This study aims to investigate the factors influencing Greek employees’ intention to adopt wearable devices in the workplace, focusing on privacy management and trust. It addresses the organizational challenges of balancing innovation with employee concerns about surveillance and data protection.
Based on Privacy Calculus Theory, extended with trust, a research model was tested through a large-scale survey of 2,243 employees in Greece using structural equation modeling.
Perceived effectiveness of privacy policies, control and benefits strongly predicted adoption intention, while privacy concerns and risks acted as barriers. Trust showed limited direct effects, with its influence primarily operating through indirect relationships. The model explained 46% of adoption intention variance.
The findings highlight the importance for managers to design transparent privacy policies, strengthen employee control over data and build organizational trust to facilitate wearable integration into HR and productivity practices.
The study extends Privacy Calculus Theory to organizations, introduces institutional variables such as privacy policy effectiveness and clarifies trust’s indirect role. It provides new insights from a regional context rarely examined.
1. Introduction
The rapid advancement of digital technologies has transformed workplaces, with wearables emerging as a fast-growing trend valued at nearly $70bn in 2019 (Shen et al., 2022). These internet-connected devices (e.g. smartwatches, VR and EEG headsets, fitness trackers) collect and transmit physical, cognitive or emotional data (Sun and Gu, 2024; Singh, 2023; Rahman et al., 2022; Felea et al., 2021; Sergueeva et al., 2020; Shafique et al., 2019; Debnath et al., 2018; Canhoto and Arp, 2017; Khakurel et al., 2017; Wright and Keith, 2014). In organizational contexts, they are promoted for monitoring productivity, enhancing safety and supporting employee well-being (Jacobs et al., 2019).
Despite these potential benefits, the organizational adoption of wearable technologies remains limited due to concerns regarding data protection and trust in employers. Although research on wearable technology adoption has grown considerably, most existing studies have focused on consumer contexts rather than organizational settings (Al-Emran et al., 2023; Kim and Chiu, 2019; Canhoto and Arp, 2017; Chuah et al., 2016; Jung et al., 2016; Lee et al., 2016; Lunney et al., 2016; Yang et al., 2016; Kim and Shin, 2015; Zhang and Rau, 2015). Employees often perceive wearables not only as tools for efficiency but also as instruments of surveillance. These contrasting perceptions make it important to examine not only employees’ willingness to adopt wearable technologies but also the organizational conditions and governance mechanisms under which such adoption becomes acceptable (Fox et al., 2021; Magni et al., 2021; Yun et al., 2019; Wirth, 2018; Xu et al., 2011).
In this context, the organizational environment differs fundamentally from consumer settings, as employees’ decisions are shaped by hierarchical relationships, limited autonomy and the potential use of data for monitoring and performance evaluation. These conditions suggest that privacy-related perceptions, control and trust may operate differently in workplace environments, extending the application of Privacy Calculus Theory.
To achieve effective integration of wearable technologies, organizations must balance data collection with privacy protection (Fox et al., 2021; Jozani et al., 2020). Previous studies also suggest extending research beyond smartwatches and adopting theoretical frameworks other than the technology acceptance model (TAM), which primarily emphasizes ease of use and usefulness (Shafique et al., 2019; Asadi et al., 2019; Lin et al., 2007; Davis, 1989). Scholars further highlight the need to incorporate trust as a determinant of adoption, given its role in shaping employee perceptions of data disclosure (Duan and Deng, 2021; Kim and Chiu, 2019).
Moreover, prior research is geographically concentrated in countries such as the USA, China and Ireland, limiting generalizability, while evidence from Southern Europe and developing economies remains scarce (Liang et al., 2024; Duan and Deng, 2021; Rowe, 2020; Asadi et al., 2019; Li et al., 2016; Gao et al., 2015). The present study aims to address these gaps by examining the factors that influence employees’ intention to adopt wearable devices in the workplace. Grounded in Privacy Calculus Theory and extended with trust, it explores how perceptions of privacy policies, control, risks and benefits shape adoption. The research focuses on Greece, a country where organizational adoption of wearables is still at an early stage and official statistics cover mainly internet and computer use (Hellenic Statistical Authority, 2023).
Using a large and diverse sample of employees across age groups and occupational sectors, the study captures perspectives before large-scale implementation, providing insights into both opportunities and barriers to integration (Al-Emran et al., 2023; Magni et al., 2021; Kim and Chiu, 2019; Shafique et al., 2019; Guo et al., 2012; Xu et al., 2011). Based on the above, the study addresses the following research question:
What factors influence employees’ intention to adopt wearable devices in the workplace, and how do privacy-related perceptions, control, risks, benefits and trust shape this decision within an organizational context?
2. Literature review
2.1 Related studies on wearable devices adoption
The adoption of, and intention to use, wearable technologies devices have been widely studied across population groups such as adolescents, elderly, parents and consumers (Rahman et al., 2022; Magni et al., 2021; Cilliers, 2020; Dwivedi et al., 2019; Tavares and Oliveira, 2018; Khakurel et al., 2018; Nalchigar and Yu, 2018; Wang et al., 2018; Ng et al., 2017; Lismont et al., 2017; Mendoza et al., 2017; Kerner and Goodyear, 2017; Rogers and Park, 2016; Lunney et al., 2016; Turhan, 2013).
Most studies on wearable technology adoption have focused on health-monitoring devices (Ren et al., 2023; Asadi et al., 2019). Using the TAM, Felea et al. (2021) found, based on a sample of 192 students, that young people being receptive and curious about technology, have a natural predisposition to acceptance. Similarly, Mohammed and Redzuan (2020), based on a survey of 304 parents, concluded that wearable devices are not adopted due to privacy concerns and concerns about data security. Li et al. (2019) examined the acceptance of wearable health devices among older adults, while Chiu et al. (2018) investigated the adoption of sports and fitness wearables among Korean consumers (sample 146 and 247, respectively). Previous studies consistently identify privacy concerns, perceived privacy risks, limited organizational support and insufficient incentives for personal data disclosure as key barriers to wearable technology adoption (Singh, 2023; Tang and Ning, 2023; Xu et al., 2011).
