Master thesis
Working in the 21
stcentury
How using ICTs can affect employees´ levels of exhaustion and engagement
Sabrina Schmidt (11368438)
Graduate School of Communication Master´s programme Communication Science
Corporate communication track Supervisor: Ward van Zoonen
Abstract
The advancement of information and communication technologies is characterized by a “dual nature”. While ICTs often generate positive business outcomes, they can also cause negative consequences for individuals. The aim of this study was to examine how the
different dimensions of technostress creators are related to levels of exhaustion and employee engagement. A quantitative online-survey, with respondents working a minimum of 30 hours per week, was conducted (N = 193). The findings showed four significant hypotheses,
concerning the positive relationship between techno-overload and exhaustion, as well as between techno-invasion and exhaustion. Furthermore, the negative relationships between techno-overload, as well as techno-complexity and lower employee engagement have shown to be significant. Comparing the effect sizes of techno-overload and techno-invasion and their relation to exhaustion, as well as techno-overload and techno-complexity and their relation to engagement, it could be examined that the effects were not significantly different from each other, which concludes that the dimensions of technostress equally affect exhaustion, as well as engagement. It was found that managerial support did not have an effect on the
relationships, between the technostress dimensions and the dependent variables exhaustion and engagement. Future research should take into account, other moderating variables, such as co-worker support or social norms, to further explore the underlying process, which defines the direction of the relationship, between technostress and exhaustion, as well as engagement. !
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Introduction
“We are the distracted generations, wasting hours a day checking irrelevant emails and intrusive social media accounts” (Belton, 2016, p. 1). The Quality of Working Life report published in 2016, from the Chartered Management Institute, found that the addiction with checking emails outside of working hours is making it difficult for the population to switch off (Belton, 2016). The "always on" culture - reinforced by the smartphone - is in fact making the population more stressed and less productive (Belton, 2016). The introduction of the information and communication technologies (ICTs) was expected to make lives easier by providing faster communication, more efficient work processes and connectivity (Salanova et al., 2013). But as described by Tarafdar et al. (2007), the advancement of information and communication technologies is characterized by a “dual nature”. While ICTs often create positive business outcomes, they can also provoke negative consequences for individuals (Tarafdar et al., 2007).
This “dual nature” of ICTs can be illustrated by two global trends, which are growing together. On the one hand more and more technology finds its way into the workplace. Technologies like cloud and mobile computing, big data, advanced robotics, drones or ubiquitous computing, not just help people to do things better and at a faster pace, they foster new ways of working in organizations (Cascio & Montealegre, 2016). On the other hand, the rise of stress levels in western workplaces is becoming a serious problem for employers and their staff members. The increasing amount of stress, reinforced by the implementation of ICTs, can lead to negative job outcomes, such as exhaustion or lower engagement in work. The Gallup news for instance, have evaluated that 67% of employees worldwide are no more engaged with their work tasks (Gallup, 2017). Additionally, a survey conducted in 2016 revealed, that 31% of German workers are feeling stressed, while 24% are considering their selves burned out (Gallup, 2016). On these grounds, this study will aim to combine two
global trends, pointing out that the more technology is implemented in working environments, the more exhaustion or lower levels of employee engagement occur.
Negative stress in the workplace and associated with information and communication technologies, was initially explained by Brod (1984, p. 1) as technostress “a modern disease of adaptation caused by an inability to cope with the new computer technologies in a healthy manner”. In the digital era, it is important to understand the different characteristics of
technostress, due to the fact that stress in the work place is causing poor employee well-being, lower employee productivity and higher health expenses for companies (Ayyagari, 2007).
Because of that, this study will research the relationship between technostress and exhaustion, as well as employee engagement, by focussing on the different dimensions of technostress. Since previous studies in the field of communication science, haven’t elaborate on the sources of technostress and how they are related to exhaustion and employee
engagement, it is important to look at these dimensions and therefore contribute to existing literature.
Researchers have obtained those technostress dimensions, which are explained by the terms overload, insecurity, invasion, uncertainty and techno-complexity (see Ragu-Nathan et al., 2008). Due to the fact that “stress is a cognitive response that individuals experience when they anticipate their inability to respond adequately to the perceived demands of a given situation” (Cascio & Montealegre, 2016, p. 4), employees might feel stressed differently according to the various forms and tasks of ICTs. While one employee could be stressed by ICTs to work faster and longer and therefore would experience techno-overload, another employee might be more overworked by staying connected to the company 24/7 and therefore suffering from techno-invasion.
By looking at the different dimensions, this research will help to show, which of the technostress sources are related more strongly to levels of exhaustion and lower engagement
and therefore support managers to understand and differentiate the dimensions and their work related outcomes, in order to come up with optimal stress reduction measures and to promote a stress-relieved working environment.
Additionally, it will be explored, how supportive management can influence the relationships between the different dimensions of technostress and exhaustion, as well as lower engagement. The term ‘managerial support’ is described, as the employee’s perceptions, that managers value their contributions, are supportive and look after their subordinates’ well-being (Eisenberger et al., 2002). That is why, supportive managers might steer the negative effects, employees experience by using ICTs.
A model is introduced as a framework, to study the relationship between the different technostress dimensions and exhaustion, as well as employee engagement, while taking into account levels of managerial support.
From a practical standpoint, the findings can help organizations and managers to understand the outcomes of ICTs use and which factors, for example managerial support, can strengthen the reduction of stress during this new type of work, so that both managers and employees can benefit from using communication technologies in an appropriate manner.
Focussing on the different dimensions of technostress can help managers to implement work norms, which are customized for every form of technostress. This can help to reduce stress in every area where communication technology is used, which in turn would reduce levels of exhaustion and encourage or improve the levels of employee engagement. The present study therefore aims to answer the following research question:
RQ: How are the different dimensions of technostress related to the negative work outcomes, of exhaustion and lower employee engagement? And to what extent can supportive
Theoretical Framework
In the following theoretical framework the relationships between the different core concepts of technostress, exhaustion, employee engagement and managerial support will be examined. By investigating their interrelation, different hypotheses will be outlined leading to the study’s overall research model.
