Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison

Xiaoyu Huang

doi:10.47297/wspiedWSP2516-250033.20261006

PDF
Export
Share
Collection
Album

Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison

Volume 10, Issue 6

Views: 0 Downloads: 0

Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison
Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison
Huangpu Xinzhi Kindergarten,Guangzhou, Guangdong, 510700, China
Vol. 10, Issue 6, Pages: 142-145(2026)
Online First：11 August 2026，

Published：30 June 2026
DOI：10.47297/wspiedWSP2516-250033.20261006
Accepted：

Scan QR Code

Huang Xiaoyu.Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison[J].Journal of International Education and Development,2026,10(06):142-145.
DOI：

Huang Xiaoyu.Structure Validation and Core Dimension Identification of Teachers' Technophobia in AI Context: An Empirical Study Based on Second-Order Confirmatory Factor Analysis and Model Comparison[J].Journal of International Education and Development,2026,10(06):142-145. DOI： 10.47297/wspiedWSP2516-250033.20261006.

摘要

Abstract

This study adopts the resource conservation framework and CB-SEM with second-order CFA to test technophobia's factor structure and core dimensions among 685 kindergarten teachers. Online data were gathered in March 2026 from practicing teachers in Guangdong

Guangxi and Chongqing (81.9% valid response rate). Key findings: (1) Model comparisons validated a second-order technophobia construct covering techno-anxiety (β=.958)

behavioral avoidance (β=.865) and cognitive resistance (β=.724)

supporting H2; (2) AI-induced technostress strongly predicts technophobia (β=.924

p<.001)

accounting for 85.4% of its variance

supporting H1; (3) Techno-anxiety bears the largest loading (β=.958) on the higher-order technophobia factor as its primary manifestation

supporting H3. This research rigorously confirms technophobia's second-order structure and delivers evidence for targeted administrative intervention strategies.

关键词

Keywords

references

Chen X. Q. , & Min L . B . ( 2024 ). Research on kindergarten teachers' digital literacy based on national standard framework. Early Childhood Education Research , ( 3 ).

Brislin R . W . ( 1970 ). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology , 1 ( 3 ): 185 – 216 .

Brod C. ( 1984 ). Technostress: The human cost of the computer revolution . Addison-Wesley.

Dong Y. , Xu C. , Chai C. S. , & Zhai X . ( 2020 ). Exploring the structural relationship among teachers' technostress, technological pedagogical content knowledge (TPACK), computer self-efficacy and school support . Asia-Pacific Education Researcher , 29 ( 2 ): 147 – 157 .

Fornell C. , & Larcker D . F . ( 1981 ). Evaluating structu ral equation models with unobservable variables and measurement error. Journal of Marketing Research , 18 ( 1 ): 39 – 50 .

Hair J. F. , Black W. C. , Babin B. J. , & Anderson R . E . ( 2019 ). Multivariate data analysis (8th ed.). Cengage Learning .

Hobfoll S . E . ( 1989 ). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist , 44 ( 3 ): 513 – 524 .

Khasawneh O . Y . ( 2018 ). Technophobia: Examining its hidden factors and defining it. Technology in Society , 54 , 93 – 100 .

Kline R. B. ( 2016 ). Principles and practice of structural equation modeling (4th ed .). Guilford Press .

Marsh H. W. , Hau K . -T., & Wen Z . ( 2004 ). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes. Structural Equation Modeling , 11 ( 3 ): 320 – 341 .

Mlambo P. B. , Mthethwa S. E. , & Faloye S . T . ( 2025 ). Examining factors contributing to technophobia: A case of secondary school teachers in KwaZulu-Natal province. Discover Education , 4 ( 1 ): 415 .

Podsakoff P. M. , MacKenzie S. B. , Lee J . -Y., & Podsakoff N . P. ( 2003 ). Common method biases in behavioral research: A critical review of the literatu re and recommended remedies. Journal of Applied Psychology , 88 ( 5 ): 879 – 903 .

Qureshi M. I. , Iftikhar M. , Al-shami S. A. , & Qayyum S . ( 2026 ). Technostress and technophobia: Unmasking the impact of coerced AI adoption in higher education institutions . Global Business and Organizational Excellence .

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 .