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Factors that Influence Engineers to be Founding Members
of Technology-Based Start-Ups in South Africa
A Research Report
presented to
The Graduate School of Business
University of Cape Town
In partial fulfilment
of the requirements for the
Masters of Business Administration Degree
by
Jaston Sikaundi
December 2013
Supervised by: Associate Professor Hamieda Parker
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Abstract
South Africa has a surplus of engineering graduates who choose their field of study as a result of
industrial and political pressures, because the country is said to be in need of more engineers.
However, South Africa has a high unemployment rate. To reduce unemployment, the country
needs more entrepreneurs. Engineers have the potential to become entrepreneurs in the field of
technology. This will solve unemployment issues particularly and broadly, because the
organisations they establish will create jobs. Accordingly, the main purpose of this research is to
describe the factors that influence engineers in South Africa to establish technology-based start-
up businesses.
Literature is used to generate initial propositions regarding the factors that influence engineers to
become part of a founding team of technology-based enterprises. Phase 1 follows with a
qualitative study, which filters these factors from the initial proposition, as well as adds those
factors that are specific to South Africa. Phase 2 consists of an online survey for engineers,
which prompts the respondents about the factors that either influenced them or deterred them
from being founding members of technology-based start-ups. This survey was distributed to
platforms where engineers are expected to be located. The survey was halted at 150 respondents,
which consisted of 32 founding members and 118 non-founding members. These results were
analysed to filter the main factors of influence and to determine and attempt to explain the
relationships between the identified factors.
The results of this analysis show that the focus of policy-makers and academia, who would like
to increase the level of uptake of founding members, should be on making engineers aware of
funding prospects; making them aware of available opportunities to become a founding member
of a start-up business; and most importantly, tackling the issue of personal finance to help reduce
the number of responsibilities that engineers take on early in their careers, which prevents them
from taking the financial risk of becoming entrepreneurs. Lastly, the research also shows that
focus for policy-makers must be on reducing the effects that labour laws and unions as well as
lack of political connections have on deterring new entrants from entrepreneurship.
Keywords: Entrepreneurship, Technopreneurship, Technology, founding members, Start-
ups
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Plagiarism Declaration
I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is one’s
own.
I have used a recognised convention for citation and referencing. Each significant contribution
and quotation from the works of other people has been attributed, cited and referenced.
I certify that this submission is my own work.
I have not allowed and will not allow anyone to copy this essay with the intention of passing it
off as his or her own work
Jaston Sikaundi
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Acknowledgements
I would like to thank the following people:
My supervisor, Associate Professor Hamieda Parker, for her continuous support and
wisdom throughout the research.
The ten founding members and ten non-founding members of technology-based start-ups
that participated in the qualitative interviews.
My friends for being there for me when times were hard and providing the stress relief
that were needed.
Dina Dabo for assisting with editing and the support that she provided.
My mother for believing in me when I doubted myself, and my father, the late Dr Martin
Ben Sikaundi for instilling in me the value of education and giving me hope for the
future.
Finally I would like to thank God my saviour for bringing me back up every time I fell
down.
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Table of Contents
Abstract ........................................................................................................................................... ii
Plagiarism Declaration ................................................................................................................... iii
Acknowledgements ........................................................................................................................ iv
Table of Contents ............................................................................................................................ v
List of Figures ............................................................................................................................... xii
List of Tables ............................................................................................................................... xiii
Abbreviations ............................................................................................................................... xiv
Glossary of Terms ......................................................................................................................... xv
1. INTRODUCTION ................................................................................................................... 1
1.1. Research Area and Problem ............................................................................................. 1
1.2. Research Questions and Scope ......................................................................................... 2
1.3. Research Assumptions ..................................................................................................... 3
1.4. Research Ethics ................................................................................................................ 4
2. LITERATURE REVIEW ........................................................................................................ 5
2.1. Introduction to Entrepreneurship ..................................................................................... 5
2.2. Entrepreneurship in Technology-based Environments .................................................... 8
2.2.1. Technology Entrepreneurs ........................................................................................ 8
2.2.2. Companies Created by Technology Entrepreneurs ................................................... 9
2.3. The Founding Team in Technology-based Environments ............................................. 10
2.4. The Technical Background-based Entrepreneur as a Leader of the Organisation ......... 12
2.5. Engineers in South Africa .............................................................................................. 13
2.6. The Enablers for Engineers to Become Part of Technology-based Start-ups ................ 15
2.6.1. Comfort with Uncertainty and Taking Risks .......................................................... 15
2.6.2. Human Capital ........................................................................................................ 16
2.6.3. Pattern Recognition ................................................................................................. 18
2.6.4. Entrepreneurship Personality .................................................................................. 18
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2.6.5. Social Capital .......................................................................................................... 19
2.6.6. Being Part of a Small Firm ..................................................................................... 19
2.6.7. Desire for More Money .......................................................................................... 20
2.6.8. Desire for Work Flexibility ..................................................................................... 20
2.6.9. Perceived Support of New Businesses .................................................................... 20
2.6.10. The Business Opportunities Created for Black/EE Engineers by BBBEE ......... 20
2.6.11. The Employment Conditions for White Engineers Created by B-BBEE ........... 21
2.7. The Inhibitors for Engineers to Become Part of Technology-based Start-ups .............. 22
2.7.1. Lack of Interest in Entrepreneurship ....................................................................... 22
2.7.2. Lack of Funding ...................................................................................................... 23
2.7.3. Job Comfort ............................................................................................................ 23
2.7.4. Lack of Business Skills ........................................................................................... 23
2.7.5. Lack of Time or Facilities to Develop the Idea ...................................................... 23
2.7.6. The High Salaries and Job Opportunities brought by B-BBEE for Black/EE
Engineers. .............................................................................................................................. 24
2.8. Initial Research Propositions .......................................................................................... 25
2.8.1. Proposition 1 ........................................................................................................... 25
2.8.2. Proposition 2 ........................................................................................................... 25
2.9. Conclusion ...................................................................................................................... 25
3. RESEARCH METHODOLOGY .......................................................................................... 27
3.1. Research Approach ........................................................................................................ 27
3.2. Research Strategy ........................................................................................................... 27
3.2.1. The Mixed Methods Research Strategy .................................................................. 27
3.2.2. Limitations of Research Strategy ............................................................................ 28
3.2.3. Justification of Research Strategy ........................................................................... 28
3.3. Research Design ............................................................................................................. 29
3.4. Phase 1 - Qualitative Research ....................................................................................... 29
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3.4.1. Sampling ................................................................................................................. 30
3.4.2. Data Collection ....................................................................................................... 30
3.4.3. Data Analysis Methods ........................................................................................... 31
3.4.4. Limitations .............................................................................................................. 31
3.5. Phase 2 – Quantitative Research .................................................................................... 32
3.5.1. Sampling ................................................................................................................. 32
3.5.2. Data Collection Methods ........................................................................................ 32
3.5.3. Data Analysis Methods ........................................................................................... 33
3.5.4. Limitations .............................................................................................................. 34
3.6. Research Criteria ............................................................................................................ 34
3.6.1. Reliability ................................................................................................................ 34
3.6.2. Validity ................................................................................................................... 35
4. PHASE1: QUALITATIVE FINDINGS ................................................................................ 37
4.1. Introduction .................................................................................................................... 37
4.2. Interviewed Sample ........................................................................................................ 37
4.3. Previously Identified Enablers ....................................................................................... 38
4.3.1. Comfort with Taking Risks ..................................................................................... 38
4.3.2. Pattern Recognition Ability .................................................................................... 39
4.3.3. Entrepreneurship Personality .................................................................................. 39
4.3.4. Social Capital .......................................................................................................... 40
4.3.5. Work Experience .................................................................................................... 40
