Factors that influence user perception of ubiquitous monitoring environments: An empirical study in a developing country

Abstract

Over the years, conventional monitoring devices such as video cameras and tape-recorders have been redesigned into smarter and smaller forms which can be integrated seamlessly into an environment. The purpose of these ubiquitous monitoring devices is to enable the provision of innovative applications and services that support user wellbeing. Despite improving operations in essential areas such as health, there are still concerns associated with ubiquitous monitoring. For benefits associated with ubiquitous monitoring to be fully realized, there is the need to understand the role of user perceptions. This study investigates the factors that influence user perceptions of ubiquitous monitoring devices by drawing samples from a developing country. Users’ response on seven recurring ubiquitous monitoring perceptions were collected using a survey questionnaire. The relationships among these factors were analysed using Partial Least Square Structural Equation Modelling. The results suggest a significant relationship between Perceived Natural Border Crossing and Perceived Privacy Invasion. Also Perceived Affordance, Perceived Coverage and Perceived Privacy Invasion predicted Perceived Trust. The findings imply that more emphasis must be given to educating and familiarizing users with ubiquitous monitoring devices. Future studies are expected to replicate the study in other developing societies to validate these claims.

Keywords

Monitoring ubiquitous monitoring privacy invasion surveillance

1. Introduction

Ubiquitous monitoring (UM) describes the use of computing devices to continuously gather information about users of an intelligent pervasive space [50]. These devices are generally unrestricted by boundaries such as walls, floors or distance. Hence, their coverage and capabilities exceed previous methods [12] and they facilitate the provision of innovative applications and services that support users’ wellbeing [55]. Especially, UM devices have improved operations in areas including healthcare, marketing, security and surveillance [24,48,58,70]. For example, Moran et al. [49] explored the benefits of a UM environment for yoga practice (ExoPranayama). The researchers observed that using ExoPranayama to monitor yoga practice provided practitioners with new physiological insights such as perceived and actual breath counts. These increased participants’ self-awareness on their breath counts and facilitated group cohesion.

Nonetheless, other researchers have highlighted concerns including intrusion, privacy invasion, mistrust and anxiety due to the constant and ubiquitous collection of data from users of such spaces [32,76,78]. These affect users’ perceptions and behaviour in a UM environment [21]. Since UM devices are deployed to improve user wellbeing by studying their behaviour, it is important to understand the factors that inform their perceptions and behavioural decisions. This will minimize the undesirable effects of such devices. Again, it is important to contextualize such investigations. This is because, an individual’s understandings and reactions to the concepts associated with space monitoring are different across societies [53]. For instance, Japanese perceptions of personal space consider dining and living room separation as redundant unlike the British [57].

This paper, therefore, investigates the factors that influence user perceptions of UM environments within the African context. It draws responses from Ghana.

Berlanger and Crossler [6] confirmed that existing studies on UM related issues such as privacy have focused on student-based samples from developed countries. This suggests that findings from existing studies may not be relevant in developing countries given that technology acceptance and use behaviour are influenced by culture [31]. More importantly, perceptions of privacy differ across societies [57]. Therefore, culture-specific research on UM is essential.

The findings from this study confirmed the role of contextualization in UM research. It suggests that familiarity and clarity operationalized in Perceived Affordance contribute to perception variables including assumptions, coverage and ability to control UM devices. Thus, institutions that seek to introduce such devices in any space must educate potential users of the functionalities of the devices they seek to implement. The next section presents a review of UM and user behaviour. This is followed by the proposed research model and formulated hypotheses, the analysis, discussions and conclusions.

2. Literature review

2.1. Overview of ubiquitous monitoring

Generally, monitoring is the collection of information about an entity or situation over a period [9]. Often, monitoring and surveillance are used interchangeably. Until recently, monitoring was mainly in the form of a panopticon. It was done through direct observation by individuals. Static devices such as video cameras in environments were placed at locations that were monitored. However, current properties of technologies such as size and ubiquity now facilitate the collection of larger amounts of data for monitoring purposes. Ubiquitous devices pervade physical boundaries such as walls, floors and distance to collect data. Botan and Vorvoreanu [51] presented five main characteristics that differentiate UM from other monitoring systems. These are collection scale, collection manner, newer types of data, collection motivation and data accessibility. These characteristics affect user perception and behaviour [21].

