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Abstract

In the era of digitalization and remote work, employee surveillance has surged, and many workers feel tethered to their work through various technological devices. This study explores how communication visibility mediates the relationship between remote work and computer-mediated communication (CMC) frequency, supervisors’ surveillance practices, and employees’ connectivity to work. Analyzing data from 539 employees and supervisors at a multinational company, we found that remote work alone did not lead to increased supervisors’ surveillance ability. However, CMC frequency was positively associated with surveillance, both directly and indirectly, through enhanced communication visibility. Additionally, workplace flexibility and CMC frequency contributed to constant connectivity, regardless of communication visibility. These results contribute to theoretical understandings of visibility management and surveillance by highlighting how communication visibility provides opportunities for surveillance in flexible work environments. Practically, these findings emphasize the need to develop policies that balance the benefits of connectivity and flexibility with increased opportunities for surveillance.

Keywords

connectivity communication visibility computer-mediated communication employee surveillance technology use workplace flexibility

The demand for software to monitor at-home or off-site workers surged by 60% since 2019 (Migliano, 2023) due in large part to an “unprecedented COVID-19-induced explosion in digital surveillance” that reconfigured power relationships in professional settings (Aloisi & de Stefano, 2022, p. 289). These changes in monitoring practices are likely to have ongoing consequences because once surveillance practices are adopted throughout an organization, they are rarely reversed (Walsh, 2020). Shifts in surveillance are accompanied by broader increases in the digitalization of work, which has facilitated more diverse, fluid, and disparate forms of organizing among workers (Leonardi & Treem, 2020). Globally, organizations must grapple with the challenges and opportunities of decentralized workforces (Nurmi & Hinds, 2020) and digitized work environments that alter the ways communication among workers, and communication about work, is visible to observers (Leonardi, 2021).

These multi-locational and digital work arrangements highlight the importance of communication visibility as a double-edged sword, offering both the boon of enhanced opportunities for interaction and the potential bane of pervasive surveillance opportunities and practices (Cunha et al., 2023; Leonardi, 2021). As digital technologies dissolve traditional spatial and temporal boundaries, they simultaneously construct new panopticons of surveillance, where every digital footprint—such as timestamps for each click or keyboard stroke—becomes a potential metric for monitoring (Aaltonen & Stelmaszak, 2023; Armstrong et al., 2023). The rapid advancement and involvement of communication technologies within organizations make workplace surveillance increasingly granular and invasive (Allen et al., 2013; Stark et al., 2020).

The ubiquity of computer-mediated communication (CMC) in organizations also offers workers new opportunities for connection with others or information—i.e., interacting, collaborating, or using organizational resources (Nurmi & Hinds, 2020). Specifically, when in remote work contexts, workers signal their productivity, availability, and connectivity through CMC activity (Cristea & Leonardi, 2019). A consequence of this is that individuals’ behaviors are increasingly visible to others in the organization and can be documented, aggregated, and evaluated over time (Leonardi & Treem, 2020). This environment presents a dualism of control whereby CMC both enhances control by offering opportunities for surveillance, but also may dilute control by allowing employees to manage their visibility and connectivity (Cunha et al., 2023).

Historically, managers’ disinclination for remote work rests on concerns regarding control of employees’ behaviors (Kurland & Egan, 1999); however, less is known about these views in the current socio-material context of work that affords new possibilities for visibility management and monitoring (Leonardi & Treem, 2020). Because employees’ digital activities are easily visible and can be aggregated and evaluated in various ways, those activities become increasingly constitutive of how other organizational members and management may perceive employees’ work, behaviors, and identities (Flyverbom, 2022; Leonardi, 2021). This trend is aligned with, and an extension of, historical efforts by organizations to implement technologies to assert greater control and oversight over workers’ actions (Bernstein, 2017; Brayne, 2022).

Hence, both managers and employees face new challenges and opportunities in navigating surveillance and connectivity in remote and digital work environments. Scholars and practitioners have noted how CMC inevitably produces digital exhaust, digital footprints, or digital traces that can be used to monitor, analyze, and evaluate the behaviors of individuals (Armstrong et al., 2023; Leonardi, 2021) as well as promote the escalation of connectivity to work (Van Zoonen, Treem, & Sivunen, 2023). What remains uncertain is the extent to which surveillance activities by management are a product of the visibility of workers’ activities, the connectivity of workers, both, or neither. For instance, research on employee surveillance and monitoring typically adopts a technology- or employee-centric approach (Kellogg et al., 2020; Mettler, 2023) and documents the downstream impacts of surveillance (Ball et al., 2012; Botan, 1996; Marwick, 2022; Vitak & Zimmer, 2023) often failing to acknowledge managers’ perspective on surveillance (White et al., 2020). Our study focuses on both sides of this visibility management, i.e. actors’ attempts to influence what is made visible and observers’ attempts at surveilling what is visible.

We propose several refinements that could help us understand connectivity and surveillance in digital and remote work settings. First, we propose that communication visibility (Treem et al., 2020) represents an important mechanism explaining how remote work frequency and CMC use may result in tighter organizational surveillance. While remote work is often believed to increase the necessity to tighten managerial oversight, the increased reliance on CMC use that facilitates this way of working also increases the opportunity for such surveillance. As such, workers’ visibility management in remote work settings may create a tension between increasing one’s visibility and connectivity but also affording and facilitating their own surveillance in doing so (Ball, 2021; Levy & Barocas, 2018; Patil & Bernstein, 2022). In other words, we demonstrate how remote work and CMC use may impinge on employees’ privacy and autonomy by affording, and possibly necessitating, unprecedented surveillance and monitoring. These findings are important for organizational communication scholarship because they lay bare a difficult bind that emerges in contemporary digital and remote workspaces, one where workers’ efforts to enhance connectivity may inadvertently expand managerial surveillance. Finally, we position communication visibility as a critical lens for examining how the interplay between remoteness and CMC use drives these dual outcomes.

Theoretical Framework

For decades, scholars have studied teleworkers, remote workers, and distributed work, where individuals are reliant on the use of communication technologies to facilitate interaction and coordination (Bailey & Kurland, 2002; Olson, 1983; Raghuram et al., 2019; Ter Hoeven & Van Zoonen, 2015; Venkatesh & Vitalari, 1992; Wang et al., 2021; Woo et al., 2023). We define remote work as a flexible arrangement whereby workers conduct organizational tasks away from their central offices or at different times as their colleagues, necessitating the use of information and communication technologies (Wang et al., 2021). Hence, remote work comprises two dimensions—i.e., flexibility over time and place and technology use (Allen et al., 2013). Together, these dimensions can be viewed as a socio-material context that gives rise to new ways for workers to connect and for organizations to expand the surveillance of workers (Aloisi & De Stefano, 2022) through increased visibility of workers’ activities and networks (Leonardi, 2021). Several studies have considered how remote work and digitalization challenge existing notions of workplace control, expanding surveillance opportunities (Cunha et al., 2023; Grisold et al., 2024; Mettler, 2023; Vitak & Zimmer, 2023). Hence, we examine how these two dimensions of contemporary work facilitates connectivity and surveillance through communication visibility.

Communication Visibility and Supervisor-Rated Remote Surveillance

Surveillance has been a central aspect of managerial ideology since the early days of Taylor’s Scientific Management when organizations sought to dictate forms of embodied, physical labor. Today, employee monitoring and surveillance technologies assist management in measuring, shaping, and controlling a variety of behaviors of employees, for instance, through email or video monitoring (Aiello, 1993; Ball, 2022; Spitzmüller & Stanton, 2006; Stanton & Stam, 2006). Several studies have highlighted trade-offs between employee privacy management and organizational control and surveillance (Watkins Allen et al., 2007). Indeed, the proliferation of surveillance technologies has given rise to debates about the balance between protecting organizational interests and preserving individual privacy (Cox et al., 2005; D’Urso, 2006).

As remote work has become mainstream, concerns about the loss of managerial oversight have become more salient, giving rise to a desire to increase employee surveillance and monitoring (Ball, 2021; Leonardi, 2021; Sewell & Taskin, 2015). Broadly, surveillance is an organizing principle (Ball, 2022) referring to “management’s ability to monitor, record, and track employee performance, behavior, and personal characteristics in real-time (for example, internet or telephone monitoring) or as part of broader organizational processes” (Ball, 2010, p. 87). Hence, we focus on supervisors’ ability to surveil their employees. In the context of workplace flexibility, research indicates that digital technologies that enable flexibility present features that enable managerial control through previously unseen forms of remote surveillance (Sewell & Taskin, 2015).

