Understanding teleworkers ’ technostress and its influence
on job satisfaction
Ayoung Suh
School of Creative Media, Department of Information Systems, City University of Hong Kong, Hong Kong, Hong Kong, and
Jumin Lee
Department of Marketing, Kyung-Hee Cyber University, Seoul, South Korea
Abstract
Purpose–The purpose of this paper is to develop and test a theoretical model that predicts a teleworker’s job satisfaction.
Design/methodology/approach–By drawing on the technostress model and job characteristics theory, this study proposed a theoretical model. The proposed model was tested through a survey of 258 teleworkers from two global IT companies that have adopted telework programs.
Findings–The results show that technology and job characteristics jointly induce teleworkers’technostress, which in turn reduces their job satisfaction. The results also indicate that the manner in which technology and job characteristics influence teleworkers’technostress varies depending on the intensity of teleworking (IOT).
Interestingly, this study finds that teleworkers with a low IOT are more vulnerable to technostress than those with a high IOT.
Research limitations/implications–By discussing the magnitude of the different factors that determine teleworkers’technostress and job satisfaction, this study contributes to a more nuanced understanding of teleworkers’challenges. The study provides insights and prescriptive guidelines that will help managers and companies develop strategies to maximize the benefits of teleworking implementation.
Practical implications – This study provides insights and prescriptive guidelines for managers or companies to develop strategies to maximize the benefits of teleworking implementation.
Originality/value–This paper is one of the first to develop and empirically test an integrated model of technostress and job characteristics. The paper outlines relevant research avenues for researchers investigating remote work and virtual collaboration.
KeywordsTelework, Job satisfaction, Technostress, Intensity of teleworking, Technology paradox Paper typeResearch paper
1. Introduction
The advancement of information technologies (ITs) and the market penetration of various mobile devices have produced significant changes in the business practices of organizations in recent years (Sewell and Taskin, 2015). Workers are now able to accomplish their tasks anywhere and anytime without spatial and temporal constraints by using diverse digital media tools and enterprise collaboration systems ( Jeske and Axtell, 2014). Such IT-enabled remote work is referred to as“telework”(Debora and Carolyn, 2014; Marlieset al., 2015).
Telework provides workers with greater flexibility and control with respect to the time and place of their work, thus helping them balance work and life (Coenen and Kok, 2014;
Kossek et al., 2015). For organizations, telework can reduce office space and operational costs and provide opportunities for the sharing of various information resources by connecting the required human resources, information, knowledge, and systems via networks to create new values that allow employees to use organizational resources more
Internet Research Vol. 27 No. 1, 2017 pp. 140-159
© Emerald Publishing Limited 1066-2243
DOI 10.1108/IntR-06-2015-0181 Received 13 June 2015 Revised 31 August 2015 4 January 2016 16 February 2016 21 February 2016 Accepted 21 February 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1066-2243.htm
This research was supported in part by Grants No. CityU 21500714 from the Research Grants Council of the Hong Kong SAR, China. This work was also partly supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A3A2054667).
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efficiently (Kaplanet al., 2014; Vegaet al., 2015). However, the benefits of technology always come at a cost. Researchers have pointed out that workers experience new types of work-related stress in teleworking environments where communication and interaction are dependent on ITs (Fonner and Roloff, 2012; Weinertet al., 2014). Teleworkers who have engaged in face-to-face work practices for a long time experience increased work stress because they have to cope with rapid and fundamental changes in the nature of work environments and they are required to keep pace with technological changes. Such work stress caused by ITs is called“technostress”(Ayyagariet al., 2011; Lei and Ngai, 2014) and is regarded as an impediment to teleworkers’job satisfaction (Srivastavaet al., 2015).
Previous studies have focused on the causes and effects of technology-induced stress, but they have ignored how job characteristics offset or intensify workers’ technostress.
Because job characteristics determine individual workers’stress levels ( Jacobset al., 2014), it is important to examine how job characteristics jointly influence teleworkers’ technostress. Furthermore, although researchers have found that teleworkers spend varying degrees of their scheduled time on telework (Fonner and Roloff, 2012), they have not fully addressed the role played by the intensity of the telework in shaping the dynamics underlying teleworkers’technostress and job satisfaction.
To fill these gaps in understanding, this study develops and tests a research model that explains teleworkers’job satisfaction by combining two theoretical lenses: the technostress model and the job characteristics theory. The former explains how technology-related stressors influence employees’ job satisfaction, whereas the latter describes how job characteristics exert additive or offsetting influences on employees’technostress. A more nuanced understanding of teleworkers’ technostress will lead to a clearer picture of the challenges and opportunities of telework and can thus help researchers design more effective telework strategies to be used in organizations.
This study therefore aims to provide researchers and practitioners with meaningful insights concerning ways to increase the effectiveness of telework implementation in organizations. Specifically, this study addresses the following questions:
(1) How do technology and job characteristics jointly induce teleworkers’technostress?
(2) How does technostress influence the job satisfaction of teleworkers?
(3) How do the technostress patterns that teleworkers experience vary depending on the intensity of their teleworking?
2. Literature review and theory development 2.1 Telework
Telework is defined as an“an alternative work arrangement in which employees perform tasks elsewhere that are normally done in a primary or central workplace, for at least some portion of their work schedule, using electronic media to interact with others inside and outside the organization”(Gajendran and Harrison, 2007, p. 1525). Although organizations are increasingly providing employees with telework opportunities, the effects of telework on workers’ job satisfaction are inconsistent. Some researchers have argued that telework positively influences workers’job satisfaction by providing enhanced flexibility in work schedules and facilitating cross-functional collaboration and extensive knowledge sharing across organizational boundaries (Coenen and Kok, 2014; Peterset al., 2014; Vegaet al., 2015). However, other researchers have identified diverse factors that may inhibit teleworkers’satisfaction, such as physical and social isolation and the lack of a sense of organizational belonging (Bartelet al., 2012; Kosseket al., 2015). These studies commonly indicate that teleworkers are concerned about being excluded from decision making and being regarded as less committed to their work group, which leads them to rely extensively
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on ITs to signal their presence and to reduce their sense of social isolation at the remote workplace to a greater degree than when they work in an office (Sewell and Taskin, 2015).
