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Google Glass: An Evaluation of Social Acceptance
Sheilagh Kernaghan
Digital Arts BA (Hons), School of Engineering and Digital Arts, University of Kent
May 2016
TABLE OF CONTENTS
List of Figures
i.
List of Tables
ii.
INTRODUCTION
Google Glass
1
Social Acceptance of Wearable Devices
3
Research Questions
4
LITERATURE REVIEW
Technological Acceptance Model (TAM-2)
4
Social Determinants
6 - 13
i. Perceived Ease of Use
6
ii. Perceived Usefulness
6
iii. Subjective Norm
6
iv. Image
8
v. Aesthetics
9
vi. Privacy and Security
10
vii. Input and Interaction
12
Key Moderating Factors
13 – 18
i. Age
14
ii. Gender
15
iii. Culture
16
iv. Technological Expertise
17
Summary of Review
18
METHODOLOGY
Theoretical Framework
19
Research Approach
Data Sampling
23
23
Data Collection
24
Procedure
25
Analysing the Data
26 – 28
i. Quantitative Analysis of Social Determining Factors
26
ii. Quantitative Analysis of Key Moderating Factors
27
iii. Qualitative Analysis of Open-Ended Responses
28
FINDINGS
Research Question One
28 – 33
i. Quantitative Findings for Social Determinants
28
ii. Qualitative Findings for Social Determinants
32
Research Question Two
34 – 41
i. The Moderating Effect of Age
36
ii. The Moderating Effect of Gender
38
iii. The Moderating Effect of Technological Expertise
39
DISCUSSION
Aesthetics
42
Input and Interaction
43
Privacy and Security
44
Perceived Ease of Use
45
Perceived Usefulness
46
Subjective Norm
47
Image
48
Limitations
49
CONCLUSIONS AND FURTHER RESEARCH
49
BIBLIOGRAPHY
52
APPENDICES
Appendix A – Ethics
64
Appendix B – Study Materials
66 – 84
Information Sheet
66
Consent Form
69
Contact Details Form
71
Survey
72
Advanced/Further Tasks
84
Appendix C – Assumptions
85 – 87
The Assumption of Errors
85
The Assumption of Linearity and Homoscedasticity
85
The Assumption of No Multicollinearity
86
The Assumption of Normality
86
Appendix D – SPSS Output
88 – 97
Descriptive Statistics
88 – 91
i. Full Data Sample
88
ii. Descriptive Statistics by Age Group
89
iii. Descriptive Statistics by Gender
90
iv. Descriptive Statistics by Technological Expertise
91
Inferential Statistics
92 - 97
i. Multiple Regression
92
ii. Age-Moderated Multiple Regression
93
iii. Gender-Moderated Multiple Regression
94
iv. Technological Expertise-Moderated Multiple
Regression
95
v. ANOVA for Age
96
vi. t-test for Age
96
vii. t-test for Gender
97
viii. ANOVA for Technological Expertise
97
Appendix E – Qualitative Themes
98
Appendix F – Observations
100
i
LIST OF ILLUSTRATIONS
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LIST OF TABLES
Table 1
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1
Introduction
Wearable technology is predicted to become an extension of the human body and
mind (Starner 2013, 15). It will act as the bridge between biology and technology, to be the
catalyst for convergence between reality and virtuality: transforming human-computer
interaction, whilst simultaneously encouraging wearers to reengage with each other and their
environment. Wearable technology is becoming capable of catering to the specific needs of
the wearer (Buenaflor and Kim 2013, 103). Thus, it has considerable potential to augment
and enhance the lives of the individuals who use it.
Wearable technology is defined as technology, which has been integrated into
garments or accessories. Presently, the functionality of wearable devices is analogous to
smartphones. However, unlike mobile technologies, wearables are sometimes equipped with
sensors capable of measuring physiological data, such as heart rate. The predominant
application of wearable technology is currently within specialist fields such as military and
healthcare. Nevertheless, wearables are slowly emerging as products for the general
consumer (Tehrani and Michael 2014).
Wearable devices come in many forms, including smart clothing, watches, glasses and
jewellery but regardless of their form, the fundamental aim of a wearable is to offer seamless
and immersive real-time access to information. One of the most prevalent wearable devices is
Google Glass (Ha et al. 2014, 69); the primary focus of this research study.
Google Glass
Google Glass is a wearable computer with a heads-up display, worn like a pair of
glasses. A small screen rests just above the wearer’s right eye. Content is partially
transparent, allowing wearers to connect with virtual and physical worlds simultaneously.
2
Glass can be navigated using voice commands, hands-free gestures such as winking and
nodding, and its built-in touchpad. Currently, applications for Google Glass are fairly limited;
performing tasks that smartphones are already capable of. These tasks include making and
receiving calls or messages, finding directions and taking photographs or videos (Strickland
2014).
The concept of Google Glass was first relayed to the public in 2012. In 2013, Google
invited ten-thousand people to participate in the Google Glass Explorer Program, giving
them the chance to test Google’s latest product: Google Glass Explorer Edition 2.0. Then in
2014, Google Glass was released to general consumers with a £1000 retail price (CNET
2013; Hattersley 2015).
In January 2015, although still in its infancy, sales of Google Glass were terminated.
However, Google have insisted that “you’ll start to see future versions of Glass when they’re
ready” (Optometry Today 2015). Research suggests that Google’s withdrawal of the device is
a result of the poor reception it received and its lack of social acceptance by users (Oremus
2015). Several public spaces forbade its use, its privacy issues caused media controversy and
its success in the mass-market was limited (McCormick 2015, 7).
Nonetheless, the premise behind Google Glass has potential: the idea of consuming
information at a single glance (Metz 2014, 80). Therefore, an understanding of why Google
Glass received a largely negative response and how it could be redesigned according to the
needs of users is essential. However at present, there is a limited amount of research
exploring the factors at the core of its social acceptance. Gao and Luo (2015) emphasise that,
opposed to its social issues, the technology is the focus of studies. Others theorise that
research is limited because wearables are a “new phenomenon”, only recently becoming
commercial products (Rauschnabel, Brem and Ivens 2015, 636; Yang et al. 2016, 256)
3
Social Acceptance of Wearable Devices
Social acceptance describes the extent to which a device has been accepted by its
target users. It determines the success or failure of a technology; success being defined as the
adoption and subsequent circulation of the device (Buenaflor and Kim 2013, 103). Despite its
potential, Google Glass did not become as commonplace as anticipated. Research analyst
Tony Danova (2013), predicted that 21 million units of Google Glass would be sold annually
by the end of 2018. However, as aforementioned, sales of Google Glass were discontinued in
2015.
The disruption of established social norms may have posed a significant challenge to
the acceptance of Google Glass. Profita et al. (2013, 89) argue that in order for individuals to
gain social acceptance, they must integrate within society without attracting negative
attention. Existing devices such as mobile phones are concealed in pockets when they are not
in use. Conversely, Google Glass rests indiscreetly upon a user’s face, potentially drawing
negative attention from others even when inactive (Baraniuk 2014).
The few studies investigating social acceptance have focused on how wearables raise
privacy concerns (Boscart et al. 2008, 218) or fulfil only “basic humanistic needs” (Duval
and Hashizume 2010, 162) . This research project aspires to address the current gap in
knowledge and understanding of wearable technology acceptance. It will look to identify and
investigate the factors influencing the social acceptance of Google Glass by gathering data
directly from potential users. A study of the key constructs may present substantial
contributions to wearable technology academia and practitioners, and through its observation
of attitudes towards the technology, this research may offer assistance in the ongoing
development of Google Glass.
4
Research Questions
The research questions guiding this study are:
RQ1.) Which factors have the greatest influence upon the social acceptance of Google
Glass?
RQ2.) To what extent does socio-demographic background affect attitudes towards
Google Glass?
Literature Review
Technology Acceptance Model (TAM-2)
The theoretical Technology Acceptance Model (TAM-2) aims to explain and predict
the social acceptance of new technologies by identifying and assessing its determining factors
(Venkatesh and Davis 2000, 186). TAM-2 is an extension of the original TAM (Davis 1989)
and integrates social factors that its predecessor omitted. TAM-2 theorises that the extent of
novel technology acceptance depends upon the influence of eight factors: perceived
usefulness, perceived ease-of-use, subjective norm, image, voluntariness, output quality,
result demonstrability and job relevance (Venkatesh and Davis 2000, 187).
Perceived usefulness is outlined as the extent to which a user believes a device might
enhance their job performance (Venkatesh and Davis 2000, 187). Perceived ease-of-use is the
user’s estimation of how simple the device is to operate (192). Voluntariness is the user’s
belief that acceptance of the novel technology is not compulsory (188). Output quality is the
user’s assessment of how well the device assists in the performance of useful tasks (191).
Result demonstrability is the perception of how much success in a task can be attributed to
the novel technology (192). Job relevance is defined as how well the device’s functionalities
match specific tasks in an individual’s job (191). Image denotes the degree to which using the
5
technology can be perceived to enhance social status (189). Finally, subjective norm is the
user’s perception of whether the people they value approve of their use of the technology
(187).
To establish the extent to which a novel technology has been accepted, TAM-2
assesses the direct or indirect effect of these factors on an individual’s intention to use the
device. Intention to use is defined as an individual’s willingness to use a system (Venkatesh
and Davis 2000, 201). Measuring behavioural intention is more practical than measuring
actual behaviour and has frequently been verified as an accurate indicator of real behaviour
(Hopp 2013, 350). Reliability and validity studies further demonstrate that TAM-2 is a
consistent, effective model (Šumak et al. 2011, 95; Wu et al. 2011, 143).
Whilst TAM-2 has provided a comprehensive account of the factors determining the
social acceptance of new technologies, it may not account for every factor. For instance, the
results of a study led by Kuru and Erbuğ (2013, 919) found that perceived usefulness and
perceived ease-of-use are fundamental determinants of an individual’s behavioural intention
to use a novel device. However, they also discovered the importance of other factors, such as
aesthetic attributes and gesture-based interactions.
Therefore, the following section will dissect modern wearable research and examine
the social determinants employed by TAM-2, as well as introduce new explanatory factors
that are not currently accounted for by the model. Job relevance, output quality, result
demonstrability and voluntariness are cognitive, non-social factors (Venkatesh and Davis
2000, 190), thus they were excluded from the review.
6
Social Determinants
i. Perceived Ease of Use
Perceived ease-of-use has been established as a strong predictor of intention to use
(Kuru and Erbuğ 2013, 919). Users who believe a system will be simple to use, usually
develop more positive attitudes towards the technology (Kim and Shin 2015, 528; Tsai, Wang
and Lu 2011, 68). One study found that perceived ease-of-use had the strongest effect upon
intention to use compared to other factors (Huang et al. 2015). However, whilst reviews of
Google Glass suggest that the device is easy to operate (Häger 2015, 104; ITPro 2014;
Shanklin 2013), its adoption has not been widespread, which suggests that while perceived
ease-of-use may be essential, there are other important factors to be considered.
ii. Perceived Usefulness
Perceived usefulness is frequently cited as a core determinant of intention to use
(Kuru and Erbuğ 2013, 919; Umrani and Ghadially 2008, 217). Metz (2014, 82) asserts that
Google Glass offers no value to users because it is an unfinished product which fails to
“perform a valuable function.” Yet it is consumers who are able to identify an advantage in
using wearable technology that are more likely to adopt a new device (Rauschnabel, Brem
and Ivens 2015, 635). Nevertheless, Hong (2013, 11) proposes that the population will
become accustomed to new technologies, which may encourage a change in perceptions of
Google Glass. Ultimately, this could lead to increased levels of social acceptance. This study
sought to clarify to what extent perceived usefulness affects an individual’s intention to use
Google Glass.
iii. Subjective Norm
Existing studies show that subjective norm has a significant effect upon intention to
7
use (Choi and Chung 2013; Hopp 2013; Umrani and Ghadially 2008; Wang and Wang 2010).
Some researchers argue that the visible nature of smart glasses, compared to more discreet
wearable devices such as smart watches, is more likely to influence the opinions of others
(Rauschnabel, Brem and Ivens 2015, 643).
Ware (2014, 2) conducted a study investigating subjective norm as a determining
factor of students’ intention to use Google Glass. Feedback indicated that a majority of the
students felt they were treated differently when they wore the device and relayed concerns
about appearing strange to others. Subsequently, only a minority of the students elected to use
Google Glass in their spare time, suggesting that the opinions of others directly affected
students’ decisions. Therefore, it could be concluded that apprehensions about subjective
norms are highly influential on the social acceptance of Google Glass.
Contrary to Venkatesh et al. findings (2003, 469), students’ concerns about violating
social norms did not dissipate after becoming more familiar with the device (Ware 2014, 2).
Rauschnabel, Brem and Ivens (2015, 639) consider that such apprehensions might diminish
over time as opinions of novel technologies change but until then, they posit that individuals
will identify using wearable devices as a high social risk; considering their usage as a failure
to conform to social norms. Bellezza, Gino and Keinan (2013, 35) explain that conforming
“is driven by a desire to gain social acceptance” and nonconformity may lead to social
disapproval, embarrassment and rejection.
Most studies involving subjective norm only consider the opinions of significant
others’ such as colleagues, friends and family (Hopp 2013; Lai, Wang and Lei 2012).
However, Bailly et al. (2012, 1246) investigated the effect of non-significant others’
opinions. They found that people were least willing to interact with a novel device in front of
strangers compared to friends, family or colleagues. This suggests that the opinions of
8
unfamiliar others significantly affects the acceptability of novel technologies and should be
included within definitions of subjective norm. Hence, this study expanded TAM-2 to
consider the effect of unfamiliar others’ opinions, for instance strangers and acquaintances.
In summary, subjective norm has been highlighted as a potentially vital factor for the
social acceptance of Google Glass; a person may reject it merely in an attempt to conform to
social norms. Upon the introduction of a new technology, individuals’ perceptions of the
device are typically underdeveloped and in looking for reinforcement, they are often
influenced by the opinions of others (Hartwick and Barki 1994, 458). This study will examine
to what extent the opinions of familiar and unfamiliar others affect an individual’s intention
to use Google Glass.
iv. Image
Image is outlined as the extent to which the “use of an innovation can be perceived to
enhance one’s status” (Moore and Banbasat 1991, 195). Wearable technology can provide
users with video and audio recording capabilities and access to relevant databases without
giving visual cues to observers (Dvorak 2003, 6). This may alter the nature of interpersonal
interactions and create an imbalance in status during exchanges; elevating one converser
above the other. Noble and Roberts (2016, 6) believe that the recording capability of Google
Glass gives the user greater standing than their counterpart.
As an iconic and expensive device, Google Glass could be considered a status
symbol. Forrest (2014) emphasises that wearing Google Glass broadcasts to others that the
wearer had £1000 of disposable income to purchase the device. This might imply that the
wearer belongs to a higher social class. Furthermore, individuals may use the device as a tool
to appear more “technologically sophisticated” than others (Yang et al. 2016, 259; Kim and
Shin 2015, 530; Umrani and Ghadially 2008, 218).
9
However, wearable technology may also impair a person’s status, as evidenced in a
smart-jewellery study. Although participants were enthusiastic about the device concept, it
was negatively reviewed overall, especially by female respondents who felt it “geeked-up”
their appearance (Dvorak 2003, 7). Nevertheless, unconventionality and nonconformity may
also enhance status. When an individual deviates from established social practices, they
display an autonomous quality, which translates to other positive characteristics such as
assertiveness and leadership (Bellezza, Gino, and Keinan 2013, 35). However, other
researchers argue that adopters of novel technologies are viewed as “conceited” and
“arrogant” (Nurun 2015).
In conclusion, it is likely that Google Glass can affect a person’s social standing.
However, it is unclear whether the device would be viewed as a symbol of prestige, arrogance
or unconventionality. This study attempted to ascertain whether participants felt that Google
Glass could improve social standing and whether this affects their intention to use the device.
v. Aesthetics
Heads-up displays began as uncomfortable and unattractive products used solely by
specialist industries such as the military. As more hardware has become available, wearable
devices have moved into the consumer market. Ha et al. (2014) maintain that devices such as
Google Glass have gradually become more stylish as a result. Technology design company
Artefact contests this, believing that Google Glass is “devoid of style” for the average
consumer (In: Miller 2013). Garfinkel agrees (2014, 77), describing Google Glass as “ugly as
sin and impossible to miss”. One explanation for Google Glass’ current aesthetic issues is that
the device has been developed for technology enthusiasts or “early-tech adopters”, opposed
to the general consumer (Kalinauckas 2015, 36). Therefore, aesthetics have not been a
priority during its development.
10
Beecham Research emphasise that although aesthetics is important for any product, it
is imperative for wearable technology. They state that “putting something on a person’s body
is a very different paradigm.” If a device is a reflection of a person’s identity, its design must
complement its user (In: Bilton 2014). Ha et al. (2014, 70) assert that the technological
capabilities of a device should be secondary to aesthetics, which is one of the “most sought-
after features” of wearable technology. Previous studies have revealed that aesthetics play a
vital role in consumer purchase behaviours. Unattractive design is the factor most likely to
prevent individuals from purchasing a new device (Ariyatum et al. 2005, 15; Yang et al. 2016,
266). Subsequently, the integration of fashion and technology could facilitate the success of
Google Glass (In: Kalinauckas 2015, 36).
