Abstract
Providing online instruction has become a normal part in primary and secondary education. The teachers’ work engagement and intention are critical to the sustenance of high-quality online teaching. Therefore, this mixed-methods study proposed and validated a theoretical model from the perspective of the Job Demands-Resources Model to shed light on what drives teachers’ online teaching work engagement and its consequent effect on continuance intention. The partial least squares structural equation was applied to 1066 primary and secondary school teachers’ valid self-reported questionnaires to examine the proposed model. We found that it is the perceived usefulness of technology, rather than the ease of use, that affects teachers’ online teaching engagement and intention; institutional support has the most influence on sustaining teachers’ online teaching; urban teachers focus more on the perceived usefulness of technology, while rural teachers are concerned with their online teaching readiness. The follow-up qualitative research further yielded three major themes, including the availability of technical equipment and guidance constitutes teachers’ online teaching basic needs, teachers emphasize the institutional support from parent, and teachers are not troubled by the use of technology but focus on its functions. These themes provided rich details and in-depth knowledge regarding the key aspects that influence teachers’ online teaching work engagement and continuance intention in quantitative study. This study extends our understanding of the theory of work engagement in the context of online education and provides practical guidance for online teaching instruction in primary and secondary education.


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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Abbreviations
- COVID-19:
-
Corona Virus Disease 2019
- The JD-R model:
-
The Job Demands-Resources Model
- MGA:
-
Multigroup analysis
- HTMT:
-
Heterotrait–Monotrait ratio of correlations
- PLS-SEM:
-
Partial Least Squares Structural Equation Modelling
- CR:
-
Composite reliability
- AVE:
-
Average variance extracted
- ICTs:
-
Information and communication technologies
References
Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31(2), 863–875. https://doi.org/10.1080/10494820.2020.1813180
Al-Furaih, S. A. A., & Al-Awidi, H. M. (2020). Teachers’ change readiness for the adoption of smartphone technology: Personal concerns and technological competency. Technology, Knowledge and Learning, 25(2), 409–432. https://doi.org/10.1007/s10758-018-9396-6
Alhumaid, K., Ali, S., Waheed, A., Zahid, E., & Habes, M. (2020). COVID-19 & elearning: Perceptions & attitudes of teachers towards E-learning acceptancein the developing countries. Multicultural Education, 6(2), 100–115. https://doi.org/10.5281/zenodo.4060121
Alkis, N., Coskunçay, D. F., & Yildirim, S. Ö. (2014). A systematic review of technology acceptance model in e-learning context. Paper presented at the Proceedings of the XV International Conference on Human Computer Interaction.
Almerich, G., Orellana, N., Suárez-Rodríguez, J., & Díaz-García, I. (2016). Teachers’ information and communication technology competences: A structural approach. Computers & Education, 100, 110–125. https://doi.org/10.1016/j.compedu.2016.05.002
Almusawi, H. A., Durugbo, C. M., & Bugawa, A. M. (2021). Innovation in physical education: Teachers’ perspectives on readiness for wearable technology integration. Computers & Education, 167. https://doi.org/10.1016/j.compedu.2021.104185
Al-Nuaimi, M. N., & Al-Emran, M. (2021). Learning management systems and technology acceptance models: A systematic review. Education and Information Technologies, 26(5), 5499–5533. https://doi.org/10.1007/s10639-021-10513-3
An, Y., Kaplan-Rakowski, R., Yang, J., Conan, J., Kinard, W., & Daughrity, L. (2021). Examining K-12 teachers’ feelings, experiences, and perspectives regarding online teaching during the early stage of the COVID-19 pandemic. Educational Technology Research and Development, 69(5), 2589–2613. https://doi.org/10.1007/s11423-021-10008-5
Bai, X., & Gu, X. (2022). Effect of teacher autonomy support on the online self-regulated learning of students during COVID-19 in China: The chain mediating effect of parental autonomy support and students’ self-efficacy. Journal of Computer Assisted Learning, 38(4), 1173–1184. https://doi.org/10.1111/jcal.12676
Bakker, A. B. (2011). An evidence-based model of work engagement. Current Directions in Psychological Science, 20(4), 265–269. https://doi.org/10.1177/0963721411414534
Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