Transaction-based model of stress
To understand the relation between the main concepts, technostress, exhaustion, employee engagement and managerial support, the transaction-based model of stress is introduced, as the overarching theory for this study.
The term “being under stress” was defined by (McGrath 1976, p. 1351) as “a state experienced by an individual, when there is an environmental situation that is perceived as presenting a demand, which threatens to exceed the person’s capabilities and resources for meeting it, under conditions where he or she expects a substantial differential in the rewards and costs from meeting the demand versus not meeting it”.
The interest in stress has lead to the development of the transaction-based model of stress, the groundwork for a number of studies on stress (McGrath, 1976). This model describes “the phenomenon of stress as a combination of a stimulating condition and the individual’s response to it” (Ragu-Nathan et al., 2008, p. 419). The model is characterized by four different components: stressors, situational factors, strain and other organizational outcomes (Ragu-Nathan et al., 2008). “Stressors” are different demands or conditions, which are faced by employees, as triggers that create stress, for example task difficulties or
uncertainty (McGrath, 1976). Second, “situational factors” are organizational influences, which can buffer the effects of certain “stressors”, for example in terms of social support (McGrath, 1976). The component “strain” is characterized by the behavioural, psychological and physiological outcomes, an employee might experience, when being under stress
(McGrath, 1976). Finally, those “strains”, for example job dissatisfaction, can lead to “other organizational outcomes”, like turnover or absence (McGrath, 1976). Stressors often increase strain, while situational factors can buffer these effects, as shown in Figure 1.
Figure 1. The transaction-based model of stress
Technostress and exhaustion
Today’s different forms of stress in the workplace are recognized as contributing to a multitude of health and liveability issues, which could have extensive consequences, such as burnout symptoms, work-home conflicts or turnover intentions (Ayyagari et al., 2011). The application of information and communication technology is receiving greater attention in the context of stress, health and well-being (Ninaus et al., 2015). Through its virtual appearance, ICTs invade all areas of life in modern societies and have therefore become an essential part of both free and working time (Ninaus et al., 2015). The inability to cope or deal with ICTs is explained by the term ‘technostress’, which is a consequence of the endless use of
information and communication technologies and the speed at which technological
“negative psychological state associated with the use or threat of ICT use in the future. This experience is related to feelings of anxiety, mental fatigue, scepticism and inefficacy”
(Salanova et al., 2007, p. 1). There is evidence emphasized by practitioners, that technostress eventuates in perceived work overload, frustrated users, information tiredness, decline of motivation and discontent at work (Ragu-Nathan et al., 2008).
As mentioned in the introduction, researchers have obtained certain factors that stimulate ‘technostress’ and are explained by the term ‘technostress creators’ such as techno-overload, techno-insecurity, techno-invasion, techno-uncertainty and techno-complexity (see Ragu-Nathan et al., 2008). Those dimensions present the stimuli that are creating stress levels in organizations, when working with ICTs (see Ragu-Nathan et al., 2008).
To address the relationship between technostress and exhaustion, the transaction based-model of stress is applied. “Stressors” (see Figure 1), which are demands creating stress, correspond to the different dimensions of technostress. Employees might encounter a “working environment that is perceived with different demands, which threatens to exceed the employee’s capabilities and resources for meeting it” (McGrath 1976, p. 1351). Those
demands, for example working with changing ICTs, create stress - more precisely technostress. According to Ragu-Nathan et al. (2008) the dimensions of technostress are described as follows: the first technostress creator is labelled as techno-overload, situations where ICTs pressure employees to work faster and longer as well as to work with a higher amount of information. Employees feeling to be constantly connected and reachable, characterize the second dimension techno-invasion. Techno-complexity can be seen as a feeling associated with inadequate skills regarding computer use. Techno-insecurity leads employees to situations, where they feel threatened by other co-workers, who might
and upgrade of ICTs and therefore forces the user, to constantly learn and train him about new communication technologies.
Those technostress creators or “stressors” lead to certain outcomes of stress for an individual, which is termed “strain”. Strain is representative for person´s behavioural, psychological and physiological outcomes. For the research at hand, those psychological outcomes can eventuate as higher levels of exhaustion and therefore reduce employees’ obtainable cognitive energies toward work-related assignments (Shuck & Reio, 2014).
Additionally, this mechanism can be affected by “situational factors”, such as managerial support, which will be explained in the up-coming chapters.
Taking into account the negative consequences of the different dimensions of ‘technostress’ for employees and therefore the result of exhaustion or health problems, the following is hypothesized:
H1a: Techno-overload is positively related to exhaustion H1b: Techno-invasion is positively related to exhaustion H1c: Techno-complexity is positively related to exhaustion H1d: Techno-insecurity is positively related to exhaustion H1e: Techno-uncertainty is positively related to exhaustion
To clarify, which of the technostress dimensions affects exhaustion more strongly, a comparison between the effect sizes of all five technostress dimensions will be made. To test for theses effect sizes, an additional research question is introduced:
Technostress and engagement
In contrast to feeling exhausted from work – “engaged employees have a sense of energetic and effective connection with their work activities and they see themselves as able to deal completely with the demands of their job” (Schaufeli et al., 2000, p. 73).
Nevertheless, previous studies have shown, that an increase of job demands, for example staying connected to the organization 24 hours every day of the week, exceeds the increase of job resources such as communication technology usage, which may entail the decrease of work engagement and the increase of exhaustion (Fujimoto et al., 2016).
Since the use of communication technologies creates stress, in that they are complex and change frequently, they can be seen as challenging job resource, leading to lower employee engagement, due to the fact that the employee is feeling overworked (Ter Hoeven et al., 2016). A study performed by Ragu-Nathan et al. (2008) among end users of
communication technologies also disclosed, that the existence of technostress resulted in a decrease in job satisfaction. As a consequence, declining job satisfaction also leads to decrease in organisational engagement (Ahmad et al., 2014).
Taking into account the negative outcomes of working with ICTs, this study will furthermore concentrate on the different dimensions of technostress and how they are related to a lower level of employee engagement.
