Faculty of Engineering, Built Environment and Information Technology
School of Engineering
Department of Engineering and Technology Management
Selected Highlights from Research Findings
A substantial body of knowledge has amassed on entrepreneurship in the sales, services and technology-based business domains in the developed world. Research in this field has concentrated on issues such as personal attributes that distinguish successful entrepreneurs from unsuccessful ones, as well as environmental factors that influence the establishment and growth of new business ventures.
By contrast, relatively little is known about the entrepreneur or about new venture creation and business growth processes in developing countries. This dearth of knowledge is especially severe in the arena of technology-based entrepreneurship (in other words, enterprises that depend on technology for their products or operations).
Frans Lotz, a student of Prof Andre Buys, addressed this shortcoming through research for his PhD thesis entitled Technological entrepreneurship in an emerging economic region: A model developed from a multi-cultural provincial study. In this study, he focused on a group of more than two hundred entrepreneurs at the helm of technology-based enterprises in the province of KwaZulu-Natal.
Over 25 000 data points were collected through questionnaires and subjected to statistical analysis. The study yielded a number of interesting findings. For instance, it was found that formal entrepreneurship training and education in the primary and secondary schooling system is virtually non-existent. Where such training does occur (primarily in tertiary institutions), it is regarded as poor or totally inadequate.
The study also revealed that environmental factors such as growing-up experiences and cultural heritage – including age when first introduced to entrepreneurship, prevailing social attitudes towards entrepreneurship and self-employment status of parents – contribute significantly to the “making” of technological entrepreneurs in emerging societies.
The study culminated in the development of a model that identifies the most prominent influences on technological entrepreneurship in an emerging economy. This model incorporates three categories of variables: technology-specific factors, start-up assistance factors and personal attributes of technological entrepreneur
Contact person: Prof MW Pretorius.
There is evidence to suggest that South Africa’s research and development (R&D) capacity is currently undergoing a period of disinvestment and decay. However, it is difficult to pinpoint the exact nature and magnitude of the problem or to design appropriate policy interventions.
This difficulty partly derives from the fact that R&D investment often takes a long time to translate into R&D output (new knowledge, research publications, industrial innovations and the like). Hence, the link between these two variables is not readily amenable to quantitative analysis.
Saartjie Grobbelaar investigated the matter during research conducted for her PhD degree under the supervision of Prof Andre Buys of the Department of Engineering and Technology Management.
Her work brings dynamic systems modelling to bear on the problem. Such modelling has the advantage that it is able to elucidate feedback effects and time delays in the influence that variables exert on one another.
The first step in dynamic systems modelling is to identify the variables that have to be included in the model. To this end, Grobbelaar employed the Delphi technique (a structured discussion aimed at eliciting the consensus opinion of an expert panel) to identify and rank the most pressing issues facing the South African R&D system in the next 20 years.
The aggregated group opinion of the participants converged on two main themes: the poor prospects of retaining researchers and rejuvenating the R&D system, and the shortage of sufficient R&D funding.
The next step was to construct a mathematical model of the South African R&D system, populating it with the relevant variables and estimating the parameters governing the relationships among those variables.
Data to support the model was collected from various sources, including R&D surveys conducted between 1977 and 2003. The parameters associated with the model’s production functions were estimated through statistical techniques, and the model was subjected to rigorous testing.
The model was then realised by means of computer simulation to test a number of scenarios. These simulations reveal that shrinking R&D capacity and output are partly the result of increasing student-to-staff ratios, and that policy interventions aimed at significantly expanding and nurturing South Africa’s R&D workforce are required if the country is to regain and improve its international position as knowledge creator.
Scenario tests also indicated that, should South Africa’s R&D system be allowed to decay any further through insufficient investment, the cost of rebuilding capacity is likely to increase considerably.
Another conclusion from the study is that the deteriorating R&D capacity in the Higher Education sector can be arrested to a significant degree by revisiting the current division of work of academics between research and teaching and the application of innovative techniques to the delivery of teaching.
The model offers a valuable tool for testing and comparing the probable consequences of various policy alternatives. As such, it can assist decision-makers in enhancing the effectiveness with which problem areas in the South African R&D system are addressed.
Contact person: Prof MW Pretorius.
|