Bakker, A. B., & Demerouti, E. (2008). Towards a model of work engagement. Career Development International, 13(3), 209–223. https://doi.org/10.1108/13620430810870476
Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
Bakker, A. B., Emmerik, H. V., & Euwema, M. C. (2006). Crossover of burnout and engagement in work teams. Work and Occupation, 3(4), 464–489. https://doi.org/10.1177/0730888406291310
Bakker, A. B., Schaufeli, W. B., Leiter, M. P., & Taris, T. W. (2008). Work engagement: An emerging concept in occupational health psychology. Work & Stress, 22(3), 187–200. https://doi.org/10.1080/02678370802393649
Barnes, S. J. (2020). Information management research and practice in the post-COVID-19 world. International Journal of Information Management, 55, 102175. https://doi.org/10.1016/j.ijinfomgt.2020.102175
Bauwens, R., Muylaert, J., Clarysse, E., Audenaert, M., & Decramer, A. (2020). Teachers’ acceptance and use of digital learning environments after hours: Implications for work-life balance and the role of integration preference. Computers in Human Behavior, 112. https://doi.org/10.1016/j.chb.2020.106479
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Burić, I., & Macuka, I. (2017). Self-efficacy, mmotions and work engagement among teachers: A two wave cross-lagged analysis. Journal of Happiness Studies, 19(7), 1917–1933. https://doi.org/10.1007/s10902-017-9903-9
Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement: A study at the school level. Journal of School Psychology, 44(6), 473–490. https://doi.org/10.1016/j.jsp.2006.09.001
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis: sage.
Chen, I. S. (2017). Work engagement and its antecedents and consequences: A case of lecturers teaching synchronous distance education courses. Computers in Human Behavior, 72, 655–663. https://doi.org/10.1016/j.chb.2016.10.002
Chin, W. W. (1998). The partial least squares approach to structural equation modeling (Vol. 295).
Chiu, T. K. F. (2021). Student engagement in K-12 online learning amid COVID-19: A qualitative approach from a self-determination theory perspective. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2021.1926289
Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology, 64(1), 89–136. https://doi.org/10.1111/j.1744-6570.2010.01203.x
Çınar, M., Ekici, M., & Demir, Ö. (2021). A snapshot of the readiness for e-learning among in-service teachers prior to the pandemic-related transition to e-learning in Turkey. Teaching and Teacher Education, 107. https://doi.org/10.1016/j.tate.2021.103478
Collie, R. J., Granziera, H., & Martin, A. J. (2018). Teachers’ perceived autonomy support and adaptability: An investigation employing the job demands-resources model as relevant to workplace exhaustion, disengagement, and commitment. Teaching and Teacher Education, 74, 125–136. https://doi.org/10.1016/j.tate.2018.04.015
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research: Sage publications
Crompton, H., Burke, D., Jordan, K., & Wilson, S. (2021). Support provided for K-12 teachers teaching remotely with technology during emergencies: A systematic review. Journal of Research on Technology in Education, 54(3), 473–489. https://doi.org/10.1080/15391523.2021.1899877
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Durst, S., Davila, A., Foli, S., Kraus, S., & Cheng, C.-F. (2023). Antecedents of technological readiness in times of crises: A comparison between before and during COVID-19. Technology in Society, 72. https://doi.org/10.1016/j.techsoc.2022.102195
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452. https://doi.org/10.1177/002224378201900406
Francis, J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V. A., Eccles, M., & Grimshaw, J. M. (2010). What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychology & Health, 25, 1229–1245.
Garson, G. D. (2016). Partial least squares. Regression and structural equation models. In: Statistical Publishing Associates.
German Ben-Hayun, S., & Perry-Hazan, L. (2023). In the same boat: Parents’ and teachers’ role in protecting elementary school students’ online rights. Children and Youth Services Review, 146. https://doi.org/10.1016/j.childyouth.2022.106751
Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory: Strategies for qualitative research: Routledge.
Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance. Information & Management, 38(2), 87–101. https://doi.org/10.1016/s0378-7206(00)00057-4
Gu, J. (2021). Family conditions and the accessibility of online education: The digital divide and mediating factors. Sustainability, 13(15). https://doi.org/10.3390/su13158590
Hair, J. F., Risher, J. J., Sarstedt, M., Ringle, C. M. J. E., & b. r. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM): SAGE Publications.
