A Post-Pandemic View of Work-From-Home and Employee Performance: A Moderated-Mediation Model

A Post-Pandemic View of Work-From-Home and Employee Performance: A Moderated-Mediation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Post-Pandemic View of Work-From-Home and Employee Performance: A Moderated-Mediation Model Muhammad Imran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7095652/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The primary objective of the study is to assess employee performance among individuals working remotely, considering the mediating role of digital literacy and the moderating influence of technostress within public universities of Malaysia. A structured questionnaire was employed to gather a total of 320 responses from university lecturers. The data were analysed using the partial least squares structural equation modelling (PLS-SEM) technique through smartPLS-4. The results of the study indicated that working from home does not exhibit a direct significant relationship with employee performance, except for an indirect relationship mediated by digital literacy. Furthermore, the findings suggest that technostress has a significant negative moderating effect on the relationship between digital literacy and employee performance. Besides, technostress moderated the indirect relationship between work-from-home and employee performance through digital literacy. Consequently, digital literacy is essential for the successful implementation of effective work-from-home strategies that enhance employee performance. It is recommended that university management prioritise the enhancement of digital literacy prior to implementing remote work strategies. Additionally, management should address employee stress levels, as these can influence both digital literacy and employee performance. Future research may explore these findings in other service sectors, such as information technology and customer service. Work from Home Employee performance Digital literacy Technostress Universities Figures Figure 1 Figure 2 Introduction In the month of March 2020, most countries, including Malaysia, implemented lockdowns to control the spread of Covid-19. These lockdowns enforced restrictions on movement, international travel, and the closure of industries, businesses, government offices, and educational institutions (Shah et al., 2020 ). In response to the lockdown, most companies and educational institutions forced their employees to work from home (WFH) to accomplish their daily tasks (Almeida et al., 2024 ). Almost two years later, most governments opened their markets and resumed work as usual. However, the pandemic has left a permanent mark on the principles of the workplace, as many companies' management and their employees prefer to continue working from home, especially in the education sector. Therefore, it is reasonable to assume that work from home (WFH) is a permanent practice (Smite et al., 2023). Besides, WFH also provides benefits to disabled employees and those who are taking care of dependents. Furthermore, from an employer's perspective, it can also reduce operational costs such as workspace, parking, and facilities (Shafizadeh et al., 2007 ). Anyway, it is clear that the pandemic has transformed remote working from a rare occurrence for many firms and their employees to a new norm (Yao et al., 2023 ). This study is going to show how the WFH strategy brings changes to employee performance. Basically, the WFH strategy brings changes to the working environment, which is a challenge for the management of employee performance as well (Saleem et al., 2021 ). Thus, the purpose of the study is to explore a better understanding of WFH and its impact on employee performance in public universities in a developing country from a post-pandemic view. Additionally, this study seeks to clarify how digital literacy and technostress influence the connection between WFH and employee performance, filling a gap in existing research. After a brief introduction to the study, the next section will cover the literature and propose the hypothesis. The third and fourth sections will cover the methodology and data analysis of the study, respectively. The last portion of the study will discuss the conclusion, implications, limitations, and future recommendations. 2.1 Employee Performance It is very hard to quantify the employee performance due to multiple tasks and their daily role on the job. In some perspectives, it is described as the completion of tasks under the given job descriptions(Pawar, 2013 ). In other words, employee performance also can be explained as the difference between excellent performers and poor-performing employees and how actively they perform or complete their tasks on time or within the deadline provided. Consequently, employee performance can significantly influence an organization’s overall performance (Okolie & Kawedo, 2018 ). Moreover, past studies have operationalised employee performance in different ways; for instance, the first group of researchers defined employee performance as the total output of an individual (Sonnentag et al., 2008). The second group stated that they completed tasks under the given job descriptions (Darsana, 2014 ). The third and last group identified positive behaviour towards extra role performance, which can have significant implications for overall firm performance (Motowidlo, 2013 ). Besides, one more study explained employee performance as the total number of quantitative and qualitative contributions of an individual or group to the overall performance of the firm (Sen & Dulara, 2018 ). Aside from operational definitions of employee performance during or after a pandemic, more organisations are giving their employees the freedom to work in the office or from home. According to Patanjali and Bhatta ( 2025 ) that work from home is saving companies expenses in respect to space, utilities, etc., but on the other hand, these things are also affecting employee performance, such as space, internet facilities, family burden, house environment, etc. Thus, this study is going to investigate the factors, such as the influence of WFM, technology skills, and technological stress on employee performance. 2.2 Work from Home and employee performance Work from home refers to a work arrangement where employees perform their job duties remotely, typically from their homes or any location outside of the traditional office environment (Pullokaran & Joseph, 2023 ). Instead of commuting to a physical workplace, employees use digital technologies and communication tools to connect with colleagues, access work-related resources, and carry out their responsibilities (Lal et al., 2021 ). Generally, employees will be operated remotely through telecommuting and virtual work environments where a physical body is not required, which is an opposite situation from working in a traditional way. The same idea behind remote working is applied to working from home. Many employees work from home, but they still come to the office. The term “remote work” refers to a broader set with four components: work location, which can be anywhere; diversity of employment relationships; time distribution; and use of ICT. The focus of the work-from-home research topic is how the idea affects employee performance (Jalagat & Jalagat, 2019 ). Working from home is also known as the intra-firm decentralisation of power, where it can improve employees’s freedom and influence over the tasks, pace, and location of their jobs. According to Angelici and Profeta ( 2020 ) who conducted their research within the context of a large company in Italy, working with flexibility (both in terms of location and time) boosts employee performance and wellness. This result is in marked contrast to the employer’s concern that workers would perform poorly because of a lack of accountability while working from home. Furthermore, working from home provides employees with greater flexibility and autonomy in managing their work schedules (Yu & Wu, 2021 ). This flexibility can enable individuals to work during their most productive hours, balance personal and professional responsibilities, and create a conducive work environment tailored to their preferences. When employees have control over their work, it can enhance motivation, job satisfaction, and overall performance (van der Kolk et al., 2019 ). While physical distance may pose challenges for collaboration, advancements in digital communication tools and technologies have made remote collaboration more feasible. Video conferencing, instant messaging, and project management tools enable employees to connect and collaborate with colleagues effectively (Rysavy & Michalak, 2020 ). However, effective communication and coordination become even more critical in remote work settings to ensure that tasks are completed efficiently, and teamwork is maintained. Moreover, working from home can contribute to improved work-life balance, as employees have the flexibility to integrate personal and professional commitments more seamlessly. Achieving a better balance between work and personal life can enhance well-being, reduce stress, and ultimately have a positive impact on performance. According to data from the British, employees who are working from home during the pandemic feel more motivated and independent (Pelly et al., 2021 ). One more study stated that employees who are working from home can improve communication with their organisation by using digital tools extensively (Hauret et al., 2020 ). A study conducted in Germany during the pandemic found that working from home can increase employees’s motivation and performance (Kifor et al., 2022 ). The following hypothesis is proposed. H1: The work from home influences employee performance in public universities of Malaysia. 