unlike VR, AR technology visualizes virtual objects in a real-

world environment (Azuma et al., 2001).

2.1.3 Retail Segments Using Mobile AR Applications

Today, a variety of retailers use AR technology to empower their customers, improve

customers’ shopping experience and help their products stand out (Sheehan, 2018). The

technology is either used for in-store experience or for AR experience at home. Examples of

AR in-store experiences are the AR enabled mirror and the AR fitting room (Sheehan, 2018).

However, AR mobile applications are more often used for online shopping experiences,

although some mobile applications encourage engagement with in-store signage and displays

(American Apparel, 2018). Most of the literature is about the acceptance of AR in

entertaining and education context (Balog & Pribeanu, 2010; Sumadio & Rambli, 2010).

However, according to Rese et al. AR apps in other contexts, such as fashion and toys, should

also be explored with regard to consumers’ acceptance models (2017) as well. As this

research focuses on mobile AR applications, only the retailers who developed a mobile AR-

based application will be included (before January 2018). Since mobile phones have been

compatible with the AR software for less than a year, not much is written about the different

segments. For this reason, four segments were created, and all existing AR apps are listed in

Table 1 below (Kolo, 2018; Sheehan, 2018). Only the AR apps that aim to sell products, and

therefore belong to retail, were included. Furthermore, a distinction was made between the

front camera used to explore AR or the normal phone camera. These are called AR mirror


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function and AR view, respectively. Table 1 displays the four retail segments with a

description and the corresponding applications, those were available before January 2018. For

every retail segment two applications were used for this study, which are the following: (1)

Beauty and Cosmetics; Sephora Virtual Artist and L’Oreal, (2) Food; KabaQ and Yummly,

(3) Home Décor; IKEA place and Amazon AR view, (4) Fashion; Topology and Gap. The

function AR mirror is indicated with a (M) behind the app name.

Table 1.Retail segments using augmented reality for m-commerce

Retail segment Description Mobile Applications

Beauty and Cosmetics AR mirror software used to try on new make-up styles, and see how different products look on the customer’s face.

Modiface (M), L’Oreal (M), Sephora Virtual Artist (M), Rimmel (M).

Food Yummly (a recipes app) uses the phone’s camera to catalogue food items a customer has in his or her kitchen and recommends recipes that include the ingredients the customer has at home. The additional products needed for the meal can be ordered via the app. KabaQ gives restaurant customers the opportunity to see meals in AR before they choosing their dinners.

Yummly, KabaQ

Home Décor The apps allow customers to visualize what new furniture or paint colour might look like in their living rooms.

IKEA place, Dulux Visualizer, Amazon AR view

Fashion These apps allow a customer to visualize a piece of clothes or shoes on their bodies. Converse and Ray-Ban are two of the first apps to use this technology, so the app quality is low. The Gap is still developing its app, and Topology gives people the opportunity to measure the best fit of glasses and see the end result.

Converse, Ray-Ban (M), Gap, Topology (M).


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2.2 Technology Acceptance Models

2.2.1 Technology Acceptance Model

Understanding consumers’ acceptance of technology has become a central topic in previous

research because of the fast developments in information technology. Over the years, many

researchers have written theories and proposed models to better understand the acceptance of

new technologies. One of the first models to measure consumer acceptance is the technology

acceptance model (TAM) from Davis (1989) (shown in Figure 1). The TAM is an information

system theory that models how consumers come to accept a new technology and make use of

the following variables: perceived usefulness, perceived ease of use, attitude toward using and

behavioural intention to use (Davis, 1989).

Existing empirical studies have focused on the TAM (Davis, 1989) when they were

evaluating online customers’ acceptance of augmented reality (Rese, Baier, Geyer-Schulz &

Schreiber, 2017). The TAM is often modified with external variables, such as perceived

informativeness and perceived enjoyment. Both external variables have been tested and are

confirmed to have a positive impact on perceived usefulness. However, it was also indicated

that the acceptance of AR applications could vary among cultures (Choi, Lee, Sajjad & Lee,

2014; Lee, Chung & Jung 2015). Since the Dutch consumer has not yet been scrutinized and

the acceptance of new technologies could vary among cultures, it would be beneficial for

Dutch retailers to know the drivers of technology acceptance for Dutch consumers.

Figure 1. Technology Acceptance Model (Davis, 1989).


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Furthermore, there may be differences between hedonic and functional utility (Rese et al.,

2017).

2.2.2 UTAUT: Unified Theory of Acceptance and Use of Technology

Multiple papers describe different models to test the technology acceptance with different

determinants. Venkatesh, Moris, Davis & Davis reviewed eight of these user acceptance

models (2003). In their research, they formulated and validated a unified model that

integrated all determinants of these eight models (Venkatesh et al., 2003) (shown in Figure 2).

In fact, the UTAUT model is a revised version of the TAM. The original UTAUT has four

main determinants: performance expectancy (PE), effort expectancy (EE), social influence

(SI) and facilitating conditions (FC). These determinants explain 70% of the variance in the

behavioural intention to use (Venkatesh, Thong & Xu, 2012).

Performance expectancy is defined as to what extent individuals believe the

technology will help them to better perform tasks. This determinant corresponds to the

determinant perceived usefulness in the TAM (Venkatesh et al., 2003). Venkatesh et al.

indicates also that the PE construct is the strongest predictor of behavioural intention to use

(2003). Effort Expectancy corresponds to perceived ease of use in the TAM and relates to

what extent the individual believes the technology is easy to use. Social Influence relates to

what extent others’ opinions influence an individual’s intention to use the technology, mostly

in that individual’s own network of family and friends (Venkatesh et al., 2003). Facilitating

Conditions corresponds to what extent an individual believes he or she has the resources to

make use of the technology (Venkatesh et al., 2003).

The model also has four different moderators: PE by age and gender; EE by age,

gender and experience; SI by age, gender, experience and voluntariness of use and FC by age

and experience. In addition, the original study has proved a positive relationship between FC

and use behaviour.


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The original UTAUT model was constructed when investigations into technology

acceptance were more concerned with employees in large corporations than with the

individual consumer. Therefore, Venkatesh, together with Thong and Xu, developed and

empirically tested an extended version of the UTAUT model (2012). This model is called the

UTAUT2 and is designed to understand technology acceptance from a consumer’s

perspective.

2.2.3 UTAUT2: Unified Theory of Acceptance and Use of Technology 2

The UTAUT2 model includes three additional determinants, hedonic motivation (HM), price

value (PV) and habit (H), which predict consumers’ behavioural intention to use a new

technology (Venkatesh et al., 2012) (shown in Figure 3).

According to Venkatesh et al. HM is the extent to which an individual can enjoy a

technology and the pleasure derived from using it (2012). This determinant was added to the

UTAUT model, as it is an important factor in consumer technology use (Brown & Venkatesh,

2005). The second determinant, PV, relates to the individual’s belief the technology is worth

its monetary costs (Brown & Venkatesh, 2005). This determinant was added because the

Figure 2, Unified theory of acceptance and use of technology (Venkatesh, Moris, Davis & Davis, 2003).


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UTAUT2 model is designed from a consumer’s perspective, and therefore, consumers, rather

than an organization, bear the monetary costs of a technology (Venkatesh et al., 2012). The

third addition is the determinant H, which relates to the individual's previous use of the

technology and the customer’s belief that their use of that particular technology is automatic

(Limayem, Hirt & Cheung 2007) (Kim, Malhotra & Narasimhan 2005). The fourth

moderator, voluntariness of use, was removed from the revised UTAUT2 model because, in

the consumer context of technology acceptance, customers can decide for themselves whether

to adopt the new technology (Venkatesh et al., 2012).

2.3 Personal Innovativeness in the domain of IT

While the UTAUT2 model has seven determinants of the consumer’s behavioural intention to

use a new technology, it fails to investigate individual traits, such as personal innovativeness.

