Table 2.
Measurement model results for first- and second-order constructs.
| Construct and items | Loadings |
|---|---|
| Challenge Research Stressor (1st-order reflective) (Cα = 0.805; CR = 0.862; AVE = 0.622) | |
| I often experience significant time pressure in my research work | 0.789 |
| My research work requires me to take on important responsibilities | 0.798 |
| Research work demands high-level and complex skills | 0.711 |
| I am expected to complete a heavy workload in my research | 0.851 |
| Hindrance Research Stressor (1st-order reflective) (Cα = 0.833; CR = 0.851; AVE = 0.595) | |
| I feel uncertain about how to become an outstanding scholar | 0.728 |
| I find it difficult to balance the dual roles of being an effective teacher and a productive scholar | 0.818 |
| My academic skills and capabilities seem to have plateaued | 0.757 |
| I experience a persistent lack of job security in my academic career | 0.791 |
| I have inadequate resources to conduct my academic research | 0.759 |
| Teaching-Research Time Conflict (1st-order reflective) (Cα = 0.913; CR = 0.918; AVE = 0.794) | |
| My research work occupies a significant amount of time, making it difficult to devote sufficient effort to teaching | 0.836 |
| I often sacrifice lesson preparation time to meet urgent research project deadlines | 0.931 |
| Urgent demands from research frequently force me to cancel or postpone teaching preparation plans | 0.896 |
| Time pressures from research projects prevent me from fully designing innovative teaching activities | 0.899 |
| AI Literacy (2nd-order reflective) (Cα = 0.939; CR = 0.942; AVE = 0.802) | |
| AI Basic (1st-order reflective) (Cα = 0.855; CR = 0.857; AVE = 0.883) | |
| I understand the basic concepts and principles of generative AI | 0.930 |
| I can clearly explain how generative AI systems work to others | 0.939 |
| AI Proficiency (1st-order reflective) (Cα = 0.805; CR = 0.812; AVE = 0.836) | |
| I can effectively design or optimize prompts for generative AI according to different task requirements | 0.924 |
| I frequently use generative AI in my study or work | 0.904 |
| AI Insight (1st-order reflective) (Cα = 0.862; CR = 0.863; AVE = 0.878) | |
| I have a deep understanding of the limitations and potential risks of using generative AI | 0.934 |
| I can effectively use generative AI to explore or develop new ideas and approaches in teaching or research | 0.940 |
| AI Analysis Capability (1st-order reflective) (Cα = 0.901; CR = 0.902; AVE = 0.910) | |
| I am capable of using generative AI to analyze complex problems and propose feasible solutions | 0.956 |
| I am able to use generative AI to predict trends or outcomes | 0.952 |
| Golden Course Quality (2nd-order reflective) (Cα = 0.782; CR = 0.831; AVE = 0.622) | |
| High-Order Thinking (1st-order reflective) (Cα = 0.732; CR = 0.733; AVE = 0.789) | |
| My course objectives are designed to integrate knowledge, skills, and competencies organically | 0.884 |
| My course content includes problem-solving-oriented tasks | 0.892 |
| Innovation (1st-order reflective) (Cα = 0.655; CR = 0.656; AVE = 0.743) | |
| Over 30% of my course content is updated annually (e.g., with new industry reports or research findings). | 0.855 |
| I employ digital tools (e.g., virtual labs, online collaboration platforms) or blended learning models in my teaching | 0.869 |
| Challenge level (1st-order reflective) (Cα = 0.738; CR = 0.743; AVE = 0.792) | |
| My course assessments include high-difficulty tasks | 0.902 |
| In my course, the ratio of in-class to out-of-class learning time is ≥1:2 | 0.877 |
Cα, Cronbach’s alpha; CR, composite reliability; AVE, average variance extracted.