Exploring the Structural Relationship Among Teachers’ Technostress, Technological Pedagogical Content Knowledge (TPACK), Computer Self-efficacy and School Support

  • Yan Dong
  • Chang XuEmail author
  • Ching Sing Chai
  • Xuesong Zhai
Regular Article


With the rapid development of technologies and the gradually increasing requirements of technology integration into teaching, teachers have been facing stress to keep pace with new technologies and to design pedagogical usage of technologies. Although prior studies have examined the creators and negative impacts of technostress, insights into the effective factors relieving teachers’ technostress are rather limited. To facilitate teacher improvement with technology usage and help school administrators develop preventive stress management strategies, this study constructed a structural model among teachers’ technostress, TPACK, computer self-efficacy, administration support, and collegial support, which were examined through a composite instrument adapted from previous studies. Data were collected from 366K-12 in-service teachers in China. After the exploratory factor analysis and confirmatory factor analysis, the results showed that the adapted instrument had adequate validity and reliability. Further, through structural equation modeling, the results indicated that administration support predicts teachers’ computer self-efficacy, and collegial support predicts both teachers’ TPACK and computer self-efficacy, which in turn negatively predict their technostress. The findings imply that primary and secondary school principals need to support teachers both administratively and through the creation of collegial professional learning communities to develop TPACK and computer efficacy to reduce teachers’ technostress.


Technology integration Computer self-efficacy TPACK Technostress School support 



The research is funded by the International Joint Research Project of Faculty of Education, Beijing Normal University [ICER201902].