In parallel to the relationship of technostress and exhaustion – the relationship between technostress and engagement can also be explained by using the transaction based-model of stress (as shown in Figure 1). Technostress creators or stressors lead to certain outcomes of stress for individuals. They influence outcomes, like job dissatisfaction, poor task performance or lack of creativity, which are the most frequent stress outcomes in the professional environment (see Ragu-Nathan et al., 2008). The transaction based-model of stress therefore helps to understand, that technostress creators are demands, which are
encountered by employees, for example in terms of techno-overload or techno-invasion. Employees might be unable to respond adequately to those perceived demands, which as a result could influence the negative work outcome of lower engagement (Ragu-Nathan et al., 2008).
Furthermore, as new communication technologies continue to overwhelm and frustrate employees, understanding how different employee types experience and cope with
technostress creators and according to that, engage less in work related tasks, presents an area of theoretical as well as practical interest (Srivastava et al., 2015). Realising that technology can create stress in the workplace and recognising the influence of stress on employee engagement, this study aims to examine the relationship between the different dimensions of technostress and employee engagement.
Taking into account the implications, which arise when employees are dealing with different communication technologies and therefore with different dimensions of technostress the following is hypothesized:
H2a: Techno-overload is negatively related to employee engagement H2b: Techno-invasion is negatively related to employee engagement H2c: Techno-complexity is negatively related to employee engagement H2d: Techno-insecurity is negatively related to employee engagement H2e: Techno-uncertainty is negatively related to employee engagement
To clarify, which of the technostress dimensions affects lower engagement more strongly, a second additional research question is introduced:
RQ (2): Which of the dimensions of technostress has the strongest effect on lower engagement?
Managerial support as a moderator
As mentioned in previous sections, the increasing complexity in organizational working environments, often leads to a higher level of perceived job-related stress by staff members (Sosik & Godshalk, 2000). “Organizational support theory supposes that employees personify the organization, infer the extent to which the organization values their
contributions and cares about their well-being and reciprocate such perceived support with increased commitment, loyalty, and performance” (Rhoades and Eisenberger 2002, p. 711). This is relevant since employees, who experience support are more likely to engage themselves and less likely to receive high levels of stress. Especially organizational support, in terms of managerial support, can play an important role to diminish work-related stress. The term ‘managerial support’ is defined as “the degree to which employees are under the general impression that their managers appreciate their contributions, are supportive and care about their subordinates’ well-being” (Eisenberger et al., 2002, p. 700).
Additionally, managerial support is a valued resource, due to the fact that it ensures workers that help by their management will be available when it is needed, to carry out their jobs efficiently and to cope with demanding situations (Sawang, 2010). Managers
consequently provide organizational support, through their managerial role by looking after their staff members’ well-being and by providing advice especially in stressful work
situations (Sawang, 2010).
Previous studies have proven, that managers are aware of detecting certain signals, which indicate that people in their organization are under stress (Dewe & O´Driscoll, 2002). Therefore, it is crucial for managers to look for these signals, as well as strategically influence employee’s perception of stress, as well as technostress (Fieseler et al., 2014). Research by Fieseler et al. (2014, p. 536) found that “leadership represents a valuable instrument to defend employees from the negative impacts, that may result from the use of information and
communication technologies or to at least buffer the negative outcomes by positively influencing job satisfaction.”
Additionally, social exchange theorists have established the norm of reciprocity. This concept is based on how people treat each other. If one person treats another well, the norm of reciprocity leads to return of favourable treatment (Rhoades & Eisenberger, 2002). This leads to the assumption, that if an employee receives good treatment by his management for
example in terms of support, the employee in turn reciprocates this behaviour in regard to higher work engagement.
Furthermore, an employee might experience some relaxation, when perceiving stress in the working environment, if the right manager leads him (Fieseler et al., 2014).!Job satisfaction and in turn employee engagement could increase, if managers are being both a supervisor, as well as a mentor towards their employees and therefore gaining the maximum efficiency and results from their employees (Sawang, 2010).
Applying managerial support also to the transaction-based model of stress, managerial support takes over the role of “situational factors”, which can buffer the effects of stress on outcomes for individuals. A supportive supervisor or management level could on these grounds, buffer the negative outcomes related to technostress. Therefore the following is hypothesized:
H3: The relationship between the different dimensions of technostress and exhaustion is moderated by managerial support, in such a way that the negative effects are less pronounced for employees that experience higher levels of support.
H4: The relationship between the different dimensions of technostress and employee
engagement is moderated by managerial support, in such a way that the negative effects are less pronounced for employees that experience higher levels of support.
Figure 2 shows the proposed research model including all hypotheses and relations between the different constructs and concepts:!!
! !
Figure 2. The research model
Techno-complexity Techno-overload Techno-invasion Techno-insecurity Techno-uncertainty Exhaustion H1b H1c H1d H1e H1a Engagement Techno-invasion Techno-complexity Techno-insecurity Techno-overload Techno-uncertainty H2a H2b H2e H2d H2c Managerial support H3 H4
Method
In this method section, separate subsections will elaborate on the design of the study, the procedure, the sample, the factor analysis as well as reliability for all variables and the plan for analysis, in order to make the study replicable.
Design
This research was conducted by using a cross-sectional online-survey with employees, who at least work 30 hours per week in a medium-sized business or in a concern. It was relevant for this study to survey employees, who work at least 30 hours per week and are therefore more likely to use communication technologies more often, than employees with a part-time contract. Since those employees are more often present in the office and hence connected more regularly to their co-workers, as well as managers, it is presumable that they are using ICTS more frequently.
Procedure
The online survey was created in Qualtrics, a survey platform for academic research. The data collection started on the 15th of November 2017 and the survey was open for 18 days. The link to the online questionnaire was forwarded via direct e-mails as well as shared on the social networks “Linkedin” and “Facebook” to reach a capital number of informants. Therefore participants were selected through convenient snowball sampling. The informants were self-reporting about their experiences during an individual setting, filling out the online-survey. A dataset was created with the data collected in the survey and was analysed using SPSS.
