Exploring the influencing factors of academic doctoral students' academic innovation behavior in the context of generative artificial intelligence: Self-determination theory and motivation-opportunity-ability perspectives.

PURPOSE: Although generative artificial intelligence (GenAI) has increasingly reshaped academic research practices, existing studies have mainly examined its technical affordances or isolated factors such as AI literacy. Less attention has been paid to how different types of academic motivation are translated into doctoral students' academic innovation behavior under varying opportunity and ability conditions. Drawing on Self-Determination Theory and the Motivation-Opportunity-Ability framework, this study examines the effects of autonomous motivation, controlled motivation, and amotivation on academic doctoral students' academic innovation behavior, and further investigates the moderating roles of resource conditions, encouragement and support, creative self-efficacy, and AI literacy. DESIGN/METHODOLOGY/APPROACH: Based on survey data from 502 academic doctoral students, this study tested the direct effects of autonomous motivation, controlled motivation, and amotivation on academic innovation behavior. It also examined whether resource conditions, encouragement and support, creative self-efficacy, and AI literacy moderated the relationships between motivation and academic innovation behavior. Data were analyzed using SPSS 27.0, AMOS 26.0, and PROCESS 4.0. FINDINGS: Autonomous motivation and controlled motivation positively predict academic innovation behavior, whereas amotivation negatively predicts such behavior. Resource conditions, encouragement and support, creative self-efficacy, and AI literacy significantly moderate these relationships. Specifically, these opportunity and ability factors strengthen the positive effects of autonomous and controlled motivation on academic innovation behavior and buffer the negative effect of amotivation. RESEARCH IMPLICATIONS: Theoretically, this paper complements the integrated application of SDT and MOA in doctoral education research and enriches the multi-factor moderating mechanism of academic innovation under GenAI context. Practically, universities need to build supportive academic environments that combine motivational support, adequate research resources, responsible GenAI training, and the cultivation of creative self-efficacy. ORIGINALITY: It clarifies how motivation, opportunity, and ability jointly shape academic doctoral students' academic innovation behavior, thereby providing a more nuanced explanation of doctoral students' innovation in the GenAI-supported academic environment.