Previous studies, such as Goldsmith and Hofacker (1990), have shown that instead of

Figure 3. Unified theory of acceptance and use of technology 2 (Venkatesh, Thong and Xu, 2012).


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measuring a global view of innovativeness, a domain specific view of personal innovativeness

will give retailers a more specific way to measure consumers’ behavioural intention to

purchase a certain product. From this perspective, Agarwal and Prasad conducted a study to

define personal innovativeness in the domain of information technology (PIIT) (1998).

Personal innovativeness can, therefore, be defined as ‘the willingness of an individual to try

out any new information technology’ (Agarwal & Prasad, 1998) (shown in Figure 4).

2.3.1 Personal Innovativeness in the domain of IT as Moderator

The authors Agarwal and Prasad argue for the use of personal innovativeness in the domain of

IT as a moderator on the relationship between the perceptions of a technology and the

behavioural intention of consumers (Rosen, 2005). Therefore, in the modified UTAUT2

model, Personal innovativeness is a moderator between all the factors that could influence

consumers’ behavioural intention to use mobile AR retail applications (H7a-H7f). Moreover,

Agarwal and Prasad only found a significant moderation effect between one determinant of

the behavioural intention to use: compatibility. Therefore, it is of interest to retest their theory

on the UTAUT2 model.

Figure 4. Personal innovativeness in the domain of IT (Agarwal and Prasad, 1998).


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2.3.2 Personal Innovativeness in the domain of IT as a predictor of UTAUT2

In contrast with Agarwal and Prasad’s theory, in which they argue personal innovativeness in

the domain of IT is a moderator, marketing researchers have found a direct link between

personal innovativeness and the behavioural intention to use a new technology (Rosen, 2005).

These researchers believe if highly innovative people are targeted first, they will eventually

improve word-of-mouth advertising (Rosen, 2005). Limayem, Khalifa and Frini empirically

tested this theory and found support for the direct positive link between PITT and purchase

intentions (2000). As this thesis was interested in the determinants that increase the

behavioural intention to use mobile AR retail applications, the PIIT was also tested as a direct

predictor of behavioural intention to use.

2.4 Conceptual Framework

After considering all available technology acceptance models, UTAUT2 was adopted for this

research because this model is specially made to measure the consumer’s technology

acceptance. Furthermore, the UTAUT model explains as much as 70% of the variance in

behavioural intention to use a technology (Venkatesh et al., 2012). Therefore, this model

seems to be most appropriate for this study on consumers’ acceptance of mobile AR-based

retail applications. To test the drivers for consumers’ acceptance of retail AR-based

applications, several hypotheses were formulated based on the currently proposed research

model. The standard moderators of UTAUT2, age, gender and experience, were not included

due to the time limit of this study. However, these moderators were used as control variables,

which allowed for side analyses.

A new moderator, personal innovativeness in the domain of IT (Agarwal & Prasad,

1998), was tested. In addition, a direct link between this determinant and the behavioural

intention to use mobile AR retail apps was tested.


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The dependent variable use behaviour (US) was not researched because, due to the short time

frame, it was not possible to measure the continuous use of the technology. As a result,

behavioural intention was selected as the main dependent variable of this study.

The independent variable, price value (PC), was not included in this study because

there are no monetary costs related to mobile AR-based apps used by consumers, as these

apps are provided by the retailer and so free to download.

The independent variables of this study are as follows: performance expectancy, effort

expectancy, social influence, facilitating conditions, hedonic motivation, habit and personal

innovativeness in the domain of IT. These independent variables are proposed to have a

positive direct effect on the behavioural intention to use mobile AR-based retail apps. In

addition, personal innovativeness in the domain of IT is proposed to positively moderate the

relationship between the first six independent variables and behavioural intention to use

mobile AR-based retail app. In Figure 5, the conceptual model used in this research is

presented.

Figure 5. Conceptual Model of behavioural intention to use mobile AR retail apps


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2.5 Hypotheses

Hypotheses 1 through 6 (H1-H6) are designed to test which factors of the revised UTAUT2

model are validate for the consumers’ acceptance of mobile AR retail applications.

Performance Expectancy is defined as to what extent an individual believes the technology

will help them to better completes their tasks (Venkatesh et al., 2003). Venkatesh et al.

indicate the PE construct is the strongest predictor of behavioural intention to use a new

technology (2003). As these mobile apps are developed to increase retailer’s m-commerce

sales, the performance is likely to have a positive effect on consumers’ behavioural intention

to use mobile AR-based retail apps. Therefore the following hypothesis is proposed:

H1: Performance expectancy will have a positive influence on behavioural intention to

use mobile AR retail applications.

Effort Expectancy relates to what extent the individual believes the technology is easy to use

(Venkatesh et al., 2003). In addition, EE is based on the perceived ease of use of the TAM

model (Davis, 1989). Furthermore, this factor is expected to be important, as AR is new to

mobile devices. In this study, it is therefore expected that if the consumer expects mobile AR-

based retail apps to require little effort learn to use, this assumption could positively affect

their behavioural intention to use these apps:

H2: Effort expectancy will have a positive influence on behavioural intention to use

mobile AR retail applications.

Social influence relates to what extent others’ opinions influence an individual’s intention to

use the technology, mostly in that individual’s own network of family and friends (Venkatesh


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et al., 2003). Social influence is therefore expected to have a positive effect on the

behavioural intention to use mobile AR-based retail apps:

H3: Social influence will have a positive influence on behavioural intention to use

mobile AR retail applications.

Facilitating conditions relates to what extent an individual believes he or she has the resources

to make use of the technology (Venkatesh et al., 2003). A consumer needs to have a mobile

device with software compatible with the AR function to make use of these AR-based retail

apps. It is therefore expected that FC will positively influence the behavioural intention to use

mobile AR-based retail apps:

H4: Facilitating conditions will have a positive influence on behavioural intention to

use mobile AR retail applications.

Hedonic motivation is the extent to what an individual can enjoy the technology and derives

pleasure from using it (Venkatesh et al., 2012). This determinant is important in consumer

technology use (Brown & Venkatesh, 2005). As Rese et al. argued it is important to measure

not only the functional utility of AR technology, but also the hedonic utility, as the use of new

technologies is perceived as entertaining (2017). Therefore, it is expected that HM positively

influences the behavioural intention to use mobile AR-based retail apps.

H5: Hedonic motivation will have a positive influence on behavioural intention to use

mobile AR retail applications

Habit relates to the individual's previous use of the technology and the customer’s belief that

the use of that particular technology is automatic to them (Limayem, Hirt & Cheung 2007)

(Kim, Malhotra & Narasimhan 2005). Considering AR technology habitual is expected to


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have a positive effect on the behavioural intention to use mobile AR-based retail apps. This is

mainly because of the experience consumers obtained via other AR-based apps, such as

Snapchat and Instagram filters.

H6: Habit will have a positive influence on behavioural intention to use mobile AR

retail applications

Hypotheses 7a through 7f (H7a-H7f) measure whether personal innovativeness in the domain

of IT is a positive moderator on the relationship between the factors and the behavioural

intention to use mobile AR-based retail apps. Agarwal & Prasad’s paper illustrated that the

more innovative consumers are, the more likely they are to develop positive perceptions that

positively influence their behavioural intention to use a new technology (1998). As AR is in

the begin phase of m-commerce, early adaptors are expected to positively moderates the

relationship between the factors and the behavioral intention to use mobile AR-based retail

apps. Therefore, this study hypothesized the following:

H7a: Personal innovativeness in the domain of IT positively moderates the

relationship between performance expectancy and behavioural intention to use, so this

relationship is stronger for consumers with higher values of personal innovativeness.

