perceptions of a wearable ubiquitous monitoring device

56 | IEEE TECHNOLOGY AND SOCIETY MAGAZINE | fALL 20131932-4529/13/$31.00©2013IEEE

STUART MORAN, TOYOAKI NISHIDA, AND KEIICHI NAKATA

Perceptions of a Wearable Ubiquitous Monitoring Device

FITBIT

Digital Object Identifier 10.1109/MTS.2013.2276672

Date of publication: 26 September 2013

IEEE TECHNOLOGY AND SOCIETY MAGAZINE | fALL 2013 | 57

world of ubiquitous com-puting, full of networked

mobile and embed-ded technologies, is approaching. The

benefits of this technology are numerous, and act as the major driving force behind its develop-ment. These benefits are brought about, in part, by ubiquitous monitoring (UM): the continu-ous and wide spread collection of significant amounts of data about users [1].

While UM is a source of many of the benefits of the technol-ogy, it may also play a role in its failure. To appreciate the poten-tial negative aspects of this new type of monitoring, we must look at existing monitoring technolo-gies and methods such as closed circuit television cameras, human observation, health-based sys-tems in hospitals, and automated city travel passes. These systems share a common social implication where they can often be found to undesirably influence the behav-ior of those using them (i.e., being observed) [2]–[5]. In addition these systems cause feelings of stress and distrust [2].

In light of this, we anticipate that behavioral changes and other undesirable effects are likely to continue to occur in UM systems. furthermore, given the reduction in physical constraints (e.g., walls, floors, and distance) that hinder existing technologies, the effects of the monitoring are likely to become amplified. The problem this cre-ates for the field of ubiquitous com-puting is that if the system used to collect naturalistic data about users is influencing their behaviors and cognitive state, then the data collected is unlikely to be accurate. As a result, the system may provide a sub-optimal response or, at worst, suffer a failure.

In response to this issue, a num-ber of predictive models have been developed with the aim of mini-mizing or preventing undesirable

behaviors prior to a UM systems development and implementation. Such models would allow designers to explore the potential behavioral impacts of their system designs without costly developments and negative social reactions, which could resolve many social issues before they occur. The Perceptions of System Attributes-Behavioral Intention (PSA-BI) model [1], is one such model, specifically designed to predict user behaviors in ubiquitously monitored environ-ments. Although parts of the model have been empirically tested [1],

one key aspect that remains unex-plored is the impact of culture on user salient perceptions, attitudes, and intentions. Thus far, studies involving the model have been limited to U.K. universities and workplaces.

Hence, the focus of this article is on exploring specific compo-nents of the PSA-BI model through a quantitative study of office work-ers in Kyoto University, Japan. An established set of questionnaire measures was used to capture the workers salient perceptions, atti-tudes, and intentions toward wear-ing a monitoring device. Using structural equation modeling [6], the relationships in the model were tested, and a series of regression coefficients observed that reaffirm the relationships in the model.

Existing FrameworksThere are number of different existing frameworks and predic-tive models in the literature such as the ubiquitous computing accep-tance model [7], and the aware-ness monitoring model [8]. These highly relevant frameworks pro-pose to help guide future research or model the undesirable effects of pervasive computer systems. While both are useful for conceptualizing

the problem space, they fail to con-sider many of the different aspects of large-scale monitoring sys-tems. The Perceptions of System Attributes – Behavioral Intention (PSA-BI) (see fig. 1) model was developed to address these issues [1], through clear identification of specific behavioral influencing system characteristics.

Like the above models, the PSA-BI model is grounded in aspects of sociological theories such as the Theory of Planned Behavior (TPB) [9] and Technology Acceptance Model (TAM) [10]. The model was

developed with the intention of pre-dicting users responses to monitor-ing systems based on the systems characteristics.

Exploring the model from left to right, the technology and applica-tion spaces contain the design char-acteristics of a monitoring system which influence users. These are external variables in the sense that they are directly controllable by the designers and engineers who build the monitoring systems.

