The impact of coercive, normative, and mimetic Stress on Chinese teachers' continuance intention to use generative AI: An integrated perspective of the Expectation-Confirmation Model and Institutional Theory

2026-05-01

AI Summary

This study investigates Chinese teachers’ intention to continue using generative artificial intelligence (GenAI) and the underlying mechanisms driving this behavior. Integrating the Expectation Confirmation Model with institutional theory, the research employs a sequential explanatory mixed-methods design—combining survey data with in-depth interviews—and is the first to incorporate the three dimensions of institutional pressure (coercive, normative, and mimetic) into a technology continuance framework. Findings reveal that perceived usefulness, satisfaction, and institutional pressures all significantly and positively predict teachers’ continued use intentions. Teachers adopt a pragmatic orientation, applying GenAI primarily to lesson preparation while maintaining critical scrutiny of its content reliability. The study thus uncovers a dual mechanism in which both individual cognitive evaluations and institutional environmental factors jointly shape sustained educational technology adoption.

Abstract

This study investigates Chinese teachers' continuance intention to use generative artificial intelligence (AI) by integrating the Expectation-Confirmation Model with Institutional Theory. A sequential explanatory mixed-methods design was employed. Questionnaire data from 437 teachers were analysed using structural equation modelling, followed by semi-structured interviews with 15 teachers to further interpret the findings. The results indicate that confirmation, perceived usefulness, and satisfaction play important roles in shaping teachers' continuance intention, while institutional pressures, including coercive, normative, and mimetic influences, also contribute to continued use. Qualitative findings further reveal that teachers often use generative AI pragmatically to support tasks such as lesson preparation and idea generation, while simultaneously exercising caution and critically evaluating the reliability of AI-generated content. These findings highlight the combined influence of individual evaluations and institutional contexts on teachers' sustained engagement with generative AI in education.

Problem

• generative AI
• continuance intention
• institutional pressure
• teachers
• Expectation-Confirmation Model

Innovation

• Expectation-Confirmation Model
• Institutional Theory
• generative AI
• continuance intention
• mixed-methods design