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Article

The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia

by
Aini Farmania
1,*,
Riska Dwinda Elsyah
2 and
Ananda Fortunisa
2
1
Management Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia
2
Management Program, Bakrie University, Jakarta 12940, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8669; https://doi.org/10.3390/su14148669
Submission received: 6 June 2022 / Revised: 1 July 2022 / Accepted: 7 July 2022 / Published: 15 July 2022
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Abstract

:
The regulation of work from home (WFH) was suddenly instructed by many companies in Indonesia at the beginning of the COVID-19 pandemic. It improved the demand for information and communication technology, which triggered the emergence of technostress among workers. Therefore, this study aimed to analyze the technostress phenomenon in Indonesia due to the regulation of WFH by involving 819 respondents collected using an online questionnaire. This quantitative study using an SEM-Model investigated the correlation of technostress on productivity and role stress. Moreover, this model research also analyzed the role of computer self-efficacy and techno-addiction toward technostress among Indonesia’s workers. Finally, the findings showed a significant correlation between variables, becoming the first empirical evidence of the technostress phenomenon during work from home in Indonesia. This research brings new insight to companies about the technostress phenomenon during work from home that has never been discussed thoroughly before in Indonesia, suggesting companies should find the right strategy to balance the use of ICT at work based on workers’ job roles.

1. Introduction

A new virus called coronavirus (COVID-19) was identified for the first time in late 2019 and changed the way people live and work around the world. This virus rapidly spread to other countries, resulting in a global pandemic due to the late handling of global traveling. To reduce the mobilization and cut the spread of the virus, many countries, including Indonesia, were locked down and practiced social distancing among societies [1,2]. Lockdown in Indonesia started at the beginning of March 2020 [3]. Companies simultaneously instructed workers to work from home for social distancing purposes. It refers to a condition of a flexible working approach that is not constrained by time, place, and the use of communication and information [4]. Work from home (WFH), also known as remote working/telecommuting [5,6,7], has actually been practiced in many countries before the COVID-19 pandemic, especially in IT and technology business [8]. Moreover, studies about working from home or telecommuting that emerged 20 years ago [9] were conducted on companies with flexible work locations.
WFH has many advantages not only for companies but also for workers. In this case, workers will be more disciplined in completing the task, the absenteeism rate will decline [10,11], the operational cost will reduce [12], work–life balance will improve [8,13,14], and there will be more time to perform household activities, spend with family [12], and undertake leisure activities [15]. WFH is also more likely to improve job satisfaction [16], organizational commitment [17], and productivity (based on social exchange theory) [11,13,18] among employees.
Even though many existing studies have focused on explaining the advantages of WFH, the disadvantages also need to be discussed when introducing WFH to all employees [3,4]. This is because the sudden shift to mobile connectivity, including the regulation of WFH during the COVID-19 pandemic, exacerbated rather than ameliorated the requirement for companies to provide better work regulations for employees [19]. For example, the interaction between staff during WFH was not as effective as when they met in person [8,11,20]. Many of them still found it challenging to operate technology, and it was not easy to seek assistance from their coworkers due to WFH [21,22,23].
The challenges during WFH potentially increase the occurrence of stress, including technostress, which is a modern illness caused by the need to adapt to technology, resulting in psychological distress [24]. Therefore, this paper will specifically discuss the technostress phenomenon during WFH using the concept of social cognitive theory (SCT). We used SCT in this research as a unique point that differs from the existing literature and that could be a solid concept in explaining the technostress phenomenon thoroughly and systematically.
Theoretically, SCT consists of three elements: individual, behavioral, and environmental [25]. Firstly, this study analyzed the technostress phenomenon, which can attack employees (as the individual element) in Indonesia during COVID-19. Secondly, this study examined the impact of the technostress phenomenon on productivity and role stress as behavioral elements of employees during work from home. Thirdly, we used compulsory WFH as the environmental element, which could cause another issue to arise, such as technology addiction. Based on a previous study, workers with a high level of techno-addiction are expected to feel more stress due to constant exposure to technology [26].
Hence, this study assumed that technology addiction pushed by WFH promotes the technostress phenomenon among employees. Besides understanding the promoting factor, it is also crucial to analyze the inhibiting factor of technostress. Computer self-efficacy (CSE) is deemed the factor that can prevent a high level of technostress because, practically, CSE is the level of self-confidence to handle technology that can help a worker manage technostress [27,28]. Therefore, we proposed that CSE is the factor that potentially averts technostress among employees. This study used these two contradicting factors (inhibit and promoting factors) to describe two sides of employees’ behavior in facing the technostress phenomenon.
The complexity of this research’s framework is our first novelty offered to explain the technostress phenomenon. Although many studies have investigated the technostress phenomenon in different objects and constructs, none were in the context of work from home in Indonesia. This is the first comprehensive analysis of the technostress phenomenon in Indonesia during WFH, and it fills the gap in the existing literature about human–technology interactivity during ‘extreme events’ such as the COVID-19 pandemic.
Furthermore, the urgency of this research is demanded by the fact that WFH has been a popular system recently, and more companies intend to maintain it even after the emergency of the COVID-19 pandemic [3,8,29]. In addition, the ubiquitous use of technology in Indonesia makes the technostress phenomenon inevitable [29]. This is supported by recent research from Irawanto et al. [4], who mentioned that WFH in Indonesia during the COVID-19 pandemic positively influenced work stress. This becomes a rational background that encourages us to conduct this research. Analyzing this issue will bring sustainability guidelines based on empirical evidence for companies or employees to better deal with technology-induced stress during work from home and avoid the unintended consequences immediately. Based on the discussion above, the objectives of this study were to answer the following five research questions:
  • How is the technostress phenomenon experienced by Indonesian workers during WFH due to the COVID-19 pandemic?
  • What are the impacts of computer self-efficacy on technostress during WFH due to the COVID-19 pandemic in Indonesia?
  • What are the impacts of technology addiction on technostress during WFH due to the COVID-19 pandemic in Indonesia?
  • What are the impacts of technostress on productivity during WFH due to the COVID-19 pandemic in Indonesia?
  • What are the impacts of technostress on role stress during WFH due to the COVID-19 pandemic in Indonesia?
The following sections of this study are a literature review, research model, and hypothesis. We used social cognitive theory (SCT) as the grounded theory and four hypotheses to describe the correlation between chosen variables. A further section is the research method, consisting of research design, sample, measurement items, and analysis method. The following section shows the analysis results, including the validity, reliability, and SEM-Model. This study also provides a conclusion section to describe the results and compare the findings with the existing literature. The last section is the limitations and directions for future research and the study’s theoretical and practical implications.