Conversely, Hwang (2014, 61) found that aesthetics had an insignificant effect on an
individual’s attitude or purchase intention, concluding that consumers generally place more
importance on the functionality of wearable technology than its aesthetic qualities.
Nevertheless, a number of Hwang’s participants reported concerns about the wearable’s
appearance (62). Therefore, the extent to which aesthetics influences a person’s attitude
towards and intention to use wearable technology appears to be conflicted.
TAM-2 does not consider the importance of aesthetic qualities as a determining factor.
However, the majority of the above research suggests that aesthetics and style may be more
influential upon social acceptance than functionality. Therefore, TAM-2’s account of how
social acceptance of new technologies is determined may be lacking a crucial factor. This
research study aimed to clarify the influence of aesthetics upon intention to use Google Glass.
vi. Privacy and Security
A substantial amount of literature debates the legitimacy of privacy and security
concerns surrounding Google Glass. Whilst the press generate a considerable amount of
11
privacy anxiety, these issues have been largely unaddressed by Google (Hong 2013, 10).
Josie Ensor (2014) at The Telegraph reported that Google Glass could be misused to
purposefully violate another person’s security. She argued that Google Glass could
“surreptitiously pick up” PIN codes, leaving people susceptible to malicious security attacks.
Moreover, Charles Arthur (2013) at The Guardian suggests that Google will monitor usage of
Google Glass and access user’s data on the device. As such, users could compromise the
privacy both of themselves and unknowing bystanders by recording videos, with little regard
for consent or who owns and uses that data (Michael and Michael 2016, 26). As a result,
some Google Glass Explorers recount experiences of confrontation with individuals who feel
certain they are being covertly recorded (Garfinkel 2014, 73).
In contrast, some academics commend wearable technology for encouraging socially-
acceptable “looking behaviours” (Fradera 2014, 741). One study deduced that an awareness
of gaze, which was the result of wearing an eye-tracking device, actively encouraged
individuals to be more considerate of where they focused their attention (Risko and
Kingstone 2011, 294). This demonstrates that individuals censor their gaze when they believe
it is being noticed by others. Therefore, because Google Glass records from a first-person
perspective, it could replicate the effect of an eye-tracking device. As such, Google Glass
wearers may actually demonstrate greater sensitivity towards others’ privacy. Furthermore,
some researchers dispute that wearable devices are a cause for concern because mobile
devices can already be used for covert recording (Lemos 2013).
Privacy and security is another social factor that is not already accounted for by TAM-
2. However, the literature suggests that the effect of privacy concerns on intention to use
could be significant. Thus, this research investigated how the social acceptance of Google
Glass is affected by concerns surrounding privacy and security violations.
12
vii. Input and Interaction
The majority of people are no longer unnerved by others appearing to talk to
themselves. Mobile headsets and earphones are a familiar sign that a person is having a
telephone conversation, sometimes reinforced by the user visibly holding their mobile device
(Dvorak 2003, 6). However, it could be posited that the novelty of Google Glass and
therefore its unfamiliar interaction methods make both the wearer and bystanders feel
uncomfortable. Rico and Brewster (2009, 1) assert that as Google Glass is reliant on gesture-
based interaction, it ties the social acceptance of the device to the acceptability of its
unconventional gestures.
A secondary study by Rico found that people are highly concerned with their
appearance when performing gesture-based interactions. Responses revealed that people
consider the wider social context when deciding on the acceptability of different gestures
(2010, 2889), namely their audience and environment (Serrano, Ense and Irani 2014, 3188).
The results were also consistent with another study, which demonstrated that the effect of
social determinants declined with increased device interaction (Morris and Venkatesh 2000,
375). This means that a user’s acceptance of a novel device increases over time. However,
this conflicts with Ware’s aforementioned finding that increased interaction failed to improve
user acceptance (2014, 2). This ambiguity warranted further investigation into the effect of
input and interaction methods and social context upon individuals’ intention to use Google
Glass.
Serrano et al. (2014, 3188) examined the social effects of hand-to-face gestural
interactions as an input method for head-worn displays and found that participants favoured
“calm” gestures, arguing that some gestures carry negative cultural connotations. Similarly,
participants in Rico’s study preferred to perform gestures which mimicked existing,
13
conventional actions such as foot tapping, rather than unfamiliar gestures (2010, 2889).
Therefore, if a person is required to perform unusual actions to operate Google Glass, the
device might be received negatively until societal conventions adapt to account for gesture-
based interactions.
Alternative studies found that participants preferred gestural interaction to voice
commands. More specifically, hand-based gestures are rated as more intuitive, effective,
interesting and comfortable than other methods (Kollee, Kratz and Dunnigan 2014, 43; Lv et
al. 2015, 564, Wilson and Daugherty 2015). Statistics further revealed that hand-based
gestures received a stronger “positive emotional response” (Kollee, Kratz and Dunnigan
2014, 48). Operating wearable technology using a touchpad was also well-received.
However, researchers pointed out that Google Glass’ touchpad offers limited usability due to
its size, location and restricted interactions (41). Therefore, unconventional interaction
methods have the potential to be received positively and to be accepted by users but Google
Glass’ current interaction methods appear to be limited and may inhibit its acceptability.
Despite evidence supporting input and interaction as a determining factor of social
acceptance (Profita et al. 2013; Rico and Brewster 2009; Rico 2010; Serrano et al. 2014),
TAM-2 fails to incorporate it as a social determinant. This research study endeavoured to
determine which of Google Glass’ modes of interaction are preferable to users and to what
extent input and interaction methods and social context affect an individual’s intention to use
Google Glass.
Key Moderating Factors
The majority of those who own Google Glass are white, middle-aged, western men
(Segan 2013). However, the exclusion of people outside of this demographic may have
contributed to the limited success of Google Glass. Therefore, this study sought to understand
14
the moderating effect of socio-demographic background on the social acceptance of Google
Glass. Specifically, this section will explore the effect of age, gender, culture and
technological expertise on attitudes towards the device (RQ2). Whilst social determinants
directly influence intention to use, the effect of key moderating factors is indirect.
i. Age
Researchers postulate that privacy is largely a generational concern and users from
younger age groups will be less apprehensive about the potential for Google Glass to cause
privacy and security violations (Reed and Stephenson 2014, 11). One possible explanation is
that younger people are less concerned with the consequences of divulging personal
information (Morgan, Snelson and Elison-Bowers 2010, 1406). Subsequently, younger users
may be more accepting of Google Glass than older users because they are less intimidated by
privacy issues.
Whilst age may inhibit the effect of factors such as privacy, it could amplify the effect
of others. For instance, younger individuals may acknowledge more worth in using Google
Glass because they place greater value on technical skills than older people do (Umrani and
Ghadially 2008, 223). This suggests that age moderates the effect of perceived usefulness.
Chen and Chan (2011, 3) discovered that even when older consumers perceive value in a
novel device, they struggle to learn how to use it or believe they lack the skills to operate it.
Consequently, age may also have a considerable impact on ease-of-use ratings for Google
Glass, with older users considering difficult usability a significant barrier to their acceptance
of the device.
The literature further suggests that younger people’s decision to use Google Glass
may be more influenced by the opinions of others, compared to older people. Lower levels of
subjective norm were reported as age increased (Magsamen-Conrad 2015, 18). Therefore, age
15
may affect to what extent individuals consider the opinions of others important when
assessing novel technologies.
In summary, existing research supports the idea that age may have a significant effect
on multiple social determining factors, with younger users being more susceptible to the
opinions of others and older users being more concerned with the usability or usefulness of a
device. Although research has identified some factors which may be mediated by age, it has
not established which age groups are most accepting of Google Glass or if they are equally
resistant to Google Glass but for different reasons. This study attempted to clarify the extent
to which age influences attitudes towards Google Glass.
ii. Gender
Gender may also play a mediating role. Several studies have found that male users are
more influenced by their perception of a device’s usefulness compared to women (Padilla-
Meléndez, del Aguila-Obra and Garrido-Moreno 2013, 315; Zhang and Rau 2015, 156). In
contrast, women are more concerned with usability (Padilla-Meléndez, del Aguila-Obra and
Garrido-Moreno 2013, 314; Terzis and Economides 2011, 2119). Conversely, other studies
have found that gender did not significantly affect any of TAM-2’s constructs for technology
adoption (Faqih and Jaradat 2014, 48; Umrani and Ghadially 2008, 222).
Another study found that gender heavily influenced attitudes towards wearable
placement and gestural interaction. Device placement and interactions were perceived as
more acceptable when performed by men (Profita et al. 2013, 95), which suggests that use of
wearable technology and unconventional gestures by women is viewed as less socially
acceptable. Research also indicates that women are more affected by the opinions of others
than men (Tarhini, Hone and Liu 2014, 177). Therefore, they may be less inclined than men
to adopt Google Glass because of their apprehensions about violating social norms. However,
16
one study found the opposite result, with subjective norm only affecting men’s intention to
use (Wang, Wu and Wang 2009, 112).
To conclude, there is some debate about the extent to which gender moderates
technology adoption. Prior research has produced conflicting accounts of which determining
factors are influenced by gender, if any. Furthermore, the research tends to be restricted to the
original TAM-2 constructs of perceived usefulness and perceived ease-of-use, with little
investigation into gender’s effect on any other social determinants. Finally, no gender studies
appear to have been conducted using Google Glass specifically. This study investigated the
moderating effect of gender on the social determinants of Google Glass.
iii. Culture
There may also be cultural differences in the prioritisation of social determinants. The
results of a cross-cultural study found that South Korean participants typically placed
importance on a wearable’s ability to blend-in and prevent the user from looking “weird or
awkward” (Profita et al. 2013, 95). Furthermore, whilst American participants favoured
devices which were simple to use, only 6.9% of South Korean participants identified ease-of-
use as important. This suggests that eastern individuals are more influenced by the opinions
of others than the usability or functionality of the device.
Eastern cultures may be more affected by subjective norm than western cultures
because they are “more susceptible to the influence of social groups” (Im, Hong, and Kang
2011, 19; Jackson and Wang 2013, 910). This is supported by Baptista and Oliviera (2015,
422) whose study also found that collectivist cultures were more influenced by the opinions
of their social groups when adopting a new technology. Therefore, if individuals within a
social group do not already use Google Glass, then it may not be readily accepted by others
within that group because they perceive a higher social risk from using the device.
17
Cultures with low uncertainty avoidance are more likely to adopt new technologies,
compared to cultures with high uncertainty avoidance. This can be defined as a “lack of
tolerance for ambiguity” (Jackson and Wang 2013, 911). Western populations typically
experience low uncertainty avoidance, whereas eastern populations customarily display high
uncertainty avoidance (Im, Hong and Kang 2011, 11). Therefore, eastern individuals may be
more likely to avoid Google Glass because of the unconventional and ambiguous input and
interaction methods that are required to operate the device.
In summary, existing literature suggests that cultural background is fundamental to
technology adoption. In particular, cross-cultural research suggests that opinions of others
are central to technology adoption decisions in eastern cultures, whereas western cultures
place more importance on functional aspects of devices. Although past research has been
consistent about how the significance of social determinants varies across cultures, relatively
little research focuses on how this affects rates of novel technology acceptance.
iv. Technological Expertise
It has also been hypothesised that the social determinants influencing the acceptance
of Google Glass are dependent on the technological expertise of the user. Some researchers
suggest that those with more experience and greater skill with technology may be less critical
and apprehensive of novel technologies (Reed and Stephenson 2014, 11). Hong (2013, 11)
agrees that an individual’s personal lack of experience with Google Glass leads to inaccurate
perceptions of the device. Therefore, an individual with low expertise may feel more
intimidated about using Google Glass than a skilled technology user, subsequently avoiding
the device. A study by Schaar and Ziefle corroborates this theory, revealing that individuals
with more technological expertise exhibited greater social acceptance towards wearable
technology (2011, 607).
18
In contrast to other literature, a study by Varma and Marler suggests that greater
technological expertise impairs social acceptance. Their study found a curvilinear effect
between expertise and intention to use (2013, 1478). The positive effect of expertise on
acceptance plateaued with high levels of experience and then began to decline where users
became less inclined to adopt a new system. The researchers suggested that this correlation is
a result of users believing that learning to operate new devices is too time-expensive. They
further propose that frequent and prolonged use of technology can lead individuals to foster
an “unconscious habitual negative reaction” towards technology, which leads them to be
dismissive of new devices (1480). These suggestions might offer an explanation for Google
Glass’ current limited success. Individuals with greater technological expertise may be
predisposed to reject novel technologies and be unprepared to spend time learning how to use
them.
To conclude, prior research has provided preliminary evidence suggesting that
technological expertise moderates social acceptance. However, the research is inconsistent;
while some sources suggest that greater technological expertise has a positive effect on social
acceptance, other research suggests that it has a strong negative effect. This research study
measured the effect of technological expertise upon each of the social determining factors. It
also clarified whether technological expertise has a positive or negative effect on an
individual’s behavioural intention to use Google Glass.
Summary of Review
The literature review demonstrates that numerous factors may have affected the social
acceptance of Google Glass, either directly or indirectly. However, to what extent they affect
an individual’s acceptance has not yet been determined. The review further highlighted that
only some of these factors are accounted for by TAM-2, therefore TAM-2 should be updated
19
to more effectively measure the social acceptance of novel technologies such as Google
Glass. Therefore, this study investigated the effect of four TAM-2 factors (perceived ease-of-
use, perceived usefulness, subjective norm, image), three new factors (input and interaction,
aesthetics, privacy and security) and four socio-demographic moderating factors (age,
culture, gender, technological expertise).
Methodology
This section offers a detailed explanation and evaluation of the research methods
adopted throughout this study. In order to investigate the key factors in the social acceptance
of Google Glass, the researcher developed a new framework based on the original TAM-2 to
clarify and quantify individual reactions to the device. This section will detail how TAM-2
was adapted to answer the research questions and accommodate the literature findings.
Furthermore, it will discuss how novel factors were assessed to comprehensively investigate
the social acceptance of Google Glass.
Theoretical Framework
Although TAM-2 has been established as a reliable model (Šumak et al. 2011, 95; Wu
et al. 2011, 143), it is specific to technology acceptance in the workplace. Therefore, TAM-2
was revised by the researcher to accommodate the use of Google Glass in any social context.
Four work-specific or non-social TAM-2 factors were excluded from the study: job
relevance, output quality, result demonstrability and voluntariness.
The remaining TAM-2 factors were tailored to measure attitudes specifically towards
Google Glass (see table 1) and most required only minor changes. However, subjective norm
was modified to account for the influence of both familiar and unfamiliar others. TAM-2 only
recognises the effect of familiar others but as the literature survey highlighted individuals
20
may be more reluctant to engage with a novel device around unfamiliar individuals (Bailly et
al. 2012, 1246). The literature also demonstrated that social context is important to wearers of
novel devices (Rico 2010, 2889). Therefore, subjective norm was further updated to consider
how the opinions of others in public or private contexts might affect acceptance. Subjective
norm had four sub-factors: familiar others, unfamiliar others, public context, private context.
The literature findings also warranted the introduction of new factors to TAM-2. It
almost unanimously indicated that aesthetics, privacy and security, and input and interaction
are direct determinants of social acceptance. Input and interaction had five sub-factors (public
context, private context, voice commands, hands-free gestures, touchpad) which considered
how social context and alternate methods of interaction might have affected the social
acceptance of Google Glass. The literature survey also justified the addition of four key
moderators (age, gender, culture and technological expertise). Figure 1 shows a diagram of
the revised TAM-2 model.
Figure 1. Revised TAM-2 model with four original TAM-2 factors, three new social determinants and
four key moderating factors.
Note: Arrows show relationships between factors.
21
TABLE 1. Original TAM-2 Measurement Scales and Revised TAM-2 Measurement Scales
Original TAM-2 Measurement Scales
Revised TAM-2 Measurement Scales
Perceived Ease of
Use
My interaction with the system is clear
and understandable.
Interacting with the system does not
require a lot of my mental effort.
I find the system to be easy to use.
I find it easy to get the system to do
what I want it to do.
I find Google Glass easy to use.
Interacting with Google Glass is clear
and understandable.
I find it easy to get the system to do
what I want it to do.
Perceived
Usefulness
Using the system improves my
performance in my job.
Using the system in my job increases
my productivity.
Using the system enhances my
effectiveness in my job.
I find the system to be useful in my
job.
Using Google Glass would improve my
performance in daily tasks.
Using Google Glass would increase my
productivity at school, university or
work.
I believe Google Glass would be useful
to me.
Subjective Norm
People who influence my behaviour
think that I should use the system.
People who are important to me think
that I should use the system.
The opinions/reactions of people who
are important to me would influence
my decision to use Google Glass in
public.
The opinions/reactions of people who
are important to me would influence
my decision to use Google Glass in
private e.g. in my own home.