One aspect of digital and remote surveillance (i.e., dataveillance) that differs from efforts to monitor physically embodied work, is that behaviors are inherently collected and available for surveillance as a byproduct of work itself (Clarke, 1988). Because employees are increasingly dependent on the use of software, databases, and digitally connected hardware, every click, entry, email, and document is available for potential observation and evaluation (Aaltonen & Stelmaszak, 2023). As a result, people can make communication more visible in more ways than ever through technologies, yet have far less control over how and by whom that communication is viewed (Treem et al., 2020). The increasing digitalization of work expands the potential scope and reach of surveillance to such a degree that it has become an important a priori condition for organizational control (de Vaujany et al., 2021). Research on dataveillance and IT monitoring has emphasized the need to consider what is made visible and how (Zorina et al., 2021). Flyverbom (2022) noted that the ubiquity of digital technologies makes our lives “overlit,” providing unprecedented and undesirable visibility into our activities that are used to make meaningful evaluations and judgments. This necessitates forms of visibility management that workers may be unaccustomed to or ill-prepared for. While organizations increasingly rely on monitoring employee data without detriment to performance or protests from employees, scholars caution against these practices (Patil & Bernstein, 2022).

This study builds on visibility management as an important set of dynamics in digital spaces and distributed organizational contexts (Flyverbom, 2022; Hafermalz, 2021). Broadly, managing visibility means attempting to act on the world by shaping possibilities for seeing, knowing, and governing (Flyverbom et al., 2016). As such, “visibility specifies how easy it is to monitor employees’ practices and performances” (Cunha et al., 2023, p. 206). Individuals seek to limit, shape, or shift how others observe them to align with situated goals and observe available information and communication in pursuit of those goals (Treem et al., 2020). Hence, visibility is a control process best understood as the mutual constitution of observers’ (managers) attempts at surveilling what is visible and actors’ (employees) attempts to influence what is made visible. Ganesh (2016) argued that increases in digital visibility gives rise to forms of surveillant individualism whereby monitoring of others (and how one is seen) becomes engrained in the daily practices of workers as they use various technologies. This dynamic is widely studied in the context of cybervetting (e.g., Berkelaar & Buzzanell, 2015). Though visibility management in CMC contexts has largely been associated with efforts of self-presentation and associated theorizations (see e.g. DeVito et al., 2017; Hogan, 2010), visibility management in contemporary organizations may differ in various ways.

First, digital communication creates a form of interdependence and relative visibility such that individuals are engaged in visibility management whether or not they are intentional, purposive, or strategic in their behaviors. In other words, invisibility is not possible for organizational members because the absence of activity is itself visible and comparable to the actions of peers or managerial expectations. Second, a self-presentation lens largely assumes a known, intended, or imagined audience for digital communication (Marwick & boyd, 2011). Yet the digitalization of work means that individuals’ activities are visible in more expansive, passive, and unknown ways – i.e., individuals produce visible communication as a byproduct of regular work practices and have little insight into how this communication is seen or used. Both employers and employees navigate visibility in a dynamic socio-material context, where different actors experience varying levels of visibility (Kim, 2018). However, organizations are asymmetrically advantaged, gaining insights into the activities of workers that offer a new visibility-based organizational control paradigm (Leonardi & Treem, 2020).

Workplace Flexibility, CMC, and Supervisor-Rated Surveillance

The visibility of information about work and employees has made surveillance more salient (Anteby & Chan, 2018; Leonardi, 2021). This creates an interdependent dynamic where organizations pursue heightened visibility of workers’ activities. In turn, employees need to make appropriate knowledge and connections visible to ensure they are evaluated, acknowledged, and recognized for their efforts (Ball, 2022). Importantly, individuals can exert a meaningful degree of agency over what data becomes visible and are likely to utilize this ability to manage evaluations that may be formed from that data (Endacott & Leonardi, 2022; Treem et al., 2020). Ball (2021) suggests that employees may “make themselves seen in order to be ontologically recognized as well as to expose their performance and limit the aspects of their person which are brought into the gaze” (p. 65).

Our discussion highlights how recent developments in work arrangements and reliance on technologies have propelled, or at least foregrounded, surveillance opportunities for managers and organizations. We emphasize the role of remote work (consisting of workplace flexibility and CMC) in driving supervisors’ ability to surveil their subordinates. Specifically, we suggest that workplace flexibility and technology use invite employee surveillance by highlighting a necessity (remote workers) and an opportunity (mediated work practices) to do so. This relationship is partly explained (i.e., mediated) by how employees make their knowledge and behaviors visible (i.e., create a target for surveillance).

Hence, we argue that remote work and CMC not only create a perceived need for surveillance (due to physical and temporal distance enabled by flexible work practices) but also a more accessible opportunity (through mediated work practices). While our mediation model suggests that this relationship is partly explained by employees’ visibility—where technology use and remote work make behaviors and knowledge more observable—it is unlikely that visibility alone fully accounts for the effect. For instance, the ability for surveillance is also likely to be shaped by organizational norms and expectations (Ball, 2010; Sewell, 1998). In addition, even when employees engage in minimal knowledge-sharing or self-presentation, digital trace data (e.g., logins, timestamps, keystroke monitoring) may still provide opportunities for surveillance (Leonardi, 2021). Thus, full mediation would be an overly restrictive assumption (one that is often untenable in practice; Maxwell et al., 2011), as it would ignore other potential drivers of surveillance that operate alongside visibility. Our expectation of partial mediation aligns with research suggesting that workplace monitoring is influenced not only by observable behaviors but also by structural and technological factors that independently contribute to surveillance practices. Specifically, we hypothesize:

Workplace flexibility (a) and CMC frequency (b) are positively associated with supervisors’ ability to surveil subordinates.

Workplace flexibility (a) and CMC frequency (b) are positively related to communication visibility, which in turn is positively related to supervisors’ ability to surveil subordinates.

Workplace Flexibility, Technology use, and Connectivity

Beyond the burgeoning surveillance and monitoring practices in the workplace, research has raised concerns about how workers seem unable to disconnect in the era of widespread communication visibility (Büchler et al., 2020; Van Zoonen, Treem, & Sivunen, 2023). Connectivity refers to “the mechanisms, processes, systems, and relationships that link individuals and collectives by facilitating material informational and/or social exchange” (Kolb, 2008, p. 138). Employees are linked together through technologies, creating a complete, closed network wherein all workers can potentially reach other workers, other content, or other tools (Kolb et al., 2012; Nurmi & Hinds, 2020). Yet despite technological advances, achieving requisite connectivity (i.e., ideal levels of connectivity) to facilitate optimal work in distributed work settings remains a prevalent and ongoing challenge (Hafermalz & Riemer, 2020).

Ironically, early concerns related to workplace flexibility revolved around efforts to increase workers’ connectivity, presence, and engagement. Currently, scholars note that digital technologies can make it difficult for workers to disconnect from work (Boswell et al., 2016). Digital technologies that enable workplace flexibility have increased the number of hours workers engage in supplemental work away from the office, creating states of perpetual or, more accurately, extended connectivity (Fenner & Renn, 2010; van Zoonen et al., 2021). Access to technologies that allow for connectivity is often interpreted as an implicit expectation for connectivity, and combined with the interdependence of work, this often expands response norms (van Zoonen et al., 2021). Research has suggested that digital technologies may represent an “electronic leash” (Büchler et al., 2020) that keeps workers tethered to work, whether out of their own volition or a felt pressure to stay connected.

However, we propose that individual’s choices to stay connected are more complex and are possibly driven by the extent to which behaviors and communication are made visible. In her influential study, Mazmanian (2013), demonstrated that observing how others used mobile work devices influenced expectations of appropriate use, leading to escalating engagement and a norm of constant availability. Seeing the information and networks of others may lead individuals to infer appropriate communication practices. In other words, connectivity is not an inherent outcome of flexible work arrangements, but when workers see others’ communication practices it can drive constant connectivity (Büchler et al., 2020).