Excessive technology dependency and the constant need to adapt to emerging IT applications lead to increases in workers’stress levels (Srivastavaet al., 2015). In this sense, recent literature has identified the stress caused by the use of ITs as a major obstacle to teleworkers’satisfaction (Lei and Ngai, 2014; Weinertet al., 2014; Srivastavaet al., 2015).
2.2 Technostress model
The term,“technostress”was coined in the field of pathological psychology. It refers to“a modern disease of adaptation caused by an inability to cope with the new computer technologies in a healthy manner” (Brod, 1984, p. 16). Arnetz and Wiholm (1997) defined technostress as“a state of arousal”found in workers who depend on computers for much of their work. Ragu-Nathanet al.(2008) defined it as“any stress experienced by end-users of information and communication technologies”(p. 417). Whereas earlier work in the field of psychology focused on the physical consequences of the stress associated with increased use of IT in organizational processes, including fatigue, headache, restlessness, and irritability (Arnetz and Wiholm, 1997), current research has focused on the different forms of psychological state that constitute technostress by distinguishing between the sources of stress (stressors) and the outcomes of stress (strain) (Tarafdaret al., 2010). The technostress model explains how IT artifacts create stressors and how the stressors influence workers’ strain in organizations (Ayyagariet al., 2011; Tarafdaret al., 2011). The main tenet of the technostress model is that workers feel distress when they perceive a discrepancy between their abilities (e.g. skills, knowledge, time, and energy) and the demands placed on them by their work environment (Lei and Ngai, 2014).
Teleworkers are increasingly likely to face situations in which the very technology expected to remove constraints imposed on workers results in other types of constraint:
“anytime, anywhere connectivity” allows employees to utilize organizational resources better, but it simultaneously requires them to work harder than ever before. For example, workers can communicate with their customers or managers anytime and anywhere through diverse IT applications, even after work hours or during vacations; this not only leads to additional work, but also compromises workers’ privacy. This phenomenon is known as the “technology paradox” (Hajli et al., 2015). By applying the tenet of the technology paradox to teleworkers, we can infer that the telework enabled by IT provides new opportunities for workers to enhance their ability to utilize time and space, thus increasing their productivity; at the same time, however, it creates new challenges that workers must address.
2.3 Job characteristics theory
Job characteristics theory provides a theoretical basis for understanding how job characteristics determine workers’attitudes and behaviors (Oldham and Hackman, 2005).
Empirical evidence suggests that job characteristics, such as job autonomy and task interdependence, affect workers’technostress (Wallgren and Hanse, 2007). Job autonomy is the degree of freedom or discretion a worker has in terms of how tasks are accomplished (Langfred, 2000). Research has shown that high levels of autonomy lead to greater motivation and an increased sense of responsibility among workers (Deci and Ryan, 2000).
Workers who can exert more discretion over the methods, procedures, plans, and judgment required to perform their tasks are likely to have more positive feelings about their work (Golden, 2007). Hence, we posit that job autonomy offsets the effects of technology on teleworkers’technostress because workers with high levels of autonomy are more likely to be resilient to stress. Task interdependence is the extent to which a worker depends on other group members to accomplish work goals (Campionet al., 1993). Previous studies suggest
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that workers with a high degree of task interdependence require a higher frequency of communication with other group members and face more complex decision-making processes than workers with a low degree of task interdependence (Maznewski and Chudoba, 2000). Because teleworkers are more likely to rely on digital communication tools, which are less effective than face-to-face meetings in transferring, exchanging, and interpreting implicit knowledge (Leonardi and Barley, 2008), we posit that task interdependence has an additive effect on teleworkers’technostress.
2.4 Intensity of teleworking (IOT)
IOT refers to “the extent or amount of scheduled time employees spend doing tasks away from a central work location” (Gajendran and Harrison, 2007, p. 1529). The degrees of IOT reflect work modes that vary between remote work and work co-located with partners (Fonner and Roloff, 2012). These variations create a sense of conflicting identities between nonconventional and conventional workers (Paset al., 2014). Current research has empirically shown that a worker’s sense of inconsistency with respect to diverse roles, work modes, and identities shapes his or her stress level and job satisfaction (Horton et al., 2014). Given that teleworkers have different stressors and motivations depending on their IOT (Belle et al., 2015), we posit that the dynamics underlying teleworkers’technostress and job satisfaction will vary depending on their IOT. Figure 1 outlines the theoretical links between technology and job characteristics, stressors, strain, and job satisfaction.
3. Research model and hypotheses
Figure 2 illustrates our theoretical model. The model examines how the dynamics underlying teleworkers’technostress and job satisfaction vary depending on their IOT.
3.1 Technology characteristics and stressors
Telework is characterized by connecting with members of one’s own organization, or from another organization, by using broadband communication technology and computers.
The first characteristic of technology that induces teleworkers’ technostress is IT complexity. IT complexity is the degree to which the use of ITs for work-related tasks is difficult and challenging (Ragu-Nathanet al., 2008). Previous studies on organizational behavior have reported that when individual workers feel that the use of computer technology is difficult, they experience negative emotions, such as fear, avoidance, or worry (Fonner and Roloff, 2012). Because updates and changes to application programs occur regularly, and new technological functions and terms become increasingly complicated (Ragu-Nathanet al., 2008), teleworkers who rely on ITs for their work are prone to experience higher degrees of work overload due to the additional time and effort
Technology Characteristics
Stressors Strain
Job Characteristics
Sources of stress
Techno-stress:
The overall transactional process from stressors and strain Outcome of stress
Job Satisfaction Intensity of Teleworking
Figure 1.