Sample
369 participants started the online survey, 193 participants returned a completed questionnaire (N = 193). Only participants, who fully completed the questionnaire, were
counted and used for testing the hypotheses. The invitation text for the survey made clear, that participants had to be over 18 years of age and currently employed at a company working at least 30 hours per week, as well as using communication technologies during their working hours, to participate in this study. Furthermore, respondents were asked to read and
understand English, in order to fill out the questionnaire, as the survey was conducted in English. The respondents took part in the study voluntarily and were not offered any compensation. The final sample consisted of people aged between 20 and 59 years (M = 30.88, SD = 8.65). Among these, 49% were male and 51% female. On average, they work 37.48 hours a week according to their labour agreement (SD = 5.22).
24% of the participants work in industries, which were not mentioned in the questionnaire, but described by the respondents (e.g. tourism, consulting and advertising). Followed by 20% of participants, who work in the communication industry (M = 9.62 SD = 6.02). On average, respondents had been working 43.03 hours a week including all overtime hours (SD = 12.49). Most of the participants 24%, worked for organizations with more than 5000 employees (M = 4.45 SD = 2.69). Followed by 23% of respondents, who worked for small business with a maximum of 29 employees.
Measures
To filter for employees, who actually work at least 30 hours per week and are therefore very knowledgeable about all organizational processes and communications, a selection question was used at the beginning of the online survey: “Are you at least working 30 hours per week for your organization?” The respondents could only continue the survey, if they have chosen answer option 1 (yes). Choosing answer option 2 (no) automatically
directed them to the end of the online survey.
Technostress. Technostress is the independent variable of this study and defined as “a problem of adaptation that an individual experiences when he or she is unable to cope with or
get used to information and communication technologies” (Tarafdar et al., 2007, p. 4). In organizations, ‘technostress’ is provoked by employees’ attempts and struggles to deal with permanent changing ICTs and the shifting social and cognitive demands referring to their application (Tarafdar et al., 2007). This variable is operationalized with a 23-item scale measuring technostress creators including overload, invasion, techno-complexity, techno-insecurity and techno-uncertainty. The items are extracted from the original 38-item scale measuring technostress creators and technostress-inhibitors developed by Ragu-Nathan et al. (2008). All items were adjusted from “technology” to “communication technology” for the purpose of exclusively focussing on communication technologies in this study. Items of this scale are therefore for example “I am under pressure by using
communication technologies to work much faster” or “I am under pressure by using communication technologies to do more work than I can handle” (for all items refer to Appendix A). The questions were designed with a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”).
A principal component analysis (PCA) shows that the 23 items for technostress form a scale with 5 dimensions: five components have an eigenvalue above 1 factor 1 (eigenvalue 7.83) factor 2 (eigenvalue 2.74), factor 3 (eigenvalue 2.19), factor 4 (eigenvalue 1.66) and factor 5 (1.23). All items correlate positively with the first component, the variable “I am under pressure by using communication technologies to work with very tight time schedules” has the strongest association (factor loading is .84). Factor loadings for all factors ranged from (.51 to .84). Reliability of the scale is good (α = .91). Therefore it appears the scale measures technostress.
Five new variables were computed for overload, invasion, techno-complexity, techno-insecurity and techno-uncertainty using the mean for all items of the different subscales. For exact executions refer to (Appendix A).
Exhaustion. Exhaustion is the first dependent variable of this study and defined as “a chronic state of physical and emotional depletion that results from excessive job demands and continuous hassles” (Wright and Cropanzano, 1998, p. 486). The variable exhaustion was operationalized by five items of the MBI-General survey, generated by Schaufeli et al. (1996). The MBI-General survey was developed to measure employees’ burnout by three different dimensions, namely “exhaustion”, “cynicism” and “professional efficacy” (Schaufeli et al., 2002). This study only concentrated on the first dimension “exhaustion”, which was measured by five different items, exploiting employees’ fatigue without making direct reference to other co-workers as the origin of one’s tiredness (Schaufeli et al., 2002). All items were scored on a 7-point frequency rating scale ranging from 0 (“never”) to 6 (“always”). Example items used in the scale are “I feel emotionally drained from my work” or “A full day of work is a heavy burden for me” (for all items refer to Appendix A).
A principal component analysis (PCA) shows that the 5 items for exhaustion form a single uni-dimensional scale: only one component has an eigenvalue above 1 (eigenvalue 3.40) and there is a clear point of inflexion after this component in the scree plot. All items correlate positively with the first component, the variable "A full day of work is a heavy burden for me” has the strongest association (factor loading is .74). Factor loadings for all factors ranged from (.60 to .74). Reliability of the scale is good (α = .88). Therefore it appears the scale measures exhaustion. One new variable was computed for exhaustion. For exact executions refer to (Appendix A).
Employee engagement. Employee engagement is the second dependent variable of this study and defined as the “employees’ willingness and ability to help their company succeed, largely by providing discretionary effort on a sustainable basis” (Kompaso & Sridevi, 2010, p. 90). Additionally, engagement is defined “as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (Schaufeli et
al., 2002, p. 74). To operationalize the variable “employee engagement” the engagement scale developed by (Salanova et al., 2001) consisting of 24 self-constructed items, including the concepts “vigor”, “dedication” and “absorption” was used. The study at hand focussed exclusively on the concept of “vigor”, which is characterized by “high levels of energy and mental resilience while working, the willingness to invest effort in one’s work and persistence even in the face of difficulties” (Schaufeli et al., 2002, p. 74). The employee version of the 6-item vigor scale included 6-items like “When I get up in the morning, I feel like going to work” or “At my work, I feel bursting with energy” (for all items refer to Appendix A). All items are scored on a 7-point frequency rating scale ranging from 0 (“never”) to 6 (“always”).
A principal component analysis (PCA) shows that the 5 items for employee engagement form a single uni-dimensional scale: only one component has an eigenvalue above 1 (eigenvalue 3.52) and there is a clear point of inflexion after this component in the scree plot. All items correlate positively with the first component, the variable “At my job I feel strong and vigorous” has the strongest association (factor loading is .68). Factor loadings for all factors ranged from (.44 to .68). Reliability of the scale is good (α = .85). Therefore it appears the scale measures engagement. One new variable was computed for engagement. For exact executions refer to (Appendix A).