2.3 Mediating role of digital literacy Digital literacy refers to the ability to use and navigate digital technologies effectively and responsibly. It encompasses a range of skills and competencies that enable individuals to find, evaluate, create, and communicate information using digital devices and online platforms (Fraillon et al., 2014 ). Digital literacy can be referred to as the fundamental organisational and physical structures required for the running of a society or business, as well as the services and amenities required for an economy to run smoothly (Hanseth & Lyytinen, 2016 ). However, some key components of digital literacy, which is important to achieve affective digital literacy, include basic computer skills, internet skills, information literacy, communication and collaboration, digital security and privacy, and media literacy (Niu et al., 2024 ). Digital literacy is important in today's digital world because digital literacy empowers individuals to fully participate in the digital society, access information, communicate effectively, and make informed decisions (Sharma et al., 2016 ). Thus, these skills are essential for academic staff to acquire new knowledge, secure employment, engage in civic activities, and pursue personal development. In respect of the mediating role of digital literacy between working from home and employee performance, in terms of digital literacy skills, they are crucial for adapting to the work-from-home environment(Sadik Tatli et al., 2023 ). Lecturers who possess strong digital literacy skills can quickly adapt to remote teaching platforms, online collaboration tools, and virtual communication channels (Moorhouse & Wong, 2022 ). They can efficiently use digital resources, manage online classes, and engage with students effectively. This adaptability can positively impact their overall performance as a lecturer. Moreover, work-from-home heavily relies on digital tools and technologies. Lecturers with high digital literacy can utilise these tools efficiently, such as learning management systems, video conferencing platforms, and online assessment tools. They can effectively deliver lectures, share resources, interact with students, and provide timely feedback (Hafiz & Fitria, 2022 ). Proficient use of digital tools enhances productivity and can positively influence lecturer performance. Furthermore, digital literacy skills aid in efficient time and task management. Lecturers can organise their work schedules, create digital calendars, set reminders, and prioritise tasks effectively. They can also leverage productivity tools and project management platforms to streamline their workflow. Effective time and task management contribute to increased productivity and improved lecturer performance. On this basis, the current study proposed the following hypothesis. H2: The digital literacy mediating between work from home and employee performance in public universities of Malaysia. 2.4 Moderating role of technostress Technostress refers to the psychological and physiological strain or discomfort experienced by individuals because of their interaction with technology (Salanova et al., 2013 ). It arises when individuals perceive that the demands and pressures associated with technology use exceed their ability to cope effectively (Finstad et al., 2024 ). Technostress can manifest in various ways and impact different aspects of well-being and performance. Another study stated that technostress was expanded to include “any adverse effect induced directly or indirectly by technology on attitudes, thoughts, behaviours, or psychology” (Shu et al., 2011 ). Furthermore, technology users experienced a psychological pressure that is distinguished by displeasure and dissatisfaction because of the times brought on by technology, which are changing too quickly to accommodate individuals’s locations (Taylor, 2022 ). Anxiety, mental weariness, sadness, and nightmares are among the symptoms of technostress; nevertheless, many people also had frequent rage attacks driven by the challenges of using computer software and by managing errors or roadblocks that prevented them from finishing their task (Weinert et al., 2020 ). Besides, information overload, constant connectivity, technology complexity, lack of control, digital distractions, and fear of technology obsolescence are the causes of technostress (Haque et al., 2019 ). Therefore, technostress has negative effects on individual mental health as well as on the overall performance of employees. In respect of the moderating role of technostress, high levels of digital literacy can help lecturers cope with technostress (Haque et al., 2021 ). With strong digital literacy skills, lecturers are better equipped to handle technological challenges, troubleshoot issues, and find alternative solutions. This can reduce the negative impact of technostress on employee performance. Conversely, low levels of digital literacy can exacerbate technostress. Lecturers who lack digital literacy skills may struggle to navigate digital tools, feel overwhelmed by technological demands, and experience heightened technostress. This can further hinder their performance. However, the degree of fit between lecturers' digital literacy skills and the technological demands of their work can influence the impact of technostress on their performance. Lecturers with a high level of digital literacy that aligns well with the technology they use may experience less technostress and achieve better performance outcomes. In this respect, the present study proposes the following hypothesis. H3: The moderating role of technostress negatively affects the relationship between digital literacy and employee performance in public universities of Malaysia. Besides, this study conceptualised the mediating role of digital literacy between work-from-home and employee performance; this view motivates the present study to investigate the effect of technostress on the indirect relationship between work-from-home and employee performance through digital literacy. On this basis, the following hypothesis is proposed. H4: Technostress moderates the indirect relationship between work-from-home and employee performance through digital literacy, whereby this indirect relationship will be stronger for employees experiencing lower technostress and weaker for those with higher technostress. 2.5 Theoretical grounds for research work This study's research work covers the transaction model of stress and coping theory (TMSC). According to Lazarus and Folkman ( 1984 ) that understanding the individual level of appraisal and responding to stressors in their working environment. This theory has been used by many past studies in human resources, education, health, and sports (Lim et al., 2023 ). In respect of the present study, work-from-home is treated as an external demand, which can be a stressful event. On the other side, individual evaluation to cope with the stressor event, such as in our study case, the appraisal component, can be the digital literacy skill. Moreover, technostress can be the coping barrier in the framework, and all these jointly influence the outcome, which is employee performance in the framework. In simple words, working from home sometimes can be the source of stress, which is also called a stressor as per TMSC theory. Employees are searching for ways to learn work-from-home technologies to handle this stress, which is also called appraisal as per TMSC theory. On the other hand, employees are confident with their digital literacy to cope with the stress, which is also called a coping resource as per TMSC theory. Furthermore, employee-mean university lecturers are unable to handle the work-from-home technological stress, and they feel overloaded, which is known as technostress. Even if they are skilled, this is called a coping barrier in the framework. Lastly, the outcome offer employees means their performance while working from home. The proposed connection between variables under TMSC theory can be seen in Fig. 1. Research Methods The study focuses on the academic staff (lecturers) employed at Malaysian public universities. According to Sekaran and Bougie ( 2016 ), a sample size of the study between 30 and 500 is appropriate in social science studies. However, the present study has collected 320 responses from academic staff (lecturers) at public universities in Malaysia. In this study, the unit of analysis consists of individual respondents. Moreover, the convenience sampling technique has been used to approach the lecturers who are engaged in online classes through their official e-mails, contact numbers and face to face interaction after their informed consent through written message requests, which clearly stated the purpose of the study. Basically, convenience sampling is a non-probabilistic technique often used in quantitative studies, where subjects are selected based on accessibility to the researcher (Suen et al., 2014 ). While it can be applied in both qualitative and quantitative research, it lacks generalisability to the target population (Obilor & Isaac, 2023 ). 3.1 Measurement The measurement scales utilised in this study were adapted from previous research. The employee performance scale, comprising 12 items, was derived from the work of Kamal et al. ( 2016 ), which reported a Cronbach's alpha of .911, indicating an acceptable level of reliability. Also, the shorter version of the technostress scale was taken from Saleem et al. ( 2021 ), which was first created by Tarafdar et al. (2007), and it showed a Cronbach's alpha of .946.he work-from-home scale, consisting of 7 items, was adapted from the research of Ishak et al. ( 2022 ) and demonstrated a Cronbach's alpha of .694. Lastly, the digital literacy scale was sourced from the study by Mohd Abas et al. ( 2019 ), which reported a Cronbach's alpha of .985. The response format employed in this study was a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). 3.2 non-response biased test Non-response bias can happen when actual survey respondents are different from sampled respondents. There are multiple methods to test the non-response bias; however, this study adopted the comparison of early to late respondents, which is widely accepted by previous studies(Wagner & Kemmerling, 2010 ). Basically, in this method, separate the 50 early and 50 late responses from the data set and run the paired t-test on these two early and late groups. If results found significant that non-response bias exists, on the other side, results revealed the not significant findings among groups, which warrants that there is no non-response bias. In simpler terms, the two groups (early and late responses) do not show significant differences. This study, Table 1 shows the insignificant findings of 4 paired groups, meaning all variables are not significantly different. This study concludes that there is no significant difference between the respondents who answered and those who did not, indicating that further data collection from the target population is unnecessary. This study can test the research model on the basis of collected data, and results can generalise the target population. Table 1 non-response biased test results Paired Samples Test Paired Differences t df Significance Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference One-Sided p Two-Sided p Lower Upper Pair 1 E_technostress - L_technostress .03222 1.06217 .14166 − .25155 .33722 .268 49 .276 .677 Pair 2 E_digital_literacy - L_digital_literacy − .01400 .51737 .11569 − .25613 .22813 − .121 49 .452 .905 Pair 3 E_Work_from_home - L_work_from_home .08571 .91696 .20504 − .34344 .51486 .418 49 .340 .681 Pair 4 E_Employee_Performance - L_employee_performance − .14167 1.03347 .23109 − .62534 .34201 − .613 19 .274 .547 3.3 Common Method Bias (CMB) Common method bias, also known as common method variance (CMV), can occur during research. During research, a researcher faces different types of bias, such as response bias, researcher bias, and instrument bias. However, the CMB method