Theoretical background and hypothesis development

In this chapter, we review the related work that forms the basis for our theoretical framework. Specifically, individual theories were examined for five constructs (individual performance, privacy invasion, organizational trust, individual stress and perceived EPM) and substantiated with empirical results from the EPM literature in order to generate theoretically informed and—where possible—empirically grounded hypotheses. Importantly, we selected the above-mentioned five constructs as they have been identified recently as major phenomena in the workplace surveillance and electronic monitoring settings (Ball, 2021; Kalischko & Riedl, 2021; Ravid et al., 2020). Moreover, our dependent measure (individual performance), as well as the three mediators (privacy invasion, organizational trust and individual stress), are major constructs in IS research and beyond, as signified by meta-research analyzing IS publications’ research topics (e.g. Sidorova et al., 2008; Steininger et al., 2009) and by seminal research papers in IS and related disciplines (e.g. Spiekermann and Cranor (2009) for privacy, Mayer et al. (1995) for organizational trust and Ayyagari et al. (2011) for (techno)stress).

2.1 Defining and conceptualizing EPM

To develop a common understanding of the term electronic performance monitoring, definitions of the three most frequently cited articles in this field are given below (Table 1). These definitions are complemented by a seminal definition by the US Congress back in 1987.

Analysis of these definitions indicates that they consider different aspects of EPM. Using the definition of the US Congress as a basis, the definition of Aiello and Kolb (1995) emphasizes the possibility to monitor employees at any time during the working day. Stanton (2000) also focuses on continuous monitoring but also refers to the large amount of data it generates. Moreover, Holman et al. (2002) stress automatic monitoring and the collection of quantitative data. Despite the fact that the definitions consider different aspects, if considered collectively they provide a consistent overall picture of the possibilities of EPM. Monitoring has always been a practiced method to ensure the economic efficiency of a company; however, the most important distinction from traditional monitoring is the electronic component. A major distinction is also made between different monitoring levels (Edwards, Martin, & Henderson, 2018).
The technological evolution of employee surveillance through time is summarized by Edwards et al. (2018) in a recent publication. In essence, they introduce three distinct levels of surveillance: Surveillance 1.0 refers to extensive analog monitoring, Surveillance 2.0 records keyboard inputs, application usage and mouse clicks, while Surveillance 3.0 tracks emails and website activity, giving access to information on “personal relationships, thoughts, opinions, preferences, and interactions” (p. 5). It has recently become possible to conduct “real-time, ubiquitous and unobtrusive surveillance of employees […] by small cheap sensor technology capable of being embedded within the working environment,” (p. 6) also known as Surveillance 4.0, thanks to ubiquitous computing and the Internet of Things (IoT). Currently, EPM is focusing on Surveillance 5.0. The age of algorithms is becoming more and more prevalent, in which “data analytics algorithms are designed to generally spot patterns in large amounts of data, enabling categorization and profiling [… enabling] automated or assisted decision making about hiring, firing, and internal promotion or disciplining” (Edwards et al. p. 6). The technological foundation for this most advanced kind of monitoring is made up of machine learning algorithms, big data and artificial intelligence (Kalischko & Riedl, 2021; Wenzel and Van Quaquebeke, 2018). Numerous EPM techniques are already in use, including video monitoring using CCTV (Kalischko & Riedl, 2021; Sarpong and Rees, 2014), the webcam of laptops (Claypoole and Szalma, 2019), location and movement tracking such as digital camera surveillance or location tracking (Ball, 2021), call monitoring (as it is common in call centers) (Bhave, 2014), tracking computer content and usage times, GPS tracking (Jeske and Santuzzi, 2015), biometric monitoring such as smartwatches (Ball, 2021; Kalischko & Riedl, 2021), emotion monitoring (Ball, 2021), electronic time clock systems, e-mail as well as Internet usage monitoring (Ravid et al., 2020). However, the future of work monitoring may lie in techniques like microchip wrist implants (Kalischko & Riedl, 2021; New York Times, 2017; Ravid et al., 2020) and body heat sensor desk hardware (Ravid et al., 2020). All these EPM techniques can be used to exploit vast amounts of data about employees.
EMP differs significantly from traditional monitoring without ICT use. In the traditional setting, data is gathered via human observation. Specifically, supervisors usually keep an eye out for particular behaviors in various work settings (Ravid et al., 2020). However, monitoring possibilities are limited by supervisor attention and perception. In contrast, in the EPM setting, employees can be monitored continuously and without their notice (Ajunwa, 2017). Thus, two main differences to traditional, non-electronic monitoring are the frequency and level of detail, as a manager’s observational resources are limited and those of an EPM system seem inexhaustible (Ravid et al., 2020). Moreover, the electronic storage of monitoring data seems to be unlimited, while the storage in human (supervisor) memory is not (Ravid et al., 2020).

