Didn’t you see my email last night?
Why communication technology use is burning you out - a relationship through work-life conflict, work intensification and the influence of colleague surveillance and
capacity for self-regulation.
Master’s Thesis
Graduate School of Communication
Master’s programme Communication Science: Corporate Communication
Carla Ojeda Bautista Student ID: 12016306
Supervisor: Claartje ter Hoeven Date of completion: 28/06/2019
Abstract
The consequences of increased communication technology use (CTU) in the workplace have not only allowed for increased employee flexibility and autonomy, but also heightened concerns about employees’ well-being – particularly burnout. The current study aims to identify to what extent there is a relationship between CTU and burnout via work-life conflict and work intensification, and if capacity for self-regulation and colleague surveillance influence it. Using convenience sampling, data from 185 respondents who worked for a minimum of 20 hours per week was collected through an online, cross-sectional survey. The study replicated findings which uncover a relationship between CTU and burnout explained through work-life conflict, but failed to find a significant relationship through work intensification. Those employees who’s CTU make it challenging for them to deal with personal obligations, seemed to be facing higher levels of emotional exhaustion. Moreover, findings reveal that undesired norms shared by colleagues about CTU and their subsequent surveillance are strengthening the relationship between CTU and work-life conflict experienced by employees. Capacity for self-regulation does not seem to be influencing this relationship. The study encourages future research to identify what factors cause certain environments to promote increased connectivity norms and colleague control and stresses the importance of promoting healthier expectations of CTU by management and organisations.
Keywords: Communication technology use; burnout; work-life conflict; work intensification; colleague surveillance; social norms; capacity for self-regulation; employee well-being
Introduction
Undoubtedly, communication technology use (CTU) has become increasingly common among organisations over the past years, and in so, easing employees’ connectedness to work. This has allowed work to leak outside the organisation’s physical and temporal boundaries and makes it increasingly more difficult to separate work from non-work domains (Boswell &
Olson-Buchanan, 2007). Consequently, the effect that this shift can pose on individuals’ well-being is of great interest both for researchers and management. However, the outcomes are not clear and studies have found that CTU has paradoxical effects on employee well-being. At the same time that it brings several advantages such as autonomy and flexibility, it also has been related to loss of autonomy, longer working hours and potential health threats simultaneously (Adisa, Gbadamosi, & Osabutey, 2017; Mazmanian, Orlikowski, & Yates, 2013; Ter Hoeven, Van Zoonen, & Fonner, 2016). Initially, organisations might promote the extension of working hours into employees’ personal time because they believe that it can increase work production without incurring higher costs. However, what they sometimes fail to see are the long-term negative effects that it can bring to their workforce and consecutively, the overall performance of the organisation. As the ultimate goal would be to only benefit from communication technology, it is essential that research determines the reasons behind the identified negative effects on employee well-being, particularly burnout (Ter Hoeven et al, 2016; Wright et al., 2014), not only to protect the quality of life of those who suffer from it, but to prevent the subsequent economic losses derived from absenteeism, low performance and job turnover (Awa, Plaumann, & Walter, 2010; Bakker, Demerouti, & Sanz-Vergel, 2014).
Nonetheless, researchers such as Ter Hoeven et al., (2016) have found the relationship between CTU and burnout to be complex, as there seems to be an indirect relationship enabled through distinct and sometimes opposing mediations. As CTU allows for increased workloads and faster paced environments, work intensification has been identified to be a potential underlying mechanism in the CTU-burnout relationship (Chesley, 2010; 2014). Now that employees are able to work outside organisational boundaries (Derks & Bakker, 2014), conflicts between work and personal life may arise if employees are unable to deal with personal obligations because they have to handle work obligations instead (Brummelhuis et al., 2012; Diaz et al., 2012). In turn, continuous work-life conflict could develop feelings of
frustration and exhaustion amongst employees, which are apparent in burnout (Derks & Bakker, 2014; Geurts, Kompier, Roxburgh, & Houtman, 2003; Wright et al., 2014; Murray & Rostis, 2007).
Of course, not all employees or organisations experience the negative effects of CTU. It is because of this, that the current research aims to shed light on potential individual and organizational level factors which strengthen or weaken the relationship between CTU and work intensification/work-life conflict. Previous studies have already contributed to literature by identifying certain factors that could be influencing the relationship between CTU and negative outcomes. Barber and Santuzzi (2015) found that the urge to respond to messages, conceptualised as work telepressure, predicted burnout, whereas Boswell and Olson-Buchanan (2007) identified that employees with higher ambition and job involvement do tend to experience higher work-life conflict due extensive use of communication technologies. However, traits such as ambition cannot be easily altered and maintaining high levels of job involvement is in the interest of organisations since it is linked to work motivation (Caillier, 2013). Therefore, the objective of this study is to build upon previous literature by focusing on certain factors which, even though challenging, could be corrected for – either by the employees themselves or through managerial efforts.
At an individual level, capacity for self-regulation has been determined a crucial boundary competence for employees with flexibility benefits (Mellner, Aronsson, & Kecklund, 2014) and has also been found to improve the effective management of workloads – therefore reducing stress and emotional exhaustion (Mattern & Bauer, 2014). This is because individuals with high capacity for self-regulation show greater control over thoughts, feelings, impulses and task performances (Baumeister, Gailliot, Dewall, & Oaten, 2006). If we are able to find evidence suggesting that individuals with higher capacity for self-regulation experience less work-life conflict and work intensification, our findings will be able to recommend concrete
action plans like undertaking trainings that improve this skill and thus, potentially reducing burnout levels. The work of Baumeister et al. (2006) already suggest that this would be possible as it summarises several self-regulation exercises that showed large improvements, while Mattern and Bauer (2014) also argue that these skills can be learned and taught.