In addition, research indicates that the visibility and persistence of communication in organizations may pressure individuals to engage with such communication, blurring the spatiotemporal boundaries of work (Van Zoonen, Treem, & Sivunen, 2023). The remote work context further fuels such pressures out of fear one may be left out, ignored, or shunned by organizational members (Hafermalz, 2021). Singh and colleagues (2022) have suggested that employees working remotely are compelled to stay visible online to demonstrate they are not shying away from work responsibilities while working from home. Hence, we suggest that workplace flexibility and CMC will likely increase workers’ connectivity to work partly through communication visibility (Büchler et al., 2020; Singh et al., 2022). As noted previously, other factors beyond visibility may explain how technology use and remote work may affect connectivity such as persistence (Van Zoonen, Treem, & Sivunen, 2023) and social norms (Mazmanian, 2013). Thus, visibility may explain part, but not all, of the relationship between remote work, CMC use and connectivity. Specifically, we hypothesize (see Figure 1):

Figure 1.

Hypothesized relationships.

Workplace flexibility (a) and CMC frequency (b) are positively associated with constant connectivity.

Workplace flexibility (a) and CMC frequency (b) are positively related to communication visibility, which in turn is positively related to constant connectivity.

Method

Sample

Data were collected at a large multinational organization operating in the industrial machinery industry by providing automation and integration solutions to business clients. The organization is headquartered in the Nordics but operates in 300 locations in 50 countries. Employees worked in a team structure, where supervisors were responsible for annual personal development discussions with each employee. In addition to these official performance evaluations, employees were encouraged to discuss their performance and work climate in their teams continuously. Office personnel in all locations around the world could work remotely and adjust their work schedules. According to the company policy, a flexible working culture was embraced, consisting of flexible work schedules with a mix of office work, remote work, or hybrid work.

We obtained access to the organization through our company liaisons, who provided access to human resource data as well as the contact details of employees and supervisors who were targeted for an online questionnaire. We included only employee responses for which corresponding supervisor data was available. The response rate for the employee survey was 15.5% (N = 1127), while the response rate for the supervisor survey was 37.4% (N = 539). Employees were matched to their respective supervisors, resulting in 539 dyads. We ensured that each dyad was unique by retaining only one employee per supervisor in the dataset. This approach prevented non-independence in the data structure and allowed for a clear interpretation of the findings.

Most employees were male (74.8%), which aligns with the company demographics. The average age of the respondents was 43.40 years (SD = 10.92), and they reported an average organizational tenure of 8.42vyears (SD = 8.60). On average, they worked 39.55 hours per week (SD = 3.51) spread across 4.5 workdays (SD = 1.58). The data included responses from 38 countries, including the United States (22.6%), Sweden (7.4%), United Kingdom (6.7%), Italy (6.5%), China (5.4%), and Malaysia (4.5%).

The supervisor survey was administered two weeks after the employee survey was closed. The majority of supervisors identified as male (84%), and their average age was 47.19 years (SD = 8.54). They worked at the company for an average of 10.57 years (SD = 9.30) and reported an average work week of 39.79 hours (SD = 1.73). The supervisors were from 28 different countries as some employees had remote supervisors working in another country. The supervisors were from countries including the United States (23.6%), Sweden (8.5%), Italy (6.7%), China (5.6%), and Malaysia (4.5%).

Measures

Table 1 reports the descriptive statistics and factor loadings for the measurement instrument. Unless indicated otherwise, all statements were answered using a five-point Likert type disagree to agree scale.

Table 1.

Survey Items Latent Constructs Measurement Model.

Item	Mean (SD)	R ²	St Factor loading	Unst. Factor loading^a	Se
Computer Mediated Communication (CMC) frequency^c
Email	4.80 (1.07)	-	-	-	-
Phone calls	3.78 (1.37)	-	-	-	-
Text messages	3.41 (1.57)	-	-	-	-
Instant messages	3.75 (1.59)	-	-	-	-
Enterprise social media	1.75 (1.16)	-	-	-	-
Cloud sharing software	3.68 (1.59)	-	-	-	-
Public social media (for work purposes)	1.85 (1.23)	-	-	-	-
Workplace flexibility
I frequently change the starting and quitting times of my work.	2.69 (1.22)	.79	.889	1.000^b
I frequently choose a work schedule that varies from the typical work schedule at my worksite.	2.67 (1.15)	.68	.826	0.877	.07
I frequently work from an off-site location (such as home) for part (or all) of the regular work week.	3.25 (1.35)	.17	.410	0.509	.06
Communication visibility^d
Message Transparency	2.87 (0.97)	.78	.799	1.000 ^b
I can see content that is shared among my colleagues even though I am not the designated recipient	2.84 (1.10)	.69	.827	1.000^b
I can see the content and messages shared amongst others.	2.83 (1.11)	.70	.837	1.021	.05
I can see what others are working on based on the information they exchange	2.95 (1.09)	.62	.785	0.941	.05
Network translucence	2.65 (1.00)	.64	.881	0.950	.13
I can see who my colleagues are connected with on their social networks	2.53 (1.10)	.74	.863	1.000^b
I can see who my coworkers are connected to, based on who they mention in online interactions	2.64 (1.09)	.86	.927	1.065	.04
I can see who others know based on the information they share	2.77 (1.04)	.73	.852	0.941	.04
Constant connectivity
Through my mobile work device, I am always available for my colleagues and/or clients, including during non-work hours	3.62 (1.04)	.66	.813	1.000^b
During non-work hours, I monitor my work through my mobile work device (e.g., checking e-mails or similar work-related messages, intranet etc.)	3.48 (1.43)	.89	.941	1.180	.04
Through my mobile work device, I know what awaits me at work before I get there	3.57 (1.36)	.78	.882	1.058	.04
For me, it is common to check and answer emails or other work-related messages during non-work hours	3.42 (1.39)	.80	.891	1.092	.04
Through the use of my mobile work device, I stay connected to work during non-work hours	3.47 (1.42)	.88	.938	1.171	.04
Supervisor-rated surveillance
The technologies used by the team enable me to…
... evaluate team member’s tentative knowledge contributions.	3.46 (0.86)	.52	.721	1.000^b
... keep track of the changes over time in any shared document used in the team.	3.72 (0.84)	.40	.630	0.852	.06
... link different contributions because they are made visible through online communications.	3.47 (0.81)	.48	.692	0.901	.06
... compare different contributions of knowledge to the team’s output.	3.41 (0.84)	.57	.755	1.025	.07
... monitor team progress through visible online communication patterns	3.41 (0.97)	.51	.713	1.112	.07
... monitor the information shared among team members	3.50 (0.89)	.48	.689	0.988	.07
... evaluate organizational networks through the online connections that are made visible.	3.30 (0.84)	.55	.743	1.009	.06

^aAll factor loadings are significant at p < .05.

^bUnit loading indicator constrained to 1. AVE is reported in bold.

^cCMC use is computed as an index and, therefore, is not included in the CFA.

^dCommunication visibility is comprised of message transparency and network translucence.

Workplace Flexibility

In this study, workplace flexibility refers to the flexibility employees have to adjust their work schedules and locations (Shockley & Allen, 2007). We measured workplace flexibility using three statements based on Halinski and Duxbury (2020).

CMC Frequency

CMC frequency refers to individuals’ self-reported frequency of organizational communication technology used to interact with their colleagues. We assessed the frequency of use of various technologies (ranging from (1) never to (6) hourly), including email, online conferencing, text or instant messaging, enterprise and public social media, and collaboration tools. In line with previous research on CMC and remote work, we created an overall index of CMC frequency by summing the responses related to individual technologies (Hoch & Kozlowski, 2014; Van Zoonen & Sivunen, 2022). Higher scores indicate a greater frequency of mediated communication with colleagues.

Communication Visibility

We conceptualize communication visibility as the extent to which CMC enables individuals to see what others know and whom others know (Leonardi, 2015). We adopted six items from van Zoonen et al. (2021).

Constant Connectivity

Constant connectivity refers to workers’ extended connectivity to work through CMC. We adopted five items from Büchler et al. (2020) to measure constant connectivity.

Supervisor-Rated Surveillance

Supervisors were asked to what extent the technologies used in the organization allowed them to engage in various surveillance and monitoring behaviors. We adapted seven items previously deployed by Malhotra and Majchrzak (2012) and Malhotra et al. (2007) to measure surveillance.