H7b: Personal innovativeness in the domain of IT positively moderates the

relationship between effort expectancy and behavioural intention to use, so this


H7c: Personal innovativeness in the domain of IT positively moderates the

relationship between social influence and behavioural intention to use, so this



25

H7d: Personal innovativeness in the domain of IT positively moderates the

relationship between facilitating conditions and behavioural intention to use, so this


H7e: Personal innovativeness in the domain of IT positively moderates the

relationship between hedonic motivation and behavioural intention to use, so this


H7f: Personal innovativeness in the domain of IT positively moderates the relationship

between habit and behavioural intention to use, so this relationship is stronger for

consumers with higher values of personal innovativeness.

Hypothesis 8 (H8) tests whether personal innovativeness in the domain of IT is a determinant

of the behavioural intention to use mobile AR retail applications, as this study is focused on

the determinants. According to Rosen (2005), marketing researchers found a direct positive

link between PIIT and behavioural intention to use, and therefore, this study hypothesized the

following.

H8: Personal innovativeness in the domain of IT will have a positive influence on

behavioural intention to use mobile AR retail applications


26

3. Data & Method

This quantitative research is an explanatory study that obtained its data via a cross-sectional

survey design, as this is the most appropriate when testing a particular phenomenon at a

particular time. Four different surveys were distributed. Each focused on a specific retail

segment: Beauty and Cosmetics, Food, Home Décor and Fashion Items. The survey data was

used to test the hypotheses and generalize research and managerial implications. The online

survey consisted of multiple 5-point Likert-scale questions and the obtained data was

analysed in SPSS. The survey was translated into Dutch to lower language barriers to

encourage respondents to complete the survey. In order to verify the translation the survey

was checked by individuals who are proficient in both English and Dutch (Appendix 1).

3.1 Methodology

The online survey asked the respondents to complete demographical questions about their

gender (nominal variable), age (ratio variable) and educational background (ordinal variable).

The other constructs from the questionnaire were adopted from the original UTAUT2 paper

from Venkatesh et al. (2012) and were therefore measured using Venkatesh et al.’s (2012)

scale. A validated Likert Scale was used on a 5-point scale ranging from ‘strongly disagree’

(1) to ‘strongly agree’ (5) at the interval level. Some scales were shortened because using all

items would make the survey too long, risking a lower response rate. Personal innovativeness

was measured with three items on Agarwal and Prasad’s 5-point Likert-scale of (1998). All

the scales and corresponding items are shown in Table 2.


27

Table 2. UTAUT2 survey items (Venkatesh et al., 2012)

Scale Items Performance Expectancy

PE1. I find these kinds of Augmented Reality Retail Applications useful in my daily life PE2. Using these kinds of Augmented Reality Retail Applications increases my chances to make better purchase decisions PE3. Using these kinds of Augmented Reality Retail Applications helps me to make purchase decisions quicker

Effort Expectancy

EE1. Learning how to use such Augmented Reality Application is easy for me EE2. My interaction with Augmented Reality Applications is clear and understandable EE3. I find these Augmented Reality Applications easy to use (or perception) EE4. It is easy for me to become skilful at using these Augmented Reality Applications

Social Influence

SI1. People who are important to me think that I should use Augmented Reality Retail Applications SI2. People who influence my behaviour think that I should use Augmented Reality Retail Applications SI3. People whose opinions that I value prefer that I use Augmented Reality Applications

Facilitating Conditions

FC1. I have a mobile phone (IOS 11 or Samsung S9) where I can use these Augmented Reality Applications on FC2. I have the knowledge necessary to download and use Augmented Reality Applications FC3. I can get help from others when I have difficulties using Augmented Reality Applications

Hedonic Motivation

HM1. Using Augmented Reality Applications is fun HM2. Using Augmented Reality Applications is enjoyable HM3. Using Augmented Reality Applications is very entertaining

Habit

H1. The use of Augmented Reality Applications has become a habit for me H2. I am addicted to using Augmented Reality Applications H3. I must use Augmented Reality Applications H4. Using Augmented Reality Applications has become natural to me

Personal Innovativeness

PIIT1. If I heard about a new information technology, I would look for ways to experiment with it PIIT2. Among my peers, I am usually the first to try out new information technologies PIIT3. I like to experiment with new information technologies

Behavioural Intention

BI1. I intend to continue using these kinds of Augmented Reality Applications in the future BI2. I will always try to use these kinds of Augmented Reality Applications in my daily life BI3. I plan to continue to use these kinds of Augmented Reality Applications frequently

3.2 Data collection and sample

The survey was distributed online via personal social media channels and public channels,

such as forums and websites, from 19 April to 7 May 2018. The total number of respondents


28

to the survey was 254. However, 24 respondents did not complete the whole survey, and

therefore, these incomplete surveys were removed from the sample.

From the remaining 230 respondents, 198 indicated that they had used mobile AR

applications before. Since this study wanted to test the acceptance of mobile AR apps in

retail, only the data of the respondents who knew how this AR technology works was used.

The sample was checked for normality; all histograms of frequencies were bell

shaped. However, the Kolmogorov-Smirnov test failed, indicating, the null hypothesis of

normal distribution should be rejected. However, almost all the QQ plots (appendix 2) were

on the diagonal, which means they are normally distributed. According to Field, for a large

sample size (±200), it is more important to look at the shape of the distribution and the

statistical value of the skewness and kurtosis (2009). The skewness and kurtosis values for

this research were all between -1 and 1, except for the variable HM. Therefore, the outliers

were examined and trimming the data reduced the impact of bias. Using the boxplot test, 11

extreme outliers were identified and excluded for further analyses. Furthermore, the Z-scores

were checked for values above 3 and below -3; these outliers were also excluded from further

analyses. The remaining 179 respondents were used as the sample size for this study. Again, a

normality check was performed, and all skewness and kurtosis values were acceptable values

between -1 and 1 (Field, 2009), shown in appendix 3. Of the variables, BI, PE, FC and PIIT

had negative skewness, indicating a pile-up of scores on the right of the distribution, while

EE, SI, HM, and H had positive skewness, indicating a pile-up to the left of the distribution.

The positive values of kurtosis of BI, PE, FC and HM indicate a pointy and heavy-tailed

distribution. The negative values of kurtosis of EE, SI, H and PIIT indicate a flat and light-

tailed distribution (Field, 2009).


29

Of respondents, 64.8% were female, while 35.2% were male. The sample was skewed

to more highly educated people, as displayed in Table 3, where 79.3% of the people had a

high educational level: applied sciences, university bachelor’s, master’s or PhD. The

education groups were split into low, middle and high educated according to the national

guidelines of Centraal Bureau Statistiek (CBS, 2016).

Table 3. Educational Level

Frequency Percent Valid Percent Cumulative

Percent

Valid Low Educational Level 2 1.1 1.1 1.1

Middle Educational Level 35 19.6 19.6 20.7

High Educational Level 142 79.3 79.3 100.0

Total 179 100.0 100.0

As Table 4 illustrates, the sample mainly represents a young population between the

ages of 16 and 25 years old (81.6%). According Strauss and Howe’s (2007) book, Millennials

Go To College, the age groups can be divided into the Baby Boomer Generation (1943-1960),

Generation X (1961-1981), Millennials (1982-2003) and Generation Z (2004 and later). Thus,

using Strauss and Howe’s guidelines, 93.9% of the respondents belong to the Millennial

generation.

Table 4. Age Groups

Frequency Percent Valid Percent Cumulative

Percent

Valid 16-25 146 81.6 81.6 81.6

26-35 22 12.3 12.3 93.9

36-45 10 5.6 5.6 99.4

46-55 1 0.6 0.6 100.0

Total 179 100.0 100.0


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4. Results

In this chapter, the results of the study’s analyses are described. First, the preliminary steps

were completed. Second, the reliability and validity tests are presented. Third, the results of

the regression, stepwise regression, process and one-way ANOVA are described. Last, the

hypotheses’ testing is described.