Conceptual framework
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required relative to their regular work (Tarafdaret al., 2011). Thus, we formulated the following hypothesis:
H1. IT complexity is positively associated with teleworkers’perceived work overload.
IT presenteeism is the extent to which technology enables users to be reachable (Ayyagari et al., 2011). Electronic connectivity has increased via various types of collaborative application programs, while mobile communication tools force workers to process constant information flows from inside and outside their organizations (Yu, 2011). Teleworkers are often required to process large volumes of information quickly in order to cope with ever-increasing information demands. Thus, electronic connectivity through ITs facilitates information overload (i.e. exposure to more information than workers can utilize and handle efficiently and effectively), resulting in “information fatigue” (Fonner and Roloff, 2012).
That is, IT presenteeism may aggravate the work overload experienced by teleworkers.
Thus, we formulated the following hypothesis:
H2. IT presenteeism is positively associated with teleworkers’perceived work overload.
Workers feel that the higher their dependence on IT use, the more their privacy is infringed upon (Bestet al., 2006). Personal privacy can be compromised by constant IT connectivity.
For example, even during the official holiday period, workers may need to respond to constant business-related requests via their mobile phones. Furthermore, their IT-related activities can easily be traced and monitored by supervisors or companies. Accordingly, the ubiquity of IT and connectivity has expanded personal work hours (Mandelet al., 2005). Because various information devices have different levels of connectivity, workers conduct their work using different devices, such as mobile phones and notebook computers. IT connectivity has given rise to an implicit norm in which workers are expected to work from home even outside of their regular working hours (e.g. 9 a.m. to 5 p.m.). This implicit expectation among organizational members is often unavoidable and frequently generates unwanted additional
Intensity of Teleworking
Technology Characteristics
Job Characteristics
IT Complexity
IT Presenteeism
Pace of IT Change
Job Autonomy
Task Interdependence
Techno-Stressors
Work-overload
Invasion of Privacy
Role Ambiguity
Strain Job Satisfaction
Figure 2.
Research model
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working hours (Maznewski and Chudoba, 2000). As such, although electronic connectivity through ITs can increase work efficiency, it can make work-home boundaries ambiguous (McFarlane and Latorella, 2002). Thus, the personal time of teleworkers is vulnerable to infringement. In recent years, this phenomenon of infringement on personal time due to IT devices has increased to the extent that it is now called the“digital shackle”(Ayyagariet al., 2011). Accordingly, we formulated the following hypothesis:
H3. IT presenteeism is positively associated with teleworkers’ perceived invasion of privacy.
Organizations adopt various new ITs and application programs to construct telework environments. Thus, individual workers experience changes in the ITs they use and although they may initially be interested in the new technology, learning and familiarizing themselves with the new technologies can be a stress-inducing factor (Brod, 1984).
Companies adopting telework systems may introduce new enterprise information systems, mobile applications, wired and wireless integration or video conferencing, and/or cloud-based platforms. If the ITs used in a business change frequently or new information systems are introduced quite often, teleworkers are required to invest time and effort in familiarizing themselves with the new IT systems in addition to conducting their regular work on the older systems. Furthermore, when teleworkers face technical problems during the process of learning new IT manuals and adaptations, they may experience inner conflicts about whether their primary role is solving technical problems (Ragu-Nathanet al., 2008; Ayyagariet al., 2011). Thus, we formulated the following hypotheses:
H4. The pace of IT change is positively associated with teleworkers’perceived work overload.
H5. The pace of IT change is positively associated with teleworkers’ perceived role ambiguity.
3.2 Job characteristics and stressors
Generally, workers who perceive high levels of job autonomy tend to feel that they have the appropriate skills to perform and fulfill the tasks assigned to them. Research has shown that job autonomy is important for teleworkers who are highly reliant on ITs to perform their tasks (Clear and Dickson, 2005). Furthermore, these studies provide evidence that job autonomy is more important in the IT industry than in other industries. For example, Ahuja and Thatcher (2005) argue that job autonomy reduces IT workers’perceived work-life conflict, which is a type of job stressor caused by work overload. Work-life conflict is intensified when workers do not maintain their personal lives because of excessive work-related responsibilities (Greenhaus and Beutell, 1985). Job autonomy reduces perceived work overload (Ahujaet al., 2007) because workers can determine how to allocate their time in performing their tasks. In addition, when teleworkers feel they have job autonomy, they experience less invasion of privacy because high levels of autonomy at work contribute to a balance between work and life (Kirkman and Rosen, 1997). Therefore, we formulated the following hypotheses:
H6. Job autonomy is negatively associated with teleworkers’perceived work overload.
H7. Job autonomy is negatively associated with teleworkers’ perceived invasion of privacy.
In addition to job autonomy, task interdependence is a job characteristic that can influence perceived work overload. Teleworkers who have high task interdependence need to make a greater effort to share knowledge and mitigate conflict with other teleworkers, and they often experience conflict with non-teleworkers who require formal interactions or face-to-face
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meetings (Kosseket al., 2015). Although one of the benefits of teleworking is having greater flexibility in time and space for work, teleworkers who have high task interdependency need to address other colleagues’requests for communication even outside of their regular work hours, which leads to an increase in perceived work overload. Teleworkers are usually connected with other colleagues through media technologies, and they can thus be accessed easily by others.
Such high connectivity through the media technologies blurs the boundary between work and life, and this leads to an increase in perceived invasion of privacy and role ambiguity.
Therefore, we developed the following hypotheses:
H8. Task interdependence is positively associated with teleworkers’ perceived work overload.
H9. Task interdependence is positively associated with teleworkers’perceived invasion of privacy.
H10. Task interdependence is positively associated with teleworkers’ perceived role ambiguity.