The technology space includes elements such as device obtrusion, device control, device coverage, and number of devices [11], focus-ing on the physical behavior influ-encing characteristics of a system. In contrast, the application space focuses on the use of the technol-ogy and includes characteristics such as: frequency of data collec-tion, data integration, user knowl-edge, application control, data access, and data sharing [11].

Object based beliefs are the beliefs/perceptions a user has about a system, and its specific character-istics. Both application and technol-ogy perceptions directly correspond to the equivalent characteristics. for example, a designed open level of access to the data collected will influence user perceptions of the

A

Ubiquitous monitoring systems can cause feelings of stress and distrust.

58 | IEEE TECHNOLOGY AND SOCIETY MAGAZINE | fALL 2013

level of access, which in turn may influence their perceptions of pri-vacy invasion. The system charac-teristics themselves will not directly influence behavior, but will do so only through a user’s perceptions of those characteristics.

A number of exogenous fac-tors will influence how a user per-ceives the system, ranging from their age and gender, to their past experiences and the social con-text within which the data collec-tion takes place [11]. A number of “adjusters” will also influence a user’s perception of the system, including the passage of time and experience. It is these perceptions, or object based beliefs (the system is the object) which act as a bridge to modified TPB and UTAUT models [12].

A user’s perceptions, or object based beliefs, will influence their attitudes (positive or negative) toward the object/system. Applica-tion and technology object based attitudes are influenced by the pre-ceding perceptions. Based on these attitudes, a user will form attitudes

regarding a behavior related to the system (e.g., acceptance, sharing of specific data, use at work). A user will also formulate their perception of social influence. These percep-tions include the social pressure from people who are important to them to perform the behavior and facilitating conditions, i.e., whether or not the conditions exist to give them control over whether they per-form the behavior (an easily acces-sible “off-switch” facilitates the behavior of shutting a device down).

The combined influence of atti-tudes toward the behavior, social influence and facilitating condi-tions affect a user’s intention to carry out the behavior. This in turn is the direct antecedent and predic-tor of behavior itself.

The model has been empirically tested using a series of question-naires, and the results analyzed using structural equation modeling [1] to identify the regression coef-ficients between variables. These were then used as a part of a simu-lation which propagated the effects of input variables and accurately

made a prediction about a behavior [1], [12], thus validating the model. While the model has been applied in a number of domains such as ubiquitous computing [1], assistive technologies [12], and persuasion [13], the impact of culture as an exogenous moderating variable has yet to be explored. One of the moti-vations for exploring this variable in particular (as opposed to age or gender) is an anticipated strong con-trast between people from different countries and cultures in their views of monitoring systems.

Study DesignJapan as a country has a unique and intriguing culture, and as such is an ideal environment to explore the potential impact of culture on user perceptions, particularly as the PSA-BI model was based on data collected primarily in the U.K. One of the notable aspects of Japanese culture is the general pos-itive attitude towards technology adoption, e.g., i-mode [14]. Given that monitoring or surveillance in the Japanese public sphere is less

Exogenous Moderating Variables

Moderating Anchors

PastExperience

ComputerSkill Level

Context Age Gender Environment Role Culture

Behavior BasedAttitudes

SocialInfluence

External VariablesAnchors

Object BasedBeliefs Object Based Attitudes

ApplicationSpace

TechnologySpace

ApplicationPerceptions

TechnologyPerceptions

AttitudeToward

Application Attitude TowardBehaviour

Behavior Intention

FacilitatingConditions

Adjusters

NewInformation

Experience Time

AttitudeToward

Technology

Fig. 1. The perception of system attributes – behavioral intention model (based on [1]).


pervasive than in other countries such as the U.K. [15] (likely due to low crime rates in Japan [16]), Japanese experience of large scale monitoring systems is likely to be limited. further to this, it is inter-esting to note that some studies have suggested there is no direct translation for the word “privacy” in Japanese [17]. This might partly reflect the state of Japanese atti-tudes and perceptions towards data collection and use. However, it does not necessarily mean the concept of privacy does not exist in modern Japan. for example, Japanese workplaces and monitor-ing are commonly large open plan offices (e.g., [18]) and as such there exists a “direct” form of observa-tion, though it may not necessarily be perceived this way.

