Thesis - Final Draft (Domenic Boni) PDF

Royal Holloway University of London

2015/2016

The acceptance of Near Field Communication technology among millennial social media users.

Domenic Boni

Student ID: 100832118

Word Count: 10 066

9th May 2016

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Independent Research Project Declaration Form PRE-MASTERS DIPLOMA FOR INTERNATIONAL STUDENTS INDEPENDENT RESEARCH PROJECT (FINAL DRAFT) Declaration to be signed by the student: I have read the following guidelines on plagiarism and declare that all work submitted is my own and that full reference has been made to other material used. “All work submitted by students as part of the requirements for any examination or other assessment must be expressed in their own words and incorporate their own ideas and judgments. Plagiarism - that is the presentation of another person’s thoughts or words as though they were one’s own - must be avoided, with particular care in coursework and essays and reports written in students’ own time. Deliberate plagiarism in coursework is as serious as deliberate cheating in an examination. Direct quotations from the published or unpublished work of others must always be clearly identified as such by being placed inside quotation marks, and a full reference to their source must be provided in the proper form. A series of short quotations from several different sources, if not clearly identified as such, constitutes plagiarism just as much as does a single unacknowledged long quotation from a single source. Equally, if a student includes a summary of another person’s ideas or judgments the source must be acknowledged and the work referred to included in the bibliography. Failure to observe these rules may result in an allegation of cheating. Students should therefore consult their Personal Tutor, Personal Adviser or Course Director if they are in any doubt about what is permissible.” Student’s signature: Domenic Boni ………………………………….……………………………… Date: 9 May 2016……………………………………………………………………………………

Submission to IN002 Signature of member of staff …………………………………………………………………

Date: ………………………………………………………………………… Time: …………………………………….…………………………………………..

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Abstract

Near Field Communication (NFC) is a short range, wireless data transfer technology

able to interact and communicate even in the absence of an Internet connection. The

technology has grown rapidly since its inception less than a decade ago, bringing

with it new opportunities for innovative mobile applications and services. In order to

keep up with the pace of the growth of mobile devices with NFC capability, it is

imperative for both academics and processionals to understand the factors

influencing the acceptance of this technology. Therefore, this study set out to

discover how millennials perceived NFC technology in a social media contact

sharing capacity.

Drawing from secondary research and utilizing the Unified Theory of Acceptance and

Use of Technology (UTAUT) and the Technology Acceptance Model (TAM), a new

conceptual framework was created. Key factors influencing the attitude millennials

form when accepting NFC were tested. Compatibility and Perceived Usefulness

were the most influential on attitude formation followed closely by Social Influence.

Perceived Risk appeared to have no significant influence over the formation of

attitudes towards NFC for social media contact sharing. Furthermore, the results

identified there being a significant positive relationship between attitude formed and

behavioural intention to adopt NFC in a social media environment.

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This research has successfully contributed to filling a prior knowledge gap in

academic research and has the ability of facilitating marketers of NFC services to

successfully reach millennial consumers.

Keywords: Innovative Technology – Technology Acceptance – Behavioural

Intention – Millennial - NFC

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Acknowledgements I would like to thank my personal tutor Laryssa Whittaker and academic supervisor

Chloe Preece for the support and feedback they have both given me throughout the

duration of this research, without them none of this would have been possible. Thank

you for helping me pursue research in an area I am passionate about and thank you

for the endless guidance in helping me make this paper something I am truly proud

of.

Declaration Abstract………………………………………………………………………………………i Acknowledgements………………………………………………………………………..ii List of Appendices ........................................................................................................ 1 List of Tables ................................................................................................................. 1 List of Figures ................................................................................................................ 2 Chapter 1: Introduction ................................................................................................ 3 1.1) Dissertation Outline ................................................................................................................................................ 6 Chapter 2: Literature Review ....................................................................................... 7 2.1) Millennials .................................................................................................................................................................... 7 2.2) Social Media ............................................................................................................................................................ 10 2.3) Near Field Communication .............................................................................................................................. 13 2.3.1) Tag Reader / Writer ........................................................................................................................................ 14 2.3.2) Peer-to-Peer ....................................................................................................................................................... 15 2.3.3) Card Emulation ................................................................................................................................................. 17

2.4) UTAUT/TAM Models ........................................................................................................................................... 19 2.5) Conceptual Model and Hypotheses ............................................................................................................ 23 2.5.1) Hypotheses ......................................................................................................................................................... 24

2.6) Conclusion ................................................................................................................................................................ 26 Chapter 3: Methodology ............................................................................................. 28 3.1) Introduction .............................................................................................................................................................. 28 3.2) Research Methodology ...................................................................................................................................... 28 3.3) Research Design .................................................................................................................................................. 31 3.4) Data Collection ....................................................................................................................................................... 32 3.4.1) Secondary Data ................................................................................................................................................ 32 3.4.2) Primary Data ....................................................................................................................................................... 32

3.5) Questionnaire Design ......................................................................................................................................... 33 3.6) Data Analysis .......................................................................................................................................................... 35 3.7) Validity ........................................................................................................................................................................ 36 3.8) Ethical Considerations ....................................................................................................................................... 37 Chapter 4: Findings .................................................................................................... 38 4.1) Demographics ........................................................................................................................................................ 38 4.1.1) Age ........................................................................................................................................................................... 38 4.1.2) Gender ................................................................................................................................................................... 39 4.1.3) Smartphone ownership ................................................................................................................................ 40 4.1.4) Experience with NFC technology ........................................................................................................... 41

4.2) Reliability Testing .................................................................................................................................................. 42 4.3) Descriptive Statistics ........................................................................................................................................... 43 4.3.1) Perceived Usefulness ................................................................................................................................... 45 4.3.2) Perceived Ease of Use ................................................................................................................................. 46 4.3.3) Social Influence ................................................................................................................................................ 47 4.3.4) Compatibility ....................................................................................................................................................... 48 4.3.5) Perceived Risk .................................................................................................................................................. 49 4.3.6) Attitude towards Use ..................................................................................................................................... 50 4.3.7) Behavioural Intention ..................................................................................................................................... 51

4.4) Hypothesis Testing and Regression Analysis ....................................................................................... 52 4.4.1) Hypothesis Testing ......................................................................................................................................... 52 4.4.2) Regression Analysis ...................................................................................................................................... 57

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Chapter 5: Discussion of Findings ........................................................................... 60 5.1) Social Influence and Perceived Ease of Use ......................................................................................... 60 5.2) Compatibility and Perceived Usefulness .................................................................................................. 61 5.3) Perceived Risk ....................................................................................................................................................... 61 5.4) Attitude Toward Use and Behavioural Intention to Adopt ............................................................... 63 Chapter 6: Conclusion ................................................................................................ 65 6.1) Contribution ............................................................................................................................................................. 65 6.1.1 Academic Contribution ................................................................................................................................... 65 6.1.2 Applied Contribution ........................................................................................................................................ 66

6.2) Limitations and Future Research Direction ............................................................................................. 66 References ................................................................................................................... 68

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List of Appendices

1. Appendix 1 – Online Questionnaire

2. Appendix 2 – Hypothesis Testing

3. Appendix 3 – Regression Analysis

4. Appendix 4 – Ethics Code Certificate

List of Tables 1. Reliability Test

2. Descriptive Statistics - Perceived Usefulness

3. Descriptive Statistics - Perceived Ease of Use

4. Descriptive Statistics - Social Influence

5. Descriptive Statistics - Compatibility

6. Descriptive Statistics - Risk

7. Descriptive Statistics - Attitude Towards Use

8. Descriptive Statistics - Behaviour Intention

9. Hypothesis Testing

10. Regression Analysis – Model Summary

11. Regression Analysis – Significance Testing (ANOVA)

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List of Figures 1. Dissertation Outline

2. NFC Operation Modes

3. Technology Acceptance Model

4. Unified Theory of Acceptance and Use of Technology – Constructs

5. Unified Theory of Acceptance and Use of Technology – Model

6. Conceptual Model

7. Deduction and Induction

8. Age Demographics

9. Gender Demographics

10. Smartphone Type

11. Experience NFC

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Chapter 1: Introduction

For some time, experts and researchers have studied the possible benefits of

linking the virtual world of the Internet with a physical one. Since the

introduction of Near Field Communication (NFC) technology, the idea of a link

between these two worlds is fast becoming a ubiquitous reality.

NFC is a short-range wireless technology, already very common in

smartphones globally (Want, 2011). The NFC chip inside a device

communicates with other NFC ready enabled devices, ranging from transport

systems, ticketing procedures through to seamless and efficient payments.

The technology has grown in popularity since the formation of the NFC Forum

in 2004. According to IHS Technology (NYSE: IHS) by 2018, 1.2 billion

mobile devices will contain NFC chips, igniting a new wave of potential

applications introduced to compliment this technology.

There are approximately 2 billion Internet users on social networking

platforms. This number is growing rapidly due to increased mobile device

usage;; mobile social media is at the forefront of technological advancement

(Leading global social networks 2016 | Statistic, no date). Twenge (2009)

highlights the importance of understanding the millennial generation when

considering innovative technology adoption due to their increasingly different

traits compared to their older counterparts, Generation X.