was used to see the instrument bias during data collection. In this regard, Herman’s single-factor test was used to evaluate the CMB(Podsakoff et al., 2012 ). This study employed SPSS to run the single Harman single-factor test using factor analysis for one factor. Therefore, according to Table 2 , the total variance of 23.231% is below the 50% threshold, indicating that there are no common method bias (CMB) issues in this study. In simple words, data is ready for further analysis. Table 2 common method bias results Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 13.474 23.231 23.231 13.474 23.231 23.231 2 6.864 11.835 35.066 3 5.533 9.540 44.606 4 3.115 5.371 49.977 5 2.055 3.544 53.520 6 1.829 3.153 56.673 7 1.528 2.635 59.308 8 1.411 2.432 61.741 9 1.316 2.269 64.010 10 1.205 2.077 66.087 11 .944 1.627 67.714 12 .924 1.593 69.307 13 .824 1.421 70.728 14 .778 1.342 72.070 15 .753 1.298 73.368 16 .732 1.261 74.629 17 .716 1.234 75.863 18 .658 1.134 76.998 19 .644 1.110 78.108 20 .635 1.096 79.203 21 .592 1.021 80.225 22 .570 .982 81.207 23 .542 .934 82.141 24 .530 .914 83.055 25 .493 .850 83.904 26 .487 .840 84.744 27 .468 .807 85.551 28 .456 .786 86.337 29 .435 .750 87.087 30 .409 .706 87.792 31 .407 .702 88.495 32 .385 .663 89.158 33 .370 .637 89.796 34 .359 .619 90.415 35 .355 .611 91.026 36 .337 .582 91.608 37 .320 .552 92.160 38 .318 .548 92.708 39 .299 .515 93.223 40 .290 .500 93.723 41 .283 .488 94.211 42 .277 .478 94.689 43 .255 .440 95.130 44 .247 .426 95.555 45 .241 .415 95.971 46 .228 .393 96.364 47 .224 .385 96.749 48 .220 .379 97.128 49 .200 .345 97.473 50 .191 .329 97.802 51 .189 .326 98.127 52 .184 .318 98.445 53 .176 .304 98.749 54 .166 .287 99.036 55 .162 .280 99.316 56 .152 .262 99.578 57 .130 .224 99.802 58 .115 .198 100.000 Extraction Method: Principal Component Analysis. Data analysis and findings The smartPLS-4 (PLS-SEM) has been used to evaluate the collected data and draw the conclusions from this study. The PLS-SEM has run into two stages: measurement and structural model. The measurement model evaluated the construct’s reliability and validity, and the structural model evaluated the relationship between constructs and accepted or rejected the proposed hypothesis. 4.1 Measurement model Cronbach's alpha coefficients were calculated to assess the internal consistency of the constructs, with a threshold of 0.70 indicating acceptable reliability. The results of this study met this criterion. Composite reliability was assessed using two alternative measures, rho_A and rho_C. The composite reliability values (rho_A and rho_C) exceeded the threshold of 0.70 for all constructs, thereby indicating that their reliability was achieved. Additionally, the average variance extracted (AVE) was calculated to assess the convergent validity of the constructs. Specifically, AVE values should exceed 0.50, and the recorded AVE values for each construct were above this threshold. Thus, the convergent validity of the constructs was established. The reliability and validity measures of the constructs are presented in Table 3 . Table 3 Reliability of constructs Constructs Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) Digital Literacy 0.904 0.908 0.919 0.510 Employee performance 0.929 0.939 0.939 0.566 Technostress 0.896 0.923 0.909 0.559 Work from Home 0.763 0.769 0.850 0.587 Furthermore, the HTMT (Heterotrait-Monotrait Ratio) criteria is used to validate the discriminant validity of the contrast. The HTMT matrix presents the correlation values between constructs in the study. The diagonal shows the correlation of each construct with itself, which is always 1, so the correlation values of each construct should be less than 1. Hence, all values of constructs are recorded less than 1, which shows that there are no validity issues; the results of HTMT can be seen in Table 4 . Table 4 Validity of constructs Constructs Digital Literacy Employee performance Technostress Work from Home Technostress x Digital Literacy Digital Literacy Employee performance 0.284 Technostress 0.214 0.249 Work from Home 0.764 0.279 0.255 Technostress x Digital Literacy 0.104 0.11 0.255 0.136 4.2 Structural Model The structural model aims to explain the causal or predictive relationships among study variables. To assess the path coefficient, SmartPLS provides t-statistics; the value of t-statistics should be greater than 1.96 at a significant level of 0.5. Hence, as per Table 5 results, hypotheses H2 and H3, except H1, recorded greater t-statistics, 2.708 and 2.379, respectively, at the 0.007 and 0.018 significance levels. Thus, all hypotheses H2 and H3 are accepted, and hypothesis H1 is rejected. Table 5 Hypothesis findings Hypothesis Beta Standard deviation t-statistics p-values H1: WFH ->Employee performance 0.054 0.066 0.812 0.417 H2: WFH ->Digital Literacy ->Employee performance 0.109 0.040 2.708 0.007 H3: Technostress x Digital Literacy ->Employee performance -0.100 0.042 2.379 0.018 Furthermore, the PROCESS macro was run on smartPLS-4 to test the 2nd-stage moderated mediation to verify hypothesis 4. Table 6 and the moderated-mediation graph in Fig. 2 show the conditional indirect effect and its level of confidence (CI) within the limit, meaning no zero is involved, which accepts hypothesis 4. Table 6 Results of conditional indirect effect Moderated indirect relationship Effect Standard error Lower limit CI Upper limit CI ST X WFH-DL-EP -0.074 0.043 -0.143 -0.004 Level of Moderated indirect relationship Effect Standard error Lower limit CI Upper limit CI WFM ->DL ->EP conditional on EP at + 1 SD (+ 1.365) 0.061 0.059 0.002 0.120 WFM ->DL ->EP conditional on EP at -1 SD (-1.365) 0.193 0.066 0.127 0.259 WFM ->DL ->EP conditional on EP at Mean (0) 0.127 0.050 0.077 0.177 Bootstrap Process sample size: 5000, CI: confidence interval Discussion of the findings The direct relationship between work from home (WFH) and employee performance was examined. As per table-3, the value of t=-0.812, p = 0.417 shows the insignificant relationship between work from home and employee performance at public universities of Malaysia. Moreover, the mediating role of digital literacy between work from home (WFH) and employee performance was examined. Table 3 findings show the positive and significant mediating role of digital literacy between work from home and employee performance. The type of mediation is perfect mediation; in simple words, when there is perfect mediation, the influence of work from home on employee performance disappears entirely once the digital literacy mediating variable is considered. The employees become more adept at utilising digital tools and platforms; they are better positioned to fulfil their job responsibilities effectively. Basically, through the intervention of digital skills, remote work provides work engagement flexibility, which leads to higher employee performance. Basically, the experience of employees who are working from home has improved job satisfaction, reduced commuting stress, and increased autonomy; this all enhances employee performance. The moderating role of technology stress in the relationship between digital literacy and employee performance was examined. The results show the negative and significant (t=-2.379, p = 0.018) moderating role of technostress on the relationship between digital literacy and employee performance. In other words, the negative moderating role of technostress weakens the relationship between digital literacy and employee performance in public universities of Malaysia. While digital literacy is positively associated with employee performance, the presence of technostress dampens this relationship. Technostress, which arises from the continuous use of technology, can hinder the positive effect of digital literacy on performance by causing anxiety, burnout, or resistance to technology adoption among employees. Moreover, the moderated role of technostress on the indirect effect between work-from-home and employee performance through digital literacy was tested. Table 6 shows the significant negative moderated mediation interaction. In respect of the level of moderated mediation, it revealed that the indirect relationship was strong when technostress was low (effect = 0.193, CI [0.127, 0.259]) and moderate at the mean level (effect = 0.127, CI [0.077, 0.177]). Finally, when technostress recorded high, then the indirect relationship was weakest (effect 0.061, CI [0.002, 0.120]). In simple words, the higher the level of technostress, the more diminished the effect of digital literacy to transport the benefits of work-from-home to improve the lecturer's performance. 5.1 Implications of the study Implications related to work from home or remote working, digital literacy, and technostress are not only important for universities but also important for individuals. However, in respect of theory, it is the first study that investigated the direct relationship between work from home and employee performance in public universities of Malaysia along with the mediating role of digital literacy and the moderating role of technostress. Moreover, with respect to practical implementation, organisations such as universities should think about implementing or enhancing their policies for work from home. Furthermore, the positive relationship between work from home and employee performance advises organisations that flexible working hours can improve employee performance as per expectations. These arrangements might include telecommuting opportunities and flexible working schedules. Additionally, digital literacy is very important for higher employee performance; hence, organisations such as universities should prioritise providing training and resources to enhance employee digital skills, such as workshops and online courses, and empower employees to use digital tools, communicate online, and manage their tasks remotely. Moreover, universities should put their efforts into developing a helpful technological environment that reduces the stress that is brought on by continual connectivity and information overload. Promoting responsible technology use and providing tools for controlling technostress can enhance employee performance and well-being. Thus, considering the above implications, the universities can improve their employees’s performance, satisfaction, and overall success in the digital-centric work landscape. Finally, this study also contributed to the transaction model of stress and coping theory (TMSC), which examines work-from-home, employee performance, digital literacy, and technostress. This finding potentially contributed to existing knowledge of TMSC, which will be useful for academicians and policymakers. 