Although wearable technology adoption has been widely examined across different population groups, most existing studies focus on consumer-oriented contexts, where technology use is typically voluntary and driven by individual preferences. In contrast, research addressing employees and the organizational benefits of wearable device use remains limited. However, organizational settings introduce additional dimensions, such as hierarchical relationships, performance monitoring and data governance, which may significantly influence employees’ perceptions and decision-making processes. Recent research further highlights that AI-enabled employee surveillance and digital monitoring practices may undermine workplace trust unless supported by transparent governance and ethical HRM practices (Qureshi et al., 2026).
In this context, wearable technologies in the workplace cannot be examined solely through the lens of technology acceptance, as they are closely associated with broader organizational dynamics. In particular, the use of wearable devices is often linked to workplace surveillance and employee monitoring practices, raising concerns about autonomy, fairness and power asymmetries between employees and employers (Moore, 2017; Ball, 2010). Existing research highlights that data collection in organizational environments is not neutral but embedded in governance structures that define how employee data are accessed, interpreted and used (Kellogg et al., 2020; Ajunwa et al., 2017).
Moreover, the increasing datafication of work has intensified the importance of organizational trust and transparency, as employees must rely on their employers to manage sensitive personal data responsibly (Pawirosumarto and Kurniawan, 2026; Ball, 2010). Recent conceptual work further argues that the effects of workplace surveillance depend not only on monitoring itself but also on how HRM systems are designed, particularly regarding transparency, employee participation and organizational governance (Patel et al., 2026). These issues position wearable adoption within a wider framework of organizational data governance, where privacy, control and trust are shaped not only by individual perceptions but also by institutional policies, governance mechanisms and workplace surveillance regulations (Howe et al., 2026).
Magni et al. (2021) empirically investigated the intention to use wearable devices in an organizational environment using an expanded TAM with a sample of 523 temporary employees. Jacobs et al. (2019) made an important contribution by demonstrating, through a sample of 1,273 employees, the organizational conditions required for the successful implementation of wearable technology programs. Using the Theory of Planned Behavior (TPB), Shafique et al. (2019) conducted a case study with a sample of 150 employees in pharmaceutical companies and examined the relationship between wearable device adoption and organizational benefits.
2.2 Privacy Calculus Theory
Privacy Calculus Theory posits that individuals decide whether to disclose personal data by weighing the perceived benefits against the associated privacy risks (Tang and Ning, 2023; Liu et al., 2021; Jozani et al., 2020; Dinev and Hart, 2006). When perceived benefits outweigh perceived risks, individuals are more likely to adopt a technology, although this balance may vary across different contexts (Fox et al., 2021; Li et al., 2016; Gao et al., 2015). The theory also encompasses related constructs, including perceived privacy control, privacy concerns and the perceived effectiveness of privacy policies.
Privacy Calculus Theory was selected as the theoretical foundation of this study because it captures both the findings of prior research and the distinctive characteristics of organizational settings. Unlike models such as TAM, which primarily emphasize perceived usefulness and ease of use, Privacy Calculus Theory focuses on the fundamental trade-off employees face when adopting workplace wearables: potential gains in productivity, safety, or personalization versus privacy loss, surveillance and reduced trust. Its adaptability across contexts involving sensitive personal data makes it particularly suitable for workplace research, where technology adoption decisions are influenced not only by individual preferences but also by organizational policies, governance mechanisms and power relationships (Fox et al., 2021; Li et al., 2016).
3. Research model and hypothesis development
Privacy Calculus Theory offers flexibility to evaluate technology adoption through a cost-benefit lens. It is particularly suitable for workplace wearables, where employees often have limited prior experience and must balance perceived benefits against risks. To extend prior models, this study incorporates “trust” as an additional factor shaping adoption intention (Fox et al., 2021; Trepte et al., 2020; Jozani et al., 2020). The proposed research model is presented in Figure 1.
The model begins with Perceived Effectiveness of Privacy Policy. H 1 connects it to Perceived Benefits, comprising Perceived Benefits for Work and Perceived Personalized Benefits. H 2 connects Perceived Effectiveness of Privacy Policy to Perceived Risks, comprising Perceived Privacy Risk, Performance Risk and Perceived Social Risk. H 3 connects Perceived Effectiveness of Privacy Policy to Privacy Control. H 4 connects Privacy Control to Privacy Concerns. H 5 connects Privacy Concerns to Perceived Risks. H 6 connects Privacy Concerns to Trust. Perceived Benefits connects to Intention to use through H 7. Perceived Risks connects to Intention to use through H 8. Trust connects to Intention to use through H 9.Proposed employee intention assessment model
Source: Authors’ own work
The model begins with Perceived Effectiveness of Privacy Policy. H 1 connects it to Perceived Benefits, comprising Perceived Benefits for Work and Perceived Personalized Benefits. H 2 connects Perceived Effectiveness of Privacy Policy to Perceived Risks, comprising Perceived Privacy Risk, Performance Risk and Perceived Social Risk. H 3 connects Perceived Effectiveness of Privacy Policy to Privacy Control. H 4 connects Privacy Control to Privacy Concerns. H 5 connects Privacy Concerns to Perceived Risks. H 6 connects Privacy Concerns to Trust. Perceived Benefits connects to Intention to use through H 7. Perceived Risks connects to Intention to use through H 8. Trust connects to Intention to use through H 9.Proposed employee intention assessment model
Source: Authors’ own work
3.1 Perceived effectiveness of privacy policy
Effective corporate privacy policies inform employees how their data will be used and provide safeguards against misuse. When employees perceive such policies as credible, they are more likely to trust their employer, feel a sense of control and perceive fewer risks (Tang and Ning, 2023; Xu et al., 2011; Milne and Culnan, 2004):
Perceived effectiveness of privacy policy is positively associated with perceived benefits.