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Considering the staggering amount of mobile social media usage, millennials

are increasingly demanding ecosystems that are more compatible with their

digital lives (Twenge 2009).

It has become apparent in reviewing secondary research that the majority of

academic and applied focus in the recent past has been placed on NFC

technology for contactless payment services. Research conducted by Dutot,

(2015), Pham and Ho, (2015), Chen and Chang, (2013) have all focused on

user behaviour and attitudes towards adoption of NFC mobile payments.

While this research offered excellent grounding for this study, Hongwei Du

(2013) introduces the idea of peer-to-peer data sharing via NFC enabled

mobile devices, and briefly touches on the impact this technology can have on

social media ecosystems. Minimal research has been conducted on this area

of NFC technology. With a rapidly growing number of NFC enabled mobile

devices coming to market and considering mobile devices are at the forefront

of social media usage, more research concerning peer-to-peer functionality

for NFC needs to be conducted. Additionally, with no emphasis whatsoever

on millennial adoption patterns of this innovative technology, this research will

contribute to the gap in academic and applied knowledge.

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The aim of this research was to uncover how millennials perceive NFC

technology for social media contact sharing purposes. The research set out to

devise a framework, which will capture the most important factors that

influence millennials’ attitudes towards the use of the technology in their social

media environment. Furthermore, the result of the attitude formed was then

measured against the behavioural intention to adopt the technology.

This study revised previous secondary literature in the field of technology

adoption and NFC technology, combined with an online questionnaire for

primary data in the quantitative research adopted in this page. The study

aimed to:

1) Understand the degree of influence, Perceived Ease of Use, Perceived

Usefulness, Social Influence, Compatibility and Perceived Risk had on

the attitudes millennials formed towards the use of NFC for social

media purposes.

2) Understand if there was a relationship between the attitude towards the

use of NFC formed by millennials and their behavioural intention to

adopt the technology for social media contact sharing.

Given the lack of knowledge in this subject area, this research was conducted

to offer professionals and marketers of NFC technology additional information

when dealing with millennial consumers.

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1.1) Dissertation Outline

Figure 1: Dissertation Outline

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Chapter 2: Literature Review This research will attempt to uncover the millennial generation’s technology

acceptance habits looking specifically at Near Field Communication

Technology (NFC) in a social media environment. The literature below

highlights important prior research conducted in the area of Millennials, Social

Media and Near Field Communication technology. Finally, this chapter looks

at various relevant behaviour models previously used to understand user

adoption and acceptance of the technology. A conceptual framework is then

derived from two relevant models to further the study of millennials’ attitudes

and behaviours when accepting or rejecting the technology.

2.1) Millennials

“Born between roughly 1982 and 2002, millennials are the first generation to

grow up with cell phones, personal computers, camcorders, digital music

players, and the World Wide Web” (Schwalbe, p1. 2009). Although there are

no definitive dates to determine millennials, it is widely agreed that millennials

or Generation-Y are those born following Generation X. For the purpose of

this research and due to ethical age considerations, I have defined millennials

as being between the ages of 18-30 years old.

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Marc Prensky (2001), in his article on United States education standards,

expands on the impact the “internet generation” or “Digital Natives”, have on

the current state of not only education but the progression of the world

compared to their older counterparts or “Digital Immigrants” (p.1). The need to

understand the millennial generation and their behaviour towards the use of

digital media and technology has ignited vast academic and business interest.

However, in their book “Millennials Rising: The next generation” Howe and

Strauss (2000, cited in Schwalbe, 2009, p 54) dedicate only one paragraph to

millennials’ use of digital media. Furthermore, 2007 Pew Internet & American

Life Project survey results categorised Americans into types of technology

users, but failed to differentiate between university-aged students and other

adults, as noted by Horrigan (2007, quoted in Schwalbe, 2009, p 54). While

much attention has been given to the millennial generation, there is a lack of

literature in technology adoption attitudes and behaviours of this generation.

This is where this study will look to fill that gap.

Although the above-mentioned research offers valuable information about

millennials, this research will attempt to understand millennials' technology

acceptance habits further by contributing to an area other than educational

settings. This lack of literature on behaviour is reflected in the work of Prensky

(2001) in thinking millennials are always connected and therefore accepting

technology is a phenomenon so deeply engrained in the generation that a

study on the matter would render predictable results.

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Schwalbe (2009) attempts to understand millennials’ digital media usage and

uncovers detailed statistics concerning the generation’s habits. This research

will look to discover what influences millennials’ attitudes when accepting new

technology.

According to Fromm et al. (2011) millennials are 2.5 times more likely to be

early adopters of new technology than any other generation. Furthermore, the

writers contend that, once new technology is introduced, millennials are more

inclined to seek peer affirmation and advice about new technology

advancements from social influences rather than from the companies or

organisations releasing the products. Social affirmation for the millennial

generation is one of the most important influencers in millennial technology

acceptance habits and this study will look to expand on this.

Once they’ve done their research — which includes consulting with

friends and family for advice, both in person and through texting and

social sites — they have a high degree of confidence in the decisions

they’ve made. This may seem contradictory, but it describes how many

Millennials behave. - Fromm et al. (2011)

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2.2) Social Media

Social media platforms allow Internet users to communicate, connect and

interact with new or mutual friends through social networking sites (SNS) and

instant messages (Correa et al., 2010). For the purpose of this study, social

media refers to Social Networking Sites (SNS), which in reality is only one

channel of social media. The reason for narrowing down this definition is that

for the purpose of this study, acceptance of NFC technology will be explored

concerning peer-to-peer contact sharing of social networking sites such as

Facebook, Twitter, Instagram and LinkedIn. Kietzmann, et al., (p 241. 2011)

provide a deeper definition of Social Media by emphasising the fact that these

media platforms are powering the world into a “new communications

landscape”, where users and communities amplify user-generated content in

real time. Ninety percent of users on social networking sites use the platforms

as a means of remaining in touch with their personal contact lists (Lenhart et

al., 2009). The author highlights that three-quarters of users on social

networking sites are under the age of 25. Furthermore, Raacke & Bonds-

Raacke’s (2008) research revealed that one-third of young adults was

checking their profile pages daily with time spent on SNS ranging from one to

three hours. This suggests that further research needs to be conducted in

this area.

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In an interview with wire.com, Mark Zuckerberg, regarded as one of the

“founding fathers” of social media highlights the importance of social media in

today's world by comparing individual social networking sites to that of

companies trying to build a brand for themselves “…It is almost a

disadvantage if you’re not on it now” (Vogelstein, p2, 2009). In their research

on the impact that social media may have on educational transformation,

Davis, et al. (2015), assert that social media enhances the process of

transformation of human relationships and connections, and alters the manner

in which humans “think, organise and act politically” (p. 410).

Kaplan (2012) introduces the concept of mobile social media as a group of

mobile applications that allow the “creation and exchange of user-generated

content” (p. 131). The writer explains the process in which information is

transferred between 1) the sender sharing information and 2) the receiver,

which listens to the information. When investigating the impact mobile devices

play in today’s social communication landscape, understanding the origins of

mobile social media communications will inform this research. For example, a

study conducted by Iwatani and Reuters, (1998, cited in Humphreys, 2013,

pp. 21-22) discusses a product named the “Lovegety”. This device facilitated

the communication between two of the same products within a 5m radius, with

pre-set functions of “let’s chat”, “let’s karaoke” or “get2” (p. 21). When

encountering another device, the product flashed to indicate a match made.

The Lovegety introduced two basic forms of communication.

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Firstly, identification of a new user and secondly the information regarding the

type of social interaction the user was searching for.

An interesting part of the Lovegety research was the consumer’s attitude

towards the adoption of the new technology at the time. The Japanese

adoption of the new technology was as a result of social influence by their

peers and family members (p. 22). With this early understanding of social

interaction technology, social influence in the acceptance of new technologies

deserves further research. Studies such as Li (pp. 265-266, 2013) on social

influence distinguish between ‘informational social influence’ and ‘normative

social influence.’ The former is where users accept information from social

groups on the premise of experience, to make informed, quality decisions.

The latter is where social influence pressures users to conform by accepting.

As moderate research has been conducted in this area, more research into

the impact social influence has on millennials is warranted.

It is important to understand the paradigm shift from desktop social media

usage to mobile social media. According to Cortesi et al., (2013)

approximately 100% of teens in Western Europe, Northern America and Asia-

Pacific own a mobile phone, with a high percentage of these being

smartphones. These staggering statistics underscore the shift to increase

mobile social media usage.

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2.3) Near Field Communication

Near Field Communication (NFC) is a variation of Radio Frequency

Identification communication technology. NFC has been widely applauded for

its “hassle-free” stance in communication technology (Chang & Chen 2011).

