Determinants of Artificial Intelligence Adoption in Training and Development: A Regression-Based Study of the Automobile Manufacturing Industry in Delhi/NCR

Abstract

Artificial Intelligence (AI) has rapidly transformed organizational training and development (T&D) practices across industries. This research paper investigates the adoption of AI in T&D functions within the automobile manufacturing sector in the Delhi/NCR region of India. Anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study analyzes the impact of four core constructs: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions on Behavioral Intention and actual adoption of AI. The moderating roles of age, gender, experience, voluntariness of use, and leadership style are also assessed. Utilizing a quantitative methodology with a sample of 383 respondents from 15 automobile companies and employing Multiple Linear Regression, the study confirms Behavioral Intention as the strongest determinant of AI adoption. Performance and Effort Expectancy significantly influence intention, while Social Influence and Facilitating Conditions have secondary effects. Leadership emerges as a vital moderator, whereas gender and voluntariness exert limited impact. The findings provide strategic implications for enhancing AI integration in corporate training settings and propose avenues for future research.