Managerial support. Managerial support is the moderating variable of this study and defined as “the degree to which employees are under the general impression that their
managers appreciate their contributions, are supportive and care about their subordinates’ well-being” (Eisenberger et al., 2002, p. 700). To operationalize the moderating variable, a short version of the Survey of Perceived Organizational Support (SPOS) was used as
recommended by Rhoades and Eisenberger (2002) and originally developed by Eisenberger et al. (1986). The scale was established to measure organizational support, but previous studies often measured managerial support by replacing the word “organization” with “manager”
(e.g.: Kottke & Sharafinski, 1988; Rhoades, Eisenberger & Armeli 2001; Shore & Tetrick, 1991; Yoon & Lim, 1999). This was also done for the purpose of this this research,
substituting the word “organization” with “manager”. The eight-item scale included questions as “The manager would ignore any complaint from me” and “The manager really cares about my well-being”. Each item was evaluated on a 7-point Likert scale from 1 (“strongly
disagree”) to 7 (“strongly agree”).
A principal component analysis (PCA) shows that the 8 items for managerial support form a single uni-dimensional scale: only one component has an eigenvalue above 1
(eigenvalue 4.64) and there is a clear point of inflexion after this component in the scree plot. All items correlate positively with the first component, the variable “The manager really cares about my well-being” has the strongest association (factor loading is .67). Factor loadings for all factors ranged from (.37 to .67). Reliability of the scale is good (α = .89). Therefore it appears the scale measures managerial support. One new variable was computed for
managerial support. For exact executions refer to (Appendix A). All results of the factor- and reliability analysis are shown in Table 1 (Appendix C).
Analysis
The first step to analyse the data and prepare for regression analysis was to execute a correlation analysis between all variables in the conceptual model. Secondly, two multiple regression analyses were conducted. The first multiple regression analysis was conducted with all dimensions of “technostress”, the moderator variable “managerial support” and the configured interaction variables on the dependent variable “exhaustion”. This first regression analysis was conducted to test hypotheses 1 and 3.
The second multiple regression analysis was conducted with all dimensions of “technostress”, the moderator variable “managerial support” and the configured interaction variables on the
dependent variable “employee engagement”. This analysis was conducted to test hypotheses 2 and 4.
To ensure the possibility of proceeding with a multiple regression analysis the following assumptions were controlled for: Firstly, the dependent variables (exhaustion and engagement) were measured on a continuous scale. Secondly, multiple independent variables were identified (dimensions of technostress). Curve estimations were conducted for all the relationships in the model and indicated that all these relationships were linear (see Table 3 and 4 in Appendix C). The fourth assumption of homoscedasticity was also met, as it is shown in the scatterplot that no cone shape could be identified (see Figure 7 and 8 in Appendix B). No multicollinearity was found as all VIF values were found to be below 10 (see Table 5 Appendix C). Lastly the residuals were normally distributed (see Figure 9 and 10 in Appendix B). The last preparatory step was to standardize the independent variables and therefore converting them into standard units of measurement (technostress dimensions). This helps to avoid multicollinearity, when working with interaction variables. As all assumptions were met and the variables were standardized, the multiple regression analyses could be conducted.
Results
This section will discuss all results from the analyses that have been executed in order to test all hypotheses.
Correlation of variables
The first step to analyse the data and prepare for multiple regression analysis was to execute a correlation analysis between all variables in the conceptual model (as shown in Figure 2). The results from the correlation analysis for the direct relationships in the
conceptual model are the following: the variables “technooverload” r(191) = .38, p < .05, “technoinvasion” r(191) = .33, p < .05, “technocomplexity” r(191) = .21, p < .05 and “technoinsecurity” r(191) = .20, p < .05 have been found to be significantly positively correlated to the variable “exhaustion”. Only the variable “technouncertainty” showed no significant correlation to “exhaustion” r(191) = .06, p > .05.
Secondly, the correlation analysis showed a significant correlation between the
variables “technooverload” r(191) = -.27, p < .05, “technocomplexity” r(191) = -.34, p < .05 and “technoinsecurity” r(191) = -.33, p < .05 and “engagement”. Only the variables
“technoinvasion” r(191) = -.13, p > .05 and “technouncertainty” r(191) = -.04, p > .05 showed no significant correlation to “engagement”. All results of the correlation analysis are shown in Table 2.
Table 2 Correlation matrix Notes: N = 193. *Significance at p < .05. Variable M (SD) 1 2 3 4 5 6 7 8 9 10 11 1. Gender 1.51 (0.50) 2. Age 3. Overtime 30.88 (8.65) 43.03 (12.49) -.18* -.18* -.01 4. Technooverload 4.03 (1.45) .08 -.04 .04 5. Technoinvasion 3.86 (1.48) -.14* -.11 .12 .51* 6. Technocomplexity 2.85 (1.22) .13 .07 -.06 .39* .31* 7. Technoinsecurity 2.75 (1.20) .07 -.12 .03 .43* .44* .59* 8. Technouncertainty 4.60 (1.29) -.05 -.03 .07 .31* .35* .21* .32* 9. Exhaustion 3.15 (1.22) .17* -.13 -.02 .38* .33* .21* .20* .06 10. Engagement 4.64 (1.10) -.07 .07 .08 -.27* -.13 -.34* -.33* -.04 -.22* 11. Managerial support 4.89 (1.13) .03 -.02 .05 -.27* -.26* -.27* -.40 -.03 -.29* .34*
Hypothesis 1a-1e & Hypothesis 3
The predictors, overload, invasion, complexity, techno-insecurity, techno-uncertainty and managerial support account for 26% of the variance in exhaustion. The overall regression model was significant. F(12, 175) = 5.16, p < .001, R2
= .26.
It was found that techno-overload significantly predicted exhaustion (b* = .38 p < .001), as did technoinvasion (b* = .25, p = .013). Techno-complexity (b = .06, p = .595), techno-insecurity (b = -.06, p = .601), as well as techno-uncertainty (b = -.14, p = .124) did not significantly predict exhaustion (see Table 6). H1a and H1b are therefore supported. H1c, H1d and H1e could not be supported.