The opinions/reactions of people who
are unfamiliar to me (e.g.
acquaintances, strangers) would
influence my decision to use Google
Glass in public.
The opinions/reactions of people who
are unfamiliar to me (e.g.
acquaintances, strangers) would
influence my decision to use Google
Glass in private e.g. in my own home.
Image
People in my organisation who use the
system have more prestige than those
who do not.
People in my organisation who use the
system have a high profile.
Having the system is a status symbol in
my organisation.
People who use Google Glass have
more prestige than those who do not.
People who use Google Glass have a
high profile.
Google Glass is a status symbol.
Voluntariness
My use of the system is voluntary.
My supervisor does not require me to
use the system.
Although it might be helpful, using the
system is certainly not compulsory in
my job
—
22
TABLE 1 (continued)
Job Relevance
In my job, usage of the system is
important.
In my job, usage of the system is
relevant.
—
Output Quality
The quality of the output I get from
the system is high.
I have no problem with the quality of
the system’s output.
—
Result
Demonstrability
I have no difficulty telling others about
the results of using the system.
I believe I could communicate to
others the consequences of using the
system.
The results of using the system are
apparent to me.
I would have difficulty explaining why
using the system may or may not be
beneficial.
—
Aesthetics
—
The design of Google Glass is
attractive.
The design of Google Glass is stylish.
Privacy and Security
—
If I was using Google Glass, I would
have concerns about my privacy.
If I was using Google Glass, I would
have concerns about my security.
Input and
Interaction
—
I would feel comfortable using voice
commands to interact with Google
Glass in public.
I would feel comfortable using voice
commands to interact with Google
Glass in private e.g. in my own home.
I would feel comfortable using hands-
free gestures to interact with Google
Glass in public.
I would feel comfortable using hands-
free gestures to interact with Google
Glass in private e.g. in my own home.
I would feel comfortable using the
touchpad to interact with Google Glass
in public.
I would feel comfortable using the
touchpad to interact with Google Glass
in private e.g. in my own home.
Intention to Use
Assuming I have access to the system,
I intend to use it.
Given that I have access to the system,
I predict that I would use it.
I intend to use Google Glass again in
the future.
Given that I had access to Google Glass,
I predict that I would use it.
23
Research Approach
The original TAM-2 is exclusively quantitative; participants used 7-point Likert scales
to rate the extent to which they agreed or disagreed with a given statement (Venkatesh and
Davis 2000, 201). However, due to the innovative nature of Google Glass and the current
lack of understanding surrounding the acceptance of wearable technology, utilising a solely
quantitative approach for this research study would not have provided an ample, contextual
explanation of Google Glass’ social acceptance. Therefore, this research study took a mixed-
method approach; applying both quantitative and qualitative practices during data collection.
The researcher was able to quantify and establish broad trends using the quantitative data,
whilst open-ended qualitative responses facilitated the interpretation of this data.
Data Sampling
Thirty-two individuals volunteered to participate in this study (see table 2). The
researcher recruited participants from local community groups and organisations. They were
offered entry into a prize draw for a £20 Amazon voucher as an incentive to take part. To be
eligible to participate, respondents had to meet the minimum age criteria of 18.
TABLE 2. Demographic distribution of the sample.
Gender
Frequency
Age
Frequency
Male
12
18-29
6
Female
20
30-49
5
50-64
6
65+
15
Ethnicity
Frequency
Technological Expertise
Frequency
White British
30
Low
7
Irish
1
Average
15
White/Black African
1
High
9
Very High
1
24
Data Collection
Data was gathered using a self-report questionnaire (see Appendix B). The survey was
divided into twelve sections. In total, there were 50 questions: 3 demographic questions, 27
Likert-scale questions (1 demographic) and 20 open-ended questions. The key moderating
factors were allocated to section one. In parts a, b and c, participants specified their gender,
age group and ethnicity. In part d, respondents indicated their level of technological expertise
on a 5-point Likert scale using a classification table designed by the researcher (see table 3).
This table aimed to prevent inconsistencies across participant responses by offering
benchmarks, against which respondents could rate themselves accurately and fairly to prevent
discrepancies between their self-perceived expertise level and actual expertise level.
TABLE 3. Technological expertise classifications
Level of Expertise
Description
Very low expertise
Little to no experience or understanding of digital technology.
Low expertise
Infrequent and basic use of digital technology.
Able to perform basic tasks at home or in a workplace. For instance,
emailing.
Average expertise
Comfortable and frequent use of digital technology.
High expertise
Confident and daily use of digital technology.
Able to use multiple devices e.g. computers, tablets, smartphones, etc.
Very high expertise
Specialist use and knowledge of digital technology.
The next ten sections examined the social determining factors and sub-factors. Each
social determinant section included a series of revised TAM-2 statements (see table 1) and
respondents scored their level of agreement to the statements using a 7-point Likert scale (1 =
Strongly Disagree, 7 = Strongly Agree); replicating studies using the original TAM-2. Each
section also included open-ended questions, allowing participants to justify their given scores
25
and enabling the researcher to interpret their ratings. The final section asked participants to
evaluate to what extent they enjoyed using Google Glass.
Procedure
The researcher conducted six study sessions, each with a different focus group. Each
group had between four and six participants and each session lasted two hours. All groups
had access to two pairs of Google Glass. Prior to using Google Glass, each participant was
given an information sheet, a consent form and a contact details form (see Appendix B). The
information sheet outlined the project aims and informed individuals what their participation
would entail. It detailed how their data would be used, how they could withdraw from the
study and assured them of good confidentiality practices. The consent form ensured that
participants had read and understood the information sheet. The contact details form allowed
the researcher to contact the prize draw winner.
Afterwards, the researcher gave a short demonstration to participants, instructing them
how to use Google Glass and offering an overview of wearable technology. Each participant
then completed three tasks, utilising each of Google Glass’ interaction methods with the
researcher’s guidance. Firstly, they performed a Google search using voice commands. Next,
they took a photograph using only the winking gesture. The final compulsory task involved
operating the device using only the touchpad to record a 10-second video.
After completing the guided-tasks, participants were encouraged to discuss their
perceptions of Google Glass and to practise with the device without assistance. This involved
either repeating the previous tasks or attempting new tasks from a list given by the researcher,
which included performing calculations, translating foreign-language phrases and finding
directions (Appendix B). Once all participants had finished interacting with the device,
participants completed the self-report questionnaire. The researcher then informed
26
participants that the study was concluded and thanked them for their time.
Analysing the Data
i. Quantitative Analysis of the Social Determining Factors (RQ1)
Quantitative data collected using the questionnaire was manually entered into SPSS.
Likert-scale values given by respondents were used to calculate individuals’ mean scores for
each social determining factor, sub-factor and intention to use. Descriptive statistics
summarised the whole data sample. Using individuals’ mean scores, an overall mean score
was calculated for each factor to describe the central tendency of the sample. Standard
deviation scores measured the spread of the data around the mean. Frequencies were
computed to measure the percentage of participants who felt positively or negatively about a
particular factor. Some questions were missed by participants, leaving nine missing values in
the data set. Rather than excluding these cases from the analyses, the researcher used the
single imputation method to substitute the missing values with the sample’s mean for the
respective factor.
Next, Cronbach’s alpha scores were computed to test the reliability of the social
determinant measures i.e. the statements in the questionnaire. A number of key error
assumptions also had to be met to ensure the revised model’s validity: the assumptions of
normality, no multicollinearity, independent errors and, linearity and homoscedasticity (See
Appendix C).
Multiple regression analyses are used to study the effect of several predictor variables
on one dependent variable, in this case the effect of each social determining factor on
intention to use. Using this method, the researcher was able to establish the contributions of
each factor on the social acceptance of Google Glass (RQ1), assess what percentage its social
27
acceptance or intention to use is accounted for by these factors, and determine how
generalisable the data sample results were to the wider population.
ii. Quantitative Analysis of the Key Moderating Factors (RQ2)
To investigate research question two, the mean sample scores for each demographic
sub-group were calculated. Culture was excluded because there was insufficient data
variability; only two participants identified themselves as non-white British (see table 2).
Frequencies computed the percentage of each sub-group who felt either positively or
negatively towards a given factor.
t-tests were run to compare the differences between men and women’s mean scores on
each social factor and intention to use. However, as age and technological expertise were
made up of three or more sub-groups (see table 4), ANOVA tests were used to compare
scores. One-way ANOVAs with four levels were run for age. Likewise, one-way ANOVAs
with three levels were conducted for technological expertise. Factors highlighted by the
ANOVAs as significant were investigated using further t-tests to clarify which specific sub-
groups had differed in their ratings of various aspects of Google Glass.
The researcher created a regression model for each demographic sub-group using the
multiple regression method. The regression models for age, gender and technological
expertise were then compared to investigate how socio-demographic background affected
attitudes towards Google Glass (RQ2). This method also revealed which social determinants
were important to each sub-group and how the importance of each factor was moderated by
socio-demographic background. For instance, a social determinant may have been important
for one sub-group and not another or it may have been important for several sub-groups but
to different extents.
28
TABLE 4. Demographic sub-groups
Key Moderating Factor
Sub-group
Key Moderating Factor
Sub-group
Gender
Male
Female
Age
18-29
30-49
50-64
65+
Technological Expertise
Low
Average
High
iii. Qualitative Analysis of Open-Ended Responses
Participant comments, recorded using the questionnaire, were assessed by the researcher to
provide contextual support to the numerical data. Keywords were taken from participants’
responses and common themes were identified for each of the factors (See Appendix E). The
researcher also used observational data, recorded during and after each focus group (See
Appendix F).
Findings
Research Question One (RQ1)
i. Quantitative Findings for Social Determinants
Figure 2 shows the data sample’s mean scores and standard deviations for each social
determining factor and intention to use. Mean scores below 4 are generally negative. Scores
above 4 are generally positive. The exception to this rule is privacy and security. The mean
score for intention to use was 3.66. Therefore, consistent with the initial reception of Google
Glass (Oremus 2015), participants were not keen to use the device in the future.
29
The mean score for aesthetics was 4.05. However, more participants found the device
attractive (50%) than unattractive (28%). The mean score for input and interaction was 5.08
therefore respondents were somewhat comfortable interacting with the device. The majority
of the sample found voice commands to be the least comfortable method of interaction, with
touchpad interaction being the most comfortable (see table 5). Furthermore, the majority of
participants expected to be more comfortable using the device in private (M = 6.17) than in
public (M = 4.00).
The mean score for privacy and security was 5.16, thus participants were somewhat
concerned about the privacy issues that Google Glass presents. The mean score for perceived
TABLE 5. Percentage of participants who are comfortable, neutral or uncomfortable using
Google Glass input and interaction methods.
Data Sample Mean Scores
Voice Commands
Hands-free Gestures
Touchpad
Uncomfortable (< 3.5)
6.2%
0.0%
0.0%
Neutral (≥ 3.5 ≤ 4.5)
59.4%
34.4%
18.7%
Comfortable (> 4.5)
34.4%
65.6%
81.3%
0
1
2
3
4
5
6
7
Aesthetics Input and
Interaction
Privacy and
Security
Perceived
Ease of Use
Perceived
Usefulness
Subjective
Norm
Image Intention
to Use
Data Sample Mean Scores
Social Determining Factors and Intention to Use
Figure 2. Sample mean scores for the social determining factors and intention to use (+/- 1 S.D.)
30
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Familiar/Significant Others Unfamiliar/Non-significant Others
Percent
Relationship to others
Unimportant (≤ 3.4)
Neutral (≥ 3.5 ≤ 4.5)
Important (≥ 4.6)
Figure 3. Percentage of participants who consider the opinions of others to be
important, neutral or unimportant in their decision to use Google Glass.
ease-of-use was 4.60, which suggests that people considered Google Glass only slightly easy
to use. Nevertheless, more participants found Google Glass easy to use (59.4%) than difficult
(18.8%). The mean score for perceived usefulness was 3.44 therefore respondents did not
find Google Glass useful.
The mean subjective norm score was 3.33 as such individuals did not consider the
opinions of others to influence their decision to use Google Glass. The majority of
participants considered the opinions of unfamiliar others to be less important than familiar
others’ opinions (see figure 3). Furthermore, although participants were less concerned about
the opinions of others in private (M = 2.72) than in public (M = 3.91), respondents did not
consider the opinions of others to have an effect on their decision in either context. Finally,
the mean image score was 3.25. Subsequently, participants did not consider Google Glass to
enhance social status.
31
Cronbach’s alpha statistics demonstrated the variable reliabilities (see table 6). All
measures were found to be highly reliable (α > .70). The multiple regression analyses
established that the revised TAM-2 model accounts for 79.5% of the variance of intention to
use Google Glass. This finding was significant (R² = .795, F (7, 24) = 13.26, p < .001). In
contrast, the original TAM-2 model accounted for between just 37% and 52% (Venkatesh and
Davis 2000, 195). Results also showed that the R Square and Adjusted R Square values were
close, thus this model is generalisable (R²Adjusted = .735, R² = .795). This means that these
research findings can be applied to the wider population and predict intention to use with
accuracy.
TABLE 6. Variable reliabilities using Cronbach’s Alpha
Variables
Cronbach’s α Scores
Aesthetics
α = .948
Input and Interaction
α = .717
Privacy and Security
α = .758
Perceived Ease of Use
α = .909
Perceived Usefulness
α = .915
Subjective Norm
α = .759
Image
α = .925
Intention to Use
α = .877
The multiple regression analyses also computed the significance of each social
determining factor, and calculated which factors had the greatest influence on the social
acceptance of Google Glass (RQ1). Aesthetics (ß = -.129, p =.290), perceived ease-of-use (ß
= -.199, p = .118), subjective norm (ß = .126, p = .256) and image (ß = -.109, p = .379) were
not found to have a significant effect on an individual’s intention to use Google Glass. In
contrast perceived usefulness (ß = .710, p < .001), input and interaction (ß = .385, p < .01),
and privacy and security (ß = -.236, p < .05) were all found to be significant predictors.
Perceived usefulness had the highest beta value. Therefore, it has the greatest
influence on the social acceptance of Google Glass. The more useful an individual believes
Google Glass is to them, the more they intend to use it. Input and interaction has the second
32
greatest influence. The more comfortable an individual is engaging with novel interaction
methods to operate the device, the greater their intention to use becomes. Input and
interaction also had a significant positive effect on participants’ level of enjoyment (M = 5.71,
β = .421, p < .05). Privacy and security has the third greatest influence. Its beta value reflects
its negative relationship with intention to use. Therefore, as an individual’s privacy concerns
increase, their intention to use Google Glass decreases.
ii. Qualitative Findings for Social Determinants
Qualitative analysis identified five common themes in participant opinions of Google
Glass’ aesthetics. It was most frequently described as unattractive, cumbersome,
odd/awkward, stylish and futuristic, demonstrating conflict across responses. The researcher
observed that participants were surprised by the lightweight and sleek design of the device.
Input and interaction responses varied based on the method of interaction. Participants
were especially concerned about looking weird and drawing attention to themselves when
using voice commands in public. However, participants revealed that they would feel
confident/comfortable using voice commands in the privacy of their own home. The use of
hands-free gestures in public was considered ambiguous/open to interpretation. For instance,
respondents proposed that winking to take a photograph could appear suggestive.
Nevertheless, participants recognised that over time gestural interaction could become the
norm. The touchpad appeared to negate all issues for participants, whether the interaction
took place in public or private. Common qualitative themes described touchpad use as
unobtrusive, non-ambiguous and unproblematic.
The main privacy concern was being overheard/overlooked. Participants were worried
about other people knowing what they were using Google Glass for. However, many
individuals were more concerned about the privacy of bystanders; expressing anxiety at
33
inadvertently recording or photographing others. Conversely, many respondents felt that
Google Glass presented the same privacy concerns as existing devices. Two security themes
also emerged from the qualitative data. Firstly, participants suggested that Google Glass
would be easy to steal. Secondly, respondents suggested that Google Glass’ security features
needed development. Participant 26 said “if it had equivalent security features to mobile
phones/computers etc, it would be okay.”
Qualitative analysis also highlighted several themes for perceived ease-of-use.
Participants described Google Glass as straightforward. However, some respondents felt
more time would be required to learn how to use the device confidently. Multiple participants
also commented on the poor usability of Google Glass. Participant 23 said,
I thought it was really good but with very poor usability. The commands need to be
more intuitive. For example, I should be able to say “menu” to go home.
Furthermore participant 30 wrote “I find Google Glass quite slow and unresponsive at times
and oversensitive at others.”
The prominent theme for perceived usefulness was that Google Glass offered nothing
new. Participant 31 actually felt the device would hinder their productivity,
I can’t really think of how this would make any of my daily tasks easier. I think it
would take me a lot longer to do things if I had Glass.
Nevertheless, some participants suggested that Google Glass could be useful within their
work environment. For instance, participant 21, a United Utilities employee stated “in the
right application this could revolutionise the way we work.” On the whole, participants
consistently and enthusiastically recognised the potential of Google Glass for future
applications in education, medicine and business but believed the device needs further
34
development before it can be successful.