2.2. Ubiquitous monitoring, user perceptions and behaviour

Monitoring has both desirable and undesirable effects and it influences human behaviour [18]. Monitoring affects trust [32] and may cause gridlock, blame, distrust and privacy concerns [38]. Some researchers have argued that it has a negative impact on job attitudes such as satisfaction and commitment [34]. It is intrusive, oppressive, stressful and invasive [33,61,64,68]. Yet, considering their capabilities, there is the need for reducing the undesired effects they caused. Thus, it is arguable to draw conclusions that UM devices are deployed to provide efficient and comfortable lives for users without considering the undesirable effects they present. Measures must, therefore, be taken to anticipate and understand the factors that influence the perceptions of those who are monitored in such environments. This will facilitate the mitigation of undesirable effects.

Consequently, attempts have been made to uncover what informs users’ perceptions in UM environments. System characteristics (such as systems design, device coverage, number of devices, obstructiveness and frequency of monitoring) and human factors (such as prior experience, user assumptions, user control, affective attitude, perceived usefulness and risk) influence UM perceptions and behaviour [11,42,49,50,63,67,74,78]. User perceptions of UM environments are influenced by seven recurring factors [51]. These include the context within which the monitoring takes place, whether or not there is enough justification for monitoring, the levels of intrusion, awareness and control implied by technology use, the boundaries of the monitoring and users’ trust for those monitoring, and how the data collected is used. Nonetheless, there are concerns that perceptions of UM environments and their effects on users have not been adequately explored [21,50]. Particularly, technology acceptance and use differ across societies [59]. Developing societies are mostly late adopters of technology, mainly because of their cultural reluctance to innovation [31]. Moreover, collectivist societies maintain strong relationships and look after themselves [30]. Therefore, their conceptualization of phenomena such as privacy differs from individualists [7]. Collectivist societies give less weight to privacy than individualist [43]. Individuals equate their interactions with computers with real-world scenarios and this makes perceptions of monitoring to differ across societies. In other words, perceptions of UM environments in collectivist societies differ from individualists. Thus, studies that have overly relied on student-based samples from developed societies may not be applicable in developing worlds [6].

3. Theoretical framework and definitions

3.1. Theoretical foundation

A number of frameworks have been adopted to explore user behaviour in UM environments. Spiekermann [63] developed the Ubiquitous Monitoring Acceptance Model (UCAM). The model provides a means for measuring interrelationships between perceived privacy invasion, perceived control, perceived usefulness and risks, and how these influence users’ attitude towards information technology (IT) systems. Others have also proposed similar models that include variables such as knowledge of monitoring system, control over technology and justification for monitoring a technology [78]. Although these models provide a better assessment of user perception of privacy and fairness, they failed to capture all relevant factors that influence their perception [46]. For instance, perception influencing factors of UM technologies may arise from a device, the software running the device or external factors such as culture and age.

To address this, the Perceptions of System Attributes (PSA) model [46] was specifically designed for understanding user perceptions in UM environments. It encompasses factors that influence perceptions of UM and accounts for the limitations of earlier models. It facilitates the study of perception, which is imperative in understanding actions toward UM technology. Users’ actions are informed by their perceptions of reality but not reality itself [52]. Therefore, users’ perception toward systems characteristics may be important than the characteristics themselves. This makes it critical since it influences system designs [46]. According to Moran and Nakata [50], seven dominant relationships influence user perceptions. These include Perceived Affordance, Perceived Natural Border Crossing, Application Assumptions, Perceived Coverage, Perceived Data control, Perceived Privacy Invasion and Perceived Trust.

Perceived Affordance (PA) denotes a user’s understanding that a device collects data about him or her. An individual may perceive UM devices differently when he or she is not familiar with the device. Thus, based on the physical qualities and capabilities of UM devices, a user interacts with devices differently. For instance, Beckwith [5] demonstrated that people interact with monitoring badges differently because they afford minimal physical interaction.

Perceived Natural Border Crossing (PNBC) describes the extent to which an individual feels his/her natural borders have been crossed. Natural border helps to define physical areas of privacy [44]. For instance, people perceive their homes as private because they are bounded by walls. In UM context, a user’s conceptualization of personal space indicates their natural borders. Therefore, invading such boundaries account for natural border crossing which suggests privacy invasion. UM devices can be seamlessly integrated into any environment to unrestrictedly gather data. Yet, a user may perceive that his/her personal space has been invaded based on the locations of these devices.

