Impact of Technostress on Job Satisfaction of Laguna State Polytechnic University Faculty

Impact of Technostress on Job Satisfaction of Laguna State Polytechnic University Faculty

Camille Khimberly J. Elcano

Laguna State Polytechnic University /College of Arts and Sciences 

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90300328

Received: 05 March 2025; Accepted: 15 March 2025; Published: 18 April 2025

ABSTRACT

Teachers experience some level of stress concerning the use of technology, especially that they are anticipated to actively and efficiently integrate technology into their teaching methods ((Graham et al., 2009, as cited by Yang, D., 2025). The purpose of the present study was to determine whether the level of technostress experienced by teachers affects their level of job satisfaction. Furthermore, this study also determined whether the teachers’ individual differences affect their level of technostress. The research findings revealed that in terms of gender, females experienced a higher level of techno-invasion than males, while there was no significant difference found in the levels of techno-overload, techno-complexity, and techno-uncertainty. The findings also suggest that there was no significant difference in the level of the four techno-stressors when the faculty were grouped according to their digital literacy, age, and marital status. Nonetheless, a significant difference was found in the level of techno-complexity and techno-uncertainty of the faculty members when they were grouped according to their employment period. Faculty members who are employed for at least 25 years are experiencing higher techno-complexity when compared with an employment period of 1-5 years. Moreover, those who are employed for 6– 10 years are experiencing more techno–uncertainty when compared also to the same group, that is, those who are employed for 1 – 5 years. On the other, the level of techno–overload and techno–invasion of the respondents did not vary across the levels of the employment period.  Findings also revealed that the level of technostress did not affect the level of job satisfaction of the faculty members despite having a high level of technostress in a period of intense use of technology.

Keywords: technostress, job satisfaction, techno-stressors, techno-invasion, techno-overload, techno-complexity, techno-uncertainty

INTRODUCTION

Technology continuously grows in importance in today’s society. It has been an essential tool in almost every workplace in the digital age, including schools and universities. Teachers need to constantly adapt to the advancement of technology to provide quality education to their students. Moreover, teachers are progressively anticipated to actively and efficiently integrate technology into their teaching methods (Graham et al., 2009, as cited by Yang, D., 2025), this obligation to upgrade ICT skills and use ICT fully in tasks creates anxiety and stress, namely technostress.

According to Weil & Rosen (2019), as cited by Norshibam (2021), technostress is the stress one feels when technology takes center stage in their work life, home environments, and at play. Technology not only replaces the workplace but also invades individuals’ private lives and makes them rely on technology which causes frustrations. Technology itself is not stressful, but rather some aspects and characteristics relating to its use (Finstad & Giorgi, 2021). Brod’s technostress theory identified several sources of technostress including situations such as users being forced by technology to work faster and longer (techno-overload), feeling the need to be constantly “connected” that it invades personal life and privacy (techno-invasion), inability to deal with technology complexity, being threatened about losing their jobs(techno-uncertainty), and fear of technology uncertainty (techno-complexity) (Tarafdar et al., 2007).

The paper of Tarafdal and his colleagues (Tarafdar et al., 2007) showed that there is an inverse relationship between technostress and productivity. Various studies also found that technostress negatively affects employees’ job satisfaction in different professions, as revealed by researchers such as Al-Ansari & Alshare (2019), Cahapay & Bangoc II (2021), Suh & Lee (2017), and Selvi, Shanthi, Rani, Muthulakshmi, & Pushparani (2021). Norhisham (2021) stated that most researchers study the effects of technostress on business or industrial work, and recently an increasing amount of research has been focused on the effect of technostress on educational context (e.g., Jena, 2015; Cahapay & Bangoc II, 2021; Narasalagi, Shintri, & Saraih, 2021; and Aktan & Toraman, 2022). However, only a few studies were conducted to determine the impact of technostress on teachers’ job satisfaction in the Philippines. Today, Philippine education relies on technology for teaching, learning, communication, and research. Yet not all teachers have good knowledge and understanding of modern technology. Nonetheless, they are forced to incorporate technology in their teaching methods which creates technostress among teachers. This situation led to the researcher’s idea to conduct research that aimed to determine the impact of technostress on the job satisfaction of faculty members at Laguna State Polytechnic University Santa Cruz Campus. Specifically, Specifically, this study intends to answer the following questions:

