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Cited by 200

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling

Özgür, Hasan·2020·Computers in Human Behavior

Abstract

Abstract The proliferation of information technology products and services in learning environments as a result of rapid innovation leads to a greater requirement of meticulous planning in integration processes. In this regard, the burden placed upon teachers within their classroom increases. This phenomenon, referred to as technostress, has recently been studied extensively in terms of its causes and negative effects. However, there is not much research into factors that contribute to the alleviation of technostress of teachers. This study employs statistical structural equation modeling (SEM) in order to examine the causal relationships among certain variables that are thought to relieve technostress, over data collected from 349 in-service high school teachers in Turkey. Results of the modeling effort indicate that both school support and teachers’ technological-pedagogical content knowledge (TPACK) negatively predict their technostress level.

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Metadata

Authors
Özgür, H.
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2020-11
Volume
112
Pages
106468
ISSN
0747-5632 (ISSN-L)
OpenAlex
W3039801489
Cited by
200
References
7
FWCI
29.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling. https://app.cochat.ai/papers/10.1016/j.chb.2020.106468
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Özgür, H. (2020). Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2020.106468

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.