4.3.6. Desire for More Money .......................................................................................... 41
4.3.7. Desire for Work Flexibility ..................................................................................... 42
4.4. New Enablers ................................................................................................................. 42
4.4.1. Identified Opportunity ............................................................................................ 42
4.4.2. Exposure to Entrepreneur(s) ................................................................................... 43
4.4.3. Job Creation ............................................................................................................ 43
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4.4.4. Many Business Opportunities in SA ....................................................................... 44
4.4.5. Globalisation ........................................................................................................... 44
4.5. Previously Identified Inhibitors ...................................................................................... 45
4.5.1. Risk-averse .............................................................................................................. 45
4.5.2. Low Social Capital .................................................................................................. 45
4.5.3. Lack of Interest in Entrepreneurship ....................................................................... 46
4.5.4. Lack of Funding ...................................................................................................... 46
4.5.5. Job Comfort ............................................................................................................ 46
4.5.6. Lack of Business Skills ........................................................................................... 47
4.6. New Inhibitors ................................................................................................................ 48
4.6.1. Lack of Opportunities ............................................................................................. 48
4.6.2. Many Responsibilities ............................................................................................. 48
4.6.3. Lack of Political Connections ................................................................................. 49
4.6.4. Lack of ECSA Registration .................................................................................... 49
4.6.5. Labour Laws and Unions ........................................................................................ 49
4.7. Summary of Results ....................................................................................................... 50
4.8. Final Propositions ........................................................................................................... 51
4.8.1. Proposition 1: Factors Enabling Engineers to Become a Part of Technology-based
Start-ups ................................................................................................................................. 51
4.8.2. Proposition 2: Factors Inhibiting Engineers to Become a Part of Technology-based
Start-ups ................................................................................................................................. 51
5. PHASE2: QUANTITATIVE FINDINGS ............................................................................. 52
5.1. Introduction .................................................................................................................... 52
5.2. Descriptive Statistics of the Sample ............................................................................... 52
5.2.1. Sample Geographical Distribution .......................................................................... 52
5.2.2. Sample Population Groups ..................................................................................... 53
5.2.3. Sample Gender ........................................................................................................ 53
5.2.4. Sample Business Education .................................................................................... 54
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5.2.5. Sample Engineering Disciplines ............................................................................. 54
5.2.6. ECSA Professional Registration Status .................................................................. 55
5.2.7. Number of Years since Graduation......................................................................... 55
5.3. The Proportion of Engineering Graduates that have been involved as Founding
Members of a Technology-based Start-Up ............................................................................... 56
5.4. The Main Factors that Enable Engineers to be Founding Members of Technology-based
Start-Ups in South Africa .......................................................................................................... 56
5.4.1. Summary of Enablers Data ..................................................................................... 57
5.4.2. Comparisons of Inhibitors between Africans and Whites ...................................... 58
5.5. Relationships between Factors that Enable Engineers to be Founding Members of
Technology-based Start-Ups ..................................................................................................... 58
5.6. The Main Factors that Inhibit Engineers to be Founding Members of Technology-based
Start-Ups in South Africa .......................................................................................................... 59
5.6.1. Summary of Inhibitors Data.................................................................................... 59
5.6.2. Comparisons of inhibitors between Africans and whites ....................................... 61
5.7. Relationships between Factors that Inhibit Engineers to be Founding Members of
Technology-based Start-Ups ..................................................................................................... 62
5.8. Summary ........................................................................................................................ 64
6. DISCUSSION OF FINDINGS .............................................................................................. 65
6.1. Discussion on the Main Factors that Enable Engineers to be Founding Members of
Technology-based Start-Ups in South Africa ........................................................................... 65
6.2. Discussion on the Main Factors that Inhibit Engineers to be Founding Members of
Technology-based Start-Ups in South Africa ........................................................................... 69
6.3. Limitations and Constraints ........................................................................................... 73
6.3.1. Sample of Founding Members ................................................................................ 73
6.3.2. Sampling Technique ............................................................................................... 73
6.3.3. Access to Sample .................................................................................................... 73
7. RESEARCH CONCLUSIONS ............................................................................................. 74
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7.1. Research Question 1: What proportion of engineering graduates has been involved as
founding members of technology-based start-ups? ................................................................... 74
7.2. Research Question 2: What are the main factors that enable engineers to be part of the
founding team of technology-based start-ups?.......................................................................... 74
7.3. Research Question 3: What are the main factors that inhibit engineers from being a part
of a founding team of technology-based start-ups? .................................................................. 75
7.4. Research Question 4: Are there relationships amongst the main factors that influence
engineers to become part of a founding team of technology-based start-ups in South Africa? 75
7.5. Research Question 5: What are the factors that influence engineers to be a part of
technology-based start-ups that are unique to the South African environment? ....................... 76
7.6. Research Question 6: Are there issue or factors that could be further emphasised in the
academic programmes that would promote increased entrepreneurship from its graduates? ... 76
7.7. Contributions to Research .............................................................................................. 76
7.8. Implications for Policy-Makers ...................................................................................... 77
8. FUTURE RESEARCH DIRECTIONS ................................................................................. 78
8.1. The Influence of BEE in the Transition to becoming a Founding Member of a
Technology-based Start-up Business ........................................................................................ 78
8.2. Difference in Perceptions of Founding Members and Non-founding Members as to
what Influences the Transition to Becoming a Founding Member of a Technology-based Start-
up Business ................................................................................................................................ 78
8.3. Study to verify relationships amongst factors engineers to transition into becoming a
founding member of a start-up .................................................................................................. 78
BIBLIOGRAPHY ......................................................................................................................... 79
APPENDIX A – QUALITATIVE INTERVIEW GUIDES ......................................................... 87
Qualitative interview guide for founding members .................................................................. 87
Qualitative interview guide for non-founding members ........................................................... 88
APPENDIX B – ONLINE QUESTIONNAIRE ........................................................................... 90
APPENDIX C – QUALITATIVE FINDINGS............................................................................. 94
Previously Identified Enablers not studied further .................................................................... 94
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Salary ceiling and scarce job for white engineers brought about by the implementation of
BEE ........................................................................................................................................ 94
The business opportunities brought by B-BBEE for black/EE engineers ............................. 95
Non-Influencing Previously Identified Enablers....................................................................... 95
Postgraduate education .......................................................................................................... 95
Being part of a small firm ...................................................................................................... 96
High Job turnover .................................................................................................................. 96
Perceived support of new businesses ..................................................................................... 96
Non-Influencing Previously Identified Inhibitors ..................................................................... 97
Lack of time to develop an idea ............................................................................................. 97
Lack of facilities to develop an idea ...................................................................................... 97
The high salaries and job opportunities brought by B-BBEE is for black/EE engineers. ..... 97
The BEE as a barrier for white engineers to start their business ........................................... 98
APPENDIX D – QUANTITATIVE FINDINGS ......................................................................... 99
APPENDIX E – FACTOR ANALYSIS OF INFLUENCING FACTORS ................................ 105
Non-Founding Members ......................................................................................................... 105
Founding Members ................................................................................................................. 108
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List of Figures
Figure 1: Literature review flow diagram ....................................................................................... 5
Figure 2: Enabling factors for technology entrepreneurship ........................................................ 22
Figure 3: Inhibiting factors for technology entrepreneurship ....................................................... 24
Figure 4: Influencing forces for technology entrepreneurship ..................................................... 26
Figure 5: Research process ........................................................................................................... 29
Figure 6: Geographical spread of the sample ............................................................................... 52