5.2 Limitations and future recommendations The current study is only limited to public universities; the future studies can consider private universities for better generalisations of the results. Basically, it raises concerns about the population’s representation since the employees from different institutions may have divergent perceptions and consequently generate different results. Moreover, the results obtained may not be generalised to employees in different fields at public universities, whereas the research majorly focused on the education sector. For instance, lecturers have adapted to situations have more exposure to the use of advanced technology than staff in private. This research also reveals the gap in technological skills among staff members at public and private universities. So, future research can be more interesting if it expands the samples used, not just public university employees. The future study may also broaden the scope of study beyond universities in Malaysia to increase the accuracy of the result, where overall understanding from the populations can be achieved. The lack of relative factors has led to the limitations of this research. To improve outcomes, more relative factors influencing employee performance should be included in future research, such as e-training and adaptive expertise. The cross-sectional approach has been used for the current study; the future study can collect the data through the longitudinal approach to generalise the findings. Conclusion of the study This study's findings revealed the perfect mediating role of digital literacy between work from home and employee performance, and in respect of moderation, this study's findings showed the negative moderating role of technostress on the relationship between digital literacy and employee performance in public universities of Malaysia. This study also found the technostress moderated the indirect effect between work-from-home and employee performance through digital literacy. Basically, working from home or remote work has a positive influence on employee performance through digital literacy because it offers flexible duty hours and reduces commuting stress, which leads to the positive impact of higher employee performance. Besides, the mediating role of digital literacy enhances employees’ abilities to use digital tools, so these abilities give leverage to employees to take advantage through work from home to improve their performance. Moreover, in respect to the moderating role of technostress, it is playing a negative role in weakening the relationship between digital literacy and employee performance; basically, employees experiencing high levels of technostress seem to struggle to effectively translate their digital literacy into improved performance due to the stress associated with technology. Thus, in the case of technostress, universities should provide training to neutralise the employees’ stress regarding technology use and allow them to fully utilise their digital skills. Declarations Ethical approval and consent participate: Ethical approval was not needed, as the research involved non-invasive data collected anonymously from the university lecturers, which maintained the research standards. This study did not involve any experiments or animals, which would require approval from our university's ethical committee. Furthermore, respondents informed consent was taken through clearly written message requests at the beginning of the questionnaire, which clearly stated the purpose of the study. Publication consent: This manuscript involves no personal information, such as images, videos, or identification. Thus, there is no need to require any consent for this manuscript. Funding: There is no funding involve Data availability : Data is not publicly available due to the privacy of the respondents, but can provide on demand References Almeida, F., Rodrigues, H., & Freitas, P. (2024). “No Need to Dress to Impress” evidence on teleworking during and after the pandemic: A systematic review. Administrative Sciences , 14 (4), 76. Angelici, M., & Profeta, P. (2020). Dondena Working Papers. Darsana, M. (2014). The Influence of Personality and Organizational Culture on Employee Performance Through Organizational Behavior. The International Journal of Management 35- 42. Finstad, G. 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Digital Literacy and its Relationship with Employee Performance in the 4IR. Journal of International Business, Economics and Entrepreneurship (JIBE) , 4 (2), 29-37. Moorhouse, B. L., & Wong, K. M. (2022). Blending asynchronous and synchronous digital technologies and instructional approaches to facilitate remote learning. Journal of Computers in Education , 9 (1), 51-70. Motowidlo, S. J. (2013). Job Performance. ResearchGate . Retrieved January, from https://www.researchgate.net/publication/303918880_Job_performance Niu, W., Zhang, W., Zhang, C., & Chen, X. (2024). The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective. Sustainability , 16 (3), 1276. Obilor, E., & Isaac. (2023). Convenience and Purposive Sampling Techniques: Are they the Same? Okolie, U. C., & Kawedo, O. P. (2018). Factors Influencing Employees' Performance at Workplace. An Integrated Perspective. Journal of Economics & Business Research , 24 (1). Patanjali, S., & Bhatta, N. (2025). Work from home during the pandemic: The impact of organizational factors on the productivity of employees in the IT industry. Vision , 29 (3), 326-338. Pawar, B. S. (2013). A proposed model of organizational behavior aspects for employee performance and well-being. Applied Research Quality Life, Vol 8 No.3 , 339-359. Pelly, D., Doyle, O., Daly, M., & Delaney, L. (2021). Worker well-being before and during the COVID-19 restrictions: A longitudinal study in the UK . Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology , 63 (1), 539-569. Pullokaran, L., & Joseph, P. (2023). Working remotely: Employees benefits and challenges. International Journal of Engineering Technology and Management Sciences , 7 (1), 34-37. Rysavy, M. D., & Michalak, R. (2020). Working from home: How we managed our team remotely with technology. Journal of Library Administration , 60 (5), 532-542. Sadik Tatli, H., Sefa Yavuz, M., & Ongel, G. (2023). The Mediator Role of Task Performance in the Effect of Digital Literacy on Firm Performance. Marketing and Management of Innovations . Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress among users of information and communication technologies. International journal of psychology , 48 (3), 422-436. Saleem, F., Malik, M. I., Qureshi, S. S., Farid, M. F., & Qamar, S. (2021). Technostress and employee performance nexus during COVID-19: training and creative self-efficacy as moderators. Frontiers in Psychology , 12 , 595119. Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach . john wiley & sons. Sen, C., & Dulara, S. (2018). Job characteristics and performance: A mediational role of work engagement. International Journal of Social Sciences , 138-142. Shafizadeh, K. R., Niemeier, D. A., Mokhtarian, P. L., & Salomon, I. (2007). Costs and benefits of home-based telecommuting: A Monte Carlo simulation model incorporating telecommuter, employer, and public sector perspectives. Journal of infrastructure systems , 13 (1), 12-25. Shah, A. U. M., Safri, S. N. A., Thevadas, R., Noordin, N. K., Abd Rahman, A., Sekawi, Z., Ideris, A., & Sultan, M. T. H. (2020). COVID-19 outbreak in Malaysia: Actions taken by the Malaysian government. International Journal of Infectious Diseases , 97 , 108-116. Sharma, R., Fantin, A.-R., Prabhu, N., Guan, C., & Dattakumar, A. (2016). Digital literacy and knowledge societies: A grounded theory investigation of sustainable development. Telecommunications Policy , 40 (7), 628-643. Shu, Q., Tu, Q., & Wang, K. (2011). The impact of computer self-efficacy and technology dependence on computer-related technostress: A social cognitive theory perspective. International Journal of Human-Computer Interaction , 27 (10), 923-939. Suen, L.-j. W., Huang, H.-M., & Lee, H.-H. (2014). [A comparison of convenience sampling and purposive sampling]. Hu li za zhi The journal of nursing , 61 3 , 105-111. Taylor, T. E. (2022). The User’s Experience. Exploring the Impact our Interactions with Technology Have on Us. Electronic Workshops in Computing . van der Kolk, B., van Veen-Dirks, P. M., & ter Bogt, H. J. (2019). The impact of management control on employee motivation and performance in the public sector. European Accounting Review , 28 (5), 901-928. Wagner, S. M., & Kemmerling, R. (2010). Handling nonresponse in logistics research. Journal of Business Logistics , 31 (2), 357-381. Weinert, C., Maier, C., Laumer, S., & Weitzel, T. (2020). Technostress mitigation: an experimental study of social support during a computer freeze. Journal of Business Economics , 90 , 1199-1249. Yao, S., Lu, J., Wang, H., Montgomery, J. J. W., Gorny, T., & Ogbonnaya, C. (2023). Excessive technology use in the post-pandemic context: how work connectivity behavior increases procrastination at work. Information Technology & People . Yu, J., & Wu, Y. (2021). The impact of enforced working from home on employee job satisfaction during COVID-19: An event system perspective. International journal of environmental research and public health , 18 (24), 13207. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7095652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488210064,"identity":"e32eb3ad-cfee-424c-8bea-ebde21acfb58","order_by":0,"name":"Muhammad Imran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACHjDJ2MAMpm2QBYnTkkaKFgjvMGEtujNyD366mWMn28DOnfbh547ziRuuHWB88LaNIdrgAHYtZjfykqVztyUbNzDzbp7Ze+Z24obbCcyGc9sYcjfg1JJjANTCnAjSwsDbBtbCJs2LX4vx79xt9WAtjH/bzoG0sP8moMUMaMthsBZm3rYDYFuY8Wo588bMOnfbceM2kBbZtmTjmbcTmyXnnJPInYlLy/Ec49u526pl+/nPbmZ822Yn23c7+eCHN2U2uX04tMABG4IJjiMJBgVCWjCBfAPJWkbBKBgFo2B4AgAaO2HC0XvqnAAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Utara Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Imran","suffix":""}],"badges":[],"createdAt":"2025-07-10 18:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7095652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7095652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87327577,"identity":"52782130-fd46-43a1-ab76-490279bd7c93","added_by":"auto","created_at":"2025-07-22 17:56:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22066,"visible":true,"origin":"","legend":"\u003cp\u003eFramework of the Research\u003c/p\u003e\n\u003cp\u003eNote: Doted line shows the mediation effect\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7095652/v1/0246d976d3b0f7b523bd3f97.png"},{"id":87327578,"identity":"80c91576-063e-4855-bb2a-0adb83d27229","added_by":"auto","created_at":"2025-07-22 17:56:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43451,"visible":true,"origin":"","legend":"\u003cp\u003eConditional indirect effect for moderation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7095652/v1/1839c51569c8ebbd5470e31d.png"},{"id":87910579,"identity":"94250387-5e27-4cc0-8182-4e9b03abd989","added_by":"auto","created_at":"2025-07-30 09:47:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1143943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7095652/v1/10783f8c-6809-4801-b56a-04949e4d0a87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Post-Pandemic View of Work-From-Home and Employee Performance: A Moderated-Mediation Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the month of March 2020, most countries, including Malaysia, implemented lockdowns to control the spread of Covid-19. These lockdowns enforced restrictions on movement, international travel, and the closure of industries, businesses, government offices, and educational institutions (Shah et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In response to the lockdown, most companies and educational institutions forced their employees to work from home (WFH) to accomplish their daily tasks (Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Almost two years later, most governments opened their markets and resumed work as usual. However, the pandemic has left a permanent mark on the principles of the workplace, as many companies' management and their employees prefer to continue working from home, especially in the education sector. Therefore, it is reasonable to assume that work from home (WFH) is a permanent practice (Smite et al., 2023).