2. Literature Review

2.1. Technostress

George Fink [30,31] defined stress as the body’s nonspecific response (both physically and psychologically) due to burdening tasks. Technostress is one of the more specific concepts of stress. This term was initially presented by Craig Brod [32] as the lack of ability of users to deal with technology. According to Bell, Tu, Wang and Shu [33,34], technostress is the negative effect of technology on attitude, behavior, thought, and psychological condition. Therefore, technostress is the feeling of stress due to using information systems [35]. Stressful feelings about technology emerge due to the inability to apply it or adapt to its rapid development [27], which is drawn in terms of job demand and resource theory [36]. A study by Ayyagari and Purvis [37] explained that technostress is a type of strain at work due to information and communication technology. In addition, Valiff et al. and Lee [38,39] stated that technostress could happen when the complexity of technology is beyond the ability of someone to operate it.
Some of the situations to depict the phenomenon of technostress include constant connectivity, information overload, the insecurities of job-related technology, multitasking, system upgrades frequently, and technical problems associated with technology [29,40]. Technostress is the feeling against technology that potentially causes work overload, discouragement, information fatigue, lack of motivation, and satisfaction with work [33,41]. Moreover, the dimensions of technostress are techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty [27,38,39,41].
The first dimension is techno-overload, referring to strain due to the overloaded use of information technology. In this case, information and communication technology forces the user to study harder and quickly to operate it [14], causing them to feel strained (technostress). According to Tarafdar [42], techno-invasion, as the second dimension of technostress, refers to the development of technology and its invasion of humans’ personal lives. Techno-invasion can cause ambiguity and conflict between work and family or personal lives. Techno-invasion can remove work–home boundaries [43], which influences workers’ health and leisure time (off hours) [44]. Based on the preinterview carried out with staff at a private university in Jakarta, during work from home, it was found that they tended to work all the time. This is because they should directly reply to messages on WhatsApp for almost 24 h, participate in Zoom meetings outside working hours or learn how to use some new applications to work faster. This condition refers to techno-invasion, in which technology invades private life and takes more energy and the time of users, resulting in psychological distress and reducing staff performance [45].
Techno-complexity is the complexity of operating information and communication technology, which can cause mental breakdown and raise the feeling of a lack of confidence in using technology. The term complexity refers to the extent to which users perceive technology as difficult to use, and they do not have enough capacity to handle it [46]. As a consequence of techno-complexity, workers will usually underestimate themselves, forcing themselves to learn the technology, which leads to stress [24,39,47]. While working from home, workers and students should learn and adapt to technology that is becoming more complex. The complexity of technology is part of techno-complexity, which can trigger users.
Techno-insecurity is a feeling of fear and feeling threatened with the development of technology (afraid of losing their job or being replaced with another person with better competence in using technology). Due to the regulation of working from home, workers should operate many new applications that trigger insecurity due to tool’s difficulty. Techno-uncertainty is a psychological condition to worry about uncertainty due to the continuous development of information and communication technology [48]. During working from home, workers had a greater chance to experience techno-uncertainty, notably because of job uncertainty due to the situation of the COVID-19 pandemic.

2.2. Social Cognitive Theory (SCT)

Albert Bandura [25] firstly introduced social cognitive theory in 1995. Social cognitive theory is based on the premise that three variables are influencing each other (environment, behavior, and individual), as illustrated in the triangular graph (triadic reciprocal) in Figure 1:
Based on the triadic reciprocal theory above, a person can be influenced by their social environment or beliefs. Studies about technostress found that self-efficacy is the dominant factor influencing work performance, including technology. The SCT model can guide us to identify the correlation between individual and technology and how technostress can emerge based on two factors, the social environment and behavior [25,27,49]. Based on the SCT model, the emergence of technostress is influenced by behavior and environment. Behavior and cognitive skills influence someone’s mindset on technology development, resulting in technostress. In contrast, the environmental factor is related to the obligation to work with technology. Both conditions can trigger technostress, such as feeling insecure about being replaced with technology, being unable to adapt quickly to technology, and difficulties in learning information and communication technology.
A study from [50] found that technostress can influence productivity and the level of strain in completing a task (role stress) [37,42,51]. The current study aimed to analyze the emergence of technostress during work from home based on social cognitive theory related to environmental and behavioral factors. The environmental factor refers to technology addiction and the condition of the pandemic and WFH system, while the behavioral factor refers to computer self-efficacy. This study also investigated the influence of technostress on productivity and role stress as the elements of behavior affected by the technostress phenomenon.