Figure 7: Sample population groups ............................................................................................. 53
Figure 8: Gender of the sample ..................................................................................................... 53
Figure 9: Proportion of sample with a business education ........................................................... 54
Figure 10: Sample engineering disciplines ................................................................................... 54
Figure 11: Sample proportion of ECSA professionals ................................................................. 55
Figure 12: Number of years since graduation ............................................................................... 55
Figure 13: Proportion of founding members in sample ................................................................ 56
Figure 14: Relationships amongst enabling factors ...................................................................... 65
Figure 15: Relationships amongst inhibiting factors .................................................................... 69
Figure 16: Scree plot for inhibitors ............................................................................................. 105
Figure 17: Scree plot for enablers ............................................................................................... 109
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List of Tables
Table 1: Race population distribution in South Africa (Statistics South Africa, 2013) ............... 13
Table 2: Founding members’ description ..................................................................................... 37
Table 3: Non-founding members’ description .............................................................................. 38
Table 4: Table showing the results of qualitative study for enabling factors ............................... 50
Table 5: Table showing the results of qualitative study for inhibiting factors ............................. 51
Table 6: Quantitative results of enablers in ranking order ............................................................ 57
Table 7: Spearman’s correlations for related main enablers ......................................................... 59
Table 8: Quantitative results of inhibitors in ranking order .......................................................... 60
Table 9: Results of Mann-Whitney U test for no business education vs. business education ...... 60
Table 10: Results of Mann-Whitney U test for non-ECSA registered vs. ECSA registered ........ 60
Table 11: Filtered results of ECSA registered and those with business education ....................... 61
Table 12: Results of Mann-Whitney U test comparing Africans to whites .................................. 61
Table 13: Inhibitor results for African non-founding members.................................................... 62
Table 14: Inhibitor results for white non-founding members ....................................................... 62
Table 15: Spearman’s correlations amongst main inhibitors ........................................................ 63
Table 16: Spearman’s correlations for inhibitors for those without business skills ..................... 63
Table 17: Pearson’s correlations amongst main inhibitors specific to White engineers .............. 63
Table 18: Summary of main influencing factors .......................................................................... 64
Table 19: Summary of correlations amongst the main influencing factors .................................. 64
Table 20: Summary of relationships amongst influencing factors ............................................... 75
Table 21: Mann-Whitney U Tests for enablers for Africans vs. Whites ...................................... 99
Table 22: Mann-Whitney U Tests for no business education vs. business education .................. 99
Table 23: Mann-Whitney U Tests for Non-ESCA registered vs. ECSA registered ................... 100
Table 24: Mann-Whitney U Tests for inhibitors for Africans vs. Whites .................................. 100
Table 25: Full table of inhibitors amongst Africans ................................................................... 101
Table 26: Full table of Inhibitors for White engineers ............................................................... 101
Table 27: Full spearman’s correlation matrix for enablers ......................................................... 102
Table 28: Full spearman’s correlation matrix for inhibitors ....................................................... 102
Table 29: Full spearman’s correlation matrix for inhibitors for White engineers ...................... 103
Table 30: Descriptive statistics for enablers ............................................................................... 103
Table 31: Descriptive statistics of Inhibitors .............................................................................. 104
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Abbreviations
BEE Black Economic Empowerment
B-BBEE Broad-based Black Economic Empowerment
CEO Chief Executive Officer
ECSA Engineering Council of South Africa
EE Employment Equity
EFA Exploratory Factor Analysis
FM Founding Member
HDI Historically Disadvantaged Individuals
MIT Massachusetts Institution of Technology
NFM Non-Founding Member
NTBF New Technology-Based Firms
PDI Previously Disadvantaged Individuals
RSE Researchers Scientists and Engineers
SA South Africa
SAGDA South African Graduates Development Association
TLO Technology Licensing Offices
UCT University of Cape Town
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Glossary of Terms
Black: African, Coloureds and Indian/Asian
Enabler: Something that promotes/motivates a specific behaviour
Founding Member: Someone who is part of the founding team of a business
Inhibitor: Something that hinders/demotivates a specific behaviour
Non-Founding Member: Someone who is not part of the founding team of a business.
Start-up: A new business
Technopreneur: An entrepreneur in the field of technology
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1. INTRODUCTION
1.1. Research Area and Problem
This research report focuses on investigating the factors that influence engineers to be part of the
founding team of technology-based start-ups in South Africa. The aim is to contribute to the
field of entrepreneurship in technology-based ventures, also known as Technopreneurship (Lee
& Wong, 2004).
This research is relevant to the South African context because the country is considered to be in
need of skilled technologists, such as engineers (Department of Labour, 2008). Consequently,
this influences many young people to take this direction of study. However, it is important to
note that the engineering unemployment rate still remains very high (Pauw, Oosthuizen & van
der Westhuizen, 2008). According to the South African Graduates Development Association’s
(SAGDA) chief executive, Thamsanqa Maqubela, engineers are the largest number of
unemployed graduates on the SAGDA database (“Young, jobless and desperate,” 2012). This
status highlights the criticality for the availability of more options for unemployed graduates.
One such option is for engineers to become involved in entrepreneurship as technology
entrepreneurs, or as other founding members of technology-based start-ups. This will not only
solve their unemployment problem, but also help to reduce unemployment in the country as a
whole, as this type of enterprise will create more jobs. This is supported by the fact that if the
companies founded by graduates from Massachusetts Institution of Technology (MIT),
recognised for its high level of technology-based innovation, were one country, this country
would be the 11th biggest economy in the world (MIT Tech Talk, 2009). This shows how
graduates from a technology-based background can contribute immensely towards the economic
development of a country. In countries such as South Africa, where unemployment is rife, it is
well-known that entrepreneurship and innovation are the more sustainable means of addressing
this problem (Co & Mitchell, 2006).
Do the Universities in South Africa produce enough entrepreneurs to help build the South
African economy? The University of Cape Town (UCT), a leading South African and African
university (Ndlovu, 2012), has shown such initiative. The university’s collaborative efforts with
MIT and the V&A Waterfront have resulted in an innovation hub called Warehouse 17, which
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aims to foster innovation (Wild, 2013). Consequently, this shows recognition of the importance
of innovation to the country.
According to Morrison (2000, p. 65) the South African “formal education system has been
recognised as a strong influence in the development of conformist, anti-entrepreneurial
behaviour”. This research report aims to contribute to the knowledge of engineers in technology-
based ventures. This knowledge may be used by those who set engineering faculty programmes
to assist in influencing more entrepreneurship initiatives amongst graduates or unemployed
engineers seeking to become entrepreneurs. It may also be used by the government in their
policy settings, to further influence and facilitate funding for more engineers and individuals
from other technical backgrounds to become entrepreneurs.
The main purpose of this research is to be descriptive; literature and the interviews conducted
will be used to describe the influencing factors. Due to the mixed methods research approach
that will be used, there will be a mild exploratory nature which will also aid in contributing to
the field of entrepreneurship in technology-based ventures.
1.2. Research Questions and Scope
This research fundamentally aims to answer the question: What are the factors that influence
engineers to be part of the founding teams of technology-based start-ups in South Africa?
This question gives rise to the following sub-questions about engineers in South Africa:
1. What proportion of engineering graduates has been involved as founding
members of technology-based start-ups?
2. What are the main factors that enable engineers to be part of the founding team of
technology-based start-ups?
3. What are the main factors that inhibit engineers from being a part of a founding
team of technology-based start-ups?
4. Are there relationships amongst the main factors that influence engineers to
become part of a founding team of technology-based start-ups in South Africa?
5. What are the main factors that influence engineers to be a part of technology-
based start-ups that are unique to the South African environment?
6. Are there issues or factors that could be further emphasised in the academic
programmes that would promote increased entrepreneurship from its graduates?
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The scope of this research is limited to engineering graduates of all disciplines from South
African universities. This includes those from the universities of technology. Another limitation
is the time available for the execution, which will limit the amount of data that can be gathered
and analysed. In addition to this, the finance to gather the necessary data is limited. A further
limitation is the word count of this research report, which constrains the amount of analysis that
can be presented.
1.3. Research Assumptions
The assumptions of this report were as follows:
1. There are enough engineers involved in technology-based ventures who are
willing to contribute towards answering the questions of this research.
This assumption was met, as a fair amount of engineers participated in this
research to draw results. Naturally, it was expected that there would not be as
many as those who have not been involved as founding members.
2. There are similarities amongst those that have become parts of founding teams of
technology-based ventures.
This assumption appears to have been met by the research, considering that they
have similar tertiary training.
3. There will be similarities amongst those from the different engineering
disciplines.
This assumption was met, with all engineers giving similar indicators.
Additionally, the different engineering disciplines share similar approaches to
teaching and some basic courses.
4. The university that the candidates come from will not influence the
entrepreneurship abilities. Although there may be differences, the engineering
departments have to meet minimum standards of the Engineering Council of
South Africa (ECSA) in order for their graduates to be registered.
The consistent results indicate that this assumption has been met.
5. There are similarities between engineers and those from other technology-based
disciplines that become entrepreneurs.
This assumption appears to have been met as most of the influences emanated
from literature of different technology-based disciplines.
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1.4. Research Ethics
This research was approached in as ethical a manner as possible. The interviewees were offered
the right to remain anonymous and no names have been used in this report. A copy of this report
is available to those that participated in the formal interview. This report did not aim to ridicule
any of the participants, but sought only to gather information necessary to contribute to the field
in a meaningful manner.
The standard GSB research ethics form is signed.