3.3 Stressors, strain, and job satisfaction
The stressor-strain model proposed by Frese and Zapf (1988) explains that employees are easily exposed to stressors that may lead to negative psychological states, thereby increasing strain. Drawing on Ayyagariet al.’s (2011) technostress model, we identify three technology-induced stressors–work overload, invasion of privacy, and role ambiguity– and we hypothesize that these stressors increase strain. Our assumption is consistent with existing job design theories, which explain that factors causing workers’distress increase their job strain (Fairbrother and Warn, 2003). Specifically, work overload due to the excessive amounts of information that teleworkers need to process, invasion of privacy due to excessive electronic connectivity, and role ambiguity due to increased technology-related duties all increase strain. We therefore formulated the following hypotheses:
H11. Work overload is positively associated with teleworkers’strain.
H12. Invasion of privacy is positively associated with teleworkers’strain.
H13. Role ambiguity is positively associated with teleworkers’strain.
Job satisfaction is determined by the extent to which an organization fulfills its employees’ requirements (Rutherfordet al., 2009). It has been argued that IT users’work satisfaction is closely related to the cognitive and mental factors perceived during IT use (Cheney and Scarpello, 1985) and that mental stress perceived in a work environment significantly influences personal work satisfaction (Cooper et al., 2001). Thus, we formulated the following hypothesis:
H14. Strain is negatively associated with teleworkers’job satisfaction.
3.4 The moderating effect of IOT
Consistent with previous studies (Gajendran and Harrison, 2007; Fonner and Roloff, 2012;
Belleet al., 2015), we contend that teleworkers’psychological and behavioral responses to technostress vary depending on IOT. Our rationale for conceptualizing IOT as a global moderating variable is based on the theory of virtuality (Leonardi and Barley, 2008;
Suhet al., 2011). Telework entails spatial and temporal separation from other collaborators, which forces teleworkers to be involved in virtual collaboration using digital technologies.
Some studies have shown that virtual workers tend to be less satisfied with their peers and easily feel isolated (Robertet al., 2008) because the barriers created by physical separation
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lead to challenges in identifying who knows what and in knowing where to locate relevant knowledge and expertise (Kanawattanachai and Yoo, 2007). In addition, people who rely on remote communication via digital tools for virtual collaboration tend to experience difficulties in transferring, exchanging, and interpreting implicit knowledge (Cramton, 2001). Thus, teleworkers with high IOT, who are involved in remote communication via digital technologies, may be more vulnerable to technology-induced stress than those with low IOT. Furthermore, we speculate that the effects of job characteristics that can offset or intensify technology-induced stress may be more salient for teleworkers with high IOT than for those with low IOT. Therefore, we formulated the following hypothesis:
H15. The patterns underlying technology and job characteristics, technostressors, and strain will vary depending on teleworkers’IOT.
4. Methods 4.1 Measurement
This study both adopted and adapted existing validated scales. All items were measured using five-point Likert scales, which ranged from“strongly disagree”to“strongly agree.” The dependent variable of this study, job satisfaction, was measured by adopting items from Rutherford et al. (2009). To measure the technology characteristics, such as IT complexity, IT presenteeism, and pace of IT change, we adapted the measurement items used by Ayyagariet al.(2011). For job autonomy, we adopted the measurement items used by Ahuja et al. (2007). For task interdependence and job satisfaction, we adopted the measurement items of Staples and Webster (2008) and Bhattacherjee (2001), respectively.
To assess the perceptions of stressors, we measured work overload by adopting the items used by Moor (2000), and we measured invasion of privacy and role ambiguity by adopting the items used by Ayyagari et al.(2011). We classified IOT on the basis of the average number of days per week the respondent claimed they allotted for telework. We coded greater than 2.5 days as high IOT and the rest as low IOT, in accordance with Gajendran and Harrison’s (2007) classification of fewer than 2.5 days per week as low-intensity telework. Finally, we controlled for the effects of age, gender, and education on job satisfaction. Table I shows the measurement items.
4.2 Data collection
We contacted several executives of two global IT companies that have adopted telework programs. Both companies are located in South Korea, where telework has received growing attention and the number of teleworkers is growing. Through the executives, we invited the two IT companies to participate in our research. We obtained e-mail lists of 300 employees who were engaged in the companies’telework programs. We created a website for the online survey and sent e-mails containing the link to the identified employees, asking them to complete the questionnaire. Of the 287 questionnaires that were completed by the employees, we removed 29 that contained unanswered items. Finally, responses from 258 questionnaires were used for the data analysis. Table II shows the demographic information of the respondents.
5. Results and analysis
We used the data analysis technique of partial least squares (PLS) (Chin, 1998) to test the research model. As a second-generation SEM technique, PLS can be used to estimate the loadings of indicators on constructs and the causal relationships among constructs in multistage models (Fornell and Bookstein, 1982). Additionally, compared with other SEM techniques, PLS does not require a large sample (Chin, 1998), which is the case in our study because the sample was divided for subgroup analyses. Accordingly, PLS was considered
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appropriate for our study. We conducted the data analysis following a two-stage analytical procedure (Anderson and Gerbing, 1988). The first stage assessed the measurement model for reliability and validity. The second stage examined the structural model to test the research hypotheses (Hairet al., 2006).