When studying user responses and behaviors involving a tech-nology, their perceptions of the technology are of particular importance to understanding their actions. This is because these user’s actions are grounded in their perceptions of reality, and not the reality itself. Hence perceptions of a system’s char-acteristics are arguably more important than the character-istics themselves, particularly salient perceptions. These are the immediate perceptions (or attitudes) that a user has about a technology based purely on their previous experiences, with no other information available. for example, when you hear a song

for the first time, your stron-gest initial responses to it can be described as salient. In terms of technology acceptance, percep-tions such as ease of use and use-fulness are frequently described as salient [19], [20]. Salient per-ceptions are of critical impor-tance because they will likely influence the users’ subsequent

experiences with the technology, and thus play a significant part in determining system acceptance and use [21].

A questionnaire has been designed to measure user salient perceptions of a specific UM device (see fig. 2). The image was used to invoke participant’s salient perceptions based purely on what the device affords. Beyond a short text description of the device as “wearable” and “deployable in their work environment,” no other information was given to partici-pants. This was intended to retain the salience of the participant’s perceptions and reactions to the idea of the devices introduction. The more information shared about the device with users, the weaker the salience of their reac-tions becomes.

The device shares a number of affordances with a name badge type object, including wearability and mobility, which we anticipate participants will focus on. One of the benefits of using this image is that participants can cognitively place the device in their own work environments. The device is also presented and introduced as a workplace monitoring system in the same way for all participants.

While there are a number of perceptions in the PSA-BI model

[1], it is difficult to explore them all in a single study. As such, a series of six prominent perceptions and attitudes were selected for testing (see Table I):

Object Based Beliefs ■ Perceived Natural Border

Crossings (PNC): defined as the degree to which a person

feels that any natural borders have been crossed [1], based on Marx [22]. In the context of a monitoring system, PNC is related to its physical place-ment in relation to a person.

■ Perceived Privacy Invasion (PPI): defined as the degree to which a person feels that the monitoring is invasive of their privacy [1].

Object Based Attitudes ■ Attitude Toward Technology

(ATT): defined as a person’s positive or negative view of a technology based on what it affords [1].

■ Behavior Based Attitudes ■ Attitude Toward Behavior

(ATB): defined as a person’s positive or negative view toward performing a specific behavior related to the technol-ogy, e.g., use [1] .

■ Facilitating Conditions (fC): defined as the degree to which an individual believes that the conditions exist which gives them control (or choice) over whether they perform a behavior [9]. for example, if the behavior were use of a wearable device, then the facilitating conditions would be related to whether or not a person is able to remove it.

Fig. 2. Wearable ubiquitous monitoring device [28].

If a system used to collect naturalistic data about users is influencing their behaviors, then the data collected is unlikely to be accurate.


■ Behavioral Intention (BI): defined as an individual’s readi-ness to perform a given behavior, and acts as the direct anteced-ent of behavior according to the TPB [23]. The behavioral intention under investigation in this study is whether or not a participant would wear the tag presented in fig. 2.

The hypothesized (and tested) relationships between each of these components, as determined by the PSA-BI model [1] (see fig. 1). These hypotheses are grounded in evidence from the

ubiquitous computing and sur-veillance/monitoring literature. One of the main hypotheses for this study is that the Japanese participants are expected to have positive attitudes and intentions toward wearable workplace UM systems. This is based on existing work practices (i.e., observation in open plan offices) and a pro-technology attitude.