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2. LITERATURE REVIEW
This section covers the literature that was reviewed regarding the roles of engineers as founding
team members of technology-based start-ups. Its aim is to gain an understanding of
entrepreneurship as a field of study; to examine the roles of entrepreneurship in technology start-
ups; and to investigate the role that engineers play in those founding teams – whether as the lead
entrepreneur or other necessary roles in the field of entrepreneurship. Thereafter, the research
focuses on identifying the factors that influence engineers to be involved in start-ups. Lastly,
hypotheses of the factors that influence engineers to become involved in technology-based start-
ups in South Africa are derived. Figure 1 illustrates the structure of this literature review.
The literature does not focus only on engineers, but also takes into account technology-based
professionals. As stated earlier, it is assumed that there are similarities amongst these
professionals.
Figure 1: Literature review flow diagram
2.1. Introduction to Entrepreneurship
Over the years, the definition of entrepreneurship has been debated by scholars worldwide, yet
there is still no consensus on the concept. According to Lado and Voszikis (1996), there is no
single comprehensive definition and each definition only emphasises specific aspects of
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entrepreneurship. This section aims to identify commonly cited definitions of an entrepreneur
and the act of entrepreneurship; such definitions emanate mainly from literature that focuses on
technology-based backgrounds.
Recently, entrepreneurship research has achieved more attention in top management journals
(Phan & Der Foo, 2004). The concept of entrepreneurship is formally introduced to management
and commerce students through the study of economics, as one of the four factors of production
(Parkin, Powel, & Mathews, 2012). The entrepreneur is a necessary element for the growth of an
economy, and entrepreneurship is defined as a factor of production alongside land, labour and
capital. Entrepreneurship is the act that organises the other three factors of production in order to
produce goods and services (Parkin et al., 2012).
According to Deeds (2001), the fundamental purpose of entrepreneurship is to create new wealth
through innovative activities. The author goes on to state that entrepreneurship plays a
significant role in the development of new technologies and their commercialisation. Garud and
Karnøe (2003) posit that entrepreneurship involves discovering new opportunities, creating
opportunities, and then exploiting these opportunities. The manner of describing
entrepreneurship depends on the perspective from which the term or concept is considered.
Mansevelt (2008) identifies three ways of considering the entrepreneur. The first is the
management activities associated with the entrepreneur; the second is the entrepreneur as an
agent of economic change; and the last is the entrepreneur’s character.
The ability to recognise opportunity is an important aspect of entrepreneurship (Marvel, Mathew
& Lumpkin, 2007). Bygrave and Hofer (as cited in Marvel et al., 2007, p 810) define the
entrepreneur as a person who “perceives an opportunity and creates an organisation to pursue it”.
However, according to Shane and Venkataraman (2000), for entrepreneurship to exist there must
be entrepreneurial opportunities. Casson (as cited in Shane & Venkataraman, 2000, p. 220)
describes entrepreneurial opportunities as “new goods, services, raw materials, and organizing
methods can be introduced and sold at greater than their cost of production”. Although
entrepreneurship is said to be about the discovery of opportunities, when the entrepreneur in the
field of technology discovers an opportunity there is usually a small window of time for them to
exploit it (Katila & Mang, 2003). Moreover, Mosey and Wright (2007) identify three types of
entrepreneurs on the basis of level of action, namely: nascent; novice; and habitual/serial
entrepreneurs. Nacent entrepreneurs are those individuals considering making a transition into
entrepreneurship (Ucbasaran, Wright, Westhead, & Busenitz, as cited in Mosey & Wright,
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2007). Individuals that have made the leap into entrepreneurship for the first time are known as
novice entrepreneurs. According to Westhead and Wright (as cited in Mosey & Wright, 2007),
habitual/serial entrepreneurs are those individuals who have made the leap into entrepreneurship
multiple times.
According to Friar and Meyer (2003), the use of entrepreneurship to stimulate economic growth
in emerging markets has grown. Shane and Venkataraman (2000, p. 218) define
entrepreneurship as “the field [which] involves the study of sources of opportunities; the
processes of discovery, evaluation, and ex-performance of individuals or firms in the context of
small or new businesses”. However, it should be emphasised that the types of businesses that are
needed to create growth are high-growth ventures rather than micro-businesses (Friar & Meyer,
2003). Micro-businesses are small businesses with typically fewer than 25 employees, and are
usually established to generate wealth for the owner (Friar & Meyer, 2003). On the other hand,
high-growth ventures create value through innovation and by creating new jobs that do not draw
from other businesses in the economy.
No discussion about entrepreneurship can be considered complete without discussing risk and
uncertainty. According to Wu and Knott (2006), a common theme in entrepreneurship literature
is that entrepreneurs play the economic role of risk-bearing. In the myriad of articles that have
been written on entrepreneurship, the vast majority make mention of risk and or uncertainty. The
main risks are investing time and money into an activity that will not generate sufficient returns.
However, in contrast, Wu and Knott (2006, p. 1315) state that “the empirical record indicates
that entrepreneurs’ risk profiles are indistinguishable from those of wage earners”.
There are various debates about whether or not entrepreneurship can be taught, and whether or
not some people are just born with this ability (Lee & Tsang, 2001). For example, “many studies
have found that entrepreneurs generally have a higher need to achieve than non-entrepreneurs”
(Lee & Tsang, 2001, p. 586). Nevertheless, on the other side of the spectrum are those that feel
that the abilities of an entrepreneur can be learnt (Lüthje & Prügl, 2006; Martin, 2009).
Furthermore, Gartner, Shaver, Gatewood and Katz (as cited in West, 2007) suggest that the
study of entrepreneurship should account more for the fact that businesses are started by a
founding team as opposed to only a single entrepreneur. The authors advise that this should be
used in the theory development.
For the purpose of this research the entrepreneur is simply be defined as one who creates a
business that can create jobs, because this definition is in line with the research problem that is
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being addressed. This definition is similar to Low and MacMillan’s (as cited in Lado & Vozikis,
1996, p. 57) definition of entrepreneurship, as used in the spirit of “explaining and facilitating
the role of new enterprises in furthering economic progress”. This report will now focus on
exploring the entrepreneur in technology-based ventures.
2.2. Entrepreneurship in Technology-based Environments
According to Phan and Der-Foo (2004, p.1), “serious research in technological entrepreneurship
only began to receive greater prominence as scholars in the management of technology and
engineering management fields began considering the role of the entrepreneur in the
organization”. Interestingly, as pointed out by Stuart and Sorenson (2003), sociology can make
strong contributions to the study of technology entrepreneurship due to the importance of the
social and professional relationships needed in the field.
Additionally, Phan and Der-Foo (2004, p.2) describe the research in technology entrepreneurship
as being “foremost about understanding the conditions and driver that lead to the identification
and exploitation of opportunity for value creation”. Moreover, the authors identify that
technology entrepreneurship research occurs at both the individual and organisational level. This
document will focus mainly on the individual level. At the individual level “the focus is on
scientist/entrepreneurs, venture capitalists and other individuals that initiate and drive
technology innovation” (Phan & Der-Foo, 2004, p. 2).
2.2.1. Technology Entrepreneurs
Marvel et al. (2007, p. 809) describe technology entrepreneurs as “individuals who recognize
and exploit opportunities by leveraging technology knowledge and experience to create new
value through the venture creation process”. Additionally, Garud and Karnøe (2003, p. 277)
suggest that “technological entrepreneurship is a larger process that builds upon the efforts of
many”. Therefore there are many people in the supply chain that contribute to technology
entrepreneurship.
Many technology-based entrepreneurs are engineers, have technology expertise, and have
managerial ability (Wu & Knott, 2009). Additionally, engineers that have worked in small
companies are more likely to become entrepreneurs than their counterparts (Elfenbein, Hamilton
& Zenger, 2010). Furthermore, according to Gross (2000), the education that engineers receive
allows them to cope well when doing technical work in research and development (R&D) and
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manufacturing. The author goes on to identify that engineers have increasingly started successful
companies; however they have had to learn the necessary business skills while operating these
companies.
However, Gross (2000, p. 648) states that “Engineers have tended to seriously underestimate the
business skills, especially finance, marketing and legal matters, as well as the high intelligence,
wisdom, energy, and luck required to lead a start-up business into substantial growth”. On the
other hand engineers have been taught to be problem-solvers and this is what is required in
growing a business (Gross, 2000). Therefore, this demonstrates how educational backgrounds
may influence those who decide to become technology entrepreneurs. A further example drawn
from Israel reveals that some of the most successful technology-based ventures came from
entrepreneurs formerly working in the defence industry where they were taught the technical
skills similar to skills fostered by engineers (Chorev & Anderson, 2006).