1. What is the profile of the faculty in terms of:

1.1. Age

1.2. Sex

1.3. Marital Status

1.4. Work experience

1.5. Digital Literacy

2. What is the technostress level of the faculty in terms of:

2.1. Techno-overload

2.2.Techno-invasion

2.3.Techno-complexity

2.4. Techno-uncertainty

3. What is the level of job satisfaction of faculty?
4. Does the profile of the faculty affect their technostress level?
5. Is there a significant relationship between the level of technostress and job satisfaction of faculty?
6. What institutional intervention can be proposed to decrease the technostress level of LSPU faculty to further enhance their level of job satisfaction?

METHODOLOGY

This study used the descriptive research method to gather information about the participants. Descriptive research involves collecting data to test hypotheses or to answer questions concerning the current status of the subject of the study, trying to get deep data and information about the object by giving detailed data and information (Gay, 1992, as cited in Sahin & Mete, 2022).  The purpose of descriptive research is, of course, to describe, as well as explain, or validate some sort of hypothesis or objective when it comes to a specific group of people.

This research was conducted upon the approval of the university campus director and deans of the participating colleges. Random sampling was used for the research. A sample of 34 faculty members, generally from two colleges (Engineering and Arts and Sciences), from Laguna State Polytechnic University Santa Cruz Campus participated in the study.

A survey via Google form was used to determine the profile and level of technostress and job satisfaction of faculty at Laguna State Polytechnic University. The information gathered was also used to assess whether the profile of the participants affects their technostress level, as well as, the impact of technostress on the job satisfaction of the participants.

This study used the technostress measure developed by Tarafdar et al. (2007) and the short form of the Minnesota Satisfaction Questionnaire (MSQ) of the University of Minnesota which was developed by Weiss, Dawis, England & Lofquist (1967). Both survey questionnaires were found to be valid and reliable to be used across professions which include educators or teachers.

RESULTS AND DISCUSSION

Table i Participating Faculty’s Demographic Profile

Table 1 shows the demographic profile of the LSPU SCC faculty who participated in the study. Most of the participants are male (51.5%) and 48.5 % are female. The majority are 41 to 50 years old (30.3%), married (72.7%), with less than 6 years of employment period (36.4), and have an intermediate digital literacy (48.5%).

Table ii Technostress Level Of The Faculty

Table 2 shows the technostress level of the faculty was High (mean=3.70). Among the responses of the faculty to the technostress scale, the statements “I am forced to change my work habits to adapt to new technologies,” “I have to be in touch with my work even during my vacation and weekends due to this technology,” “I am forced by this technology to do more work than I can handle,” and “I am forced by this technology to work much faster” obtained the highest mean scores of 4.18 (SD=0.83), 4.15 (SD=0.96), 4.09 (SD=0.87), and 4.03 (SD=0.76), respectively.

This study shows that the faculty respondents are experiencing higher levels of techno-overload among the given technostressors. Similarly, the excessive workload was found to be the highest stress-causing factor among university teachers (Fatima et al., 2019). Moreover, they also exhibit a high level of techno-invasion, which supports findings that suggest, in general, teachers exhibit many challenges that contribute to a lack of work-life balance (Johari, Tan, & Zulkarnain, 2018; Punia & Kamboji, 2013, as cited in Califf & Brooks, 2020).

On the other hand, the lowest mean scores referred to the remarks “I need a long time to understand and use new technologies,” “I do not know enough about this technology to handle my job satisfactorily,” “I often find it too complex for me to understand and use new technologies,” and “I do not have enough time to study and upgrade my technology skills.”

This result was supported by the study of Aktan & Toraman, (2022) who stated that the intense use of technology during the COVID-19 pandemic process creates too much workload and invasion of privacy in teachers. However, this situation also forces them to adapt to new technologies.