Measure Assessment and Sample DistributionA series of three 7-point Likert items were adopted from previous

research [1] for each factor (see Table I). The letter “r” indicates that this measure was reversed dur-ing statistical analysis, and was included as a negative/positive wording to prevent acquiescence bias. The measures were profes-sionally translated into Japanese, and then crosschecked by both native and foreign fluent Japanese speakers. The items are designed to measure perceptions and attitudes of the device in fig. 2.

A paper-based questionnaire was distributed to 500 office based administrative and support staff at

Table I Japanese measures with English Translations

Factor ID English Item Japanese Item

PNC Pnc1 The placement of the device is desirable

この機器の装着位置は望ましい

Pnc2r This device invades my personal space

この機器は、自分のパーソナルスペースを侵害してしまう

Pnc3r The placement of the device is uncomfortable

この機器の装着位置は、不快に感じる

PPI Ppi1r The information the device collects is an invasion of my privacy

この機器が集めた情報は、プライバシーの侵害だと感じる

Ppi2r I am uncomfortable with the information the device collects

機器が自分に関する情報を収集するということについて、不快に感じる

Ppi3r I am concerned with the information the device collects

機器が収集する自分に関する情報について、懸念している

ATT Att1r I like this monitoring device このモニタリング機器が好きだ

Att2r I think this monitoring device is good

このモニタリング機器は良いと思う

Att3 I think this monitoring device is bad

このモニタリング機器はよくないと思う

ATB Atb1r Wearing this device at work would be fun

仕事中にこの機器を装着するのは、楽しいだろうと思う

Atb2r I would hate using this device at work

仕事中にこの機器を使用するのは、嫌だと感じる

Atb3r I would like wearing the device at work

仕事中にこの機器を装着したいと思う

FC Fc1r I have a choice in wearing the tag or not

タグを装着するかしないかは、自分で選択できる

Fc2r It is within my power to stop wearing the tag

タグを装着したとしても、自分の裁量で装着をやめることができる

Fc3r I am permitted to remove the tag at work

仕事中、自分はタグを取り外すことを許可されている

BI Bi1 I would wear the tag regularly at work

仕事中、定期的にタグを装着すると思う

Bi2 I would frequently remove the tag at work

仕事中、頻繁にタグを取り外すと思う

Bi3r I would wear the tag most of the time at work

仕事中はほとんどタグを装着すると思う


Kyoto University, Japan. A total of 164 useable responses were received, giving a 33% response rate. With recommendations of 15 respondents per parameter, this sample size is considered sufficient for a structural equation modeling (SEM) analysis [6]. There was an equal 50% split between male and female respond-ers. The majority of respondents (92.7%) work in open plan offices, most participants (81.0%) work full time, and the most common (92.1%) reported computer skill level was between novice and intermediate (a modest response). The measures were assessed for both convergent and discriminant validity. Con-vergent validity was demonstrated

by all factor loadings and average variance extracted (AVE) values being > 0.5, and all composite reli-ability (CR) and alphas are ≥ 0.7 [6] (see Table II). Alpha measures for BI were reduced by the inclusion of Bi1, so it was removed to improve the reliability.

Discriminant validity was confirmed by values of AVE > 0.5, and the square roots of AVE being higher than all other corre-lations among constructs [6] (see Table III). However, it should be noted that measure for ATB showed weak discriminant validity, but was still included in the analysis.

SEM simplifies the exploration of multiple hypotheses [6] making

it an ideal approach to test the rela-tionships selected from the PSA-BI model. Given that the measures were not normally distributed, the Generalized Least Squares (GLS) statistic was used to estimate the structural model as it is unaffected by non-normality [6].

Results and DiscussionThe structural model produced by AMOS is shown in fig. 3. All of the hypothesized relationships were met, with strong estimates for relations at the p < 0.001 level. The regression coefficients (β) are unstandardized and R2 shows the variance. The GfI (0.864) and RMSEA (0.58) measures were

used to assess how well the model fits the data, and indicated a medium to good fitting model [6].