Table 6
Regression results dependent variable exhaustion
Variable Unstandardized B Standard Error Standardized B t p 95% Confidence interval Technooverload .38 .10 .31 3.75 .000 [.18, .58] Technoinvasion .25 .10 .21 2.52 .013 [.06, .45] Technocomplexity .06 .10 .05 .53 .595 [-.15, .26] Technoinsecurity -.06 .12 -.05 -.52 .601 [-.29, .17] Technouncertainty -.14 .09 -.11 -1.55 .124 [-.32, .04] Managerialsupport -.18 .10 -.15 -1.90 .059 [-.37, .01] Overtime -.04 .08 -.03 -.44 .661 [-.20, .13] Technooverload * Support .06 .10 .05 .64 .521 [-.13, .25] Technoinvasion * Support -.15 .08 -.14 -1.85 .067 [-.32, .01] Technocomplexity * Support -.18 .10 -.15 -1.75 .081 [-.39, .02] Technoinsecurity * Support .17 .13 .13 1.35 .178 [-.08, .42] Technouncertainty * Support .05 .09 .04 .58 .560 [-.12, .22]
!
In order to test that the techno-overload (b = .38) and techno-invasion (b = .25) standardized beta weights were statistically significantly different from each other, their corresponding 95% confidence intervals were estimated via bias corrected bootstrap (1000 re-samples). In the event that the confidence intervals overlapped by less than 50% the beta weights would be considered statistically significantly different from each other (p < .05). Half of the average of the overlapping confidence intervals was calculated (.10) and added to the techno-overload beta weight lower bound estimate (.14), which yielded (.238). As the techno-invasion upper bound estimate of (.18) exceeded the value of (.28), the
difference between the techno-overload and techno-invasion standardized beta weights (Δ b = 0.13) was not considered statistically significantly larger than the techno-invasion beta weight (p < .05). Which leads to conclude, that no significant differences between the effect sizes of techno-overload and techno-invasion could be examined.!
For this reason it was not possible to sufficiently answer RQ (1): Which of the dimensions of technostress has the strongest effect on exhaustion? There were no
significant differences between the technostress creators and their effects on exhaustion. !
!
Hypothesis 3 interaction effect
The moderation effect of managerial support on techno-invasion shows a trend to the direction expected (b = −0.15, CI 95%[−0.32; 0.01], p = .067). Figure 3 represents this effect. Employees who experience techno-invasion do experience less exhaustion in case of high levels of managerial support. The moderation effect of managerial support on techno-complexity also shows a trend to the direction expected (b = −0.18, CI 95% [−0.39; 0.02], p = .082). Figure 4 represents this effect. Employees who experience
However, the moderation effects for the different dimensions of technostress and exhaustion were not significant. H3 is therefore not supported. All graphs can be found in (Appendix B).
Hypothesis 2a-2e & Hypothesis 4
The predictors, overload, invasion, complexity, techno-insecurity, techno-uncertainty and managerial support account for 23% of the variance in engagement. The overall regression model was significant. F(12, 175) = 4.33, p < .001, R2 = .23.
It was found that technooverload significantly predicted lower engagement (b* = -.19, p = .042), as did technocomplexity (b* = -.21, p = .029). Technoinvasion (b = .12, p = .210), techno-insecurity (b = -.15, p = .168) as well as techno-uncertainty (b = .08, p = .356) did not significantly predict engagement (see Table 7). H2a and H2c are therefore supported. H2b, H2d and H2e could not be supported.
Table 7
Regression results dependent variable engagement
Variable Unstandardized B Standard Error Standardized B t p 95% Confidence interval Technooverload -.19 .09 -.17 -2.12 .036 [-.37, -.01] Technoinvasion .12 .09 .11 1.37 .171 [-.05, .30] Technocomplexity -.21 .09 -.19 .52 .023 [-.15, .25] Technoinsecurity -.08 .12 -.07 -2.29 .179 [-.40, -.03] Technouncertainty .08 .08 .07 .93 .354 [-.09, .24] Managerialsupport .25 .09 .23 2.97 .003 [.09, .42] Overtime .05 .08 .04 .61 .542 [-.11, .20] Technooverload * Support .03 .09 .02 .28 .784 [-.15, .20] Technoinvasion * Support -.04 .08 -.04 -.50 .617 [-.19, .11] Technocomplexity * Support .06 .10 .05 .62 .535 [-.13, .25] Technoinsecurity * Support .02 .12 .02 .17 .868 [-.21, .25] Technouncertainty * Support -.13 .08 -.12 -1.56 .121 [-.29, .03]
In order to test that the techno-overload (b = -.19) and techno-complexity (b = -.21) standardized beta weights were statistically significantly different from each other, their corresponding 95% confidence intervals were estimated via bias corrected bootstrap (1000 re-samples). In the event that the confidence intervals overlapped by less than 50% the beta weights would be considered statistically significantly different from each other (p < .05). Half of the average of the overlapping confidence intervals was calculated (.16) and added to the techno-overload beta weight lower bound estimate (-.37), which yielded (-.21). As the techno-complexity upper bound estimate of (.25) exceeded the value of (-.21), the difference between the techno-overload and techno-complexity standardized beta weights (Δ b = 0.02) was not considered statistically significantly larger than the techno-complexity beta weight (p < .05). Which leads to conclude, that no significant differences between the effect sizes of techno-overload and techno-complexity could be examined.!
For this reason it was not possible to sufficiently answer RQ (2): Which of the dimensions of technostress has the strongest effect on lower engagement? There were no significant differences between the technostress creators and their effects on engagement. !
Hypothesis 4 interaction effect
The moderation effect of managerial support on techno-complexity shows a trend to the direction expected (b = 0.6, CI 95% [−0.13; 0.25], p = .535). Figure 5 represents this effect. Employees who experience techno-complexity do experience higher levels of engagement in case of high levels of managerial support. However the moderation effects for the different dimensions of technostress and engagement were not significant. H4 is therefore not supported. All graphs can be found in (Appendix B).
Figure 6. Adapted research model
The relationships indicated by the black arrows, have been predicted by the research model and have found to be significant. The grey concepts and arrows were not found to be statistically significant by testing the model, thus they have been extracted from the model.
Conclusion and Discussion
The purpose of the present study was to examine, how the different technostress creators, namely techno-overload, techno-invasion, techno-complexity, techno-insecurity and techno-uncertainty are related to exhaustion, as well as to employee engagement and if managerial support might function as a buffer between those relationships.