Application Assumptions (AA) are a user’s level of understanding with regards to the motives for an installed UM device. Speculation arises once individuals do not understand the purpose of a device and negative speculations accounts for technology rejection [56]. This is often when trust is low. Therefore, a user’s perception of the purpose of UM in a space affects his or her perception of UM.

Perceived Coverage (PC) describes a user’s knowledge of the physical area covered by the UM device. Users may have different perceptions about a device. This is based on their understanding of its monitoring and coverage ability. Thus, they may perceive a device with higher coverage when compared to others. For instance, perceptions on wearable device may differ from a Closed-Circuit Television (CCTV) camera installed in an office location.

Perceived Device Control (PDC) is the extent to which a user believes to have direct control of the monitoring device. Control varies per device and it is manifested in the ability to avoid, remove or switch off. User’s ability to control a device that collects data about them promotes device acceptance. Such a provision gives them control over their natural borders and privacy.

Perceived Privacy Invasion (PPI) is the degree to which a user believes that monitoring has invaded his or her privacy. Moran [46] suggested that privacy invasion is a function of the type of information, how it is disseminated and the value the user gets from it. Users perceive privacy invasion when they attain less value, yet more data is collected and disseminated. Given that UM devices pervasively gather data, it raises privacy concerns

Perceived Trust (PT) defines the extent to which a user is confident that the observer is trustworthy. It is based on the belief that the observer’s intention is genuine and will not cause harm. As such, PT is relevant when examining users’ perceptions. Table 1 gives a summary of the definitions of the various factors proposed influence perceptions in UM space.

Table 1
Definition of constructs and their sources

Construct Definition Reference

Perceived Affordance (PA) A user’s degree of familiarity and clearness that a monitoring device can collect data [5]

Perceived Natural Border Crossing The extent to which a user believes or perceives that a monitoring device invades his or her natural boundaries [44,77]

Application Assumption A user’s understanding of the purpose of a monitoring system [19,56]

Perceived Coverage A user’s understanding of the physical area that a monitoring device covers [46]

Perceived Device Control A user’s ability to avoid, remove or switch off a monitoring device [50]

Perceived Privacy Invasion The degree to which a user believes that a monitoring device invades their privacy [51,79]

Perceived Trust The degree to which a user believes that the observer of a monitoring device is trustworthy [60,66]

Construct	Definition	Reference
Perceived Affordance (PA)	A user’s degree of familiarity and clearness that a monitoring device can collect data	[5]
Perceived Natural Border Crossing	The extent to which a user believes or perceives that a monitoring device invades his or her natural boundaries	[44,77]
Application Assumption	A user’s understanding of the purpose of a monitoring system	[19,56]
Perceived Coverage	A user’s understanding of the physical area that a monitoring device covers	[46]
Perceived Device Control	A user’s ability to avoid, remove or switch off a monitoring device	[50]
Perceived Privacy Invasion	The degree to which a user believes that a monitoring device invades their privacy	[51,79]
Perceived Trust	The degree to which a user believes that the observer of a monitoring device is trustworthy	[60,66]

3.2. Hypothesis formulation

The PSA model postulated that Perceived Affordance positively affects Perceived Coverage, Perceived Device Control, Perceived Natural Border Crossing and Application Assumptions, whilst Perceived Coverage negatively affects Perceived Device Control and positively impacts Perceived Natural Border Crossing. However, although Perceived Natural Border Crossing positively affects Perceived Privacy Invasion, the latter negatively relates to Perceived Trust. The model failed to examine all the possible relationships among these perceptions. For example, the effect of a user’s perception of privacy invasion and trust is unaccounted for. Yet such a relationship is imperative. Again, this is substantial in developing countries where users of such spaces may be less familiar with some of these monitoring technologies [8].

3.2.1. Perceived affordance, application assumptions and perceived trust

Affordances are object characteristics that define interactions [23]. However, users’ perceivable actions and nature of interaction are informed by their previous knowledge or experiences with the object. Perceived Affordance of UM devices denotes the extent to which users are familiar or recognize the capabilities of a device [5] and it influences users’ assumptions. A user’s interaction with unfamiliar devices differs from familiar ones. For instance, a user who has been exposed to conventional monitoring systems such as CCTV may not associate well with wearable devices [53]. There is enough evidence that technological affordances influence assumptions and perceptions such as trust and assumptions of use [25,45,65]. It is therefore hypothesized that:

Perceived Affordance positively influences Application Assumptions within a ubiquitous monitoring environment.