3. Research Model and Hypothesis

3.1. Research Framework

The construct of the research framework for this study is as follows (Figure 2):

3.2. Research into Technology Addiction and Technostress

The rapid development of technology brings not only positive effects but also stimulates negative impacts [26,52]. The negative effect can arise when someone uses or interacts with technology for a long time [53]. Long-time exposure to technology can stimulate interest, which changes one’s behavior [54]. In the beginning, someone may only use technology when they need it, but the more they use it, the more they may become addicted to it [55]. Based on theory, this phenomenon is considered technology addiction or technology dependency [54].
Technology addiction is the excessive and continuous use of technology due to the particular addictive features it has [27,56]. Working from home or the telecommuting system, which causes the dependency of all jobs on technology [14,29], triggers technology addiction. Technology addiction positively influences technostress, as Tarafdar, Cooper, and Stich [35] stated that technostress is a phenomenon caused by overexposure to technology [57]. Furthermore, Jamal, Anwar, and Khan [58] found that working from home during the COVID-19 pandemic triggered workaholism [59] using technology and led to technostress [2,59]. This system blurred the boundary between work and leisure time [7] and caused constant exposure to new posts on smartphones and social media [26], which increased stress.
Shu, Tu, and Wang [27] emphasized that technology dependence/addiction positively influences technostress. In addition, Salanova [57] and Tarafdar [46] also found that techno-addiction improves technostress. Although there have been several earlier studies theoretically discussing the influences of technology dependence/addiction on technostress, none have investigated the correlation between technology addiction and technostress in working from home, especially in the context of Indonesian workers. Thus, this study aimed to analyze the correlation between technology addiction on technostress with the following hypothesis:
Hypothesis H1.
Technology addiction positively influences technostress during work from home.

3.3. Research into Computer Self-Efficacy and Technostress

Computer self-efficacy, or CSE, is an essential topic in the framework of information technology [60,61], observing human behavior using information and communication technology (ICT) [62] that determines the work performed on the job dealing with technology. Computer self-efficacy is in line with the definition of self-efficacy: trust to do something [25]. Thus, computer self-efficacy can be defined as a trust to use and handle computer technology [60,63]. Computer self-efficacy improves the confidence to operate a computer. Moreover, computer self-efficacy motivates someone to perform better at work [64]. A study by Teo and Hwee [65] mentioned that computer self-efficacy has three dimensions: basic computer skills, media-related skills, and web-based skills.
Some previous studies investigated social cognitive theory (SCT) in the context of human–technology interaction and found individual behavior toward technology was related to the theory of computer self-efficacy or CSE [66,67,68]. A study by Sheng et al. [69] argued that CSE positively correlates with human behavior in using information and communication technology. In addition, Venkatesh and Davis [70] stated that CSE influences the perceived ease of using the technology, positively affecting work performance [69].
Computer self-efficacy also has a strong relationship with technostress [27,38,71], which becomes a factor influencing someone to accept and adapt to technology [32]. Someone with high computer self-efficacy tends to be motivated to understand and master technology [72]. During work from home, someone should use information and communication technology independently, affecting their computer self-efficacy level. Moreover, people who have telecommuted before and indicated they have a strong capability in using technology may work better during work from home [7] and easily reduce stress.
La Paglia et al. [71] explained that low computer self-efficacy predicts anxiety toward computer usage. Other studies also found that computer self-efficacy negatively influences technostress [27,73]. Levels of computer self-efficacy, which refer to confidence in the ability to operate technology [49], the authors of [73] found are also influenced by the level of computer skill, as John [28] found that someone with adequate computer skills will have high computer self-efficacy. The ability to use technology is necessary for workers during WFH [14], as it can reduce technostress levels [74]. Therefore, this study aimed to investigate the influence of computer self-efficacy on technostress with the following hypothesis:
Hypothesis H2.
Computer self-efficacy negatively influences technostress during work from home.

3.4. Research into Technostress and Productivity

Technostress inflicts low performance and productivity at work [37,40,50]. This is because technostress is a negative psychological condition caused by computer technology that can threaten or influence users by triggering stress and strain while using technology. Some conditions usually experienced by someone attacked with technostress are anxiety, mental fatigue, self-doubt, and lack of confidence to use the technology [26,57]. The authors of [75] stated that anxiety is related to techno-insecurity, which is a fear of losing a job due to the lack of ability to use the technology. This negative feeling can reduce productivity [24,40,50,76]. Moreover, the negative feeling or strain can reduce work performance, job satisfaction, and involvement in teamwork [40,75,77].
Technostress may appear when someone is demanded to use technology when working but lacks adequate skill or competence [75]. Consequently, technostress can be a barrier to achieving the standard performance set by a company. According to Tarafdar and Ragu-Nathan [78], techno-complexity can lead someone to spend more time completing a task due to the complexity of the technology they use at work. This condition possibly reduces their productivity, as they have to learn how to operate a computer before working with it. In addition, research by Park and Cho [75] found that techno-overload can become a burden for someone who works with technology, as they believe that they should finish their task more quickly than usual.
In the study by Tarafdar [24], it was found that technostress negatively influenced workers’ productivity at two public sector organizations in the US, while another study from Tarafdar [78] found that technostress negatively influenced sales performance. Alam [79] mentioned that technostress also negatively influenced the productivity of aviation workers, such as pilots, maintenance crew, flight engineers, and other crews. Research from Califf and Brooks [39] found that technostress can significantly increase job burnout in teachers in the United States. Molino [29] found that technostress has a positive relationship with workload and work–family conflict in Italy, influencing low performance. Park and Cho [75] found that technostress reduces job satisfaction, affecting work productivity. Many studies have investigated the influence of technostress on performance or productivity, but, unfortunately, studies analyzing the two variables in Indonesia are rather rare. Therefore, the current research aimed to analyze the influence of technostress on productivity using the following hypothesis:
Hypothesis H3.
Technostress negatively influences productivity during work from home.