Construct Code Items Mean SD Cronbach’sα
IT complexity CO 1. Learning to use ITs is difficult for me 2. Use of ITs is complicated to use
3. It is difficult to get results that I desire from ITs
3.525 0.618 0.86
IT presenteesim PS 1. ITs enable others to have access to me 2. ITs make me accessible to others
3. The use of ITs enables me to be in touch with others 4. ITs enable me to access others
3.633 0.659 0.72
Pace of IT change
PC 1. I feel that there are frequent changes in the features of ITs I use
2. I feel that characteristics of ITs I use change frequently
3. I feel that the capabilities of ITs I use change often
3.343 0.661 0.74
Job autonomy AU 1. I control the content of my job
2. I have a lot of freedom to decide how I perform assigned tasks
3. I set my own schedule for completing assigned tasks
3.590 0.677 0.74
Task
interdependence
TS 1. I frequently must coordinate my efforts with other team members
2. Goal attainment for me helps goal attainment for others
3. To perform my tasks well, I must communicate with other colleagues well
3.502 0.642 0.71
Work overload OV 1. ITs create many more requests, problems, or complaints in my job than I would otherwise experience
2. I feel busy or rushed due to ITs 3. I feel pressured due to ITs
3.306 0.737 0.85
Invasion of privacy
PV 1. I feel uncomfortable that my use of ITs can be easily monitored
2. I feel my employer could violate my privacy by tracking my activities using ITs
3. I feel that my use of ITs makes it easier to invade my privacy
3.353 0.775 0.81
Role ambiguity AB 1. I am unsure whether I have to deal with IT problems or with my work activities
2. I am unsure what to prioritize: dealing with IT problems or my work activities
3. I cannot allocate time properly for my work activities because my time spent on ITs-activities caries
3.168 0.755 0.79
Strain SR 1. I feel drained from activities that require me to use ITs 2. I feel tired from my IT activities
3. Working all day with ITs is a strain for me
3.130 0.797 0.88
Job satisfaction ST How do you feel about your overall experience of your job
1. Very dissatisfied/very satisfied 2. Very displeased/very pleased 3. Very frustrated/very contented
3.684 0.639 0.73
Notes: Before the survey, we emphasized use of ITs for work-related tasks (i.e. not for personal use).
Following Ayyagariet al.(2011), in this study, ITs involve a collection of information, processing, storage, network, and communication technologies
Table I.
Measurement items
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5.1 Measurement model
To assess convergent validity, we examined the loadings andt-statistics of the indicators on their corresponding construct. To do so, we conducted a confirmatory factor analysis using the PLS technique. If all the item loadings exceed the recommended value of 0.7 and are statistically significant, this is viewed as evidence supporting the convergent validity of the indicators. In addition, Cronbach’s α was used to check internal consistency. All the reliability measures were 0.7 or higher, indicating adequate internal consistency. Table III contains item factor loadings and cross-loadings for the total sample and subgroups.
Subsequently, we conducted Harman’s one-factor analysis to determine if the variance of our data comes from a common method source. If a substantial amount of common method variance is present, either a single factor emerges from the factor analysis or one general factor accounts for the majority of the covariance in the independent and criterion variables (Podsakoff and Organ, 1986, p. 536). We subjected all measurement items of constructs used in our study to a principal component analysis and examined how many orthogonal components emerged to explain the variance of our data. The results revealed that the largest single component accounted for 35.3 percent of the variance in our data, indicating that a single factor could not explain the majority of the variance in our data. Common method bias was thus not a serious validity concern for this study.
5.2 Structural model
The results for the path coefficients in the structural model are shown in Figure 3. As expected, the results showed that IT presenteeism positively influenced invasion of privacy, and the pace of IT change positively influenced work overload and role ambiguity. In contrast, the results revealed that IT complexity and IT presenteeism had no significant influence on work overload. The results also showed that job autonomy negatively influenced invasion of privacy but did not influence work overload, whereas task interdependence positively influenced work overload and invasion of privacy. The three IT-induced stressors led to greater strain, accounting for 38 percent of the variance in strain, which in turn led to a decrease in job satisfaction. Thus, all hypotheses were supported, except forH1,H2,H6, andH10.
Total sample High IOT (n¼104) Low IOT (n¼154) Item Category Frequency Ratio (%) Frequency Ratio (%) Frequency Ratio (%)
Gender Male 147 57.0 61 58.7 79 51.3
Female 111 43.0 43 41.3 75 48.7
Total 258 100.0 104 100.0 154 100.0
Age ⩽30 5 1.9 3 2.9 4 2.6
31-39 168 65.1 68 65.4 96 62.3
40-49 52 20.2 21 20.2 31 20.1
⩾50 33 12.8 12 11.5 23 14.9
Total 258 100.0 104 100.0 154 100.0
Education College 167 64.7 63 60.6 77 50.0
Post-
graduate 53 20.5 23 22.1 63 40.9
Above 38 14.7 18 17.3 14 9.1
Total 258 100.0 104 100.0 154 100.0
Organizational Tenure ⩽5 65 25.2 29 27.9 23 14.9
6-10 84 32.6 35 33.7 54 35.1
11-15 58 22.5 22 21.2 28 18.2
16-20 33 12.8 12 11.5 31 20.1
⩾21 18 7.0 6 5.8 18 11.7
Total 258 100.0 104 100.0 154 100.0
Table II.
