An integrated partial least squares structural equation modeling, artificial neural network, and importance–performance map analysis framework for Industry 4.0 readiness in university startups

Since Industry 4.0 entered international policy and scholarly debate in 2011, the convergence of cyber-physical systems, the Internet of Things, cloud computing, data analytics, artificial intelligence, and intelligent automation has reshaped how university-linked startups are conceived, incubated, and scaled. This study evaluates an integrated partial least squares structural equation modeling (PLS-SEM), artificial neural network (ANN), PLSpredict/cross-validated predictive ability test (CVPAT), and importance–performance map analysis (IPMA) framework linking technological infrastructure (TI), innovation capacity (IC), entrepreneurship education (EE), digital capability (DC), and Industry 4.0 readiness (I4R). A quantitative cross-sectional survey using criterion-based purposive sampling produced 250 usable responses from student entrepreneurs, incubator resident venture representatives, faculty startup advisers, and innovation center employees. PLS-SEM with 5,000 bootstrap resamples showed that DC had the strongest positive association with I4R (β = 0.485, p < 0.001), followed by TI (β = 0.241, p < 0.001). EE showed a small, marginally significant positive association (β = 0.095, p = 0.047), whereas IC was not significant (β = 0.083, p = 0.093). The model explained 53.8% of the variance in I4R (R2 = 0.538). PLSpredict yielded positive Q2predict values for all I4R indicators, and construct-level CVPAT showed significantly lower PLS-SEM prediction loss than the linear-model benchmark (difference = −0.059, t = 2.602, p = 0.010). A fully specified, repeated cross-validated multilayer perceptron ANN ranked DC first (100% normalized importance), followed by TI (47.6%), EE (10.9%), and IC (10.0%); however, the matched linear benchmark achieved lower held-out root mean square error, so ANN is interpreted as a complementary ranking tool rather than evidence of superior predictive accuracy. IPMA identified DC (importance = 0.485; performance = 48.222) and TI (0.241; 46.037) as the leading intervention priorities. The framework therefore integrates explanatory assessment, out-of-sample prediction, predictive ranking, and intervention prioritization without implying causal proof from cross-sectional data.