Analysis

To ensure the validity and reliability of our measurement instrument, we first estimated a measurement model. Subsequently, we tested our hypotheses by estimating a structural equation model. Indirect effects were estimated using bootstrapping procedures (5,000 resamples) to obtain bias-corrected parameter estimates and confidence intervals. Partial mediation was examined by exploring whether the direct effects remained significant when accounting for the mediator (i.e., communication visibility).

We evaluated the model fit of our models using various indices, including the chi-square/df ratio, Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). A chi-square/df ratio below 3, TLI and CFI values greater than .95, and RMSEA and SRMR values less than .06 indicate a good model fit (Hu & Bentler, 1999).

Notably, we examined the influence of various control variables, including gender, age, tenure, supervisor age, supervisor gender, and supervisor tenure. However, because we did not hypothesize their influence, we do not have strong theoretical grounds for their inclusion, there is no assumption that their inclusion would “purify” the results (Spector & Brannick, 2011, p. 296), and they were not found to influence the hypothesized relationships, we present the results from the more parsimonious hypothesized model without the controls (Kline, 2023).

Results

Measurement Model

The measurement model indicated good model fit: χ2/df = 2.424, CFI = .963, TLI = .956, RMSEA = .051 [CI90% .046; .057], SRMR = .038, PCLOSE = .335. The model parameters indicated reliable and valid measurements. Specifically, all composite reliabilities and maximum reliabilities based on Hancock’s coefficient H ranged between .77 and .95 and .83 and .96, respectively. This is well above the suggested threshold of .70. To examine the validity of the measurement instrument, we examined convergent and discriminant validity. Convergent validity is typically assumed when the Average Variance Extracted (AVE) exceeds .50. Convergent validity can be assumed as the AVE values range between .50 and .80. Finally, discriminant validity was established by ensuring that the maximum shared variance across constructs was smaller than the average variance extracted within constructs. Finally, we established that the square root of the AVE was greater than the inter-construct correlations (See Table 2). Hence, the Confirmatory Factor Analysis results did not raise any reliability or validity concerns.

Table 2.

Reliability and Validity Statistics.

	Mean (Sd)	MaxR (H)	CR	AVE	MSV	1	2	3	4	5	6	7	8	9	10	11
1. CMC frequency	3.29 (0.84)	-	-	-	-	-
2. Workplace flexibility	2.87 (0.99)	.88	.77	.55	.06	.11*	.74
3. Communication visibility	2.76 (0.89)	.83	.83	.70	.04	.26*	.19*	.84
4. Surveillance	3.40 (0.66)	.88	.88	.50	.03	.15*	−.03	−.42*	.71
5. Constant connectivity	3.51 (1.28)	.96	.95	.80	.06	.24*	.24*	.15*	.17*	.89
6. Gender^a	0.25 (0.43)	-	-	-	-	.10*	−.07	.05	.09*	−.06	-
7. Age	43.40 (10.92)	-	-	-	-	−.07	.05	−.17*	−.17*	.05	−.15*	-
8. Tenure	8.42 (8.60)	-	-	-	-	−.05	.06	−.06	−.11*	.03	−.17*	.51*	-
9. Work hours	39.55 (3.51)	-	-	-	-	.09*	−.03	.08	.13*	−.02	−.08	−.08	−.04	-
10. Supervisor age	47.19 (8.54)	-	-	-	-	.00	.08	.02	−.08	.18*	−.12	.21*	.13	−.08	-
11. Supervisor gender^a	0.16 (0.37)	-	-	-	-	.05	.03	.10*	.03	−.02	.30*	−.01	−.02	.00	−.02	-
12. Supervisor tenure	10.57 (9.30)	-	-	-	-	−.03	.08	.03	−.06	.01	−.11*	.10*	.22*	.02	.29*	−.13*

Note. MaxR(H) = Maximum Reliability H; CR = Composite Reliability; AVE = Average Variance Extracted; MSV = Maximum Shared Variance; the Square Root of the AVE is reported on the diagonal. Reliability and validity statistics are only provided for latent constructs retained in the final measurement and structural model. Significant correlations are flagged*.

^aGender was coded 0 = male, 1 = female.

Structural Model

Hypotheses Testing

The structural model demonstrated good model fit: χ2/df = 2.396, CFI = .962, TLI = .956, RMSEA = .051 [CI90% .045; .057], SRMR = .040, PCLOSE = .388. Figure 1 depicts the structural model with standardized direct effects, Table 3 provides the results of hypotheses testing. Below, unstandardized solutions are provided. Hypothesis 1 suggests that (a) workplace flexibility and (b) CMC frequency are positively associated with supervisor-rated surveillance ability. The results indicate that workplace flexibility is not significantly related to supervisor-rated surveillance (B = −.032, CI 95% −.088; .025, p = .265). Notably, the results indicate that CMC frequency positively correlates with supervisor-rated surveillance (B = .087, CI 95% .012; .160, p = .023). These findings do not support hypothesis 1a but support hypothesis 1b.

Table 3.

Hypotheses and Results.

Hypotheses		Bootstrapping		BC 95% CI		p	Result
Hypotheses		Estimate	SE	Lower	Upper	p	Result
H1a	Workplace flexibility → ability to surveil	−.032	.029	−.088	.025	.265	Not supported
H1b	CMC use → ability to surveil	.087	.038	.012	.160	.023	Supported
H2a	Workplace flexibility → visibility → ability to surveil	.013	.006	.002	.036	.020	Supported
H2b	Workplace flexibility → visibility → ability to surveil	.028	.013	.003	.064	.033	Supported
H3a	Workplace flexibility → constant connectivity	.222	.061	.103	.344	.001	Supported
H3b	CMC use → constant connectivity	.321	.065	.198	.454	.001	Supported
H4a	Workplace flexibility → visibility → constant connectivity	.043	.085	−.112	.220	.600	Not supported
H4b	Workplace flexibility → visibility → constant connectivity	.006	.011	−.014	.031	.427	Not supported

Hypothesis 2 reflects the assumption that workplace flexibility and CMC frequency positively relate to supervisors’ ability to surveil their subordinates through communication visibility. The findings demonstrate a significant relationship between workplace flexibility and supervisor-rated surveillance through communication visibility (B = .013, CI 95% .002; .036, p = .020). In addition, the findings demonstrate a positive and significant indirect relationship between CMC frequency and supervisor-rated surveillance through communication visibility (B = .028, CI 95% .003; .064, p = .033). These results support H2a and H2b.

Hypothesis 3 articulated that (a) workplace flexibility and (b) CMC frequency are positively associated with constant connectivity. The results indicate that workplace flexibility positively relates to constant connectivity (B = .222, CI 95% .103; .344, p = .001). In addition, CMC frequency is positively associated with constant connectivity (B = .321, CI 95% .198; .454, p < .001). These results support hypotheses 3a and 3b.

Finally, hypothesis 4 posits that workplace flexibility and CMC frequency positively relate to constant connectivity through communication visibility. Notably, the results indicated that communication visibility was not significantly associated with constant connectivity (B = .043, CI 95% −.112; .220, p = .600). As a result, the mediator does not carry an effect to the outcome resulting in the indirect relationships failing to reach significance. The findings indicate that the relationship between workplace flexibility and constant connectivity through communication visibility is not significant (B = .006, CI 95% −.014; .031, p = .427). In addition, the relationship between CMC frequency and constant connectivity through communication visibility was not significant (B = .013, CI 95% −.033; .061, p = .570). Hence, the findings do not support hypotheses 4a and 4b.

Discussion

This study reveals that workplace flexibility is not directly associated with supervisors’ ability to surveil and monitor employees. However, CMC frequency is positively and directly associated with supervisors’ ability to assess employees’ knowledge contributions and online interactions, as well as indirectly through communication visibility. Specifically, employees’ CMC enables supervisors’ surveillance, partly through higher visibility of knowledge and networks. For instance, supervisors reported being able to evaluate employees’ tentative knowledge contributions and link different contributions made visible through online communication. CMC frequency was related to supervisory monitoring abilities because it facilitated greater visibility into organizational networks and messages—that is, it enhanced supervisors’ ability to observe and evaluate workers’ communication and knowledge contributions. Furthermore, the findings suggest that workplace flexibility and CMC are directly associated with connectivity, irrespective of the extent to which these contexts facilitate communication visibility. This indicates that employees who work flexibly and rely on CMC tend to expand their connectivity by remaining accessible to colleagues and engaged with their work, regardless of how visible their communication is to others – including supervisors.