\u003c/p\u003e\u003cp\u003eBesides, WFH also provides benefits to disabled employees and those who are taking care of dependents. Furthermore, from an employer's perspective, it can also reduce operational costs such as workspace, parking, and facilities (Shafizadeh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Anyway, it is clear that the pandemic has transformed remote working from a rare occurrence for many firms and their employees to a new norm (Yao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study is going to show how the WFH strategy brings changes to employee performance. Basically, the WFH strategy brings changes to the working environment, which is a challenge for the management of employee performance as well (Saleem et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, the purpose of the study is to explore a better understanding of WFH and its impact on employee performance in public universities in a developing country from a post-pandemic view. Additionally, this study seeks to clarify how digital literacy and technostress influence the connection between WFH and employee performance, filling a gap in existing research.\u003c/p\u003e\u003cp\u003eAfter a brief introduction to the study, the next section will cover the literature and propose the hypothesis. The third and fourth sections will cover the methodology and data analysis of the study, respectively. The last portion of the study will discuss the conclusion, implications, limitations, and future recommendations.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Employee Performance\u003c/h2\u003e\u003cp\u003eIt is very hard to quantify the employee performance due to multiple tasks and their daily role on the job. In some perspectives, it is described as the completion of tasks under the given job descriptions(Pawar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In other words, employee performance also can be explained as the difference between excellent performers and poor-performing employees and how actively they perform or complete their tasks on time or within the deadline provided. Consequently, employee performance can significantly influence an organization\u0026rsquo;s overall performance (Okolie \u0026amp; Kawedo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, past studies have operationalised employee performance in different ways; for instance, the first group of researchers defined employee performance as the total output of an individual (Sonnentag et al., 2008). The second group stated that they completed tasks under the given job descriptions (Darsana, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The third and last group identified positive behaviour towards extra role performance, which can have significant implications for overall firm performance (Motowidlo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Besides, one more study explained employee performance as the total number of quantitative and qualitative contributions of an individual or group to the overall performance of the firm (Sen \u0026amp; Dulara, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAside from operational definitions of employee performance during or after a pandemic, more organisations are giving their employees the freedom to work in the office or from home. According to Patanjali and Bhatta (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) that work from home is saving companies expenses in respect to space, utilities, etc., but on the other hand, these things are also affecting employee performance, such as space, internet facilities, family burden, house environment, etc. Thus, this study is going to investigate the factors, such as the influence of WFM, technology skills, and technological stress on employee performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Work from Home and employee performance\u003c/h2\u003e\u003cp\u003eWork from home refers to a work arrangement where employees perform their job duties remotely, typically from their homes or any location outside of the traditional office environment (Pullokaran \u0026amp; Joseph, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Instead of commuting to a physical workplace, employees use digital technologies and communication tools to connect with colleagues, access work-related resources, and carry out their responsibilities (Lal et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Generally, employees will be operated remotely through telecommuting and virtual work environments where a physical body is not required, which is an opposite situation from working in a traditional way. The same idea behind remote working is applied to working from home. Many employees work from home, but they still come to the office. The term \u0026ldquo;remote work\u0026rdquo; refers to a broader set with four components: work location, which can be anywhere; diversity of employment relationships; time distribution; and use of ICT.\u003c/p\u003e\u003cp\u003eThe focus of the work-from-home research topic is how the idea affects employee performance (Jalagat \u0026amp; Jalagat, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Working from home is also known as the intra-firm decentralisation of power, where it can improve employees\u0026rsquo;s freedom and influence over the tasks, pace, and location of their jobs. According to Angelici and Profeta (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who conducted their research within the context of a large company in Italy, working with flexibility (both in terms of location and time) boosts employee performance and wellness. This result is in marked contrast to the employer\u0026rsquo;s concern that workers would perform poorly because of a lack of accountability while working from home.\u003c/p\u003e\u003cp\u003eFurthermore, working from home provides employees with greater flexibility and autonomy in managing their work schedules (Yu \u0026amp; Wu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This flexibility can enable individuals to work during their most productive hours, balance personal and professional responsibilities, and create a conducive work environment tailored to their preferences. When employees have control over their work, it can enhance motivation, job satisfaction, and overall performance (van der Kolk et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While physical distance may pose challenges for collaboration, advancements in digital communication tools and technologies have made remote collaboration more feasible. Video conferencing, instant messaging, and project management tools enable employees to connect and collaborate with colleagues effectively (Rysavy \u0026amp; Michalak, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, effective communication and coordination become even more critical in remote work settings to ensure that tasks are completed efficiently, and teamwork is maintained.\u003c/p\u003e\u003cp\u003eMoreover, working from home can contribute to improved work-life balance, as employees have the flexibility to integrate personal and professional commitments more seamlessly. Achieving a better balance between work and personal life can enhance well-being, reduce stress, and ultimately have a positive impact on performance. According to data from the British, employees who are working from home during the pandemic feel more motivated and independent (Pelly et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One more study stated that employees who are working from home can improve communication with their organisation by using digital tools extensively (Hauret et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A study conducted in Germany during the pandemic found that working from home can increase employees\u0026rsquo;s motivation and performance (Kifor et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The following hypothesis is proposed.\u003c/p\u003e\u003cp\u003eH1: The work from home influences employee performance in public universities of Malaysia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Mediating role of digital literacy\u003c/h2\u003e\u003cp\u003eDigital literacy refers to the ability to use and navigate digital technologies effectively and responsibly. It encompasses a range of skills and competencies that enable individuals to find, evaluate, create, and communicate information using digital devices and online platforms (Fraillon et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Digital literacy can be referred to as the fundamental organisational and physical structures required for the running of a society or business, as well as the services and amenities required for an economy to run smoothly (Hanseth \u0026amp; Lyytinen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, some key components of digital literacy, which is important to achieve affective digital literacy, include basic computer skills, internet skills, information literacy, communication and collaboration, digital security and privacy, and media literacy (Niu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Digital literacy is important in today's digital world because digital literacy empowers individuals to fully participate in the digital society, access information, communicate effectively, and make informed decisions (Sharma et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, these skills are essential for academic staff to acquire new knowledge, secure employment, engage in civic activities, and pursue personal development.