3.5. Research into Technostress and Role Stress

Besides influencing productivity, technostress can also trigger role stress in someone working with technology [37,48,51]. According to [48], some can experience strain in performing a role in a company. Role stress can be defined as an awareness or feeling of personal dysfunction resulting from an event that causes psychological reactions, such as uncomfortable, undesirable, or threatened, by the workplace condition [51]. The rapid development of technology can trigger role stress when someone has to be up to date with it [48,80]. This is because the role at work determines the particular requirement someone has to fulfill. Role stress consists of two types: role conflict and role stress. Technology changes the work procedure. Someone who feels stress due to changes in work procedures using technology faces techno-complexity, leading to role conflict [29,51,80]. Role conflict refers to the perception of the contradiction between requirements set by the organization and the workers’ wishes [81], and it usually occurs when a worker is obligated to complete too many tasks (overwhelming) [42,51,80].
Information and communication technology is one of the factors causing role conflict [51] because it requires standardization that workers have to fulfill to master it [82]. Companies’ new technologies can change the working process and demand new skills. Moreover, the development of information technology used during work from home can cause role conflict. Workers should adapt to technology for involvement in virtual meetings to discuss a topic with other staff with various arguments [51]. Role conflict due to technology is also because staff experience conflict in completing tasks, as they need longer time to learn the features of the technology [83]. Role conflict is a part of role stress, which stimulates stress due to tasks or roles beyond an individual’s ability [42,48,57].
Role stress also consists of role overload. Role overload occurs when one cannot complete an abundant task beyond their ability [48] within a concise limit. In another study, role overload can also be identified as when a worker is facing more than one role within a limited time. Communication and information technology development effectively influences routines even outside working hours, such as when on holiday, someone should continue replying to emails or messages, which creates a workload outside the workplace, leading to role overload [24,37].
Jones [84] stated that this condition is related to information fatigue syndrome (IFS), which is the effect of fatigue due to the use of technologies, such as the Internet, email, handphone, or social media, as the users are continuously exposed to information overload. IFS is closely related to role stress or strain emerging due to the requirement to work outside the workplace and working hours. It can cause difficulties in making a decision, low focus on an activity being performed, and other psychological feelings such as fatigue, stress, and excessive anxiety [57]. Moreover, other research has found that technostress positively influences role stress [42,51]. Research from Aldo et al. [2] also found that technostress positively influences exhaustion as a sign of role stress. Another study mentioned some factors causing stress due to information technology, such as job demands.
Wallgren and Hanse [85] found that job demands positively influence perceived stress. Because of the COVID-19 pandemic, the unpredicted situation because of the COVID-19 pandemic demanded that workers reach a higher performance, which then positively influenced role stress. Role overload can also occur when someone is trying to fulfill their role but finds it challenging to manage it effectively. The tasks are too many, and they require the use of advanced technology beyond their ability. Other researchers have also mentioned that the sophistication and complexity of technology force users to make a great deal of effort simultaneously, such as updating software, learning how to operate new technology, integrating files, performing tasks, and so on. These conditions signify technostress (techno-complexity and techno-overload) [48]. As a result, workers are burdened with more roles unexpectedly.
Moreover, research from [51] also mentioned that techno-uncertainty related to information security affects role conflict since workers need to deal with ICT security and job requirements. Based on the explanation above, it can be concluded that technostress increases role stress among workers. Even though existing studies have already found a correlation between technostress and role stress, there is no study correlating technostress and role stress during working from home. Thus, the aim of the current study was to examine those aspects with the following hypothesis:
Hypothesis H4.
Technostress positively influences role stress during work from home.

4. Research Methods and Results

4.1. Design and Sample

This quantitative study collected data using an online questionnaire distributed to the Indonesian workers residing in Jakarta, Bogor, Depok, Tangerang, Bekasi, Surabaya, Bali, Lombok, Medan, Surabaya, Palembang, and Kalimantan. We used purposive random sampling based on age, working status, and education level. In this case, they had to be 21–60 years old, currently working, and have a minimum education of D3. The online questionnaire was distributed from 26 September to 29 December 2021. The questionnaire was initially filled with 870 responses. Before continuing to the statistical analysis, data screening was processed to ensure all collected data were appropriate for this study. Based on the completeness of data, it was found that 39 respondents did not answer all questions and were removed. Furthermore, conformity was also checked in the data screening process. It was found that 12 respondents did not fulfill the criteria, 7 were unemployed, and 5 were not qualified for the minimum education criteria. Therefore, the result of data screening left 819 responses in the final sample.

4.1.1. Research Instruments

There were 48 item statements used in this study using a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Statements on the questionnaire were formulated based on the items asked in earlier studies’ instruments, including technostress [24], which consisted of five dimensions (techno-insecurity, techno-overload, techno-complexity, techno-uncertainty, and techno-invasion), computer self-efficacy [60], technology addiction [54], and the correlation with productivity [42] and role stress [57]. Therefore, this study applied 10 scales, as shown in Table 1 below:

4.1.2. Method of Analysis

The study underwent three stages, including EFA (explanatory factor analysis); CFA (confirmatory factor analysis) to evaluate the validity, reliability, and fitness of the model [89]; and an SEM-Model to test the hypotheses of the research model. EFA and CFA tests were carried out using SPSS 25 and AMOS v21.0 software, respectively. Firstly, Harman’s single factor test was applied to identify whether there was a biased sample or other problems with the data, which resulted in 26.33% and <50%, indicating that the samples were not biased and could be tested further [90].
Furthermore, we examined the common method for bias using the full collinearity test to obtain the values of VIF (variance inflation factor) [91]. Secondly, we performed a Pearson correlation matrix and square root AVE tests to identify discriminant validity. Then, all latent variables from the EFA tests were assessed using the eigenvalue test. The CFA test was performed to measure the validity and reliability of data by comparing the standardized loading factor, Cronbach’s alpha, t-values, construct reliability (CR), and average variance explained (AVE). Ultimately, SEM (structural equation modeling) was applied to test the hypotheses of the research model using AMOS Graphics software. This study also provides the result of the goodness-of-fit model using the GFI (goodness of fit index), AGFI (adjusted goodness of fit index), CFI (comparative fit index), TLI (Tucker–Lewis index), NFI (normed fit index), and RMSEA (root mean square error of approximation) [89].

