Steering the sustainability of entrepreneurial start-ups

Abstract

Entrepreneurship is a promising business avenue that individuals, groups and governments can explore and adopt for economic progress. However, there have been high rates of failure of entrepreneurial start-ups within their first 5 years across the globe. In this regard, the aim of the study was to explore ways of improving the sustainability of entrepreneurial start-ups. The study employed a thematic synthesis exploratory analysis on 105 articles. The paper sheds light on survival tools which may be applicable particularly during the setting-up of entrepreneurial ventures as well as in the course of difficult economic periods. The research findings were that, firstly, the entrepreneurial start-ups are prone to risks that if left uncontrolled would impact on their survival. Secondly, there exists a strong theoretical basis for birth (start-up) and death (failure/exit) of start-ups although failure is a learning opportunity. Thirdly, club theory provides a platform for a sharing economy which resource constrained start-ups would find cost friendly. Fourthly, building of relationships through collaboration and social networking has been in existence but favourably emerging approach that could offer start-ups a lifeline through accruing social capital. Fifthly, incubation and responsible innovation have continually been embraced wherein science and research are key ingredients for start-ups' sustainability. Finally, sustainability of entrepreneurial start-ups is a cumulative effect from existing and new product(s) and process(es) aspects. The insights emanating from this exploratory study may serve as signals to governments and private stakeholders of the urgency to initiate frameworks that would incorporate and facilitate entrepreneurial start-ups in implementing sustainability programmes. The findings derived from the study are expected to influence the sustainability management theory especially for entrepreneurial start-ups. Based on the limitations of manual exploratory analysis, the study recommends consideration of machine learning methods in conducting future exploratory studies.