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This paper aims to identify the antecedents’ factors that positively and negatively influence the intention to use big data analytics (BDA) by future managers of companies.

The sample comprised 364 business students from a public university in Brazil. The methodology had a quantitative approach, with the use of structural equation modeling.

This paper presented a robust model with a high explanatory factor for the intention to use BDA, in which the elements of positive influence on the intention to use are expected performance, social influence and cost–benefit, and the negative influence factor is resistance to use.

Research on BDA has improved the understanding of the phenomenon, mostly emphasizing the technical dimensions of BDA and underestimating organizational and human dimensions. This research contributed to the literature by presenting new insights into these organizational and human aspects by presenting influencing factors for future managers. User resistance is a variable that can incorporate technology adoption theories in BDA.

The results present a positive perception of future managers in the decision on financial resources in the acquisition of new technologies and enable managers to improve planning, investment and choice of technologies while presenting insights from the next generation. Issues regarding privacy, security and ethical aspects are key to minimizing user resistance.

This paper fills a significant research gap on the adoption of BDA, presenting the perception of future managers on fundamental aspects of adoption in a developing country. In addition, the research offers a theoretical model with new latent variables for a current and relevant topic.

This paper aims to enlighten existing gender differences in the linkages between university ecosystem, self-efficacy and entrepreneurial intention in a developing country.

The empirical exercise relies on Partial Least Squares Structural Equations Modeling based on data from a probabilistic sample of 467 Brazilian students of 70 universities across the country.

The university ecosystem positively influences self-efficacy and entrepreneurial intention. Entrepreneurial intention is also influenced by self-efficacy. Gender difference lies in females’ self-efficacy, which presents a higher impact on entrepreneurial intentions.

The research used a secondary database, which offered a limited view of the constructs analyzed. Moreover, by evaluating perception conditions, students’ self-evaluation may not offer conditions to better comprehend higher education organizations conditions.

This paper provided an evaluation of entrepreneurship in universities and gender differences, also presenting an analysis tool for university ecosystems. The assessment of the university ecosystem in two dimensions helps to analyze the strengths and weaknesses of university ecosystems and also the impact they can have on the entrepreneurial ecosystems in which they are inserted.

This study added in-depth information on the dynamics of entrepreneurial universities and gender differences in the context of a developing country, with a probabilistic sample. Both the comprehensive analysis of the model and the assessment at the variable level bring new evidence that can guide entrepreneurship-oriented initiatives in universities.