According to Wright, Hmieleski, Siegal and Ensley (2007, p. 173) an important measure for
technology entrepreneurs is “the extent to which they are able to develop and bring to market
radically innovative new products and/or services”. These innovations are important for both the
economic impact they have and the behavioural changes that they make for consumers, which
often improves their lives.
The next section discusses the companies created by entrepreneurs in the field of technology.
2.2.2. Companies Created by Technology Entrepreneurs
Almeida, Dokko and Rosenkopf (2003, p. 301) state that the “opportunities that are presented to
firms are often technological in nature and the ability to respond to these opportunities (or
technological entrepreneurship) is increasingly tied to a firm’s success”. Additionally, according
to Gans and Stern (2003, p. 333) “the past two decades have witnessed a dramatic increase in
investments in technology entrepreneurship – the founding of small, start-up firms developing
inventions and technology with significant potential commercial application”. Moreover,
Brinckmann, Salomo and Gemuenden (2011) state that new technology-based firms (NTBFs)
are important for job creation, national competitiveness, and innovation. Consequently, many
countries in emerging markets have identified technology entrepreneurs as drivers of economic
growth (Phan & Der-Foo, 2004).
Many technology entrepreneurs have built successful companies by integrating innovations into
already existing value chains in cooperation with established industry players (Gans & Stern,
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2003). Additionally, technology entrepreneurs can develop competencies over more established
firms because the latter are less agile and may not be able to organise and market new
opportunities effectively (Gans & Stern, 2003). However, there are two types of technology-
based start-ups, namely technology adopters and technology developers (Clarysse & Moray,
2004). Technology adopters start businesses using new technologies that were developed by
others, while technology developers use in-house developed technology.
In high-technology industries the windows of opportunity close quickly, therefore access to the
means of exploiting these opportunities can be the difference between great success and failure
(Katila & Mang, 2003). This is supported by Park (2005, p. 750) who states that “an ability to
improvise is so often a critical feature of the entrepreneurial venture in the face of ever changing
technologies and external market conditions”. Nevertheless, technical ability is necessary for
technology entrepreneurship; however, the success of the venture depends on the entrepreneur’s
ability to develop the business skills to exploit this expertise (Oakey, as cited in Chorev &
Anderson, 2006). Unfortunately, “most start-ups stem from engineers and scientists who often
believe, erroneously, that a good product will sell. Marketing is not always seen as a profession
and founders, inexperienced in marketing, may take on the role” (Chorev & Anderson, 2006, p.
168). A further interesting observation by Di Gregorio and Shane (2003) is that start-ups that
emerge from university technology licensing offices (TLOs) have a much higher success rate
than other start-ups. The authors suggest that the reason is that “the university’s status enhances
the entrepreneur’s credibility” (p.226).
As suggested earlier, there is a general agreement that teams rather than single entrepreneurs
create high-tech start-ups (Brinckmann et al., 2011; Roberts, as cited in Clarysse, 2004). This
leads to the next section discussing founding teams in technology-based environments.
2.3. The Founding Team in Technology-based Environments
In technology ventures, the other founding team members are usually as important as the
entrepreneur (Wu, Wang, Tseng, & Wu, 2009). According to Utterback (as cited in Friar &
Meyer, 2003), the majority of innovative start-ups were founded by teams rather than by a single
entrepreneur. The lead entrepreneur in this team is also known as the champion, because he/she
is the driver of the venture. Without him/her the business would not be established, and the idea
would not proceed into the first phase of commercial development (Clarysse & Moray, 2004).
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For technology-based start-ups the diversified knowledge, harmony, and expertise are essential
for its success (Chorev & Anderson, 2006). Previous research has shown that the main criteria
that venture capitalists consider when investing are the skills and experience of the combined
founding team (Clarysse & Moray, 2004; West, 2007). Research on founding teams examines
how the composition of the team brings together the diverse knowledge base needed to make the
start-up successful. There has also been research identifying that the team members will also
differ in their propensity for networking, an aspect that is necessary for the start-up to survive
(West, 2007).
Additionally, Beckman and Burton (2008) state that past company affiliations of the founding
team are an important element that has not been sufficiently explored. For example, the authors
found that it is more probable that founding teams consisting of individuals who previously
worked together will bring a product to market quicker than those individuals who hailed from
different companies. However, founding teams where the members come from different
companies are more likely to be based on innovative activities – using their diversity to develop
innovatively.
Moreover, Bave and Hoffer (as cited in Park, 2005) suggest that successful start-ups generally
have more than one founding entrepreneur, giving the business a broad knowledge base
necessary for higher profitability. This is supported by Oakey (as cited in Park, 2005) who finds
that technology-based ventures need a combination of managerial and technical skills.
Therefore, those individuals from a technical background may benefit by partnering with those
individuals from commercial backgrounds. Additionally, Gruber, Macmillan and Thompson
(2008, p. 1663) state that “teams possessing a mix of prior entrepreneurial experience as well as
experience in technology (or marketing) identify a larger number of opportunities than teams
with technology (or marketing) experience only”. Furthermore, Lüthje and Prügl (2006, p. 212)
find that “cross-disciplinary teams have a higher propensity for success in the process of new
venture formation”. On the other hand, the engineer with a technical background can still learn
these required commercial skills (Lüthje & Prügl, 2006).
Apart from being the venture leader, the founding team member with a technical background can
take on other important roles such as the technical expert, project leader, or gatekeeper (Clarysse
& Moray, 2004).
The literature presented illustrates the value of the start-up team towards the success of the
organisation. Consequently, the Friar and Meyer’s (2003, p. 150) findings lead the authors to
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suggest “that more effort go into helping teams of people become entrepreneurs”. The next
section discusses the entrepreneurs from technical backgrounds as leaders of organisations.
2.4. The Technical Background-based Entrepreneur as a Leader of the Organisation
There are many articles that paint the entrepreneur as someone who develops a product in a
small room or garage and goes on to run large, successful companies. The first mention of a
technology entrepreneur sparks names such as Microsoft’s Bill Gates and the late Steve Jobs of
Apple. However, these entrepreneurs are rare in the sense that these inventors came from a
technology background, yet they were also able to establish and grow very successful
organisations (Wasserman, 2008).
According to Wasserman (2008), most entrepreneurs need to figure out what is more important
to them – being the boss or making money. Most of the time, the entrepreneur does not have the
skills necessary to see the business past the start-up phase. In most circumstances a different
kind of leadership is needed for this. A large percentage of founders of successful companies
gave up control a few years before their company’s initial public offering (IPO) (Wasserman,
2008). Perhaps this is the reason why investors normally look at the structure of the full core
team, and not just the lead entrepreneur. Additionally, investors sometimes instate their choice of
CEO or mentor (Clarysse & Moray, 2004). In situations where the investors do not install a new
CEO from the start, they often fail to consider the time that it takes for the lead entrepreneur to
learn the business skills necessary when creating a timeline for the business (Clarysse & Moray,
2004).
In a founding team, the champion is often the initial leader of the organisation (Clarysse &
Moray, 2004). However, investors normally want someone with enough business experience but
may face resistance with the founding team when trying to bring in someone new to lead. It may
be worthwhile for investors to allow the members of the founding team to find their roles in the
business first, before investing too much money into the business (Clarysse & Moray, 2004).
The authors suggest that doing this may give the other members in the founding team enough
time to realise that the technical background champion cannot grow the company to the right
heights. Additionally, the business needs different personalities at different stages of its growth.
The beginning stage may require someone like the champion. This is due to reasons such as the
fact that most “CEOs with business experience do not establish the ‘‘technical authority’’
needed to run a team of engineers” (Clarysse & Moray, 2004, p. 77).
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2.5. Engineers in South Africa
The aim of this section is to contextualise the engineering environment of South Africa so that
the factors that influence engineers to be a part of technology-based start-ups, unique to South
Africa, can be proposed.
South Africa is a relatively new democracy with ethnic group distributions depicted in the table
below from Statistics South Africa (2013).