At an organizational level, a tendency for colleague surveillance at the workplace has been identified (Barker, 1993), where employees themselves adhere to norms and expectations about their own constant online availability (Mazmanian et al., 2013; Derks, Duin, Tims, & Bakker, 2015). Colleague surveillance could be both, influencing work intensification through expectations of increased availability and lengthened working hours (Duxbury, Higgins, Thomas, & Carr, 2006), and influencing work-life conflict due to external pressures which prevent employees to achieve their preferred level of work-life balance (Kreiner, Sheep, & Hollensbe, 2009; Kreiner, 2006). If significant results are found, it could draw theoretical and practical implications about workplace culture and the importance of paying attention to the subtle cues, comments and behaviour regarding communication technology’s common use. If a toxic environment in which undesired norms amongst employees, such as expectations of availability after hours, are not identified and corrected, potential negative consequences on employee health may arise. Introducing boundary work tactics to create an ideal level of segmentation among peers has been found useful to reduce work-life conflict (Kreiner et al., 2009) and could help in situations where employee well-being is threatened. Therefore, the current study aims to answer the following question: to what extent is there a relationship between CTU and burnout via work-life conflict and work intensification, and does capacity for self-regulation and colleague surveillance influence it?
Theoretical Background
Communication technology use and Burnout: a relationship through work intensification and work-life conflict
Many organisations have redesigned their work approaches by facilitating employees use and choice of communication technologies – thereby allowing employees to decide when and where they work (Brummelhuis et al., 2012).We define communication technologies as those technology-enabled communication mediums (e.g. email) that are used to connect employees with colleagues and supervisors and also allow them to execute work-related tasks outside the organisation’s physical and time boundaries (Wright et al., 2014). CTU was introduced into the workplace with the intention to increase employees’ flexibility and productivity but has been seen to have paradoxical effects as it also hinders their ability to disconnect from work – resulting in the merging of work and non-work boundaries, escalating commitment and stress (Mazmanian et al., 2013). Similarly, the connectivity paradox also argues that even though CTU can be a means to successfully perform tasks, it can also impede work due to perpetual connectivity (Leonardi, Treem, & Jackson, 2010).
Several studies have already discussed a relationship between CTU and negative effects on employee well-being (Geurts et al., 2003; Murray & Rostis, 2007; Ter Hoeven et al, 2016; Chesley 2014; Barber & Santuzzi, 2015) in particular burnout, a phenomenon that occurs when work becomes draining and emotional resources are depleted due to chronic job stress (Maslach & Jackson, 1981). Adisa, et al. (2017) identified CTU as potentially having negative impacts on employee’s health derived from perceived pressure that makes workers feel tired and worn-out. Likewise, Ter Hoeven et al. (2016) also established a relationship between CTU and burnout due to increased work interruptions and unpredictability. Burnout is defined as “a state of exhaustion in which one is cynical about the value of one’s occupation and doubtful of one’s
capacity to perform” (Maslach, Jackson, & Leiter, 1996, p.20) that is said to be composed of three main elements: emotional exhaustion, cynicism and decreased sense of personal accomplishment (Reichl, Leiter, & Spinath, 2014). It is different from stress in a way that those who suffer from burnout deal with prolonged symptoms (Awa et al., 2010). For the purpose of this study we will only be focusing on emotional exhaustion, as it is often referred to as burnout’s core element and most obvious manifestation (Reichl et al., 2014).
Furthermore, the use of certain communication technologies has been identified to positively influence perceived increments in workload as it allows non-paid work to be performed during non-work time (Diaz, Chiaburu, Zimmerman, & Boswell, 2012) and daily users report having to work faster (Chesley, 2010; 2014). This idea of increased efforts and workloads can be linked with work intensification, defined as a state of feeling constantly active promoted by organisational practices, especially those enabled through CTU, that encourages more intense and demanding work experiences where employees sense that they should be working harder and faster (Chesley, 2014). Thereby, it depletes employees from the resources necessary to keep up with the increased pace and workloads, causing them to feel stressed, overwhelmed and ultimately burn out (Chesley, 2014; Kroon & Veldhoven, 2009). These findings make us believe that prolonged exposure to these pressures can lead to severe symptoms of exhaustion and that therefore, work intensification could be one of the underlying mechanisms which explain why CTU can result in burnout. H1(a): The positive relationship between CTU and employee burnout will be mediated by work intensification.
Moreover, the flexibility and autonomy enabled through CTU can become an issue when employees find it difficult to disconnect from work at home, preventing them from dealing with personal obligations and enjoying leisure time (Brummelhuis et al., 2012; Diaz et al., 2012). The conflicts that arise due to incompatibilities between work and non-work roles have been given different terms, often looked as the interference between work and family
responsibilities (Wright et al., 2014). However, although family is relevant when analysing this conflict, employees may also have other significant responsibilities and/or interests to pursue (Fisher, Bulger, Smith, & Tetrick, 2009). Thus, the term work-life conflict has been chosen to be more inclusive, and it is defined in this paper as the interference between work and non-work domains, referring to, not only family aspects, but also other significant roles in employees’ personal lives (Reichl et al, 2014). On occasion, research has made a distinction between work obligations interfering with non-work and, vice versa, non-work obligations interfering with work (Wright et al., 2014). Yet, this research will only be focusing on the former as even though previous literature has found that both conflicts use up individual’s resources, it is work’s interference with personal life which has been found to have higher correlation with exhaustion (Reichl et al, 2014). The link that has been established between work-life conflict and feelings of exhaustion (Derks & Bakker, 2014; Geurts et al., 2003; Wright et al., 2014; Murray & Rostis, 2007) could be explained due to conflicting roles (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964), in which work’s interference with personal life makes complying with life demands more difficult, as it is not possible to engage in different activities simultaneously (Jarvenpaa & Lang, 2005).