Perceived effectiveness privacy policy is negatively associated with perceived risk.
Perceived effectiveness privacy policy is positively associated with employee perception of privacy control.
3.2 Privacy control
Privacy control refers to employees’ belief that they can decide how much data to share and how it is disseminated. Higher perceived control reduces privacy concerns and strengthens adoption intention by providing a sense of autonomy over sensitive information (Tang and Ning, 2023; Jozani et al., 2020; Ioannou et al., 2020; Wang et al., 2016; Chen et al., 2013; Xu et al., 2011; Malhotra et al., 2004):
Perceived privacy control is negatively associated with privacy concerns.
3.3 Privacy concerns
Privacy concerns refer to employees’ belief that using wearables at work may expose their personal data and reduce their control over it (Singh, 2023; Duan and Deng, 2021). Prior research consistently shows that such concerns increase perceived risks and erode trust, acting as a significant barrier to adoption (Sun and Gu, 2024; Duan and Deng, 2021; Liu et al., 2021; Jozani et al., 2020; Ioannou et al., 2020; Guo et al., 2012; Xu et al., 2011; Dinev and Hart, 2006):
Privacy concerns are positively associated with perceived privacy risks.
Privacy concerns are negatively associated with trust.
3.4 Perceived benefits
Perceived benefits are the advantages employees expect from using wearables at work, such as improved productivity, access to information and personalized support. When employees believe that sharing data leads to tangible work-related gains, they are more willing to adopt such technologies (Tang and Ning, 2023; Fox et al., 2021; Sergueeva et al., 2020; Aguirre et al., 2016; Guo et al., 2016; Li et al., 2016; Sutanto et al., 2013; Guo et al., 2012; Junglas and Watson, 2006; Schafer et al., 2001):
Perceived benefits are positively associated with the intention to use a wearable device.
3.5 Perceived risks
Perceived risks capture the negative consequences employees associate with sharing data through wearables, including privacy violations, performance issues and social stigma. When risks are seen as high, employees are less likely to adopt such technologies (Sun and Gu, 2024; Liu et al., 2021; Magni et al., 2021; Jozani et al., 2020; Mohammed and Redzuan, 2020; Li et al., 2019; Li et al., 2016; Yang et al., 2016; Gao et al., 2015; Zhang, et al., 2015; Dinev et al., 2013; Xu et al., 2011; Fensli et al., 2010; Dinev and Hart, 2006):
Perceived risks are negatively associated with the intention to use a wearable device.
3.6 Trust
Trust is a key factor in technology adoption, especially when employees have little prior experience with wearables. Confidence in employers and colleagues reduces uncertainty and facilitates acceptance, making employees more willing to adopt such devices (Tang and Ning, 2023; Magni et al., 2021; Mohammed and Redzuan, 2020; Santoro et al., 2020; Asadi et al., 2019; Miltgen and Smith, 2019; Khasawneh, 2018; Kalantari, 2017; Lin et al., 2015; Guo et al., 2012; Komiak and Benbasat, 2006; Jarvenpaa et al., 1999):
Trust is positively associated with the intention to use a wearable device.
4. Methods
A structured questionnaire was developed to measure employees’ intention to adopt workplace wearables, following approval from the University Ethics and Deontology Committee. The questionnaire comprised two sections:
demographic and job related information; and
items measuring the seven research constructs.
A total of 37 items, adapted from validated scales in prior studies (Table 1), were rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The questionnaire items were adapted to the workplace wearable context to ensure content validity, while the full instrument is provided in the Appendix.
Questionnaire composition and origins
| Factors | No. of items | References |
|---|---|---|
| Perceived effectiveness of privacy policy | 3 | Xu et al., 2008 |
| Privacy control | 4 | Xu, 2007 |
| Privacy concerns | 4 | Dinev and Hart, 2006 |
| Perceived benefits | 6 | Fox et al., 2021; Li et al., 2016; Xu et al., 2011 |
| Perceived risks | 10 | Yang et al., 2016; Verdegem and De Marez, 2011; Malhotra et al., 2004; Jarvenpaa et al., 1999; Stone and Grønhaug, 1993 |
| Trust | 6 | Magni et al., 2021; Gu et al., 2009 |
| Intention to use | 4 | Li et al., 2019; Pan and Jordan-Marsh, 2010 |
| Total number of items | 37 |
To ensure clarity and accuracy, the questionnaire was translated into Greek following a forward-backward translation procedure. Prior to distribution, a pilot study was conducted with a small group of participants to assess clarity and identify potential ambiguities. Minor revisions were subsequently made to improve the wording of selected items. In addition, the instrument included definitions and visual examples (e.g. VR and EEG headsets) to facilitate respondents’ understanding (Jacobs et al., 2019; Li et al., 2019).
The questionnaire was distributed online via Google Forms between July and October 2024. A convenience sampling approach was used, and participants were recruited through professional networks (e.g. LinkedIn contacts) and online distribution channels, including social media and email sharing. Eligibility required participants to be over 18 years old and currently employed in Greece. Participation was voluntary and anonymous, and the confidentiality of all responses was ensured. A total of 2,305 responses were collected.
Data screening procedures were applied to ensure data quality. Responses were examined for completeness and consistency, and no substantial missing data were identified. Outliers were detected using the Mahalanobis Distance Test (p < 0.002 based on χ2 distribution and model degrees of freedom), resulting in the removal of 62 cases and a final sample of 2,243 participants.