With its ability to assure user-friendly (no discovery, no pairing as experienced

in Bluetooth) short distance (up to 10cm) wireless communication between

two devices, NFC technology has numerous benefits for both individuals and

industry use (Dutot 2015). Kantner et al. (2008) highlight the key benefit of

NFC is its simplicity of use. A users pre-empted feelings or attitude towards

new technology, positive or negative, according to Holloway et al. (2005) will

be key indicators to the extent to which a user is willing to engage with new

technology. Taylor and Todd (1995) state that users are more ready to accept

a new phenomenon if it is perceived to add more benefit to an older model or

version of the technology. While outside influences are important to

acceptance behaviour, a user’s personal experience is as valuable in forming

an attitude towards a new technology (Petty et al. 1983). Building on the

theory of simplicity, Kantner et al. (2008) highlight the added benefits the

technology offers mobile device users, citing the example of the most

commonly used area of the technology today- contactless payments.

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Also, Csapodi & Nagy, (2007) discuss the further benefits of NFC’s use by

highlighting the sensitivity of energy consumption and state that the

technology may be powered by only one of the two devices in the

communication mode, adding to the sophistication of the technology.

Figure 2: NFC Operation Modes (Source: nfc-forum.org ‘What It Does’, no date)

2.3.1) Tag Reader / Writer

According to the NFC Forum (nfc-forum.org: ‘What It Does’, no date), the

reading and writing functionality of NFC-enabled devices “connects the world

of apps with the physical world”. An NFC device will be able to read

information stored on multiple platforms where another NFC tag exists. In his

study of NFC, Du (2013) discusses the impact the technology is already

having on transportation globally. Simplifying transport logistics and allowing

NFC-enabled users to use their mobile devices to gain further information

about their journey by “smart advertisements”.

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Commuters can hold up their phone to a “smart poster” and instantaneously

communicate with the NFC enabled tag to gain further transport information.

Expanding on Tag Reading and Writing, Du (2013) cites examples of NFC-

enabled devices replacing a hotel room key by the use of a mobile device.

Building on Du (2013) discussion, Roy Want of Google (2011) furthermore

points to the benefits of NFC’s ability of simplifying and transforming boarding

procedures in public transport by the simple tap of an NFC-enabled device.

The author introduces the topic of gaining virtual store coupons by a simple

swipe of the phone and eradicating out-dated methods of collecting stamps in

a paper orientated loyalty card.

2.3.2) Peer-to-Peer

The nature of NFC peer-to-peer communication is similar to Tag Reading and

Writing specifications in that an NFC-enabled mobile device will interact with

another device in the same way it does with, for example, a smart poster,

(nfc-forum.org: ‘What It Does’, no date). Considering the simple nature of the

transfer of information, peer-to-peer NFC communication is highly attractive to

individual users and businesses alike (Want, p.5. 2011). With the ability to

initiate a “viral transfer of information” (Clark, 2011, no pagination) an NFC-

enabled mobile device can share information already acquired from a smart

poster to another NFC-enabled mobile device instantaneously.

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Movie trailers, coupons and games are a few of the endless opportunities for

marketers to penetrate an audience via viral communication and seed

consumers in allowing them to initiate the further communication of

information. Du (2013) introduces the topic of contact sharing and social

media complementing the use of NFC for instantaneous contact sharing

across platforms. Furthermore, an increase in the use of “check-ins” again

benefits the individual user and businesses.

Looking further into mobile commerce, in 2011 PayPal introduced the ability to

include person-to-person transactions (PayPal United Kingdom: Pay, Send

Money & Accept Payments, no date). For the purpose of this study, user

attitudes to NFC will be considered and analysed. At the same time,

perceived risk and security related issues would be a primary consideration

when new users look to adopt this technology. The NFCforum.org offers

various scenarios of possible ‘cybercrimes’ through NFC technology;; one

significant concern is ‘data corruption or modification’ (Security Concerns with

NFC Technology -NearFieldCommunication.org, no date). This research

attempts to understand the variables affecting millennials acceptance of NFC

technology of social media use. Taking this into consideration, it is important

to realise the perceived risks of using this technology that impacts the

attitudes of users in the acceptance process. Determining if millennials

perceive NFC as a threat to their personal information will form part of this

study.

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2.3.3) Card Emulation

NFC-enabled mobile devices also take the form of moving smartcards, (nfc-

forum.org: ‘What It Does’, no date). By just tapping an NFC mobile device

against an NFC terminal, a user can make unlimited small amount purchases

without the use of their credit cards. The benefits for business as stated by Du

(2013) will be the satisfaction the customer feels because of the simplicity of

the transaction. At the same time, the information the merchant receives

about buying habits and preferences will be useful for marketing purposes.

In 2012, global payment transactions accounted for over 15,3% of all

transactions and estimates suggest that by the year 2016 this figure will rise

to over 40.8% according to Portio Research (2012, quoted in Liébana-

Cabanillas et al., 2015, p.2). The most important element of NFC-enabled

mobile devices for payment is the security factor involved with transactions as

agreed by Liébana-Cabanillas et al. (2015), Du (2013) and Want (2011).

Csapodi & Nagy (2007) highlight the users deliberate intent to conduct a

transaction as a justification for security. The implication of the anxiety a user

feels of not being able to physically control the exchange of funds (i.e., with a

pin code or some form of verification) is an important factor for further

research (Chen and Chang 2011). The risk associated with the use of non-

traditional monetary payments has been contentious for many years. The

reliance on the Internet, as well as new forms of payment for goods and

services has brought with it a change in consumer attitudes in the adoption or

acceptance of new technologies.

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Risk is a major influencer of consumer attitudes, which in turn affect the

consumer’s engagement levels (Taylor and Todd, 1995). According to

Forsythe et al. (2006), many innovative technologies have been hindered by

the perceived risk a given technology or service brings. For example, two of

the main concerns is identity theft and credit card fraud. Dutot (2015) explains

the lack of physical contact concerning payments with ‘online’ services to be

an influencer in consumer’s decisions to accept or reject new technology like

NFC. As mentioned above, the lack of physical control (i.e., pin code)

heightens the distrust of consumers because of a fear of transactions being

intercepted and fraud or cybercrime being initiated. These perceived risks

concerning NFC contactless payments will be used in this NFC for social

media contact sharing research and adapted accordingly.

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2.4) UTAUT/TAM Models

With the landscape of information system technology always changing, users’

acceptance of new technology is of utmost importance to technology

manufacturers, corporations and even individual users. Numerous studies on

user behaviour have been carried out to gauge the manner in which people

perceive new technologies. The most common model used in the study of

acceptance of technology is the Technology Acceptance Model (TAM)

constructed by Davis in 1989. It is considered to be one of the most impactful

and relevant research tools of technology acceptance (Tan et al... 2012,

quoted in Dutot, 2015, p 47). Offering two distinct elements influencing

acceptance, namely perceived ease of use and perceived usefulness, the

model presents a very simplified method to understand user’s acceptance of

new technology.

Figure 3: Technology Acceptance Model (Source: Davis 1989)

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Davis (1989) in his design of the TAM, proposed “Perceived Usefulness” (the

extent to which a user finds an application or technology useful) to be directly

affected by “Perceived Ease of Use”. The author believed that even if a user

viewed an application or technology as useful, the perception of the difficulty

to manipulate the application or technology was more important when forming

an attitude. Considering this study is focused on millennials, throughout their

lives, according to Prensky (pp. 1-3. 2001), they have become more

susceptible to adopting new technology, therefore, rendering this relationship

by Davis (1989) less impactful to these “Digital Natives”. Although the

relationship between these two variables will not be discussed in this study,

the variables independent of one another are still of great importance to the

study at hand. The extensive use of this model has been adopted in research

in the field of Information Technology and Information Systems due to its ease

of use (Rupanjali et al., 2013, quoted in Dutot, 2015, p. 46).

With the limitation of this model hindering deeper knowledge of the

acceptance of technology, further research conducted by Venkatesh, Morris,

Davis, and Davis (2003) was carried out to uncover additional variables and

behaviour theories affecting a user's attitude towards acceptance.

A new concept was introduced by the authors, namely The Unified Theory Of

Acceptance And Use Of Technology (UTAUT) model to explain users’

behavioural intention of information technology (Chen & Chang 2011).

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Accounting for constructs across nine critical theories, the UTAUT model

offers a more efficient framework, with a 70% explanatory power, more so

than any other integrated method to date (Venkatesh et al., 2003).

The UTAUT includes constructs from (In chronological order);;

1) Theory of reasoned action (TRA) (Fishbein & Ajzen 1967)

2) Technology Acceptance Model (TAM) (Davis 1989)

3) Model of PC Utilisation (MPCU) (Thompson et al. 1991)

4) Theory of Planned Behaviour (Ajzen 1991)

5) Combined TAM & TPB (C-TAM-TPB) (Taylor and Todd 1995)

6) Innovation Diffusion Theory (IDT) (Rogers 1995)

7) Social Cognitive Theory (SCT) (Compeau and Higgins 1995)

8) Motivational Model (MM) (Vallerand 1997)

9) TAM2 (Vankatesh & Davis 2000)

Combining these nine models indicates a user's attitude towards acceptance,

as well as the intention to use technology.

Upon testing each of the nine above mentioned theories, Venkatesh et al.

(2003) extracted the four key constructs best explaining behavioural intentions

and usage behaviour.