\u003c/p\u003e\u003cp\u003eIn respect of the mediating role of digital literacy between working from home and employee performance, in terms of digital literacy skills, they are crucial for adapting to the work-from-home environment(Sadik Tatli et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lecturers who possess strong digital literacy skills can quickly adapt to remote teaching platforms, online collaboration tools, and virtual communication channels (Moorhouse \u0026amp; Wong, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They can efficiently use digital resources, manage online classes, and engage with students effectively. This adaptability can positively impact their overall performance as a lecturer. Moreover, work-from-home heavily relies on digital tools and technologies. Lecturers with high digital literacy can utilise these tools efficiently, such as learning management systems, video conferencing platforms, and online assessment tools. They can effectively deliver lectures, share resources, interact with students, and provide timely feedback (Hafiz \u0026amp; Fitria, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Proficient use of digital tools enhances productivity and can positively influence lecturer performance.\u003c/p\u003e\u003cp\u003eFurthermore, digital literacy skills aid in efficient time and task management. Lecturers can organise their work schedules, create digital calendars, set reminders, and prioritise tasks effectively. They can also leverage productivity tools and project management platforms to streamline their workflow. Effective time and task management contribute to increased productivity and improved lecturer performance. On this basis, the current study proposed the following hypothesis.\u003c/p\u003e\u003cp\u003eH2: The digital literacy mediating between work from home and employee performance in public universities of Malaysia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Moderating role of technostress\u003c/h2\u003e\u003cp\u003eTechnostress refers to the psychological and physiological strain or discomfort experienced by individuals because of their interaction with technology (Salanova et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It arises when individuals perceive that the demands and pressures associated with technology use exceed their ability to cope effectively (Finstad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Technostress can manifest in various ways and impact different aspects of well-being and performance. Another study stated that technostress was expanded to include \u0026ldquo;any adverse effect induced directly or indirectly by technology on attitudes, thoughts, behaviours, or psychology\u0026rdquo; (Shu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, technology users experienced a psychological pressure that is distinguished by displeasure and dissatisfaction because of the times brought on by technology, which are changing too quickly to accommodate individuals\u0026rsquo;s locations (Taylor, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Anxiety, mental weariness, sadness, and nightmares are among the symptoms of technostress; nevertheless, many people also had frequent rage attacks driven by the challenges of using computer software and by managing errors or roadblocks that prevented them from finishing their task (Weinert et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Besides, information overload, constant connectivity, technology complexity, lack of control, digital distractions, and fear of technology obsolescence are the causes of technostress (Haque et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, technostress has negative effects on individual mental health as well as on the overall performance of employees.\u003c/p\u003e\u003cp\u003eIn respect of the moderating role of technostress, high levels of digital literacy can help lecturers cope with technostress (Haque et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With strong digital literacy skills, lecturers are better equipped to handle technological challenges, troubleshoot issues, and find alternative solutions. This can reduce the negative impact of technostress on employee performance. Conversely, low levels of digital literacy can exacerbate technostress. Lecturers who lack digital literacy skills may struggle to navigate digital tools, feel overwhelmed by technological demands, and experience heightened technostress. This can further hinder their performance.\u003c/p\u003e\u003cp\u003eHowever, the degree of fit between lecturers' digital literacy skills and the technological demands of their work can influence the impact of technostress on their performance. Lecturers with a high level of digital literacy that aligns well with the technology they use may experience less technostress and achieve better performance outcomes. In this respect, the present study proposes the following hypothesis.\u003c/p\u003e\u003cp\u003eH3: The moderating role of technostress negatively affects the relationship between digital literacy and employee performance in public universities of Malaysia.\u003c/p\u003e\u003cp\u003eBesides, this study conceptualised the mediating role of digital literacy between work-from-home and employee performance; this view motivates the present study to investigate the effect of technostress on the indirect relationship between work-from-home and employee performance through digital literacy. On this basis, the following hypothesis is proposed.\u003c/p\u003e\u003cp\u003eH4: Technostress moderates the indirect relationship between work-from-home and employee performance through digital literacy, whereby this indirect relationship will be stronger for employees experiencing lower technostress and weaker for those with higher technostress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Theoretical grounds for research work\u003c/h2\u003e\u003cp\u003eThis study's research work covers the transaction model of stress and coping theory (TMSC). According to Lazarus and Folkman (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) that understanding the individual level of appraisal and responding to stressors in their working environment. This theory has been used by many past studies in human resources, education, health, and sports (Lim et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In respect of the present study, work-from-home is treated as an external demand, which can be a stressful event. On the other side, individual evaluation to cope with the stressor event, such as in our study case, the appraisal component, can be the digital literacy skill. Moreover, technostress can be the coping barrier in the framework, and all these jointly influence the outcome, which is employee performance in the framework.\u003c/p\u003e\u003cp\u003eIn simple words, working from home sometimes can be the source of stress, which is also called a stressor as per TMSC theory. Employees are searching for ways to learn work-from-home technologies to handle this stress, which is also called appraisal as per TMSC theory. On the other hand, employees are confident with their digital literacy to cope with the stress, which is also called a coping resource as per TMSC theory. Furthermore, employee-mean university lecturers are unable to handle the work-from-home technological stress, and they feel overloaded, which is known as technostress. Even if they are skilled, this is called a coping barrier in the framework. Lastly, the outcome offer employees means their performance while working from home. The proposed connection between variables under TMSC theory can be seen in Fig.\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e"},{"header":"Research Methods","content":"\u003cp\u003eThe study focuses on the academic staff (lecturers) employed at Malaysian public universities. According to Sekaran and Bougie (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a sample size of the study between 30 and 500 is appropriate in social science studies. However, the present study has collected 320 responses from academic staff (lecturers) at public universities in Malaysia. In this study, the unit of analysis consists of individual respondents. Moreover, the convenience sampling technique has been used to approach the lecturers who are engaged in online classes through their official e-mails, contact numbers and face to face interaction after their informed consent through written message requests, which clearly stated the purpose of the study. Basically, convenience sampling is a non-probabilistic technique often used in quantitative studies, where subjects are selected based on accessibility to the researcher (Suen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While it can be applied in both qualitative and quantitative research, it lacks generalisability to the target population (Obilor \u0026amp; Isaac, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Measurement\u003c/h2\u003e\u003cp\u003eThe measurement scales utilised in this study were adapted from previous research. The employee performance scale, comprising 12 items, was derived from the work of Kamal et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which reported a Cronbach's alpha of .911, indicating an acceptable level of reliability. Also, the shorter version of the technostress scale was taken from Saleem et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which was first created by Tarafdar et al. (2007), and it showed a Cronbach's alpha of .946.he work-from-home scale, consisting of 7 items, was adapted from the research of Ishak et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and demonstrated a Cronbach's alpha of .694. Lastly, the digital literacy scale was sourced from the study by Mohd Abas et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which reported a Cronbach's alpha of .985. The response format employed in this study was a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 non-response biased test\u003c/h2\u003e\u003cp\u003eNon-response bias can happen when actual survey respondents are different from sampled respondents. There are multiple methods to test the non-response bias; however, this study adopted the comparison of early to late respondents, which is widely accepted by previous studies(Wagner \u0026amp; Kemmerling, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Basically, in this method, separate the 50 early and 50 late responses from the data set and run the paired t-test on these two early and late groups. If results found significant that non-response bias exists, on the other side, results revealed the not significant findings among groups, which warrants that there is no non-response bias. In simpler terms, the two groups (early and late responses) do not show significant differences. This study, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the insignificant findings of 4 paired groups, meaning all variables are not significantly different. This study concludes that there is no significant difference between the respondents who answered and those who did