The data were analyzed using SPSS and AMOS, and the structural model was estimated using Maximum Likelihood (ML). The assumptions underlying Maximum Likelihood estimation, including multivariate normality and sample adequacy, were assessed prior to model estimation.
In addition to the hypothesized model, several additional paths were identified after the initial model estimation. These relationships are treated as post hoc modifications and are reported as exploratory. Their inclusion is supported by theoretical reasoning and prior literature.
5. Results
5.1 Descriptive statistics
Table 2 summarizes the demographic and occupational characteristics of the participants. Most employees who participated in the survey belong to the age group 36–50 (31.2%), while the remaining age groups show relatively similar proportions, ranging between 22% and 24.4%. Almost half of the participants (46.5%) held a university degree, while 28.6% had completed postgraduate studies and 24.9% had completed secondary education. Regarding professional experience, 43.6% of employees have more than 15 years of experience, indicating a highly experienced sample. In contrast, 23.8% have 1–5 years of experience, while smaller proportions are observed for those with less than one year (9.7%), 6–10 years (13.1%) and 11–15 years (9.9%).
Results of descriptive questions
| Demographics | Categories | Frequency | % |
|---|---|---|---|
| Age group | 18–25 | 503 | 22.4 |
| 26–35 | 494 | 22 | |
| 36–50 | 699 | 31.2 | |
| 50+ | 547 | 24.4 | |
| Education | Secondary | 560 | 24.9 |
| University degree | 1,043 | 46.5 | |
| Postgraduate degree | 640 | 28.6 | |
| Experience in years | < 1 | 218 | 9.7 |
| 1–5 | 533 | 23.8 | |
| 6–10 | 292 | 13.1 | |
| 11–15 | 221 | 9.9 | |
| 15+ | 979 | 43.6 | |
| Years in current company | < 1 | 444 | 19.8 |
| 1–5 | 719 | 32.1 | |
| 6–10 | 287 | 12.8 | |
| 11–15 | 167 | 7.4 | |
| 15+ | 626 | 27.9 | |
| Years in current position | < 1 | 421 | 18.8 |
| 1–5 | 764 | 34.1 | |
| 6–10 | 316 | 14.1 | |
| 11–15 | 180 | 8 | |
| 15+ | 562 | 25.1 | |
| Industry sector | Industrial/Manufacturing | 552 | 24.6 |
| Construction | 196 | 8.7 | |
| Tourism | 156 | 7 | |
| Trade | 303 | 13.5 | |
| Services | 887 | 39.6 | |
| Banks | 149 | 6.6 |
Regarding organizational tenure, 31.1% of participants had worked in their current company for 1–5 years, while 27.9% has remained with the same employer for more than 15 years. The remaining participants reported organizational tenure of less than one year (18.8%), 6–10 years (12.8%) or 11–15 years (7.4%). Regarding tenure in their current position, 34.1% had held the same position for 1–5 years, 25.1% for more than 15 years and 18.8% for less than a year.
Most participants were employed in the service sector (39.6%), followed by industry/crafts (24.6%). Smaller proportions worked in trade (13.5%), construction (8.7%), tourism (7.0%) and banking (6.6%).
Beyond demographics, participants were also asked about their familiarity and perceptions regarding wearable devices such as VR and EEG headsets. Awareness was generally limited: nearly half (47%) reported no prior knowledge, 42.8% had only general awareness and 7.5% had accessed information through reading or videos. Only a very small proportion (2.7%) had prior hands-on experience. Despite this low level of direct exposure, attitudes toward workplace use were cautiously positive: 60.8% stated they would be willing to use such devices in some processes, while 39.2% expressed reluctance.
Expectations regarding productivity varied across respondents. A notable proportion of respondents (24.7%) anticipated a slight productivity improvement, while 18.7% expected a moderate improvement and 12% a significant increase. By contrast, 26.7% believed their productivity would remain unaffected, and 17.9% expected it to decline. Table 3 shows that, despite limited familiarity, a considerable proportion of employees expressed willingness to use wearable technologies under appropriate conditions.
Perceptions and awareness of wearable devices
| Question | Categories | Frequency | % |
|---|---|---|---|
| Did you know before you participated in the survey about the existence of wearable devices like VR, EEG, etc.? | Yes, but I didn’t know that many details | 960 | 42.8 |
| Yes, I have read and seen videos about this device | 169 | 7.5 | |
| Yes, I have used such a device | 60 | 2.7 | |
| No, I was not aware of their existence | 1,054 | 47 | |
| Would you use such a device in the context of your work? | Yes, I would use it in some processes | 1,364 | 60.8 |
| No, I wouldn’t use it | 879 | 39.2 | |
| How do you think using these devices would affect your productivity? | It won’t affect my productivity | 600 | 26.7 |
| It will slightly improve my productivity | 553 | 24.7 | |
| It will moderately improve my productivity | 419 | 18.7 | |
| It will significantly improve my productivity | 270 | 12 | |
| It will reduce my productivity | 401 | 17.9 |
5.2 Factor analysis and discriminant validity analysis
To assess validity and reliability, a factor analysis was conducted using the indicators in Table 4. The KMO index exceeded 0.50 and Bartlett’s test was significant (p < 0.001). TVE values were above 60% for all factors. Factor loadings were above 0.50 and Cronbach’s α exceeded 0.70 (Fabrigar and Wegener, 2012; Thompson, 2004).