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Figure 4: Four constructs of the UTAUT Model (Source: Venkatesh et al., 2003)

Figure 5: UTAUT Model (Source: Venkatesh et al., 2003)

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2.5) Conceptual Model and Hypotheses

The above-reviewed literature shows that Social Influence (SI), Perceived

Usefulness (PU), Perceived Ease of Use (PEU), Compatibility (Com) and

Perceived Risk (Risk) are all factors that influence the preconceived attitudes

when millennials accept new technology. This research paper will combine

the UTAUT model (Venkatesh et al., 2003) and the TAM (Davis, 1989) to

construct a research questionnaire that was designed to investigate the

constructs influencing attitude towards use and behavioural intention to adopt

NFC. While there has been a moderate amount of research conducted on the

adoption of NFC for mobile payment services with the use of mobile devices

(Dutot 2015, Chen and Chang 2013), research into millennials’ attitudes and

acceptance of NFC has yet to be uncovered.

Figure 6: Proposed Conceptual Model (Source: own contribution)

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2.5.1) Hypotheses

By forming the six hypotheses below and using secondary data provided

mainly by Chen & Chang (2013);; Dutot, (2015);; Pham & Ho (2015) (among

others) I have also used my experience as a millennial technology user. By

combining the previous research with real life experience with NFC

technology and social media, the following hypotheses were formed and will

be tested in chapter 4.

The hypotheses are as follows:

H1: There is a positive relationship between Attitude towards the use of NFC

and the Social Influence experienced by a millennial user.


and the perceived usefulness of the technology by a millennial user.


and the perceived ease of use of the technology by a millennial user.


and the compatibility in a millennial user's lifestyle.

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H5: There is a negative relationship between Attitude towards the use of NFC

and the risk associated with the technology experienced by a millennial user.

H6: Millennial Social Media user’s attitude towards NFC has a significant

positive relationship with the user’s behavioural intention to use the

technology.

The proposed research model includes the variables of Gender, Age and

Experience using NFC as moderators of the user’s behavioural intention

(Chen and Chang, 2011).

26

2.6) Conclusion

The chapter above offers detailed research on previous literature of the

millennial generation, Social Media, NFC and also previously investigated

models presenting methods to uncover user attitudes and adoption behaviour

of new technologies.

The literature demonstrates factors needed to study and understand user’s

attitude about technology. This includes Social Influences, Perceived ease of

use and usefulness of the technology, Compatibility of the technology to the

lifestyle of the user, as well as the risk factors involved when forming

preconceived attitudes towards technology adoption. Through a detailed

review of technology acceptance models, it was concluded that the two most

suitable models in drawing up the framework for this study were the UTAUT

(Venkatesh et al., 2003) and the TAM (Davis, 1989). The studies of Chen &

Chang, (2013), Dutot, (2015) and Pham and Ho (2015) on the adoption of

NFC technology with mobile payments gave valuable insight into the

necessary elements required to focus on conducting research in NFC

technology.

27

The vast amount of literature gives valuable insight into answering the

following research questions:

1. Which variables affect millennials’ attitudes when accepting new

innovative technologies?

2. Do millennial user’s attitudes towards NFC affect their behavioural

intention to adopt the technology?

28

Chapter 3: Methodology

3.1) Introduction

The primary purpose of the research is to gain a deeper understanding of

human behaviour when accepting new technologies, specifically the

behaviour of millennial social media users and NFC technology. This chapter

explains the methodologies that were used for the study. Two schools of

thought concerning research methods are discussed and justification is given

for the chosen case. Following this, the research design and data collection

methods will be discussed, introducing the use of both primary and secondary

data. The questionnaire design will be examined, as well as the manner in

which the data was analysed. Lastly, the validity and ethical considerations of

the methodology will be discussed.

3.2) Research Methodology

In conducting research in social sciences, two main schools of thought need

to be considered when applying methodology - Subjectivism (or

interpretivism) and Positivism. Labelling the difference between these two

schools of thought as the “paradigm war”, Kirby (2007) believes the division of

these two approaches is a simplification of research.

29

According to Kantona and Morgan (1952), positivism refers to a scientific,

objective approach when observing behaviours of human beings that exist in

the natural world. Larrson (1993) further expands on the theory, which begins

with researchers forming hypotheses before gathering and analysing

statistical data to prove or disapprove predetermined theories. Through the

use of statistical analysis, this approach is also commonly referred to as

deductive research according to Kirby (2007). Deductive research can often

misrepresent a researcher’s influence over his or her research by assuming

that the researcher has no control over results. In this study, I found this to be

partially false. While conducting the literature review and forming

predetermined hypotheses, I have included my beliefs and experiences as a

millennial technology and social media user. Therefore, my perspectives have

partially influenced the direction of the study.

When contrasting positivism to subjectivism, Aliaga and Gunderson (2000)

report a Subjectivist approach as a research method where the researcher is

involved with and has influence over or is affected by the study. Synonymous

to subjectivism is the use of subjective research tools such as, but not

restricted to: interviews, focus groups and ethnography, where the researcher

is included directly in the process, which affect outcomes.

The subjective approach yields hypotheses only after data has been

collected, leaving interpretations to be made and patterns to be discovered by

the researcher (Gummesson 2005, Lemke, 1998).

30

Figure 7: (Deduction & Induction, no date)

For the purpose of this paper, a quantitative approach will be utilised to

confirm or disprove hypotheses and theories established earlier in the

literature review. NFC is an innovative technology with a moderate amount of

recent academic interest, which has primarily taken a quantitative approach.

This study has borrowed from a vast amount of literature already conducted

on technology adoption and has adapted it for NFC and millennials in a social

media environment.

For this reason, this study has used a quantitative methodology approach to

expand and interact with previously conducted research.

31

3.3) Research Design

The primary objective of the research was to uncover variables affecting

attitude and adoption of NFC technology among millennials in a social media

environment. Before designing the questionnaire, which was used as a

primary data collection tool, a conceptual model was developed in line with

previous research conducted in the subject area. As explained in the chapter

above, when carrying out a detailed analysis of the variables which affect

millennials’ attitudes towards the acceptance of new technologies, two models

were used to facilitate the design. The models used were the UTAUT by

Venkatesh et al., (2003) and the TAM by Davis (1989). Detailed analysis

revealed the variables Social Influence, Perceived Usefulness and Ease of

use, Compatibility and Perceived Risk influenced the attitudes millennials

formed when accepting new technologies. Drawing from the TAM model, the

attitude was then measured with the intention of using the technology. This

formed the foundation of the conceptual model designed for this study.

Carrying out the study in this way, allowed the research to contribute to the

field of technology acceptance, while touching on millennials’ social

behaviours in the contemporary world powered by social media.

On completing the conceptual model, the questionnaire was designed in line

with the model and proved to be an effective way in the collection of data for

this study.

32

3.4) Data Collection

3.4.1) Secondary Data

Defined as previously accumulated data from earlier forms of research,

secondary data is a vital part of the research process for a quantitative

approach (Saunders, 2007). Saving time and providing a higher quality of

data, secondary data provides the basis for theories and frameworks used in

this paper. Secondary data drawn from the research discussed in the

literature review chapter was used in conjunction with primary data collected

(see section 3.3.2). The data was used to develop the aims and objectives of

the research, as well as formulate theories and hypotheses. The data used in

this paper was accumulated through predominately online sources, including

peer-reviewed journals, books and websites.

3.4.2) Primary Data

In contrast to secondary research, where data is accumulated through

reviewing and borrowing from previous researchers, primary research data is

collected as a direct result of their own implementation of tools or for the case

of this paper, a questionnaire (Saunders, 2007). An online questionnaire

gathers as many responses as possible, with a far greater reach than what

would have otherwise been possible in an offline questionnaire according to

Wiersma (n.d) of the Oxford Internet Institute. I used a convenience sampling

approach via two social media platforms.

33

The sampling approach was chosen because of the convenience of reaching

as many people as possible in a short period of time (Davies, 2007). The

research conducted of millennial social media users, was distributed across

two of the most active social media channels in the contemporary world:

Facebook and Twitter, (Milanovic, 2015). Posted on April 4th, 2016, the

questionnaire had a potential reach of over 3000 potential participants,

especially when sharing and re-tweeting were taken into consideration.

3.5) Questionnaire Design

The online questionnaire was separated into eight sections, making the

progression and understanding for the participant as simple as possible to

hold interest throughout. Fallowfield (1995) notes that respondents find it

easier to answer questionnaires when they are separated into sub-sections,

allowing for consistent participation.

The questionnaire began with a short introduction of NFC technology,

accompanied by a description of how the technology could be used for the

purpose of the study. This was done to explain and educate the participant on

the subject matter to minimise the chance of answering the questions without

any knowledge. (See Appendix 1 for Questionnaire)

34

The first section dealt with demographics (Age and Gender) followed by a

question on experience with the technology, as well as the type of

smartphone the respondent owned. All four questions were multiple-choice

answers.

The remaining seven sections were designed as close-ended questions

(except one in the risk section which will be explained further on). According

to Maylor and Blackmon (2005), more accurate data may be drawn from

close-ended questions compared to open-ended ones, which requires further

interpretation.