Table iii Difference In The Level Of Techno-Stress Of Male And Female

Table 3 shows the difference in the level of techno-stress between males and females. Based on the table there is a significant difference in the level of Techno – invasion (Welch’s t Statistic = 2.2656, df = 22.985, p – value = 0.0332) of males and females.  In fact, females are experiencing more technostress in terms of Techno – invasion (Mean Difference  = 0.6765) than males. This result was contrary to the findings of Ragu-Nathan, Tarafdar, and Ragu-Nathan (2008) which revealed that males experienced more technostress than women. Moreover, Sareen (2019) also discovered that males experienced techno-invasion more than females. However, the findings of other related research revealed that females experience more technostress than males (Syvänen et al., 2016, Wang, Tan, & Li, 2020; Sahin, & Mete, 2021; Cahapay & Bangoc II, 2021). La Torre, De Leonardis, and Chiappetta’s (2020) study also showed that techno-invasion was significantly associated with the female gender. Female faculty, when compared to male faculty, tend to feel more pushed by ICT to stay connected and available anytime, and consequently feel that their personal lives are invaded by work-related ICT.

However, there is no significant difference in the level of techno – stress, Techno – overload (Student’s t Statistic = 1.6736, df = 31, p – value = 0.1043), Techno – complexity (Student’s t Statistic = 1.646, df = 31, p – value = 0.1099), and Techno – uncertainty (Student’s t Statistic = 0.6951, df = 31, p – value = 0.4922) of male and female. This suggests that males and females have the same level of Techno – overload, Techno – complexity, and Techno – uncertainty.  Several results obtained the same results, indicating that there was no significant difference between male and female teachers in the development of technostress (Li & Wang, 2020; Yadav and Rahaman, 2020).

Table iv Difference In The Level Of Techno-Stress When Grouped According To Digital Literacy, Age, And Marital Status

Table 4 shows the difference in the level of techno-stress of the respondents when grouped according to their levels of digital literacy, age, and marital status. Based on the results, there is no significant difference in the level of Techno – complexity (F = 1.6899, df1 = 2, df2 = 30, p – value = 0.20164), Techno – overload (χ² = 1.5663, df = 2, p – value = 0.45697), Techno – invasion (χ² = 3.8729, df = 2, p – value = 0.14421), and Techno – uncertainty (χ² = 0.6284, df = 2, p – value = 0.73038) of the respondents when their digital literacy is taken as a factor.  The level of technostress in terms of Techno- complexity, Techno-overload, Techno-invasion, and Techno-uncertainty of the respondents is the same whether they belong to groups with basic, intermediate, or advanced digital literacy. Contrary to the findings of related research (Ragu-Nathan et al., 2008; Çoklar et al., 2016; Dong et al., 2019) that computer confidence influences technostress.

Table 4 also suggests that there is no significant difference in the level of Techno – complexity (F = 1.2681, df1 = 3, df2 = 29, p – value = 0.30369), Techno – uncertainty (F = 3.2586, df1 = 3, df2 = 14.6705, p – value = 0.05196), Techno – overload (χ² = 4.606, df = 3, p – value = 0.20303), Techno – invasion (χ² = 0.7922, df = 3, p – value = 0.85132) of the respondents when their age is taken as a factor.  The level of Techno – complexity, Techno – uncertainty, Techno – overload, and Techno – invasion of the respondents are all the same regardless of their age. This is similar to the view that age might be unrelated to techno-stressors since younger and older individuals experienced a similar number of stressful situations related to ICT use in their working lives (Hauk et al., 2019).

Furthermore, the results also show no significant difference in the level of Techno – complexity (F = 1.7061, df1 = 2, df2 = 30, p – value = 0.19871), Techno – uncertainty (χ² = 0.3104, df = 2, p – value = 0.39361), Techno – invasion (χ² = 1.6725, df = 2, p – value = 0.43333), and Techno – overload (χ² = 1.8648, df = 2, p – value = 0.90447) of the respondents when they are grouped according to their marital status.  The respondents have the same level of techno-stress regardless of their marital status. Contrastingly, Cahapay & Bangoc II (2021) and La Torre et al., (2020) stated that techno-stressors are significantly associated with being married. However, the results of the present study are consistent with the previous study by Jamadin et al., 2015 that being married does not contribute to the level of stress of employees. The reason may be, despite additional responsibilities as spouse and parent, married couples may share the responsibilities and problems with their spouse while unmarried individuals may have to face problems and bear responsibilities alone.