4.2. Results

4.2.1. Respondent Profile

Before analyzing the data, we performed a descriptive test on the characteristics of respondents using demographic data consisting of age, job, latest education, and gender. The results of the test are presented in Table 2 below:
This study involved 819 respondents. According to the questionnaire filled in previously, they had worked from home during the pandemic. Based on Table 2 above, most respondents were women, with a total number of 469 (57.3%). In terms of age, the respondents were dominated by workers aged 21–30 years old (59.8%), followed by respondents aged 31–40 (28.2%), 41–50 (9.4%), and 51–60 (2.6%). The data show that the respondents were from different generations.
Furthermore, based on job status, most of them, almost half of the total respondents, were permanent employees (406). The second biggest proportion was freelancers and nonpermanent employees, 196 and 182 respondents, respectively. The remaining (35) were self-employees who also worked from home during the COVID-19 pandemic. The demographic data also indicate that most respondents held a bachelor’s degree—more than half (52.1%) of the total respondents. This was followed by workers holding a diploma 3 certificate with 224 respondents (27.4%), and a master’s degree with 154 respondents (18.8%). The remaining (14 respondents) held a doctoral degree. The data show that respondents had different educational degrees.

4.2.2. Explanatory Factor Analysis (EFA)

The first analysis was EFA (explanatory factor analysis) using SPSS 25 to measure the influence of instruments on the factor. The first test was performed to determine the values of the Kaiser–Meyer–Olkin (KMO) and Bartlett’s test, and the results are presented in Table 3 below.
Based on Table 3 above, it can be observed that the value of the KMO and Bartlett’s test was 0.821, which was higher than the standard (0.6), meaning that the data could be continued for further analysis. Next, a full collinearity test was performed to identify whether there was a biased sample or other problems with the data. The full collinearity test on this study is shown in Appendix A, Table A1. As shown in Table A1, the values of the variance inflation factor (VIF) for all variables are lower than 3.3, indicating that the samples are free of collinearity [91] and could continue to the following test.
The next was to test all latent variables using the Pearson correlation matrix. This study also examined discriminant validity using average variance extracted (AVE). As presented in Table 4, the results show that each latent variable’s AVE values (shown in bold on the diagonals) are higher than the correlation values of other latent variables, meaning that they fulfilled the discriminant validity criteria [92]. The results can be observed in Table 4 below.
After the correlation matrix, we performed the EFA test using eigenvalues, as shown in Appendix A, Table A2. This table shows 10 components, indicating 10 factors have an eigenvalue of >1, meaning that all instruments used in the study have good scores. Therefore, this study passed all requirements of the EFA test and could continue to the next test.

4.2.3. Confirmatory Factor Analysis (CFA)

The validity and reliability were examined using the confirmatory factor analysis (CFA) test. The reliability test was performed using SPSS 21 with Cronbach’s alpha value. The validity test used AMOS software with standard loading factors, t-values, coefficient of determination (R2) values, construct reliability (CR) values, and AVE values. In this study, we performed the CFA test twice, namely CFA 1 (CFA first order) and CFA 2 (second order/CFA revision). The results of the CFA test are provided in Appendix B, Table A3, and show that the Cronbach’s alpha value of each variable (>0.6) indicated that all of them were reliable. The results of the final CFA and CFA 2 also show that all 48 instrument items had standardized loading factor value > 0.7, t-value > 2, CR > 0.7, and AVE > 0.5 [89], indicating that all instrument items were valid and reliable.

4.2.4. Structural Equation Modeling (SEM) Result

Before this study analyzed the SEM-Model, a goodness-of-fit model was analyzed first. The result of the goodness-of-fit model indicates that all items in this study met the standard value for each measurement (GFI, AGFI, CFI, TLI, NFI, and RMSEA). The model fit of this research can be observed in Table 5 below.
All items were appropriate for the hypothesis test based on the model fit above. To test the hypothesis, an SEM-Model test was performed. If it shows a t-value > 1.96 and p-value < 0.05, it means that one variable affects another variable [89,93]. Based on the SME model test, each technostress instrument was accepted, meaning that respondents experienced stress during work from home due to techno-invasion, techno-overload, techno-complexity, techno-insecurity, and techno-uncertainty. Moreover, this study also showed that techno-addiction positively influenced technostress (hypothesis 1 is accepted), and technostress positively influenced role stress (hypothesis 4 is accepted). The results indicated that the higher the technostress, the more serious the role stress experienced by workers.
However, the current findings on Hypotheses 2 and 3 were different from earlier studies. The SEM-Model test showed that computer self-efficacy significantly positively influenced technostress; thus, Hypothesis 2 is rejected. Furthermore, the findings showed that someone confident in operating technology would feel under pressure to use it (technostress increases) while working from home. However, it was found that although technostress increased during the pandemic, work productivity kept increasing. The SEM-Model result of this study is shown below in Figure 3.
The SEM-Model test above showed that techno-addiction significantly positively influenced technostress at 0.315 or 31.5%, and technostress influenced role stress at 0.782 or 78,2%. In contrast, computer self-efficacy (CSE) significantly positively influenced technostress at 0.466 or 46.6%, and technostress positively influenced productivity during work from home at 0.378 or 37.8%. The present research findings are different from older ones related to the correlations between technostress and computer self-efficacy and productivity because it is in the context of working from home due to the COVID-19 pandemic. The SEM-Model results are shown in Table 6 below.