At the same time, research studies have also supported the existence of a relationship between CTU, and particularly its use outside working hours, with work-life conflict (Wright et al., 2014), perceived not only by employees themselves, but by their significant other too (Boswell & Olson-Buchanan, 2007). This is because CTU has become the medium that allows employees to perform work related tasks outside the organisation and during personal time (Derks & Bakker, 2014). In other words, now that work can be done outside organisational boundaries, an employee might find themselves trying to finish up a project that was left incomplete in the evening, instead of spending time with their friends. If this occurs consistently, it could cause increased strain levels as they cannot give attention to both activities
at the same time. Boswell and Olson-Buchanan’s (2007) findings regarding perceived work-life conflict by one’s significant other are particularly interesting, as it implies that friends and family could increase their demands for one’s attention and time. This could be pressuring employees even further, causing exhaustion levels to rise. Work-life conflict could therefore, also explain the relationship between CTU and burnout. H1(b): The positive relationship between CTU and employee burnout will be mediated by work-life conflict.
The moderating role of capacity for self-regulation and colleague surveillance
Due to increased use of communication technologies, employees need to manage work-life boundaries which are increasingly blurred and establish an effective balance between the two domains (Derks et al., 2015). This, at the same time allows for more autonomy but also reduces the advantages that having a fixed schedule might bring (Mellner et al., 2014). At an individual level, employees therefore depend on their own self-management abilities to assure autonomy and prevent themselves from wasting time and energy (Duxbury et al., 2006). Skills such as time management, referring to goal setting and prioritisation, become essential as a form of behaviour planning (Lapierre & Allen, 2012; Macan, 1994). Claessens, Van Eerde, Rutte, and Roe (2004) argued that skills such as planning behaviour should allow for greater efficiency of time and energy distribution, which in turn would ease work-life conflict and work overload. Meanwhile, Lapierre and Allen (2012) also found that these skills are useful when keeping control at work.
These findings are associated with individuals’ capacity for self-regulation, a competence which involves: “standards of thought (feeling, or behavior that individuals endorse, mentally represent, and monitor), sufficient motivation to invest effort into reducing discrepancies between standards and actual states and sufficient capacity to achieve this in light of obstacles and temptations along the way” (Hofmann, Schmeichel, & Baddeley, 2012, p.174).
Studies already suggest that individual capacity for self-regulation can be important for boundary control as it involves capacities like action planning, performance control, self-monitoring, and self-reflection (Mellner et al., 2014; Mattern & Bauer, 2014). Now that individuals’ professional email accounts are often connected to smartphone devices that they constantly carry around with them, it is not unlikely for a work-related email to, for example, pop up amidst a family reunion. In theory, the individual might be eager to be fully present and enjoy quality time with their family, but their ability to ignore the temptation to read the email could depend significantly on their personal capacity for self-regulation. Thus, capacity for self-regulation could be influencing the relationship between CTU and work-life conflict. It is predicted that those employees with higher capacity for self-regulation would have overall, better organisation skills and self-control that will allow them to have a better balance between work and non-work domains and therefore, reduced work-life conflict. H2(a): The positive relationship between CTU and work-life conflict will be stronger for employees with low self-regulation capacity (as opposed to high self-self-regulation capacity).
Furthermore, capacity for self-regulation has also been linked with traits such as resilience, which is particularly interesting as it encompasses one’s capacity to become emotionally distant from work and be resistant to failure (Klusmann, Kunter, Trautwein, Lüdtke, & Baumert, 2008). Additionally, it has been argued by Mattern and Bauers (2014) on their study focusing on teachers, that actions displaying higher capacity for self-regulation can help overcome difficulties with the excessive workloads that are present in work intensification. In line with their study, we suggest that this occurs because of the flexibility enabled by CTU and the lack of external usage supervision after working hours, which leaves employees to reply upon their own ability to regulate their impulses, manage distractions and prevent procrastination. A better capacity for self-regulation in which employees develop more efficient planning practices, become aware of the most competent working strategies and have
better control over feelings of stress, could therefore reduce working hours and help cope with the pressure experienced in fast paced environments (Mattern & Bauer, 2014). Conversely, being inefficient or unable to regulate one’s schedule and meet deadlines could lead to even longer working hours and the impression of never having enough time to complete tasks. For this reason, it is believed that capacity for self-regulation could also influence the relationship between CTU and work intensification. H2(b): The positive relationship between CTU and work intensification will be stronger for employees with low self-regulation capacity (as opposed to high self-regulation capacity).
Besides, at an organisational level, researchers have identified a shift in control from supervisors to employees who reach concertive control by negotiating and accepting a set of values and norms that regulate each other’s behaviour. (Barker, 1993). This control can manifest itself in the form of norms, which describe how one should behave at work and are more implicit measures, as well as colleague pressure whereby they explicitly express their dissatisfaction with those who do not comply with the norms created (De Jong, Bijlsma-Frankema, & Cardinal, 2014) – we conceptualise this as colleague surveillance. Enforcing these norms increases the amount of control in an organization in a more subtle way than managerial control, as workers are relatively unaware of it (Barker, 1993). An example of this can be seen in Kirby and Krone’s (2002) study, which examined how negative talk sent messages to other colleagues, creating peer pressure, and rewarding work done in the public sphere while flexibility was perceived as time off.
In terms of CTU, studies have argued that expectations about its use can create norms of constant connectivity where employees should be available and online whenever and wherever (Green, 2002). Derks et al. (2015) suggest that norms regarding availability after working hours are pressuring workers to comply with it in order to feel part of the group. This concept is also explored by Mazmanian et al. (2013) in the autonomy paradox where they argue
that CTU allows for collective escalated engagement whereby in order to perform at work, employees sensed that they had to be always alert and available to respond, which ultimately hindered their autonomy. Expectations of increased availability when using communication technologies and that one should continue working even after working hours (Duxbury et al., 2006) could therefore be strengthening the relationship between CTU and work intensification. In other words, instead of simply leaving work at 5pm, employees might now be receiving emails from their colleagues later in the evening which they feel pressured to reply to – ultimately lengthening their working day. H3(a): The positive relationship between CTU and work intensification will be stronger for employees working under high colleague surveillance (as opposed to low colleague surveillance).
