DETERMINANTS OF ARTIFICIAL INTELLIGENCE ADOPTION IN SCHOLARLY WORKFLOWS: AN EMPIRICAL ANALYSIS OF RESEARCHERS IN MADHYA PRADESH USING STRUCTURAL EQUATION MODELING

Abstract: The rapid integration of Artificial Intelligence (AI) into global academic ecosystems has necessitated a closer examination of technology adoption within regional contexts. This study investigates the socio-technical factors influencing the adoption of AI-enhanced research tools such as machine learning algorithms, automated citation mining, and data processing software among researchers in the state of Madhya Pradesh, India. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the research proposes a structural model to explain the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and actual AI usage behavior.Data were collected via a stratified random survey of 342 active researchers (PhD scholars and faculty members) across state and central universities in Madhya Pradesh. Analysis was performed using Covariance-Based Structural Equation Modeling (CB-SEM) via IBM SPSS Amos version 27. The measurement model was validated through Confirmatory Factor Analysis (CFA), with all constructs demonstrating robust reliability (Cronbach’s alpha greater than 0.75) and meeting the Fornell–Larcker criterion for discriminant validity. The structural model yielded a good fit to the data (chi-square divided by degrees of freedom equal to 2.41, Comparative Fit Index equal to 0.942, Root Mean Square Error of Approximation equal to 0.061). Statistical findings reveal that Performance Expectancy is the strongest predictor of behavioral intention (beta coefficient equal to 0.485, p-value less than 0.001), followed by Social Influence (beta coefficient equal to 0.310, p-value less than 0.001). While Effort Expectancy was statistically significant, its relatively lower effect size suggests that researchers in Madhya Pradesh prioritize perceived utility over ease of use. Crucially, Facilitating Conditions significantly influenced actual usage behavior (beta coefficient equal to 0.218, p-value less than 0.001), highlighting the critical role of institutional infrastructure. The model explained 52 percent of the variance in adoption intention. These findings provide a strategic roadmap for the Madhya Pradesh Higher Education Department to bridge the digital divide through targeted artificial intelligence literacy initiatives and infrastructure investment.