Looking at the struc-tural model, PNC showed a medium strength (β = .485) positive relationship with PPI, but only explained 20% of the variance. It is interesting to observe that the Japanese respondents felt the device would be physically invasive given that Japan is often per-ceived as more technologi-cally orientated than other cultures. While interest-ing, it is not necessarily surprising given the find-ings reaffirm the relation-ship between physical and privacy invasions found in other research [24].

PPI displayed a strong negative relationship (β = -0.625) with ATT, explain-ing 52% of the variance. This shows that the more invasive of privacy the device was perceived to be, the less positive a respondent’s attitude was toward it. Again, as Japan has a wide acceptance of technology, perceived privacy invasion in this

Table II Assessment of Convergent Validity

Factor Item Item Loading Mean AVE CR Alpha

PNC

Pnc1 .748 3.28

.570 .690 .817Pnc2r .772 3.78

Pnc3r .743 3.56

PPI

Ppi1r .784 5.35

.740 .860 .907Ppi2r .901 5.47

Ppi3r .889 5.43

ATT

Att1r .853 2.51

.650 .780 .832Att2r .850 2.96

Att3 .705 3.34

ATB

Atb1r .730 1.96

.540 .650 .757Atb2 .819 2.90

Atb3r .640 2.10

FC

Fc1r .846 4.78

.800 .910 .926Fc2r .967 4.82

Fc3r .865 4.51

BI

Bi1 Excluded Excluded.540 .550 .697Bi2 0.792 3.87

Bi3r 0.66 4.513.69

Table III Assessment of Discriminant Validity (Diagonal = √AVE)

p<0.01 PNC PPI ATT ATB FC BI

PNC .750

PPI .359 .860

ATT -.346 -.441 .810

ATB -.285 -.554 .671 .540FC .048 -.048 .132 .158 .890

BI -.426 -.389 .405 .402 -.224 .730


instance appears to be influenc-ing the generally positive attitudes toward technology. One reason for this might be the (deliberate) lack of explicit justification, which may have improved attitude toward use of the technology.

ATT shows a strong relationship with ATB (β = 0.750), explaining 82% of the variance. The behavior in this study (wearing the tag) is strongly associated with the tech-nology itself. The salient character-istics of the system (e.g., position on the body) are influencing the atti-tude towards the technology itself, which are in turn influencing atti-tudes towards the way the device is intended to be used (as anticipated through affordance). This demon-strates the propagation of influ-ence through the model, which is further supported by the strong relationship between ATB and BI

(β = 0.792); as anticipated based on the TPB [9].

There is also a weak relation-ship (β = -0.231) between fC and BI, where the more control/choice a person feels they have in wear-ing the device, the less likely they are to wear it. This is a particularly interesting result, as Japanese peo-ple frequently comply with obliga-tions when their behavior (in this case wearing/not wearing the tag)

is visible [25]. This suggests that participants salient perception is that they are not obliged to where the tag. We are likely to see dif-ferent values for this relationship if the tag were strongly justified or enforced for use in the workplace. Given attitudes towards the device, it is apparent that participants who felt they had a choice in wearing the tag would likely exercise it. Combined, fC and ATB explain 42% variance in BI, which sug-gests that the other factors that were not included for testing in the PSA-BI model may be important in explaining the remaining variance.

The study is limited in a num-ber of ways: the hypothetical intro-duction of a device may not have invoked realistic responses from participants, although participants were able to cognitively place the device in their own environments.

Secondly, it should be acknowledged that the sample size and response rate were small, limiting the gener-alizability of the results. However, the sample was sufficient to justi-fiably perform an analysis using SEM, and provides some insights into Japanese perceptions of work-place UM systems. finally the pre-diction of the behavioral intention to wear the tag was not validated, which will require further studies.