  1. Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers & Education, 51(3), 1103–1110.Google Scholar
  2. Altınay-Gazi, Z., & Altınay-Aksal, F. (2017). Technology as mediation tool for improving teaching profession in higher education practices. EURASIA Journal of Mathematics, Science & Technology Education, 13(3), 803–813.Google Scholar
  3. Ansyari, M. F. (2015). Designing and evaluating a professional development programme for basic technology integration in English as a foreign language (EFL) classrooms. Australasian Journal of Educational Technology, 31(6), 699–712.Google Scholar
  4. Bandura, A. (1986). Social foundation of thought and action: A social-cognitive view. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  5. Bandura, A. (1997). Editorial. American Journal of Health Promotion, 12(1), 8–10.Google Scholar
  6. Blackwell, C. K., Lauricella, A. R., & Wartella, E. (2016). The influence of TPACK contextual factors on early childhood educators’ tablet computer use. Computers & Education, 98, 57–69.Google Scholar
  7. Blonder, R., Jonatan, M., Bardov, Z., Benny, N., Rap, S., & Sakhnini, S. (2013). Can You Tube it? Providing chemistry teachers with technological tools and enhancing their self-efficacy beliefs. Chemistry Education Research & Practice, 14(3), 269–285.Google Scholar
  8. Blonder, R., & Rap, S. (2017). I like Facebook: Exploring Israeli high school chemistry teachers’ TPACK and self-efficacy beliefs. Education and Information Technologies, 22(2), 697–724.Google Scholar
  9. Brod, C. (1984). Technostress: The human cost of the computer revolution. Boston: Addison Wesley Publishing Company.Google Scholar
  10. Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54.Google Scholar
  11. Byrne, B. M. (2010). Structural equation modeling using AMOS. Basic concepts, applications, and programming (2nd ed.). New York: Routledge.Google Scholar
  12. Chai, C. S., Koh, J. H. L., & Teo, Y. H. (2018). Enhancing and modeling teachers’ design beliefs and efficacy of technological pedagogical content knowledge for 21st century quality learning. Journal of Educational Computing Research, 57, 360–384.Google Scholar
  13. Chai, C. S., Koh, J. H. L., & Tsai, C. C. (2011). Exploring the factor structure of the constructs of technological, pedagogical, content knowledge (TPACK). Asia-Pacific Education Researcher (De La Salle University Manila), 20(3), 595–603.Google Scholar
  14. Chen, C. H. (2008). Why do teachers not practice what they believe regarding technology integration? The Journal of Educational Research, 102(1), 65–75.Google Scholar
  15. Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362–370.Google Scholar
  16. Çoklar, A. N., Efilti, E., & Şahin, Y. L. (2017). Defining teachers’ technostress levels: A scale development. Online Submission, 8(21), 28–41.Google Scholar
  17. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.Google Scholar
  18. Drent, M., & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers & Education, 51(1), 187–199.Google Scholar
  19. Drossel, K., Eickelmann, B., & Gerick, J. (2017). Predictors of teachers’ use of ICT in school: The relevance of school characteristics, teachers’ attitudes and teacher collaboration. Education & Information Technologies, 22(2), 551–573.Google Scholar
  20. Edwards, J. R., Caplan, R. D., & Harrison, R. V. (1998). Person-environment fit theory: Conceptual foundation, empirical evidence, and directions for future research. In C. L. Cooper (Ed.), Theories of organizational stress. Oxford: Oxford University Press.Google Scholar
  21. Eickelmann, B., Gerick, J., & Koop, C. (2017). ICT use in mathematics lessons and the mathematics achievement of secondary school students by international comparison: Which role do school level factors play? Education & Information Technologies, 22, 1–25.Google Scholar
  22. Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61.Google Scholar
  23. Fuglseth, A. M., & Sørebø, Ø. (2014). The effects of technostress within the context of employee use of ICT. Computers in Human Behavior, 40(40), 161–170.Google Scholar
  24. Graham, C. R., Borup, J., & Smith, N. B. (2012). Using TPACK as a framework to understand teacher candidates’ technology integration decisions. Journal of Computer Assisted Learning, 28(6), 530–546.Google Scholar
  25. Graham, R., Burgoyne, N., Cantrell, P., Smith, L., St Clair, L., & Harris, R. (2009). Measuring the TPACK confidence of inservice science teachers. TechTrends, 53(5), 70–79.Google Scholar
  26. Hennessy, S., Ruthven, K., & Brindley, S. (2005). Teacher perspectives on integrating ICT into subject teaching: Commitment, constraints, caution, and change. Journal of Curriculum Studies, 37(2), 155–192.Google Scholar
  27. Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223–252.Google Scholar
  28. Hwang, I., & Cha, O. (2018). Examining technostress creators and role stress as potential threats to employees’ information security compliance. Computers in Human Behavior, 81, 282–293.Google Scholar
  29. Inan, F. A., & Lowther, D. L. (2010). Factors affecting technology integration in K-12 classrooms: A path model. Educational Technology Research and Development, 58(2), 137–154.Google Scholar
  30. Jena, R. (2015). Technostress in ICT enabled collaborative learning environment: An empirical study among Indian academician. Computers in Human Behavior, 51, 1116–1123.Google Scholar
  31. Joo, Y. J., Lim, K. Y., & Kim, N. H. (2016). The effects of secondary teachers’ technostress on the intention to use technology in South Korea. Computers & Education, 95, 114–122.Google Scholar
  32. Kim, M. C., & Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education, 56(2), 403–417.Google Scholar
  33. Koehler, M. J., & Mishra, P. (2005). What happens when teachers design educational technology? The development of technological pedagogical content knowledge. Journal of Educational Computing Research, 32(2), 131–152.Google Scholar
  34. Koh, J. H. L., Chai, C. S., & Lim, W. Y. (2017). Teacher professional development for TPACK-21CL: Effects on teacher ICT integration and student outcomes. Journal of Educational Computing Research, 55(2), 172–196.Google Scholar
  35. Koo, C., & Wati, Y. (2011). What factors do really influence the level of technostress in organizations? An empirical study. International Journal on Information, 14(11), 339–348.Google Scholar
  36. Krishnan, S. (2017). Personality and espoused cultural differences in technostress creators. Computers in Human Behavior, 66, 154–167.Google Scholar
  37. Lam, S. F., Cheng, W. Y., & Choy, H. C. (2010). School support and teacher motivation to implement project-based learning. Learning & Instruction, 20(6), 487–497.Google Scholar