Theoretical Implications

The findings have several implications for theorizing about communication visibility in an age of workplace flexibility and mass employee surveillance. First, while technological advancements have been recognized as expanding computer-aided supervisory monitoring for at least three decades (Aiello, 1993), the increasing prevalence of workplace flexibility has led many organizations to double down on employee surveillance and monitoring (Aloisi & De Stefano, 2022). Interestingly, our findings suggest that greater temporal and spatial flexibility does not affect supervisors’ reported ability to surveil their subordinates. Employees’ CMC frequency was positively related to supervisors’ perceived ability to monitor and evaluate their team members. This aligns with recent findings that technology-mediated work practices may extend the scope and reach of employee surveillance (de Vaujany et al., 2021). Importantly, the mediating role of communication visibility in the relationship between work flexibility and supervisors’ perceived surveillance ability indicates that managers feel more capable of enacting surveillance practices as opportunities arise, not merely out of a need to supervise employees who work flexibly.

One might view the relationships in this study and the different effects of workplace flexibility and communication visibility as a distinction without a difference; after all, the same CMC that enables workplace flexibility also increases communication visibility. However, we argue that understanding communication visibility as a mediator of managerial surveillance abilities has two important implications. First, individuals may opt for non-traditional, distributed work arrangements for various reasons, such as meeting individual needs and preferences, balancing care and professional obligations, or protecting and enhancing personal well-being. This implies an inevitable tradeoff between flexibility and the level of surveillance, which paints a rather bleak picture of the future of work. Alternatively, when communication visibility is seen as a driver of surveillance, it offers more potential for workers to exhibit agency over the types of behaviors and networks they engage in and, in turn, how the organization evaluates those behaviors. Although the required CMC in remote work inherently makes communication visible in new and different ways, research demonstrates that when workers are aware that their behaviors and networks can be monitored by management, they can develop creative, situated ways to make their activities and connections more or less visible in a manner that reasserts control over work (Treem et al., 2023). Hence, centering communication visibility as the mechanism driving surveillance can offer new avenues for forms of visibility management by workers.

Second, viewing surveillance more as a product of opportunity—where supervisors perceive they can monitor employees because the information is readily available—rather than an inevitability required by the work arrangements indicates that managers’ decisions regarding surveillance practices are context-dependent and may be more malleable than previously thought. Although managerial surveillance is often attributed to efforts to control workers and extract optimal labor, many contemporary monitoring efforts may be driven by supervisors’ curiosity, perceived convenience, or even complacency. The multidimensional nature of communication visibility offers different avenues to counter the increased perception of surveillance capabilities among supervisors. One path is to communicate to managers more explicitly that they have agency in utilizing their perceived ability to surveil. Another option is to design the implementation of CMC to limit managers’ access to data regarding workers’ practices and connections, thereby affecting their perceived ability to monitor. The former approach encourages potential observers to manage visibility by consciously limiting what they see, and the latter leverages the material aspects of technology to manage users’ visibility. Both of these strategies demonstrate the theoretical value of a communication visibility perspective and indicate how research on visibility management can incorporate observers’ perceptions (e.g., managers’ reported ability to surveil) and the socio-material context of work (e.g., features of digital tools and the ways they are used), in addition to the common focus on actors’ activities and networks (e.g., ways that workers are visible).

More broadly, this study indicates how CMC frequency, communication visibility, surveillance, and connectivity all contribute to a logic of extraction among contemporary organizations in which companies seek to gather as much information as possible from technology users (Sadowski, 2019). As this study shows, when this data is available, organizations and their members are more likely to use it and see related outputs as valid representations of reality. The datafication of work then feeds into efforts to develop new metrics, rankings, scores, and other evaluative tools that quantify workers’ behaviors (Aaltonen & Stelmaszak, 2023). Ongoing work is needed to examine how aspects of communication visibility are adapted by organizations and managers as a means of evaluating workers and work practices.

The findings regarding a lack of association between connectivity and communication visibility was somewhat surprising considering previous evidence of this relationship (e.g., Büchler et al., 2020; van Zoonen et al., 2021, 2023). Research has argued that connectivity and supplemental work can result from the widespread availability of information (i.e., communication visibility) that creates an expectation (Mazmanian, 2013; Van Zoonen, Treem, & Sivunen, 2023) and pressure to act upon that information (van Zoonen et al., 2021). However, our findings indicate that workplace flexibility and CMC frequency are associated with constant connectivity to work regardless of increased communication visibility. Notably, while the bivariate correlations indicated a positive relationship between visibility and constant connectivity, this relationship did not hold in the structural models where CMC frequency and remote work directly and significantly predicted constant connectivity.

In addition, our findings demonstrate a moderate positive correlation between CMC use and constant connectivity. This implies that more CMC use is associated with higher levels of connectivity. Research has shown that CMC use enables different connectivity states, including requisite connectivity (i.e., connective flow: Dery et al., 2014), and also affords disconnection (Gibbs et al., 2013). In line with previous findings emphasizing that increased digital communication does not necessarily result in constant connectivity (Leonardi et al., 2010; Mazmanian, 2013), our results show a moderate relationship highlighting the need to consider how technology is enacted in practice.

Furthermore, our assumed partial mediation analysis tested whether visibility explained the link between CMC use, remote work, and connectivity, but the non-significant path between visibility and connectivity indicated that this assumption was not supported. Instead, CMC use and remote work emerged as direct and stronger predictors of connectivity, suggesting that they function as more immediate and robust drivers of constant connectivity than visibility. Our study aligns with earlier research, as evidenced by the positive and significant correlation between visibility and connectivity. However, the lack of a significant association in our structural models and mediation analysis suggests that scholars should consider competing or alternative explanations to more robustly assess the relationship between visibility and its implications—here, for connectivity. Future research should further examine whether visibility operates as a contextual condition rather than a direct driver, or whether its effects depend on unexamined moderators that shape how employees engage with visible communication.

Practical Implications

Interestingly, the findings of the study suggest that increased temporal and spatial flexibility within the workforce does not automatically lead to heightened surveillance and monitoring practices. While the abrupt transition to remote work during the pandemic drew an incredible amount of attention to employee surveillance and monitoring practices (Ball, 2021), our findings indicate that technological opportunities and communication visibility facilitate surveillance. Hence, organizations should be aware that technology use by all workers—remote, distributed, or co-located—can spark supervisors’ surveillance and monitoring practices, and they should consider implementing appropriate policies and guidelines to ensure ethical and effective monitoring practices (White et al., 2020).

Furthermore, our findings indicate that communication visibility is important in facilitating surveillance and monitoring practices. As such, organizations should recognize that increased visibility of communication and networks can lead to increased surveillance opportunities for supervisors. Both message transparency and network translucence are inherently performative, meaning employees have opportunities to curate their knowledge and the communication networks they seek to make visible. This also raises important questions about the target of supervisors’ surveillance and monitoring practices. For instance, organizations that view the results of surveillance efforts as objective reflections of workers’ practices risk rewarding the activities of employees who engage in misleading, deviant, or opportunistic practices designed to evade surveillance (Aaltonen & Stelmaszak, 2023). From the perspective of organizational policies, surveillance is shaped by a complex interplay of control and privacy concerns. D’Urso (2006) reminds us to consider how panoptic effects—the feeling of being constantly surveilled by communication technologies—are driven by both technological and organizational factors. Policies that govern employee monitoring may set expectations, while inconsistent enforcement or lack of transparency can lead to confusion and resistance from workers (Cox et al., 2005). Effective policies must balance organizational needs for security and productivity with employees’ right to privacy, ensuring clear communication and ethical practices in the digital workspace (D’Urso, 2006; Watkins Allen et al., 2007).