Aside of these limitations, a number of important conclusions can be drawn from this study. It is interesting to observe that there are clear patterns in participant’s salient perceptions of the device. It suggests that people, when first presented with a device, think along similar lines. A better under-standing of salient perceptions could lead to more effective ways of introducing technology to new users. The results also provide an interesting insight into how Japanese people negatively view and, saliently perceive, monitor-ing technologies in the workplace, where personal space and privacy invasion appear to be prominent issues. This is in direct contrast to the more positive and open view to the technology we expected. The results of this study suggest that choice and privacy are more important to Japanese people than first anticipated.

Social Implications and ConsiderationsThe potential for ubiquitous moni-toring is significant, and it stands to influence almost every aspect of our lives. As such, from the results in this study and previous work in the area, we present a number of reflective social implications and considerations.

Salience and Acceptance of Monitoring TechnologyWhen users are first presented with a technology, their initial reactions may have a lasting effect on sub-sequent interactions. Interestingly, given that much of ubiquitous com-puting is intended to be embedded and hidden from view, there is in fact likely to be little to “react” to. This places stronger emphasis on the initial assumptions that users make given the limited informa-tion available. Particularly given the degree to which such embed-ded systems will be able to silently manage and support large aspects of our daily activities. Hence,

PNC PPI ATT ATB BI

FC

β = 0.485 β = –0.625 β = –0.231β = 0.750 β = 0.792

R 2 = 0.198 R 2 = 0.519 R 2 = 0.819 R 2 = 0.423

p < 0.001

Fig. 3. Structural model including parameter estimates and variance.

The potential for ubiquitous monitoring is significant, and it stands to influence almost every aspect of our lives.


salient perceptions, attitudes, and intentions should be a consider-ation at the forefront of the minds of those who introduce any new technology.

There are a number of different approaches to introducing the ideas and concepts behind the technol-ogy, and in this article we see there is a strong relationship between attitudes towards technology and attitudes toward behavior related to that technology. Therefore any efforts made to improve users views of the technology could help influence their initial (and long-term) intentions to use it.

The long-term acceptance of monitoring technology is impor-tant given the likely permanence of their installation. The results from this paper indicate a weak relation-ship between facilitating conditions and intention to use, suggesting that if users have a choice, they would not use the technology. This raises several issues regarding scenar-ios where there is no choice, e.g., current closed-circuit television (CCTV), and the effects caused by such coercive interaction. How could such systems be first pre-sented and introduced to users? How can positive views of the sys-tem be sustained over time? How would any tensions be resolved?

Unintentional and Undesireable EffectsWe know from existing research that when people are observed their behavior changes and they act dif-ferently [2]–[5]. As monitoring sys-tems become more complex, and increase in scale, this is anticipated to become a key point in their suc-cess. While this is one of the major and clearest possible undesirable effects of monitoring systems, there are likely to be others. for example, users have been shown to become stressed, distrustful, and uncomfortable when observed [2]-[5]. furthermore, given the scale and wide range of applications of new monitoring systems we

anticipate that not only will exist-ing undesirable effects occur, but they will become amplified. Even more disconcerting is the potential for new undesirable effects will arise. This highlights an important question of who is accountable for the consequences of such undesir-able and unknown effects? What are the long-term implications of an initial or on-going bad experience with such wide-spread technology? In most cases, any undesirability will be highly subjective, and inter-preted in different ways depending

on the role of the stakeholder in the system. This brings into question the intentions of system designers and owners.

Intended Behavioral Change and CoercionOne of the long-term goals of this research is to develop a complete model of how system characteristics influence user behaviors in monitor-ing systems. This model can then be used to develop a tool that simulates the effects, and predicts a behavioral outcome. Although such a tool is intended to minimize the undesirable effects, one of its major implications is that it could be used by designers to potentially manipulate the behaviors of the systems users in the ways they want. for example, the knowledge of being watched can increase produc-tivity [26]. Hence a workplace may be designed to have a highly obtru-sive monitoring system, consciously creating a social presence. This argu-ably leaves the responsibility and assessment of the morality surround-ing the outcomes of the monitoring system in the hands of the designer: “wisely or not, users trust system designers to protect them from [any] unintended consequences” [27, p.