Techno-complexity Techno-overload Techno-invasion Techno-insecurity Techno-uncertainty Exhaustion H1b H1c H1d H1e H1a Engagement Techno-invasion Techno-complexity Techno-insecurity Techno-overload Techno-uncertainty H2a H2b H2e H2d H2c Managerial support H3 H4
First, the findings confirm that techno-overload, as well as techno-invasion
significantly predicted exhaustion. In line with the approach of the transaction-based model of stress (McGrath, 1976), this reveals that stressors or demands like techno-overload and techno-invasion result in negative outcomes, such as feeling exhausted.
Techno-complexity, techno-insecurity, as well as techno-uncertainty did not significantly predict exhaustion. Those last three dimensions might serve as demanding challenges for
employees, but with either no rise of or a slight increase in exhaustion. For that reason H1a and H1b could be supported, while H1c, H1d and H1e had to be rejected.
Second, the results showed that techno-overload, as well as techno-complexity significantly predicted a decrease in engagement. Previous studies have shown that an overload of communication technology use and therefore an overload of information may result in the decrease of work engagement (Fujimoto et al., 2016). Techno-complexity and therefore the feeling of having inadequate skills regarding computer use, could lead to the assumption that employees cannot keep up with the pace their co-workers might have, in terms of ICTs skills. This perception could also result in the decrease of work engagement and the increase of exhaustion (Fujimoto et al., 2016). Additionally, this finding made clear that techno-overload and techno-complexity as demands for employees, which create stress, result in negative work outcomes, such as lower employee engagement. Techno-invasion, techno-insecurity and techno-uncertainty did not significantly predict lower engagement. Those three dimensions might serve as demanding challenges for employees, but with either no loss of or a slight decline in engagement. This why H2a and H2c were supported, while H2b, H2d and H2e had to be rejected.
Furthermore, this study aimed to examine, if one of the different technostress dimensions was related more strongly to exhaustion, as well as to lower engagement. Comparing the effect sizes of the technostress dimensions, it could be examined that the effects were not significantly different from each other, which concludes that the
dimensions of techno-overload and techno-invasion do not diversify they both equally affect exhaustion. The same was found for the dimensions of overload and techno-complexity and their effects on engagement. The study examined, that the effects were equally strong, which means that both of them likewise affect engagement.
Finally, the findings detected that managerial support did not significantly moderate the relationships between the different dimensions of technostress and exhaustion. The same was examined for the relationships between the dimensions of technostress and engagement. However, a trend in the direction expected, that managerial support buffers the negative outcomes of technostress, was apparent for the relationships between techno-invasion/techno-complexity and exhaustion. Nevertheless H3 and H4 were rejected.
Managerial support as a situational factor, hence did not significantly function as the expected buffer, between the stressors employees are facing and the negative outcomes of exhaustion and decreasing engagement. This indicates, that even if managerial support is high, employees might feel exhausted or not able to fully engage in their work tasks. Other forms of social support, for example co-worker support, might be a more important factor, to reduce the implications of technostress. Previous studies have suggested, that social support, in terms of co-worker support, can have a direct influence on the experience of stressors and the related stress outcomes (Swanson and Power, 2001).
Theoretical and practical implications
This study contributes to existing literature, by concentrating on the relationships between the different dimensions of technostress creators and the outcomes of exhaustion and decreased work engagement. To date, the literature lacks, with regard to studies that investigate all dimensions of technostress and their relations to different work outcomes.
Since the dual nature of ICTs, characterized by positive business outcomes and negative consequences for individuals, is developing further, it is important to keep up
research, that focuses on the demands employees are facing in the current technological age and which strains those demands might provoke.
Shuck and Reio (2014) found, that workers who experienced negative
psychological climates, as well as stress at work, were more likely to receive higher levels of exhaustion. Because not all of the technostress creators showed relations to exhaustion or lower levels of engagement, this study also demonstrates that work-related stress, created by the use of ICTs, may not compulsory lower the level of engagement or increase the level of exhaustion (Ahmad et al., 2014).
Not finding significant relationships between some of the technostress dimensions and the outcomes of exhaustion and lower engagement might be due to the fact, that some employees indeed experience technostress as ‘positive’ or ‘enhancing’ work experiences. Technostress can therefore be seen as a ‘challenge’, which needs to be overcome.
Challenges refer to conditions of high demands. It is crucial to cope with those demands by overcoming obstacles, in order to grow as an individual (Lazarus, 1995). Challenges, in terms of technostress, therefore might lead to enthusiastic and engaged employees, who like to overcome certain demands and to grow with the challenge.
On these grounds, it is necessary to understand, when and why employees are experiencing technostress, as a vector leading to positive outcomes, such as dealing with challenges.
The findings of this research lead to multiple implications for practitioners. Considering the significant results it becomes evident, that managers should consider the different forms of technostress, which can emerge from working with communication technologies. Since techno-overload and techno-invasion were both positively related to exhaustion, it would be advisable for managers and organizations to introduce measures, which buffer those technostress effects.
work faster and longer, as well as to work with a higher amount of information, it would be advisable to predefine exact working hours and deadlines and for example to reduce e-mail communication to a relevant minimum, so that employees can follow exact schedules and decrease their overtime hours, as well as level of exhaustion.
Employees feeling to be constantly connected and reachable, termed techno-invasion, was also positively related to exhaustion. It is a social phenomenon, that employees replicate the behaviour of their co-workers. If co-workers are using ICTs also after work hours, for example to send late e-mails, an employee might feel compelled to keep up with that behaviour and to respond even after required working time. Previous studies suggest “shared expectations of constant accessibility and responsiveness to incoming messages are at the root of many of the negative ramifications associated with ICTs” (Mazmanian, 2013, p. 1227). Therefore it would be judicious for organizations to create a working environment, where employees are having the same perceptions when and how to use ICTs and therefore not getting into peer pressure, to constantly being accessible. This is important to establish clear work-life boundaries and to prevent exhaustion.
It is relevant to focus on both dimensions techno-overload and techno-invasion, which were related to higher levels of exhaustion and hence to come up with appropriate measures, in order to reduce stress in the workplace.