According to a typology of EPM put forward by Ravid et al. (2020), the monitoring traits of purpose, invasiveness, synchronicity and transparency interact to influence individual-level work outcomes. The typology developed by Ravid et al. (2020) offers a conceptual framework and a vocabulary for discussing and researching EPM characteristics.

Purpose refers to the function or justification for EPM use (Ravid et al., 2020). Different monitoring objectives convey various organizational values, affecting reactions to electronic surveillance (Jeske & Kapasi, 2017; Wells et al., 2007). For instance, if employees are only monitored to quantify the work they perform, this may impair the quality of their work (Stanton and Julian, 2002). However, when used more constructively, performance evaluations can increase organizational commitment, motivation, work satisfaction and feelings of procedural fairness (Bartels & Nordstrom, 2012; Fairness and Wells, 2003; Wells et al., 2007). Also, it has been argued that when EPM is utilized in training and development, it may give learners insightful feedback so they can grow (Holman et al., 2002). When EPM is employed to guarantee safety, it can reassure staff members that they are protected in risky situations (Sewell et al., 2012). However, monitoring can also lead to significant negative attitudes, such as perceptions of diminished fairness and justice (McNall and Roch, 2007), decreased satisfaction, increased stress and negative effects on performance when employees do not know the explicit purpose of EPM use (Ball, 2021; Becker and Marique, 2014).

Invasiveness describes how intrusive and restricting EPM use is, particularly, when it comes to a person’s feeling of privacy or autonomy (Ravid et al., 2020). Individualized monitoring is often seen as a privacy breach (Zweig and Webster, 2003). Monitoring that is task-focused is more acceptable than monitoring that is person- or location-focused (Jeske and Santuzzi, 2015). When employees have control over how information is utilized, monitoring is seen as fairer and less invasive (Alge, 2001). When employees can influence when monitoring occurs, they also view it as fairer and less invasive. Evidence also indicates that giving workers the option to turn off monitoring can improve performance (Ball, 2021; McNall and Stanton, 2011).

EPM’s temporal properties, including the synchronicity of feedback transmission and data gathering, are characterized by synchronicity (Ravid et al., 2020). Employees can be monitored continuously or at specific times. Interestingly, continuous monitoring may be preferred to monitoring at certain times (if employees are not informed about the specific times) because such a situation, ironically, comes along with a higher level of perceived control (Jeske and Santuzzi, 2015). However, research has also shown that some people prefer monitoring at certain times (Aiello & Kolb, 1995; Lund, 1992). Differences in research findings have been attributed to the studies’ monitoring designs and variations in cultural expectations regarding job monitoring (Ball, 2021).

Transparency refers to the level of access to monitoring information (Ravid et al., 2020). Transparency of monitoring is positively related to perceived fairness and justice (Hovorka-Mead et al., 2002). Transparency of EPM also has a positive impact on performance (McNall and Roch, 2009). Higher levels of informational fairness and managerial trust follow from more openness and transparency, reducing employee turnover. Importantly, evidence indicates that low transparency is likely to give the impression that monitoring is dictatorial and without a purpose (Alder et al., 2006).