Table 1: Race population distribution in South Africa (Statistics South Africa, 2013)
Race White African Indian/Asian Coloured
Population % 8,7 79,8 2,5 9,0
Before democracy in 1994, the professional and managerial fields were white dominated
(Booysen, 2007). There were very few black engineers or other professionals for that matter. In
South Africa, the official definition of black refers to African, Indian, and Coloureds (Sithole,
2006). Therefore, to understand the factors that influence engineers to be part of technology-
based start-ups in South Africa, it is important to realise that it may be different for the black and
white groups respectively. Since 1994, the government have legislated policies such as
Employment Equity (EE), (Black Economic Empowerment (BEE) and Broad-Based Black
Economic Empowerment (B-BBEE), which are aimed at addressing the imbalances in the
workplace and rectifying the inequalities created by the previous apartheid regime (Sithole,
2006; Black Economic Empowerment Commission, 2001). These three policies will be used
interchangeably in this research.
Black Economic Empowerment “aimed at redressing the imbalances of the past by seeking to
substantially and equitably transfer and confer the ownership, management and control of South
Africa’s financial and economic resources to the majority of its citizens. It seeks to ensure
broader and meaningful participation in the economy by black people to achieve sustainable
development and prosperity” (Black Economic Empowerment Commission, 2001, p. 11). For a
more detailed explanation of B-BBEE see the Broad-Based Black Economic Empowerment Act
2003 of the Republic of South Africa, as this is a complex policy and it is not the main focus of
this research report.
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Luiz (2007, p. 74) states that the “aim of the [BEE] policy was to advance the development of
SMEs and historically disadvantaged individuals (HDI): to promote the role of women and
physically handicapped people; to create new jobs; to promote local enterprises in designated
provinces; and to support local products”. Consequently, engineering organisations are
incentivised to comply with BEE when doing business with the government, and those that do
not comply are penalised. Hence, South African companies are desperate for highly skilled black
South Africans so that they can meet their procurement and enterprise development objectives
(Nzukuma & Bussin, 2011).
However, according to Alessandri, Black and Jackson (2011), there is an unsatisfactory amount
of black engineering graduates. Despite a higher number of black student enrolment, the rate of
graduating black students is less than half that of their white counterparts (Scott et al., as cited in
Alessandri et al., 2011). This needs to be seen in context, because as stated in Section 1of this
research report, the highest number of unemployed graduates are engineers so the demand is not
as high as industry seems to indicate. The reason is that the demand is for ‘highly skilled’ blacks
and not simply recent graduates. However, the authors’ statement is based on research conducted
a few years ago, which may not reflect the current climate. Additional reasons as to why there
appears to be a shortage of black engineering graduates are suggested by Sithole (2006, p.95); he
proposes that “the number of black engineers has reached critical mass; however, the limited
pool of practicing engineers is caused by engineers leaving the profession to pursue other careers
or moving into management positions because of the many difficulties experienced in the
profession”. It is important to note that the author’s inferences were based on a small sample;
however they should not be ignored.
According to (Booysen, 2007), a job-hopping phenomenon is occurring amongst black managers
because of the high demand and favourable job opportunities for them in South Africa. In
contrast, Sithole (2006) suggests that the commonly cited myths are that black South African
engineers have premium salaries due to BEE legislation, the job-hopping phenomenon, and their
preference to management positions because engineering is not glamorous, are not true. The
author finds that the choices that most black South African engineers make are driven by the
conditions under which they operate in their profession (Sithole, 2006). However, the author
does indicate that this is a complex subject requiring further research to be conclusive.
The relationship between the South African government’s objective to reduce the injustices of
the past through B-BBEE and its influence on engineers becoming entrepreneurs is a complex
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one. This is because of the multifaceted interrelating elements of BEE. For example, one may
draw the inference that there is less of a demand for white engineers in the workplace than that
for black engineers. Consequently this may result in BEE and other governmental policies as
enablers for white engineers to become entrepreneurs. Alternatively, it could be an inhibitor
because their enterprises may not get government contracts without BEE accreditation, thus
white entrepreneurs may have to deal with the private sector only or partner with black
entrepreneurs.
Similarly, BEE may also be an enabler for black entrepreneurs because they may be classified as
founding members of these technology-based start-ups. Although it is less likely that they would
leave their high paying jobs to join a start-up, they are more likely to join when the organisation
is already showing signs of future success. If this is the case, then they cannot be considered
founding members. These issues will be dealt with in the next few sections.
The literature presented thus far leads to the next two sections that discuss the enabling and
inhibiting factors for engineers to become part of technology-based start-ups.
2.6. The Enablers for Engineers to Become Part of Technology-based Start-ups
The enablers for engineers to become part of technology-based start-ups were predominantly
examined in articles that focus mainly on the technology entrepreneur. It is presumed that the
technology entrepreneur will have a technology-based background, and consequently there will
be similarities to engineers.
2.6.1. Comfort with Uncertainty and Taking Risks
Risk-taking is something that entrepreneurs have to do because there is uncertainty as to whether
or not their ventures will succeed (Wu & Knott, 2006). According to Baron (1998, p 276),
entrepreneurs “formulate new ideas, recognize opportunities, and translate these into added
value to society by assuming the risk of starting a business”. There is usually a lot of time and
money invested in a start-up business, and in most situations, the person is only employed in the
venture. Those people with jobs normally have to resign from permanent employment in order to
make these ventures succeed. However, society needs these risk-takers as a factor of production.
Palich and Bagby (1995) showed that entrepreneurs perceive a greater potential for gain in
highly uncertain situations than non- entrepreneurs. This is supported by Baron (1998, p. 285)
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who states that “entrepreneurs often underestimate risks and overestimate the likelihood of
success”.
There are debates as to whether entrepreneurs are less risk-averse than non-entrepreneurs. Some
have found that entrepreneurs have the same level of risk-taking behaviour as non-entrepreneurs
(Palich & Bagby, 1995; Wu & Knott, 2006). On the other hand, these studies reflect the opinions
of the individuals about their own propensity to take risks (Palich & Bagby, 1995). Additionally,
these tests are done after the entrepreneur has already become an entrepreneur, and do not take
into account the preceding process that may have changed their character.
An increase in human capital can also increase the chances of entrepreneur’s new business
succeeding (Zahra & Dess, 2001). Human capital will be discussed in the next section.
2.6.2. Human Capital
A high level of human capital is associated with the propensity for individuals to become a part
of a start-up firm (Marvel et al., 2007). The authors describe the main elements of human capital
as the level of a person’s education and their experiences. Human capital, which can also be
developed over time, is an enabler for entrepreneurship (Wright et al, 2007). The next sub-
sections will explore these two elements separately.
2.6.2.1. Education
It has long been argued that the level of education of an entrepreneur is an enabler for
entrepreneurship. Extensive literature suggests that the more formal education an individual has,
the higher the human capital of the individual (Marvel et al., 2007). According to Oviatt and
McDougall (1995), a strong education, amongst other things, is a key to making a global
entrepreneur. Supporting this is the study done by Marvel et al. (2007) who show that the level
of one’s postgraduate education is linked to the level of entrepreneurship. For example, those
with a doctorate degree were more likely than those with lower degrees to be involved in
entrepreneurship (Marvel et al., 2007). Knowing how to develop and exploit a new technology is
specific to the entrepreneur (Cable & Shane, 1997).
Within the South African context, African engineers interviewed by Sithole (2006) mentioned a
postgraduate education as one of the things needed to help them achieve their goals, of which
entrepreneurship was the second highest mentioned goal and being a senior manager as the
highest goal.
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2.6.2.2. Experience
According to Marvel et al. (2007) research has shown that the labour market experience,
management, and the previous entrepreneurial experience that a person has, significantly
correlates to both the likelihood of becoming an entrepreneur as well as the likely success of
their venture. This is supported by Agarwal, Echambadi, Franco and Sakar (2004) who suggest
that previous employment affiliations may influence a new start-up formation, its product-
market strategies, and its survival. Additionally, employment may provide opportunities for a
spin-off, as some organisations have abundant underexploited knowledge.
On the other hand, people can develop skills and information that they need for entrepreneurial
activities, such as the acquisition of resources, entrepreneurial strategy, and the process of
organising (Marvel et al., 2007). Furthermore, experience reduces the uncertainty related to the
opportunities (Marvel et al., 2007). Multiple researchers have also “concluded that the greatest
number of start-up ideas come from previous employment” (Marvel et al., 2007, p. 810).
Likewise, Fiet (as cited in Marvel et al., 2007) observed that experience gained over time
increases awareness of opportunities, triggering when to take action. However in contrast,
Ucbasaran, Westhead and Wright (2009, p. 103) state that, “entrepreneurs may become
constrained by their past experience so that, beyond a certain level, experience becomes a
liability and they cite lower levels of innovativeness”.