5. Discussion: The Influenced and Influencing Factors of Technostress

The pandemic has changed the working system (from WFO to WFH). This study found that working from home in Indonesia had increased the rate of technostress among workers due to the more intensive use of information and technology communication. It is indicated by the results above that the long-term use of technology, which causes techno-addiction, leads to technostress. Moreover, technostress may emerge because of the absence of formal policy regulating workers, who were not accustomed to remote working, to follow or adapt to the working from home system [14]. This is because not all business activities can be carried out digitally. This explanation is supported by the latest research into the adaptive response to COVID-19 in Indonesia, which explained that many companies found it difficult to adapt to the WFH system during the COVID-19 pandemic [94]. Further, the research mentioned that 62% of Indonesian companies experiencing difficulties adapting to lockdown and WFH had inadequate digital training.
This study suggests that companies should provide digital training for all employees since it can be a solution to reduce technostress. Besides inadequate digital training, technostress in Indonesia was also probably caused by the high demand to perform tasks independently during WFH. In fact, not all tasks can be performed independently [5,7]. Several tasks need to be completed by involving multiple coworkers [95]. High-task interdependence triggered stress (including technostress) during WFH [14]. These findings support a previous study that found all benefits of the WFH system can be obtained if employees gain managerial and technological support [4]. Therefore, this research’s finding emphasizes the importance for companies to provide digital training before applying the WFH system to all employees.
Moreover, this research also found that technostress positively influences role stress. It supports previous studies that found technostress and role stress can trigger excessive anxiety [71], pressure at work [35], unbalanced work–life, feeling threatened by technology, job burnout [23], and inability to adapt to new technology. Therefore, based on the finding of Hypothesis 2 in this study, we suggest that all companies in Indonesia need to implement the right strategy in distributing tasks for all employees during WFH to eliminate the destructive impact of technostress and role stress.
The sudden shift in the working system due to the COVID-19 pandemic demanded that workers adapt to new technology. It also caused them to experience techno-insecurity and led them to be stressed. They had to work harder, especially in dealing with the new technology [13,17]. Based on the third hypothesis, the increase in technostress among Indonesian workers during WFH caused them to work longer (workaholism) [2,96], which led to an improvement in productivity. This result supports existing studies that found WFH positively influences productivity [13,59]. Another possible reason for the increase in productivity was that during WFH, workers were more autonomous and had less pressure from the office [11,13,18].
Unfortunately, we found that there was also a possibility that productivity was not provoked by their own willingness. Nevertheless, they did it because of the pressure caused by the pandemic. This is supported by information obtained from a local news website in Indonesia, www.cnnindonesia.com (accessed on 24 September 2021), which reported that on 23 March 2020, a labor union in Indonesia protested the salary and holiday allowance deduction due to the prevailing massive technology transformation and less involvement of workers within a company. It has led to techno-insecurity, which is one of the spheres of the technostress [37,39,42,74,97] phenomenon during work from home.
The economic recession due to the COVID-19 pandemic increased productivity because workers were afraid of being fired. Thus, it is concluded that someone experiencing technostress might be more motivated to work harder to avoid being replaced by other more competent workers or technology [41], which increases role stress. The increase in productivity due to technostress found in this study shows that workers in Indonesia had low self-control, especially in controlling work–life boundaries. It answered the statement in hypothesis 2 that computer self-efficacy (CSE) positively influences technostress. While earlier studies have generally shown that CSE negatively influences technostress, as people with high CSE will have better self-control when experiencing stress due to technology usage [27,71,73], the current study showed a contradictory finding.
This research found that workers with a high CSE level were more likely to experience technostress. This finding is the novelty of this research and differs from previous studies. This happened because the demand to use technology during WFH caused staff with a high level of CSE to work more, leading them to feel stressed during working [98], increasing techno-invasion [38]. When CSE is high, technostress occurs because workers need to work using technology more frequently. This finding indirectly shows that Indonesian workers had low self-control when working from home. Therefore, we suggest that companies more carefully manage workers’ roles to work more effectively and efficiently. This can prevent them from some long-term negative impacts such as job burnout, low job satisfaction [80], and early resignation.

6. Conclusions, Strengths, Limitations, and Implications

6.1. Conclusions

This research explains the current situation of technostress during work from home due to the COVID-19 pandemic in Indonesia. In Indonesia, the compulsion to perform work from home for all workers increases technostress since they need to perform all work using technology and deal with new systems and applications simultaneously. It correlates with the ability and confidence to use technology. The research framework used in this study is the first comprehensive research model to describe the technostress phenomenon and is a novelty for this study. This result shows that the greater computer self-efficacy among workers, the more they feel the confidence to use technology, which can increase the level of technostress. This is quite different from previous studies. Therefore, this finding is a novelty of this study, as it represents workers’ behavior toward technology usage.
Even though our study shows that CSE positively impacts the level of technostress, the finding also explains that a high level of technostress can bring a positive relationship to productivity because the situation of the COVID-19 pandemic forced workers to do the best for their job. Therefore, even though Indonesia’s workers have a high level of stress using technology, it also improves their want to be more productive during work from home. The high level of productivity shows the capability to learn new systems and technologies from different resources or partners outside the company to stay productive. The findings of this study are rather different from previous studies, and it is our next novelty to represent the new insight into the positive correlation between technostress and productivity. It describes not only the relationship among variables but also contributes to explaining the behavior of Indonesia’s workers during work from home due to the COVID-19 pandemic. Hopefully, this study will provide the best insight into human–technology interaction, which can potentially bring new ideas for innovation in the future and become the first empirical research into the technostress phenomenon during work from home in Indonesia.

6.2. Strengths, Limitations, and Future Research

The strength of this study is in explaining the technostress phenomenon; we considered studying this issue by implementing social cognitive theory (SCT) as the grounded theory to choose latent variables of the research model. The chosen variables in this research framework are viewed as the strength of this study because of the complexity and completeness in studying the technostress phenomenon. We used workers’ behavior and considered environmental and individual as the elements affecting the occurrence of technostress. Therefore, this research not only describes the correlation between each variable but also describes the behavioral and environmental conditions of employees in Indonesia in facing WFH during the COVID-19 pandemic. Moreover, this study used extensive data from 819 respondents scattered throughout 12 large cities in Indonesia. Thus, the data were appropriate to represent the issues and can be the first empirical research into the context of work from home in Indonesia.
However, this study also has limitations. Firstly, it did not discuss the influences of control variables in explaining WFH systems in Indonesia. Future research is suggested to investigate control variables such as gender, age, and occupation of employees in explaining their behavior toward technostress. In addition, other environmental elements such as access to the Internet and the area where the employees lived should also be analyzed in discussing the level of computer self-efficacy among employees to support these findings.
Secondly, we obtained data through an online questionnaire because of the barrier to conducting face-to-face interviews. Subsequent research is suggested to collect respondents’ data by performing physical contact to avoid the bias of collected data. Thirdly, this study visualized all respondents using the same criteria. To satisfy these findings, we suggest the following research to examine the technostress phenomenon by classifying respondents based on digital workers and nondigital workers. Lastly, successive research can also analyze of WFH system using qualitative research methods to obtain deeper perspectives from different departments and positions facing the technostress phenomenon.