The authors continue to highlight the ease with which close-ended question

can be quantified and analysed by the researcher. The close-ended questions

in all seven sections (with an exception of one) were designed using a Five

Point Likert Scale with 1=Strongly Disagree and 5=Strongly Agree. The

writers Maylor and Blackmon (2005) propose Likert scales as being easier for

the respondent to interpret when answering questions and the authors point

out the wide use and popularity of Likert scales throughout academic

research. The limitations of a Likert scale will be discussed further in section

(3.7).

35

3.6) Data Analysis

Considering the nature of the structure of the questionnaire being 95%

closed-ended questions, the data gathered was analysed by using ‘Statistical

Package for the Social Sciences’ (SPSS). This software allowed for in-depth

statistical analysis of the various outcomes. Using descriptive statistics

allowed for processes of understanding through ‘means’ between data sets,

correlations as well as regression analysis and Cronbach’s Alpha to conduct

reliability testing of the data.

Upon dealing with the single open-ended question, answers were analysed in

detail, resulting in finding recurring trends throughout respondents’ answers.

Considering there was only one question of this nature, coding various

themes that occurred throughout the answers to formulate categories or

‘buckets’ was conducted (Zapin, no date). This strategy coupled with

secondary data in the literature review was used to quantify answers for

statistical analysis.

36

3.7) Validity

Drawing from the positivist, quantitative nature of the research, respondents

were able to answer the questionnaire without pressure from any outside

forces (i.e. the researcher) at their pace, and at any location of their choice.

Andrews et al. (2007) further justify the validity of a quantitative methodology

by stating a scientific approach is more suitable for analysing technology

adoption and acceptance. During the process of collecting data for the

research, as mentioned above, a Likert scale was used as the primary

function for respondents to answer questions (except one question). Maylor

and Blackmon (2005) are of the opinion that a Likert scale offers more validity

compared to other forms of answering methods (i.e. multiple choice). The

authors justify their view by explaining the thought process required for

responding to a Likert scale question compared to a multiple choice question,

where answers could be picked at random. Contesting this school of thought,

Podsakoff et al. (2003) highlight a possible problem with Likert scaling. The

authors believe that respondents, afraid of answering with either extreme of

the scale (i.e. 1 or 5), choose answers within the three neutral groups, diluting

the actual attitude of respondents. To keep the questionnaire valid,

respondents were asked only to answer if they were between the ages of 18-

30 years of age. Respondents were assured that all data collected would

remain anonymous and the data would be used only for the purposes of

research only. Hidden identity allowed participants to feel more comfortable

about answering questions truthfully, increasing the validity of the sample.

37

Lastly, according to a study on NFC technology, Chen & Chang (p 616, 2013)

advised that researchers should conduct a pilot review of the questionnaire to

strengthen its reliability and validity. The pilot review was conducted by a

teaching fellow at Royal Holloway University of London as well as a millennial

working in a tech start-up (RoundMenu.com) who had previous knowledge of

NFC technology. Both participants in the pilot study had knowledge and

experience in academic research and were of opposite genders and different

in age which added to their ability to give valuable feedback in order to

streamline the questionnaire.

3.8) Ethical Considerations

The research conducted received approval of ethical procedures from the

Royal Holloway University of London ethics code. (See Appendix 4 for Ethics

Code Certificate)

All data was securely stored on Google Docs with only the researcher and

supervisor having access to any information from the questionnaire

participants.

38

Chapter 4: Findings

This chapter will present the raw findings collected from the primary data

questionnaire discussed in the chapter above. This is separated into four

sections dealing with frequency statistics, reliability testing, descriptive

statistics, hypothesis testing and regression analysis.

4.1) Demographics

The questionnaire began with the demographics of respondents. Analysing

the frequency of the 103 millennial responses below gives the reader a clear

idea about who participated in the questionnaire, as well as their experience

with the technology before participating in the study.

4.1.1) Age

As displayed in the pie chart below, 69% of respondents fell into the 22-25-

year-old category followed by 27% in the 26-30-year-old category, with only

4% of respondents falling into the youngest 18-21-year-old grouping.

39

Figure 8: Age Demographics (Source: own contribution)

4.1.2) Gender

The pie chart below shows dominance of male respondents, with 68% and

32% female.

Figure 9: Gender (Source: own contribution)

40

4.1.3) Smartphone ownership

The majority of respondents, 62.2%, owned an Apple iPhone. Half as many

respondents were Samsung owners with 32.1% and only 1.9% of

respondents owned a Blackberry. Lastly, 5.9% of respondents stated owning

a smartphone other than the three mentioned.

Figure 10: Smartphone type (Source: own contribution)

41

4.1.4) Experience with NFC technology

As displayed in the pie chart below, a significant percentage of respondents

had never used NFC technology services (59%). 12% of respondents were

not sure if they had encountered the technology, and 29% stated they had

previously used NFC.

Figure 11: Experience with NFC (Source: own contribution)

42

4.2) Reliability Testing

Drawing from the research of Dutot, (2015);; Chen & Chang, (2013);;

Pham & Ho, (2015), using a Cronbach's Coefficient Alpha as a measurement

of reliability is the most common statistical reliability test for Likert scale

answers. Bryman and Bell (2007) state that an alpha below .70 is deemed

unreliable. The authors continue by explaining this only to be true for scales

with ten or more items. Due to this study containing a maximum of three items

per construct, alphas below .70 commonly arise according to Bryman and Bell

(2007). An alpha of one (1) indicates a perfect internal reliability and 0

showing no internal reliability. As displayed in the table below, all constructs

rendered medium to high internal reliability alphas.

Table 1: Reliability Test (Source: own contribution)

43

4.3) Descriptive Statistics

Having met minimum requirements for validity of the questionnaire, the focus

will be shifted onto the descriptive statistics of each construct, by looking

specifically at the mean, mode and standard deviation of each question.

Davies (2007, pp. 124-125) clearly highlights the importance of using

measures of central tendency as the bedrock for further exploration into the

findings of a given data set. The mean and mode values were used in order

“to provide information about the centre of a distribution of values” (Larson, p.

77, 2006). The mean is an average of the total responses to each question

and the mode is the most frequent recurring response in a particular answer

(Larson, 2006). Using the information provided by respondents of the primary

data, the mean and the mode offer insight as to how respondents perceived

NFC technology in a social media setting. As shown in the result tables below,

each construct is grouped with each question being numbered per the order in

which it appeared in the questionnaire (i.e. Perceived Usefulness = PU_1,

PU_2, etc.). Considering the answering method was constructed in a Likert

scale format with 1= Strongly Disagree and 5= Strongly Agree, an average

mean of 3 and above was accepted as being a positive indication. The same

was true for the mode values, 3 and higher. The rationale for accepting an

average mean of 3 and average mode of 3 is drawn from an earlier stated

limitation concerning Likert scales, where Podsakoff et al. (2003) stated that

respondents were often uncomfortable answering a scale question with either

extreme (in this case 1 or 5).

44

According to Larson (2006), dispersion statistics, variance and standard

deviation offer researchers valuable statistical information concerning the

“variability of the data about the measures of central tendency” (p. 77). The

standard deviation is the distance that a variable deviated from the mean, with

a lower standard deviation showing a more consistent set of results.

45

4.3.1) Perceived Usefulness

The construct of Perceived Usefulness (PU) was adapted from the

Technology Acceptance Model (TAM) created by Davis (1989). Having been

further adapted for NFC technology, it was used to gauge the Perceived

Usefulness of NFC for social media among millennials. The results showed

millennials’ perceiving the use of the technology for social media as useful

with a mean of 3.36 and a mode showing high responsiveness with an

average of 3.33 throughout the three questions.

Table 2: Descriptive Statistics Perceived Usefulness (Source: own contribution)

46

4.3.2) Perceived Ease of Use

The construct of Perceived Ease of Use (PEU) from Davis (1989), further

adapted for NFC technology, was used to understand the manner in which

millennials perceive how easy it is to use NFC technology. With a mean value

of 4.40 and a mode of 5, clear evidence is given as to the extent millennials

perceive NFC technology to be easy to use.

Table 3: Descriptive Statistics Perceived Ease of Use (Source: own contribution)

47

4.3.3) Social Influence

The construct of Social Influence (SI) from the Unified Theory of Acceptance

and Use of Technology (UTAUT) model by Venkatesh et al. (2003), further

adapted for NFC technology, was used in order to uncover whether

millennials are influenced by their social surroundings (i.e. friends, family, role

models) when accepting new technology. Keeping in line with the opinion of

Fromm et al. (2011) quoted in the literature review;; millennials are influenced

by their social surroundings when accepting new technology. This was

evident to see with a mean of 3.085 and a mode of 3.5,

Table 4: Descriptive Statistics- Social Influence (Source: own contribution)

48

4.3.4) Compatibility

The construct of Compatibility (Com) from Yang et al., (2012), further adapted

for NFC technology, was used to understand to what extent respondents

valued the compatibility of the technology to their social media activities.

According to Pham and Ho (2015), the extent to which a user finds a

technology compatible with their lifestyle (in this case social media) the more

willing the user will be to accept the technology. With a mean and mode of

3.5, clear evidence is given that users perceive NFC technology to be

compatible with their social media activities.