Factor analysis
| Factor | Mean | SD | KMO | Bartlett’s sig | TVE | Cronbach α | Loadings | CR | AVE |
|---|---|---|---|---|---|---|---|---|---|
| Privacy concerns | 3.53 | 0.92 | 0.83 | < 0.001 | 74.82 | 0.89 | > 0.79 | 0.92 | 0.75 |
| Privacy control | 2.65 | 0.94 | 0.85 | < 0.001 | 84.11 | 0.94 | > 0.89 | 0.96 | 0.84 |
| Perceived effectiveness of privacy policy | 3.31 | 0.91 | 0.74 | < 0.001 | 86.45 | 0.92 | > 0.90 | 0.95 | 0.86 |
| Perceived risks | |||||||||
| Perceived privacy risk | 3.39 | 0.80 | 0.82 | < 0.001 | 76.37 | 0.90 | > 0.85 | 0.93 | 0.76 |
| Performance risk | 3.30 | 0.74 | 0.70 | < 0.001 | 66.91 | 0.75 | > 0.82 | 0.86 | 0.67 |
| Perceived social risk | 2.87 | 1.01 | 0.75 | < 0.001 | 81.56 | 0.89 | > 0.89 | 0.93 | 0.82 |
| Trust | 3.45 | 0.75 | 0.82 | < 0.001 | 67.63 | 0.90 | > 0.75 | 0.93 | 0.68 |
| Perceived benefits | |||||||||
| Perceived benefits for work | 3.41 | 0.78 | 0.72 | < 0.001 | 80.64 | 0.88 | > 0.86 | 0.93 | 0.81 |
| Perceived personalized benefits | 3.67 | 0.73 | 0.74 | < 0.001 | 85.53 | 0.92 | > 0.91 | 0.95 | 0.86 |
| Intention to use | 3.10 | 0.85 | 0.83 | < 0.001 | 80.73 | 0.92 | > 0.87 | 0.95 | 0.81 |
Discriminant validity was assessed using Fornell–Larcker criterion (Fornell and Larcker, 1981). The square root of AVE for each factor exceeded its highest correlation with other constructs, indicating satisfactory discriminant validity (Table 5).
Discriminant validity (Fornell–Larcker criterion)
| Factor | Privacy concerns | Privacy control | Perceived effectiveness of privacy policy | Perceived privacy risk | Performance risk | Trust | Perceived social risk | Perceived benefits for work | Perceived personalized benefits | Intention to use |
|---|---|---|---|---|---|---|---|---|---|---|
| Privacy concerns | 0.865 | |||||||||
| Privacy control | −0.314 | 0.917 | ||||||||
| Perceived effectiveness of privacy policy | −0.168 | 0.491 | 0.930 | |||||||
| Perceived privacy risk | 0.507 | −0.240 | −0.192 | 0.874 | ||||||
| performance risk | 0.396 | −0.198 | −0.121 | 0.512 | 0.818 | |||||
| Trust | −0.133 | 0.326 | 0.519 | −0.162 | −0.111 | 0.903 | ||||
| Perceived social risk | 0.199 | −0.080 | −0.103 | 0.229 | 0.276 | −0.162 | 0.822 | |||
| Perceived benefits for work | −0.086 | 0.279 | 0.342 | −0.161 | −0.181 | 0.347 | −0.134 | 0.898 | ||
| Perceived personalized benefits | 0.084 | 0.145 | 0.263 | −0.057 | 0.300 | −0.060 | 0.514 | 0.925 | ||
| Intention to use | −0.242 | 0.447 | 0.367 | −0.293 | −0.270 | 0.390 | 0.148 | 0.601 | 0.392 | 0.898 |
Descriptive statistics indicate that employees express moderate to high concern (μ = 3.53) about providing data to wearable devices and about issues of excessive data collection and use for unknown purposes. Furthermore, employees state that they have limited control over their personal data (μ = 2.65), as they feel that they do not control the type of data that wearable devices collect but also cannot adequately manage who has access to their data. Regarding employees’ perception of the effectiveness of the privacy policy of the company they work for, they believe that there is respect for their personal data and that to a certain extent they will remain confidential given the moderate positive attitude (μ = 3.31).
Perceived risk was moderate to high, particularly with respect to privacy and performance risks. Respondents expressed concerns about potential data disclosure, uncertainty, loss of privacy and the possibility of unexpected problems (μ = 3.39). They also reported moderate concern regarding the performance of wearable devices, including whether these technologies would deliver the expected benefits and provide accurate and reliable results (μ = 3.30). In contrast, perceived social risk was lower (μ = 2.87), indicating relatively limited concern about colleagues’ perceptions of wearable device use.
Perceived benefits were rated at a moderate to positive level (μ = 3.41). Participants believed that wearable technologies could provide personalized services tailored to their needs and interests, while productivity-related benefits received an even higher rating (μ = 3.67), particularly regarding productivity enhancement, improved access to information and the quality of work processes. Trust in the workplace was also moderate to positive (μ = 3.45). Participants generally perceived their employer and colleagues as trustworthy and reported feeling reasonably confident when encouraged to use wearable devices, although some reservations remained.
Overall, employees reported a moderate intention to adopt wearable technologies (μ = 3.10), suggesting cautious acceptance under appropriate conditions.
5.3 Structural equation model
The structural model (Figure 2) explained 46% of the variance in employees’ intention to use wearable devices. The results indicate that perceived benefits have the largest impact (0.42/H7) on intention to use, followed by perceived control (0.25), perceived concerns (−0.10) and trust (0.09/H9).