Similarly, colleague surveillance could also be strengthening the relationship between CTU and work-life conflict. Employees might have their own preferred way of managing work and non-work domains in order to achieve balance, and are not necessarily in favour of carrying out work-related tasks during personal time. However, external influences, such as the pressure to adapt to social norms about CTU, could prevent them from managing their time in their preferred way (Rothbard, Philips, & Dumas, 2005; Kreiner et al., 2009; Kreiner, 2006). In short, and employee might prefer to spend their personal time pursuing hobbies or spending time with their loved ones, but replying to emails after hours might be norm at work. Therefore, they might end up choosing to work in fear of the social consequences it would bring if they do not or simply because they want to feel accepted by their colleagues. If due to this they are unable to meet their personal demands, work-life conflict could increase.H3(b): The positive relationship between CTU and work-life conflict will be stronger for employees working under high colleague surveillance (as opposed to low colleague surveillance).
Methodology
Sample and Procedures
Data was collected using an online, cross-sectional survey administered in both English and Spanish language (backwards translation from English to Spanish was used for this). A total of 239 adult respondents were recruited by convenience, through the researcher’s social networks and subsequent snowball technique. Only respondents who worked for a minimum of 20 hours a week (excluding self-employed and freelance) were taken into account. An initial set of demographic questions was exposed in order to assure that the requirements were met. Subsequently, 54 respondents who did not meet the requirements were filtered out, leaving a final sample size of 185 where 66.5% replied to the Spanish version and 33.5% to the English version. The average age of the sample was 37 years old (SD=13.26), 55.7% were female, 43.2% male (2 respondents did not disclose this information), most of them single (50.8%), with no children (67%) and an average of 37.66 working hours per week (SD=9.19). All participants were presented with a document of informed consent and explicitly agreed to their voluntary participation in the study.
Measures
Communication technology use (CTU). This measure intends to evaluate the frequency of respondents’ communication technology use for work-related purposes. A 7-item scale was adapted from Chesley (2010) ‘technology use frequency use context’ variable with the most common, current workplace communication technologies: desktop or laptop, smartphone, tablet, e-mail, text, Skype and workplace specific instant messaging platforms. Respondents were asked to answer a 5-point Likert scale where 1 equals ‘never’ and 5 ‘very often’. CTU (M=3.85, SD= 0.92) appeared to have a negatively skewed distribution, meaning that most respondents ranked relatively high on this scale. Principal axis factor analysis (PAF)
showed that the 7 items form a bi-dimensional scale with two components having an eigenvalue above 1 (eigenvalue 2.34, 1.78) and a cumulative explained variance of 45.3% in the original items. After a direct oblimin rotation, items correlated positively with 2 factors of which 4 items in factor 1 were selected. The items, tablet, Skype and workplace specific instant messaging platforms were left out as their factor loadings were below .30. The scale is acceptable, Cronbach's alpha = .65.
Work intensification. Work intensification (M=3.11, SD=0.87) was measured using an adapted 4-item scale inspired by Karasek’s et al. (1998) psychological job demands: work fast, work hard, excessive work and hectic job. Respondents were asked to reply to items such as “Do you need to work really fast” on a 5-point Likert scale where 1 equals ‘never’ and 5 equals ‘very often’. PAF showed that the 4 items form a single uni-dimensional scale with only one component having an eigenvalue above 1 (eigenvalue 2.51) and an explained variance of 39,7% in the original items. The item “Do you need to work really hard?” has the strongest association (factor loading is .81) and the scale is reasonably reliable, Cronbach's alpha = .77.
Work-life conflict. Work-life conflict (M=2.14, SD=0.79) was measured using Geurts et al. (2003) negative work-home interference scale composed of 9 items. Respondents were asked to answer how often do they experience certain situations on a 5-point Likert scale ranging from 1 ‘never to 5 ‘very often’, examples include “How often does it happen that you find it difficult to fulfil your domestic obligations because you are constantly thinking about your work?”. PAF showed that the 9 items form a single uni-dimensional scale with only one component having an eigenvalue above 1 (eigenvalue 5.14) and an explained variance of 51.8% in the original items. The item “How often does it happen that you have to work so hard that you do not have time for any of your hobbies?” has the strongest association (factor loading is .79). The scale is good and reliable, Cronbach's alpha = .91.
Capacity for self-regulation. Capacity for self-regulation (M=3.35, SD=0.79) was measured using 9 items from Lapierre and Allen’s (2012) scale on measures of planning behaviour. Respondents were asked to reply on a 5-point Likert scale ranging from 1 ‘never’ to 5 ‘very often’ to items such as “I set short-term goals for what I want to accomplish in a few days or weeks.” PAF showed that the 9 items form a bi-dimensional scale with 2 components having an eigenvalue above 1 (eigenvalues 4.44, 1.25) and a cumulative explained variance of 41.44% in the original items. After a direct oblimin rotation, items correlated positively with 2 factors of which 5 items loading above 0.55 in factor 2 were selected. The item “I break complex, difficult projects down into smaller manageable tasks.” has the strongest association (factor loading is .73) and the scale is reasonably reliable, Cronbach's alpha = .77.