Perceived Affordance positively influences Perceived Trust within a ubiquitous monitoring environment.

3.2.2. Application assumptions, perceived natural border crossing, perceived device control and perceived coverage

Application Assumption expresses users’ level of understanding about the objective of a monitoring device [19]. When people do not appreciate the relevance of a technology, they perceive them differently. Likewise, users’ perceptions of monitoring technologies may change when they are not convinced about the device [56]. When users are exposed to newer technologies such as hidden devices, their perception of reality may not be accurate. Especially regarding the boundaries size. Such perceptions have been demonstrated to influence their behaviour [74]. Similarly, the embeddedness of some UM devices, if not well understood, may suggest hidden motives for data collection and thus provoke users’ awareness on their natural border. These make them evaluate the level of coverage [46] and their ability to control the device [51]. Considering these in the context of developing countries where surveillance and UM systems are less pervasive [26], it is postulated that:

Application Assumptions negatively influence Perceived Natural Border Crossing within a ubiquitous monitoring environment.

Application Assumptions positively influence Perceived Data Control within a ubiquitous monitoring environment.

Application Assumptions negatively influence Perceived Coverage within a ubiquitous monitoring environment.

3.2.3. Perceived natural border crossing, perceived device control and perceived privacy invasion

As already indicated UM devices are not limited by physical boundaries. Thus, knowing that a device is constantly collecting personal data constitutes natural border crossing [39] and influences users’ perception of privacy invasion [40]. Burrows, Coyle, & Gooberman-Hill [10] indicated that perceptions of natural border crossing are associated with perceived privacy invasion. However, a user’s ability to control a monitoring situation affects their perceptions of privacy invasion [62]. When users perceive they are incapable of limiting the volume, nature and frequency of data a UM device collects about them, it raises speculative assumptions about their privacy [1]. However, the cultural orientation of many developing societies suggests a mutual care system where individuals serve as each other’s keeper [30]. It is therefore imperative to examine how users perceive their natural borders in UM environments in such societies. Hence, it is hypothesized that:

Perceived Natural Border Crossing positively influences Perceived Privacy Invasion within a ubiquitous monitoring environment.

Perceived Device Control negatively influences Perceived Privacy Invasion within a ubiquitous monitoring environment.

3.2.4. Perceived device control, perceived coverage and perceived trust

Perceived Device Control relates to users abilities to switch off, remove or avoid a UM device [17]. Users are comfortable when they are allowed to control when and how data is collected about them: this increase trust [47]. Again, when users are unsure about the coverage limitations of a device, they question the intentions of observers [50]. This reduces trust. Accordingly, it is hypothesized that:

Perceived Device Control positively influences Perceived Trust within a ubiquitous monitoring environment.

Perceived Coverage negatively influence Perceived Trust within a ubiquitous monitoring environment.

3.2.5. Perceived privacy invasion and perceived trust

Privacy is a relevant perception in UM, because it influences users’ perception of a monitoring device [79]. It is a function of trust towards the observer and promotes honesty and responsibility [60]. Thus, there is a significant relationship between trust and Perceived Privacy Invasion [69]. However, an individual’s characteristics and cultural background influence perceptions of trust [13,36]. To investigate existing conceptualization within UM context, it is proposed that:

Perceived Privacy Invasion negatively influences Perceived Trust within a ubiquitous monitoring environment.

Figure 1 is a diagrammatic representation of the relationship between the various constructs as hypothesized. The next section is a discussion on the research methodology used for validating the various hypothesis formulated above.

Fig. 1.

Hypothesized model.

4. Research methodology

4.1. Survey design

A quantitative survey design was adopted for this study. Google Forms was used to design an English-based questionnaire. Links to the questionnaire were sent to the Human Resource (HR) departments of 20 public and private organizations in Ghana. These organizations were chosen using a convenience sampling method. Each organization has at least one monitoring device installed at their workplace. Thus, all participants have encountered a form of ubiquitous monitoring. The HR departments were requested to forward the questionnaire to 50 people in their respective organisations also utilising the convenient sampling method. This gave a total of 1000 sampled participants. However, participation was voluntary, and the questionnaire ensured anonymity. Our justification for the selection of participants from a developing country was to present a different perspective from existing studies that have mainly drawn samples from students in developed countries.