Perceived Effectiveness of Privacy Policy connects to Trust at 0.44, Perceived Benefits at 0.29, Perceived Risks at minus 0.13, and Privacy Control at 0.52. Privacy Control connects to Perceived Benefits at 0.12, Privacy Concerns at minus 0.34, and Intention to Use at 0.25. Privacy Concerns connects to Perceived Risks at 0.48, Perceived Benefits at 0.13, and Intention to Use at minus 0.10. Perceived Risks connects to Trust at minus 0.08, Perceived Benefits at minus 0.16, and Intention to Use at minus 0.14. Perceived Benefits connects to Trust at 0.23 and Intention to Use at 0.42. Trust connects to Intention to Use at 0.09. Values displayed with the constructs are Trust 0.36, Perceived Benefits 0.17, Perceived Risks 0.27, Privacy Control 0.27, Privacy Concerns 0.12, and Intention to Use 0.46.Structural equation model results
Note: Values on the paths represent standardized regression coefficients (β), while R2 values indicate the explained variance of the endogenous variables
Source: Authors’ own work
Perceived Effectiveness of Privacy Policy connects to Trust at 0.44, Perceived Benefits at 0.29, Perceived Risks at minus 0.13, and Privacy Control at 0.52. Privacy Control connects to Perceived Benefits at 0.12, Privacy Concerns at minus 0.34, and Intention to Use at 0.25. Privacy Concerns connects to Perceived Risks at 0.48, Perceived Benefits at 0.13, and Intention to Use at minus 0.10. Perceived Risks connects to Trust at minus 0.08, Perceived Benefits at minus 0.16, and Intention to Use at minus 0.14. Perceived Benefits connects to Trust at 0.23 and Intention to Use at 0.42. Trust connects to Intention to Use at 0.09. Values displayed with the constructs are Trust 0.36, Perceived Benefits 0.17, Perceived Risks 0.27, Privacy Control 0.27, Privacy Concerns 0.12, and Intention to Use 0.46.Structural equation model results
Note: Values on the paths represent standardized regression coefficients (β), while R2 values indicate the explained variance of the endogenous variables
Source: Authors’ own work
The fit indices of the model are listed in Table 6, where the values indicate that the model under analysis is of good fit. All indices are within or beyond the acceptable limits, concluding that the model describes the data well with high accuracy and is suitable for interpretation. Table 7 reports the findings regarding the research hypotheses but also lists the new relationships between the factors that emerged after the statistical analysis.
Model fit indices
| Indices | x2/df | GFI | CFI | RMSEA | RMR |
|---|---|---|---|---|---|
| Scores | 2.145 | 0.999 | 0.999 | 0.023 | 0.007 |
Hypothesis testing
| Hypothesis testing | ||
|---|---|---|
| Hypothesis | Path | Result |
| H1 | Perceived effectiveness of privacy policy → Perceived benefits | Accepted |
| H2 | Perceived effectiveness of privacy policy → Perceived risks | Accepted |
| H3 | Perceived effectiveness of privacy policy → Privacy control | Accepted |
| H4 | Privacy control → Privacy concerns | Accepted |
| H5 | Privacy concerns → Perceived risks | Accepted |
| H6 | Privacy concerns → Trust | Rejected |
| H7 | Perceived benefits → Intention to use | Accepted |
| H8 | Perceived risks → Intention to use | Accepted |
| H9 | Trust → Intention to use | Accepted |
| New proposed paths | ||
| Perceived effectiveness of privacy policy → Trust | ||
| Perceived benefits → Trust | ||
| Perceived risks → Perceived benefits | ||
| Privacy control → Perceived benefits | ||
| Privacy concerns → Perceived benefits | ||
| Privacy concerns → Intention to use | ||
| Perceived risks → Trust | ||
| Privacy control → Intention to use | ||
All supported hypotheses are significant at p < 0.001
6. Discussion
6.1 Direct and indirect effects of the model factors
Table 8 summarizes the direct, indirect and total effects among the research factors of the model. The effectiveness of the privacy policy plays a central role in the model, as it is linked either directly or indirectly to the other factors. More specifically, the effectiveness of the company’s privacy policy directly affects privacy control (r = 0.517) and indirectly affects privacy concerns (r = −0.177). Previous studies have similarly shown that effective organizational privacy policies enhance employees’ perceived control while reducing privacy concerns (Zhang et al., 2025; Tang and Ning, 2023; Liu et al., 2021; Mutimukwe et al., 2020; Xu et al., 2011). When a company’s privacy policy is effective, employees feel in control of who has access to the data they provide on their wearable devices and the type of data collected. Consequently, employees become less concerned about how their personal data are collected, accessed and used within the organization.
Direct and indirect effects of the model factors
| Factor | Perceived effectiveness of privacy policy | Privacy control | Privacy concerns | Perceived risks | Perceived benefits | Trust |
|---|---|---|---|---|---|---|
| Privacy control | ||||||
| D | 0.517 | |||||
| I | ||||||
| T | 0.517 | |||||
| Privacy concerns | ||||||
| D | −0.343 | |||||
| I | −0.177 | |||||
| T | −0.177 | −0.343 | ||||
| Perceived risks | ||||||
| D | −0.133 | 0.481 | ||||
| I | −0.085 | −0.165 | ||||
| T | −0.218 | −0.165 | 0.481 | |||
| Perceived benefits | ||||||
| D | 0.294 | 0.122 | 0.135 | −0.163 | ||
| I | 0.075 | −0.019 | −0.079 | |||
| T | 0.369 | 0.103 | 0.056 | −0.163 | ||
| Trust | ||||||
| D | 0.444 | −0.080 | 0.233 | |||
| I | 0.103 | 0.037 | −0.025 | −0.038 | ||
| T | 0.548 | 0.037 | −0.025 | −0.118 | 0.233 | |
| Intention to use | ||||||
| D | 0.247 | −0.100 | −0.136 | 0.424 | 0.093 | |
| I | 0.382 | 0.104 | −0.044 | −0.080 | 0.022 | |
| T | 0.382 | 0.351 | −0.144 | −0.216 | 0.446 | 0.093 |
Privacy policy effectiveness also negatively impacts perceived risks (r = −0.218), positively impacts perceived benefits (r = 0.369) and trust (r = 0.548). These findings are consistent with previous research (Aluri, 2025; Liu et al., 2021; Mutimukwe et al., 2020; Wang et al., 2020; Xu et al., 2011). A clear and transparent privacy policy increases perceived benefits, strengthens trust in the organization and reduces employees’ perceived risks associated with workplace wearables. Consequently, it indirectly promotes employees’ intention to use wearable devices (r = 0.382), highlighting the central role of privacy policy effectiveness in workplace technology adoption (Liu et al., 2021). This finding is also consistent with recent research suggesting that transparent governance, participatory HRM practices and organizational governance mechanisms can mitigate the negative consequences of workplace surveillance while strengthening workplace trust employees’ acceptance of digital monitoring technologies (Kayas et al., 2025; Patel et al., 2026).