Qualitative results varied across the subjective norm sub-factors. Respondents were
conflicted over the importance of familiar others’ opinions in public. Some felt using Google
Glass was their own independent decision, whereas others were concerned about looking
unusual. Participants reported fewer concerns about the opinions of familiar others in private.
They also stated that they would disregard others’ opinions if they believed the device would
be useful to them. Participant 9 stated “[I’m] not influenced by what others think if I found it
useful.” When considering the opinions of unfamiliar others in public, participants were
divided. Some individuals were unconcerned, however others worried about violating privacy
or appearing unusual. In contrast, the dominant theme when considering strangers’ opinions
in private was indifference.
Qualitative analysis of the image factor indicated that participants felt it is too early to
discern whether Google Glass can enhance social status. Participant 28 wrote “at present
there is no precedence for the glasses so we cannot say one way or the other as to whether it
would become a status symbol.” However, some participants felt that Google Glass was a
symbol of arrogance or technological superiority. For instance, participant 29 said “[you]
may look like you have more money than sense or just look like a bit of a nerd.” Nevertheless,
some respondents felt it could be considered a status symbol because of its price. Participant
32 felt “if you have it, it’s because you can afford it, which means you’re probably in a very
good job, which means you probably come across like you have more ‘status’”.
Research Question Two (RQ2)
The data sample’s mean scores for each social determinant were computed and
organised by three key moderating factors: age (see figure 4), gender (see figure 5) and
technological expertise (see figure 6).
35
0
1
2
3
4
5
6
7
Aesthetics Input and
Interaction
Privacy
and
Security
Perceived
Ease of
Use
Perceived
Usefulness
Subjective
Norm
Image Intention
to Use
Data Sample Mean Score
Social Determining Factors and Intention to Use
18-29
30-49
50-64
65+
Figure 4. Sample mean scores for each social determining factor and intention to use, based
on participant age group.
0
1
2
3
4
5
6
7
Aesthetics Input and
Interaction
Privacy
and
Security
Perceived
Ease of
Use
Perceived
Usefulness
Subjective
Norm
Image Intention
to Use
Data Sample Mean Score
Social Determining Factors and Intention to Use
Male
Female
Figure 5. Sample mean scores for each social determining factor and intention to use, based
on participant gender.
36
i. The Moderating Effect of Age
Age was not found to have a significant moderating effect on aesthetics (F (3, 28) =
.27, p = .85). More participants in each age group found Google Glass attractive and stylish,
compared to those who found it unattractive (see table 7).
TABLE 7. Percentage of participants that find Google Glass attractive/unattractive
organised by age group.
Data Sample Mean Scores
18-29
30-49
50-64
65+
Unattractive (≤ 3.4)
33.3%
40.0%
33.3%
20.0%
Neutral (≥ 3.5 ≤ 4.5)
16.7%
0.0%
16.7%
33.3%
Attractive (≥ 4.6)
50.0%
60.0%
50.0%
46.7%
Age did not moderate the effect of input and interaction (F (3, 28) = .62, p = .61). All
age groups were somewhat comfortable using the input and interaction methods as mean
scores ranged from 4.83 to 5.42. Similarly, age had no moderating effect on privacy and
security (F (3, 28) = 1.21, p = .32). The 65+ group had the strongest privacy concerns (M =
5.67) but more young people were concerned overall. Age did not moderate perceived ease-
0
1
2
3
4
5
6
7
Aesthetics Input and
Interaction
Privacy
and
Security
Perceived
Ease of
Use
Perceived
Usefulness
Subjective
Norm
Image Intention
to Use
Data Sample Mean Scores
Social Determining Factors and Intention to Use
Low
Average
High
Figure 6. Sample mean scores for each social determining factor and intention to use, based
on participant technological expertise.
37
of-use (F (3, 28) = .76, p = .52).
Age was found to moderate the importance of perceived usefulness for the 30-49 (ß =
.944, p < .05) and 65+ age groups (ß = .714, p < .01). Therefore, the more value these groups
perceived in Google Glass, the more they intended to use it. On the whole, sample mean
scores for all age-groups ranged from 3.14 to 4.07 which means that no age group currently
perceives Google Glass to be useful.
Age also had a significant moderating effect on subjective norm (F (3, 28) = 3.72, p <
.05). In particular, the 65+ age group scored significantly lower than the 18-29 (t (19) = -2.41,
p < .05) and 30-49 groups (t (18) = -2.73, p < .05). Therefore, older participants did not
consider the opinions of others to be important in their decision to use Google Glass. Just
16.7% of the 18-29 group and 20% of the 30-49 group considered the opinions of others to be
unimportant, compared to 73.3% of the 65+ group.
Age was not found to moderate the effect of image. The sample mean scores ranged
from 2.83 to 3.73 which suggests that no age group currently perceives Google Glass to
enhance status. The majority of participants within each age group did not view Google Glass
as a status symbol (see table 8). Finally, age did not significantly affect intention to use (F (3,
28) = 1.14, p = .35). The 18-29 group was slightly more inclined than other groups to use
Google Glass again (M = 4.25), however the 65+ group was the least persuaded (M = 3.13).
In total, up to 60% of each group did not intend to use Google Glass again in the future.
TABLE 8. Percentage of participants that find Google Glass to be a status symbol,
organised by age group.
Data Sample Mean Scores
18-29
30-49
50-64
65+
Not Status Symbol (≤ 3.4)
66.6%
40.0%
50.0%
46.7%
Neutral (≥ 3.5 ≤ 4.5)
16.7%
40.0%
50.0%
33.4%
Status Symbol (≥ 4.6)
16.7%
20.0%
0.0%
20.0%
38
ii. The Moderating Effect of Gender
Aesthetics was found to be a significant predictor for women (ß = -.43, p < .05) but
not for men (ß = .22, p = .18). Therefore, aesthetics only affected women’s intention to use
Google Glass. However, male participants (M = 4.33) found Google Glass slightly more
attractive than female participants did (M = 3.88). Gender was not found to have a significant
moderating effect on input and interaction. Both male (M = 5.14) and female participants (M
= 5.05) were somewhat comfortable interacting with Google Glass.
The level of privacy and security concerns surrounding Google Glass was similar for
both male (M = 5.00) and female participants (M = 5.25); both were somewhat concerned.
However, privacy and security was only significant for women (ß = -.53, p < .05). Therefore,
the greater women’s privacy concerns were, the less they intended to use Google Glass,
whereas men were unaffected by their privacy and security concerns.
Gender did not have a significant moderating effect on perceived ease-of-use. The
sample mean scores revealed that women (M = 4.75) found Google Glass slightly more
simple to use than men did (M = 4.36). Nevertheless, gender significantly moderated the
importance of perceived usefulness for both males and females. The more useful they
perceived Google Glass to be, the more they intended to use it. However, usefulness was
more important for men (ß = 1.02, p < .01) than women (ß = .61, p < .01). Nevertheless,
neither gender currently views Google Glass as useful. Mean scores were 3.42 and 3.49
respectively.
Neither men (M = 3.45) nor women (M = 3.25) considered the opinions of others
important in their decision to use Google Glass. Therefore, gender was not found to have a
moderating effect on subjective norm. However, it was found to have a significant
moderating effect on image (t (30) = 2.48, p < .05). Women (M = 2.72) were less convinced
39
than men (M = 4.14) that Google Glass could enhance status.
Gender did not significantly affect intention to use (t (30) = .62, p = .54). Men (M =
3.88) intended to use Google Glass slightly more than women did (M = 3.53). However, the
majority of both genders responded that they did not intend to use Google Glass again or that
they were undecided (see table 9).
TABLE 9. Percentage of participants who intend to use Google Glass again in the future,
organised by gender.
Data Sample Mean Scores
Male
Female
No Intention to Use (≤ 3.4)
41.7%
40.0%
Neutral (≥ 3.5 ≤ 4.5)
24.9%
45.0%
Intention to Use (≥ 4.6)
33.4%
15.0%
iii. The Moderating Effect of Technological Expertise
No participants reported themselves as having ‘very low’ expertise. Only one
participant was listed as having ‘very high’ expertise. This individual was excluded from
these tests due to insufficient data variability. Technological expertise did not have a
significant moderating effect on aesthetics (F (3, 28) = .74, p = .54). Results showed that
participants with high expertise found Google Glass the least attractive (M = 3.56), compared
to individuals with average (M = 4.33) and low expertise (M = 4.14).
Conversely, technological expertise significantly moderated the importance of input
and interaction on intention to use, with only individuals with average expertise considering it
important (ß = .64, p < .05). The more comfortable they were using the novel interaction
methods, the more they intended to use Google Glass. Nevertheless, the amount of
participants listing themselves as comfortable using the interaction methods increased with
levels of expertise (see figure 7). Sample mean scores ranged from 4.50 to 5.28. Therefore,
all expertise levels were slightly-to-somewhat comfortable operating the device.
40
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low Average High
Percent
Level of Technological Expertise
Uncomfortable (≤ 3.4)
Neutral (≥ 3.5 ≤ 4.5)
Comfortable (≥ 4.6)
Technological expertise was not found to have a significant moderating effect on
privacy and security (F (3, 28) = 1.41, p = .26). Nevertheless, table 10 shows that more
participants with high expertise were concerned about the privacy of Google Glass, compared
to other expertise levels. Similarly, individuals with high expertise had slightly stronger
concerns (M = 5.50) than those with average (M = 5.13) and low expertise (M = 4.50).
TABLE 10. Percentage of participants concerned about Google Glass privacy issues,
organised by participant level of technological expertise.
Data Sample Mean Scores
Low
Average
High
Unconcerned (≤ 3.4)
14.3%
6.7%
11.1%
Neutral (≥ 3.5 ≤ 4.5)
28.6%
26.7%
11.1%
Concerned (≥ 4.6)
57.1%
66.6%
77.8%
It was also discovered that technological expertise significantly moderated the
importance of perceived ease-of-use, with only average expertise individuals considering it
important (ß = .57, p < .05). Furthermore, technological expertise was found to significantly
moderate the importance of perceived usefulness on intention to use for individuals with high
(ß = .74, p < .05) and average expertise (ß = .78, p < .01). The more useful these groups
considered Google Glass to be, the more they intended to use it. Nonetheless, mean scores
Figure 7. Participant levels of comfort interacting with Google Glass, based
on technological expertise level.
41
ranged from 3.26 to 3.72, which means Google Glass is not currently viewed as useful,
regardless of expertise level.
Subjective norm was not moderated by technological expertise (F (3, 28) = 1.25, p =
.31). Fewer participants considered the opinions of others to be important in their decision to
use Google Glass than those who considered it unimportant (see table 11).
TABLE 11. Percentage of participants who consider opinions of others important or
unimportant in their decision to use Google Glass, organised by technological expertise
levels.
Data Sample Mean Scores
Low
Average
High
Not Important (≤ 3.4)
28.6%
73.4%
44.4%
Neutral (≥ 3.5 ≤ 4.5)
57.1%
13.3%
22.2%
Important (≥ 4.6)
14.3%
13.3%
33.3%
The importance of image was significantly moderated by technological expertise.
Individuals with high technological expertise regarded Google Glass’ effect on their social
status as important (ß = .79, p < .05). The more they believed Google Glass could increase
social status, the more they intended to use it and vice versa. However, sample mean scores
demonstrated that individuals with high expertise did not perceive Google Glass as a status
symbol (M = 2.37).
Finally, technological expertise had a non-significant moderating effect on intention to
use (F (3, 28) = .67, p = .58). Mean scores suggested that users with high expertise were most
inclined to use Google Glass again (M = 3.78), compared to individuals with average (M =
3.73) and low expertise (M = 3.64). However they also indicated that, on the whole,
participants did not intend to use Google Glass again, regardless of their expertise level.
42
Discussion
This study aimed to understand why Google Glass failed to achieve social acceptance
and had limited commercial success. This section will interpret and discuss the findings
recorded in the previous section, answer the research questions which guided this research
project, address conflicts found within the literature and offer practical implications.
Aesthetics
Consistent with Hwang’s findings (2014) but contrary to other literature (Ariyatum et
al. 2005; Ha et al. 2014; Yang et al. 2016), aesthetics was not found to influence the social
acceptance of Google Glass (RQ1). The findings suggested that users are more concerned
with how Google Glass functions than how it looks. Participants frequently commented on
the device’s poor battery life and tendency to overheat. Therefore, aesthetics may be more
valued once Google Glass becomes more technologically efficient.
Overall, Google Glass’ aesthetics were viewed as neutral; neither strongly attractive
nor unattractive. Some literature posits that familiarity increases aesthetic ratings (Leder at al.
2004, 496; Faerber and Carbon 2012, 554). Therefore, increased exposure to Google Glass
may encourage a more positive response to its design. However, other researchers propose
that the “novelty effect” may result in a more favourable evaluation, which could diminish
over time and result in a lower aesthetic score (Michael and Michael 2016, 27). Future
studies should be conducted over a longer period of time to assess how attitudes towards
aesthetics change with increased familiarity and decreased novelty.
Although aesthetics was not found to affect the social acceptance of Google Glass
generally, it strongly influenced women’s attitudes towards the device, whereas men were
unaffected (RQ2). This may be due to societal differences in gender roles, as physical
43
appearance is often linked to a woman’s value and competence (Bliss 2015, 8). Beecham
Research stated that wearable technology such as Google Glass is intrinsically linked to a
person’s own appearance due to its placement on the body (In: Bilton 2014). Therefore, it
follows that Google Glass’ aesthetics would affect a woman’s intention to use because its
appearance is a reflection of their own attractiveness. One practical implication of this finding
is that a device which allows women to customise the design to fit their personal style would
permit them greater control over their self-presentation, potentially increasing their
acceptance of a novel technology.
Input and Interaction
Input and interaction is a significant determinant of social acceptance (RQ1) and user
enjoyment. Consistent with the literature review, the results found that voice commands were
the least comfortable method of interaction (Kollee, Kratz and Dunnigan 2014, 43; Lv et al.
2015, 564; Wilson and Daugherty 2015). The findings also reinforced earlier suggestions
that some hands-free gestures carry negative cultural connotations (Serrano et al. 2014,
3188). Participant 11 wrote “winking in a crowded room could be misinterpreted.”
Touchpad interaction was considered the most comfortable method. This may be
because it is a comparatively discreet method of interacting with Google Glass and a common
method of interacting with existing devices. Participant 26 explained “touching the side of the
head is less noticeable than, for example, speaking on your own or winking.” Therefore, use
of the touchpad integrates well within current social norms. However, future studies could
examine the effect of familiarity upon levels of comfort with multimodal interaction methods.
Parallel to Rico’s findings (2010), this study found that social context affected
participant’s levels of comfort interacting with Google Glass. In particular, participants rated
all interaction methods as more comfortable to perform in private. Empirical evidence
44
implies that this is because participants were concerned about drawing attention or looking
strange in public. Therefore, a wider choice of inconspicuous interaction methods could put
wearers at greater ease using the device in public. Socio-demographics were not found to
affect input and interaction (RQ2).
Privacy and Security
Contrary to media-hype, people were relatively unconcerned by serious privacy and
security issues such as identity fraud, governmental monitoring and ownership of data.
Privacy and security concerns were instead dominated by more domestic or personal issues,
such as self-consciousness during internet searches. Stop the Cyborgs, an anti-Glass
organisation, campaigned to restrict the use of Google Glass in public places. They aim to
discourage the public from accepting what they argue is the current course of technology, a
future without privacy and with total surveillance (Stop the Cyborgs 2015; Williams 2013).
However, the findings of this study instead demonstrated that respondents were highly
conscious of invading bystanders’ privacy; they were more concerned about accidentally
recording others, than being recorded themselves. Therefore, as Risko and Kingstone
predicted (2011, 294), devices such as Google Glass may actually encourage wearers to adopt
a more respectful and less-intrusive gaze.
Socio-demographics were found to influence attitudes towards Google Glass’ privacy
issues (RQ2). As expected, older people had the strongest privacy concerns. Research submits
that older age groups perceive more danger in using technology due to their lack of
experience using it (López, Marín and Calderón 2015, 255). Unexpectedly however, a greater
proportion of younger people had privacy concerns overall. One possible explanation is that
younger generations are better informed about the dangers of technology (Park 2015, 252)
but they are also better-equipped to take appropriate precautions against privacy breaches
45
(Steijna and Veddera 2015, 301).
Gender was found to mediate the importance of privacy and security concerns (RQ2).
While privacy concerns dissuaded women from accepting Google Glass, men appeared to be
undeterred. Prior research suggests that men are more technologically-skilled than women
(He and Freeman 2010; Wilkowska and Ziefle 2011). Consequently, men’s concerns may be
lessened because they have more confidence in their ability to handle privacy and security
threats (Park 2015, 256). Conversely, women’s concerns may be amplified by societal
pressure to behave cautiously and due to their higher susceptibility to online harassment
(López, Marin and Calderón 2015, 247).