Relevant respondents’ demographics, experiences and perceptions of UM devices within their workplace environment were collected. All participants were literates (see Table 2) and confirmed that they had experience on computerised monitoring systems such as CCTV camera, e-mail and world wide web browsing, telephone call recording, attendance tracking and access codes/cards tracking. A seven-point Likert scale questions relating to (i) Perceived Affordance, (ii) Perceived Natural Border Crossing, (iii) Perceived Trust, (iv) Perceived Device Control, (v) Perceived Privacy Invasion and (vi) Application Assumptions were measured. All constructs and question items were modifications from prior studies (see Table 1). The questionnaire was pretested with a sample of 20 responses. These responses were not included in the analysis. The suggestions gathered from the pre-test were considered and the questionnaire was amended appropriately. In addition, a minimum of three questions was used per construct and an initial reliability check was performed on the pre-test data to ensure that the question items are reliable. The minimum Cronbach’s Alpha value recorded on the pre-test data was 0.72. Thus, the questionnaire was deemed appropriate for data collection exercise.

4.2. Respondent characteristics

A total of 657 responses were received over a period of three months of administering the questionnaire. The period for data collection was between February and April 2019 for all 20 organizations. All respondents were from and live in a developing country. Out of that, 630 responses were valid for analysis. The other 27 respondents did not answer all questions; hence their responses were discarded. This gives a response rate of 63%. The first 20% of the valid responses were compared with the later 80% to establish non-response bias. There was no significant difference between the two groups (see Appendix 2). Sixty-six percent (66%) of the respondents were males and the remainder were females. Majority of the responses (i.e. 82%) were completed by individuals who are below 30 years. Seventeen percent (17%) were between 30 and 40 years and 1% were above 40 years. Also, majority (76%) of the respondents were undergraduates, whereas the remaining (24%) had graduate degrees. A summary of respondents’ demographics is presented in Table 2.

Table 2
Respondents demographics

Demographics Value Frequency Percentage

Sex Male 416 66%

Female 214 34%

Age Below 30 516 82%

30–35 107 17%

Above 35 7 1%

Education background Bsc/BBA/LLB 499 76%

Msc/Mphil/MRes 158 24%

Demographics	Value	Frequency	Percentage
Sex	Male	416	66%
Female	214	34%
Age	Below 30	516	82%
30–35	107	17%
Above 35	7	1%
Education background	Bsc/BBA/LLB	499	76%
Msc/Mphil/MRes	158	24%

5. Analysis and findings

The hypothesized model (see Fig. 1) was validated using Partial Least Square Structural Equation Modelling (PLS-SEM). This technique was adopted because it is suitable for exploratory studies [27], predicting relationships between latent variables [28] and also robustness to errors in multivariate distributions [22]. Furthermore, since the sample size is larger than ten times the number of structural paths directed at a construct, PLS-SEM is suitable for this study. SmartPLS 3.0 was used to analyse the structural model.

5.1. Measurements

In Structural Equation Modelling (SEM), the analysis of the measurement model must focus on item reliability, internal consistency, convergent validity and discriminant validity [16]. All item loadings (see Appendix 1) were above Barclay, Higgins, & Thompson’s [4] recommended threshold of 0.7 hence they were valid. Internal consistency was measured using Cronbach’s Alpha and Composite Reliability. Table 3 indicates that all constructs were above 0.7 as suggested by Bagozzi & Yi [3]. The Average Variance Extracted (AVE) was used to test the convergent validity. All AVE values greater than 0.5 were acceptable to be valid [75].

Table 3
Construct validity and reliability

Cronbach’s Alpha rho_A Composite Reliability Average Variance Extracted (AVE)

AA 0.761 0.868 0.815 0.596

PA 0.723 0.728 0.844 0.643

PC 0.915 0.921 0.940 0.797

PDC 0.742 0.703 0.803 0.583

PNBC 0.700 0.713 0.833 0.626

PPI 0.785 0.800 0.781 0.544

PT 0.875 0.900 0.912 0.723

	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
AA	0.761	0.868	0.815	0.596
PA	0.723	0.728	0.844	0.643
PC	0.915	0.921	0.940	0.797
PDC	0.742	0.703	0.803	0.583
PNBC	0.700	0.713	0.833	0.626
PPI	0.785	0.800	0.781	0.544
PT	0.875	0.900	0.912	0.723

Discriminant validity was tested by finding the square root of the AVE of a latent variable and compared it to the correlations between that latent variable and other latent variables. It is expected that the square root of the AVE of the latent variable should be greater than the correlations of all other latent variables [20]. The highlighted diagonal entries shown in Table 4 represents the results for discriminant validity.