Mobility across different markets is associated with whether or not someone will become an
entrepreneur (Marvel et al., 2007). It gives access to a broad information base that may be useful
in finding new opportunities, whether or not the person is looking for it. Additionally,
entrepreneurs with a greater work experience tend to stumble upon opportunities accidentally
(Marvel, 2013). This is supported by Elfenbein et al. (2010) who suggest that the broader the
scope of work the person does, the higher the propensity to become an entrepreneur.
Moreover, according to Lee and Wong’s (2004) investigation on researchers, scientists and
engineers (RSEs), it is suggested that those with a strong managerial anchor had significant
positive intentions to establish a business. Additionally, those that had a “strong technical anchor
had significantly greater intentions to start a business in their own field of technical expertise”
(Lee & Wong, 2004, p. 8). According to Baker, Miner and Eesley (2003), interactions with a
company’s customers are an enabler for entrepreneurship. The businesses that these
entrepreneurs formed were spinoffs of the organisation in which they were involved.
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Peters and Roberts (1969) found that some of those that did not start their own business thought
that they had ‘general inexperience’. Furthermore, the study by Sithole (2006) also found that
exposure and more experience were stated as enablers for African engineers in South Africa to
achieve their goals of becoming entrepreneurs.
2.6.3. Pattern Recognition
Pattern recognition is an attribute that has been associated with entrepreneurship because it is
necessary for opportunity recognition. The ability to interlink between markets, technology, and
policies, amongst other factors, is what enables entrepreneurship (Baron, 2006; Grégoire et al.,
2012; Martin, 2009). The patterns seen by entrepreneurs in these connections can be used as a
basis for new ventures. Additionally, Zanakis, Renko and Bullough (2012, p. 1250001-3)
mention that “individuals with creative ideas may be motivated to pursue entrepreneurship
because of their potential to capitalise on their investments”.
2.6.4. Entrepreneurship Personality
According to Cardon, Wincent, Singh and Drnovsek (2009), entrepreneurship passion is an
entrepreneurship enabler. The authors theorise that passion is an attribute that is prevalent
amongst entrepreneurs. They suggest that it is loosely defined, and that it is misunderstood and
confused with other emotions.
Additionally, according to Lado and Vozikis (1996), social psychologists correlate
entrepreneurship with certain personality traits such as the need to achieve, the propensity to
take risk, and locus of control. This is supported by Lee and Tsang (2001, p. 586) who state that
“entrepreneurs generally have a higher need to achieve than non-entrepreneurs”. This could be
linked to entrepreneurship passion. Additionally, Peters and Roberts (1969) find that the “lack of
personality traits” is a reason that people do not get into entrepreneurship, thus suggesting that
believing that one has the personality trait could be associated with entrepreneurship.
Moreover, Zanakis et al. (2012) mention the “belief in one’s ability to face market challenges”
and “high levels of persistence and self-confidence” as elements that are positively correlated
with the decision to participate in a start-up business.
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2.6.5. Social Capital
It is well-known and accepted that a social network is necessary for entrepreneurship. Social
capital is the network resourcefulness that a person has (Zhang, Souitaris, Soh & Wong, 2008).
Higher levels of social capital are an enabler for entrepreneurship. Lack of industry network ties
can constrain opportunities (Mosey & Wright, 2007). Mosey and Wright (2007, p. 932) “also
find that the nature of social capital is associated with the academic discipline base”. For
example, new entrepreneurs from engineering have fewer problems building a network than
those from biological science (Mosey & Wright, 2007).
Additionally, according to Stuart and Sorenson (2003, p. 230) “social relationships play an
essential role in attracting the resources to create new organisations”. The social capital required
to begin the resource utilisation lies in the relationships that the entrepreneur currently has and
those that the entrepreneur can make in the future (Stuart & Sorenson, 2003). Moreover, some
opportunities that the entrepreneurs encounter come from the social contacts that he/she has
(Baker et al., 2003). A certain amount of human capital is necessary in order to utilise social
capital (Mosey & Wright, 2007).
In founding teams, trust in the lead entrepreneur is necessary in order to attract other members of
the team (Stuart & Sorenson, 2003). Therefore, the champion needs to have a good amount of
social capital to attract these members. Members of a founding team also have varying levels of
social capital. Consequently, being a team as opposed to just being an individual can benefit the
firm as a whole (Wright et al., 2007). Moreover, Zanakis et al. (2012) mention the “lack of social
support” as being negatively associated with deciding to go into entrepreneurship.
2.6.6. Being Part of a Small Firm
Elfenbein et al.’s (2010) study on entrepreneurs suggested that engineers had a higher propensity
to be involved in entrepreneurship if they came from smaller firms. However, no link between
the salary level of these subjects and the propensity to become an entrepreneur were found. It
was postulated that the reason may be due to small firms attracting those individuals that would
naturally become entrepreneurs, considering that they normally have a performance-based pay
structure, which is similar to entrepreneurship.
Another explanation is that employees in small firms perform a wider variety of work and the
authors compare it to the description of an entrepreneur being a jack of all trades (Elfenbein et
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al., 2010). These concepts also complement the human capital theory described earlier. It was
also found that those individuals that belong to small firms were more likely to have been
employed by a larger number of firms than those individuals from big companies. This would
also increase the level of human capital, since they would have a broader level of experience.
2.6.7. Desire for More Money
Individuals with prior managerial experiences have a higher desire to earn more, and therefore
are driven into entrepreneurship (Lee & Wong, 2004). Having greater needs for monetary
rewards and recognition was associated with entrepreneurship (Lee & Wong, 2004).
Additionally, those who have low wages, or are unemployed, may be forced into self-
employment as a means of income (Zanakis et al., 2012).
2.6.8. Desire for Work Flexibility
A study by Zanakis et al. (2012) found that nascent entrepreneurs who made the transition to
start a business were more motivated by the independence and work flexibility that comes with
owning a business, rather than being motivated by financial wealth. This may be even more m
for the generation Ys who emphasise flexibility of work and quality of life in their careers and
personal lives (Twenge & Campbell, 2008).
2.6.9. Perceived Support of New Businesses
The study by Zanakis et al. (2012) found that “perceived support of new businesses by
established institutions” motivates nascent entrepreneurs to start a business. These institutions
include financial institutions, local governments, and community groups.
2.6.10. The Business Opportunities Created for Black/EE Engineers by BBBEE
As mentioned previously, BEE has brought about business opportunities for firms that score
high on the B-BBEE score-card. Firstly, for many government contracts, the B-BBEE level is a
criterion in assessing whether or not a firm will be granted government contracts. Thus, there is a
motivating factor for engineers that meet the B-BBEE requirements to establish organisations.
Other non-compliant firms are lining up to partner with these B-BBEE firms in order to secure
government contracts. This is supported by Nzukuma and Bussin (2011, p. 5) who state that
“BEE is an attraction for talented Black professionals to enter the ranks of entrepreneurs”.
Important reasons for this are the opportunities occasioned by B-BBEE, where companies have
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to meet their enterprise development objectives of developing black-owned organisations, as
well as procurement objectives of procuring from black-owned organisations (Nzukuma &
Bussin, 2011).
2.6.11. The Employment Conditions for White Engineers Created by B-BBEE
The effects caused by B-BBEE have made the employment of black engineers essential for
engineering firms dealing with the government. For example, in Luiz’s (2007) study of SMEs in
engineering manufacturing, it was found that 15% of the sample specified that they implemented
B-BBEE to obtain government contracts. For this and similar reasons, white engineers may not
follow the same career prospects as those of black engineers. This is supported by Booysen
(2007. p. 16) who noted that some white males feel that there is an employment equity ceiling in
the workplace, with consequently limited career growth. Some individuals in this sample
indicated that previously disadvantaged individuals (PDIs) get appointed and promoted above
their ability – then we have to report to them, while doing the work”.
Additionally, the author also notes that these individuals do not feel valued and feel that they
lack job security. Moreover, the shortage of skilled black South Africans has led to them being
head-hunted and thus driving up their income, leading to an income disparity between whites
and blacks (Booysen, 2007). Consequently, the white engineers may not be as happy in their
jobs.
