Purpose 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. Design/methodology/approach 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. Findings 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 limitations/implications 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. Practical implications 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. Originality/value 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.

Purpose Social contexts and academic environments are key elements in the debate about drivers of entrepreneurial intention and behavior in tertiary students. Nonetheless, the underlying dynamics of student entrepreneurship remain elusive. This study aims to contribute to this discussion by creating an original model that addresses the perception of entrepreneurs and potential entrepreneurs regarding the relationship between social norms, the university environment of support to entrepreneurship and the perceived satisfaction about universities’ conditions to nurture entrepreneurial orientation. Design/methodology/approach To investigate the hypotheses, a quantitative approach has been chosen through multivariate data analysis using partial least squares structural equation modeling applied to a sample of 595 students from 66 Brazilian universities. Findings The results indicate that social norms affect how students perceive their university environment in terms of entrepreneurial support. In turn, students’ impressions about such environment shape their levels of satisfaction. However, in contrast with the theory of intention–action gap, differences between actual and potential entrepreneurs could not be identified. Originality/value The originality of the research lies in filling an entrepreneurial intention–action gap among undergraduate students, with consistent results in a developing country. Additionally, the research presents new insights for researchers, policymakers and practitioners, exploring the students’ perceived satisfaction in relation to the university environment to support entrepreneurship.

Knowing relevant information about students entering the higher education (HE) system is becoming increasingly important, thus enabling higher education institutions (HEIs) to design effective studentcentred support programmes. Therefore, HEIs should ascertain all relevant information about their students before the commencement of the academic year. Doing so means that institutions have a head start in understanding the types of support that will be required for different students throughout the year. This article describes the design, implementation and application of a student biographical questionnaire (BQ) online platform at the University of the Witwatersrand (Wits), as well as some of the lessons learned in this regard. The BQ online platform was fully implemented for the first time in January 2016 during the student registration process and has now become an integral part of the university student registration process. Once data collection and analysis is done, a BQ report is compiled and presented to various high-level decision-making structures of the university. The Faculty Student Advisers are the most critical users of the BQ data, as they utilise the data to inform and improve the various student support interventions that each faculty is providing. The planning process for BQ data collection includes questionnaire review; updates on the BQ online platform; testing of the BQ online platform; stakeholder meetings and BQ training of involved stakeholders. Some of the lessons learned when implementing this online platform include buy-in and support from University Management; understanding of the BQ online platform by those dealing directly with students during the registration process; and continuous review and improvements of the BQ online platform. The BQ online platform has proven to be a valuable tool in providing Wits with a head start in understanding the needs of the students and the support they might require to succeed in their first year of study.