Table v Difference In The Level Of Techno-Stress When Grouped According To Digital Literacy, Age, And Marital Status

Table 5 shows the difference in the level of techno-stress of the respondents when grouped according to employment period.  Based on the results, there is a significant difference in the level of Techno – complexity (F = 3.2881, df1 = 3, df2 = 29, p – value = 0.03461) and Techno – uncertainty (F = 4.4131, df1 = 3, df2 = 29, p – value = 0.01127) of the respondents when they are grouped according to their employment period. This supports the view that the longer the user’s length of professional experience is, the more these users are affected by problems associated with technology (Marchiori et al., 2018).

Table vi  Tukey Post-Hoc Test – Techno-Complexity

Post – hoc test reveals respondents who are employed for at least twenty – five (25) years are experiencing higher techno – complexity when compared to respondents with an employment period of 1 – 5 years. Moreover, those who are employed for 6 – 10 years are experiencing more techno–uncertainty when compared also to the same group, that is, those who are employed for 1 – 5 years.

A possible explanation may be that faculty members who are employed for at least 10 years are older faculty than those with 1-5 years of experience. Thus, it supports the study by Marchiori et al., (2018) and Yadav and  Rahaman  (2020) which revealed that older employees perceived the organizational technology environment (techno-complexity) to be more complex than younger employees. Furthermore, younger people are more exposed to ICT because they have used it for a longer period (Teo & Noyes, 2012), thus they feel less uncertain and unsettled with the changes and upgrades in the ICT applications.

Table vii Tukey Post-Hoc Test – Techno-Complexity

On the other, the level of Techno – overload (χ² = 0.5647, df = 3, p – value = 0.90447), and Techno – invasion (χ² = 1.1224, df = 3, p – value = 0.77168) of the respondents does not vary across the levels of employment period.  The level of Techno – overload, and Techno – invasion of the respondents are all the same regardless of their employment period. As mentioned in the previous discussion, the length of service may be associated with age, faculty with longer experience may be older than those with shorter experience, and age might be unrelated to techno-stressors such as techno-overload and techno-invasion since younger and older individuals experienced a similar number of stressful situations related to ICT use in their working lives (Hauk et al., 2019).

Table viii Level Of Faculty Job Satisfaction

The relationship between Techno-stress and Job Satisfaction is shown in Table 7.  The results shown in the table suggest that there is no significant linear relationship between Job Satisfaction and Techno-invasion (r = -0.1333, p – value = 0.45949), Job Satisfaction and Techno-complexity (r = -0.3322, p – value = 0.05888), and Job Satisfaction and Techno-uncertainty (r = -0.0366, p – value = 0.83964).

Also, there is no significant monotonic relationship between Job Satisfaction and Techno-complexity (rs = -0.241, p – value = 0.17676).  This implies that any change in the level of techno – stress is not associated with any change in the level of job satisfaction.

This is in contrast with the study of Maxwell (2021) which found that three techno-stressors decreased job satisfaction among teachers in K-12 education. Furthermore, Selvi, Shanthi, Rani, Muthulakshmi, & Pushparani (2021) also found in their study that technostress is related to job satisfaction.

However, the result of this study is coincident with the works of Aktan & Toraman’s (2022) and Narasalagi, Shintri & Saraih (2021). They also found that technostress does not affect teachers’ job satisfaction. Aktan & Toraman (2022) stated that, according to teachers, distance education is an inefficient, exhausting, stressful, job-repellent, workload-increasing, and wearisome process that leads to the invasion of private life. Nonetheless, some teachers also found the process positive due to participating in self-improvement activities from home, saving free time, getting motivation for technological improvement, and working with home comfort (Aktan & Toraman, 2022).

CONCLUSIONS

The study’s findings on LSPU SCC faculty member’s technostress level revealed that teachers experienced a high level of stress due to too much usage of technology at work (techno-overload), interference of technology in personal life (techno-invasion), too much demand to learn and understand the use of new applications (techno-complexity) and feeling of being uncertain and unsettled since technology is continuously changing and needs upgrading. The purpose of this study was to find out whether or not individual differences are associated with technostress. The results indicated that females experienced more technostress in terms of techno-invasion (Mean Difference  = 0.6765) than males. However, males and females have the same level of techno–overload, techno-complexity, and techno-uncertainty. The findings also suggest that there was no significant difference in the level of the four techno-stressors when the faculty were grouped according to their digital literacy, age, and marital status. Nonetheless, a significant difference was found in the level of techno-complexity and techno-uncertainty of the faculty members when they were grouped according to their employment period. Faculty members who are employed for at least 25 years are experiencing higher techno-complexity when compared with an employment period of 1-5 years. Moreover, those who are employed for 6– 10 years are experiencing more techno–uncertainty when compared also to the same group, that is, those who are employed for 1 – 5 years. On the other, the level of techno–overload and techno–invasion of the respondents did not vary across the levels of the employment period.