Table 5: Descriptive Statistics- Compatibility (Source: own contribution)

49

4.3.5) Perceived Risk

The construct of Perceived Risk from Brown et al., (2003) and Pham and Ho

(2015) was to understand the perceived risk respondents felt towards NFC

technology for social media activities. As shown in the table below, a mean of

3.11 and mode of 3.5 proves that respondents felt NFC technology would

have a high level of risk when used in a social media capacity. Interestingly,

Risk_1 rendered a low mean value, and this finding will be discussed later in

the chapter.

Table 6: Descriptive Statistics- Risk (Source: own contribution)

50

4.3.6) Attitude towards Use

The construct of Attitude towards use (Att) from Davis’ (1989) TAM and

Venkatesh et al.’s (2003) UTAUT model, further adapted for NFC, is used to

uncover the attitudes respondents have towards accepting the use of NFC

technology for social media activities. With an average mean of 3.46 and

average mode of 3.5;; respondents’ attitudes were positive when considering

the use of NFC technology in a social media capacity.

Table 7: Descriptive Statistics- Attitude Towards Use (Source: own contribution)

51

4.3.7) Behavioural Intention

The construct of Behavioural Intention to accept technology was adapted from

Davis’ (1989) TAM and Venkatesh et al.’s (2003) UTAUT model. The

construct was used to understand respondents’ behavioural intention to

accept NFC technology in a social media capacity. With an average mean of

2.81 and mode of 3, it can be noted that a neutral to negative response

towards behavioural intention was discovered. Although a mode of 3 showed

positive signs, a mean of less than 3 indicates users’ lack of intention to

accept the technology. These statistics could be influenced by many factors,

including a lack of knowledge of the technology to make an informed decision

about a user's intention to use NFC.

Table 8: Descriptive Statistics- Behaviour Intention (Source: own contribution)

52

4.4) Hypothesis Testing and Regression Analysis

4.4.1) Hypothesis Testing

To conduct reliable hypothesis testing, Pearson’s correlation coefficient

measures the strength of a linear association between two variables, and has

been implemented due to the data being normally distributed (Field, 2009).

The correlation coefficient is always between (-1 and 1). Positive one (1)

indicates perfect positive and negative one (-1) indicates perfect negative

(Field, 2009). (See Appendix 2 for raw SPSS statistics)

SI: Social Influence, PU: Perceived Usefulness, PEU: Perceived Ease of Use, Com: Compatibility, Risk, Att: Attitude Towards Use, BI: Behavioural Intention

Table 9: Hypothesis Testing (Source: own contribution)

53

H1: Social Influence à Attitude

• There is a positive relationship between Attitude towards the use of NFC

and the Social Influence experienced by a millennial user.

.574 from Table 9 indicates there to be a moderately strong positive

correlation between Social Influence and Attitude towards NFC technology.

The significance level is 0.01

.000 < .01 = There is enough evidence to suggest that the correlation

observed does exist in the population and therefore Hypothesis 1 stands.

H2: Perceived Usefulness à Attitude


and the perceived usefulness of the technology by a millennial user.

.691 from Table 9 indicates there to be strong positive correlation between

Perceived Usefulness and Attitude towards NFC technology.




54

H3: Perceived Ease of Use à Attitude


and the Perceived Ease of Use of the technology by a millennial user.

.280 from Table 9 indicates there to be weak positive correlation between

Perceived Ease of Use and Attitude towards NFC technology.




H4: Compatibility à Attitude


and the compatibility in a millennial user's lifestyle.

.709 from Table 9 indicates there to be strong positive correlation between

Perceived Ease of Use and Attitude towards NFC technology.




55

H5: Risk à Attitude

• There is a negative relationship between Attitude towards the use of NFC

and the Perceived Risk associated with the technology experienced by a

millennial user.

-.085 from table 9 indicates there to be weak negative correlation between

Perceived Risk and Attitude towards NFC technology.


.393 > .001 = There is not enough evidence to suggest the correlation and

therefore Hypothesis 4 is rejected.

Qualitative: Open-ended question.

In support of the above findings, the qualitative data collected identified key

concerns of risk when using NFC technology for social media. Of the 103

millennial respondents to the questionnaire, only 39 agreed to elaborate

further on what they perceive as the risks of using NFC. I identified three main

themes in the set of answers:

1. Privacy Concerns (Social Media Information): 30% (12 respondents)

expressed privacy of social media profile information as a risk

associated with NFC.

56

2. Privacy concerns (Personal Information): 40% (15 respondents)

raised concerns about vulnerability to hackers of personal information

with regards to identifying theft and credit card fraud as being a

potential risk when using NFC technology for social media purposes.

3. Accidental Use: 30% (12 respondents) expressed accidental and non-

intentional use of NFC as a potential risk associated with the

technology.

H6: Attitude à Intention

• Millennial Social Media user’s attitude towards NFC has a significant

positive relationship with the users Behavioural Intention to use the

technology.

.709 from table 9 indicates there to be a strong positive correlation between

Attitude towards NFC technology and Behavioural Intention to adopt the

technology.




57

4.4.2) Regression Analysis

The next two regression analysis tables will describe the relationship between

Social Influence (SI), Perceived Usefulness (PU), Perceived Ease of Use

(PEU), Compatibility (Com), Perceived Risk (Risk) and Attitude Towards Use

(Att). The tables will indicate if the conceptual model was successful in

predicting the outcomes of millennials’ attitudes toward the use of NFC. As

noted in Table 10 below, Attitude Towards Use has been used as the

dependent variable and Perceived Risk, Perceived Ease of Use, Perceived

Usefulness, Social Influence and Compatibility as the predictors influencing it

(Field, 2009).

• R in column one indicates a strong positive correlation between the five

predictors and the attitude millennials formed towards the use of NFC.

• R-squared in column two of the table 10, is the measure of how much

of the variability in attitude of use is accounted for by the predictors.

The five predictors account for 61.4% of the variation in attitude

towards the use of the NFC.

• Column three in Table 10 provides evidence of how well the conceptual

model has generalised for the population of respondents. With a

difference between Adjusted R-squared and R-squared being (.614-

.594= 0.02) = 2%, this indicates that if the entire population were to be

used in place of the sample selected, there would only be a shrinkage

of 2%, showing a positive cross-validity of the model.

58

Model Summary

Predictors (Constant): SI, PU, PEU, COM, RISK Dependent Variable: Attitude towards use

Table 10: Regression Analysis (Source: own contribution)

As shown in Table 11 below, a significance level of p < .001 (.000<0.001)

indicates that the framework significantly improved the ability to predict

millennials’ attitudes toward the use of NFC (Field, 2009).

59

ANOVA

Table 11: Significance Testing (Source: own contribution)

60

Chapter 5: Discussion of Findings

Chen and Chang (2013, p.621), state that NFC technology is a “combination

between innovative technology and an information system”. This justifies the

use of the TAM and UTAUT models to explore behavioural intention through

the moderating construct of attitude toward use of NFC. Using the conceptual

model, I highlighted six hypotheses, which were then tested through the

online questionnaire. The significance of these findings will be discussed

below.

5.1) Social Influence and Perceived Ease of Use

The construct of Social Influence (SI) was in line with the literature reviewed.

The research found that millennials' attitudes towards NFC technology was

positively influenced by their social surroundings (i.e. friends, family and role

models).

Perceived Ease of Use (PEU) scored poorly in the research, which was not

initially hypothesised. Although still rendering a positive relationship with

attitude, the results showed that when millennials form attitudes towards NFC,

Perceived Ease of Use was not considered as being an important factor.

Hence, the weak positive correlation between the two constructs.

61

5.2) Compatibility and Perceived Usefulness

The research showed Perceived Usefulness and Compatibility as the two

most important constructs influencing millennials’ attitudes towards the use of

NFC. These results were in line with previous studies of NFC technology,

where compatibility to a user’s lifestyle and perceived practicality of the

technology were deemed as the most influential factors in the adoption

process, (Pham & Ho, (2015), Everett M. Rogers, (1995), Agarwal & Prasad,

(n.d.)). The study conducted by Pham & Ho (2015), resulted in Perceived

Usefulness surpassing Compatibility as the most influential factor in the

technology acceptance process. This study however, proved the opposite to

be true. While both were instrumental in the formation of attitudes,

compatibility surpassed perceived usefulness significantly. This suggests that

millennials are more concerned with how NFC technology complemented their

social media environment, more so than any other construct.

5.3) Perceived Risk

Contradicting all previous literature reviewed, Perceived Risk showed no

significance in its influence over millennials’ attitudes when accepting NFC

technology. Dutot (2015), Pham and Ho (2015) and Chen and Chang (2013),

being the most recent studies of NFC technology adoption, all concluded that

risk has a significant influence on the formation of attitude and the behavioural

intention to adopt NFC.

62

Taking into account all of these studies focused on NFC payment related

subjects, where perceived risk was associated with fraud, theft and possible

interception of transactions, this study initially hypothesised there to be a

similar relationship between Risk and Attitude with social media contact

sharing. The rejection of this assumption came as a surprise. The millennial

respondents provided evidence that risk did not influence their attitudes

towards using the technology. Due to the surprising nature of this finding, a

more detailed analysis was conducted.