Henkin, A. B., & Holliman, S. L. (2009). Urban teacher commitment: Exploring associations with organizational conflict, support for innovation, and participation. Urban Education, 44(2), 160–180. https://doi.org/10.1177/0042085907312548
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. https://doi.org/10.1007/s11423-006-9022-5
Hobfoll, S. E., Johnson, R. J., Ennis, N., & Jackson, A. P. (2003). Resource loss, resource gain, and emotional outcomes among inner city women. Journal of Personality and Social Psychology, 84(3), 632–643. https://doi.org/10.1037/0022-3514.84.3.632
Hohlfeld, T. N., Ritzhaupt, A. D., Dawson, K., & Wilson, M. L. (2017). An examination of seven years of technology integration in Florida schools: Through the lens of the levels of digital divide in schools. Computers & Education, 113, 135–161. https://doi.org/10.1016/j.compedu.2017.05.017
Kangas, M., Siklander, P., Randolph, J., Ruokamo, H. J. T., & Education, T. (2017). Teachers’ engagement and students’ satisfaction with a playful learning environment. Teaching and Teacher Education, 63, 274–284. https://doi.org/10.1016/j.tate.2016.12.018
Khan, S., Hwang, G.-J., Azeem Abbas, M., & Rehman, A. (2018). Mitigating the urban-rural educational gap in developing countries through mobile technology-supported learning. British Journal of Educational Technology, 50(2), 735–749. https://doi.org/10.1111/bjet.12692
Khlaif, Z. N., Sanmugam, M., Joma, A. I., Odeh, A., & Barham, K. (2023). Factors influencing teacher’s technostress experienced in using emerging technology: A qualitative study. Technology, Knowledge and Learning, 28(2), 865–899. https://doi.org/10.1007/s10758-022-09607-9
Khong, H., Celik, I., Le, T. T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2022). Examining teachers' behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey. Education and Information Technologies, 1–28. https://doi.org/10.1007/s10639-022-11417-6
Kim, R., & Song, H.-D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs. The Asia-Pacific Education Researcher, 31(4), 395–408. https://doi.org/10.1007/s40299-021-00581-x
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology Learning & Performance Journal, 22(1), 35–48.
Kormos, E. M. (2018). The unseen digital divide: Urban, suburban, and rural teacher use and perceptions of web-based classroom technologies. Computers in the Schools, 35(1), 19–31. https://doi.org/10.1080/07380569.2018.1429168
Kreijns, K., Vermeulen, M., Kirschner, P. A., Buuren, H. V., & Acker, F. V. (2013). Adopting the integrative model of behaviour prediction to explain teachers’ willingness to use ICT: A perspective for research on teachers’ ICT usage in pedagogical practices. Technology, Pedagogy and Education, 22(1), 55–71. https://doi.org/10.1080/1475939x.2012.754371
Krishnan, J., Black, R. W., & Olson, C. B. (2020). The power of context: Exploring teachers’ formative assessment for online collaborative writing. Reading & Writing Quarterly, 37(3), 201–220. https://doi.org/10.1080/10573569.2020.1764888
Kukulska-Hulme, A. (2012). How should the higher education workforce adapt to advancements in technology for teaching and learning? The Internet and Higher Education, 15(4), 247–254. https://doi.org/10.1016/j.iheduc.2011.12.002
Li, Y., & Ranieri, M. (2013). Educational and social correlates of the digital divide for rural and urban children: A study on primary school students in a provincial city of China. Computers & Education, 60(1), 197–209. https://doi.org/10.1016/j.compedu.2012.08.001
Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873. https://doi.org/10.1016/j.compedu.2007.09.005
Lim, J. (2022). Impact of instructors’ online teaching readiness on satisfaction in the emergency online teaching context. Education and Information Technologies, 1–18. https://doi.org/10.1007/s10639-022-11241-y
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Linnenbrink, E. A., Pintrich, P. R. J. R., & Quarterly, W. (2003). The role of self-efficacy beliefs instudent engagement and learning intheclassroom. Reading & Writing Quarterly, 19(2), 119–137. https://doi.org/10.1080/10573560308223
Lowther, D. L., Thompson, L., Ross, S. M., McDonald, A., & Wang, W. (2004). West orange collaborative STARK program. 2003–2004 Evaluation Report. Center for Research in Education Policy and Education Innovations, 16(1).
Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153–169. https://doi.org/10.1016/j.chb.2018.08.056
Luo, H., Zuo, M., & Wang, J. (2022). Promise and reality: Using ICTs to bridge China’s rural–urban divide in education. Educational Technology Research and Development, 70(3), 1125–1147. https://doi.org/10.1007/s11423-022-10118-8
Marchlik, P., Wichrowska, K., & Zubala, E. (2021). The use of ICT by ESL teachers working with young learners during the early COVID-19 pandemic in Poland. Educ Inf Technol (dordr), 26(6), 7107–7131. https://doi.org/10.1007/s10639-021-10556-6
Martin, F., Budhrani, K., & Wang, C. (2019). Examining faculty perception of their readiness to teach online. Online Learning, 23(3), 97–119. https://doi.org/10.24059/olj.v23i3.1555
Martin, F., Xie, K., & Bolliger, D. U. (2022). Engaging learners in the emergency transition to online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S1–S13. https://doi.org/10.1080/15391523.2021.1991703
McCulloch, A. W., Hollebrands, K., Lee, H., Harrison, T., & Mutlu, A. (2018). Factors that influence secondary mathematics teachers’ integration of technology in mathematics lessons. Computers & Education, 123, 26–40. https://doi.org/10.1016/j.compedu.2018.04.008
McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496–508. https://doi.org/10.1016/j.compedu.2008.10.002
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation: John Wiley & Sons.
Ministry of Education of the People’s Republic of China. (2021). Overview of Education in China: National Education Development in 2020. Retrieved from http://www.moe.gov.cn/jyb_sjzl/s5990/202111/t20211115_579974.html.
Ministry of Education of the People’s Republic of China. (2022). Number of Female Educational Personnel and Full-time Teachers of Schools by Type and Level. Retrieved from http://www.moe.gov.cn/jyb_sjzl/moe_560/2020/quanguo/202108/t20210831_556359.html.
Moreira-Fontán, E., García-Señorán, M., Conde-Rodríguez, Á., & González, A. (2019). Teachers’ ICT-related self-efficacy, job resources, and positive emotions: Their structural relations with autonomous motivation and work engagement. Computers & Education, 134, 63–77. https://doi.org/10.1016/j.compedu.2019.02.007
Mutambara, D., & Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas. Computers & Education, 160. https://doi.org/10.1016/j.compedu.2020.104010
Nikolopoulou, K., & Kousloglou, M. (2022). Online teaching in COVID-19 pandemic: Secondary school teachers’ beliefs on teaching presence and school support. Education Sciences, 12(3), 216. https://doi.org/10.3390/educsci12030216
Potyrala, K., Demeshkant, N., Czerwiec, K., Jancarz-Lanczkowska, B., & Tomczyk, L. (2021). Head teachers’ opinions on the future of school education conditioned by emergency remote teaching. Educ Inf Technol (dordr), 26(6), 7451–7475. https://doi.org/10.1007/s10639-021-10600-5
Raskind, I. G., Shelton, R. C., Comeau, D. L., Cooper, H. L. F., Griffith, D. M., & Kegler, M. C. (2019). A review of qualitative data analysis practices in health education and health behavior research. Health Education & Behavior, 46(1), 32–39. https://doi.org/10.1177/1090198118795019
Reuge, N., Jenkins, R., Brossard, M., Soobrayan, B., Mizunoya, S., Ackers, J.,. Taulo, W. G. (2021). Education response to COVID 19 pandemic, a special issue proposed by UNICEF: Editorial review. International Journal of Educational Development, 87. https://doi.org/10.1016/j.ijedudev.2021.102485
Robinson, B. (2008). Using ICT and distance education to increase access, equity and quality of rural teachers’ professional development. The International Review of Research in Open and Distributed Learning, 9(1). https://doi.org/10.19173/irrodl.v9i1.486
Robson, J. (2018). Performance, structure and ideal identity: Reconceptualising teachers’ engagement in online social spaces. British Journal of Educational Technology, 49(3), 439–450. https://doi.org/10.1111/bjet.12551
Saldaña, J. (2013). The coding manual for qualitative researchers (2nd ed.). Sage Publications Ltd.
Schaufeli, W. B. (2012). Work engagement. What do we know and where do we go? Romanian Journal of Applied Psychology, 14(1), 3–10.