Table 4

Discriminant validity (Fornell & Lacker [75] criterion)

In addition, Heterotrait–Monotrait Ratio (HTMT) was also used to assess discriminant validity. This ratio has been confirmed by some researchers to be effective [29] and thus have been used in a number of studies [35,41,72] that seek to investigate similar relationships.

Table 5 indicates that Clark & Watson [35] required maximum of 0.85 was met. Moreover, comparing the specific item loadings and cross loadings in Appendix 1 also confirms discriminant validity.

Table 5

Discriminant validity results (Heterotrait–Monotrait Ratio)

	AA	PA	PC	PDC	PNBC	PPI
PA	0.817
PC	0.647	0.321
PDC	0.207	0.203	0.127
PNBC	0.156	0.123	0.083	0.243
PPI	0.169	0.105	0.167	0.233	1.088
PT	0.346	0.299	0.380	0.220	0.232	0.273

Variance Inflation Factor (VIF) was used to evaluate the possibility of multicollinearity. Hair et al. [28] indicated that all VIF values must be less than 3. Table 6 shows that collinearity did not disturb the findings of the study.

Table 6

Multicollinearity testing with variance inflation factor

	AA	PA	PC	PDC	PNBC	PPI	PT
AA			1.000	1.000	1.000
PA	1.000						1.094
PC							1.085
PDC						1.001	1.026
PNBC						1.001
PPI							1.011

5.2. Structural model

The bootstrap technique was used to examine the significance and strength of the predicted relationships. Significance of Path Coefficients was determine using p-values lesser than 0.05. Using a one-tailed test, the analysis showed that most of the predicted relationships were significant. Particularly, Application Assumptions negatively influenced Perceived Coverage ( $β = - 0.517$ , $p < 0.001$ ). Perceived Affordance also positively predicted Application Assumptions ( $β = 0.561$ , $p < 0.001$ ). Again, Perceived Affordance ( $β = 0.561$ , $p < 0.05$ ) and Perceive Coverage ( $β = - 0.312$ , $p < 0.001$ ) had a significant relationship with Perceived Trust.

Table 7
Significance of path coefficients

Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics ( $| O / STDEV |$ ) p values Effect size ( $f^{2}$ ) Supported

AA → PC −0.517 −0.515 0.066 7.813 0.000 0.365 Yes

AA → PDC 0.097 0.126 0.116 0.837 0.201 0.010 No

AA → PNBC −0.052 −0.045 0.085 0.612 0.270 0.003 No

PA → AA 0.561 0.561 0.063 8.925 0.000 0.460 Yes

PA → PT 0.154 0.155 0.092 1.671 0.048 0.027 Yes

PC → PT −0.312 −0.305 0.090 3.470 0.000 0.112 Yes

PDC → PPI −0.099 −0.095 0.065 1.538 0.062 0.020 No

PDC → PT 0.097 0.122 0.114 0.848 0.198 0.011 No

PNBC → PPI 0.708 0.711 0.054 13.192 0.000 1.020 Yes

PPI → PT −0.186 −0.180 0.068 2.742 0.003 0.043 Yes

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics ( $\| O / STDEV \|$ )	p values	Effect size ( $f^{2}$ )	Supported
AA → PC	−0.517	−0.515	0.066	7.813	0.000	0.365	Yes
AA → PDC	0.097	0.126	0.116	0.837	0.201	0.010	No
AA → PNBC	−0.052	−0.045	0.085	0.612	0.270	0.003	No
PA → AA	0.561	0.561	0.063	8.925	0.000	0.460	Yes
PA → PT	0.154	0.155	0.092	1.671	0.048	0.027	Yes
PC → PT	−0.312	−0.305	0.090	3.470	0.000	0.112	Yes
PDC → PPI	−0.099	−0.095	0.065	1.538	0.062	0.020	No
PDC → PT	0.097	0.122	0.114	0.848	0.198	0.011	No
PNBC → PPI	0.708	0.711	0.054	13.192	0.000	1.020	Yes
PPI → PT	−0.186	−0.180	0.068	2.742	0.003	0.043	Yes

Fig. 2.

Structural model.