Demographic information of respondents
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Totalsample(n¼258) 12345678910HighIOT(n¼104)LowIOT(n¼154) SR10.7910.0240.207−0.078−0.170−0.0280.1000.0770.0600.1680.7820.801 SR20.7800.0670.1720.0170.0210.1430.181−0.016−0.0070.0310.8710.791 SR30.7630.1700.173−0.133−0.1260.0610.0710.1220.0540.2590.8000.772 AB10.4480.8960.165−0.055−0.0160.1560.1120.207−0.1680.0870.7350.753 AB20.3960.7680.1780.2430.0920.2410.1630.004−0.0800.0290.7740.684 AB30.4570.7570.1480.1920.1070.324−0.0440.0150.0360.0160.8010.655 OV10.1700.1500.8250.0310.0820.1090.0770.148−0.0190.0330.7220.812 OV20.2600.0780.7310.031−0.0110.1340.1950.0500.2530.0210.8140.734 OV30.2670.0910.7230.0480.1000.1360.1980.080−0.2010.0570.8110.716 AU10.1560.2070.0050.7770.057−0.0360.0780.1740.159−0.1330.8100.702 AU2−0.0030.0040.0430.7450.172−0.0640.0200.0250.2620.2280.7350.773 AU3−0.0650.015−0.0310.7100.2150.0550.100−0.0340.1120.0410.7730.735 CO10.0150.1480.1400.0950.810−0.0540.0240.0230.1100.0630.8250.677 CO2−0.0820.050−0.1000.1320.7900.0020.0670.1180.1130.0700.7340.674 CO3−0.0200.0800.0760.2430.775−0.1040.1700.1190.271−0.0020.7660.778 PV10.224−0.0350.1080.091−0.0310.8020.2460.1750.059−0.0090.7610.753 PV20.3180.1740.159−0.051−0.1250.7330.254−0.0010.1480.0740.8210.724 PV30.2010.1120.386−0.110−0.0440.6930.0060.125−0.0430.1060.8230.805 PC10.1930.1100.1000.0280.0390.1220.7850.0450.1760.0740.8400.794 PC20.1820.1130.1680.1500.1000.0870.7370.217−0.010−0.0350.8310.683 PC30.0740.2710.2630.0780.1400.2190.6960.0990.1300.0270.8130.805 TS10.1350.0590.0330.0910.0380.0930.0580.8110.076−0.1540.8740.736 TS2−0.0100.1480.1090.0870.0260.0120.1850.7310.2900.0460.8850.697 TS30.138−0.0350.083−0.0080.2130.1300.0850.7160.0330.1960.8020.765 PS1−0.0380.027−0.0100.2070.2230.0480.1030.1460.741−0.0260.8610.756 PS20.010−0.1540.0110.1800.3390.0550.1050.1230.755−0.1790.8600.643 PS3−0.0480.0460.1620.3530.0610.0710.1290.1880.737−0.0910.9010.755 PS40.2880.0870.0510.0480.1880.0350.1150.1650.714−0.0490.8820.664 ST10.2790.1960.080−0.0630.0500.082−0.013−0.060−0.0360.8600.8310.575 ST20.176−0.0260.1620.2530.1230.0480.1050.155−0.2760.6850.8630.706 ST30.168−0.0280.0930.1190.0450.0110.1010.122−0.2930.6820.7330.651 Note:po0.001
Table III.
Factor loadings and cross-loadings for total sample and factor loadings for subgroups
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In this study, IOT was used as a global moderator. To perform the subgroup analyses, we split the sample into two groups (i.e. low IOT vs high IOT) at the value of IOT 2.5, after which we tested both validity and reliability by subgroup. All items in both the high IOT and low IOT subgroups exhibited acceptable loadings (W0.7). Table III shows that the loading patterns of each subgroup were very similar, which permits between-group path comparison. Drawing on the approach for the moderating effect test (Sharmaet al., 1981), we verified that IOT was neither a pure moderator nor a quasi-moderator but acted as a homologizer (Allison et al., 1992). Here, the term “homologizer” refers to a moderating variable that can change the magnitude of the relationship between variables (X and Y).
Homologizer effects can be tested by conducting subgroup analyses based on the moderating variable (Thatcher and Zhu, 2006). In this study, the differences between the two subgroups were tested by computingt-statistics following Keilet al.(2000):
Spooled¼ ðN1–1Þ=ðN1þN2–2Þ
SE21þðN2–1Þ=ðN1þN2–2Þ SE22
n o
t¼ðPC1–PC2Þ= Spooled 1=N1þ1=N2
whereSpooledis the pooled estimator for the variance, PCi is the path coefficient in the structural model of IOT groupi,Njis the sample size of the data set for IOT groupi, SEiis the standard error of the path in the structural model of IOTi, andtis thet-statistic with N1+N2–2 degrees of freedom. As shown in Figures 4 and 5, the strength and significance of the effects of technology characteristics and job characteristics on technostress (i.e. stressors) were different across the two groups. The results showed that IT complexity significantly increased work overload in the low IOT group, whereas IT complexity did not increase work overload in the high IOT group. The results also indicated that IT presenteeism increased invasion of privacy in the low IOT group but did not influence invasion of privacy in the high IOT group. Likewise, task interdependence increased work overload in the low IOT group but did not have a significant effect in the high IOT group.
The magnitude of path coefficients was different across the subgroups. Specifically, the path coefficients between: strain and job satisfaction; work overload and strain; role ambiguity and strain; IT presenteeism and work overload; job autonomy and invasion of privacy; and pace of IT change and role ambiguity differed across the two subgroups.
Table IV shows the results of homologizer effects for the path coefficients across subgroups.
IT Complexity
IT Presenteeism
Pace of IT Change
Job Autonomy
Task Interdependence
Work-overload R2= 0.24
Invasion of Privacy R2= 0.17
Role Ambiguity R2= 0.19
Strain R2= 0.38
Job Satisfaction R2= 0.22
Significant relationship Insignificant relationship 0.11**
0.40***
0.31***
–0.16**
0.15** 0.29***
0.23**
0.11**
0.56***
–0.40***
Notes: n=258. **<0.05; ***<0.01
Figure 3.
Structural model for the total sample
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IT Complexity
IT Presenteeism
Pace of IT Change
Job Autonomy
Task Interdependence
Work-overload R2= 0.24
Invasion of Privacy R2= 0.11
Role Ambiguity R2= 0.22
Strain R2= 0.53
Job Satisfaction R2= 0.20
Significant relationship Insignificant relationship 0.13**
0.12**
0.12**
0.39***
0.48***
–0.11**
0.21**
0.30***
0.23**
0.19**
0.43***
–0.44***
Notes: n=154. **<0.05; ***<0.01 Figure 5.