4.1 Preliminary steps

First, to enable the use of gender as a control variable, (1) male and (2) female were recoded

into male = 0 and female = 1. Furthermore, the categorical variable education was changed

into dichotomous variables, also called dummy coding, as this categorical variable had more

than two categories. This step was necessary due to the regression analysis that was

performed in later stage, in which education was used as a predictor. None of the variables

has items that were counter-indicative, so none had to be recoded.

4.2 Reliability and Validity

A reliability analysis was executed on all factors. Looking at the Cronbach’s alpha value is

commonly used to test the reliability of a scale. Examining the correlation between the

different items tests the reliability. A Cronbach's alpha value above .7 is an acceptable value;

values substantially lower (α < .7) indicate unreliability (Field, 2009). The PE scale had high

reliability, with a Cronbach’s alpha of .797. The corrected item-total correlations indicate that

all the items had a good correlation with the total score of the scale, as all scored above .030.

Also, none of the items would substantially affect reliability if one were deleted, by looking if

Cronbach’s alpha if item deleted was substantially different from the Cronbach’s alpha value

tests this.

The following factor scales were also checked for reliability. The EE scale had high

reliability, with a Cronbach’s alpha of .798. For the scale of SI, a high reliability was present

(α = .946). For the factor HM, high reliability applied (α = .844). For the scale of H, the


31

reliability was high, with a Cronbach’s α of .770. For personal innovativeness, Cronbach's α

was .911, and for behavioural intention, the α was .760. However, the reliability analysis

indicated a problem with the scale of FC. The Cronbach’s alpha for this factor was .461,

which is below the minimum of .7. Even when one of the items was deleted, the Cronbach’s

alpha remained too low. Since the assumption is that there must be at least three items, the

decision has made to reject this factor for further analyses. All outcomes are presented in

Table 5.

Table 5 Reliability, Cronbach's alpha

Cronbach's alpha

Performance expectancy .797

Effort expectancy .798

Social influence .946

Facilitating conditions .461

Hedonic motivation .844

Habit .770

Personal innovativeness .911

Behavioural intention .760

4.2.1 Principal Components Analysis

A principal components analysis (PCA) was performed on the scales of the independent

variables to examine similarities between the variables and to check whether this study could

reduce the set of variables into a number of factors. Since the dependent variable was known,

there was no need to use this variable in the factor analysis (Field, 2009). The Kaiser-Meyer-

Olkin (KMO) measure verified the sampling adequacy for the analysis (KMO = .777), which

is above the minimum (> |.60|) and Bartlett’s Test of Sphericity X2 was significant (< .001):

(253) = 2232.98, shown in Table 6. This indicates that correlations between the items were


32

sufficiently large for the PCA analysis. Seven components had eigenvalues over Kaiser’s

criterion of 1, and in combination, these components explained 73.39% of the variance.

Another method to check whether an eigenvalue is large enough to represent a factor

is by examining the scree plot, shown in Appendix 4. According to Cattell (1966b), the cut-

off point for selecting factors should be at the point of inflexion of this curve, also known as

the point where the slope of the line changes dramatically. The scree plot examined a point of

inflexion after the seventh factor. Therefore, seven factors were retained and rotated with an

oblimin with Kaiser normalization rotation, a method of oblique rotation (Field, 2009). With

oblique rotation, the factors are allowed to correlate.

Table 7 shows the factor loadings after the oblimin rotation. The items that cluster on

the same factor suggest that factor 1 represents HM, factor 2 represents SI, factor 3 represents

PIIT, factor 4 represents PE, factor 5 represents EE, factor 6 represents H and factor 7

represents FC. The second item of facilitation conditions indicates high cross loading on the

factor of HM. The same is true for the last item of FC, which shows high cross-loadings on

the factor of HM as well. Both results could be due to the content of the items. However, as

mentioned in the reliability analysis, the factor FC was rejected from further analyses due to a

Cronbach’s alpha of less than .7. According to Field, the pattern matrix shows evidence for

convergent validity if all the factor loadings have a score great than .4 (2009). If FC is

disregarded, all the loadings had scores greater than .6, which indicates high validity.

Table 6. KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .777 Bartlett's Test of Sphericity Approx. Chi-Square 2232.984

df 253

Sig. .000


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Table 7. Factor Loading PCA

Pattern Matrix Rotated Factor Loadings

HM SI PIIT PE EE H FC Q11-1 I find these kinds of Augmented Reality Retail Applications useful in my daily life

.129 .014 -.081 -.788 -.083 .106 -.143

Q11-2 Using these kinds of Augmented Reality Retail Applications increases my chances to make better purchase decisions

.007 -.032 .116 -.848 .080 -.027 .043

Q11-3 Using these kinds of Augmented Reality Retail Applications helps me to make purchase decisions quicker

-.018 -.117 -.053 -.804 -.003 .003 .198

Q12-1 Learning how to use such Augmented Reality Application is easy for me.

.055 .063 .084 -.145 -.549 -.082 .260

Q12-2 My interaction with Augmented Reality Applications is clear and understandable.

-.042 .037 -.023 -.008 -.889 -.018 -.110

Q12-3 I find these Augmented Reality Applications easy to use (or perception)

.000 -.026 -.073 .021 -.831 .195 .032

Q12-4 It is easy for me to become skilful at using these Augmented Reality Applications.

.047 -.120 .109 .056 -.784 -.082 -.063

Q13-1 People who are important to me think that I should use Augmented Reality Retail Applications

-.025 -.929 -.006 -.047 -.054 -.010 -.045

Q13-2 People who influence my behaviour think that I should use Augmented Reality Retail Applications

.043 -.929 .056 -.022 -.013 .000 .024

Q13-3 People whose opinions that I value prefer that I use Augmented Reality Applications

.011 -.928 .009 -.032 .012 .039 .007

Q14-1 I have a mobile phone (IOS 11 or Samsung S9) where I can use these Augmented Reality Applications on

-.079 .039 .083 -.100 .044 .043 .896

Q14-2 I have the knowledge necessary to download and use Augmented Reality Applications

.339 -.041 .051 .042 -.329 -.052 .419

Q14 -3 I can get help from others when I have difficulties using Augmented Reality Applications.

.637 -.097 -.113 .157 .115 .089 .222

Q15-1 Using Augmented Reality Applications is fun.

.801 -.022 .059 -.104 -.086 .012 -.120

Q15-2 Using Augmented Reality Applications is enjoyable.

.710 -.129 .040 -.124 -.096 .040 -.089

Q15-3 Using Augmented Reality Applications is very entertaining.

.890 .124 .091 -.080 -.002 -.063 -.084

Q16-1 The use of Augmented Reality Applications has become a habit for me.

.086 -.069 .030 .025 -.026 .792 .111

Q16-2 I am addicted to using Augmented Reality Applications

-.087 -.227 -.007 .038 .083 .734 -.109


34

Q16-3 I must use Augmented Reality Applications

-.039 .003 .074 -.193 .046 .630 -.178

Q16-4 Using Augmented Reality Applications has become natural to me.

.068 .127 .061 .020 -.127 .798 .164

Q17-1 If I heard about a new information technology, I would look for ways to experiment with it.

.033 -.020 .912 -.002 -.014 .032 -.025

Q17-2 Among my peers, I am usually the first to try out new information technologies.

.041 .005 .906 .068 .012 .093 .023

Q17-3 I like to experiment with new information technologies.

-.043 -.033 .917 -.012 .014 -.031 .044

Extraction method: PCA Rotation method: oblimin with Kaiser normalization.