Perceived control appears to influence four of the five remaining factors. The negative influence of perceived control on employees’ privacy concerns (r = −0.343) is consistent with findings in the literature (Liu et al., 2021) and is due to the fact that the more control they have over the data provided on wearable devices, the less they worry about their use for purposes they do not know and the lack of protection in the company’s databases (Virgillito et al., 2025). In addition, perceived control has a positive influence on perceived benefits (r = 0.103) and a negative influence on perceived risks (r = −0.165). Previous research has likewise shown that greater perceived control enhances perceived benefits while reducing perceived risks (Atalay and Yücel, 2024; Mutimukwe et al., 2020).
Examining the effect of control on employees’ trust in colleagues and employer and ultimately on the intention to provide data to wearable devices, r = 0.037 and r = 0.351 are obtained, respectively. Perceived control has a small, positive effect on trust, while a fairly strong, positive effect on adoption intention. These findings suggest that employees who perceive greater control over their personal data are more willing to adopt wearable technologies in organizational settings (Atalay and Yücel, 2024).
Regarding employee privacy concerns, they seem to have a positive and strong effect on perceived risks (r = 0.481) and a slightly positive effect on perceived benefits (r = 0.056). Previous studies consistently report that the relationship between these factors and conclude that the more privacy concerns increase, the more perceived risks employees will face (Tang and Ning, 2023; Fox et al., 2021; Liu et al., 2021; Jozani et al., 2020). Regarding the effect of privacy concerns on trust, the relationship is slightly negative (r = −0.025) and not statistically significant. In contrast, privacy concerns have a moderate and negative effect on intention to use (r = −0.144). These findings suggest that while privacy concerns do not appear to substantially influence trust in the workplace, they are associated with a lower intention to adopt wearable devices. This is consistent with prior research indicating that increased concerns about personal data may reduce employees’ willingness to engage with such technologies (Fox et al., 2021; Sergueeva et al., 2020; Guo et al., 2012). Similarly, recent evidence suggests that employees exposed to workplace monitoring technologies report less positive workplace attitudes when surveillance practices are perceived as intrusive or insufficiently transparent (Abrams et al., 2026).
In this context, the effect of perceived risks on perceived benefits (r = −0.163), trust (r = −0.118) and overall intention to use (r = −0.216) was also measured. Perceived risks have a moderately negative effect on all three factors. These findings suggest that the more risks they perceive that using a wearable device and providing data to it has, the fewer the perceived benefits, the less trust they show in employees and employers, and overall they are less likely to adopt such technologies (Ali et al., 2021; Magni et al., 2021; Mohammed and Redzuan, 2020; Park et al., 2019; Li et al., 2016; Gao et al., 2015). Regardless of whether these risks relate to privacy, social, or performance issues, their overall influence on adoption intention remains negative.
Trust directly and positively influences (r = 0.233) employees’ perceived benefits, a finding that is consistent with previous research linking trust to intention to use (Ali et al., 2021; Park et al., 2019). Employees’ trust in their employer and colleagues leads them to face the situation with optimism and perceive more benefits. In turn, perceived benefits significantly influence (r = 0.446) their intention to adopt a wearable device during work (Fox et al., 2021; Park et al., 2019). For the above reasons, a small direct effect of trust on intention to use is observed (r = 0.093), which is not statistically significant (Ali et al., 2021; Magni et al., 2021; Mohammed and Redzuan, 2020; Guo et al., 2012).
It should be noted that the additional structural relationships incorporated into the final model represent exploratory post hoc modifications identified after the initial model estimation. Although these relationships are supported by prior literature and contributed to an improved model fit, they were not part of the original conceptual model. Therefore, they should be interpreted with caution and validated in future studies using independent samples.
7. Conclusion
The proposed framework enhances understanding of the factors influencing employees’ intention to adopt wearable devices in the workplace. It focuses on wearable technologies that monitor and collect physical, cognitive or emotional data and contributes to the literature by providing evidence from a relatively underexplored context, where organizational adoption of wearable devices remains limited.
In addition, the study extends prior research by applying Privacy Calculus Theory in an organizational setting, offering an alternative perspective to commonly used technology acceptance models. Given that most participants had limited direct experience with wearable devices, this approach allows for a more appropriate examination of perceived risks, benefits and privacy-related considerations.
The findings indicate that the model explains 46% of the variance in intention to use. Among the examined factors, perceived benefits, privacy policy effectiveness and perceived control show stronger associations with intention to use, while other relationships are weaker or not statistically significant. These findings highlight the multifaceted nature of employees’ decision-making regarding wearable adoption.
Overall, the findings suggest that while perceived benefits play an important role in shaping adoption intentions, concerns and perceived risks may act as barriers. Organizational mechanisms such as transparent privacy policies and enhanced employee control appear to contribute to balancing these opposing forces, supporting the responsible and acceptable integration of wearable technologies in the workplace.
7.1 Practical implications
The study’s findings provide practical guidance for organizations considering the integration of wearable technologies into workplace processes. Addressing employees’ concerns about data privacy and security is essential for the successful adoption of such technologies in areas such as recruitment, performance monitoring and training (Fox et al., 2021). Organizations can benefit from strategies that position wearable technologies as supportive workplace tools, provided that appropriate organizational safeguards are established (Jacobs et al., 2019).