However, whereas Perceived Natural Border Crossing ( $β = 0.708$ , $p < 0.001$ ) had positive effect on Perceived Privacy Invasion, Perceived Privacy Invasion ( $β = - 0.186$ , $p < 0.005$ ) negatively affected Perceived Trust. The non-significant relationships included Application Assumptions, Perceived Device Control ( $β = 0.097$ , $p > 0.05$ ) and Perceived Natural Border Crossing ( $β = - 0.052$ , $p > 0.05$ ). Perceived Device Control and Perceived trust also recorded a non-significant relationship ( $β = 0.097$ , $p > 0.05$ ). Perceived Device Control did not predict Perceived Privacy Invasion ( $β = - 0.099$ , $p > 0.05$ ). Cohen’s [15] effect sizes ( $f^{2}$ ) were studied. Effect sizes is irrelevant when $f^{2} < 0.02$ , small when $0.02 < f^{2} < 0.15$ , medium when $0.15 < f^{2} < 0.35$ and large when $0.35 < f^{2}$ . Results for $f^{2}$ assessment is presented in Table 7.

6. Discussion

As an attempt to address knowledge deficits in perceptions in Ubiquitous Monitoring devices, this study investigated the relationships between seven recurring users’ perceptions of UM devices. These relationships were analysed using structural equation modelling. The structural model presented in Fig. 2 confirmed most of the hypothesized relationships. This suggests that the research model is potent for explaining users’ perceptions in UM environments, especially within developing countries. In the next sections, a discussion on the theoretical and practical contributions to the results is presented.

6.1. Theoretical contributions

The results of this study present relevant theoretical contributions to UM research. As indicated earlier, this result confirms that recurring factors in UM environments can be explored to explain various user perceptions. Moreover, the study adds to existing literature on UM. Arguably, the main purpose of UM is to enhance the provision of services to users [55]. It is therefore imperative to investigate context-specific perceptions on UM [53], particularly when technology acceptance is different in cross cultures [31]. Power distance in most developing countries is wide and as such citizens are reluctant to question authority and their decisions. Although existing literature relating to users’ perceptions and UM has been conducted in the developed countries, these findings are the first from a developing country and it provides a new perspective in UM research.

Most importantly, the cultural relativism of UM environments also feeds in the results of this study. Firstly, Application Assumptions failed to influence Perceived Device Control and Perceived Natural Border Crossing. Secondly, Perceived Device Control failed to impact Perceived Trust and Perceived Privacy Invasion. Ordinarily, in societies with high power distance, subordinates trust their superiors since they are deemed to know what is best. However, given extensive arguments and assumptions that UM devices are deployed for wellbeing, users in such societies will be unperturbed about the degree of control they have on these devices. In Aleisa and Renaud’s [2] study in Saudi Arabia whose power distance is 95, it was established that users’ need for convenience led them to ignore concerns associated with UM devices. Findings from Moran [46] and Moran and Nakata [50] are contradictory because these studies were conducted in Britain which has a lesser power distance value. Thus, as compared to users in developed countries, Perceived Device Control does not impact trust and privacy invasion in developing countries. In such countries trust is established through reciprocation and social interaction ties [36] and knowledge sharing [37].

Perceived Coverage and Perceived Affordance all predicted Perceived Trust. Similarly, the relationships between Perceived Affordance and Applications Assumptions as well as Application Assumptions and Perceived Coverage were confirmed. Users are comfortable with devices they are conversant with [23,71,73]. Having knowledge about the capabilities of new UM devices reduce doubts. On the other hand, when users are not acquainted with a device, they question the level of coverage which may affect trust.

There were significant relationships between Perceived Natural Border Crossing and Perceived Privacy Invasion. As already indicated, in developing countries individuals take care of each other, therefore their conceptualization of borders is wider than other developed societies. Thus, although this is consistent with Moran [46], it is surprising within this context. Perhaps this is because such societies are increasingly adopting the nuclear family systems hence their awareness and understanding of boundaries and privacy are evolving. These findings call for further investigations to ascertain existing notion that individuals are each other’s keeper within developing countries.

6.2. Practical contributions

In addition to the theoretical contributions, the findings also possess practical implications. Firstly, the study showed that the role of user perceptions in UM environment is significant. The findings discovered factors that inform user perceptions and the consequences of these perceptions. Therefore, UM system designers aiming to minimize the negative effects of their systems should pay attention to user perceptions in such environments. Some researchers [54] have explained that UM affords behaviour change and can be designed to deliberately alter user cognition to a predetermined one. It is imperative that designers and observers of UM devices ensure that the impact of a UM device does not result in an undesirable impact. As indicated, the relationships between the various factors differ across nations and developers should ensure that they factor cultural perspectives during designs.