4.2.2 Convergent validity

As this study wanted to assess the total validity of the model, the convergent validity is

inspected. The convergent validity is a measure of the amount of variance captured by a

construct in relation to the amount of variance due to measurement error (Fornell & Larcker,

1981). The convergent validity is measured using the values of the average variance extracted

(AVE). According to the criterion of Fornell and Larcker, a minimum AVE value of .5 is

needed (1981). For this study, all factors met this criterion. The AVE for PE was .662. For

EE, the AVE was .599; for SI, it was .862; for HM, it was .646; for H, it was .550 and for

PIIT, it was .831. These outcomes are presented in Table 8.

4.2.3 Correlation Matrix

Before the correlation matrix was performed, means were first computed. The items per factor

were computed into one and standardized. Next, a bivariate Pearson correlation was

performed. Results displayed in Table 8. The correlation matrix indicated no Pearson’s

correlation coefficients were above .9. Therefore, this study assumed that multicollinearity

was not a problem (Field, 2009). There was a statistically significant (p < 0.01) positive linear

relationship between PE and behavioural intention (.520). However, the correlation


35

coefficient did not indicate in which direction the causality operated between these two

variables. This positive relationship also applied to the relationship between PIIT and BI

(.487) and between HM and BI (.395). Furthermore, there were statistically significant (p <

.01) positive linear relationships between EE and BI (.231), between SI and BI (.356) and

between H and BI (.312).

Table 7. Mean, Standard Deviation and Correlations

Variables M SD 1 2 3 4 5 6 7 8 9 10

AVE .662 .599 .862 .646 .550 .831

1.BI 3.4 .649 (.760)

2.Age 24.41 5.127 .138

3.Gender 0.65 .479 -.205** -.113

4.Education 6.54 1.435 .136 .379** .039

5.PE 3.53 .729 .520** -.035 -.027 .016 (.797)

6.EE 4.13 .484 .231** .028 -.082 .160* .209** (.798)

7.SI 2.33 .861 .356** .084 -.088 .022 .325** .109 (.946)

8.HM 3.99 .547 .395** .021 -.058 .101 .319** .430** .264** (.844)

9.H 2.18 .781 .312** -.013 -.090 -.145 .270** .106 .386** .205** (.770)

10.PIIT 3.12 1.069 .487** .049 -.299** .114 .223** .268** .154* .249** .305** (.911)

**. Correlation significant at the 0.01 level (two-tailed).

*. Correlation significant at the 0.05 level (two-tailed).


37

4.3 Analyses

First, the results of the multiple linear regression analyses will be discussed to find the

maximum explained variance model. Thereafter, the stepwise linear regression, used to find a

model with a maximum of statistically significant variables, is presented. Third, the

moderation interaction is explained by using process, and afterward, the one-way ANOVA is

discussed.

4.3.1 Hierarchical Regression Model

For the first model only the control variables, age, gender and education, were measured as

predictors of behavioural intention to use, and the model explained 5.1% of the variance. The

model was statistically significant: F (3,175) = 4.196, p < .01. These results indicate most

variance of the variable behavioural intention to use is explained by other factors.

In model 2, the predictors PE, EE, SI, HM, H and PIIT were added, and the total

variance explained by the model as a whole was 45.3% (F (9,169) = 17.387), which was

significant (p < .001). In the second model, three out of nine predictor variables were

significantly correlated, with PE recording the highest standardized beta coefficient (β = .355,

p < .001). Thus, if PE increases by one, the behavioural intention to use mobile AR retail

applications will increase by .355. The second largest predictor correlated with BI was PIIT

with the following standardized beta coefficient: β = .298, p < .001. The third predictor

correlated with BI was HM (β = .168, p < .05). All three paths mentioned were positively

related to the behavioural intention to use mobile AR retail apps. The three paths were

consistent with H1, H5 and H8, which were associated with the intention to use mobile AR

apps as a retail tool, such that a more positive PE, HM and PIIT, respectively, lead to a greater

BI. The results of the hierarchical regression model are shown in Table 9.

Hypotheses H2, H3 and H6 were non-significant (p > .05), so that a higher level of EE

(H2, β = -.032, p = .615), SI (H3, β = .119, p = .062) and H (H6, β = .051, p = .429) were not


38

associated with a significantly higher or lower level of behavioural intention to use mobile

AR apps in retail.

In the third model, the interaction terms were also included, and the total variance

explained by the model as a whole was 45.8% (F (14,164) = 11,736), which was not

significant (p > .05). Therefore, the moderation effect of personal innovativeness does not

explain a higher variance. The interaction term was also analysed in process (Hayes, 2013),

which is elaborated in 5.3.3 in the stepwise regression, elaborated in section 5.3.2.

Furthermore, only gender was statistically significant in model 1, which measured

only the control variables as explanatory variables. In the other two models, none of the

control variables was statistically significant. This result means that age, gender and education

level do not influence the results of the predictors influencing the behavioural intention to use

mobile AR retail apps.


39

Table 8. Hierarchical Regression Model of behavioural Intention Behavioural Intention Model 1 Model 2 Model 3 Explanatory variables

B S.E. Beta B S.E. Beta B S.E. Beta

Control Variables Age .009 .010 .071 .012 .008 .094 .013 .008 .104 Gender -.273 .100 -.201** -.103 .080 -.076 -.141 .082 -.104 Education .053 .036 .117 .026 .028 .057 .018 .029 .040 Predictor Variables PE .316 .055 .355*** .318 .055 .356*** EE -.043 .085 -.032 -.025 .085 -.019 SI .090 .048 .119 .111 .049 .148* HM .199 .077 .168* .191 .079 .161* H .042 .054 .051 .039 .056 .047 PIIT .181 .039 .298*** .171 .042 .282*** Moderator Variables PE x PIIT .079 .051 .102 EE x PIIT -.110 .076 -.101 SI x PIIT -.081 .043 -.126 HM x PIIT .064 .073 .067 H x PIIT .011 .051 .014 F-value 4.196** 17.387*** 11.736*** df (df1, df2) (3,175) (9,169) (14,164) R2 .067** .481*** .500 dR2 .051 .453 .458 ***p < .001 **p < .01 *p < .01

4.3.2 Stepwise Linear Regression Model

A stepwise linear regression model was used to perform a regression on multiple variables

and simultaneously remove the insignificant variables. In this method, it is not possible to lose

a significant variable and not possible to have an insignificant variable. Therefore, this model

results in the best explanatory variables. In this stepwise linear regression model, the control

variables and the other predictors, including the interaction terms, are measured. As presented

in Table 10, five different models (in each model a predictor was added) were found

statistically significant (p < .05).


40

Table 9 stepwise regressions, model summary

Model Summaryf

Model R R Square Adjusted R Square

R Square Change

F Change

df1 df2 Sig. F Change

1a .52 .271 .267 .271 65.714 1 177 .000

2b .639 .409 .402 .138 41.049 1 176 .000

3c .663 .439 .429 .030 9.447 1 175 .002

4d .677 .458 .446 .019 6.201 1 174 .014

5e .687 .472 .457 .014 4.624 1 173 .033

a. Predictors: (Constant), PE b. Predictors: (Constant), PE, PIIT c. Predictors: (Constant), PE, PIIT, HM d. Predictors: (Constant), PE, PIIT, HM, SI e. Predictors: (Constant), PE, PIIT, HM, SI, Age f. Dependent Variable: BI

The five predictors in model 5 explained 45.7% of the total variance (F (1,173) =

4,624) and were significant (p < .05). Performance expectancy was the greatest predictor of

the behavioural intention to use mobile AR retail apps and explained 26.7% of the variance.

The second largest predictor for behavioural intention to use mobile augmented reality retail

apps was personal innovativeness, which explained another 13.8% of the variance. The third

predictor was HM, which was much lower than the other two, explaining only an additional

3% of the variance. Last, two additional small predictors were found statistically significant

(p < .05), SI and age, which respectively explained 1.9% and 1.4% of the variance. Therefore,

five predictors were significant: PE, personal innovativeness, HM, SI and age. Four of these

predictors are consistent with H1, H3, H5 and H8.