More specifically, these findings can be translated into concrete managerial scenarios. In the context of HR analytics, organizations should ensure that data collected through wearable devices are explicitly linked to employee development and performance improvement, rather than perceived as tools for surveillance. In occupational safety, wearables can be used to monitor fatigue, stress, or exposure to risks, provided that employees are clearly informed about the purpose, scope and limits of data collection. In productivity monitoring, transparency regarding how data are interpreted and used in evaluation processes is critical to avoid resistance and mistrust. Similarly, in training and development, wearable technologies (e.g. VR-based training environments) should be positioned as tools that enhance learning and skill acquisition rather than mechanisms of control.
It is therefore recommended that organizations strengthen the security of employees’ personal data and ensure transparency in how data are collected, stored and used. Clear communication of privacy policies, including updates and improvements, can enhance employees’ sense of security and foster trust in the organization (Asadi et al., 2019). Given that perceived control significantly influences intention to use, organizations should also enable employees to have a degree of control over the data collected and how they are used (Tang and Ning, 2023).
Employees’ concerns and uncertainty may also stem from previous experiences or broader perceptions of digital surveillance. Therefore, organizations should not only inform employees about data protection measures but also demonstrate them in practice. Although trust has only a moderate direct effect on intention to use, it can facilitate the successful integration of wearable technologies into work processes (Magni et al., 2021; Santoro et al., 2020). A supportive organizational environment should include open communication between management and employees, as well as employee participation in discussions and decision-making processes related to the adoption of such technologies. This approach can reduce perceived risks and concerns, including fear of social stigma, thereby limiting their negative impact on adoption decisions.
Finally, managers should leverage employees’ perceptions of the benefits associated with wearable technologies, such as improved productivity, personalization and enhanced performance in daily tasks (Sergueeva et al., 2020). However, as highlighted in recent research, a high level of willingness to adopt such technologies does not automatically translate into actual data-sharing behavior or sustained use. Effective implementation requires the development of appropriate policies, mechanisms and user-centered design approaches that can bridge the gap between intention and actual adoption (Chandrasekaran et al., 2025). Therefore, the proposed managerial implications should be viewed as recommendations that support employees’ intention to adopt wearable technologies rather than evidence of actual implementation or sustained organizational use.
7.2 Limitations and future research
A key limitation is that the study examines employees’ intention to use wearable devices rather than actual usage behavior. Given that most respondents had limited or no prior hands-on experience with such technologies, their responses reflect perceptions and expectations rather than real-world interaction. Therefore, the findings should be interpreted with appropriate caution when generalizing to actual workplace adoption.
In addition, the geographic focus exclusively on Greek workers limits the generalizability of the findings to other countries or cultural settings. Future research could examine additional organizational factors and adopt longitudinal designs to investigate actual wearable technology use in workplace settings over time.
References
Further reading
Appendix. Questionnaire items.
All items were measured using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree).
Privacy Concerns (Dinev and Hart, 2006):
I am concerned that wearable devices will collect too much data about me.
I am concerned that unauthorized individuals could access the personal data I provide to wearable devices.
I am concerned that my personal data may be used for purposes I am not aware of.
I am concerned that my personal data stored in wearable device databases will not be adequately protected.
Privacy Control (Xu, 2007):
I believe that I have control over who can access the data I provide to wearable devices.
I believe that I have control over the personal data collected by wearable devices.
I believe that I have control over how my personal data is used by wearable devices.
I believe that I can control the personal data I provide to wearable devices.
Perceived Effectiveness of Privacy Policy (Xu et al., 2008):
I believe that my organization’s privacy policy reflects respect for individuals’ privacy when using wearable devices.
Considering my organization’s privacy policy, I believe that the personal data I provide will remain private and confidential.
I am confident that my organization’s privacy policy ensures that my privacy is protected when using wearable devices.
Perceived Risks (Yang et al., 2016; Verdegem and De Marez, 2011; Malhotra et al., 2004; Jarvenpaa et al., 1999; Stone and Grønhaug, 1993)
Privacy Risk:
It would be risky to disclose my personal data to wearable devices.
There would be high uncertainty associated with providing my personal data when using wearable devices.
There would be a high likelihood of privacy loss when providing personal data to wearable devices.
Providing information to wearable devices would involve many unexpected problems.
Performance Risk:
I am concerned whether wearable devices will provide the expected benefits (e.g. functionality).
Wearable devices may lead to privacy violations.
Wearable devices may not function satisfactorily (e.g. measurement accuracy, quality issues).
Social Risk:
My colleagues would look at me differently if they saw me using wearable devices.
I would feel uncomfortable wearing wearable devices.
People around me might laugh at me if I adopted a wearable device.
Perceived Benefits (Fox et al., 2021;Li et al., 2016;Xu et al., 2011)
Personalized Benefits:
Providing data to wearable devices may offer personalized services tailored to my activity history.
Providing data to wearable devices may provide more relevant information based on my preferences and interests.
Providing data to wearable devices may deliver content that suits my personal preferences.
Work-related Benefits:
Providing data to wearable devices would improve my ability to manage my work.
Providing data to wearable devices would improve my access to information.
Providing data to wearable devices would improve the quality of my work.
Trust (Magni et al., 2021;Gu et al., 2009):
I feel safe in my workplace.
I believe that my colleagues are trustworthy.
I believe that my employer is trustworthy.
I trust my colleagues if they recommend using wearable devices.
I trust my employer if they suggest using wearable devices.
I trust my organization if it encourages the use of wearable devices.
Intention to Use (Li et al., 2019;Pan and Jordan-Marsh, 2010):
I believe that providing data to wearable devices is a good idea.
I would be interested in providing data to wearable devices.
I intend to provide data to wearable devices when given the opportunity in the future.
I am likely to provide personal data to wearable devices to better meet my needs.