not, indicating that further data collection from the target population is unnecessary. This study can test the research model on the basis of collected data, and results can generalise the target population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003enon-response biased test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003ePaired Samples Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003ePaired Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Error Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOne-Sided p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTwo-Sided p\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePair 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE_technostress - L_technostress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.03222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.14166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.25155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.33722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePair 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE_digital_literacy - L_digital_literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.01400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.51737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.11569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.25613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.22813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePair 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE_Work_from_home - L_work_from_home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.08571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.91696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.20504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.34344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.51486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePair 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE_Employee_Performance - L_employee_performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.14167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.23109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.62534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.34201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Common Method Bias (CMB)\u003c/h2\u003e\u003cp\u003eCommon method bias, also known as common method variance (CMV), can occur during research. During research, a researcher faces different types of bias, such as response bias, researcher bias, and instrument bias. However, the CMB method was used to see the instrument bias during data collection. In this regard, Herman\u0026rsquo;s single-factor test was used to evaluate the CMB(Podsakoff et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This study employed SPSS to run the single Harman single-factor test using factor analysis for one factor. Therefore, according to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the total variance of 23.231% is below the 50% threshold, indicating that there are no common method bias (CMB) issues in this study. In simple words, data is ready for further analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ecommon method bias results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTotal Variance Explained\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInitial Eigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eExtraction Sums of Squared Loadings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e% of Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCumulative %\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.530\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c4\"\u003e\u003cp\u003e95.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eExtraction Method: Principal Component Analysis.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Data analysis and findings","content":"\u003cp\u003eThe smartPLS-4 (PLS-SEM) has been used to evaluate the collected data and draw the conclusions from this study. The PLS-SEM has run into two stages: measurement and structural model. The measurement model evaluated the construct\u0026rsquo;s reliability and validity, and the structural model evaluated the relationship between constructs and accepted or rejected the proposed hypothesis.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Measurement model\u003c/h2\u003e\u003cp\u003eCronbach's alpha coefficients were calculated to assess the internal consistency of the constructs, with a threshold of 0.70 indicating acceptable reliability. The results of this study met this criterion. Composite reliability was assessed using two alternative measures, rho_A and rho_C. The composite reliability values (rho_A and rho_C) exceeded the threshold of 0.70 for all constructs, thereby indicating that their reliability was achieved. Additionally, the average variance extracted (AVE) was calculated to assess the convergent validity of the constructs. Specifically, AVE values should exceed 0.50, and the recorded AVE values for each construct were above this threshold. Thus, the convergent validity of the constructs was established. The reliability and validity measures of the constructs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability of constructs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstructs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite reliability (rho_a)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnostress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork from Home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, the HTMT (Heterotrait-Monotrait Ratio) criteria is used to validate the discriminant validity of the contrast. The HTMT matrix presents the correlation values between constructs in the study. The diagonal shows the correlation of each construct with itself, which is always 1, so the correlation values of each construct should be less than 1. Hence, all values of constructs are recorded less than 1, which shows that there are no validity issues; the results of HTMT can be seen in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidity of constructs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstructs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Literacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmployee performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTechnostress\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWork from Home\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTechnostress x Digital Literacy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnostress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork from Home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnostress x Digital Literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Structural Model\u003c/h2\u003e\u003cp\u003eThe structural model aims to explain the causal or predictive relationships among study variables. To assess the path coefficient, SmartPLS provides t-statistics; the value of t-statistics should be greater than 1.96 at a significant level of 0.5. Hence, as per Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e results, hypotheses H2 and H3, except H1, recorded greater t-statistics, 2.708 and 2.379, respectively, at the 0.007 and 0.018 significance levels. Thus, all hypotheses H2 and H3 are accepted, and hypothesis H1 is rejected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis findings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1: WFH -\u0026gt;Employee performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2: WFH -\u0026gt;Digital Literacy -\u0026gt;Employee performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3: Technostress x Digital Literacy -\u0026gt;Employee performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, the PROCESS macro was run on smartPLS-4 to test the 2nd-stage moderated mediation to verify hypothesis 4. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and the moderated-mediation graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the conditional indirect effect and its level of confidence (CI) within the limit, meaning no zero is involved, which accepts hypothesis 4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of conditional indirect effect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerated indirect relationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower limit CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper limit CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST X WFH-DL-EP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of Moderated indirect relationship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower limit CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper limit CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFM -\u0026gt;DL -\u0026gt;EP conditional on EP at +\u0026thinsp;1 SD (+\u0026thinsp;1.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFM -\u0026gt;DL -\u0026gt;EP conditional on EP at -1 SD (-1.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFM -\u0026gt;DL -\u0026gt;EP conditional on EP at Mean (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eBootstrap Process sample size: 5000, CI: confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion of the findings","content":"\u003cp\u003eThe direct relationship between work from home (WFH) and employee performance was examined. As per table-3, the value of t=-0.812, p = 0.417 shows the insignificant relationship between work from home and employee performance at public universities of Malaysia. Moreover, the mediating role of digital literacy between work from home (WFH) and employee performance was examined. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e findings show the positive and significant mediating role of digital literacy between work from home and employee performance. The type of mediation is perfect mediation; in simple words, when there is perfect mediation, the influence of work from home on employee performance disappears entirely once the digital literacy mediating variable is considered. The employees become more adept at utilising digital tools and platforms; they are better positioned to fulfil their job responsibilities effectively. Basically, through the intervention of digital skills, remote work provides work engagement flexibility, which leads to higher employee performance. Basically, the experience of employees who are working from home has improved job satisfaction, reduced commuting stress, and increased autonomy; this all enhances employee performance.