Colleague surveillance. Colleague surveillance (M=2.86, SD=0.96) was measured using a self-constructed 6-item scale inspired by social norms created by colleagues (Derks et al., 2015) and the concept of escalated engagement (Mazmanian, Orlikowski, & Yates, 2013), which include items related to peer expectations and consequences of undesired behaviours. Examples include, “My colleagues expect me to reply to messages very fast.” and “If I happen to be unavailable, I get comments from my colleagues”. Respondents were asked to reply to a 5-point Likert scale ranging from 1 ‘strongly disagree’ to 5 ‘strongly agree’. PAF showed that the 6 items form a bi-dimensional scale with 2 components having an eigenvalue above 1 (eigenvalues 2.60, 1.00) and a cumulative explained variance of 41.88% in the original items. After a direct oblimin rotation, items correlated positively with 2 factors of which 4 items loading above 0.55 in factor 1 were selected. The item “My colleagues expect me to be available when needed.” has the strongest association (factor loading is .71) and the scale is reasonably reliable, Cronbach's alpha = .75.
Burnout. Burnout (M=2.67, SD= 1.04) was measured using Maslach and Jackson’s (1981) 9 item scale of the emotional exhaustion subdimension in ‘The measurement of
experienced burnout’. Example items include “I feel used up at the end of my workday” and “I feel burned out from my work”, and was measured using a 5-point Likert scale ranging from 1 ‘strongly disagree’ to 5 ‘strongly agree’. PAF showed that the 9 items form a single uni-dimensional scale with only one component having an eigenvalue above 1 (eigenvalue 5.29) and an explained variance of 54,69% in the original items. One item was omitted as it did not have a sufficient factor loading (.51) and the item “I feel burned out from my work.” has the strongest association (factor loading is .84). The scale is good, Cronbach's alpha = .91.
Control variables. Lastly, we controlled for flexibility (M=2.56, SD= 1.22), measured using a 2-item scale including “Do you have flexibility in choosing when you perform your work responsibilities?” and “Do you have flexibility in choosing where you perform your work responsibilities?”. Respondents answered to a 5-point Likert scale ranging from 1 ‘never’ to 5 ‘very often’. Gender, measured as a dichotomous variable (0 = male, 1= female), organizational position (0 = no/lower management position, 1 = mid/upper management position), Age and Average number of working hours per week, measured as continuous variables, were also controlled for.
Results
Firstly, an independent sample T-test was conducted for all variables in order to check for differences in means between the survey administered in English and its translation to Spanish. As it can be observed in Table 2, there were no significant mean differences except for capacity for self-regulation which showed a relatively weak effect, d=0.35. Therefore, the analysis was continued assuming that the difference in language did not alter the results.
Table 1.
Survey language statistics
n M SD
Burnout Spanish 123 2.62 1.11
English 62 2.77 0.89
Colleague surveillance Spanish 123 2.83 1.03
English 62 2.92 0.82
Capacity for self-regulation Spanish 123 3.26 0.77
English 62 3.53 0.79
Work intensification Spanish 123 3.08 0.90
English 62 3.17 0.81
Work-life conflict Spanish 123 2.15 0.79
English 62 2.12 0.77
CTU Spanish 123 3.84 0.98
English 62 3.86 0.80
Table 2.
Independent samples T-test outcomes for survey language
T df p M Difference 95% CI Cohen’s d
Lower Upper
Burnout -0.98 148.17 .327 -0.15 -0.44 0,15 0.15
Colleague surveillance -0.65 149.61 .520 -0.09 -0.37 0.19 0.10
Capacity for self-regulation -2.25 183 .026 -0.27 -0.51 -0.03 0.35
Work intensification -0.68 183 .500 -0.09 -0.36 0.18 0.11
Work-life conflict 0.25 183 .802 0.03 -0.21 0.27 0.04
CTU 0.18 183 .860 -0.03 -0.31 0.26 0.02
Note. N=185
A moderated mediation regression analysis was executed using PROCESS model 9, with burnout as the dependent variable, communication technology use (CTU) as the independent variable, work intensification and work-life conflict as mediators, capacity for self-regulation and colleague surveillance as moderators, controlling for age, gender, flexibility, average number of working hours per week and organisational position. All variables were centred at the mean. See Figure 1 for a visual representation of the model and Table 3 for results.
Table 3.
Regression Outcomes Using PROCESS Model 9
Work intensification Work-life conflict Burnout
b T p b T p b T p
Constant 3.55 8.83 <.001* 2.31 6.60 <.001* -0.22 -0.46 .644
CTU 0.07 0.97 .335 0.13 2.09 .039* 0.14 2.09 .038*
Age -0.002 -0.37 .710 -0.01 -1.30 .195 0.002 0.45 .656
Average number of working hours per week
-0.002 -0.28 .783 0.01 0.75 .456 0.01 1.45 .149
Organisational position 0.06 0.38 .704 0.34 2.59 .010* -0.13 -0.90 .367
Gender -0.01 -0.09 .928 0.29 2.51 .013* 0.16 1.21 .229
Flexibility -0.12 -2.17 .031* -0.16 -3.33 .001* 0.03 0.60 .547
Capacity for self-regulation 0.25 3.08 .002* 0.05 0.74 .463 CTU* Capacity for
self-regulation
-0.03 -0.34 .731 0.02 0.22 .827
Colleague surveillance 0.21 3.08 .002* 0.15 2.47 .015*
CTU* Colleague surveillance 0.10 1.45 .149 0.16 2.60 .010*
Work intensification 0.31 3.98 <.001*
Work-life conflict 0.63 6.98 <.001*
Figure 1.
Conceptual Regression Model
Note. (*) Significant results at 95% Confidence level. Control variables: Flexibility, Average number of working hours per week, Organisational position, Age, Gender.