45]. What legislations currently exist to prevent excessive use of this type of user manipulation? What laws might need to be introduced to pro-tect users?

Ubiquity and Data SharingAs monitoring systems become adopted in different spaces and places, the boundaries of data col-lection will grow. Take offices on a university campus as an example: a monitoring system may cover a single office, an entire building or an entire campus. Do the terms of

participation and use of the tech-nology change depending on the environment? What information is shared between the local and global systems? Who has access to that information and how is it used? How long is data stored for? Where is consider a private space in the workplace, and how could this be enforced in ubiquitous systems?

Future WorkThe next steps are to make a direct comparison between the salient perceptions of the device by office workers in Kyoto University, with another study exploring the salient perceptions of office workers in the University of Reading, U.K. A series of different, theoreti-cally acceptable, combinations of relationships within the PSA-BI model will also be explored. In addition, the data collected in the study will also be further analyzed using a variety of different statis-tical methods beyond structural equation modeling. The long-term aim of research involving the PSA-BI model is to fully test all the relationships with a vari-ety of technologies, contexts and participants.

Given that much of ubiquitous computing is embedded and hidden from view, initially there may be little to react to.


Understanding PerceptionsMonitoring devices are known to influence the behavior of those being observed, potentially caus-ing issues with the data collected. This is a serious problem for ubiq-uitous computing, and motivates the need for the predictive PSA-BI model. This paper describes a study exploring the influence of culture as an exogenous moderat-ing variable on perceptions of a wearable monitoring system. The results show signs that those work-ers in the Kyoto University, Japan, saliently perceive the UM system negatively. A greater understand-ing of these perceptions could lead to more effective ways of intro-ducing technology to new users, improving the user’s subsequent experiences.

Author InformationStuart Moran is with the Mixed Reality Lab, University of Not-tingham, Nottingham, U.K. Email: [email protected].

Toyoaki Nishida is with Nishida Lab, Kyoto University, Kyoto, Japan; email: [email protected].

Keiichi Nakata is with Infor-matics Research Centre, University of Reading, Reading, U.K.; email: [email protected].

AcknowledgmentThis work was supported by a research grant from the Japanese Society for the Promotion of Sci-ence (JSPS), Japan and is an exten-sion of the conference paper titled “Japanese Salient Perceptions of Ubiquitous Monitoring” published in the Proceedings of the 2012

IEEE Conference on Technology and Society in Asia.

References[1] S. Moran, “User perceptions of system attributes in ubiquitous monitoring: Toward a model of behavioural intention,” in School of Construction Manage. and Eng., Univ. of Reading, Reading, U.K., 2011, p. 298.[2] M. Vorvoreanu and C.H. Botan, “Examin-ing electronic surveillance in the workplace: A review of theoretical perspectives and re-search findings,” in Proc. Conf. Int. Commun. Assoc., Acapulco, Mexico, 2000.[3] S. Dawson et al., “Watching learning from behind closed doors: The impact of surveil-lance on student online behaviour,” in Proc. Conf. E-learning in Corporate, Government, Healthcare and Higher Education, Vancou-ver, Canada, 2005, pp. 1978–1985.[4] R.C. Grant et al., “Computerized perfor-mance monitoring systems: Are they costing you customers?,” Sloan Manag. Rev., vol. 29, pp. 39–45, 1988.[5] J.L. Jespersen et al., “Surveillance, persua-sion, and panopticon,” in Persuasive Technol-ogy (Lecture Notes in Computer Science, vol. 4744). Berlin, Heidelberg, Germany: Springer, 2007, pp. 109–120.[6] J.f. Hair et al., Multivariate Data Analysis, 5th ed. Upper Saddle River, NJ: Prentice-Hall, 1995.[7] S. Spiekermann, User Control in Ubiqui-tous Computing: Design Alternatives and User Acceptance, Aachen, Germany: Shaker, 2008.[8] D. Zweig and J. Webster, “Where is the line between benign and invasive? An examination of psychological barriers to the acceptance of awareness monitoring systems,” J. Organiz. Behav., vol. 23, pp. 605–633, 2002.[9] I. Ajzen, “The theory of planned behavior,” Organizational Behaviour and Human Deci-sion Processes, vol. 50, pp. 179–211, 1991.[10] f.D. Davis, “Perceived usefulness, per-ceived ease of use, and user acceptance of information technology,” MIS Quart., vol. 13, pp. 319–340, 1989.[11] S. Moran and K. Nakata, “Analysing the factors affecting users in intelligent pervasive spaces,” Intelligent Buildings Int., vol. 2, pp. 57–71, 2010.[12] S. Moran et al., “Assessment of assistive technologies based on the PSA-BI Model,” in Proc.Int. Conf on Pervasive Technologies Related to Assistive Environments, Crete, Greece, 2011. [13] S. Moran et al., “ Ubiquitous monitoring and user perceptions as a persuasive strategy,” in Proc. 2011 IEEE/WIC/ACM Int. Conf. on WI-IAT, 2011, pp. 41–44.