Finally, supervisors should be educated about the implications of digital surveillance and monitoring practices. The findings highlight that managers enact surveillance practices as they become opportune. Organizations should provide training and guidance to managers on ethical surveillance practices, respecting employee privacy, and avoiding excessive surveillance that can lead to distrust and disengagement. Relatedly, managers and organizations should acknowledge the likelihood, even inevitability, that workers engage in their own forms of surveillance, countersurveillance, and resistance in efforts to manage visibility (Ganesh, 2016). Competing efforts by workers and supervisors to manage visibility, navigate surveillance efforts, and influence evaluations can produce unintended consequences (Treem et al., 2023). While surveillance may be driven by increased visibility, organizations should be mindful of the potential negative, or unpredictable, effects on employees.

Limitations and Future Research Directions

Several limitations of the study should be considered. First, while we draw from multi-sourced data and the surveillance measure was taken two weeks after the initial survey, the employee survey was single-sourced and cross-sectional. This prevents us from drawing any conclusions about causality. Second, the findings reported here are based on a sample from a single organization. While employees were recruited from different branches of the organization across the globe, the generalizability of the findings beyond the context of this study may be limited. Third, the current study examined antecedents of supervisors’ surveillance practices. While we considered it important to measure employees’ perceptions of their visibility to study visibility management in remote work, our study is limited in showing how observers’ visibility (i.e. supervisors’ perceived visibility to their employees’ communication) may be associated with supervisors’ surveillance practices. Future research could explore whether supervisors actually perceive that their ability to observe workers’ communication through CMC has enhanced and what are the ramifications of this enhanced visibility to their surveillance practices.

It is also important to note that worker surveillance and monitoring issues are always intimately shaped by differences in power, positionality, and privilege. A more direct, explicit critical examination of surveillance practices in organizations is needed but beyond the scope of what is examined in this research. In particular, there is a need to examine surveillance and monitoring in non-capitalistic economies, the potential role of bias and prejudice in practices, and ways the relationships operate in more diverse organizational settings. Future research may seek to explore how cultural and contextual factors shape surveillance dynamics in the workplace. For instance, Kayas (2023) suggested that new insights on electronic surveillance could be developed by examining the context within which technology is adopted.

Furthermore, it will be interesting to explore to what extent organizations sort through or monitor the glut of digital traces employees leave behind as by-products of their work (Leonardi, 2021). Such studies may focus on how employees’ professional identities are shaped and represented in various data sources, and what information is made visible to whom in the organization. Different layers of communication visibility might exist in parallel in the same organization, if, for instance, certain information is shared among coworkers through communication technologies with restricted visibility compared to what is visible to supervisors. In addition, it is interesting to explore to what extent surveillance practices and perceptions of being under surveillance affect overall worker experiences in a datafied work environment.

Finally, future studies may explore how these dynamics are shaped by different organizational policies and legal frameworks. For instance, organizational policies are important in shaping how and when electronic surveillance is used (D’Urso, 2006) and, as a result, to what extent workers may engage in different compliance and resistance tactics, for instance, through privacy management (Watkins Allen et al., 2007). Ultimately, bringing organizational policies on surveillance into scope will help tease out how such structural aspects in the digital work environment help shape employee communication (e.g., connectivity behavior) and surveillance in mutually reinforcing ways. Similarly, the nature and extent of surveillance reflected in organizational policies may affect perceptions of privacy (Cox et al., 2005) and motivate employees to carefully consider when and what is made visible to whom in remote and digital work.

Conclusions

This study provides novel insights into surveillance practices, communication visibility, and constant connectivity in the context of workplace flexibility and CMC. The findings revealed a complex tradeoff for employees in which their reachability to colleagues through CMC also enhances supervisors’ reported ability to monitor them. We further demonstrate that communication visibility is an important mediator between workplace flexibility, CMC frequency, and supervisors’ perceived ability to surveil their subordinates. Hence, the extent to which communication is made visible provides important opportunities for supervisors to feel more capable of increasing their surveillance activities.

We hope this study inspires further exploration. For instance, this study raises broader questions about visibility-surveillance dynamics in a digitalized environment. The increasing reliance on communication technologies, regardless of work location, blurs the boundaries between remote and office-based work as CMC use produces a glut of data and digital exhaust that creates a challenge between privacy and monitoring (Leonardi, 2021). The intersection of flexibility, connectivity, and surveillance invites further inquiry into how organizations manage these tensions and how employees navigate the trade-offs between autonomy and monitoring. Future research should examine the implications of these dynamics across diverse organizational settings, considering how workers and managers adapt to and interpret the visibility of digital traces in their daily work.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Kulttuurin ja Yhteiskunnan Tutkimuksen Toimikunta (356143).

ORCID iDs

Ward van Zoonen

Anu E. Sivunen

Jeffrey W. Treem

Author Biographies

Ward van Zoonen (Ph.D., University of Amsterdam) is an associate professor in Communication Science at Vrije Universiteit Amsterdam, the Netherlands. His main research interests include information and communication technologies, including AI at work, remote work and wellbeing.

Anu E. Sivunen (Ph.D., University of Jyväskylä) is a professor in the Communication Science at the University of Jyväskylä, Finland. Her research focuses on remote and virtual communication, with a particular interest in identity, emotions, and wellbeing themes.

Jeffrey W. Treem (Ph.D., Northwestern) is professor of integrated marketing. His research examines ways that digital technologies alter the visibility of communication in organizational contexts. His scholarship addresses the increased digitalization and datafication of work and the ways these changes transform processes such as employee surveillance, knowledge sharing and collaboration.

References

Aaltonen

Stelmaszak

(2023). The performative production of trace data in knowledge work. Information Systems Research. Advance online publication. https://doi.org/10.1287/isre.2019.0357

Aiello

J. R.

(1993). Computer-based work monitoring: Electronic surveillance and its effects. Journal of Applied Social Psychology, 23(7), 499–507. https://doi.org/10.1111/j.1559-1816.1993.tb01100.x

Allen

T. D.

Johnson

R. C.

Kiburz

K. M.

Shockley

K. M.

(2013). Work–family conflict and flexible work arrangements: Deconstructing flexibility. Personnel Psychology, 66(2), 345–376. https://doi.org/10.1111/peps.12012

Aloisi

De Stefano

(2022). Essential jobs, remote work, and digital surveillance: Addressing the COVID-19 pandemic panopticon. International Labour Review, 161(2), 289–314. https://doi.org/10.1111/ilr.12219

Anteby

Chan

C. K.

(2018). A self-fulfilling cycle of coercive surveillance: Workers’ invisibility practices and managerial justification. Organization Science, 29(2), 247–263. https://doi.org/10.1287/orsc.2017.1175

Armstrong

Briggs

Moncur

Carey

D. P.

Nicol

Schafer

(2023). Everyday digital traces. Big Data & Society, 10(2), Article 20539517231213827. https://doi.org/10.1177/20539517231213827

Bailey

D. E.

Kurland

N. B.

(2002). A review of telework research: Findings, new directions, and lessons for the study of modern work. Journal of Organizational Behavior, 23(4), 383–400. https://doi.org/10.1002/job.144

Ball

(2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106. https://doi.org/10.1080/00236561003654776

Ball

(2021). Electronic monitoring and surveillance in the workplace. European Commission Joint Research Centre. https://doi.org/10.2760/5137

10.

Ball

(2022). Surveillance in the workplace: Past, present, and future. Surveillance and Society, 20(4), 455–461. https://doi.org/10.24908/ss.v20i4.15805

11.

Ball

Daniel

E. M.

Stride

(2012). Dimensions of employee privacy: An empirical study. Information Technology & People, 25(4), 376–394. https://doi.org/10.1108/09593841211278785

12.

Berkelaar

B. L.

Buzzanell

P. M.

(2015). Online employment screening and digital career capital: Exploring employers’ use of online information for personnel selection. Management Communication Quarterly, 29(1), 84–113. https://doi.org/10.1177/0893318914554657

13.

Bernstein

E. S.

(2017). Making transparency transparent: The evolution of observation in management theory. The Academy of Management Annals, 11(1), 217–266. https://doi.org/10.5465/annals.2014.0076

14.

Boswell

W. R.

Olson-Buchanan

J. B.

Butts

M. M.

Becker

W. J.

(2016). Managing “after hours” electronic work communication. Organizational Dynamics, 45(4), 291–297. https://doi.org/10.1016/j.orgdyn.2016.10.004

15.