Perceived Ease of Use
Contrary to prior studies (Kuru and Erbuğ 2013; Huang et al. 2015; Kim and Shin
2015; Tsai, Wang and Lu 2011), this experiment found that perceived ease-of-use did not
affect the social acceptance of Google Glass (RQ1). Quantitative results suggest that users did
not find Google Glass especially easy to use but the qualitative responses revealed that
participants believed they would be capable of using the device to a reasonable level with
practice. Subsequently, potential users may not consider ease-of-use as an obstacle to their
acceptance.
In contrast with former studies (Padilla-Meléndez, del Aguila-Obra and Garrido-
Moreno 2013; Terzis and Economides 2011), perceived ease-of-use was not found to be
significant for either gender. Instead, it is technological expertise that influenced attitudes
towards Google Glass, particularly for those with average expertise (RQ2). No previous
studies have replicated this finding. It may be that those with average expertise can
sufficiently operate different devices. As such, these users can simply dismiss Google Glass
in favour of an easier alternative (Faerber and Carbon 2012, 553), whereas highly-skilled
46
users may have both the confidence and the ability to overcome usability issues. Further
research could be conducted to understand why perceived ease-of-use is unimportant to those
with low technological expertise.
Perceived Usefulness
Perceived usefulness was found to significantly inhibit the social acceptance of
Google Glass (RQ1). The findings concurred with researcher’s assertions that users do not
consider the device to be useful (Hong 2013, 11; Metz 2014, 82). It is necessary to
understand the implications of these findings if the social acceptance of Google Glass is to be
facilitated. Firstly, the two-hour sessions may not have afforded participants sufficient time
to form thorough perceptions of the device’s usefulness. Therefore, researchers replicating
this study should consider running longitudinal studies. Furthermore, this study highlighted
that developers need to invest more time overcoming the device’s technical limitations. Users
felt that issues such as poor battery life, overheating and unresponsiveness needed to be
addressed before the device would be capable of offering practical uses. Participant 3 said “I
think it would be something that I might use and enjoy once it has had some of the
shortcomings sorted out.”
Additionally, compared to other wearables which offer fitness and health applications,
Google Glass does not currently offer a specific purpose to users (Ledger and McCaffrey
2014, 3). However, feedback suggested that participants might find Google Glass useful in
the workplace. Participant 32, an events fundraiser wrote,
“I can imagine how it would be good if it worked well– taking photos at events on the
go, looking for routes/maps when at races, storing people’s details and pulling them
up in a health and safety emergency, on the go reporting on social media, pulling up
events plans … Definitely not at home though – I have no need for it.”
47
This feedback is encouraging because Google is rumoured to be developing a Glass at
Work program (Glass 2016). Based on the above implications, this edition is likely to be more
successful and achieve greater social acceptance than its predecessor.
Lastly, socio-demographics research suggested that ease-of-use would be a priority to
women, while usefulness would be insignificant (Padilla-Meléndez, del Aguila-Obra and
Garrido-Moreno 2013, 314; Terzis and Economides 2011, 2119). However, this study found
the opposite to be true; usability had little effect on women’s intention to use Google Glass
and the device’s usefulness was paramount. Nevertheless, consistent with the literature
(Zhang and Rau 2015, 156), usefulness was even more important to men than to women.
Subjective Norm
Unlike previous research (Choi and Chung 2013; Hopp 2013; Umrani and Ghadially
2008; Wang and Wang 2010), this study did not find subjective norm to influence the social
acceptance of Google Glass; neither the opinions of familiar or unfamiliar others in public or
private contexts affected user acceptance of the device (RQ1). One possible explanation is
that participants in this study tested Google Glass voluntarily, freely choosing to take part in
the experiment, whereas the original TAM-2 study only found subjective norm to be
significant in mandatory usage conditions (Venkatesh and Davis 2000, 195). Therefore, users
who make an independent decision to use Google Glass may be less concerned about other’s
opinions.
Furthermore, this study took place within a controlled setting. As such, participants
could only estimate how they would react to others’ opinions, rather than actually experience
them. Xu et al. (2015, 11) argues that familiarity with fellow participants can alleviate the
discomfort experienced by using new technologies. The respondents within this study were
colleagues and friends. These relationships may have lead participants to underestimate the
48
effect that others’ opinions could have on their behaviour.
Consistent with the literature findings (Magsamen-Conrad 2015), age moderated the
effect of subjective norm. Younger people were more apprehensive of others’ opinions than
older people (RQ2). It could be posited that they fear rejection by their peers. In contrast,
older adults already have a defined place within society and are subsequently unthreatened by
others’ opinions (Ferry 2011; Steinberg and Monahan 2007, 1532).
Image
Image also had a nonsignificant effect on the social acceptance of Google Glass
(RQ1). Participants did not believe that the device enhances social standing. Despite this,
several participants requested to have their photograph taken wearing the device; they wanted
to show family and friends that they had experienced an iconic and expensive piece of
technology. It could be posited that this behaviour in itself would increase their social
standing because they were demonstrating themselves to be “innovators” (Yang et al. 2016,
259). Therefore, it could be argued that Google Glass does enhance status to some extent.
Technological expertise affected participant attitudes towards social status (RQ2).
Although image was found to be an important factor for participants with a high level of
expertise, they did not judge Google Glass to be a status symbol. As frequent and proficient
users of technological devices, they may be more likely to demonstrate an awareness of new
technologies. Therefore, those with higher expertise may have had prior knowledge of
Google Glass and its negative reputation, whereas those with less expertise may not have
heard of Google Glass and had insufficient time to formulate opinions of its status. Again,
future research should consider longitudinal studies to overcome these limitations.
49
Limitations
Thirty-two individuals participated in this study. A larger sample may have strengthened the
findings of this research and ensured all relationships between the social determinants and
intention to use were detected. Furthermore, there was an uneven ratio of female to male
participants. Therefore, the generalisability of gender difference findings may have been
limited as a result of this imbalance. Additionally, very few respondents came from
ethnically-diverse backgrounds, preventing the inclusion of culture as a moderating factor in
the analyses. This limitation could be overcome in the future by conducting cross-cultural
studies to validate the current findings and determine the role of culture on intention to use
Google Glass. Finally, as previously recommended, future studies should utilise a
longitudinal design to monitor how intention to use and attitudes towards Google Glass
change over time with increased familiarity and decreased novelty.
Conclusions and Future Research
A review of key literature highlighted factors that were unaccounted for by existing
technology acceptance models but which might affect the social acceptance of Google Glass.
TAM-2 was adapted accordingly and used to conduct a study evaluating Google Glass’
acceptance and attitudes towards the device. Perceived usefulness, input and interaction, and
privacy and security were found to have the greatest influence on the social acceptance of
Google Glass (RQ1).
In order to be adopted, Google Glass must prove useful to potential users. At present,
it replicates existing smartphone functions without offering anything new. A promising area
for development is the implementation of Google Glass into workplace settings. Furthermore,
the input and interaction methods were an appealing aspect of the device for users. However,
the novelty and unconventionality made users reluctant to use the device in public. Therefore,
50
despite participants’ enjoyment of the interaction methods, time is required for the
development of their social acceptance. This study also suggests that in order to further the
social acceptance of Google Glass, its security features must be improved to prevent others
from freely accessing data on the device. Moreover, to minimise the chance of participants
being overheard/overlooked, there should be more discreet ways of interacting with the
device. If Google are able to successfully tackle these issues, future editions of Google Glass
may have more success.
It was further discovered that attitudes towards Google Glass were moderated by
participants’ socio-demographic background (RQ2). Each social determinant was affected by
at least one key moderating factor. Additional research might identify further moderating
factors. A previous study used the Big Five personality model and discovered a link between
“openness to experience” and intention to use. Open individuals are more willing to try new
activities; hence they may also be more accepting of novel wearable technologies such as
Google Glass. It was also posited that extraverts have a stronger need for self-presentation
(Rauschnabel, Brem and Ivens 2015, 642), which may moderate factors such as image and
input and interaction.
Studies into the effect of personality on the social acceptance of Google Glass could
investigate links between privacy and security and neuroticism. Neuroticism is characterised
by a tendency to experience more negative emotions (Fagan 2014). Consequently, individuals
who are more neurotic may have greater privacy and security concerns. Alternatively,
subjective norm may present a stronger influence on conscientious individuals since they are
generally more concerned about the expectations of others. Therefore, the integration of
personality into models of technology acceptance could further explain the social acceptance
of Google Glass.
51
The assessment of new social determinants, alongside existing TAM-2 factors, has had
both practical and theoretical implications for the future of Google Glass. This study clarified
which factors determine individuals’ intention to use, thereby providing practical guidelines
for future editions to overcome barriers to social acceptance. Finally, the introduction of new
determinants and moderating factors explained intention to use more comprehensively than
the previous TAM-2, subsequently addressing the current gap in knowledge and
understanding of wearable technology acceptance.
52
Bibliography
Ajjan, Haya, and Richard Hartshorne. 2008. “Investigating Faculty Decisions to Adopt Web
2.0 technologies: Theory and Empirical Tests.” Internet and Higher Education 11:71-
80. Accessed November 23, 2015. doi:10.1016/j.iheduc.2008.05.002
Angelini, Leonardo, Maurizio Caon, Stefano Carrino, Luc Bergeron, Nathalie Nyffeler,
Mélanie Jean-Mairet, and Elena Mugellini. 2013. “Designing a Desirable Smart
Bracelet for Older Adults.” Proceedings of the 2013 ACM Conference on Pervasive
and Ubiquitous Computing Adjunct Publication (UbiComp-13 Adjunct), 425-434.
Accessed April 9, 2016. doi:10.1145/2494091.2495974
Ariyatum, B., R. Holland, D. Harrison, and T. Kazi. 2005. “The Future Design Direction of
Smart Clothing Development.” Journal of the Textile Institute 96(4): 1-33. Accessed
July 15, 2015. http://bura.brunel.ac.uk/handle/2438/1362
Arthur, Charles. 2013. “Google Glass: Is It A Threat To Our Privacy.” The Guardian, March
6. Accessed July 11, 2015.
http://www.theguardian.com/technology/2013/mar/06/google-glass-threat-to-our-
privacy
Bailly, Gilles, Jörg Müller, Michael Rohs, Daniel Wigdor, and Sven Kratz. 2012.
“ShoeSense: A New Perspective on Hand Gestures and Wearable Applications.” Paper
presented at the SIGCHI Conference on Human Factors in Computing Systems,
Austin, Texas, May 5-10. Accessed November 23, 2015.
doi:10.1145/2207676.2208576
Baptista, Gonçalo, and Tiago Oliviera. 2015. “Understanding Mobile Banking: The Unified
Theory of Acceptance and Use of Technology Combined with Cultural Moderators.”
Computers In Human Behavior 50:418-430. Accessed November 28, 2015.
doi:10.1016/j.chb.2015.04.024
Baraniuk, Chris. 2014. “Google Glass: Why the Gadget Faces its Biggest Test.” BBC Future,
May 5. Accessed January 13, 2015. http://www.bbc.com/future/story/20140504-the-
biggest-flaw-of-google-glass
Barnard, Yvonne, Mike D. Bradley, Frances Hodgson, and Ashley D. Lloyd. 2013. “Learning
to Use New Technologies by Older Adults: Perceived Difficulties, Experimentation
Behaviour and Usability.” Computers in Human Behavior 29:1715-1724. Accessed
November 29, 2015. doi:10.1016/j.chb.2013.02.006
BBC Technology. 2015. “Google Glass Sales Halted but Firm Says Kit is Not Dead.” BBC,
January 15. Accessed June 27, 2015. http://www.bbc.com/news/technology-
30831128
Bellezza, Silvia, Francesca Gino, and Anat Keinan. 2013. “The Red Sneakers Effect:
Inferring Status and Competence from Signals of Nonconformity.” Journal of
Consumer Research 41:35-54. Accessed November 23, 2015.
http://www.hbs.edu/faculty/Publication%20Files/The%20Red%20Sneakers%20Effect
%202014_4657b733-84f0-4ed6-a441-d401bbbac19d.pdf
53
Bellezza, Silvia, Francesca Gino, and Anat Keinan. 2014. “The Surprising Benefits of
Nonconformity.” MIT Sloan Management Review, March 18. Accessed November 23,
2015. http://sloanreview.mit.edu/article/the-surprising-benefits-of-nonconformity/
Bilton, Nick. 2014. “Tech, Meet Fashion.” The New York Times, September 3. Accessed July
13, 2015. http://www.nytimes.com/2014/09/04/fashion/intel-and-opening-ceremony-
collaborate-on-mica-a-stylish-tech-bracelet.html
Bliss, Catherine. 2015. “What Is She Wearing? Gender Division and the Dilemmas of Female
Agency in Television News.” Critical Studies in Fashion & Beauty 6(1):7-25.
Accessed April 10, 2016. doi: 10.1386/csfb.6.1.7_1
Boscart, V., K. McGilton, A. Levchenko, G. Hufton, P. Holliday, and G. Fernie. 2008.
“Acceptability of a Wearable Hand Hygiene Device With Monitoring Capabilitites.”
Journal of Hospital Infection 70:216-222. Accessed January 24, 2015.
doi:10.1016/j.jhin.2008.07.008
Buenaflor, Cherrylyn, and Hee-Cheol Kim. 2013. “Six Human Factors to Acceptability of
Wearable Computers.” International Journal of Multimedia and Ubiquitous
Engineering 8(3):103-114. Accessed January 22, 2015.
http://www.sersc.org/journals/IJMUE/vol8_no3_2013/10.pdf
Champion, Edward. 2013. “Thirty-Five Arguments Against Google Glass.” Reluctant Habits,
March 14. Accessed June 21, 2015. http://www.edrants.com/thirty-five-arguments-
against-google-glass/
Chen, Ke, and Alan H. S. Chan. 2011. “A Review of Technology Acceptance by Older
Adults.” Gerontechnology 10(1):1-12. Accessed November 26, 2015.
doi:10.4017/gt.2011.10.01.006.00
Choi, Gilok, and Hyewon Chung. 2013. “Applying the Technology Acceptance Model to
Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the
Acceptance of SNS.” International Journal of Human-Computer Interaction
29(10):619-628. Accessed November 22, 2015. doi:10.1080/10447318.2012.756333
CNET. 2013. “Hands-on With Google Glass: Limited, Fascinating, Full of Potential.” Last
modified August 12. Accessed November 20, 2015.
http://www.cnet.com/uk/products/google-glass/
Cronan, Bryan. 2014. “Can Intel and Corporate America Save Google Glass?.” Christian
Science Monitor. Accessed July 13, 2015.
http://search.ebscohost.com.chain.kent.ac.uk/login.aspx?direct=true&db=a9h&AN=9
9754184&site=ehost-live
Danova, Tony. 2013. “BI Intelligence Forecast: Google Glass Will Become a Mainstream
Product And Sell Millions By 2016.” Business Insider, December 31. Accessed July
10, 2015. http://www.businessinsider.com/google-glass-sales-projections-2013-
11?IR=T
Daugherty, Kevin. 2014. “Understanding Factors that Influence College Faculty n Deciding
to Adopt Digital Technologies in their Practice.” MA thesis. University of Ontaria
Institute of Technology. Accessed November 22, 2015.
http://hdl.handle.net/10155/442
54
Davis, Fred. D. 1989. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology.” MIS Quarterly 13(3):319-340. Accessed October 18, 2015.
doi:10.2307/249008
Davis, Fred. D., and Viswanath Venkatesh. 1996. “A Critical Assessment of Potential
Measurement Biases in the Technology Acceptance Model: Three Experiments.”
International Journal of Human-Computer Studies 45: 19-45. Accessed July 5, 2015.
doi: 10.1006/ijhc.1996.0040
Due, Brian. 2015. “The Social Construction of a Glasshole: Google Glass and Multiactivity
in Social Interaction.” PsychNology Journal 13(2):149-178. Accessed April 2, 2016.
http://search.ebscohost.com.chain.kent.ac.uk/login.aspx?direct=true&db=a9h&AN=1
13572435&site=ehost-live
Duggan, Maeve, and Lee Raine. 2012. “Additional Demographic Analysis.” Pew Research
Center, November 25. Accessed April 9, 2016.
http://www.pewinternet.org/2012/11/25/additional-demographic-analysis-2/
Duval, C. H., and H. Hashizume. 2010. “Chapter Seven” In Smart Clothing Technology and
Applications edited by Gilsoo Cho, 153-187. United Kingdom: CRC Press.
Dvorak, J. L. 2003. “Social Aspects of Conformables.” Paper presented at IEE Eurowearable,
Birmingham, United Kindom, September 3-5. Accessed January 22, 2015.
doi:10.1049/ic:20030139
———. 2008. Moving Wearables into the Mainstream: Taming the Borg. New York:
Springer. Accessed August 9, 2014. http://link.springer.com/book/10.1007%2F978-0-
387-69142-8
Elgan, Mike. 2013. “Will Women Dominate The Wearable Computing Market?” Cult of
Android, November 10. Accessed November 26, 2015.
http://www.cultofandroid.com/44705/will-women-dominate-the-wearable-computing-
market/
Ensor, Josie. 2014. “How Google Glass Can Be Used To Steal Your PIN code.” The
Telegraph, June 25. Accessed July 11, 2015.
http://www.telegraph.co.uk/technology/google/10924369/How-Google-Glass-can-be-
used-to-steal-your-PIN-code.html
Faerber, Stella J., and Claus-Chritian Carbon. 2012. “The Power of Liking: Highly Sensitive
Aesthetic Processing For Guiding Us Through the World.” i-Perception 3:553-561.