[14] S.J. Barnes and S.L. Huff, “Rising sun: IMode and the wireless Internet,” Commun. ACM, vol. 46, pp. 78–84, 2003.

[15] M. Cole, “Signage and surveillance: In-terrogating the textual context of CCTV in the U.K.,” Surveillance & Society, vol. 2, pp. 430–445, 2001.

[16] N. Komiya, “A cultural study of the low crime rate in Japan,” British J. Criminology, vol. 39, pp. 369–390, 1999.

[17] R. Ozaki and J.R. Lewis, “Boundar-ies and the meaning of social space: A study of Japanese house plans,” Environment and Planning D: Society and Space, vol. 24, pp. 91–104, 2006.

[18] E. Nagy et al., “Japanese office employees psychological reactions to their underground and above-ground offices “ J. Environmental-Pyschology, vol. 15, pp. 123–134, 1995.

[19] P. Chee Wei et al., “Senior citizens’ ac-ceptance of information systems: A study in the context of e-government services,” IEEE Trans. Eng. Mgmt., vol. 53, pp. 555–569, 2006.

[20] M.Y. Yi et al., “Understanding the role of individual innovativeness in the acceptance of IT-based innovations: comparative analyses of models and measures,” Decision Sciences, vol. 37, pp. 393–426, 2006.

[21] D.H. Nguyen et al., “An empirical in-vestigation of concerns of everyday tracking and recording technologies,” in Proc. 10th Int. Conf. on Ubiquitous Computing. Seoul, Korea: ACM, 2008, pp. 182–191.

[22] G.T. Marx, “Murky conceptual waters: The public and the private,” Ethics and In-form. Technology, vol. 3, pp. 157–169, 2001.

[23] I. Ajzen and M. fishbein, Understand-ing Attitudes and Predicting Social Behav-ior. Englewood Cliffs, NJ: Prentice-Hall, 1980.

[24] C. Bryant, “Privacy, privatisation and self-determination,” Privacy. Chichester and New York: Wiley, 1978.

[25] M. Hechter and S. Kanazawa, “Group solidarity and social order in Japan,” J. of The-oretical Politics, vol. 5, pp. 455–493, 1993.

[26] f.J. Roethlisberger and W.J. Dickson, “Management and the Worker,” Cambridge, Mass: Harvard Univ. Press, 1939.

[27] R. Beckwith, “Designing for ubiquity: The perception of privacy,” IEEE Pervasive Computing, vol. 2, pp. 40–46, 2003.

[28] Wearable Tag Image, Available: http://www.engadget.com/2005/03/17/be-all- cap ta in -k i rk -wi th - s i emens -wearab le - communication-badge/.

perceptions of a wearable ubiquitous monitoring device

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