Botan

(1996). Communication work and electronic surveillance: A model for predicting panoptic effects. Communication Monographs, 63(4), 293–313. https://doi.org/10.1080/03637759609376396

16.

Brayne

(2022). The banality of surveillance. Surveillance and Society, 20(4), 372–378. https://doi.org/10.24908/ss.v20i4.15946

17.

Büchler

ter Hoeven

C. L.

van Zoonen

(2020). Understanding constant connectivity to work: How and for whom is constant connectivity related to employee well-being? Information and Organization, 30(3), Article 100302. https://doi.org/10.1016/j.infoandorg.2020.100302

18.

Clarke

(1988). Information technology and dataveillance. Communications of the ACM, 31(5), 498–512. https://doi.org/10.1145/42411.42413

19.

Cox

Goette

Young

(2005). Workplace surveillance and employee privacy: Implementing an effective computer use policy. Communications of the IIMA, 5(2), 6. https://doi.org/10.58729/1941-6687.1263

20.

Cristea

I. C.

Leonardi

P. M.

(2019). Get noticed and die trying: Signals, sacrifice, and the production of face time in distributed work. Organization Science, 30(3), 552–572. https://doi.org/10.1287/orsc.2018.1265

21.

Cunha

Errichiello

Pianese

(2023). The axis of accessibility and the duality of control of remote workers: A literature review. Journal of Information Technology, 39(1), 194–260. https://doi.org/10.1177/02683962231208218

22.

Dery

Kolb

MacCormick

(2014). Working with connective flow: How smartphone use is evolving in practice. European Journal of Information Systems, 23(5), 558–570. https://doi.org/10.1057/ejis.2014.13

23.

De Vaujany

F. X.

Leclercq-Vandelannoitte

Munro

Nama

Holt

(2021). Control and surveillance in work practice: Cultivating paradox in ‘new’ modes of organizing. Organization Studies, 42(5), 675–695. https://doi.org/10.1177/01708406211010988

24.

DeVito

M. A.

Birnholtz

Hancock

J. T.

(2017). Platforms, people, and perception: Using affordances to understand self-presentation on social media. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing (pp. 740–754). ACM. https://doi.org/10.1145/2998181.2998192

25.

D’Urso

S. C.

(2006). Who’s watching us at work? Toward a structural–perceptual model of electronic monitoring and surveillance in organizations. Communication Theory, 16(3), 281–303. https://doi.org/10.1111/j.1468-2885.2006.00271.x

26.

Endacott

C. G.

Leonardi

P. M.

(2022). Artificial intelligence and impression management: Consequences of autonomous conversational agents communicating on one’s behalf. Human Communication Research, 48(3), 462–490. https://doi.org/10.1093/hcr/hqac009

27.

Fenner

G. H.

Renn

R. W.

(2010). Technology-assisted supplemental work and work-to-family conflict: The role of instrumentality beliefs, organizational expectations, and time management. Human Relations, 63(1), 63–82. https://doi.org/10.1177/0018726709351064

28.

Flyverbom

(2022). Overlit: Digital architectures of visibility. Organization Theory, 3(3), 1–16. https://doi.org/10.1177/26317877221090314

29.

Flyverbom

Leonardi

Stohl

(2016). Digital age: The management of visibilities in the digital age—introduction. International Journal of Communication, 10(1), 98–109. https://ijoc.org/index.php/ijoc/article/view/4841

30.

Ganesh

(2016). Digital Age| Managing surveillance: Surveillant individualism in an era of relentless visibility. International Journal of Communication, 10(1), 164–177.

31.

Gibbs

J. L.

Rozaidi

N. A.

Eisenberg

(2013). Overcoming the “ideology of openness”: Probing the affordances of social media for organizational knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 102–120. https://doi.org/10.1111/jcc4.12034

32.

Grisold

Seidel

Heck

Berente

(2024). Digital surveillance in organizations. Business & Information Systems Engineering, 66(3), 401–410. https://doi.org/10.1007/s12599-024-00866-7

33.

Hafermalz

(2021). Out of the panopticon and into exile: Visibility and control in distributed new culture organizations. Organization Studies, 42(5), 697–717. https://doi.org/10.1177/0170840620909962

34.

Hafermalz

Riemer

(2020). Interpersonal connectivity work: Being there with and for geographically distant others. Organization Studies, 41(12), 1627–1648. https://doi.org/10.1177/0170840620973664

35.

Halinski

Duxbury

(2020). Workplace flexibility and its relationship with work-interferes-with-family. Personnel Review, 49(1), 149–166. https://doi.org/10.1108/PR-01-2019-0048

36.

Hoch

J. E.

Kozlowski

S. W.

(2014). Leading virtual teams: Hierarchical leadership, structural supports, and shared team leadership. Journal of Applied Psychology, 99(3), 390–403. https://doi.org/10.1037/a0030264

37.

Hogan

(2010). The presentation of self in the age of social media: Distinguishing performances and exhibitions online. Bulletin of Science, Technology & Society, 30(6), 377–386. https://doi.org/10.1177/0270467610385893

38.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

39.

Kayas

O. G.

(2023). Workplace surveillance: A systematic review, integrative framework, and research agenda. Journal of Business Research, 168(12), Article 114212. https://doi.org/10.1016/j.jbusres.2023.114212

40.

Kellogg

K. C.

Valentine

M. A.

Christin

(2020). Algorithms at work: The new contested terrain of control. The Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

41.

Kim

(2018). The mutual constitution of social media use and status hierarchies in global organizing. Management Communication Quarterly, 32(4), 471–503. https://doi.org/10.1177/0893318918779135

42.

Kline

R. B.

(2023). Principles and practice of structural equation modeling. Guilford.

43.

Kolb

D. G.

(2008). Exploring the connectivity metaphor: Attributes, dimensions, and duality. Organization Studies, 29(1), 127–144. https://doi.org/10.1177/0170840607084574

44.

Kolb

D. G.

Caza

Collins

P. D.

(2012). States of connectivity: New questions and new directions. Organization Studies, 33(2), 267–273. https://doi.org/10.1177/0170840611431653

45.

Kurland

N. B.

Egan

T. D.

(1999). Telecommuting: Justice and control in the virtual organization. Organization Science, 10(4), 500–513. https://doi.org/10.1287/orsc.10.4.500

46.

Leonardi

P. M.

(2015). Ambient awareness and knowledge acquisition. MIS Quarterly, 39(4), 747–762. https://doi.org/10.25300/misq/2015/39.4.1. https://www.jstor.org/stable/26628649

47.

Leonardi

P. M.

(2021). COVID-19 and the new technologies of organizing: Digital exhaust, digital footprints, and artificial intelligence in the wake of remote work. Journal of Management Studies, 58(1), 249–253. https://doi.org/10.1111/joms.12648

48.

Leonardi

P. M.

Treem

J. W.

(2020). Behavioral visibility: A new paradigm for organization studies in the age of digitization, digitalization, and datafication. Organization Studies, 41(12), 1601–1625. https://doi.org/10.1177/0170840620970728

49.

Leonardi

P. M.

Treem

J. W.

Jackson

M. H.

(2010). The connectivity paradox: Using technology to both decrease and increase perceptions of distance in distributed work arrangements. Journal of Applied Communication Research, 38(1), 85–105. https://doi.org/10.1080/00909880903483599

50.

Levy

Barocas

(2018). Privacy at the margins: Refractive surveillance: Monitoring customers to manage workers. International Journal of Communication, 12(1), 1166–1188.

51.

Malhotra

Majchrzak

(2012). How virtual teams use their virtual workspace to coordinate knowledge. ACM Transactions on Management Information Systems, 3(1), 1–14. https://doi.org/10.1145/2151163.2151165

52.

Malhotra

Majchrzak

Rosen

(2007). Leading virtual teams. Academy of Management Perspectives, 21(1), 60–70. https://doi.org/10.5465/amp.2007.24286164

53.

Marwick

(2022). Privacy without power: What privacy research can learn from surveillance studies. Surveillance and Society, 20(4), 397–405. https://doi.org/10.24908/ss.v20i4.16009

54.

Marwick

A. E.

boyd

(2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133. https://doi.org/10.1177/1461444810365313

55.

Maxwell

S. E.

Cole

D. A.

Mitchell

M. A.

(2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46(5), 816–841. https://doi.org/10.1080/00273171.2011.606716

56.