Table 11 presents the coefficients in model 5. In this model, PE had the highest

standardized beta coefficient (β = .355, p < .001). Therefore, if PE increases by one, the

behavioural intention to use mobile AR retail apps will increase by .355. If PIIT increases by

one, the behavioural intention to use mobile AR retail apps will increase by .332. For HM, the

increase would be lower, as the behavioural intention to use would increase by .161. If the


41

fourth predictor, SI, increases by one, the behavioural intention to use will increase by .137.

Last, the predictor age had a direct positive relationship with the behavioural intention to use;

if someone’s age increases by one, the BI will increase by .120. None of the interaction terms

were added to the model, so none of these interaction terms were statistically significant.

Table 10. Stepwise regression, coefficients

Coefficients

Model Unstandardized Coefficients

Standardized Coefficients

t Sig.

B Std. Error Beta

5 (Constant) .276 .329 0.841 .402

PE .316 .054 .355 5.798 .000

PIIT .202 .035 .332 5.731 .000

HM .191 .071 .161 2.676 .008

SI .104 .045 .137 2.303 .022

Age .015 .007 .120 2.150 .033

Dependent Variable: BI

Additionally, three case numbers had residual sizes that exceeded 3. These cases were

not excluded, as they are not the result of a mistake during data entry. Furthermore, the

Cook’s distance value was below 1. In addition, the three case numbers that exceeded 3, only

accounts for 1.67% of the total sample size; therefore, the outliers have a relatively small

impact on the model.

The histogram (Figure 6) of the residuals suggests that they are close to being

normally distributed. The scatterplot in Appendix 5 indicates the residuals are not distributed

in any pattern with the predicted values. Therefore, the model does not violate the assumption

of homoscedasticity.


42

Figure 6. Histogram of model 5

4.3.2 Moderation effect

After the regression analyses, the simple moderation effect of personal innovativeness in the

domain of IT was analysed by the process analysis of Hayes (2013). Process is a macro tool

for SPSS, to check whether personal innovativeness positively moderated the relationship

between the predictor of the model and the outcome. For this reason, the p-value of the

interaction term was checked for each interaction term in the model to determine whether

these interaction terms were significant to the model. The interaction terms were consistent

with hypotheses 7a through 7f.

The first interaction term PE x PIIT was not statistically significant (p = .2208, so p >

.05) (shown in Table 12). Thus, the effect of PE on the behavioural intention to use mobile

AR apps in the future does not depend on someone’s personal innovativeness in the domain

of IT. Therefore, H7a was rejected.


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Table 12. Process moderation effect of personal innovativeness on PE

Coefficient SE t p

Intercept -.0039 .0547 -.0718 .9428

PE (X) .4241 .0579 7.3299 .0000

PIIT (W) .3566 .0557 6.3972 .0000

PE*PIIT (XW) .0655 .0533 1.2288 .2208

The second interaction term EE x PIIT was also not statistically significant (p = .8341,

so p > .05) (shown in Table 13). Thus, the effect of EE on the behavioural intention to use

mobile AR apps in the future does not depend on someone’s personal innovativeness in the

domain of IT. Therefore, H7b was rejected.

Table 13. Process moderation effect of personal innovativeness on EE

Coefficient SE t p

Intercept .0250 .0626 .4000 .6896

EE (X) .1095 .0673 1.6282 .1053

PIIT (W) .4229 .0649 6.5114 .0000

EE*PIIT (XW) -.0121 .0578 -.2097 .8341

The third interaction term SI x PIIT was not statistically significant (p = .3634, so p >

.05) (shown in Table 14). Thus, the effect of SI on the behavioural intention to use mobile AR

apps in the future does not depend on someone’s personal innovativeness in the domain of IT.

Therefore, H7c was rejected.

Table 14. Process moderation effect of personal innovativeness on SI

Coefficient SE t p

Intercept .0301 .0585 .5141 .6078

SI (X) .2919 .0628 4.6484 .0000

PIIT (W) .4056 .0595 6.8149 .0000

SI*PIIT (XW) -.0519 .0569 -.9114 .3634


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The fourth interaction term HM x PIIT was not statistically significant (p = .9670, so p

> .05) (shown in Table 15). Thus, the effect of HM on the behavioural intention to use mobile

AR apps in the future does not depend on someone’s personal innovativeness in the domain

of IT. Therefore, H7e was rejected.

Table 15. Process moderation effect of personal innovativeness on HM

Coefficient SE t p

Intercept .0210 .0595 .3535 .7242

HM (X) .2772 .0617 4.4930 .0000

PIIT (W) .3804 .0622 6.1197 .0000

HM*PIIT (XW) -.0023 .0554 -.0414 .9670

The last interaction term H x PIIT was not statistically significant (p = .0983, so p >

.05) (shown in Table 16). Thus, the effect of H on the behavioural intention to use mobile AR

apps in the future does not depend on someone’s personal innovativeness in the domain of IT.

Therefore, H7f was rejected.

Table 16. Process moderation effect of personal innovativeness on H

Coefficient SE t P

Intercept -.0085 .0627 -.1349 .8929

H (X) .1344 .0689 1.9511 .0526

PIIT (W) .4133 .0644 6.4140 .0000

H*PIIT (XW) .1080 .0650 1.6621 .0983

According to the process analyses of Hayes, there is no moderation effect; meaning

hypotheses H7a through H7f were all rejected (2013).

4.3.3 One-way ANOVA

A one-way ANOVA was used to discover whether there were any differences in acceptance

among the four retail sectors. The results, in Table 17, have shown that there was no

statistically significant effect of sector on the behavioural intention to use mobile AR retail


45

apps in the future: F (3, 175) = 1.766, p = .155. Therefore, there was no significant difference

in acceptance among the four retail sectors (p > .05).

Table 17. One-way ANOVA retail segments

As Table 17 illustrates, the four retail sectors each have different means for the

behavioural intention to use mobile AR retail apps; therefore, there is indeed a difference in

behavioural intention, although these differences are not significant. This result could be due

to the low sample size for each retail sector, which is between 37 and 51 respondents.

The means plot in Appendix 4 shows that the Home sector (group 1) had a greater

behavioural intention than the other three groups to use these mobile AR retail apps in the

near future. However, for the beauty sector (group 3), the behavioural intention to use was

lower than for the food sector and for the fashion sector.

4.3.4 Additional side analyses

As mentioned before, this study measured three main control variables: age, gender and

education. For all these control variables a one-way ANOVA was performed. The results are

described below.

Gender

As Table 18 illustrates, there was a statistically significant effect of gender on the behavioural

intention to use mobile AR retail apps in the future: F (1,177) = 7.759, p < .01. Therefore,

males had a higher behavioural intention than females to use mobile AR retail apps in the

future (Appendix 6).

SS DF MS F Sig. Sector Mean SD N

Sector 2.206 3 .735 1.766 .155 1.Home 3.54 .54 51

Error 72.855 175 .416 2.Food 3.38 .78 42

Total 75.060 178 3. Beauty 3.22 .58 37

4. Fashion 3.40 .66 49

Total 3.40 .65 179


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Table 18. One-way ANOVA for gender

SS DF MS F Sig. Gender Mean SD N

Gender 3.15 1 3.152 7.759 .006 1.Male 3.58 .66 63

Error 71.91 177 .406 2.Female 3.3 .62 116

Total 75.06 178 Total 3.4 .65 198

Education

As Table 19 indicates, there was no statistically significant effect of education on the

behavioural intention to use mobile AR retail apps in the future: F (2,176) = .792, p > .05).

This result could be possibly due to the sample size, which was skewed to more highly

educated people, where high educational level has the largest sample size.