Structural model for the low IOT group
IT Complexity
IT Presenteeism
Pace of IT Change
Job Autonomy
Task Interdependence
Work-overload R2= 0.26
Invasion of Privacy R2= 0.19
Role Ambiguity R2= 0.20
Strain R2= 0.27
Job Satisfaction R2= 0.15
Significant relationship Insignificant relationship 0.20***
0.40***
0.12***
–0.38***
0.68***
0.30***
–0.20**
0.29***
0.22***
Notes: n=104. **<0.05; ***<0.01 Figure 4.
Structural model for the high IOT group
High IOT (n¼104) Low IOT (n¼154)
From→To Path SE t-value Path SE t-value Comparison of path coefficients
SR→ST −0.38 0.05 −7.17 −0.44 0.03 −14.67 10.97***
OV→SR 0.12 0.04 3.00 0.23 0.03 7.67 −23.87***
PV→SR ns ns ns 0.19 0.08 2.53 ns
AB→SR 0.68 0.08 7.20 0.43 0.07 6.14 25.87***
CO→OV ns ns ns 0.13 0.06 2.17 ns
PS→OV 0.20 0.06 3.33 0.12 0.05 2.40 11.21***
PC→OV 0.40 0.07 5.71 0.39 0.06 6.5 1.19
AU→OV ns ns ns ns ns ns ns
TS→OV ns ns ns 0.21 0.03 7.00 ns
PS→PV ns ns ns 0.12 0.05 2.40 ns
AU→PV −0.20 0.08 −2.50 −0.11 0.05 −2.20 −10.20***
TS→PV 0.29 0.06 4.8 0.30 0.05 5.76 1.4
PC→AB 0.30 0.04 7.5 0.48 0.06 7.73 −28.92***
TS→AB 0.22 0.02 4.4 ns ns ns ns
Note:***po0.001 Table IV.
T-statistics for comparisons of subgroups
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6. Discussion, implications, and limitations
We set out to examine how technology and job characteristics jointly induce teleworkers’ technostress, how technostress influences teleworkers’job satisfaction, and how the patterns of the technostress that teleworkers experience vary depending on their IOT. Our main findings are as follows: first, technology characteristics (IT presenteeism and the pace of IT change) and job characteristics (job autonomy and task interdependence) jointly explain the technostressors (the sources of strain). We found that task interdependence had an additive effect on strain by increasing work overload and invasion of privacy, whereas job autonomy reduced strain by decreasing invasion of privacy. Second, work overload, invasion of privacy, and role ambiguity are the main sources of teleworkers’strain. Third, the patterns underlying technology and job characteristics, technostressors, and strain vary depending on IOT.
The results show that technology-induced stressors (i.e. work overload, invasion of privacy, and role ambiguity) lead to greater strain, which in turn reduces teleworkers’job satisfaction. It is a well-known fact that a high level of work satisfaction can increase workers’ productivity, whereas a low level of work satisfaction increases employee turnover. Therefore, the study’s results imply that the technostress perceived by teleworkers directly influences the productivity of individuals and organizations.
The results demonstrate that the faster the pace of the IT changes, the higher the level of work overload and role ambiguity perceived by workers. This implies that the fast, ever-changing nature of IT may be a source of conflict between the roles required for regular work and those required to learn and utilize ITs. Companies utilizing telework programs essentially adopt new technologies and information systems, such as unified communication, wired and wireless integration (e.g. fixed mobile convergence), video conferencing, and cloud-based platforms. During this process, which requires workers to solve technical issues while simultaneously learning and using the new IT tools, teleworkers perceive a high level of work overload and role ambiguity.
No significant relationship was found between IT complexity and work overload. This can be explained from two viewpoints. First, since the IT complexity measured in this study was based on a perception of general ITs, it was limited to the complexity of specific technologies.
The perception of the complexity of general ITs can be judged as having no effect on the perception of work overload in specific work situations. Second, as office workers improve their overall IT-use capability due to the high penetration of ITs, the relationship between IT complexity and work overload could weaken. The phenomenon of the expansion of the tech-savvy–that is, of individuals, predominantly educated urban residents, who have an affinity for high technology (Dwyer, 2009) – is another reason why the perception of IT complexity does not significantly increase the perception of work overload.
More importantly, as shown in Table IV, the comparison of the path coefficients between strain and job satisfaction across the high and the low IOT groups indicates that the negative influence of strain on job satisfaction is significantly greater in the low IOT group (β¼−0.44,t¼−14.67) than in the high IOT group (β¼−0.38,t¼−7.17). In addition, the results show that the relationships between technostressors and strain are different across the subgroups. The influence of role ambiguity on strain is significantly greater in the high IOT group (β¼0.68,t¼7.20) than in the low IOT group (β¼0.43,t¼6.14); the influence of work overload on strain is significantly greater in the low IOT group (β¼0.23,t¼7.67) than in the high IOT group (β¼0.12,t¼3.00); invasion of privacy significantly influences strain in the low IOT group (β¼0.19,t¼2.53) but not in the high IOT group.
The results demonstrate that stressors induced by technology account for 53 percent of the variance in strain in the low IOT group and 27 percent of the variance in strain in the high IOT group. This implies that teleworkers with low IOT are more vulnerable to technostress than those with high IOT. This finding is contrary to our expectation that teleworkers who are more extensively engaged in telework via digital tools may struggle to cope with technostress.
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We attribute this surprising result to the ways in which technology and job characteristics jointly induce teleworkers’ stressors (e.g. work overload, invasion of privacy, and role ambiguity). The results show that teleworkers with low IOT are more susceptible to IT complexity and IT presenteeism than those with high IOT. Figures 4 and 5 show that IT complexity significantly increases work overload in the low IOT group (β¼0.13,t¼2.17), whereas it does not have any influence on work overload in the high IOT group. In addition, IT presenteeism significantly increases invasion of privacy in the low IOT group (β¼0.12, t¼2.40) but not in the high IOT group. Furthermore, the comparison of the path coefficients between pace of IT change and role ambiguity across the two subgroups indicates that the positive influence of the pace of IT change on role ambiguity is significantly greater in the low IOT group (β¼0.48,t¼7.73) than in the high IOT group (β¼0.30,t¼7.5).