6.3. Practical Implications

The study showed that although workers had more flexible time, they still faced pressure in working. We found that the working from home system increases the work pressure phenomenon due to the use of technology (technostress). Therefore, companies are suggested to create clear policies regulating staff roles during working from home to avoid disturbance on their life balance, which can trigger techno-invasion and role stress. This study also suggests that companies provide clarity in job desks for workers to prevent the phenomenon of role stress. We also suggest that companies improve digital readiness before applying new technology systems by training staff effectively.

6.4. Theoretical Implications

This study adds to the literature by explaining the working conditions in Indonesia during working from home, including the technostress experienced because of the demand to use technology. Furthermore, the findings of this study provide new theoretical contributions to studying work from home. The work from home system is considered a popular way since more companies have recently adopted this system for their workers. Therefore, this study is crucial to bringing the empirical theory of this current issue in explaining the human–technology interaction by considering three elements (individual, environmental, and behavioral). Theoretically, this study highlighted practical and psychological outcomes of telecommuting or working from home in Indonesia divided into two categories, influencing factors (computer self-efficacy and techno-addiction) and influencing factors (productivity and role stress), of the technostress phenomenon.
Another novelty of this study is the contradictory findings from existing studies that showed us that the technostress phenomenon might positively impact productivity among employees forced by environmental factors such as the unpredictable condition of the COVID-19 pandemic. Therefore, this study implies that users’ behavior is not only influenced by the complexity of technology and information systems but also by the environmental situation. Studies about technostress, especially in Indonesia, are still limited, and this study is the first one to comprehensively discuss technostress resulting from the COVID-19 pandemic and working from home.

Author Contributions

Conceptualization, A.F. (Aini Farmania); methodology, A.F. (Aini Farmania) and R.D.E.; software, R.D.E.; validation, A.F. (Aini Farmania) and R.D.E.; formal analysis, R.D.E.; investigation, A.F. (Aini Farmania); R.D.E.; and A.F. (Ananda Fortunisa); resources, A.F. (Ananda Fortunisa); data curation, R.D.E.; writing—original draft preparation, R.D.E.; writing—review and editing, A.F. (Aini Farmania); R.D.E.; and A.F. (Ananda Fortunisa); visualization, R.D.E. and A.F. (Ananda Fortunisa); supervision, A.F. (Aini Farmania); project administration, A.F. (Ananda Fortunisa); funding acquisition (Aini Farmania), A.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Full collinearity test (common method bias).
Table A1. Full collinearity test (common method bias).
Coefficients a
ModelStandardized
Coefficients
Beta		tSig.Collinearity Statistics
ToleranceVIF
1(Constant) 5.9550.000
TOVERLOAD0.0070.1430.8860.4772.097
TINVASION−0.050−0.9990.3180.4882.050
TCOMPLEXITY−0.065−1.4420.1500.6011.663
TINSECURITY−0.040−0.8310.4060.5211.918
TUNCERTAINTY0.0671.3570.1750.5041.984
EFFICACY−0.018−0.3530.7240.4522.210
ROVERLOAD0.0591.0970.2730.4272.344
DEPENDENCE0.0631.2350.2170.4642.157
RCONFLICT−0.029−0.5580.5770.4472.238
PRODUCTIVITY−0.039−0.9300.3530.6861.457
a Dependent variable: random.
Table
Table A2. Explanatory factor analysis (variance explained).
Table A2. Explanatory factor analysis (variance explained).
Total Variance Explained
ComponentInitial EigenvaluesExtraction Sums of Squared Loadings
Total% of VarianceCumulative %Total% of VarianceCumulative %
114.64927.64027.64014.64927.64027.640
25.88011.09538.7355.88011.09538.735
34.5658.61347.3494.5658.61347.349
42.7655.21852.5662.7655.21852.566
52.3084.35456.9202.3084.35456.920
62.0383.84560.7652.0383.84560.765
71.9453.67064.4351.9453.67064.435
81.4412.71867.1531.4412.71867.153
91.3232.49669.6491.3232.49669.649
101.2262.31471.9631.2262.31471.963
Extraction method: principal component analysis

Appendix B

Table
Table A3. Confirmatory factor analysis (CFA).
Table A3. Confirmatory factor analysis (CFA).
ConstructStd.Loading CFA1Std.Loading CFA2t-ValuesR2Cronbach’s AlphaConstruct ReliabilityAVE
Techno-overload 0.7790.8570.600
X110.590.78616.5520.348
X120.7520.74221.5820.566
X130.8140.83516.6720.663
X150.5830.73113.4060.339
Techno-invasion 0.8710.8750.639
X210.7310.73119.3390.535
X220.6940.69619.4150.482
X230.880.87924.590.775
X240.8740.87424.4600.764
Techno-complexity 0.8530.8680.569
X310.7450.74722.470.555
X320.8150.81822.680.663
X330.640.73417.4480.409
X340.7120.71319.7330.508
X350.7560.75520.9410.572
Techno-insecurity 0.8420.8590.606
X410.7060.69318.330.499
X430.8340.84221.8540.695
X440.820.84521.9150.673
X450.7210.72119.0130.52
Techno-uncertainty 0.7620.8290.620
X520.5220.70212.9090.272
X530.8080.83514.460.654
X540.8150.81823.6420.664
CSE 0.9150.9470.691
X630.7470.74825.3140.558
X640.7920.79223.6660.626
X650.840.8425.3430.706
X660.8760.87626.6010.767
X670.8040.80524.470.646
X680.90.927.470.81
X690.8880.88827.0320.788
X6100.7890.7923.5630.623
Techno-addiction 0.9040.9090.589
X710.7870.78823.6490.619
X720.7660.76523.6510.587
X730.6580.65919.7230.433
X740.8050.80725.3030.649
X750.860.85927.440.739
X760.7150.71421.6960.511
X770.7630.76223.5460.582
Role overload 0.8790.8830.656
Y110.7150.71421.2280.511
Y120.7760.77621.2320.601
Y130.8860.88824.1220.785
Y140.8520.8523.190.726
Role conflict 0.8640.5550.861
Y210.8110.80921.6930.658
Y220.7160.71621.5970.513
Y230.7490.75122.9310.561
Y240.6830.68420.4250.467
Y250.7570.75823.1670.573
Productivity 0.9170.9220.749
Y310.860.8636.5950.739
Y320.9160.91636.6010.839
Y330.9270.92737.3180.859
Y340.7480.74825.7670.56