\u003c/p\u003e\u003cp\u003eThe moderating role of technology stress in the relationship between digital literacy and employee performance was examined. The results show the negative and significant (t=-2.379, p = 0.018) moderating role of technostress on the relationship between digital literacy and employee performance. In other words, the negative moderating role of technostress weakens the relationship between digital literacy and employee performance in public universities of Malaysia. While digital literacy is positively associated with employee performance, the presence of technostress dampens this relationship. Technostress, which arises from the continuous use of technology, can hinder the positive effect of digital literacy on performance by causing anxiety, burnout, or resistance to technology adoption among employees.\u003c/p\u003e\u003cp\u003eMoreover, the moderated role of technostress on the indirect effect between work-from-home and employee performance through digital literacy was tested. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the significant negative moderated mediation interaction. In respect of the level of moderated mediation, it revealed that the indirect relationship was strong when technostress was low (effect = 0.193, CI [0.127, 0.259]) and moderate at the mean level (effect = 0.127, CI [0.077, 0.177]). Finally, when technostress recorded high, then the indirect relationship was weakest (effect 0.061, CI [0.002, 0.120]). In simple words, the higher the level of technostress, the more diminished the effect of digital literacy to transport the benefits of work-from-home to improve the lecturer's performance.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Implications of the study\u003c/h2\u003e\u003cp\u003eImplications related to work from home or remote working, digital literacy, and technostress are not only important for universities but also important for individuals. However, in respect of theory, it is the first study that investigated the direct relationship between work from home and employee performance in public universities of Malaysia along with the mediating role of digital literacy and the moderating role of technostress. Moreover, with respect to practical implementation, organisations such as universities should think about implementing or enhancing their policies for work from home. Furthermore, the positive relationship between work from home and employee performance advises organisations that flexible working hours can improve employee performance as per expectations. These arrangements might include telecommuting opportunities and flexible working schedules.\u003c/p\u003e\u003cp\u003eAdditionally, digital literacy is very important for higher employee performance; hence, organisations such as universities should prioritise providing training and resources to enhance employee digital skills, such as workshops and online courses, and empower employees to use digital tools, communicate online, and manage their tasks remotely. Moreover, universities should put their efforts into developing a helpful technological environment that reduces the stress that is brought on by continual connectivity and information overload. Promoting responsible technology use and providing tools for controlling technostress can enhance employee performance and well-being. Thus, considering the above implications, the universities can improve their employees’s performance, satisfaction, and overall success in the digital-centric work landscape.\u003c/p\u003e\u003cp\u003eFinally, this study also contributed to the transaction model of stress and coping theory (TMSC), which examines work-from-home, employee performance, digital literacy, and technostress. This finding potentially contributed to existing knowledge of TMSC, which will be useful for academicians and policymakers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Limitations and future recommendations\u003c/h2\u003e\u003cp\u003eThe current study is only limited to public universities; the future studies can consider private universities for better generalisations of the results. Basically, it raises concerns about the population’s representation since the employees from different institutions may have divergent perceptions and consequently generate different results. Moreover, the results obtained may not be generalised to employees in different fields at public universities, whereas the research majorly focused on the education sector. For instance, lecturers have adapted to situations have more exposure to the use of advanced technology than staff in private. This research also reveals the gap in technological skills among staff members at public and private universities. So, future research can be more interesting if it expands the samples used, not just public university employees. The future study may also broaden the scope of study beyond universities in Malaysia to increase the accuracy of the result, where overall understanding from the populations can be achieved.\u003c/p\u003e\u003cp\u003eThe lack of relative factors has led to the limitations of this research. To improve outcomes, more relative factors influencing employee performance should be included in future research, such as e-training and adaptive expertise. The cross-sectional approach has been used for the current study; the future study can collect the data through the longitudinal approach to generalise the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion of the study","content":"\u003cp\u003eThis study's findings revealed the perfect mediating role of digital literacy between work from home and employee performance, and in respect of moderation, this study's findings showed the negative moderating role of technostress on the relationship between digital literacy and employee performance in public universities of Malaysia. This study also found the technostress moderated the indirect effect between work-from-home and employee performance through digital literacy.\u003c/p\u003e\u003cp\u003eBasically, working from home or remote work has a positive influence on employee performance through digital literacy because it offers flexible duty hours and reduces commuting stress, which leads to the positive impact of higher employee performance. Besides, the mediating role of digital literacy enhances employees’ abilities to use digital tools, so these abilities give leverage to employees to take advantage through work from home to improve their performance. Moreover, in respect to the moderating role of technostress, it is playing a negative role in weakening the relationship between digital literacy and employee performance; basically, employees experiencing high levels of technostress seem to struggle to effectively translate their digital literacy into improved performance due to the stress associated with technology. Thus, in the case of technostress, universities should provide training to neutralise the employees’ stress regarding technology use and allow them to fully utilise their digital skills.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval and consent participate:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eEthical approval was not needed, as the research involved non-invasive data collected anonymously from the university lecturers, which maintained the research standards. This study did not involve any experiments or animals, which would require approval from our university\u0026apos;s ethical committee. Furthermore, respondents informed consent was taken through clearly written message requests at the beginning of the questionnaire, which clearly stated the purpose of the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePublication consent:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThis manuscript involves no personal information, such as images, videos, or identification. Thus, there is no need to require any consent for this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e There is no funding involve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e: Data is not publicly available due to the privacy of the respondents, but can provide on demand\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida, F., Rodrigues, H., \u0026amp; Freitas, P. (2024). \u0026ldquo;No Need to Dress to Impress\u0026rdquo; evidence on teleworking during and after the pandemic: A systematic review. \u003cem\u003eAdministrative Sciences\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(4), 76. \u003c/li\u003e\n\u003cli\u003eAngelici, M., \u0026amp; Profeta, P. (2020). Dondena Working Papers. \u003c/li\u003e\n\u003cli\u003eDarsana, M. (2014). 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The impact of enforced working from home on employee job satisfaction during COVID-19: An event system perspective. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(24), 13207. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Work from Home, Employee performance, Digital literacy, Technostress, Universities","lastPublishedDoi":"10.21203/rs.3.rs-7095652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7095652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe primary objective of the study is to assess employee performance among individuals working remotely, considering the mediating role of digital literacy and the moderating influence of technostress within public universities of Malaysia. A structured questionnaire was employed to gather a total of 320 responses from university lecturers. The data were analysed using the partial least squares structural equation modelling (PLS-SEM) technique through smartPLS-4. The results of the study indicated that working from home does not exhibit a direct significant relationship with employee performance, except for an indirect relationship mediated by digital literacy. Furthermore, the findings suggest that technostress has a significant negative moderating effect on the relationship between digital literacy and employee performance. Besides, technostress moderated the indirect relationship between work-from-home and employee performance through digital literacy. Consequently, digital literacy is essential for the successful implementation of effective work-from-home strategies that enhance employee performance. It is recommended that university management prioritise the enhancement of digital literacy prior to implementing remote work strategies. Additionally, management should address employee stress levels, as these can influence both digital literacy and employee performance. Future research may explore these findings in other service sectors, such as information technology and customer service.\u003c/p\u003e","manuscriptTitle":"A Post-Pandemic View of Work-From-Home and Employee Performance: A Moderated-Mediation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 17:56:32","doi":"10.21203/rs.3.rs-7095652/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"695f770d-d963-4a4c-8638-b9fe8e7cad49","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T09:39:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 17:56:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7095652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7095652","identity":"rs-7095652","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}