Mobile headsets and earphones are a familiar sign that a person is having a telephone conversation ...... Android, November 10. Accessed November 26, 2015.
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) Social Determinants
4 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
23
Data Sampling
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 Appendix B – Study Materials
64 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 Descriptive Statistics
88 – 97 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 Figure 1
20
Figure 2
29
Figure 3
30
Figure 4
35
Figure 5
35
Figure 6
36
Figure 7
40
Figure 8
64
Figure 9
65
Figure 10
65
Figure 11
85
Figure 12
87
Figure 13
87
ii
LIST OF TABLES
Table 1
21
Table 23
91
Table 2
23
Table 24
91
Table 3
24
Table 25
91
Table 4
28
Table 26
92
Table 5
29
Table 27
92
Table 6
31
Table 28
92
Table 7
36
Table 29
93
Table 8
37
Table 30
93
Table 9
39
Table 31
93
Table 10
40
Table 32
94
Table 11
41
Table 33
94
Table 12
85
Table 34
94
Table 13
86
Table 35
95
Table 14
88
Table 36
95
Table 15
88
Table 37
95
Table 16
88
Table 38
96
Table 17
89
Table 39
96
Table 18
89
Table 40
97
Table 19
89
Table 41
97
Table 20
90
Table 42
98
Table 21
90
Table 43
100
Table 22
90
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 soughtafter 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 sociallyacceptable “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 TAM2. 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 gesturebased 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 gesturebased 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 (PadillaMeléndez, 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-ofuse 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-ofuse, 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.
Image
Voluntariness
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. 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
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. 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.
—
22 TABLE 1 (continued) Job Relevance
Output Quality
Result Demonstrability
In my job, usage of the system is important. In my job, usage of the system is relevant.
—
The quality of the output I get from the system is high. I have no problem with the quality of the system’s output.
—
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
—
Input and Interaction
Intention to Use
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.
—
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 handsfree gestures to interact with Google Glass in public. I would feel comfortable using handsfree 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.
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 mixedmethod 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 Male Female
Frequency 12 20
Ethnicity White British Irish White/Black African
Frequency 30 1 1
Age 18-29 30-49 50-64 65+ Technological Expertise Low Average High Very High
Frequency 6 5 6 15 Frequency 7 15 9 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 Very low expertise
Description 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 subgroups 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
Qualitative Analysis of Open-Ended Responses
iii.
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 7
Data Sample Mean Scores
6 5 4 3 2 1 0 Aesthetics Input and Privacy and Perceived Perceived Subjective Interaction Security Ease of Use Usefulness Norm
Image
Intention to Use
Social Determining Factors and Intention to Use Figure 2. Sample mean scores for the social determining factors and intention to use (+/- 1 S.D.)
TABLE 5. Percentage of participants who are comfortable, neutral or uncomfortable using Google Glass input and interaction methods. Data Sample Mean Scores Uncomfortable (< 3.5) Neutral (≥ 3.5 ≤ 4.5) Comfortable (> 4.5)
Voice Commands Hands-free Gestures Touchpad 6.2% 0.0% 0.0% 59.4% 34.4% 18.7% 34.4% 65.6% 81.3%
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
30 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.
100% 90% 80% Percent
70% 60%
Unimportant (≤ 3.4)
50%
Neutral (≥ 3.5 ≤ 4.5)
40%
Important (≥ 4.6)
30% 20% 10% 0% Familiar/Significant Others
Unfamiliar/Non-significant Others
Relationship to others Figure 3. Percentage of participants who consider the opinions of others to be important, neutral or unimportant in their decision to use Google Glass.
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 Aesthetics Input and Interaction Privacy and Security Perceived Ease of Use Perceived Usefulness Subjective Norm Image Intention to Use
Cronbach’s α Scores α = .948 α = .717 α = .758 α = .909 α = .915 α = .759 α = .925 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
7 6
Data Sample Mean Score
5 4 18-29 3
30-49 50-64
2
65+ 1 0 Aesthetics Input and Privacy Perceived Perceived Subjective Interaction and Ease of Usefulness Norm Security Use
Image
Intention to Use
Social Determining Factors and Intention to Use Figure 4. Sample mean scores for each social determining factor and intention to use, based on participant age group.