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Sustainability 14 08669 g001 550
Figure 1. Triadic reciprocal theory. Source: [25].
Figure 1. Triadic reciprocal theory. Source: [25].
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. Research model result.
Figure 3. Research model result.
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Table
Table 1. Measurement items.
Table 1. Measurement items.
ScaleReferenceExample of Items
Techno-overload[42,86]“The use of technology during work from home forces myself to work faster.”
Techno-invasion[42,86]“My time to spend with family decreased due to the constant usage of technology during work from home.”
Techno-complexity[42,86]“I am not really understand to finish my job using new technology (software and hardware) during work from home).”
Techno-insecurity[42,86]“I feel my job will be lost due to the compulsion to use new systems and technology during work from home.”
Techno-uncertainty[42,86]“There is always constant change or disruption in the new systems and applications I used during work from home.”
Computer self-efficacy[60,87]“I am able to finish my job with the new systems during work from home if there are instructions or guidelines I can learn.”
Technology addiction[27]“Information and communication technology has been part of my daily routine during work from home.”
Role overload[42]“During work from home, I always work more than usual working hours.”
Role conflict [81]“During work from home, I often get the task that doesn’t have enough information I need in order to finish it.”
Productivity[42,88]“New systems and technology that I use during work from home help me to improve the quality of my work or the tasks that I do.”
Table
Table 2. Sample characteristics.
Table 2. Sample characteristics.
Number of UsersPercentages
Gender
Female46957.3%
Male35042.7%
Age
21–30 years old49059.8%
31–40 years old23128.2%
41–50 years old779.4%
51–60 years old212.6%
Type of job
Freelancer19623.9%
Permanent employee40649.6%
Nonpermanent employee18222.2%
Self-employee354.3%
Education
D3/equivalent22427.4%
Bachelor’s degree42752.1%
Master degree15418.8%
Doctoral degree141.7%
Table
Table 3. KMO and Bartlett’s test.
Table 3. KMO and Bartlett’s test.
Kaiser–Meyer–Olkin Measure of Sampling Adequacy0.821
Bartlett’s test of sphericityApprox. chi-square38,409.214
df1225
Sig.0.000
Table
Table 4. Pearson correlation matrix and discriminant validity.
Table 4. Pearson correlation matrix and discriminant validity.
Correlations
TOTINVTCOMTINSTUNCCSETADDROVERRCONFProductivity
Toverload0.558
Tinvasion0.503 **0.721
Tcomplexity0.272 **0.434 **0.631
Tinsecurity0.258 **0.371 **0.476 **0.624
Tuncertainty0.370 **0.228 **0.456 **0.351 **0.586
CSE0.495 **0.262 **0.318 **0.311 **0.584 **0.598
Taddiction0.366 **0.290 **0.189 **0.0220.457 **0.576 **0.646
Roverload0.512 **0.584 **0.273 **0.331 **0.199 **0.345 **0.376 **0.736
Rconflict0.481 **0.512 **0.223 **0.472 **0.165 **0.261 **0.286 **0.662 **0.649
Productivity0.312 **0.164 **0.102 **0.0360.355 **0.391 **0.525 **0.268 **0.201 **0.806
** Correlation is significant at the 0.01 level (two-tailed). The leading bold diagonal shows the squared root of the AVE: TO, techno-overload; TINV, techno-invasion; TINS, techno-insecurity; TUNC, techno-uncertainty; CSE, computer self-efficacy; ROVER, role overload; RCONF, role conflict.
Table
Table 5. Model fit.
Table 5. Model fit.
X2/dfGFIAGFICFITLINFIRMSEA
Model fit<3.000–1>0.8>0.9>0.8>0.9<0.08
CFA first order2.900.7650.8130.9310.8120.9110.073
CFA second order2.870.7398.8170.9400.8170.9190.070
SEM-Model fit2.890.7890.8250.9200.8670.9240.078
Table
Table 6. SEM-Model result.
Table 6. SEM-Model result.
Dependent Variables Independent VariablesStandardized Total Effectsp-ValueResults
Technostress<---Techno-addiction0.315***H1 is supported
(positive effect)
Technostress<---CSE0.466***H2 is not supported
(positive effect)
Role stress<---Technostress0.782***H4 is supported
(positive effect)
Techno-invasion<---Technostress0.687***Positive effect
Techno-overload<---Technostress0.786***Positive effect
Techno-complexity<---Technostress0.551***Positive effect
Techno-insecurity<---Technostress0.493***Positive effect
Techno-uncertainty<---Technostress0.594***Positive effect
Productivity<---Technostress0.378***H3 is not supported
(positive effect)
Role conflict<---Role stress0.825***Positive effect
Role overload<---Role stress0.919***Positive effect
*** means 0.000.
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Farmania, A.; Elsyah, R.D.; Fortunisa, A. The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia. Sustainability 2022, 14, 8669. https://doi.org/10.3390/su14148669

AMA Style

Farmania A, Elsyah RD, Fortunisa A. The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia. Sustainability. 2022; 14(14):8669. https://doi.org/10.3390/su14148669

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Farmania, Aini, Riska Dwinda Elsyah, and Ananda Fortunisa. 2022. "The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia" Sustainability 14, no. 14: 8669. https://doi.org/10.3390/su14148669

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