Question 1 of the “Perceived Risk” construct in the questionnaire asked:

“Using NFC for social media has risks and makes me feel insecure.”

The mean answer to this question was 2.84 as displayed in Table 6. This

indicates that respondents were positioned below the acceptable mean

response of 3. This suggests millennials do not perceive risk as a significant

influence when forming attitudes towards the adoption of NFC for social

media contact sharing.

Question 2 of the “Perceived Risk” construct in the questionnaire asked:

“Using NFC for social media could make my personal information (other

than social media information) known to others I do not intend to share

with”.

Showing more normal results (concerning previous research), the mean

answer to this question was slightly higher equalling 3.38 and a mode answer

of 4.

63

This deeper analysis suggests millennials are more concerned with personal

information (i.e. phone number, address details, identity information and credit

card details) rather than social media information, falling into the wrong

hands. This is in line with the secondary research.

While the latter seemed significant, millennials did not feel insecure when it

came to risks of the technology in a social media capacity.

5.4) Attitude Toward Use and Behavioural Intention to Adopt

Taking from the regression analysis in Table 10, it is evident that the

constructs of Social Influence, Perceived Usefulness, Perceived Ease of Use,

Compatibility and Perceived Risk account for 62% of the variability in the

dependent variable of Attitude Towards Use of NFC. Field (2009) confirms

this value to be very high when conducting regression analysis.

Attitude toward use was then measured against behavioural intention to adopt

NFC in a social media capacity. The original hypothesis stated there to be a

significant positive relationship between these two constructs. The results

proved this to be correct. With a strong relationship, it can be concluded that

the formation of a positive attitude toward NFC will lead to a high intention to

adopt the technology among millennials.

64

Considering the above-discussed findings, this research questionnaire and

conceptual model may be considered a success as it proves there to be a

direct positive relationship between attitudes formed and intention to adopt

NFC technology for social media contact sharing.

65

Chapter 6: Conclusion

6.1) Contribution

This research offers future practitioners and academics a robust framework

for analysing NFC technology adoption. The framework can be implemented

for other innovative technologies and not solely for NFC.

6.1.1 Academic Contribution

Considering NFC is a new technological phenomenon, this research offered

one of the first of its kind, linking NFC technology and social media contact

sharing, looking specifically at millennials. The secondary literature reviewed

had relatively few areas of focus for NFC technology, specifically in the peer-

to-peer functionality, resulting in this paper developing a new area of

knowledge. This research provided a framework structured to understand the

millennial consumer by identifying the key factors influencing attitude

formation. The study provided sufficient evidence that millennials’ attitudes

positively affected their intention to adopt NFC for social media contact

sharing.

66

6.1.2 Applied Contribution

The study underlines the key factors influencing the formation of attitude

towards the use of NFC as Perceived Usefulness but more importantly,

Compatibly. Furthermore, the research displayed there to be a significant

positive relationship between millennials’ attitudes and their behavioural

intention to adopt the technology for social media. Considering respondents in

this research were concerned with the risk of personal information being

shared, potential service providers looking to promote NFC services in social

media would benefit from assuring consumers that NFC does not hold risk

implications of hacking or interception of personal information (i.e. credit

cards, identification, etc.). Furthermore, marketers’ looking to attract millennial

consumers, should allocate resources towards highlighting the compatibility

the product provides as well as the usefulness of the product in a social media

environment.

6.2) Limitations and Future Research Direction

This study encountered limitations throughout its duration. Firstly, the

constraint of time affected deeper research and understanding of the topic.

With additional time, more data could have been collected, resulting in a

richer analysis.

67

Secondly, the limitations of the size of the study restricted deeper explanation

and examination of the literature, as well as the primary data collected.

Initially, the pilot study was perceived as being sufficient for the release of the

questionnaire. Yet, in retrospect a more thorough pilot study should have

been conducted.

Future research into the subject area should consider using a larger sample

size, with a wider demographic spread. Also, more qualitative research could

be utilised in conjunction with the quantitative approach to gain a deeper,

more personal understanding of each respondent. Lastly, due to the discovery

of perceived risk contradicting prior research, the questions concerning risk in

the questionnaire could be re-analysed. The pilot study did not show its flaws

initially but throughout the investigation it appeared that a perceived risk

question could have confused the respondents, which possibly explains the

contradictory risk findings. Future research could prove or disprove this

theory.

Continuing this research, I plan to implement this framework into other areas

of innovative technology to test the applicability of the conceptual model.

68

References Ajzen, I (1985) On risk, convenience and Internet shopping behavior.

Aliaga, M., and Gunderson, B.(2000) Interactive Statistics. New Jersey:

Prentice Hall.

Andrews, L., Kiel, G., and Boyle, M. (2007) 'Gendered perceptions of

experiential value in using Web-based retail channels'. European Journal of

Marketing 41, (5) 640-658. Available at http:// [Accessed 1 April 2016]

Andy P. Field (2009) Discovering statistics using SPSS: 3rd ed. Los Angeles,

Calif;; London, Sage.

Association for Computing Machinery. J. Beckman ed. Heidelberg: Springer.

Bogdan Patrut, M.P. (n.d.) Social Media in Politics. [Online]. Springer Verlag.

Available from: https://www-dawsonera-

com.ezproxy01.rhul.ac.uk/abstract/9783319046662 [Accessed: 20 February

2016].

Bruns, A., Highfield, T. & Burgess, J. (2013) The Arab Spring and Social

Media Audiences English and Arabic Twitter Users and Their Networks.

69

American Behavioral Scientist. [Online] 57 (7), 871–898. Available from:

doi:10.1177/0002764213479374.

Business Wire (2013) ‘Research and Markets: Market Research Report:

Global NFC Handsets Market 2012-2016.(Industry overview)’.

Chen, K.-Y. & Chang, M.-L. (2013) User acceptance of ‘near field

communication’ mobile phone service: an investigation based on the ‘unified

theory of acceptance and use of technology’ model. The Service Industries

Journal. [Online] 33 (6), 609–623. Available from:

doi:10.1080/02642069.2011.622369.

III, C.H.F.D., Deil-Amen, R., Rios-Aguilar, C. & Canché, M.S.G. (2015) Social

Media, Higher Education, and Community Colleges: A Research Synthesis

and Implications for the Study of Two-Year Institutions. Community College

Journal of Research and Practice. [Online] 39 (5), 409–422. Available from:

doi:10.1080/10668926.2013.828665.

Clark, S. (2011) ‘NFC Forum publishes peer-to-peer communications

specification • NFC World+’, NFC World+, 29 September. Available at:

http://www.nfcworld.com/2011/09/29/310293/nfc-forum-publishes-peer-to-

peer-communications-specification/ (Accessed: 5 April 2016).

Collis, J., and Hussey, R. (2009) Business research: a practical guide for

undergraduate & postgraduate students. 3 ed. Palgrave: MacMillan.

70

Correa, T., Hinsley, A.W. & de Zúñiga, H.G. (2010) Who interacts on the

Web?: The intersection of users’ personality and social media use. Computers

in Human Behavior. [Online] 26 (2), 247–253. Available from:

doi:10.1016/j.chb.2009.09.003.

Csapodi, M. & Nagy, A. (2007) New Applications for NFC Devices. In: Mobile

and Wireless Communications Summit, 2007. 16th IST. [Online]. July 2007

pp. 1–5. Available from: doi:10.1109/ISTMWC.2007.4299077.

Davies, M. (2007) Doing a successful research project: using qualitative or

quantitative methods. Basingstoke, Palgrave Macmillan.

Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User

Acceptance of Information Technology. MIS Quarterly. [Online] 13 (3), 319–

340. Available from: doi:10.2307/249008.

Dutot, V. (2015) Factors influencing Near Field Communication (NFC)

adoption: An extended TAM approach. Journal of High Technology

Management Research. [Online] 26 (1), 45–57. Available from:

doi:10.1016/j.hitech.2015.04.005.

Everett M. Rogers (1995) Diffusion of innovations. 4th ed. New York, Free.

71

Fallowfield, L. (1995) Questionnaire design. Archives of Disease in Childhood.

[Online] 72 (1), 76–79. Available from: doi:10.1136/adc.72.1.76.

Fishbein, M., and Ajzen, I.(1975) Belief, attitude, intention, and behavior: An

introduction to theory and research. Reading: Addison - Wesley.

Forsythe, S., Liu, C., and Shannon, Gardener. (2006) 'Development of a scale

to measured perceived benefits and risks of online shopping'. Journal of

Interactive Marketing 20, (2) 55-75. Available at http:// [Accessed 22 March

2016]

Gummesson, E. (2005) 'Qualitative research in marketing: Road-map for a

wilderness complexity and unpredictability' European Journal of Marketing

[Online] 39, (3) 309-327. Available at http:// www.ebscohost.com [Accessed 1

April 2016]

Hawthorne, J.L. (2015) Engaging the skill set of the Millennials: Librarians,

content and technology in the mobile age. QScience Proceedings. [Online]

2015 (1), 3. Available from: doi:10.5339/qproc.2015.gsla.3.