Accessed April 10, 2016. dx.doi.org/10.1068/i0506
Fagan, Patrick. 2014. "How To Make The Best of Neuroticism: Use It For Preparation and
Winning." A Winning Personality, January 21. Accessed May 6, 2016.
http://www.awinningpersonality.com/psychology/the-big-5-personality-traits/use-
neuroticism-for-winning/
Faqih, Khaled M.S., and Mohammed-Issa Riad Mousa Jaradat. 2014. “Assessing the
Moderating Effect of Gender Difference and Individualism-Collectivism at
Individual-Level on the Adoption of Mobile Commerce Technology: TAM3
perspective.” Journal of Retailing and Consumer Services 22:37-52. Accessed
November 27, 2015. doi:10.1016/j.jretconser.2014.09.006
55
Ferry, Tom. 2011. “Why Do We Care What Others Think? Our Addiction To The Opinions Of
Others Explained.” Huffington Post, November 17. Accessed April 30, 2016.
http://www.huffingtonpost.com/tom-ferry/self-help-why-do-we-care_b_615063.html
Field, Andy. 2009. Discovering Statistics Using SPSS, 3rd ed. London: SAGE Publications
Ltd.
Fishbein, Martin E., and Icek Ajzen. 1975. Belief, Attitude, Intention and Behaviour: An
Introduction to Theory and Research. Reading: Addison-Wesley.
Forrest, Conner. 2014. “The Main Reasons People Hate Google Glass, and Why They Won’t
in a Few Years.” TechRepublic, July 30. Accessed November 24, 2015.
http://www.techrepublic.com/article/the-main-reasons-people-hate-google-glass-and-
why-they-wont-in-a-few-years/
Fradera, Alex. 2014. “The Psychology of Wearable Computing - Does Google Glass Affect
Where People Look?” British Journal of Psychology 27(10):740-741. Accessed
November 17, 2015. https://thepsychologist.bps.org.uk/volume-27/edition-10/digest-
google-glass-and-more
Frizzell, Nell. 2015. “Disappointing Tech: When Did the Future Get So Ugly?” The
Guardian, May 11. Accessed November 21, 2015.
http://www.theguardian.com/commentisfree/2015/may/11/when-did-the-future-get-
so-ugly
Furlan, Rod. 2013. “Google Glass: This Wearable Computer Augments the Self, Not Reality.”
Spectrum IEEE 50: 24. Accessed October 19, 2014.
doi:10.1109/MSPEC.2013.6607007
Gao, Yiwen, and He Li Yan Luo. 2015. "An Empirical Study of Wearable Technology
Acceptance in Healthcare." Industrial Management & Data Systems 115(9): 1704 –
1723. Accessed November 19, 2015. doi:10.1108/IMDS-03-2015-0087
Garfinkel, Simson. 2014. “Glass, Darkly.” MIT Technology Review 117(2): 70-77.
Glass. 2016. “Glass at Work.” Accessed April 15.
https://developers.google.com/glass/distribute/glass-at-work
Goffman, E. 1959. The Presentation of Self in Everyday Life. New York: Doubleday Anchor.
Ha, Kiryong, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, and Mahadev
Satyanarayanan. 2014. "Towards Wearable Cognitive Assistance." Paper presented at
the 12th Annual International Conference on Mobile Systems, Applications, and
Services, Bretton Woods, New Hampshire, ACM, June 16 - 19. Accessed November
17, 2015. doi:10.1145/2594368.2594383
Häger, Johan. 2015. “An Evaluation of Google Glass: Design, Implementation and
Evaluation of a Product Assembly Application for Google Glass and Smartphones.”
MSc thesis., Karlstad University. Accessed November 25, 2015. http://www.diva-
portal.org/smash/record.jsf?pid=diva2%3A818447&dswid=-1579
56
Hanson, Vicki I. 2010. “Influencing Technology Adoption by Older Adults.” Interaction with
Computers 22:502-509. Accessed November 29, 2015.
doi:10.1016/j.intcom.2010.09.001
Hartwick, J., and H. Barki. 1994. “Explaining the Role of User Participation in Information
System Use.” Management Science 40(4):440-465.
Hattersley, Lou. 2015. “Google Glass Explorer Edition 2.0 review: Glass Has Show-Stopping
Power, But Will Soon Be Unavailable To Buy.” Tech Advisor. Accessed November
20, 2015. http://www.pcadvisor.co.uk/review/wearable-tech/google-glass-explorer-
edition-20-review-smartglasses-3528631/
He, Jun, and Lee A.Freeman. 2010. “Are Men More Technology-Oriented Than Women? The
Role of Gender on the Development of General Computer Self-Efficacy of College
Students.” Journal of Information Systems Education 21(2):203-212. Accessed
November 17, 2015. http://aisel.aisnet.org/amcis2009/672
Hohlfeld, Tina N., Albert D. Ritzhaupt, and Ann E. Barron. 2013. “Are Gender Differences in
Perceived and Demonstrated Technology Literacy Significant? It Depends on the
Model.” Education Technology Research and Development 61:639-663. Accessed
November 26, 2015. doi:10.1007/s11423-013-9304-7
Holzinger, Andreas, Gig Searle, Stephan Prückner, Silke Steinbach-Nordmann, Thomas
Kleinberger, Etienne Hirt, and Jens Temnitzer. 2010. “Perceived Usefulness Among
Elderly People: Experiences and Lessons Learned During the Evaluation of a Wrist
Device.” Paper presented at the 4th International Conference on No Permissions,
Munich, Germany, March 22-25. Accessed April 4, 2016. doi:
10.4108/ICST.PERVASIVEHEALTH2010.8912
Hong, Jason. 2013. “Considering Privacy Issues in the Context of Google Glass.”
Communications of the ACM 56(11):10-11. Accessed March 1, 2015.
doi:10.1145/2524713.2524717
Hopp, Tony. 2013. “Subjective Norms as a Driver of Mass Communication Students’
Intentions to Adopt New Media Production Technologies.” Journalism & Mass
Communication Educator 68(4):348-364. Accessed November 22, 2015.
doi:10.1177/1077695813506993
Hoy, Mariea G., and George Milne. 2010. “Gender Differences in Privacy-Related Measures
for Young Adult Facebook Users.” Journal of Interactive Advertising 10(2):28-45.
Accessed April 6, 2016.
http://search.ebscohost.com.chain.kent.ac.uk/login.aspx?direct=true&db=bth&AN=49
023250&site=ehost-live
Huang, Yong-Ming, Tien-Chi Huang, Mu-Yen Chen, Yu-Lin Jeng, Yu Shu, and Ting-Ting
Wu. 2015. “What Influences Students to Use Cloud Services? From The Aspect Of
Motivation.” Paper presented at the International Conference on Interactive
Collaborative Learning (ICL), Florence, Italy, September 20-24. Accessed November
24, 2015. doi:10.1109/ICL.2015.7318039
57
Hurford, R., A. Martin, and P. Larsen. 2006. “Designing Wearables.” Paper presented at the
10th IEEE International Symposium on Wearable Computers, Montreux, Switzerland.
October 11-14. Accessed March 3, 2015. doi: 10.1109/ISWC.2006.286362
Hwang, Chanmi. 2014. "Consumers' Acceptance of Wearable Technology: Examining Solar-
Powered Clothing." MSc thesis, Iowa State University. Accessed November 17, 2015.
http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=4957&context=etd.
Im, Il, Seongtae Hong, and Myung Soo Kang. 2011. “An International Comparison of
Technology Adoption: Testing the UTAUT Model.” Information & Management
48(1):1-8. Accessed November 26, 2015. doi:10.1016/j/im.2010.09.001
ITPro. 2014. “Google Glass Review: Hands-on.” Accessed November 25, 2015.
http://www.itpro.co.uk/mobile/21573/google-glass-review-hands-on
Jackson, Linda and Jin-Liang Wang. 2013. “Cultural Differences in Social Networking Site
Use: A Comparative Study of China and the United States.” Computers in Human
Behavior 29: 910-921. Accessed November 17, 2015. doi:10.1016/j.chb.2012.11.024
Kalinauckas, Alex, 2015. “Wearable Technology.” Engineering & Technology 10(4):36-43.
Accessed July 13, 2015. doi:10.1049/et.2015/0416
Kim, Ki Joon, and Dong-Hee Shin. 2015. “An Acceptance Model for Smart Watches:
Implications for the Adoption of Future Wearable Technology.” Internet Research
25(4):527-541. Accessed November 19, 2015. doi:10.1108/IntR-05-2014-0126
Kollee, Barry, Sven Kratz, and Tony Dunnigan. 2014. “Exploring Gestural Interaction in
Smart Spaces Using Head Mounted Devices With Ego-Centric Sensing.” Paper
presented at the 2nd ACM symposium on Spatial User Interaction SUI’14, Honolulu,
Hawaii, October 4-5. Accessed November 22, 2015. doi:10.1145/2659766.2659781
Kuru, Armağan and Çiğdem Erbuğ. 2013. “Explorations of Perceived Qualities of On-Body
Interactive Products.” Ergonomics 56(6): 906-921. Accessed November 19, 2015.
doi:10.1080/00140139.2013.788737
Lai, Chun, Qiu Wang, and Jing Lei. 2012. “What Factors Predict Undergraduate Students’
Use of Technology for Learning? A Case From Hong Kong.” Computers and
Education 59:569-579. Accessed November 24, 2015.
doi:10.1016/j.compedu.2012.03.006
Leder, Helmut, Benno Belke, Andries Oeberst, and Dorothee Augustin. 2004. “A Model of
Aesthetic Appreciation and Aesthetic Judgments.” British Journal of Psychology
95:489-508. Accessed April 9, 2016. doi: 10.1348/0007126042369811
Ledger, Dan, and Daniel McCaffrey. 2014. “Inside Wearables: How the Science of Human
Behavior Change Offers the Secret to Long-Term Engagement.” Endeavour Partners.
Accessed April 9, 2016. http://endeavourpartners.net/assets/Endeavour-Partners-
Wearables-White-Paper-20141.pdf
Lemos, Robert. 2013. “Google Glass Security, Privacy Worries Complicated Wide
Adoption.” eWeek, August 25. Accessed November 18, 2015.
http://www.eweek.com/mobile/google-glass-security-privacy-worries-complicate-
wide-adoption.html
58
Liu, Dawei, and Xiaohong Guo. 2016. “Can Trust and Social Benefit Really Help? Empirical
Examination of Purchase Intentions For Wearable Devices.” Information
Development, 1-14. Accessed April 9, 2016. doi: 10.1177/0266666916635724
Liu, Siwen. 2013. “Effects of Early Experiences with Interaction Style on Usability and
Acceptance of New Technologies by Older Adults: A Generation-Oriented Approach.”
PhD diss., North Carolina State University. Accessed April 9, 2016.
http://repository.lib.ncsu.edu/ir/handle/1840.16/8552
López, Gustavo, Gabriela Marín and Marta Calderón. 2015. “Characterizing Ubiquitous
Systems Privacy Issues by Gender and Age.” In Ambient Assisted Living: ICT-Based
Solutions in Real Life Situations, edited by Ian Cleland, Luis Guerrero, and José
Bravo, 247-258. Switzerland: Springer International Publishing. Accessed April 5,
2016. doi: 10.1007/978-3-319-26410-3_23
Lv, Zhihan, Alaa Halawani, Shenzhong Feng, Shafiq ur Rehman, and Haibo Li. 2015.
“Touch-less interactive augmented reality game on vision-based wearable device.”
Personal and Ubiquitous Computing 19(3): 551-567. Accessed November 21, 2015.
doi:10.1007/s00779-015-0844-1
Magsamen-Conrad, Kate. 2015. “Bridging the Divide: Using UTAUT to Predict
Multigenerational Tablet Adoption Practices.” Media and Communications Faculty
Publications 37:1-42. Accessed November 26, 2015.
http://scholarworks.bgsu.edu/smc_pub/37
March, Evita, and Rachel Grieve. 2014. “Sex differences and mate preferences: Contributions
and interactions of gender roles and socio-economic status.” Sensoria: A Journal of
Mind, Brain and Culture 10(2):34-42. Accessed April 4, 2016.
http://dx.doi.org/10.7790/sa.v10i2.410
Marković, Slobodan. 2014. “Object Domains and the Experience of Beauty.” Art and
Perception 2:119-140. Accessed April 10, 2016. doi:10.1163/22134913-00002020
McCormick, Emily. 2015. “Google to Halt Sales of Glass.” Optometry Today 55(2):7.
Accessed November 18, 2015.
http://search.ebscohost.com.chain.kent.ac.uk/login.aspx?direct=true&db=a9h&AN=1
03625556&site=ehost-live
Metz, Rachel. 2014. “Google Glass is Dead; Long Live Smart Glasses.” MIT Technology
Review 118(1):79-82. Accessed November 18, 2015.
http://search.ebscohost.com.chain.kent.ac.uk/login.aspx?direct=true&db=a9h&AN=1
00243731&site=ehost-live
Michael, K., and M. G. Michael. 2016. “Computing Ethics: No Limits to Watching?”
Communications of the ACM 56(11):26-28. Accessed April 9, 2016.
doi:10.1145/2527187
Miller, Claire. C. 2013. “Women at Google Looking Past the Glass Ceiling.” The New York
Times, August 23. Accessed July 13, 2015.
http://www.nytimes.com/2013/08/25/fashion/women-at-google-looking-past-the-
glass-ceiling.html
59
Miller, Hugh. 1995. “The Presentation of Self in Electronic Life: Goffman on the Internet.”
Paper presented at Embodied Knowledge and Virtual Space Conference, Goldsmiths’
College, University of London. Accessed March 11, 2015.
http://www.dourish.com/classes/ics234cw04/miller2.pdf
Moore, Gary, and Izak Benbasat. 1991. “Development of an Instrument to Measure the
Perceptions of Adopting an Information Technology Innovation.” Information Systems
Research 2(3): 192-222. Accessed June 5, 2015. doi:10.1287/isre.2.3.192
Motti, Vivian G., and Kelly Caine. 2014. “Human Factors Considerations in the Design of
Wearable Devices.” Proceedings of the Human Factors and Ergonomics Society
Annual Meeting, 58:1820-1824. Accessed April 9, 2016.
doi:10.1177/1541931214581381
Mitzner, Tracy I., Julie B. Boron, Cara Bailey Fausset, Anne E. Adams, Neil Charness, Sara J.
Czaja, Katinka Dijkstra, Arthur D. Fisk, Wendy A. Rogers, and Joseph Sharit. 2010.
“Older Adults Talk Technology: Technology Usage and Attitudes.” Human Behavior
26:1710-1721. Accessed November 30, 2015. doi:10.1016/j.chb.2010.06.020
Morgan, Elizabeth, Chareen Snelson, and Patt Elison-Bowers. 2010. “Image and video
Disclosure of Substance Use on Social Media Websites.” Computers in Human
Behaviour 26: 1405-1411. Accessed November 17, 2015.
doi:10.1016/j.chb.2010.04.017
Morris, M. G., and V. Venkatesh. 2000. "Age Differences in Technology Adoption Decisions:
Implications for a Changing Workforce." Personnel Psychology 53(2):375-403.
Accessed October 12, 2015. doi:10.1111/j.1744-6570.2000.tb00206.x
Nasiopoulos, Eleni, Evan F. Risko, Tom Foulsham, and Alan Kingstone. 2014. “Wearable
Computing: Will it Make People Prosocial?” British Journal of Psychology 106:209-
216. Accessed November 18, 2015. doi:10.1111/bjop.12080
Noble, Safiya Umjola, and Sarah Roberts. 2016. “Through Google-Colored Glass(es):
Design, Emotion, Class, and Wearables as Commodity and Control.” In Emotions,
Technology and Design, edited by S. Tettegah, 1-24. Elsevier. Accessed November 23,
2015. http://ir.lib.uwo.ca/commpub/13
Nurun. 2015. “What Are You Looking at? How Google’s Glass Will Change Public Space
(Again).” Accessed November 24, 2015. http://www.nurun.com/en/our-
thinking/emerging-behavior/what-are-you-looking-at-how-google-s-glass-will-
change-public-space-again/
O’Brien, Marita. 2010. “Understanding Human-Technology Interactions: The Role of Prior
Experience and Age.” PhD diss., Georgia Institute of Technology. Accessed April 9,
2016. https://smartech.gatech.edu/handle/1853/34000
Optometry Today. 2015. “Google Glass 2.0 Could Be Released in Early 2016.” Accessed
November 18, 2015. https://www.aop.org.uk/ot/science-and-
vision/technology/2015/09/21/google-glass-2-0-could-be-released-in-early-2016
60
Oremus, Will. 2015. “Google Glass: The Future’s Not Very Bright.” News Observer, January
25. Accessed January 26, 2015.
http://www.newsobserver.com/2015/01/25/4493829_google-glass-the-futures-not-
very.html?rh=1
Padilla-Meléndez, Antonio, Ana-Rosa del Aguila-Obra, and Aurora Garrido-Moreno. 2013.