Table 19. One-way ANOVA Education

SS DF MS F Sig. Education Mean SD N

Education .67 2 .335 .792 .455 1. Low 3.17 .24 2

Error 74.39 176 .423 2. Middle 3.29 .64 35

Total 75.06 178 3. High 3.43 .65 142

Total 3.4 .65 179

Age

As depicted in Table 20, there was no statistically significant effect of age on the behavioural

intention to use mobile AR retail apps in the future: F (3,175) = 1.426, p = .237. However,

there is a large difference between the behavioural intention of Millennials and the

behavioural intention of Generation X (Appendix 7). However, this result may be due to the

sample size: 168 for Millennials and 11 for Generation X.


47

Table 20 One-way ANOVA Age

SS DF MS F Sig. Age Mean SD N

Age 1.79 3 .60 1.426 .237 1. 16-25 3.37 .64 146

Error 73.27 194 .42 2. 26-35 3.42 .58 22

Total 75.06 197 3. 36-45 3.8 .89 10

4. 46-55 3.33 . 1

Total 3.40 .65 198

4.4 Hypotheses testing

The results of this study are related to the revised UTAUT2 model, by which the hypotheses

were tested. All the path coefficients and the significance levels of the proposed model are

shown in Figure 7. This figure shows that 4 of the 13 hypotheses were statistically significant

(shown in bold). From the original UTAUT2 model, six variables were used: PE, EE, SI, FC,

HM and H. However, only three variables were found to be statistically significant to

behavioural intention to use mobile AR retail applications. The relationship between PE and

behavioural intention (H1) was found to be significant. Likewise, the relationship between SI

and behavioural intention was found to be significant (H3). The results also indicate a

significant relationship between HM and behavioural intention (H5). Last, results indicate a

strong significant relationship between personal innovativeness and behavioural intention

(H8). However, there was no significant support found for H2, H4 and H6, which are

respectively the relationships between EE, FC and H and the behavioural intention to use

mobile AR retail applications. An interaction effect on the relationship between the factors

and the behavioural intention to use mobile AR retail apps was proposed to influence these

relationships. However, for this interaction term, personal innovativeness, none of the

moderating effects were found to be statistically significant (H7a, H7b, H7c, H7d, H7e, H7f).

Therefore, no significant support was found for the interaction effect of personal


48

innovativeness on the relationship between PE, EE, SI, FC, HM or H and behavioural

intention to use mobile AR retail apps. However, personal innovativeness did have a direct

positive significant relationship to the behavioural intention to use mobile AR retail apps;

therefore, H8 was significant.

Figure 7. Path coefficients and significance level indicated by *** p < .001


49

5. Discussion

The goal of this study was to determine the drivers that influence the behavioural intention to

use mobile AR retail applications in the future. Furthermore, research measured how personal

innovativeness relates to this model and determined whether there are any differences

between four retail segments: home interior, food, fashion and beauty and cosmetics. The

UTAUT2 model was used to investigate the theoretical drivers are for Dutch consumers to

accept mobile AR apps in retail. Additionally, personal innovativeness was added as

moderator, as well as an independent variable. In this chapter, the study’s findings will be

discussed.

5.1 Findings Confirm UTAUT 2

The first six hypotheses tested the relationships between elements discussed in the original

technology acceptance papers in the UTAUT model (Venkatesh et al., 2003) and the

UTAUT2 model (Venkatesh et al., 2012). The results of this study support the validity of

these factors on mobile AR retail applications. The strong significant effect and path

coefficient of PE provides evidence for the fact that consumers will start using mobile AR

retail apps when they expect these apps to improve their ability to find suitable products. The

study indicates that mobile AR retail apps are perceived as mobile applications that will allow

for better and quicker purchase decisions. This finding supports the research of Venkatesh et

al. in which the researched concluded that PE is the primary determinant of technology

acceptance (2003).

Effort Expectancy showed no significant support for a relationship with the

behavioural intention to use mobile AR retail apps. This insignificance may be due to the

nature of the sample, which only included people who used AR before. The result is in line


50

with Chau’s research in which he argues that through increasingly skilled users and more

user-friendly systems EE becomes a less important determinant of technology acceptance

(1996). As the respondents were mostly Millennials, they were familiar with the technology

used in social media applications: Snapchat and Instagram.

The second largest, and moderate, significant effect on the behavioural intention to use

mobile AR retail apps was HM. According to Brown and Venkatesh, HM is important in

consumer technology use and emphasizes utility (2005), which is supported by this study.

The third largest, and moderate, significant effect on the behavioural intention to use mobile

AR retail apps was SI. Thus, the influence of others (family and friends) about AR influences

consumers’ behavioural intention to use mobile AR retail apps. As the sample mostly

consisted of young people, between 16 and 25 years old, it is reasonable they are more

sensitive to SI.

Unfortunately, the construct of FC was not considered in this study, as the construct’s

items not reliable. This unreliability could be a fault during the translation of the items, or the

respondents could have incorrectly interpreted the questions. The overall results indicate that

the EE, FC and H constructs could be improved.

5.2 Role of Personal Innovativeness in the Domain of IT

In addition to the factors from the technology acceptance models validated for measuring the

technology acceptance in related studies, this research also considered the theory of PIIT. The

research results indicate there was a positive significant relationship between the additional

factor PIIT and behavioural intention to use mobile AR retail applications. Thus, someone’s

willingness to try any new information technology positively influences the behavioural

intention to use mobile AR retail applications.


51

More studies are needed to identify the specific role of PIIT in technology acceptance models,

as these results are contradictory to the theory of Agarwal and Prasad (1998).

Although Agarwal and Prasad theorize PIIT moderates the relationship between the

predictors and the behavioural intention to use this new technology (1998), the results of this

research indicate no significant effect of the moderating role of PIIT. Therefore, the

relationship between any driver and behavioural intention to use mobile AR retail apps is not

influenced by someone’s willingness to try new information technology. However, more

studies are needed to identify whether Agarwal and Prasad’s theory can be rejected, as this

study only elaborates on the acceptance of the Dutch consumer.

5.3 Retail segments

The model was tested for four different retail sectors. However, the results indicated that there

was no significant difference between these four different retail segments and the behavioural

intention to use. In fact, a consumer’s behavioural intention to use a mobile AR application

for the home interior segment was not significantly different than a consumer’s behavioural

intention to use a mobile AR application in the beauty segment, food or fashion. As the

sample size for this study consisted of 179 respondents, the sample size per retail segment

was probably too low to measure any significance (n ≤ 55).

5.4 Contribution to the theory

This study’s findings lead to some contribution to the theory of technology acceptance. First,

this study expands the literature on consumer acceptance with the topic mobile AR

applications in retail. This study confirms the reliability and validity of some constructs from

the original UTAUT and UTAUT2 model as well as for their applicability to the subject of

mobile AR applications in retail.


52

Another contribution to the theory is the evidence for an additional factor to the UTAUT2

model: PIIT. In this study, PIIT was significant as an individual factor that predicted the

behavioural intention to use.

Furthermore, this study found that the interaction term proposed by Agarwal and

Prasad, PIIT, was not statistically significant (1998). In addition, this study found that EE, FC

and H are not direct predictors of the behavioural intention to use mobile AR retail apps.

5.5 Managerial implications

Since more retailers are developing mobile applications to stimulate mobile commerce, they

are looking for multiple ways to increase sales and decrease returns. At the end of 2017,

mobile AR made its entrance in retail, and therefore, this study aids to retailers who want to

develop mobile AR-based applications. As these applications are still in the development

phase, retailers can examine the effects of user-oriented variables on users’ behavioural

intention to use mobile AR retail applications. Retailers can use this study’s results when they

developed their applications to stimulate the applications’ use by their consumers.

