Search results

Background: Educational institutions are strategic tools in disseminating knowledge on Sustainable Development Goals (SDGs) since education is an effective developmental tool. All the 17 SDGs are tied in one way or the other to education, that is, the ability of people to learn and apply. This study applies unorthodox theories which include convergence models, neo-functionalism, intergovernmentalism, neorealism and the Hofstede model to explain how educational institutions are an essential enabling environment which accelerates the attainment of SDGs.

Methods: These factors are analysed in the context of the new coronavirus (COVID-19) pandemic. Empirically, some university case studies were highlighted in addition to unclear modus operandi, small, fragmented and heterogeneous markets and economies, political stability, deficient political will, and lack of standardisation of products and procedures among other factors. These dynamics affect both the quality of educational institutions and the quality of education thereby directly or indirectly affecting the attainment of the 17 SDGs and are compounded with the emergence of the coronavirus pandemic.

Results: The study reveals that acceleration of the 17 SDGs will require a holistic approach as opposed to silos (scientific, economic, political, academic) which usually emerge when pursuing overarching goals of this magnitude.

Conclusions: It concludes that accelerating progress towards the attainment of SDGs will not only require dynamic and visionary leadership but also well-functioning institutions which are based on economic feasibility as opposed to political alliances. Priorities should be placed on addressing poverty, inequality and quality education. Moreover, partnerships will be key in achieving sustainability especially given that the COVID-19 pandemic has compounded existing challenges.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.