“Perceived Playfulness, Gender Differences and Technology Acceptance Model in a
Blended Learning Scenario.” Computers and Education 63: 306-317. Accessed
November 27, 2015. doi:10.1016/j.compedu.2012.12.014
Palomo-Lovinski, Noël. 2008. “Extensible Dress: The Future of Digital Clothing.” Clothing
and Textiles Research Journal 26(2):119-130. Accessed April 9, 2016.
doi: 10.1177/0887302X07310078
Park, Yong Jin. 2015, “Do Men and Women Differ in Privacy? Gendered Privacy and
(In)equality in the Internet.” Computers in Human Behavior 50:252-258. Accessed
April 4, 2016. doi: 10.1016/j.chb.2015.04.011.
Partala, Timo, and Timo Saari. 2015. “Understanding the Most Influential User Experiences
in Successful and Unsuccessful Technology Adoptions.” Computers in Human
Behavior 53:381-395. Accessed April 10, 2016.
http://dx.doi.org/10.1016/j.chb.2015.07.012
Profita, H., J. Clawson, S. Gilliland, C. Zeagler, T. Starner, J. Budd, and E. Do. 2013. “Don’t
Mind Me Touching My Wrist: A Case Study of Interaction with On-Body
Technology in Public.” Paper presented at the International Symposium on Wearable
Computers, Zurich, Switzerland, September 9-12. Accessed January 22, 2015.
doi:10.1145/2493988.249331
Rauschnabel, Philipp, Alexander Brem, and Björn Ivens. 2015. "Who Will Buy Smart
Glasses? Empirical Results of Two Pre-Market Entry Studies on the Role of
Personality in Individual Awareness and Intended Adoption of Google Glass
Wearables." Computers in Human Behavior 49: 635-647. Accessed November 17,
2015. doi:10.1016/j.chb.2015.03.003
Reed, Daniel, and Chris Stephenson. 2014. “First Impressions, Unexpected Benefits.”
Communications of the ACM 57: 10-11. Accessed April 21, 2015.
doi:10.1145/2601022.
Rico, Julie. 2010. “Evaluating the Social Acceptability of Multimodal Mobile Interactions.”
Paper presented at CHI '10 Extended Abstracts on Human Factors in Computing
Systems, Atlanta, Georgia, April 10-15. Accessed March 1, 2015. doi:
10.1145/1753846.1753877
Rico, Julie, and Stephen Brewster. 2009. “Gestures All Around Us: User Differences in
Social Acceptability Perceptions of Gesture-based Interfaces.” Proceedings of the 11th
International Conference on Human-Computer Interaction with Mobile Devices and
Services 64: 1-2. Accessed April 12, 2015. doi:10.1145/1613858.1613936.
Risko, Evan. F., and Alan Kingstone. 2011. “Eyes Wide Shut: Implied Social Presence, Eye
Tracking and Attention.” Attention, Perception and Psychophysics 73(2):291-296.
Accessed November 18, 2015. doi:10.3758/s13414-010-0042-1
61
Schaar, Anne Kathrin, and Martina Ziefle. 2011. “Smart Clothing: Perceived Benefits vs
Perceived Fears.” Paper presented at the 5th International Conference on Pervasive
Computing Technologies for Healthcare, Dublin, Ireland, May 23-26. Accessed
November 28, 2015.
http://ieeexplore.ieee.org.chain.kent.ac.uk/stamp/stamp.jsp?tp=&arnumber=6038875
&isnumber=6038756
Segan, Sascha. 2013. “Google Glass’s White Male Problem.” PCMag UK, May 1. Accessed
November 28, 2015. http://uk.pcmag.com/consumer-electronics-reviews-
ratings/15760/news/google-glasss-white-male-problem
Serrano, Marcos, Barrett Ense, and Pourang Irani. 2014. “Exploring the Use of Hand-to-Face
Input for Interacting with Head-Worn Displays.” Paper presented at the SIGCHI
Conference on Human Factors in Computing Systems (CHI '14), Toronto, Canada,
April 26 – May 1. doi:10.1145/2556288.2556984
Shanklin, Will. 2013. “Ten days with Google Glass.” Gizmag, December 23. Accessed
November 25, 2015. http://www.gizmag.com/2nd-google-glass-review/30227/
Starner, Thad. 2013. “Project Glass: An Extension of the Self.” Pervasive Computing IEEE
12(2): 14-16. Accessed February 8, 2015. doi:10.1109/MPRV.2013.35
Steijna, Wouter M. P., and Anton Veddera. 2015. “Privacy Concerns, Dead or Misunderstood?
The Perceptions of Privacy Amongst the Young and Old.” Information Polity 20:299-
311. Accessed April 4, 2016. doi:10.3233/IP-150374
Steinberg, Laurence, and Kathryn C. Monahan. 2007. “Age Differences in Resistance to Peer
Influence.” Developmental Psychology 43(6): 1531-1543. Accessed April 30, 2016.
doi: 10.1037/0012-1649.43.6.1531
Stephan, Karl D., Katina Michael, M. G. Michael, Laura Jacob, and Emily P. Anesta. 2012.
“Social Implications of Technology: The Past, the Present, and the Future.”
Proceedings of the IEEE 100: 1752-1781. Accessed January 22, 2015.
doi:10.1109/JPROC.2012.2189919
Strickland, Jonathon. 2014. “How Google Glass Works.” How Stuff Works. Accessed October
18, 2014. http://electronics.howstuffworks.com/gadgets/other-gadgets/project-
glass.htm
Stop the Cyborgs. 2015. “About.” Accessed June 21, 2015. http://stopthecyborgs.org/about/
Šumak, Boštjan, Marjan Heričko, Maja Pušnik, and Gregor Polančič. 2011. “Factors
Affecting Acceptance and Use of Moodle: An Empirical Study Based on TAM.”
Informatica 35(1): 91-100. Accessed July 10, 2015.
http://www.informatica.si/index.php/informatica/article/view/336/335
Tarhini, Ali, Kate Hone, and Xiaohui Liu. 2014. “Measuring the Moderating Effect of Gender
on Age on E-learning Acceptance in England: A Structural Equation Modelling
Approach for an Extended Technology Acceptance Model.” Journal of Education
Computing Research 51(2):163-184. Accessed November 27, 2015.
doi:10.2190/EC.51.2.b
62
Taylor, S. and P. A. Todd. 1995. “Understanding Information Technology Usage: A Test of
Competing Models.” Information Systems Research: 144-176. Accessed July 5, 2015.
doi: 10.1287/isre.6.2.144
Tehrani, Kiana, and Andrew Michael. 2014. “Wearable Technology and Wearable Devices:
Everything You Need to Know.” Wearable Devices Magazine, March 26. Accessed
January 27, 2015. http://www.wearabledevices.com/what-is-a-wearable-device/
Terzis, Vasileios, and Anastasios Economides. 2011. “Computer Based Assessment: Gender
Differences in Perceptions and Acceptance.” Computers in Human Behavior 27(6):
2108-2122. Accessed November 27, 2015. doi:10.1016/j.chb.2011.06.005
Tsai, Chih-Yung, Chih-Chiang Wang, and Ming-Te Lu. 2011. “Using The Technology
Acceptance Model To Analyze Ease of Use of a Mobile Communication System.”
Social Behaviour and Personality 39(1):65-70. Accessed November 24, 2015.
doi:10.2224/sbp.2011.39.1.65
Umrani, Farida, and Rehana Ghadially. 2008. “Gender and Decision-Making in Technology
Adoption Among Youth: A Study of Computer Learners in India.” Psychology and
Developing Societies 20(2):209-227. Accessed November 22, 2015.
doi:10.1177/097133360802000204
Varma, Sonali, and Janet H. Marler. 2013. “The Dual Nature of Prior Computer Experience:
More is Not Necessarily Better for Technology Acceptance.” Computers in Human
Behavior 29: 1475-1482. Accessed November 29, 2015.
doi:10.1016/j.chb.2013.01.029
Venkatesh, Viswanath, and Fred Davis. 2000. “A Theoretical Extension of the Technology
Acceptance Model: Four Longitudinal Field Studies.” Management Studies
46(2):186-204. Accessed June 3, 2015. doi:10.1287/mnsc.46.2.186.11926
Venkatesh, Viswanath, Michael Morris, Gordon Davis and Fred Davis. 2003. “User
Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly
27(3):425-478. Accessed October 14, 2015. http://www.jstor.org/stable/30036540
Wagner, Nicole, Khaled Hassanein, and Milena Head. 2010. “Computer Use by Older Adults:
A Multi-Disciplinary Review.” Computers in Human Behavior 26(5):870-882.
Accessed November 26, 2015. doi:10.1016/j.chb.2010.03.029
Wang, Hsiu-Yuan, and Shwu-Huey Wang. 2010. “User Acceptance of Mobile Internet Based
on the Unified Theory of Acceptance and Use of Technology: Investigating the
Determinants and Gender Differences.” Social Behaviour and Personality 38(3):415-
426. Accessed November 22, 2015. doi:10.2224/sbp.2010.38.3.415
Wang, Yi-Shun, Ming-Cheng Wu, and Hsiu-Yuan Wang. 2009. “Investigating the
Determinants and Age and Gender Differences in the Acceptance of Mobile
Learning.” British Journal of Educational Technology 40(1): 92-118. Accessed
November 27, 2015. doi:10.1111/j.1467-8535.2007.00809.x
Ware, Jennifer. 2014. “Social Norms Influence Journalists’ Perception of Wearable
Technologies.” Proceedings of the 32nd ACM International Conference on The Design
of Communication (SIGDOC ’14) 23:1-2. Accessed April 21, 2015.
doi:10.1086/599247.
63
Wattal, Sunil, Yili Hong, Munir Mandviwalla, and Abhijit Jain. 2011. “Technology Diffusion
in the Society: Analyzing Digital Divide in the Context of Social Class.” Paper
presented at the 44th Hawaii International Conference on System Sciences (HICSS),
Kauai, Hawaii, January 4-7. Accessed November 24, 2015.
doi:10.1109/HICSS.2011.398
Wilkowska, Wiktoria, and Martina Ziefle. 2011. “Perception of Privacy and Security for
Acceptance of E-Health Technologies.” Paper presented at the 5th International
Conference on Pervasive Computing for Healthcare (PervasiveHealth) and
Workshops, Dublin, Ireland, May 23-26. Accessed April 4, 2016.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6038874&isnumber=6038
756
Williams, Rob. 2013. “Google Glass Will Make ‘Privacy Impossible’ Warn ‘Stop the
Cyborgs’ Campaigners.” The Independent, March 26. Accessed June 21, 2015.
http://www.independent.co.uk/life-style/gadgets-and-tech/news/google-glass-will-
make-privacy-impossible-warn-stop-the-cyborgs-campaigners-8550499.html
Wilson, James, and Paul Daugherty. 2015. “Say Goodbye to the Screen.” The Wall Street
Journal, October 13. Accessed November 22, 2015.http://www.wsj.com/articles/say-
goodbye-to-the-screen-1444734983
Wu, Mei-Ying, Han-Ping Chou, Yung-Chien Weng, and Yen-Han Huang. 2011. “TAM2-
Based Study of Website User Behavior – Using Web 2.0 Websites as an Example.”
WSEAS Transactions on Business and Economics 8(4): 133-151. Accessed November
21, 2015. http://www.wseas.us/e-library/transactions/economics/2011/53-665.pdf
Xu, Qianli, Michal Mukawa, Liyuan Li, Joo Hwee Lim, Cheston Tan, Shue Ching Chia, Tian
Gan, and Bappaditya Mandal. 2015. “Exploring Users’ Attitudes towards Social
Interaction Assistance on Google Glass.” Paper presented at the Augmented Human
International Conference (AH ’15), Singapore, March 9-11. Accessed April 2, 2016.
http://dx.doi.org/10.1145/2735711.2735831
Yang, Heetee, Jieun Yu, Hangjung Zo, and Munkee Choi. 2016. “User Acceptance of
Wearable Devices: An Extended Perspective of Perceived Value.” Telematics and
Informatics 33:259-269. Accessed April 4, 2016. doi:10.1016/j.tele.2015.08.007
Yau, Hon Keung, and Alison Lai Fong Cheng. 2012. “Gender Difference of Confidence in
Using Technology for Learning.” Journal of Technology Studies 38(2):74-79.
Accessed November 26, 2015.
http://web.b.ebscohost.com.chain.kent.ac.uk/ehost/pdfviewer/pdfviewer?sid=61f2fe28
-41ea-4a18-af4e-4242277d3b92%40sessionmgr198&vid=2&hid=123
Zhang, Yubo, and Pei-Luen Patrick Rau. 2015. “Playing With Multiple Wearable Devices:
Exploring the Influence of Display, Motion and Gender.” Computers in Human
Behavior 50:148-158. Accessed November 26, 2015. doi:10.1016/j.chb.2015.04.004
64
Appendix A – Ethics
Prior to data collection, this study was granted ethics approval by the Research Ethics
Advisory Group (REAG) at the School of Engineering and Digital Arts, University of Kent. A
full risk assessment was carried out by the researcher (see figure 8).
Participants were given an information sheet, consent form and contact details form
(See Appendix B). The information sheet informed participants about confidentiality, the
study withdrawal process and the study aims. It also provided contact details of the
researcher. The consent form confirmed that participants’ involvement in this research study
was voluntary. The contact details form gave the researcher a means to contact the participant
who won the prize draw.
Public liability cover was organised for Google Glass (see figure 9). Each of the two
Figure 8. Completed Risk Assessment
65
Google Glass devices used in this study costs £1000, as such they were not covered by the
University of Kent’s standard insurance policy. However, the travel policy for business
equipment partially covered the costs. The researcher also completed the student notification
for travel insurance (see figure 10).
Figure 9. Public Liability Cover for Google Glass
Figure 10. Student Notification of Travel Insurance
66
Appendix B – Study Materials
Information Sheet
67
68
69
Consent Form
70
71
Contact Details Form
72
Survey
73
74
75
76
77
78
79
80
81
82
83
84
Advanced/Further Tasks
85
Appendix C – Assumptions
The Assumption of Errors
TABLE 12. Assumption of Independent Errors
R
R Square
Adjusted R Square
Durbin-Watson
.891a
.795
.735
2.209
Note: Durbin-Watson statistics lower than 1 or greater than 3 violate the assumption of independent errors. The
Durbin-Watson value for the revised TAM-2 model is 2.209, therefore the errors are not correlated and the
assumption of independent errors have been met (Field 2009).
The Assumption of Linearity and Homoscedasticity
Figure 11. Scatterplot showing Assumptions of Linearity and Homoscedasticity.
Note: Non-linearity is represented by the curved distribution of data points. Heteroscedasticity is
indicated by the skewed distribution of data points in a scatterplot. The data points for the revised
TAM-2 model are randomly and evenly distributed, therefore the assumptions of linearity and
homoscedasticity are met (Field 2009).
86
The Assumption of No Multicollinearity
TABLE 13. The Assumption of No Multicollinearity
Collinearity Statistics
Social Determining Factors
Tolerance
VIF
Aesthetics
.603
1.660
Input and Interaction
.695
1.439
Privacy and Security
.841
1.189
Perceived Ease of Use
.566
1.768
Perceived Usefulness
.771
1.296
Subjective Norm
.729
1.371
Image
.581
1.722
Note: The assumption of multicollinearity has been met because the highest variance inflation factor (VIF) has a
value less than 10 (Perceived Ease of Use = 1.768) and the average VIF is not substantially greater than 1
(1.492). Furthermore, no tolerance value is less than 0.1 with the lowest being .566. Therefore, none of the
social determining factors are significantly correlated, thus each one is investigating a different component of
intention to use.
The Assumption of Normality
Note: The bell curve in the histogram (see figure 12) and the line of best fit in the P-P scatterplot (see figure 13)
indicates that there is little difference between the observed data (collected via the questionnaire) and the
predicted data, computed by the multiple regression test using the revised TAM-2 model. This suggests that the
model can accurately predict intention to use, that there are no extreme outlying values and the assumption of
normality is met.
87
Figure 12. Histogram showing normal distribution of data.
Figure 13. Normal P-P plot showing the normal distribution of the data.
