Purpose This study aims to examine the intersection between knowledge-intensive entrepreneurship (KIE) and sustainable value creation (SVC), analyzing how key KIE perspectives – empathy, moral obligation, self-efficacy, perceived social support and environmental engagement – relate to the economic, environmental and social pillars of SVC. Design/methodology/approach Using a quantitative research design, primary data were collected from 284 KIE ventures participating in Brazil’s Innovative Research in Small Businesses program. Machine learning (ML) techniques served as the primary analytical approach to assess predictive relevance across multiple validation strategies. To enhance interpretability and provide confirmatory benchmarks, partial least squares structural equation modeling (PLS-SEM) was applied as a complementary method, reporting path coefficients and bootstrapped significance levels. Findings Results consistently indicate that empathy and moral obligation are central drivers of SVC, particularly for environmental and social outcomes. ML analyses reveal their strong predictive relevance, while PLS-SEM confirms positive and statistically significant associations aligned with theoretical expectations. In contrast, technical and experiential factors show weaker or context-dependent effects. Variations across validation strategies further suggest limited generalizability across entrepreneurial modes, highlighting the heterogeneity of KIE profiles. Research limitations/implications Although the use of self-reported, cross-sectional data constrains causal inference, the findings offer robust insights into individual-level drivers of SVC and encourage future research to further combine predictive and explanatory methods in sustainability studies. Practical implications The findings suggest that policymakers, incubators and educators can strengthen SVC by prioritizing empathy-oriented selection, training and support mechanisms alongside traditional technical capabilities. Originality/value This study advances SVC research by identifying affective and ethical dispositions, especially empathy, as critical yet underexplored micro-foundations of sustainable entrepreneurial outcomes. Methodologically, it contributes by integrating prediction-oriented ML with SEM-based confirmation, demonstrating how predictive and explanatory approaches can jointly inform sustainability-oriented entrepreneurship research.
Prado et al. (Mon,) studied this question.