Schaufeli, W. B. (2017). Applying the job demands-resources model. Organizational Dynamics, 46(2), 120–132. https://doi.org/10.1016/j.orgdyn.2017.04.008
Schaufeli, W. B., Salanova, M., González-romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The Measurement of work engagement with a short questionnaire a cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471
Schaufeli, W. B., & Taris, T. W. (2014). A critical review of the job demands-resources model: Implications for improving work and health. Bridging Occupational, Organizational and Public Health, 43–68. https://doi.org/10.1007/978-94-007-5640-3_4
Scherer, R., Howard, S. K., Tondeur, J., & Siddiq, F. (2021). Profiling teachers' readiness for online teaching and learning in higher education: Who's ready? Computers in Human Behavior, 118. https://doi.org/10.1016/j.chb.2020.106675
Scherer, R., Siddiq, F., Howard, S. K., & Tondeur, J. (2023). The more experienced, the better prepared? New evidence on the relation between teachers’ experience and their readiness for online teaching and learning. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107530
Shamsi, M., Iakovleva, T., Olsen, E., & Bagozzi, R. P. (2021). Employees' work-related well-being during COVID-19 pandemic: An integrated perspective of technology acceptance model and JD-R theory. Int J Environ Res Public Health, 18(22). https://doi.org/10.3390/ijerph182211888
Sofi-Karim, M., Bali, A. O., & Rached, K. (2023). Online education via media platforms and applications as an innovative teaching method. Education and Information Technologies, 28(1), 507–523. https://doi.org/10.1007/s10639-022-11188-0
Tajfel, H. (2010). Social identity and intergroup relations (7): Cambridge University Press.
Tondeur, J., Scherer, R., Baran, E., Siddiq, F., Valtonen, T., & Sointu, E. (2019). Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education. British Journal of Educational Technology, 50(3), 1189–1209. https://doi.org/10.1111/bjet.12748
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. J. M., & q. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Vermeulen, M., Kreijns, K., van Buuren, H., & Van Acker, F. (2017). The role of transformative leadership, ICT-infrastructure and learning climate in teachers’ use of digital learning materials during their classes. British Journal of Educational Technology, 48(6), 1427–1440. https://doi.org/10.1111/bjet.12478
Wang, P.-Y. (2013). Examining the digital divide between rural and urban schools: Technology availability, teachers’ integration level and students’ perception. Journal of Curriculum and Teaching, 2(2), 127–139. https://doi.org/10.5430/jct.v2n2p127
Wang, J., Tigelaar, D. E. H., & Admiraal, W. (2019). Connecting rural schools to quality education: Rural teachers’ use of digital educational resources. Computers in Human Behavior, 101, 68–76. https://doi.org/10.1016/j.chb.2019.07.009
Wang, Y., Cao, Y., Gong, S., Wang, Z., Li, N., & Ai, L. (2022). Interaction and learning engagement in online learning: The mediating roles of online learning self-efficacy and academic emotions. Learning and Individual Differences, 94. https://doi.org/10.1016/j.lindif.2022.102128
Whiteoak, J. W. (2020). Morale and burnout in an Australian public school: A socio-technical systems approach. Educational Management Administration & Leadership, 49(6), 958–975. https://doi.org/10.1177/1741143220925091
Williams, M., & Moser, T. (2019). The art of coding and thematic exploration in qualitative research. International Management Review, 15(1), 45–55.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
Yang, H. H., Zhu, S., & MacLeod, J. (2018). Promoting education equity in rural and underdeveloped areas: Cases on computer-supported collaborative teaching in China. EURASIA Journal of Mathematics, Science and Technology Education, 14(6). https://doi.org/10.29333/ejmste/89841
Yao, Y., Wang, P., Jiang, Y., Li, Q., & Li, Y. (2022). Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. Journal of Innovation & Knowledge, 7(4). https://doi.org/10.1016/j.jik.2022.100252
Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915. https://doi.org/10.1016/j.chb.2010.02.005
Yin, R. K. (2009). Case study research: Design and methods. Sage.
