Measuring university students’ technostress in technology-enhanced learning: Scale development and validation
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With technology-enhanced learning playing an increasingly important role in higher education, university students are enjoying unprecedented benefits while likely experiencing technostress due to increased requirements and different academic expectations of technology-enhanced learning. This study aimed to develop a psychometric scale to measure university students’ levels of technostress in technology-enhanced learning. An initial technostress scale was developed drawing on the person-environment fit theory and prior research on technostress. In total 620 students were recruited from two public universities in China. Three rounds of study were performed to validate the initial technostress scale using factor analysis, the Rasch model, and multilevel linear regression. The finalised technostress scale with 8 items demonstrated robust psychometric properties, including high internal consistency, validity, uni-dimensionality, and measurement invariance across students of different demographics. The development of the technostress scale is an important step in identifying maladapted students and preserving their wellbeing so as to increase their constructive and active participation in technology-enhanced learning.
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The 107 items were screened with a one-parameter Rasch model, which converts ordinal responses into linear ability estimates and flags misfitting items.
“The 107 items were screened with a one-parameter Rasch model, which converts ordinal responses into linear ability estimates and flags misfitting items, a feature valued in educational technology measurement (Sumintono, 2017; Wang et al., 2020).”
From A tridimensional model of AI literacy: An empirical analysis of student performance and demographic patterns in higher education (2025) · cites this paper as Wang et al., 2020
The Findings of the Rasch analysis (first and second rounds; page unavailable) describe screening a 10-item technostress scale, identifying two misfitting items, and repeating the analysis with eight items—not screening 107 items. The first-round findings say the authors used a rating scale model for polytomous responses, but our copy does not state that it converts ordinal responses into linear ability estimates. (LLM, conf=0.88)
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ASSESSING THE PSYCHOMETRIC PROPERTIES OF THE TECHNOSTRESS SCALE AMONG FEMALE SOCIAL WORKERS
Bostan, C. M., & Şoitu, D. (2025) · Scientific Annals of the “Alexandru Ioan Cuza” University, Iaşi. #TAB#New Series SOCIOLOGY AND SOCIAL WORK Section · doi:10.47743/asas-2025-1-811 · Cited by 2 · see citing works
Investigation of technophobia level and its related factors among nursing studentsPreprint
Toni, E., Dehghan, M., Bagheri, F., & Khajouei, R. (2023) · Research Square · doi:10.21203/rs.3.rs-2508196/v1 · Cited by 2 · see citing works
Vajda, B. K. (2023) · Egyesület a Marketing Oktatásért és Kutatásért Országos Konferencia (EMOK) · doi:10.62561/emok-2023-14 · Cited by 1 · see citing works
Penerapan Strategi Coping Untuk Mengatasi Krisis Kepercayaan Diri Dampak Pandemi Covid-19
Jamain, R. R., Sari, N. P., & Rusandi, M. A. (2022) · Journal of Community Engagement Research for Sustainability · doi:10.31258/cers.2.3.143-151 · Cited by 1 · see citing works
Balaskas, S., Tsiantos, V. D., Chatzifotiou, S., Filiopoulou, D., Komis, K., & Androulakis, G. (2025) · Knowledge · doi:10.3390/knowledge5040023 · Cited by 1 · see citing works
Khoso, A. K., & Honggang, W. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107396 · Cited by 1 · see citing works
The Impacts of Online Education on Ecology of Learning and Social Learning Processes
Bağcı, C. (2022) · Maarif global education series · doi:10.1007/978-981-16-9640-4_3 · Cited by 1 · see citing works
Gender differences and technostress vis-a-vis Open Government Data (OGD) adoption and usagePreprint
Alexopoulos, C., Rizun, N., Matheus, R., Junior, L. P. P., & Saxena, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4580806 · Cited by 1 · see citing works
Faisol, F., & Nurdiwaty, D. (2025) · Wiley eBooks · doi:10.1002/9781394411283.ch8 · Cited by 1 · see citing works
(2022) · Journal of Global Economy Business and Finance · doi:10.53469/jgebf.2022.04(02).04
Technostress and Students in Sustainable Work Environment: An Empirical Study
Ravindran, K. N., & Manjula, M. (2023) · Contributions to environmental sciences & innovative business technology · doi:10.1007/978-981-99-7058-2_8
Mapping The Impact of Technostress on the Psychological Well-Being of College Student
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Aplicaciones de la tecnología en ambientes educativos: Revisiones sistemáticas
(2025) · Ediciones Comunicación Científica eBooks · doi:10.52501/cc.330
Tourism and Hospitality Education
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The Role of TAM as a Mediator between Organizational Support and Technostress of ASN Employees
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ICT engagement and technostress among Iranian EFL teachers: a person–environment fit perspective
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Strategic prompting under technostress: A moderation study
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Do Digital Literacy Skills Mitigate Technostress? Validation and Insights from Gen Z
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Eğitim Örgütlerinde Dijital Liderlik ve Teknostres: Kuramsal Çerçeve ve Alanyazın Değerlendirmesi
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- Authors
- Wang, X., Tan, S. C., & Li, L.
- Affiliations
- Qingdao University (CN)
- Nanyang Technological University (SG)
- Venue
- Australasian Journal of Educational Technology
- Published
- 2020-04-27
- Pages
- 96-112
- ISSN
- 1449-3098 (ISSN-L)
- 1449-5554
- OpenAlex
- W3020686411
- Cited by
- 79
- References
- 53
- Citations by year
- 2026: 15 · 2025: 12 · 2024: 9 · 2023: 20 · 2022: 11 · 2021: 7 · 2020: 5
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Australasian Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Social Psychology › Technostress in Professional Settings
- Social Sciences › Psychology › Developmental and Educational Psychology › Student Stress and Coping
- Social Sciences › Psychology › Social Psychology › Stress and Burnout Research
- Keywords
- technostress, technology-enhanced learning, university students, scale development, psychometric validation, person-environment fit, student well-being, measurement invariance, higher education
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