Mazmanian

(2013). Avoiding the trap of constant connectivity: When congruent frames allow for heterogeneous practices. Academy of Management Journal, 56(5), 1225–1250. https://doi.org/10.5465/amj.2010.0787

57.

Mettler

(2023). The connected workplace: Characteristics and social consequences of work surveillance in the age of datification, sensorization, and artificial intelligence. Journal of Information Technology, 39(3), 547–567. https://doi.org/10.1177/02683962231202535

58.

Migliano

(2023). Employee surveillance software demand up 56% since pandemic started. Top10VPN. Retrieved August 10, 2024, from. https://www.top10vpn.com/research/covid-employee-surveillance/

59.

Nurmi

Hinds

P. J.

(2020). Work design for global professionals: Connectivity demands, connectivity behaviors, and their effects on psychological and behavioral outcomes. Organization Studies, 41(12), 1697–1724. https://doi.org/10.1177/0170840620937885

60.

Olson

M. H.

(1983). Remote office work: Changing work patterns in space and time. Communications of the ACM, 26(3), 182–187. https://doi.org/10.1145/358061.358068

61.

Patil

S. V.

Bernstein

E. S.

(2022). Uncovering the mitigating psychological response to monitoring technologies: Police body cameras not only constrain but also depolarize. Organization Science, 33(2), 541–570. https://doi.org/10.1287/orsc.2021.1452

62.

Raghuram

Hill

N. S.

Gibbs

J. L.

Maruping

L. M.

(2019). Virtual work: Bridging research clusters. The Academy of Management Annals, 13(1), 308–341. https://doi.org/10.5465/annals.2017.0020

63.

Sadowski

(2019). When data is capital: Datafication, accumulation, and extraction. Big Data & Society, 6(1), 1–12. https://doi.org/10.1177/2053951718820549

64.

Sewell

(1998). The discipline of teams: The control of team-based industrial work through electronic and peer surveillance. Administrative Science Quarterly, 43(2), 397–428. https://doi.org/10.2307/2393857

65.

Sewell

Taskin

(2015). Out of sight, out of mind in a new world of work? Autonomy, control, and spatiotemporal scaling in telework. Organization Studies, 36(11), 1507–1529. https://doi.org/10.1177/0170840615593587

66.

Shockley

K. M.

Allen

T. D.

(2007). When flexibility helps: Another look at the availability of flexible work arrangements and work–family conflict. Journal of Vocational Behavior, 71(3), 479–493. https://doi.org/10.1016/j.jvb.2007.08.006

67.

Singh

Bala

Dey

B. L.

Filieri

(2022). Enforced remote working: The impact of digital platform-induced stress and remote working experience on technology exhaustion and subjective wellbeing. Journal of Business Research, 151(1), 269–286. https://doi.org/10.1016/j.jbusres.2022.07.002

68.

Spector

P. E.

Brannick

M. T.

(2011). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods, 14(2), 287–305. https://doi.org/10.1177/1094428110369842

69.

Spitzmüller

Stanton

J. M.

(2006). Examining employee compliance with organizational surveillance and monitoring. Journal of Occupational and Organizational Psychology, 79(2), 245–272. https://doi.org/10.1348/096317905X52607

70.

Stanton

Stam

(2006). The visible employee: Electronic monitoring and information security. In AMCIS 2006 proceedings. Paper 407. https://aisel.aisnet.org/amcis2006/407

71.

Stark

Stanhaus

Anthony

D. L.

(2020). “I don’t want someone to watch me while I'm working”: Gendered views of facial recognition technology in workplace surveillance. Journal of the Association for Information Science and Technology, 71(9), 1074–1088. https://doi.org/10.1002/asi.24342

72.

Ter Hoeven

C. L.

Van Zoonen

(2015). Flexible work designs and employee well‐being: Examining the effects of resources and demands. New Technology, Work and Employment, 30(3), 237–255. https://doi.org/10.1111/ntwe.12052

73.

Treem

J. W.

Barley

W. C.

Weber

M. S.

Barbour

J. B.

(2023). Signaling and meaning in organizational analytics: Coping with Goodhart’s Law in an era of digitization and datafication. Journal of Computer-Mediated Communication: JCMC, 28(4), Article zmad023. https://doi.org/10.1093/jcmc/zmad023

74.

Treem

J. W.

Leonardi

P. M.

Van den Hooff

(2020). Computer-mediated communication in the age of communication visibility. Journal of Computer-Mediated Communication, 25(1), 44–59. https://doi.org/10.1093/jcmc/zmz024

75.

Van Zoonen

Sivunen

A. E.

(2022). The impact of remote work and mediated communication frequency on isolation and psychological distress. European Journal of Work & Organizational Psychology, 31(4), 610–621. https://doi.org/10.1080/1359432X.2021.2002299

76.

Van Zoonen

Sivunen

A. E.

Treem

J. W.

(2021). Why people engage in supplemental work: The role of technology response expectations and communication persistence. Journal of Organizational Behavior, 42(7), 86–884. https://doi.org/10.1002/job.2538

77.

Van Zoonen

Sivunen

A. E.

Treem

J. W.

(2023). Examining the longitudinal relationship between visibility and persistence on stress and technology-assisted supplemental work. Human Communication Research, 49(1), 13–23. https://doi.org/10.1093/hcr/hqac023

78.

Van Zoonen

Treem

J. W.

Sivunen

A. E.

(2023). Staying connected and feeling less exhausted: The autonomy benefits of after-hour connectivity. Journal of Occupational and Organizational Psychology, 96(2), 242–263. https://doi.org/10.1111/joop.12422

79.

Venkatesh

Vitalari

N. P.

(1992). An emerging distributed work arrangement: An investigation of computer-based supplemental work at home. Management Science, 38(12), 1687–1706. https://doi.org/10.1287/mnsc.38.12.1687

80.

Vitak

Zimmer

(2023). Surveillance and the future of work: Exploring employees’ attitudes toward monitoring in a post-COVID workplace. Journal of Computer-Mediated Communication, 28(4), Article zmad007. https://doi.org/10.1093/jcmc/zmad007

81.

Walsh

N. P.

(2020). 9/11 saw much of our privacy swept aside. Coronavirus could end it altogether. CNN Business. https://edition.cnn.com/2020/05/16/tech/surveillance-privacy-coronavirus-npw-intl/index.html

82.

Wang

Liu

Qian

Parker

S. K.

(2021). Achieving effective remote working during the COVID-19 pandemic: A work design perspective. Applied Psychology, 70(1), 16–59. https://doi.org/10.1111/apps.12290

83.

Watkins Allen

Coopman

S. J.

Hart

J. L.

Walker

K. L.

(2007). Workplace surveillance and managing privacy boundaries. Management Communication Quarterly, 21(2), 172–200. https://doi.org/10.1177/0893318907306033

84.

White

J. C.

Ravid

D. M.

Behrend

T. S.

(2020). Moderating effects of person and job characteristics on digital monitoring outcomes. Current Opinion in Psychology, 31(1), 55–60. https://doi.org/10.1016/j.copsyc.2019.07.042

85.

Woo

Endacott

C. G.

Myers

K. K.

(2023). Navigating water cooler talks without the water cooler: Uncertainty and information seeking during remote socialization. Management Communication Quarterly, 37(2), 251–280. https://doi.org/10.1177/08933189221105916

86.

Zorina

Bélanger

Kumar

Clegg

(2021). Watchers watched and watching in the digital age: Reconceptualization of information technology monitoring as complex action nets. Organization Science, 32(6), 1571–1596. https://doi.org/10.1287/orsc.2021.1435

Connectivity and Surveillance: The Role of Communication Visibility in the Era of Digitalization and Remote Work

Abstract

Keywords

Theoretical Framework

Communication Visibility and Supervisor-Rated Remote Surveillance

Workplace Flexibility, CMC, and Supervisor-Rated Surveillance

Workplace Flexibility, Technology use, and Connectivity

Method

Sample

Measures

Workplace Flexibility

CMC Frequency

Communication Visibility

Constant Connectivity

Supervisor-Rated Surveillance

Analysis

Results

Measurement Model

Structural Model

Hypotheses Testing

Discussion

Theoretical Implications

Practical Implications

Limitations and Future Research Directions

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

Author Biographies

References