Consumers should be convinced that when using these mobile AR apps in retail they

would make better and quicker purchase decisions. They should be aware of the advantages

of these apps, thus decreasing the chance a product will be returned. Secondly, someone’s

willingness to try new information technology will positively influence consumer’s intention

to use mobile AR retail apps. Together with the fact it is important for retailers to consider the

SI factor to encourage the adoption of mobile AR retail apps, retailers could take advantage of

early adopters and hope they will spread positive word-of-mouth advertising. Furthermore it

is recommend making the retail apps to be enjoyable for the customer. Retailers should know

that if their retail apps are more entertaining to use, the likelihood the customer will use the

app increases, ultimately creating a competitive advantage. Based on this research, retailers


53

should aim to develop the AR-based applications according to consumer’s hedonic

motivation, convince the customer of the apps’ benefits and focus on early adaptors to drives

m-commerce sales.

5.6 Limitations and future research

This research also has a couple of limitations. These limitations can provide opportunities for

the future research.

First, the data collection resulted in 179 respondents after deleting missing values,

selecting only the cases that had previous experienced with AR technology and deleting the

extreme outliers. If the sample size had been larger, then the behavioural intention to use

mobile AR apps could have had significantly different results for the four different retail

segments. This is a limitation as this could have been a contribution to the theory of

technology acceptance. Furthermore, the sample size mainly consisted of young, highly

educated people. This limitation can be resolved in future research by not using a non-

probability sampling method.

In this study, the brand names were not considered (IKEA, Amazon, Yummly, KabaQ,

Topology, GAP, Sephora and L’Oreal). For this reason, future research should consider the

retail brand name as the brand itself could influence the behavioural intention to use. Another

limitation of this study is the fact only the behavioural intention to use mobile AR apps was

investigated due to the limited time frame. Future research could study consumers’ actual

behaviour as well in a longitudinal study. Another limitation of this study is the fact there was

no distinction made between the two different kinds of mobile AR. Therefore, future research

can examine the differences in behavioural intention to use AR mirror and AR view mobile

apps in retail.


54

6. Conclusion

This study further developed the theory of technology acceptance models by addressing the

acceptance of mobile AR retail applications. This chapter will use this study’s insight to

answer the original research question: ‘What are the drivers that influence the behavioural

intention to use mobile augmented reality applications in retail and how does personal

innovativeness influence these relationships?’ In this study, the control variables explained

5.1% of the variance, the factors explained 45.3% of the variance in behavioural intention to

use, and the moderating interacting effect of personal innovativeness was not significant. The

results of the stepwise regression indicate that PE, SI, HM, PIIT and age are drivers of the

behavioural intention to use mobile AR retail apps in the future and explain 45.7% of the

variance. Thus, if benefits of using mobile AR apps in retail are clear to the customer and the

app is entertaining to use, then the behavioural intention to use the mobile app will increase.

Furthermore, the behavioural intention to use these apps is positively affected by positive

influence of others and therefore it is also recommend to retailers to focus on early adaptors to

drives m-commerce sales. Factors such as EE, FC and H seem less important in this

technology adoption. Furthermore, the relationship between PIIT and behavioural intention to

use was examined in this study. Personal innovativeness in the domain of IT was a strong

determinant of the behavioural intention to use mobile AR retail apps. Therefore, the

behavioural intention to use these apps is positively affected by someone’s willingness to try

new information technology.


55

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Appendices

Appendix 1. Survey questions – revised UTAUT2 model Augmented Reality

1. What is your age? (Age control variable)

2. Please indicate your gender? (Gender control variable)

3. What is your highest level of education? (Education control variable)

(Primary education, VMBO, MBO, HAVO, VWO, HBO, Bachelor, Masters, PhD,

Other)

4. Did you ever have used a Mobile Augmented Reality Application?

(Examples are Pokémon Go, Snapchat filter, Instagram filter, IKEA place)

(Yes/No)

5. How often did you use Augmented Reality Applications for m-commerce

activities? (This could have been on the computer as well - Ray Ban Sunglass trial,

L’Oreal Make-up, IKEA place, In-store mirror fitting room etc.)

(Never, 1-5 times, 5-10 times, more than 10 times)

A short movie will be showed to the respondent, where an Augmented Reality Application is

demonstrated – one of the four movies will be shown: 1) Beauty & Cosmetics à Sephora

Make-up 2) Food à KabaQ 3) Home Décor à IKEA place 4) Fashion à Topology

6. I have seen the following movie:

(1. Home – IKEA and Amazon View, 2. Food - KabaQ and Yummly, 3. Beauty –

Sephora and L’Oreal, 4. Fashion – Topology and GAP)

7. I have a great affinity with the industry from the video. (Beauty, Food, Home and

Fashion – the same segment is asked as shown in the movie)

(5-point Likert scale: Strongly Disagree – Strongly Agree)


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(All following scales are 5-point Likert-Scale: Strongly Disagree – Strongly Agree)

Performance Expectancy

PE1. I find these kinds of Augmented Reality Retail Applications useful in my daily life

PE2. Using these kinds of Augmented Reality Retail Applications increases my chances to

make better purchase decisions

PE3. Using these kinds of Augmented Reality Retail Applications helps me to make purchase

decisions quicker

Effort Expectancy

EE1. Learning how to use such Augmented Reality Application is easy for me.

EE2. My interaction with Augmented Reality Applications is clear and understandable.

EE3. I find these Augmented Reality Applications easy to use (or perception)

EE4. It is easy for me to become skilful at using these Augmented Reality Applications.

Social Influence

SI1. People who are important to me think that I should use Augmented Reality Retail

Applications

SI2. People who influence my behaviour think that I should use Augmented Reality Retail

Applications

SI3. People whose opinions that I value prefer that I use Augmented Reality Applications

Facilitating Conditions

FC1. I have a mobile phone (IOS 11 or Samsung S9) where I can use these Augmented

Reality Applications on

FC2. I have the knowledge necessary to download and use Augmented Reality Applications

FC3. I can get help from others when I have difficulties using Augmented Reality

Applications.

Hedonic Motivation

HM1. Using Augmented Reality Applications is fun.

HM2. Using Augmented Reality Applications is enjoyable.

HM3. Using Augmented Reality Applications is very entertaining.


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Habit

HT1. The use of Augmented Reality Applications has become a habit for me.

HT2. I am addicted to using Augmented Reality Applications

HT3. I must use Augmented Reality Applications

HT4. Using Augmented Reality Applications has become natural to me.

Personal Innovativeness

PIIT1. If I heard about a new information technology, I would look for ways to experiment

with it.

PIIT2. Among my peers, I am usually the first to try out new information technologies.

PIIT3. I like to experiment with new information technologies.

Behavioral Intention

BI1. I intend to continue using these kinds of Augmented Reality Applications in the future.

BI2. I will always try to use these kinds of Augmented Reality Applications in my daily life.

BI3. I plan to continue to use these kinds of Augmented Reality Applications frequently.

Total questions: 33


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Appendix 2 - QQ plots

BI: PE:

EE: SI:

FC: HM:


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H: PI:

Appendix 3 - Skewness and Kurtosis Table - after outliers are excluded.

N Skewness Kurtosis

Statistic Statistic Std. Error Statistic Std. Error

BI 179 -.072 .182 .432 .361 PE 179 -.598 .182 .118 .361 EE 179 .341 .182 -.159 .361 SI 179 .083 .182 -.674 .361 FC 179 -.404 .182 .308 .361 HM 179 .077 .182 .129 .361 H 179 .592 .182 -.017 .361 PIIT 179 -.176 .182 -.758 .361 Valid N 179


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Appendix 4 – Scree plot eigenvalues – PCA

Appendix 5 – Scatterplot Stepwise regression model 5


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Appendix 6 – Means Plot One-way ANOVA sector

Appendix 7 – Means plot one-way ANOVA Gender

Appendix 8 – Means plot one-way ANOVA Age

Documents

Unified Theory of Acceptance and Use of Technology applied