Step 1 of the moderated mediation model with our first mediator, work intensification, as outcome variable is significant F(10, 171) = 3.26, p =.001, R2 = .16. As it can be observed in Table 3, capacity for self-regulation and colleague surveillance appear to be significant predictors of work intensification. Contrary, CTU, the interaction term between CTU and capacity for self-regulation and the interaction term between CTU and colleague surveillance are not significant. Hence, an insignificant relationship between CTU and work intensification rules out the possibility of a mediation effect between CTU and burnout through work intensification and so, H1(a) is rejected. Additionally, as there were no moderations found H2(b) and H3(a) are not supported either. Although neither capacity for self-regulation nor colleague surveillance moderate the relationship between CTU and work intensification, they do both individually have significant effects on work intensification. Employees working under
higher levels of colleague surveillance have higher levels of work intensification. Similarly, and against our theorised prediction, employees with higher self-regulating capacity appear to have higher levels of work intensification. As it can be observed, out of the control variables, only flexibility has a significant negative effect, suggesting that higher levels of flexibility would lead to reduced work intensification.
Step 2 of the moderated mediation model with our second mediator, work-life conflict, as outcome variable is significant F(10, 171) = 4.63, p <.001, R2 = .21. Table 3 shows that neither capacity for regulation nor the interaction term between CTU and capacity for self-regulation are significant, meaning that H2(a) is not supported. Against out predictions, capacity for self-regulation does not seem to have an effect on the positive relationship between CTU and work-life conflict. On the other hand, CTU, colleague surveillance, and the interaction term between CTU and colleague surveillance appears to be significant. This means that H3(b) is supported and there is evidence to believe that there is a positive relationship between CTU and work-life conflict that is moderated by colleague surveillance. As it can be observed in Figure 2, the positive effect of the interaction term between CTU and colleague surveillance shows that the effects of CTU on work-life conflict are stronger for employees working under higher levels of colleague surveillance. Out of the control variables, organisational position, and gender have significant positive effects, suggesting that employees in higher managerial positions (as opposed to no or low managerial positions, average difference 0.34) and females (as opposed to males, average difference 0.29) tend to experience more work-life conflict. Flexibility seems to have significant negative effects in predicting work-life conflict, meaning that higher levels of flexibility could lead to reduced work-life conflict.
Figure 2.
Moderation effect of colleague surveillance on the relationship between CTU and work-life conflict.
Step 3 of the moderated mediation model with our dependent variable, burnout, as outcome variable is also significant F(8, 173) = 4.72, p <.001, R2 = .45. As observed in Table 3 and Figure 1, there is a small direct effect between CTU and burnout, b = 0.14, p= .038, 95% CI [0.01, 0.26], meaning that an increase of 1 unit in CTU would increase burnout by 0.14 when keeping all other variables constant at the mean. Work intensification and work-life conflict appear to be stronger predictors of burnout, suggesting that, in accordance with our predictions, higher levels of work intensification and work-life conflict would result in employees experiencing higher levels of burnout. All control variables are not significant in this model.
The indirect effect of CTU and burnout through work intensification does not appear to show significant moderated mediation results neither with capacity for self-regulation as moderator, b = -0.01, 95% CI [-0.07, 0.05] nor colleague surveillance, b = 0.03, 95% CI [-0.02,
1 1,5 2 2,5 3 3,5 4 4,5 5
Low CTU High CTU
W or k -li fe c on fli ct Low Colleague surveillance High Colleague surveillance
0.09], as both confidence intervals cross the meaningful 0 point. The indirect effect of CTU and burnout through work-life conflict does have a small, significant partial moderated mediation effect with colleague surveillance as moderator, b = 0.10, 95% CI [0.01, 0.19]. This means that there exists a partial mediated effect between CTU and burnout through work-life conflict and that colleague surveillance does moderate the effect between CTU and work-life conflict. This confirms that H3(b) is indeed supported and further, also shows support for H1(b). The indirect effect with capacity for self-regulation as moderator shows no significance, b = 0.01, 95% CI [-0.09, 0.12].
Discussion
Communication technologies have changed the way in which employees communicate with colleagues, supervisors and perform job-related tasks (Wright et al., 2014). Now that employees can choose where and when they work it is easier for work to seep into personal time (Brummelhuis et al., 2012) and availability seems to have increased (Chesley, 2014). It is because of this, that a link between CTU and burnout has been made (Murray & Rostis, 2007; Ter Hoeven et al, 2016; Chesley 2014). The aim of this study was to replicate findings which explained a relationship between CTU and burnout through work intensification and work-life conflict as well as mainly, identify potential individual (capacity for self-regulation) and organisational (colleague surveillance) factors which influenced the relationship.
Theoretical Implications
In line with previous literature, such as the negative health implications stated by Adisa et al., (2017), we replicated the findings suggesting that CTU seems to have a relationship with burnout, as increased feelings of fatigue and emotional exhaustion were found amongst employees with high CTU in our sample. However, our findings sustain that, indeed, this relationship does seem to be at least partially explained through certain underlying mechanisms
(Ter Hoeven et al., 2016). Particularly, it has been found that work-life conflict mediates this relationship, potentially due to the stress caused by CTU’s interference with employees’ ability to enjoy personal time (Boswell & Olson-Buchanan, 2007). Those employees with higher CTU do seem to have increased work-life conflict and in turn, experience more burnout. This contributes to theory by supporting previous findings and reinforcing that they can be generalised to a wider population.
On the other hand, an explanation through work intensification was not found. Although theoretically we do have basis to sustain this relationship, contradicting the findings of Chesley (2010; 2014), CTU did not appear to increase workloads and force employees to work faster. Nonetheless, work intensification does seem to have a significant relationship with burnout. Those employees who perceived that they had to work very hard and fast also reported higher levels of emotional exhaustion. This suggest that the intensification of work demands that can lead to burnout does not seem to be a result of the introduction of communication technologies in the workplace and might have other possible explanations. Employees might have adapted already to the increased speed brought by communication technologies, not only in the workplace, but due to its use in personal exchanges too, and therefore they might experience this as the “new normal”. It also should be noted that because a direct relationship between CTU and burnout was found, our findings fail to explain a full mediation and therefore, it is probable that other underlying mechanisms are also affecting this relationship. An example of this could be the increased interruptions promoted by CTU that were found in Ter Hoeven’s et al., (2016) study.