  38. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer.Google Scholar
  39. Liang, J. C., Chai, C., Koh, J., Yang, C. J., & Tsai, C. C. (2013). Surveying in-service preschool teachers’ technological pedagogical content knowledge. Australasian Journal of Educational Technology, 29(4), 586.Google Scholar
  40. López-Vargas, O., Duarte-Suárez, L., & Ibáñez-Ibáñez, J. (2017). Teacher’s computer self-efficacy and its relationship with cognitive style and TPACK. Improving Schools, 20(3), 264–277.Google Scholar
  41. Loyd, B. H., & Loyd, D. E. (1985). The reliability and validity of an instrument for the assessment of computer attitudes. Educational and Psychological Measurement, 45(4), 903–908.Google Scholar
  42. Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25(3), 275–308.Google Scholar
  43. Meristo, M., & Eisenschmidt, E. (2014). Novice teachers’ perceptions of school climate and self-efficacy. International Journal of Educational Research, 67, 1–10.Google Scholar
  44. Munyengabe, S., Yiyi, Z., Haiyan, H., & Hitimana, S. (2017). Primary teachers’ perceptions on ICT integration for enhancing teaching and learning through the implementation of One Laptop Per Child program in primary schools of Rwanda. Eurasia Journal of Mathematics, Science and Technology Education, 13(11), 7193–7204.Google Scholar
  45. Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the computer self-efficacy scale. Educational and Psychological Measurement, 49(4), 893–899.Google Scholar
  46. Pamuk, S., & Peker, D. (2009). Turkish pre-service science and mathematics teachers’ computer related self-efficacies, attitudes, and the relationship between these variables. Computers & Education, 53(2), 454–461.Google Scholar
  47. Paul, N., & Glassman, M. (2017). Relationship between internet self-efficacy and internet anxiety: A nuanced approach to understanding the connection. Australasian Journal of Educational Technology, 33(4), 147–165.Google Scholar
  48. Pineida, F. O. (2011). Competencies for the 21st century: Integrating ICT to life, school and economical development. Procedia-Social and Behavioral Sciences, 28, 54–57.Google Scholar
  49. Porter, W. W., & Graham, C. R. (2016). Institutional drivers and barriers to faculty adoption of blended learning in higher education. British Journal of Educational Technology, 47(4), 748–762.Google Scholar
  50. 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.Google Scholar
  51. Revilla Muñoz, O., Alpiste Penalba, F., Fernández Sánchez, J., & Santos, O. C. (2017). Reducing techno-anxiety in high school teachers by improving their ICT problem-solving skills. Behaviour & Information Technology, 36(3), 255–268.Google Scholar
  52. Sabzian, F., & Gilakjani, A. P. (2013). Teachers’ attitudes about computer technology training, professional development, integration, experience, anxiety, and literacy in English language teaching and learning. International Journal of Applied Science and Technology, 3(1), 67–75.Google Scholar
  53. Şahin, Y. L., & Çoklar, A. N. (2009). Social networking users’ views on technology and the determination of technostress levels. Procedia - Social and Behavioral Sciences, 1(1), 1437–1442.Google Scholar
  54. 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.Google Scholar
  55. Sang, G., Tondeur, J., Chai, C. S., & Dong, Y. (2016). Validation and profile of Chinese pre-service teachers’ technological pedagogical content knowledge scale. Asia-Pacific Journal of Teacher Education, 44(1), 49–65.Google Scholar
  56. Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009). Technological pedagogical content knowledge (TPACK) the development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 123–149.Google Scholar
  57. Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). New York: Routledge.Google Scholar
  58. Semiz, K., & Ince, M. L. (2012). Pre-service physical education teachers’ technological pedagogical content knowledge, technology integration self-efficacy and instructional technology outcome expectations. Australasian Journal of Educational Technology, 28(7), 1248–1265.Google Scholar
  59. 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.Google Scholar
  60. Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.Google Scholar
  61. Smith, C. S., & Hung, L. C. (2017). Using problem-based learning to increase computer self-efficacy in Taiwanese students. Interactive Learning Environments, 25(3), 329–342.Google Scholar
  62. Suh, A., Suh, A., Lee, J., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27(1), 140–159.Google Scholar
  63. Tarafdar, M., Pullins, E. B., & Ragunathan, T. S. (2014). Examining impacts of technostress on the professional salesperson’s behavioural performance. Journal of Personal Selling & Sales Management, 34(1), 51–69.Google Scholar
  64. Tarafdar, M., Tu, Q., & Ragu-Nathan, T. (2010). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334.Google Scholar
  65. Tarus, J. K., Gichoya, D., & Muumbo, A. (2015). Challenges of implementing e-learning in Kenya: A case of Kenyan public universities. The International Review of Research in Open and Distributed Learning, 16(1), 120–141.Google Scholar
  66. Vandeyar, T. (2015). Policy intermediaries and the reform of e-Education in South Africa. British Journal of Educational Technology, 46(2), 344–359.Google Scholar
  67. Voet, M., & De Wever, B. (2017). Towards a differentiated and domain-specific view of educational technology: An exploratory study of history teachers’ technology use. British Journal of Educational Technology, 48(6), 1402–1413.Google Scholar
  68. Weber, D. M., & Kauffman, R. J. (2011). What drives global ICT adoption? Analysis and research directions. Electronic Commerce Research and Applications, 10(6), 683–701.Google Scholar
  69. Wind, S. A., Jami, P. Y., & Mansouri, B. (2018). Exploring the psychometric properties of the empathy quotient for Farsi speakers. Current Psychology. Scholar
  70. Xie, K., Min, K. K., Cheng, S. L., & Luthy, N. C. (2017). Teacher professional development through digital content evaluation. Educational Technology Research and Development, 65(4), 1–37.Google Scholar
  71. Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior, 64, 591–601.Google Scholar

Copyright information

© De La Salle University 2019

Authors and Affiliations

  1. 1.School of Educational Technology, Faculty of EducationBeijing Normal UniversityBeijingChina
  2. 2.Faculty of EducationThe Chinese University of Hong KongShatinHong Kong SAR
  3. 3.Anhui Provincial Key Laboratory of Intelligent Building and Building Energy SavingAnhui Jianzhu UniversityHefeiChina

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