One possible explanation for teleworkers with low IOT being more likely to be susceptible to technostress is that they have conflicting roles and identities as both teleworkers and conventional workers. Teleworkers with low IOT have more face-to-face contact with their colleagues and supervisors than teleworkers with high IOT. Given that teleworkers with low IOT still perform a significant portion of their work within the temporal and physical organizational boundaries that facilitate conventional work norms and identification, they may be more likely to experience conflicting identities (Thatcher and Zhu, 2006; Fonner and Roloff, 2012). Teleworkers’conflicting roles and identities negatively influence their sense of consistency regarding their values, beliefs, norms, and expectations (Paset al., 2014). Therefore, teleworkers with low IOT may find it more difficult to get used to accomplishing their tasks in the varying work environments than those with high IOT.
We speculate that teleworkers with low IOT may need to change their workplaces and their ways of collaborating, coordinating, and communicating with other colleagues more frequently than teleworkers with high IOT. In contrast, teleworkers with high IOT may find it easier to get used to the new work mode via digital tools, thus coping with the technostress more easily than teleworkers with low IOT. The finding that the role of job autonomy in reducing invasion of privacy is much stronger in the high IOT group than in the low IOT group supports our conjecture. The comparison of the path coefficients between job autonomy and invasion of privacy across the two subgroups indicates that the extent to which job autonomy reduces invasion of privacy is significantly greater in the high IOT group (β¼−0.20,t¼−2.50) than in the low IOT group (β¼−0.11,t¼−2.20). The results indicate that although job autonomy is critical for workers who choose a telework arrangement and that it contributes to reducing teleworkers’stress (Coenen and Kok, 2014), those with low IOT may not fully enjoy the benefits of job autonomy in offsetting the perceived invasion of privacy due to their mixed roles and identities as both teleworkers and conventional workers.
6.1 Implications for research
This study provides several key research contributions. First, the proposed model that examines teleworkers’technostress and job satisfaction can serve as a theoretical platform to examine, verify, and advance an understanding as to why some teleworkers enjoy the benefits of telework whereas others experience technology-induced stress in telework environments. Given that the majority of previous technostress studies have focused on technological factors that influence workers’ technostress in general, by adding job characteristics to the technostress model, our work suggests that technostress should be understood jointly with job characteristics. This approach helps researchers diagnose more precisely potential problems within a teleworking program. Specifically, our findings imply that a teleworking program may fail to achieve its purposes (e.g. employees’ work-life balance, cost savings, and work efficiency) if it is implemented in a context where workers’ tasks are highly interdependent and their job autonomy is not ensured. Second, our
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study contributes to an advance in the technostress model by explaining the moderating role of IOT. Our study has empirically verified that the patterns and mechanics underlying teleworkers’ stressors, stress, and strain vary according to IOT. This is a significant contribution to telework literature because the conventional technostress model assumes that workers’ stress levels increase because of the use of IT, but it does not consider how the situation in which workers use IT shapes their perceptions of stressors, strain, and job satisfaction. Such a limitation prevents a more comprehensive understanding of teleworkers’ technostress. We have described why teleworkers who are involved in the same telework program can experience different levels of technostress and job satisfaction. Previous studies have argued that IT-enabled-telework allows for increased flexibility in work schedules and helps employees’ work-life balance. However, despite the widespread belief in the benefits of telework, it has been reported that many teleworkers experience new types of work stress, which may impede the success of a telework program.
Our work provides a possible explanation for this dilemma. By providing a more nuanced understanding of how teleworkers with different levels of IOT have varying levels of susceptible to technostress, our study improves the explanatory power of technostress in predicting a teleworker’s job satisfaction, thereby making the technostress model more granular.
6.2 Implications for practice
Based on these findings, we suggest that IT training aimed at mitigating teleworkers’ perceptions of IT complexity and presenteeism should be provided to teleworkers with low IOT. In addition, because the influence of role ambiguity on strain was stronger for those with high IOT, we suggest that organizations should define more specific roles and rules for teleworkers who extensively utilize teleworking programs. Organizational managers and information system designers may benefit from exploring the demands placed on high- and low-intensity teleworkers who experience varying combinations of telework and traditional work arrangements. We contend that such hybrid work arrangements will become more pervasive in the future. That is, managerial approaches and work-scheduling practices should be adjusted in conjunction with differing degrees of teleworking (Sewell and Taskin, 2015). Many managerial strategies have been developed based on the assumption that workers have homogenous office schedules with face-to-face supervision (Kossek et al., 2005). Our findings imply that varying strategies that take into consideration workers’ IOT should be developed to reduce teleworkers’technostress. A blanket effort to reduce teleworkers’overall levels of technostress may be wasted on some employees.
6.3 Limitations and future research
Despite our study’s intriguing findings, its limitations should be recognized. First, the samples may have been biased because the survey respondents were limited to one country (South Korea) and to IT companies only. Given that most Korean companies expect a great deal of physical presence and long work hours, the results may reflect the particular workplace experience of this country, potentially limiting the generalizability of the findings. Accordingly, future results may vary depending on sector-specific or country- specific contexts. An expansion of the technostress-job characteristics model developed in this study to include other task characteristics and cultural factors would be a valuable extension of this research. Second, although our study disentangled the mixed effects of the stressors induced by technology and strain, which allowed us to explain the different magnitude of each factor determining technostress, dependent on the IOT, we did not fully explain when or where the specific functions of technologies would induce more serious technostress among teleworkers. Future research would benefit from specifying the
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different types of communication and collaborative technologies and linking them theoretically with job-related outcomes (e.g. job performance, job satisfaction, and turnover intention). In particular, researchers should observe how teleworkers overcome the challenges posed by technology-induced stressors.
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