Entrepreneurial Opportunities and Legal Risk in AI-Driven Businesses: Evidence from the Indian Context

Purpose: India’s burgeoning AI ecosystem tells a dual story of high entrepreneurial opportunity and high legal uncertainty. We propose and empirically validate an integrated conceptual model in India that analyzes the relationships among Entrepreneurial Opportunity (EO), Legal Risk Perception (LR), Government Support (GS), Innovation Intensity (II), and Business Performance (BP) for AI-driven businesses.

Design/Methodology: The data utilized is mostly a structured questionnaire collected from 360 AI entrepreneurs, startup founders, innovation managers, and legal professionals from the six major Indian technology hubs including Bengaluru, Hyderabad, Mumbai, Delhi-NCR, Pune, and Chennai. Structural Equation Modelling (SEM) was conducted using IBM AMOS 26.0. Confirmatory Factor Analysis (CFA) was used to validate the measurement model, and Maximum Likelihood Estimation (MLE) was used to estimate the structural model. In indirect effects, bootstrapped mediation analysis (5,000 iterations) was conducted.

Findings: The results clearly indicate that EO is positively and significantly related to BP (β = 0.312, p < .001) and II (β = 0.389, p < .001). LR has a significant negative effect on BP (β = −0.218, p < .001) and EO (β = −0.143, p < .01). GS is a positive moderator of EO and directly influences BP. II partially mediates the EO–BP relationship. These seven hypotheses are supported. The model performed reasonably well (CFI = 0.953, RMSEA = 0.049, and χ²/df = 2.14).

Originality/Value: This is among the first studies to include legal risk perception using an SEM approach for AI entrepreneurship in India, with DPDPA 2023 and the National AI Strategy as references. The results provide practical recommendations for policymakers, legal academics, and entrepreneurs.