Holloway, B., Wang, S., and Parish, J. (2005) 'He role of cumulative online

purchasing experience in service recovery management'. Journal of

Interactive Marketing 19, (3) 54-68. Available at http:// [Accessed 1 March

2016]

72

Hongwei Du (2013) NFC Technology: Today and Tomorrow. International

Journal of Future Computer and Communication. [Online] 2 (4), 351–354.

Available from: doi:10.7763/IJFCC.2013.V2.183.

Humphreys, L. (2013) Mobile social media: Future challenges and

opportunities. Mobile Media & Communication. [Online] 1 (1), 20–25.

Available from: doi:10.1177/2050157912459499.

Jeff Fromm, Celeste Lindell and Lainie Decker. (2011) American Millennials:

Deciphering the enigma generation. Available at:

https://www.barkleyus.com/AmericanMillennials.pdf (Accessed: 3 April 2016)

Kaplan, A.M. (2012) If you love something, let it go mobile: Mobile marketing

and mobile social media 4x4. Business Horizons. [Online] 55 (2), 129–139.

Available from: doi:10.1016/j.bushor.2011.10.009.

Kantner, C., Langer, J., Madlmayr, G. and Scharinger, J. (2008) ‘NFC

Devices: Security and Privacy’, in Third International Conference on

Availability, Reliability and Security, 2008. ARES 08. Third International

Conference on Availability, Reliability and Security, 2008. ARES 08, pp. 642–

647. doi: 10.1109/ARES.2008.105.

73

Katona, G., and Morgan, J. (1952) 'The quantitative study of factors

determining business decisions'. The quarterly journal of economics 66, (1)

67-90. Available at http:// www.ebscohost.com [Accessed 28 March 2016]

Kietzmann, J.H., Hermkens, K., McCarthy, I.P. & Silvestre, B.S. (2011) Social

media? Get serious! Understanding the functional building blocks of social

media. Business Horizons. [Online] 54 (3), 241–251. Available from:

doi:10.1016/j.bushor.2011.01.005.

Kirby, M. (2007) 'Paradigm change in operations research: Thirty years of

debate' Operations research [Online] 55, (1) 1-13. Available at http://

www.ebscohost.com [Accessed 28 March 2016]

Larsson, R. (1993) 'Case Survey Methodology: Quantitative analysis of

patterns across case studies' The academy of management journal

[Online]36, (6) 1515-1546. Available at http:// www.ebscohost.com [Accessed

19 March 2016]

Larson, M.G. (2006) Descriptive Statistics and Graphical Displays. Circulation.

[Online] 114 (1), 76–81. Available from:

doi:10.1161/CIRCULATIONAHA.105.584474.

74

Leading global social networks 2016 | Statistic (no date) Statista. Available at:

http://www.statista.com/statistics/272014/global-social-networks-ranked-by-

number-of-users/ (Accessed: 2 May 2016).

Lecompte, M.D. (2000) Analyzing Qualitative Data. Theory Into Practice.

[Online] 39 (3), 146–154. Available from: doi:10.1207/s15430421tip3903_5.

Li, C.-Y. (2013) Persuasive messages on information system acceptance: A

theoretical extension of elaboration likelihood model and social influence

theory. Computers in Human Behavior. [Online] 29 (1), 264–275. Available

from: doi:10.1016/j.chb.2012.09.003.

Liébana-Cabanillas, F., Ramos De Luna, I. & Montoro-Ríos, F.J. (2015) User

behaviour in QR mobile payment system: the QR Payment Acceptance

Model. Technology Analysis & Strategic Management. [Online] 27 (9), 1031–

1049. Available from: doi:10.1080/09537325.2015.1047757

Marc Prensky (2001) Digital Natives, Digital Immigrants Part 1. On the

Horizon. [Online] 9 (5), 1–6. Available from:doi:10.1108/10748120110424816.

Maylor, H., and Blackmon, K.(2005) Researching Business and Management.

New York: MacMillan.

75

McGrath, J. (1982) 'Dilemmatics: The study of research choices and

dilemmas' American Behavioral Scientist [Online] 25, (2) 179. Available at

http:// [Accessed 2 April 2016]

Milanovic, R. (2015) The World’s 21 Most Important Social Media Sites and

Apps in 2015. [Online]. 13 April 2015. Social Media Today. Available from:

http://www.socialmediatoday.com/social-networks/2015-04-13/worlds-21-

most-important-social-media-sites-and-apps-2015 [Accessed: 6 April 2016].

NFC-Enabled Cellphone Shipments to Soar Fourfold in Next Five Years | IHS

Online Newsroom (no date). Available at: http://press.ihs.com/press-

release/design-supply-chain/nfc-enabled-cellphone-shipments-soar-fourfold-

next-five-years (Accessed: 6 May 2016).

PayPal United Kingdom: Pay, Send Money & Accept Payments (no date).

Available at: https://www.paypal.com/gb/webapps/mpp/home (Accessed: 8

April 2016).

Pearson’s correlation (no date). Available at:

http://webcache.googleusercontent.com/search?q=cache:NhYBP7vf_pgJ:ww

w.statstutor.ac.uk/resources/uploaded/pearsons.pdf+&cd=1&hl=en&ct=clnk&g

l=uk (Accessed: 20 April 2016).

76

Petty, M., McGee, G., and Cavender, J. (1983) 'A Meta-Analysis of the

Relationships Between Individual Job Satisfaction and Individual

Performance'. Academy of Management Review 9, (721) 712. Available at

http:// [Accessed 15 March 2016]

Pham, T.-T.T. & Ho, J.C. (2015) The effects of product-related, personal-

related factors and attractiveness of alternatives on consumer adoption of

NFC-based mobile payments. Technology in Society. [Online] 43159–172.

Available from: doi:10.1016/j.techsoc.2015.05.004.

Podsakoff, P., Mackenzies, S., and Lee, Y. (2003) 'Common method biases in

behavioral research: A critical review of the literature and recommended

remedies'. Journal of Applied Psychology 88, (5) 879-903. Available at http://

[Accessed 1 April 2016]

Raacke, J. & Bonds-Raacke, J. (2008) MySpace and Facebook: Applying the

Uses and Gratifications Theory to Exploring Friend-Networking Sites.

CyberPsychology & Behavior. [Online] 11 (2), 169–174. Available from:

doi:10.1089/cpb.2007.0056.

Saunders, M., Lewis, P., and Thornhill, A. (2007) Research Methods for

Business Students. UK: Pearson Education.

77

Schwalbe, C.B. (2009) Leveraging the Digital Media Habits of the Millennials:

Strategies for Teaching Journalism Courses. Southwestern Mass

Communication Journal. 25 (1), 53–68.

SPSS FAQ: What does Cronbach’s alpha mean? (no date). Available at:

http://www.ats.ucla.edu/stat/spss/faq/alpha.html (Accessed: 16 April 2016).

Taylor, S. and Todd, P. (1995) 'Understanding Information Technology

Usage: A Test of Competing Models'. Information Systems Research 6, (2)

144-157. Available at http:// [Accessed 1 March 2016]

Twenge, J. M. (2009) ‘Generational changes and their impact in the

classroom: teaching Generation Me’, Medical Education, 43(5), pp. 398–405.

doi: 10.1111/j.1365-2923.2009.03310.x.

Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D. (2003) User

Acceptance of Information Technology: Toward a Unified View. MIS

Quarterly. 27 (3), 425–478.

Vogelstein, F. (2009) The Wired Interview: Facebook’s Mark Zuckerberg.

[Online]. 29 June 2009. WIRED. Available from:

http://www.wired.com/2009/06/mark-zuckerberg-speaks/ [Accessed: 2 April

2016].

78

Want, R. (2011) Near field communication. Pervasive Computing, IEEE.

[Online] 10 (3), 4–7. Available from: doi:10.1109/MPRV.2011.55.

What Are Descriptive Statistics? - Definition and Uses - Video & Lesson

Transcript (no date) Study.com. Available at:

http://study.com/academy/lesson/what-are-descriptive-statistics-definition-

and-uses.html (Accessed: 18 April 2016)

‘What It Does’ (no date) NFC Forum. Available at: http://nfc-forum.org/what-is-

nfc/what-it-does/ (Accessed: 16 March 2016)

Yang, S., Lu, Y., Gupta, S., Cao, Y., et al. (2012) Mobile payment services

adoption across time: An empirical study of the effects of behavioral beliefs,

social influences, and personal traits.(Report). Computers in Human Behavior.

28 (1), 129.

Zapin, M. (no date) The Challenge of Analyzing Open-Ended Survey

Questions, SurveyGizmo. Available at: https://www.surveygizmo.com/survey-

blog/the-challenge-of-analyzing-open-ended-questions/ (Accessed: 6 May

2016).

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Appendix

1) Questionnaire

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81

82

83

84

85

86

2) Hypothesis Testing Social Influence

87

Perceived Usefulness

88

Perceived Ease Of Use

89

Compatibility

90

Perceived Risk

91

Behavioural Intention

92

3) Regression Analysis – Model Summary

Regression Analysis -‐‑ Anova

93

3) Ethics Code

94

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Thesis - Final Draft (Domenic Boni) PDF