Zhou, S., & Song, H. (2022). Exploring teacher educators’ post-pandemic intention to teach online in mainland China: The social cognitive career theory perspective. Journal of Education for Teaching, 48(4), 424–440. https://doi.org/10.1080/02607476.2022.2098006
Zigurs, I., & Khazanchi, D. (2008). From profiles to patterns: A new view of task-technology fit. Information Systems Management, 25(1), 8–13. https://doi.org/10.1080/10580530701777107
Zou, M., Kong, D., & Lee, I. (2021). Teacher engagement with online formative assessment in EFL writing during COVID-19 pandemic: The case of china. The Asia-Pacific Education Researcher, 30, 487–498. https://doi.org/10.1007/s40299-021-00593-7
Acknowledgements
The authors declare that they have no conflict of interest. We special thanks to the teachers who voluntarily took part in this study. This work was financially funded by the Major Research Project of the Key Research Institute of Humanities and Social Sciences at Universities of China (grant number 22JJD880026) and the Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province (grant number xtkjrh2021-006).
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Appendices
Appendix 1
1.1 Questionnaire
Greetings! We cordially invite you to complete an online survey based on your online teaching experiences during the suspension of the normal semester. The purpose of the survey is to understand online teaching in terms of perceived personal resources, social resources, online teaching engagement, and continuance intention. Your participation is completely voluntary. You are free to withdraw from the survey at any time without penalty.
Basic Information.
1. Your birth sex is
○Male ○Female.
2. Your school location is
○Urban ○Rural (Town).
3. What grade level you teach?
○Primary school ○Secondary school.
Institutional Support.
1. Parents and community members support our school’s emphasis on online teaching.
2. Teachers receive adequate administrative support to carry out online teaching.
3. Our school has a well-developed plan that guides online teaching.
4. I can readily obtain answers to technology-related questions.
5. Most of our school computers are kept in good working condition.
6. Materials (e.g. software, printer supplies) for online teaching are readily available.
7. My students have adequate access to up-to-date technology resources.
Task–Technology Fit.
1. Technical tools are fit for the requirements of my teaching.
2. Carrying out online teaching fits with my educational practice.
3. It is easy to understand which tool to use in online teaching.
4. Technical tools are suitable for helping me complete online courses.
Readiness.
1. I know how to meaningfully integrate technology into lessons.
2. I am able to align technology use with my district’s standardised curriculum.
3. I have received adequate training to incorporate technology into my instruction.
4. My computer skills are adequate to conduct classes that have students using technology.
Perceived Ease of Use.
1. Learning to operate online teaching platforms or software would be easy for me.
2. I would find it easy to get online teaching platforms or software to do what I want them to do.
3. My interaction with online teaching platforms or software would be clear and understandable.
4. I would find online teaching platforms or software to be flexible to interact with.
5. It would be easy for me to become skilled at using online teaching platforms or software.
6. I would find online teaching platforms or software easy to use.
Perceived Usefulness.
1. Online teaching platforms or software would improve my job performance.
2. Online teaching platforms or software in my job would increase my productivity.
3. Online teaching platforms or software would enhance my effectiveness on the job.
4. Online teaching platforms or software would make it easier to do my job.
5. I would find online teaching platforms or software useful in my job.
Work Engagement.
1. At my work of online teaching, I feel I am bursting with energy.
2. At my job of online teaching, I feel strong and vigorous.
3. When I get up in the morning, I feel like going to online teaching.
4. I am enthusiastic about my job of online teaching.
5. My job of online teaching inspires me.
6. I am proud of the online teaching that I do.
7. I feel happy when I am teaching online intensely.
8. I am immersed in my online teaching.
9. I get carried away when I am teaching online.
Continuance Intention.
1. I intend to use online teaching to assist my teaching.
2. I intend to use online teaching content to assist my teaching.
3. I intend to use online teaching as an autonomous tool to guide students’ learning.
4. I intend to use online teaching platforms or tools to assist my evaluation.
5. I intend to take online teaching as a regular teaching mode.
Appendix 2
2.1 Interview protocol
1. Please introduce yourself and tell us something about your online teaching experience.
2. Have you encountered any problems in online teaching? When you encounter problems, what measures have you taken to solve them?
3. Can you describe your status of engagement when teaching online? Please elaborate.
4. What do you think of the effect of online teaching? Please elaborate.
5. What possible reasons for your decision about implementing online teaching?
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Zuo, M., Yan, Y., Ma, Y. et al. Modeling the factors that influence schoolteachers’ work engagement and continuance intention when teaching online. Educ Inf Technol 29, 9091–9119 (2024). https://doi.org/10.1007/s10639-023-12186-6
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DOI: https://doi.org/10.1007/s10639-023-12186-6