Moreover, in accordance with literature which suggests that social norms and pressure related to CTU could have negative effects on employees (Duxbury et al., 2006; Olson-Buchanan & Boswell, 2006: Derks et al., 2015), our study did find that colleague surveillance moderates the relationship between CTU and work-life conflict. Employees who found
themselves working under environments where the expectations to comply with norms of faster replies and constant availability due to feared consequences were higher, did seem to be experiencing significantly higher work-life conflict. Boswell and Olson-Buchanan (2007) already suggested that those employees with high job involvement and ambition seem to have higher levels of work-life conflict. The current study adds to theory by showing that employees with a higher frequency of CTU, who feel the need to comply with expectations of increased availability as to be accepted by their colleagues, also have a harder time achieving a desired level of balance between work and non-work time (Rothbard, Philips, & Dumas, 2005; Kreiner et al., 2009; Kreiner, 2006). Our finding is therefore relevant when explaining the bottom-line causes that lead CTU to result in burnout and sets a path for future research, which can use this to further investigate why, and under what circumstances colleague surveillance may arise. Although previous studies observed patterns that pointed in this direction (Derks et al., 2015), they were unable to find significant results. This difference in results might have been due to the fact that our measurement of colleague surveillance is more general and encompasses norms and pressure not only after working hours, but during work hours too. This is because we acknowledge that colleague surveillance can also occur, for instance, if an employee is working from home on a certain day but still feels the need to be constantly available in fear of their colleagues suspecting that they might be taking the day off. In addition, our study looked at a wider range of CTU and not only smartphone use. Even though colleague surveillance was not found to moderate the effect between CTU and work intensification, on its own, colleague surveillance does seem to be contributing to increased work intensification. Most likely, due to the expectations of increased availability when using communication technologies and the norms inciting to continue working even after working hours proposed by Duxbury et al. (2006).
As opposed to colleague surveillance, capacity for self-regulation did not show the results we expected. Contradicting the findings of Mattern & Bauer (2014), in our sample we did not find that higher capacity for self-regulation helped overcome difficulties with excessive workloads or reduced the work-life conflict enhanced by CTU. It is believed that the difference in findings could be due to contextual circumstances, such as profession. Mattern & Bauer (2014) adapted their study to fit teachers, who share a common context. Therefore, different professions might require different measures of what capacity for self-regulation means in that context. If nothing else, increasing capacity for self-regulation was found not to be a good solution for reducing work intensification and work-life conflict. Surprisingly, by itself (as opposed to the hypothesised combination with CTU), capacity for self-regulation was found to have a positive relationship with work intensification instead of a negative one. This means that instead of easing work intensification, it might be that those employees ranking high on work intensification might have had to improve their planning and monitoring approaches in order to cope with the increased workloads and faster pace.
Practical Implications
From the findings of this study, it can be derived that management’s ability to identify the subtle norms that develop regarding CTU expectations becomes essential. If these are not acknowledged, it could be promoting a culture that not only depletes their employees’ resources and energy, but poses economic threats to their organisation (Awa et al., 2010; Bakker et al., 2014). This could be done, for instance, by staying close to their teams and carrying out regular 360-degree feedback sessions (Holloway & Kusy, 2010). What is more, management should be aware that they could be the ones who promote these undesired norms in the first place. They can initiate colleague surveillance by establishing what behaviours are appropriate and expected as well as allowing and promoting colleague control measures (Ehrhart, Naumann, Zedeck, & Klein, 2004; Chua, Lim, Sia, & Soh, 2012). For example, if
managers themselves are the ones sending and replying to emails after hours in the first place, this could act as a signal for employees, who then believe that this behaviour is appropriate and adopt it themselves.
Nonetheless, this can also be seen as an opportunity, as it also means that management has the power to establish different types of norms which do not promote colleague surveillance and, instead, encourage cultures where employees’ work life balance is respected. By signalling that taking time off and establishing boundaries is accepted, managers could lead by example and contribute to reduced exhaustion levels and better work-life balance amongst their team. This can be done in different ways. A common approach has been to introduce policies which restrict CTU times, such as a no-email policy after working hours, or turning off notifications on workplace specific instant messaging platforms (e.g. Slack) after a certain time. This, indeed, could portray that the organisation values their employees’ work-life balance. However, as Kirby and Krone’s (2002) study has shown, even though policies exist, employees might refrain from using them if it is not seen as socially accepted.
Therefore, setting expectations through communicative tactics such as the ones proposed by Kreiner et al., (2009) might be a better approach. This would imply stating preferences regarding work-life boundaries and communicating in advance was is acceptable and what is not. Managers could clearly state that they do not wish to be contacted in the weekends unless it is strictly necessary and that they will not be available during vacation. A behaviour like this set by management, could then act as an example for other employees which will adopt the same tactics. In addition, it was suggested by the same study that confronting violators of these boundaries during or after the violation has occurred was also useful. This tactic can be used by management or by employees themselves when they are confronted with those colleagues who have not respected their boundaries. Instead of becoming upset and ruminating about what happened without action, clearly expressing why this is a problem might help them protect
their work-life balance. Lastly, these tactics could be complemented through workshops on team performance and team building which enhance the level of trust amongst colleagues. For instance, if employees trust that their colleagues will reply to their messages whenever they can and that even though they are working from home they will still perform up to standard, there is less need for colleagues to check on each other and speculate about each other’s productivity.