This study also sought to determine whether the level of technostress affects the job satisfaction of the faculty members. Findings revealed that the level of technostress did not affect the level of job satisfaction of the faculty members. Despite having a high level of technostress in a period of intense use of technology, it is interesting that the faculty members’ job satisfaction is high. This is in contrast to several studies (Maxwell, 2021; Selvi, et al., 2021; Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008), but is similar to related studies such as studies by Aktan & Toraman (2022) and Narasalagi, Shintri, & Saraih (2021).

REFERENCES

1. Aronoff, , (1989). Geographic Information Systems: A Management Perspective. Ottawa: WDL Publications.
2. Aktan, O., & Toraman, Ç. (2022). The relationship between Technostress levels and job satisfaction of teachers within the COVID-19 period. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11027-2
3. Al-Ansari, M. A., & Alshare, K. (2019). The impact of Technostress components on the employees satisfaction and perceived performance. Journal of Global Information Management, 27(3), 65-86. https://doi.org/10.4018/jgim.2019070104
4. Braun, S., & Hollander, R. (1987). A study of job stress among women and men in the Federal Republic of Germany. Health Education Research, 2(1), 45-51. https://doi.org/10.1093/her/2.1.45
5. Cahapay, M. B., & Bangoc II, N. F. (2021). Technostress, work performance, job satisfaction, and career commitment of teachers amid COVID-19 crisis in the Philippines. IJERI: International Journal of Educational Research and Innovation, (16), 260-275. https://doi.org/10.46661/ijeri.6145
6. Califf, C. B., & Brooks, S. (2020). An empirical study of techno-stressors, literacy facilitation, burnout, and turnover intention as experienced by K-12 teachers. Computers & Education, 157, 103971. https://doi.org/10.1016/j.compedu.2020.103971
7. Çoklar, A. N., Efilti, E., Şahin, Y. L., & Akçay, A. (2016). Determining the Reasons of Technostress Experienced by Teachers: A Qualitative Study. Turkish Online Journal of Qualitative Inquiry, 7(2), 71. https://doi.org/10.17569/tojqi.96082
8. Dong, Y., Xu, C., Chai, C. S., & Zhai, X. (2019). Exploring the structural relationship among teachers’ Technostress, technological pedagogical content knowledge (TPACK), computer self-efficacy and school support. The Asia-Pacific Education Researcher, 29(2), 147-157. https://doi.org/10.1007/s40299-019-00461-5
9. Estrada-Muñoz, C., Vega-Muñoz, A., Castillo, D., Müller-Pérez, S., & Boada-Grau, J. (2021). Technostress of Chilean teachers in the context of the COVID-19 pandemic and teleworking. International Journal of Environmental Research and Public Health, 18(10), 5458. https://doi.org/10.3390/ijerph18105458
10. Fatima, T., Bilal, A. R., & Imran, M. (2019). Workplace ostracism and employee reactions among University teachers in Pakistan. The Qualitative Report. https://doi.org/10.46743/2160-3715/2019.4002
11. Georgia Libera, F., & Gabriele, G. (2021). The technostress questionnaire: A pilot study. Journal of Advanced Health Care. https://doi.org/10.36017/jahc2111-001
12. Hauk, N., Göritz, A. S., & Krumm, S. (2019). The mediating role of coping behavior on the age-technostress relationship: A longitudinal multilevel mediation model. PLOS ONE, 14(3), e0213349. https://doi.org/10.1371/journal.pone.0213349
13. Jamadin, N., Mohamad, S., Syarkawi, Z., & Noordin, F. (2015). Work – Family conflict and stress: Evidence from Malaysia. Journal of Economics, Business and Management, 3(2), 309-312. https://doi.org/10.7763/joebm.2015.v3.200
14. La Torre, G., De Leonardis, V., & Chiappetta, M. (2020). Technostress: How does it affect the productivity and life of an individual? Results of an observational study. Public Health, 189, 60-65. https://doi.org/10.1016/j.puhe.2020.09.013
15. Li, L., & Wang, X. (2020). Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cognition, Technology & Work, 23(2), 315-330. https://doi.org/10.1007/s10111-020-00625-0