88
Appendix D – SPSS Output
Descriptive Statistics
i. Full Data Sample
TABLE 14. Sample Mean Scores for Each Social Determining Factor and Intention to Use
Variable
Mean
Std. Deviation
Aesthetics
4.0469
1.28490
Input and Interaction
5.0833
.78288
Privacy and Security
5.1562
1.35859
Perceived Ease of Use
4.6042
1.38719
Perceived Usefulness
3.4427
1.75447
Subjective Norm
3.3255
1.42296
Image
3.2500
1.69545
Intention to Use
3.6562
1.53159
TABLE 15. Sample Mean Scores for Input and Interaction Sub-Factors
Variable
Mean
Std. Deviation
Voice Commands
4.3906
.96499
Hands-free Gestures
5.1406
.89112
Touchpad
5.7187
1.00753
Public Context
4.0000
1.08095
Private Context
6.1667
.86758
TABLE 16. Sample Mean Scores for Subjective Norm Sub-Factors
Variable
Mean
Std. Deviation
Familiar Others
3.6406
1.56181
Unfamiliar Others
3.0313
1.57571
Public Context
3.9062
1.82030
Private Context
2.7188
1.46979
89
ii. Descriptive Statistics by Age Group
TABLE 17. Age Group Sample Mean Scores for Each Social Determinant and Intention to
Use.
18-29
30-49
50-64
65+
Aesthetics
4.0000
4.0000
3.6667
4.2333
Input and Interaction
5.4167
4.8333
5.1944
4.9889
Privacy and Security
5.0833
4.3000
4.9167
5.5667
Perceived Ease of Use
4.8333
3.8667
4.3333
4.8667
Perceived Usefulness
3.4444
4.0667
3.6667
3.1444
Subjective Norm
4.0417
4.6667
2.8750
2.7722
Image
3.1111
3.7333
2.8333
3.3111
Intention to Use
4.2500
4.0000
4.0833
3.1333
TABLE 18. Age Group Sample Mean Scores for Input and Interaction Sub-Factors.
18-29
30-49
50-64
65+
Voice Commands
4.7500
4.0000
4.5833
4.3000
Hands-free gestures
5.0833
4.9000
5.4167
5.1333
Touchpad
6.4167
5.6000
5.5833
5.5333
Public Context
4.2222
3.7333
4.2222
3.9111
Private Context
6.6111
5.9333
6.1667
6.0667
TABLE 19. Age Group Sample Mean Scores for Subjective Norm Sub-Factors.
18-29
30-49
50-64
65+
Familiar Others
4.5833
5.2000
3.5833
2.7667
Unfamiliar Others
3.5000
4.3000
2.1667
2.7667
Public Context
5.4167
5.1000
3.0833
3.2333
Private Context
2.6667
4.1000
2.6667
2.3000
90
iii. Descriptive Statistics by Gender
TABLE 20. Gender Sample Mean Scores for Each Social Determinant and Intention to Use.
Male
Female
Aesthetics
4.3333
3.8750
Input and Interaction
5.1389
5.0500
Privacy and Security
5.0000
5.2500
Perceived Ease of Use
4.3611
4.7500
Perceived Usefulness
3.4861
3.4167
Subjective Norm
3.4514
3.2500
Image
4.1389
2.7167
Intention to use
3.8750
3.5250
.
TABLE 21. Gender Sample Mean Scores for Input and Interaction Sub-Factors
Male
Female
Voice Commands
4.2500
4.4750
Hands-free Gestures
5.2917
5.0500
Touchpad
5.8750
5.6250
Public Context
4.2500
3.8500
Private Context
6.0278
6.2500
TABLE 22. Gender Sample Mean Scores for Subjective Norm Sub-Factors
Male
Female
Familiar Others
3.8333
3.5250
Unfamiliar Others
3.1250
2.9750
Public Context
3.9583
3.8750
Private Context
2.8750
2.6250
91
iv. Descriptive Statistics by Technological Expertise.
TABLE 23. Sample Mean Scores for Each Social Determinant and Intention to Use Sorted
by Levels of Technological Expertise.
Low
Average
High
Aesthetics
4.1429
4.3333
3.5556
Input and Interaction
4.5000
5.2778
5.2407
Privacy and Security
4.5000
5.1333
5.5000
Perceived Ease of Use
4.5714
4.8889
4.1852
Perceived Usefulness
3.4286
3.7222
3.2593
Subjective Norm
3.6429
3.0556
3.7500
Image
3.8571
3.5111
2.3704
Intention to Use
3.6429
3.7333
3.7778
TABLE 24. Sample Mean Scores for Input and Interaction Sub-Factors Sorted by Levels of
Technological Expertise.
Low
Average
High
Voice Commands
4.0714
4.7333
4.1667
Hands-free Gestures
4.4286
5.3000
5.3889
Touchpad
5.0000
5.8000
6.1667
Public Context
3.5238
4.2444
3.8889
Private Context
5.4762
6.3111
6.5926
TABLE 25. Sample Mean Scores for Subjective Norm Sub-Factors Sorted by Levels of
Technological Expertise.
Low
Average
High
Familiar Others
3.7857
3.3333
4.3333
Unfamiliar Others
3.5000
2.8333
3.1667
Public Context
4.0714
3.6667
4.5000
Private Context
3.2143
2.4000
3.0000
92
Inferential Statistics
i. Multiple Regression
TABLE 26. Summary of Revised TAM-2 Model
R
R Square
Adjusted R Square
F
Sig.
.891
.795
.735
13.256
.000
TABLE 27. Importance and Significance of Each Social Determining Factor on Intention to
Use
Beta
Sig.
Aesthetics
-.129
.290
Input and Interaction
.385
.002
Privacy and Security
-.236
.028
Perceived Ease of Use
-.199
.118
Perceived Usefulness
.710
.000
Subjective Norm
.126
.256
Image
-.109
.379
TABLE 28. Correlations Between the Social Determining Factors.
Aesthetics
Input and
Interaction
Privacy
and
Security
Perceived
Ease of
Use
Perceived
Usefulness
Subjective
Norm
Image
Aesthetics
1.000
-.104
-.058
-.458
.015
.081
-.370
Input and
Interaction
-.104
1.000
.010
-.282
-.152
.008
-.245
Privacy
and
Security
-.058
.010
1.000
011
.089
.274
.124
Perceived
Ease of
Use
-.458
-.282
.011
1.000
-.234
.123
.328
Perceived
Usefulness
.015
-.152
.089
-.234
1.000
.032
-.233
Subjective
Norm
.081
.008
.274
.123
.032
1.000
-.319
Image
-.370
-.245
.124
.328
-.233
-.319
1.000
93
ii. Age-Moderated Multiple Regression
TABLE 29. Importance of Each Factor on Intention to Use for Different Age Groups.
18-29
30-49
50-64
65+
Beta
Sig.
Beta
Sig.
Beta
Sig.
Beta
Sig.
Aesthetics
.592
.216
.000
1.000
.491
.322
-.249
.371
Input and Interaction
.580
.228
.630
.254
.228
.664
.419
.120
Privacy and Security
-.289
.579
-.701
.187
-.325
.530
-.403
.137
Perceived Ease of Use
.440
.382
-.213
.731
.327
.527
.314
.254
Perceived Usefulness
.615
.194
.944
.016
.510
.302
.714
.003
Subjective Norm
-.222
.672
.040
.949
-.061
.909
.505
.055
Image
.400
.433
.764
.133
.320
.537
.064
.821
TABLE 30. Importance of Input and Interaction Sub-Factors on Intention to Use for
Different Age Groups
18-29
30-49
50-64
65+
Beta
Sig.
Beta
Sig.
Beta
Sig.
Beta
Sig.
Voice Commands
.373
.466
.680
.207
.530
.280
.344
.210
Hands-free Gestures
.659
.155
.434
.465
-.169
.749
.216
.440
Touchpad
.479
.336
.518
.371
.307
.554
.425
.115
Public Context
.801
.056
.456
.440
.615
.193
.168
.550
Private Context
-.274
.599
.703
.186
-.407
.423
.556
.031
TABLE 31. Importance of Subjective Norm Sub-Factors on Intention to Use for Different
Age Groups
18-29
30-49
50-64
65+
Beta
Sig.
Beta
Sig.
Beta
Sig.
Beta
Sig.
Familiar Others
.235
.655
.280
.649
-.325
.529
.567
.027
Unfamiliar Others
-.560
.248
0.12
.985
.181
.731
.371
.173
Public Context
-.728
.101
-.043
.946
.092
.863
.610
.016
Private Context
.775
.070
.023
.971
-.180
.733
.168
.549
94
iii. Gender-Moderated Multiple Regression
TABLE 32. Importance of Each Social Determining Factor on Intention to Use for Males and
Females.
Male
Female
Beta
Sig.
Beta
Sig.
Aesthetics
.218
.180
-.426
.042
Input and Interaction
-.006
.975
.295
.157
Privacy and Security
.085
.408
-.527
.011
Perceived Ease of Use
-.306
.067
-.087
.689
Perceived Usefulness
1.023
.003
.610
.009
Subjective Norm
.179
.169
-.056
.771
Image
-.228
.068
.115
.628
TABLE 33. Importance of Input and Interaction Sub-Factors on Intention to Use for Males
and Females.
Male
Female
Beta
Sig.
Beta
Sig.
Voice Commands
.580
.063
.207
.491
Hands-free Gestures
-.150
.618
.229
.882
Touchpad
.417
.175
.155
.601
Public Context
.226
.390
.351
.155
Private Context
.598
.041
.279
.783
TABLE 34. Importance of Subjective Norm Sub-Factors on Intention to Use for Males and
Females.
Male
Female
Beta
Sig.
Beta
Sig.
Familiar Others
.972
.007
-.049
.875
Non-Familiar Others
-.637
.046
.323
.305
Public Context
.551
.275
.074
.768
Private Context
-.289
.557
.259
.307
95
iv. Technological Expertise-Moderated Multiple Regression
TABLE 35. Importance of Each Social Determining Factor on Intention to Use for Different
Technological Expertise Levels.
Low
Average
High
Beta
Sig.
Beta
Sig.
Beta
Sig.
Aesthetics
-.431
.334
.153
.587
.122
.755
Input and Interaction
.231
.618
.635
.011
.645
.061
Privacy and Security
-.737
.059
-.181
.519
-.471
.201
Perceived Ease of Use
-.264
.567
.566
.028
-.412
.271
Perceived Usefulness
.705
.077
.778
.001
.737
.023
Subjective Norm
-.296
.520
.110
.695
.478
.193
Image
-.409
.362
.189
.501
.793
.011
TABLE 36. Importance Input and Interaction Sub-Factors on Intention to Use for Different
Technological Expertise Levels.
Low
Average
High
Beta
Sig.
Beta
Sig.
Beta
Sig.
Voice Commands
.642
.120
.523
.045
.448
.227
Hands-free Gestures
-.517
.235
.688
.005
.294
.443
Touchpad
.519
.233
.490
.064
.540
.134
Public Context
-.063
.893
.564
.028
.414
.268
Private Context
.228
.623
.558
.031
.306
.423
TABLE 37. Importance of Subjective Norm Sub-Factors on Intention to Use for Different
Technological Expertise Levels.
Low
Average
High
Beta
Sig.
Beta
Sig.
Beta
Sig.
Familiar Others
-.229
.621
.395
.145
.525
.147
Unfamiliar Others
-.332
.468
-.068
.811
.341
.369
Public Context
.073
.876
.209
.455
.186
.631
Private Context
-.710
.074
-.091
.748
.701
.035
96
v. ANOVA for Age
TABLE 38. ANOVA Showing Differences Between Scores Across Age Groups for Each
Social Determining Factor and Intention to Use.
df
F
Sig
Aesthetics
Between Groups
3
.265
.850
Within Groups
28
Input and Interaction
Between Groups
3
.622
.607
Within Groups
28
Privacy and Security
Between Groups
3
1.210
.324
Within Groups
28
Perceived Ease of Use
Between Groups
3
.763
.524
Within Groups
28
Perceived Usefulness
Between Groups
3
.364
.779
Within Groups
28
Subjective Norm
Between Groups
3
3.718
.023
Within Groups
28
Image
Between Groups
3
.256
.856
Within Groups
28
Intention to use
Between Groups
3
1.138
.351
Within Groups
28
Note: See Table 39 for breakdown of Subjective Norm differences across age groups.
vi. t-test for Age
TABLE 39. t-test Showing Differences Between Subjective Norm Scores Across Age Groups
t
df
Sig
18-29
30-49
-.803
9
.442
50-64
1.824
10
.098
65+
2.409
19
.026
30-49
18-29
.803
9
.442
50-64
1.881
9
.093
65+
2.725
18
.014
50-64
18-29
-1.824
10
.098
30-49
-1.881
9
.093
65+
.169
19
.867
65+
18-29
-2.409
19
.026
30-49
-2.725
18
.014
50-64
-.169
19
.867
97
vii. t-test for Gender
TABLE 40. t-test Showing Gender Differences in Social Determining Factor and Intention to
Use Scores.
t
df
Sig
Aesthetics
.976
30
.337
Input and Interaction
.306
30
.761
Privacy and Security
-.498
30
.622
Perceived Ease of Use
-.763
30
.452
Perceived Usefulness
.107
30
.916
Subjective Norm
.382
30
.705
Image
2.481
30
.019
Intention to Use
.620
30
.540
viii. ANOVA for Technological Expertise
TABLE 41. ANOVA Test Showing Differences Between Scores Across Technological
Expertise Levels for Each Social Determining Factor and Intention
df
F
Sig
Aesthetics
Between Groups
3
.742
.536
Within Groups
28
Input and Interaction
Between Groups
3
1.915
.150
Within Groups
28
Privacy and Security
Between Groups
3
1.405
.262
Within Groups
28
Perceived Ease of Use
Between Groups
3
.473
.704
Within Groups
28
Perceived Usefulness
Between Groups
3
.790
.510
Within Groups
28
Subjective Norm
Between Groups
3
1.245
.312
Within Groups
28
Image
Between Groups
3
1.264
.306
Within Groups
28
Intention to use
Between Groups
3
.670
.577
Within Groups
28
98
Appendix E – Qualitative Themes
TABLE 42. Qualitative themes based on participant open-ended responses
Social Determinant
Common Themes
Aesthetics
———
Unattractive/Cumbersome
Stylish
Futuristic
Odd/Awkward
Input and Interaction
Voice Commands (Public)
Conscious of appearing weird
Conscious of drawing attention
Uncomfortable
Voice Commands (Private)
Confident/comfortable
No risk of privacy violation
Gestures (Public)
Ambiguity/Open to interpretation
Negative cultural connotations
It will become the norm
Gestures (Private)
No issues
Confident/comfortable
Touchpad (Public)
Unproblematic
Unobtrusive
Easy to use
Same as current technology
Unambiguous
Touchpad (Private)
Unproblematic
Privacy and Security
Privacy
Same as current technology
Accidentally recording or photographing
others
Being overheard/overlooked
Security
Lack of security/needs security features
Loss or theft
99
TABLE 42. Continued
Perceived Ease of Use
———
Straightforward/easy to use
Ease to use quickly
Time required
Perceived Usefulness
Daily tasks
Nothing new offered
On the go usefulness
Latest gadget
Work, school or University
See potential
Useful in the workplace
Better alternatives
Personally useful
See potential
Specified use (Work, gaming, on the go)
Subjective Norm
Familiar Others in Public
Independent decision
Concerned
Familiar Others in Private
Care about family opinions
Independent decision
Depends on usefulness
Worried about annoying others
Unfamiliar Others in Public
Only care about familiar others
Concerned about invading others privacy
Independent decision
Concerned about drawing attention/looking
weird
Unfamiliar Others in Private
Independent Decision
Indifferent
Image
———
Too early to tell
Price enhance status
Negative symbol of nerdiness or too much
money
100
Appendix F – Observations
TABLE 43. Study session observations
Factor/Topic
Observations
Aesthetics
Participants are surprised by the lightweight, sleek design,
particularly the older participants.
Input and Interaction
Participants felt winking to take a photograph was too suggestive.
Perceived Ease of Use
People picked up navigating skills relatively quickly across the
sessions and it varied amongst age groups. Generally, people who
appeared to use technology less, struggled more.
Perceived Usefulness
Participants consistently recognised the potential for Google Glass in
multiple industries: education, healthcare, etc.
Privacy and Security
Many participants recognise that Google Glass presents very similar
issues to current devices.
Image
They couldn't see how Google Glass would imply a high social
status but they all wanted to have photographs taken to show others
that they had used it.
Intention to Use
People are interested in using Google Glass in the future once
technology has developed
Functionality
Participants frequently commented on how hot Google Glass
became during the sessions and how that slowed the performance of
the device. They also noted its poor battery life.
Age
Younger people adapted to the novel interaction methods quicker
than older people, possibly because of their more frequent use of
smartphones.
101
TABLE 43. Continued
Older people stated that if they were younger, they would be
interested in using Google Glass in the future but they did not feel
that the technology would be developed enough within their lifetime
so did not see the point in spending time learning how to use the
device.
Price
Money was a big issue. Participants felt that there were better
alternatives out there for a smaller cost.
General
People were unanimously fascinated by Google Glass.
One gentleman said that he would go out and buy Google Glass
tomorrow if it was cheaper and more reliable. He would use it to
navigate around foreign cities.
Participants often had negative preconceptions about Google Glass
but interacting with it and experience the technology first hand
helped them to overcome their discomfort.
