Design and Psychometric Evaluation of the Artificial Intelligence Acceptance and Usage in Research Creativity Scale Among Faculty Members: Insights From the Network Analysis Perspective

Abstract

The acceptance of artificial intelligence (AI) in academic settings, particularly in the context of research creativity, is a growing area of interest. This study aimed to design and validate the AI Acceptance and Research Creativity Scale (AIA&RCS) among faculty members. This exploratory mixed-method was conducted among 720 faculty members. A literature review and participant interviews were conducted in the qualitative phase to generate and develop items. In the quantitative phase, face validity, content validity, construct validity, convergent validity and reliability (internal consistency and stability) were used. Exploratory factor analysis (EFA) indicated a 4-factor model of the scale with ‘perceived usefulness and effectiveness of AI in research creativity’, ‘ethical issues in research’, ‘trusted in AI capabilities’ and ‘willingness to use AI’ accounting for 51.6% of the variance. This arrangement was verified by confirmatory factor analysis (CFA), with fit indices that were at suitable levels. Then, the network analysis took into account the four-factor structure of AIA&RCS further. Similarly, the exploratory graph analysis (EGA) indicated the four-factor configuration of the AIA&RCS. The 25-item scale is well-suited for measuring AI acceptance and research innovation among faculty because of its psychometrics.