Furthermore, Perceived Affordance impacted majority of the perception variables. Hence, users’ familiarity or degree of clearness about a monitoring device influences their assumptions about the device and other perceptions including coverage, natural border crossing and ability to control the device. However, highlighting the important role of familiarity and clarity, and given the fact that education and experiences with technology evolve rapidly, those in charge of monitoring should educate those they monitor about the systems they implement.

7. Conclusion

This study investigated the effects of user perceptions on Ubiquitous Monitoring (UM) environments. It highlights the need for contextualization in UM studies. As opposed to previous studies that drew samples from students in developed countries, this study collected responses from users from a developing country in an industrial environment, regarding their perceptions on UM. The responses were analysed using techniques in PLS-SEM. The findings indicated that Perceived Coverage, Perceived Affordance and Perceived Privacy Invasion all influence Perceived Trust within UM environments. Although Perceived Natural Border Crossing affected Perceived Privacy Invasion, Perceived Device Control did not. Hence there is the need for further studies to be conducted to analyse these findings in other countries.

Since the samples used in this study were drawn from only one developing country, future studies need to focus on other developing countries to assess other perception factors. Studies seeking to compare levels of perceptions within UM environments in developing and developed countries will also be relevant. Also, although further analysis on variations among sexes and age differences would be relevant, the skewness in sample distribution (see Table 2) will make such findings invalid. It is therefore recommended that further investigations are conducted to compare the difference in perceptions among ages and sexes.

Conflict of interest

None to report.

Appendix 1

Item loadings and cross loadings

Note: Shaded Items represents individual item loadings.

Appendix 2

Non-bias response test using construct reliability and validity

	Cronbach’s Alpha		rho_A		Composite reliability		Average Variance Extracted (AVE)

	Group 1 ( $n = 129$ )	Group 2 ( $n = 501$ )	Group 1 ( $n = 129$ )	Group 2 ( $n = 501$ )	Group 1 ( $n = 129$ )	Group 2 ( $n = 501$ )	Group 1 ( $n = 129$ )	Group 2 ( $n = 501$ )
AA	0.787	0.783	0.795	0.789	0.781	0.825	0.543	0.611
PA	0.735	0.755	0.743	0.756	0.804	0.859	0.578	0.670
PC	0.916	0.913	0.944	0.918	0.940	0.939	0.797	0.794
PDC	0.729	0.775	0.756	0.783	0.706	0.811	0.669	0.591
PNBC	0.722	0.704	0.741	0.729	0.841	0.835	0.639	0.629
PPI	0.766	0.740	0.765	0.776	0.817	0.763	0.599	0.522
PT	0.887	0.872	0.898	0.904	0.922	0.910	0.748	0.716

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Factors that influence user perception of ubiquitous monitoring environments: An empirical study in a developing country

Abstract

Keywords

1. Introduction

2. Literature review

2.1. Overview of ubiquitous monitoring

2.2. Ubiquitous monitoring, user perceptions and behaviour

3. Theoretical framework and definitions

3.1. Theoretical foundation

3.2.1. Perceived affordance, application assumptions and perceived trust

3.2.2. Application assumptions, perceived natural border crossing, perceived device control and perceived coverage

3.2.3. Perceived natural border crossing, perceived device control and perceived privacy invasion

3.2.4. Perceived device control, perceived coverage and perceived trust

3.2.5. Perceived privacy invasion and perceived trust

4.1. Survey design

4.2. Respondent characteristics

Table 2 Respondents demographics Demographics Value Frequency Percentage Sex Male 416 66% Female 214 34% Age Below 30 516 82% 30–35 107 17% Above 35 7 1% Education background Bsc/BBA/LLB 499 76% Msc/Mphil/MRes 158 24%

5.1. Measurements

6.1. Theoretical contributions

6.2. Practical contributions

7. Conclusion

Conflict of interest

References

Table 2
Respondents demographics

Demographics Value Frequency Percentage

Sex Male 416 66%

Female 214 34%

Age Below 30 516 82%

30–35 107 17%

Above 35 7 1%

Education background Bsc/BBA/LLB 499 76%

Msc/Mphil/MRes 158 24%