Limitations and future research
It should be noted that our variable CTU did not follow a normal distribution. As it was previously mentioned, CTU appeared to have a negatively skewed distribution, meaning that most respondents ranked relatively high on this scale. Convenience sampling did restrict our ability to recruit employees who have low use of communication technology – which is increasingly difficult to find. Besides, it has been noticed that the common measurements of CTU, such as the one used by Chesley (2010), could show some validity problems. The items might be highly correlated with each other and our Cronbach’s alpha for was .65, which is lower than desired. Thus, now that work-related CTU is becoming increasingly popular, it might be more useful for future research to focus on specific CTU outcomes related to increased workloads and connectivity, such as measures of constant connectivity (Mazmanian, 2013) or communication technology use after hours (Boswell & Olson-Buchanan, 2007) instead of general use. Additionally, future research could measure further underlying mechanisms affecting the relationship between CTU and burnout which were not measured in this study due to time constrictions. For instance, it has been identified that by hampering personal recovery time, employees are not able to restore the energy levels depleted after work (Reichl et al, 2014). Theories such as the Effort-Recovery (Meijman & Mulder, 1998), stress the importance of recovery during non-work time and argue that the inability to do so will imply that the following day will begin bellow optimal conditions and will require extra effort.
Psychological detachment, referring to the individuals’ capacity to “switch off” from job-related issues and concerns, has been identified as a key component of that recovery – the greater the detachment experienced, the better it relieves forms of fatigue and burnout (Etzion et al., 1998).
Furthermore, our difficulties in finding a validated scale for capacity for self-regulation including all phases proposed by literature (action planning, performance control, self-monitoring, and self-reflection), might have limited our ability to create a valid measurement. The scale that was used in our study relied solely on planning behaviour. In addition, even though people might have self-reported their ability to plan ahead and set deadlines, this does not mean that they do adhere to their schedules and accomplish their goals. Therefore, future research might want to focus on concepts such as job resourcefulness, which has been conceptualised as the ability to cope with work-related obstacles by using the available resources in the most effective way (Akgunduz, Bardakoglu, & Alkan, 2015). This could be a more precise measurement of effective time management and performance.
Lastly, it should be mentioned that this study has been developed under time constraints which limited its design. A cross-sectional design does not allow for causal relationships to be drawn and it is because of this, that we cannot demonstrate that CTU and work-life conflict are the causes of burnout. It would not be illogical to believe that higher burnout levels could also lead to more work-life conflict as individuals might feel too exhausted to achieve balance between work and non-work domains. Longitudinal studies would therefore be a better design to test these hypotheses. Relying on a convenience sample instead of a randomised one, also compromises the external validity of the study and limits our availability to generalise our findings to the wider population.
Conclusion
The increased use of communication technology seems to be irrepressible and has come to stay in organisations. The consequences that it has brought to the workplace have significantly attracted researcher’s attention as well as management’s and society’s as a whole. Even though the fast pace in which these technologies develop makes it harder to predict what changes they will bring, several outcomes have already been identified by literature. Advantages such as effective communication, accessibility (Ter Hoeven et al., 2016) and autonomy (Mazmanian et al., 2013) have been proposed, and support why communication technology use might be a useful tool in work environments. However, literature also establishes that, at the same time, communication technology use can threaten employee well-being (Adisa, et al. 2017; Ter Hoeven et al., 2016; Mazmanian et al., 2013). The current study has found support for the latter and determined that the work-life conflict that communication technology use enables, could lead to employees’ experience of higher emotional exhaustion. It has also demonstrated that the social norms and expectations about communication technology use that are created in the workplace could also have a significant effect on employees’ well-being. If the norms created by colleagues encourage increased blurred boundaries between work and non-work domains as well as constant connectivity, employees have a higher risk of becoming burnt out. Therefore, while researchers should focus on identifying what causes these unhealthy norms to arise in the first place, managers should be focusing on promoting healthier expectations of communication technology use that allow for employee work-life balance.
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Appendix
Measurement Scales
CTU. I would like to ask you a few questions about communication technology. Using the following scale, please tell me how often you use each of the following technologies for work-related tasks:
1. Laptop or desktop 2. Smartphone 3. Email
4. Text (e.g. WhatsApp)
Work intensification. In your job...
1. Do you need to work really fast? 2. Do you need to work really hard?
3. Do you have excessive amounts of work? 4. Is your working environment hectic?
1. You are irritable at home because your work is demanding?
2. You do not fully enjoy the company of your partner/family/friends because you worry about your work?
3. You find it difficult to fulfil your domestic obligations because you are constantly thinking about your work?
4. You have to cancel appointments with your partner/family/friends due to work-related commitments?
5. Your work schedule makes it difficult for you to fulfil your domestic obligations? 6. You do not have the energy to engage in leisure activities with your
partner/family/friends because of your job?
7. You have to work so hard that you do not have time for any of your hobbies? 8. Your work obligations make it difficult for you to feel relaxed at home?
9. Your work takes up time that you would have liked to spend with your partner/family/friends?
Capacity for self-regulation. The following statements are about your planning habits.
1. I review my goals to determine if they need revising.
2. I break complex, difficult projects down into smaller manageable tasks. 3. I set short-term goals for what I want to accomplish in a few days or weeks. 4. I set deadlines for myself when I set out to accomplish a task.
5. I look for ways to increase the efficiency with which I perform my activities.
Colleague surveillance. I would now like to ask you a few questions about your working environment. Please indicate how much you agree with the following statements.
1. My colleagues expect me to reply to messages very fast.
3. My colleagues expect me to be available when needed.
4. If I do not respond to emails from my colleagues, my position in the group would be questioned.
Burnout. Please indicate how much you agree with the following statements about your well-being.
1. I feel emotionally drained from my work. 2. I feel used up at the end of my workday.
3. I feel fatigued when I get up in the morning and have to face another day on the job. 4. Working with people all day is really a strain for me.
5. I feel burned out from my work. 6. I feel frustrated by my job.