16. Marchiori, D. M., Mainardes, E. W., & Rodrigues, R. G. (2018). Do individual characteristics influence the types of Technostress reported by workers? International Journal of Human–Computer Interaction, 35(3), 218-230. https://doi.org/10.1080/10447318.2018.1449713
17. Maxwell, E. E. (2021). Technostress Factors as Predictors of Job Satisfaction Among Teachers in K-12 Education (Doctoral dissertation, Grand Canyon University) [Doctoral dissertation]. ProQuest Dissertations and Theses Global.
18. Narasalagi, V. M., Shintri, S., & Saraih, U. N. (2021). Technostress and its impact on job satisfaction: Evidences during COVID-19 pandemic among faculty members in higher education in Karnataka. Journal of Management Research and Analysis, 8(4), 202-207. https://doi.org/10.18231/j.jmra.2021.039
19. Norhisham, N. (2021). Understanding Technostress during the era of COVID-19: A conceptual paper. International Journal of Academic Research in Business and Social Sciences, 11(8). https://doi.org/10.6007/ijarbss/v11-i8/10628
20. Penado Abilleira, M., Rodicio-García, M., Ríos-de Deus, M. P., & Mosquera-González, M. J. (2021). Technostress in Spanish University teachers during the COVID-19 pandemic. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.617650
21. Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of Technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417-433. https://doi.org/10.1287/isre.1070.0165
22. Sahin, S., & Mete, J. (2021). A Brief Study on Descriptive Research:: Its Nature and Application in Social Science. International Journal of Research and Analysis in Humanities,, 1(1), 11-11.
23. Saree, P. (2019). Techno Stress Creators-An Exploratory Research on Teaching and Non Teaching Staff Working in Colleges. International Journal of Management and Humanities (IJMH), 3(9), 1-7.
24. Selvi, R. T., Shanti, S., Rani, C., Muthulakshmi, M., & Pushparani, S. (2021). Techno-Stress And Job Satisfaction Among It Employees In Chennai. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal NVEO, 570-576.
25. Schomakers, E., Lidynia, C., & Ziefle, M. (2021). The role of privacy in the acceptance of smart technologies: Applying the privacy calculus to technology acceptance. International Journal of Human–Computer Interaction, 38(13), 1276-1289. https://doi.org/10.1080/10447318.2021.1994211
26. Suh, A., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27(1), 140-159. https://doi.org/10.1108/intr-06-2015-0181
27. Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of Technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301-328. https://doi.org/10.2753/mis0742-1222240109
28. Teo, T., & Noyes, J. (2012). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51-66. https://doi.org/10.1080/10494820.2011.641674
29. Wang, X., Tan, S. C., & Li, L. (2020). Technostress in university students’ technology-enhanced learning: An investigation from multidimensional person-environment misfit. Computers in Human Behavior, 105, 106208. https://doi.org/10.1016/j.chb.2019.106208
30. Zhang, J., Lu, J., Zhao, S., Lamis, D. A., Li, N., Kong, Y., Jia, C., Zhou, L., & Ma, Z. (2012). Developing the psychological strain scales (PSS): Reliability, validity, and preliminary hypothesis tests. Social Indicators Research, 115(1), 337-361. https://doi.org/10.1007/s11205-012-0222-6
31. Çoklar, A. N., Efilti, E., Şahin, Y. L., & Akçay, A. (2016). Determining the Reasons of Technostress Experienced by Teachers: A Qualitative Study. Turkish Online Journal of Qualitative Inquiry, 7(2), 71. https://doi.org/10.17569/tojqi.96082
32. Yang, D., Liu, J., Wang, H., Chen, P., Wang, C., & Metwally, A. H. S. (2025). Technostress among teachers: A systematic literature review and future research agenda. Computers in Human Behavior, 108619.
33. Brod, C. (1984) Technostress The Human Cost of the Computer Revolution. Addison-Wesley Publishing Company, Reading, USA. – References – Scientific Research Publishing. (n.d.). https://www.scirp.org/reference/References Papers?Reference ID=1741598