7
Data Sample Mean Score
6 5 4 3
Male Female
2 1 0 Aesthetics Input and Privacy Perceived Perceived Subjective Interaction and Ease of Usefulness Norm Security Use Social Determining Factors and Intention to Use
Image
Intention to Use
Figure 5. Sample mean scores for each social determining factor and intention to use, based on participant gender.
36 7
Data Sample Mean Scores
6 5 4 Low
3
Average 2
High
1 0 Aesthetics Input and Privacy Perceived Perceived Subjective Interaction and Ease of Usefulness Norm Security Use
Image
Intention to Use
Social Determining Factors and Intention to Use Figure 6. Sample mean scores for each social determining factor and intention to use, based on participant technological expertise.
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-
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 Not Status Symbol (≤ 3.4) Neutral (≥ 3.5 ≤ 4.5) Status Symbol (≥ 4.6)
18-29 66.6% 16.7% 16.7%
30-49 40.0% 40.0% 20.0%
50-64 50.0% 50.0% 0.0%
65+ 46.7% 33.4% 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
100% 90% 80%
Percent
70% 60%
Uncomfortable (≤ 3.4)
50%
Neutral (≥ 3.5 ≤ 4.5)
40%
Comfortable (≥ 4.6)
30% 20% 10% 0% Low
Average
High
Level of Technological Expertise Figure 7. Participant levels of comfort interacting with Google Glass, based on technological expertise level.
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
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 GarridoMoreno 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.
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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-willmake-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/saygoodbye-to-the-screen-1444734983 Wu, Mei-Ying, Han-Ping Chou, Yung-Chien Weng, and Yen-Han Huang. 2011. “TAM2Based 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).
Figure 8. Completed Risk Assessment
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
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 Multiple Regression
i.
TABLE 26. Summary of Revised TAM-2 Model R .891
R Square .795
Adjusted R Square .735
F 13.256
Sig. .000
TABLE 27. Importance and Significance of Each Social Determining Factor on Intention to Use Beta -.129 .385 -.236 -.199 .710 .126 -.109
Aesthetics Input and Interaction Privacy and Security Perceived Ease of Use Perceived Usefulness Subjective Norm Image
Sig. .290 .002 .028 .118 .000 .256 .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
-.407
.423
.556
.031
.186
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.
Aesthetics Input and Interaction Privacy and Security Perceived Ease of Use Perceived Usefulness Subjective Norm Image Intention to use
Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups
df 3 28 3 28 3 28 3 28 3 28 3 28 3 28 3 28
F .265
Sig .850
.622
.607
1.210
.324
.763
.524
.364
.779
3.718
.023
.256
.856
1.138
.351
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 30-49 -.803 9 .442 18-29 50-64 1.824 10 .098 65+ 2.409 19 .026 18-29 .803 9 .442 30-49 50-64 1.881 9 .093 65+ 2.725 18 .014 18-29 -1.824 10 .098 50-64 30-49 -1.881 9 .093 65+ .169 19 .867 18-29 -2.409 19 .026 65+ 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 Aesthetics Input and Interaction Privacy and Security Perceived Ease of Use Perceived Usefulness Subjective Norm Image Intention to use
Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups
df 3 28 3 28 3 28 3 28 3 28 3 28 3 28 3 28
F .742
Sig .536
1.915
.150
1.405
.262
.473
.704
.790
.510
1.245
.312
1.264
.306
.670
.577
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
Voice Commands (Public)
Conscious of appearing weird Conscious of drawing attention Uncomfortable
Voice Commands (Private)
Confident/comfortable No risk of privacy violation
Input and Interaction Gestures (Public)
Ambiguity/Open to interpretation Negative cultural connotations It will become the norm
Gestures (Private)
No issues Confident/comfortable Unproblematic
Touchpad (Public)
Unobtrusive Easy to use Same as current technology Unambiguous
Touchpad (Private)
Unproblematic
Same as current technology Privacy and Security
Privacy
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 Nothing new offered
Daily tasks
On the go usefulness Latest gadget Perceived Usefulness Work, school or University
See potential Useful in the workplace Better alternatives
Personally useful
See potential Specified use (Work, gaming, on the go)
Independent decision Familiar Others in Public
Concerned
Care about family opinions Subjective Norm
Familiar Others in Private
Independent decision Depends on usefulness Worried about annoying others Only care about familiar others
Unfamiliar Others in Public
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.