2.2 Individual performance

When analyzing reviews of EPM in the workplace of the last few years, a variety of potential outcomes emerge (Ball, 2021; Kalischko & Riedl, 2021; Ravid et al., 2020). Although the term EPM no longer exclusively refers to performance aspects, but also to behavior (Business Insider, 2020; Financial Times, 2021; Montealegre and Cascio, 2017), emotions (Ravid et al., 2020; The Guardian, 2018), as well as physiological states (Ball, 2021), the primary reason for companies to introduce such a system is to ensure and improve the productivity and performance of the organization (derStandard, 2021; Fortune, 2021; New York Times, 2021; Washington Post, 2021). Since there are currently no conclusive results regarding the performance effect of EPM use, a more detailed investigation of this construct is of high importance (Ravid et al., 2022).
Considering Zajonc’s social facilitation theory (Zajonc, 1965), the fear of being judged by others and the resulting desire to present oneself in a certain way to others is critical in human social interaction. As an explanation for the impacts of social facilitation, numerous ideas have been developed. Drive theories, social comparison theories and cognitive process theories are the three groups into which social facilitation theory can be divided (Guerin, 1993). These categories are useful for evaluating particular social presence responses that mediate performance effects. The first type of reaction is heightened arousal or drive. According to Zajonc (1965, 1980), individuals’ drive or arousal levels rise in the (mere) presence of others, and it is this rise that either improves or degrades performance of simple tasks. The second group includes worries about being judged by others. People may start to worry about how they appear or perform in comparison to others when they are around other people. These worries include the fear of being judged by others (Cottrell, 1972), the desire to appear a specific way to others (Baumeister, 1982; Bond, 1982) or the need to perform at a level that has been set by society (Aiello & Douthitt, 2001; Carver and Scheier, 1981). The third category comprises a change in cognitive processing speed caused by other people’s distraction (Baron, 1986). In essence, humans typically seek to adapt their own performance to a socially recognized standard (Aiello & Douthitt, 2001; Baumeister, 1982; Bond, 1982; Carver and Scheier, 1982; Cottrell, 1972) and research indicates that the presence of others can increase task performance, especially if the tasks are simple (Zajonc, 1965). What follows is that perceived monitoring through a passive observer (e.g. supervisor) may have—at least in some situations—a positive and direct performance effect.

Positive (Davidson and Henderson, 2000; Huston et al., 1993; Irving et al., 1986; Nebeker, Tatum, Nelbeker, & Tatum, 1993) as well as negative effects (Becker and Marique, 2014; Mallo et al., 2007; Smith et al., 1992) and also no effects (Griffith, 1993; Kolb and Aiello, 1996) characterize the literature. However, most positive employee performance effects through EPM are either based on laboratory studies (predominantly with student samples) or were found in the specific context of call center employees (e.g. Bhave, 2014; Stanton & Julian, 2002). Moreover, it is of utmost importance that the type of work may significantly affect the influence of EPM on individual performance. Research (Goomas and Ludwig, 2009) shows that when people perform physically demanding work and are observed by another person, then performance typically increases—this phenomenon constitutes a major foundation of social facilitation theory, which was published several decades ago when computer work did not play a role (Zajonc, 1965). However, whether this finding generalizes to the context of knowledge workers who predominantly perform their work based on computer technology in mentally complex and psychologically demanding, yet in relatively physically undemanding environments, is not clear today.
Based on self-determination theory (Ryan & Deci, 2000), we argue that an employee’s perception of being electronically monitored may even have negative effects on individual performance. Self-determination theory (SDT) is an approach to human motivation that emphasizes the importance of humans` evolved inner resources for personality development and behavioral self-regulation (Ryan et al., 1997). According to Ryan and Deci (2000), SDT‘s “arena is the investigation of people‘s inherent growth tendencies and innate psychological needs that are the basis for their self-motivation and personality integration, as well as for the conditions that foster those positive processes” (p. 68). People are more likely to perform better, learn better and be more appropriately adjusted when they recognize the value and purpose of their work, feel ownership and autonomy in carrying it out and receive clear feedback and support. The extrinsic focus that results from controlling motivation, whether through contingent rewards or power dynamics, can, however, limit the range of employees’ efforts and have detrimental knock-on effects on subsequent performance and engagement at work (Deci, Olafsen, & Ryan, 2017). Importantly, a non-controlling positive feedback culture positively affects self-motivation and individual performance (Deci, Connell, & Ryan, 1989). However, because an organization with an EPM system in use is quite the opposite of a non-controlling positive feedback culture, we hypothesize as follows.

H1: EPM is negatively correlated with individual performance.