Additionally, the findings showed that overload, as well as techno-complexity was related to lower levels of employee engagement. Managers should train their employees with regard to time management and encourage their staff members to reserve a part of the day, to exclusively work for themselves and therefore reducing techno-overload and the processing of irrelevant information, coming up through ICTs.
Through managing those job expectations, employees might perceive assurance and thus engage themselves more strongly in work tasks.
Techno-complexity and therefore the feeling of having inadequate skills regarding communication technology use, could be the foundation to implement targeted ICTs work groups between co-workers and hereby give employees the possibility to upgrade and refresh their communication technology skills, by exchanging with their colleagues. Benson and Dundis (2003) figured out that in the digital era, where technology is an essential part in the workplace, offering exchange between skilled and less skilled co-workers, will result in employees feeling secured, as well as needed and appreciated, which in turn leads to higher levels of commitment.
Those sanctions could not only increase levels of work engagement, since
employees would feel safeguarded, but also lead to a better working climate, between co-workers.
Limitations and future research directions
As an acknowledgement of the need for future research it is important to discuss limitations of this study. Since the sampling method for this study was a convenient sample through sharing the survey link on Facebook, Linkedin and via e-mails, only the network of the researcher was reached. The generalizability of the results is therefore limited, as the access was restricted to other individuals. Since the time frame for this master thesis was defined, it was only possible to collect data over a short period.
Fluency in English language could also be seen as a limitation for the research at hand. The individuals, who filled out the survey, were mostly from internationally diverse backgrounds, which could have led to difficulties for respondents in understanding
questions correctly. It also has to be noted that the study has limited outcomes, due to the constrained options of responses, which were preselected for the questionnaire. Some answer options might not represent the actual opinion of the respondents, but they had to choose between the given answer possibilities.
Taking into account the aforementioned limitations, future research should choose a bigger sample, for example accomplishing the questionnaire in several big industries, so that the results can be generalized to the whole population. A larger sample, for example in the work force, would not only contribute to the reliability of the study‘s outcomes, but also be a representative reflection of the population. Additionally, a longitudinal study would help to test the proposed causality of the relationships in the model and allow researchers to track changes and trends in the field of communication technology use over a longer period of time and therefore the further expansion of technostress.
Although this study has its limitations, it advanced the general understanding of technostress and will hopefully spark further inquiry into the subject of ICTs use and the negative outcomes of technostress, as many more empirical questions are waiting to be answered.
As the dual nature of ICTs is evolving further and will continue to be an integral part of modern society, it is important to trace these global trends and to keep up research in the field of stress in the workplace. To date, technology-related stress remains
understudied. For this reason, it is essential that this modern phenomenon and its consequences be comprehend.
While more and more technology is implemented in working environments, to support employees and to simplify work processes, stress in western workplaces,
reinforced by ICTs, is constantly rising. Due to the fact, that the employee´s well-being is threatened, through the increased levels of modern stress, it is necessary to research in this field and to come up with stress-reducing measures, so that the use of ICTs will in future create mostly positive business outcomes, instead of negative consequences for
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Appendices
Appendix A: scales and measures
Techno-overload Item N° Assertion
TO 1 I am under pressure by using communication technologies to work much faster TO 2 I am under pressure by using communication technologies to do more work than I
can handle
TO 3 I am under pressure by using communication technologies to work with very tight time schedules
TO 4 I am forced to change my work habits to adapt to new communication technologies TO 5 I have a higher workload because of increased communication technology
complexity
Techno-invasion Item N° Assertion
TI 1 I spend less time with my family due to these communication technologies TI 2 I have to be in touch with my work even during my vacation due to these
communication technologies
TI 3 I have to sacrifice my vacation and weekend time to keep current on new communication technologies
TI 4 I feel my personal life is being invaded by these communication technologies
Techno-complexity Item N° Assertion
TC 1 I do not know enough about these communication technologies to handle my job satisfactorily
TC 2 I need a long time to understand and use new communication technologies
TC 3 I do not find enough time to study and upgrade my communication technology skills TC 4 I find new recruits to this organization know more about communication technology
than I do
TC 5 I often find it too complex for me to understand and use new communication technologies
Techno-insecurity Item N° Assertion
TIN 1 I feel constant threat to my job security due to new communication technologies TIN 2 I have to constantly update my skills to avoid being replaced
TIN 3 I am threatened by co-workers with newer communication technology skills TIN 4 I do not share my knowledge with my co-workers for fear of being replaced
TIN 5 I feel there is less sharing of knowledge among co-workers for fear of being replaced
Techno-uncertainty Item N° Assertion
TU 1 There are always new developments in the communication technologies we use in our organization
TU 2 There are constant changes in computer software in our organization TU 3 There are constant changes in computer hardware in our organization TU 4 There are frequent upgrades in computer networks in our organization All items were measured on a 7-point Likert scale from 1 (“strongly disagree”) to 7 (“strongly agree”).
Scale used to operationalize “exhaustion”
Exhaustion
Item N Assertion
EX 1 I feel mentally drained by my work
EX 2 A full day of work is a heavy burden for me EX 3 I feel exhausted by my work
EX 4 At the end of my work day, I feel empty
All items are scored on a 7-point frequency rating scale ranging from 0 (“never”) to 6 (“always”).
Scale used to operationalize “employee engagement”
Employee engagement
Item N° Assertion
EE 1 When I get up in the morning, I feel like going to work EE 2 At my work, I feel bursting with energy
EE 3 At my work I always persevere, even when things do not go well EE 4 I can continue working for very long periods at a time
EE 5 At my job, I am very resilient, mentally EE 6 At my job I feel strong and vigorous
All items are scored on a 7-point frequency rating scale ranging from 0 (“never”) to 6 (“always”).
Scale used to operationalize “managerial support”
Survey of perceived organizational support (SPOS)
Item N° Assertion
MS 1 The manager strongly considers my goals and values MS 2 The manager fails to appreciate any extra effort from me MS 3 The manager would ignore any complaint from me MS 4 The manager really cares about my well-being
MS 5 Even if I did the best job possible, the manager would fail to notice MS 6 The manager cares about my general satisfaction at work