This has led to some white engineers leaving South Africa for countries such as the UK and
Australia. For those that stay, it is assumed that entrepreneurship may be a manner of career
progression. Although, if white engineers are interested in government contracts they would
have to partner with black-owned organisations or take on black partners in their firms. This is
highlighted in the study by Luiz (2007) whereby 77% of the family-owned engineering
manufacturing companies indicated that they had only implemented BEE because it was
required by the customer, or because they had an interest in securing government contracts.
For these reasons it is hypothesised that the BEE employment conditions for white engineers is
an enabler for entrepreneurship.
The enabling factors for technology entrepreneurship in South Africa are presented in Figure 2.
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Figure 2: Enabling factors for technology entrepreneurship
2.7. The Inhibitors for Engineers to Become Part of Technology-based Start-ups
In this section the factors that inhibit engineers from becoming technology-based entrepreneurs
are discussed. Naturally, the lack of some of the enablers mentioned in the previous section will
be also inhibitors, namely risk aversion and low social capital. This section only describes those
factors that require further explanation. The majority of the mentioned inhibiting factors come
from Peters and Roberts (1969) who conducted a study on scientists in MIT labs to determine
the reasons that they did not go into self-employment. However, these are further elaborated on,
with more recent references included to indicate their continuing relevance in today’s society.
2.7.1. Lack of Interest in Entrepreneurship
In a study by Peters and Roberts (1969), lack of interest was commonly cited as the reason that
those individuals from a technical background did not pursue entrepreneurship. This inhibitor is
the opposite of the enabler ‘entrepreneurship personality’, and is linked to entrepreneurship
passion. Lack of interest could also be associated with other factors such as being satisfied with
the levels of technical work that one is doing, etc. However, all these can be grouped under a
lack of interest.
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2.7.2. Lack of Funding
Peters and Roberts (1969) found that the lack of adequate funding limited the desire for
entrepreneurship. On the other hand, Lee and Wong (2004, p. 8) showed that the “individual’s
perceptions of their financial constraints" did not moderate their desire to become
technopreneurs. Such a desire may be specific to the geographical environment.
2.7.3. Job Comfort
Being comfortable in a job decreases the desire to venture into entrepreneurship (Peters &
Roberts, 1969; Zanakis et al., 2012; Lee & Wong, 2004). Those employees who feel challenged
and confident in their current job are less likely to make the transition to become entrepreneurs
(Zanakis et al., 2012). The reason is that there is a high opportunity cost for them to make the
switch (Zanakis et al., 2012). For these employees to move into entrepreneurship, the expected
intrinsic reward must be better than their current employment. Consequently, a person’s career
anchor can predict whether or not they will become entrepreneurs (Lee & Wong, 2004).
Additionally, according to Mitchell and Mickel (1999), it has been found that risk-averse people
prefer fixed salaries. This may also lead to job comfort.
2.7.4. Lack of Business Skills
According to Peters and Roberts (1969) business skills are a moderator for the propensity to
become entrepreneurs. A perceived lack of business skills is seen as an inhibitor for one to
become involved in entrepreneurship. Although Lüthje and Prügl (2006) found that individuals
with technical backgrounds soon picked up the business skills required while on the job. Some
individuals would not consider entrepreneurship without these skills.
2.7.5. Lack of Time or Facilities to Develop the Idea
The subjects of the Peters and Roberts (1969) study mentioned lack of time to pursue the idea
and lack of facilities to develop the idea as the two dominant inhibitors to starting a business.
These situations occur when an individual from a technology-based background wants to
develop the idea to a level of usefulness before starting a business.
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2.7.6. The High Salaries and Job Opportunities brought by B-BBEE for Black/EE
Engineers.
Earlier it was suggested that BEE was an enabler for entrepreneurship, however this depends on
which elements of BEE one considers. As pointed out earlier, the BEE criterion has also had the
effect of elevating the demand for black engineers and increasing the salaries of black engineers
(Booysen, 2007). Therefore, there are less financial incentives for black engineers to get into
entrepreneurship because the high salaries that they can command can lead to job comfort,
which is an inhibitor found in this literature. Additionally, it was shown that there is a high
turnover of skilled black professionals; this often leads to the retention strategy of increasing
salaries.
Consequently, the South African government policies such as BEE are perceived to inhibit the
transition of experienced black engineers into entrepreneurship because of the high salaries
offered to them due to the short supply and high demand of black engineers. This is supported by
Sithole (2006) who noted that the majority of black engineers who considered becoming
entrepreneurs in the future were those in the relatively lower levels of their careers, whilst those
that were in senior positions thought of becoming senior managers or executives in their
organisations.
The inhibiting factors for technology entrepreneurship are presented in Figure 3.
Figure 3: Inhibiting factors for technology entrepreneurship
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The next section lists the propositions based on the factors that have been identified.
2.8. Initial Research Propositions
The literature in this section has given rise to the following factors that influence engineers to
become part of technology-based start-ups:
2.8.1. Proposition 1
Enablers for engineers to become part of technology-based start-ups are:
Comfort in taking risks
Postgraduate education level
Work experience
High pattern recognition ability
Entrepreneurship desire personality
Social capital
Being a part of a small firm
High job turnover
Desire for more money
Desire for work flexibility
Perceived support of new businesses
Business opportunities brought by BEE for
black engineers
Salary ceilings and scarce jobs for white
engineers brought about by BEE
implementation
2.8.2. Proposition 2
Inhibitors for engineers to become part of technology-based start-ups are:
Risk-averse personality
Low social capital
Lack of interest in entrepreneurship
Lack of funding
Job comfort
Lack of business skills
Lack of time to develop the idea
Lack of facilities to develop the idea
The high salaries and job opportunities for
black engineers occasioned by BEE
2.9. Conclusion
Technology-based companies are an important growth sector for the economy. To increase job
creation, more companies that can employ people need to be established. In most circumstances,
starting a business is not an easy decision to make. There need to be entrepreneurial
opportunities even though it has been argued that some entrepreneurs create their own
opportunities through pattern recognition and being able to connect the dots. Understanding the
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factors that influence engineers to become entrepreneurs in technology-based environments will
assist in enabling policy-makers to attempt to increase the level of entrepreneurship uptake in the
country. The literature clearly identifies prevalent factors that influence those from technology-
based backgrounds to become entrepreneurs. In addition to these factors, another possible
influencing factor such as B-BBEE was identified in the South African context.
The propositions developed, with alterations and additions based on preliminary interviews, will
assist in developing a questionnaire that can add value in answering the question of what factors
influence engineers to be a part of technology-based start-ups, specifically in the South African
context. Figure 5 shows the research process from the research problem to how it will be
answered. An overview of the literature review is shown in Figure 4 below.
Figure 4: Influencing forces for technology entrepreneurship
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3. RESEARCH METHODOLOGY
3.1. Research Approach
This research aims to determine the factors that influence engineers to enter entrepreneurship as
founding members of technology-based companies in South Africa. There have been many
studies into the factors that influence people from technology-based backgrounds to become
entrepreneurs, as depicted in the literature review. Therefore, this research uses literature to
derive hypotheses, which are then subjected to empirical scrutiny to reveal whether or not they
apply to South African engineers. For that reason, it is primarily a deductive research topic
(Bryman & Bell, 2011).
3.2. Research Strategy
3.2.1. The Mixed Methods Research Strategy
This research uses a mixed methods research strategy. Johnson and Onwuegbuzie (2004, p. 17)
describe mixed methods as “the class of research where the researcher mixes or combines
quantitative and qualitative research techniques, methods, approaches, concepts or language into
a single study”. There have been numerous debates about whether mixed research should be an
accepted practice (Bryman & Bell, 2011; Johnson & Onwuegbuzie, 2007; Hanson, Creswell,
Clark, Petska, & Creswell, 2005). Most of the debates are concerned with the validity of
combining research methodologies from different paradigms when paradigms are
incommensurable (Bryman & Bell, 2011). The paradigm argument highlights ontological and
epistemological assumptions of business research. However, “it rests, as with the embedded
methods argument, on contentions about the interconnectedness method, and epistemology in
particular, that cannot – in the case of business research – be demonstrated” (Bryman & Bell,
2011, p. 629). Moreover, mixed methods research has now been recognised as its own paradigm
and is increasingly becoming an accepted practice (Johnson & Onwuegbuzie, 2004).
Some ontological and epistemological issues with the manner of conducting this research are
that it assumes that:
the subjects did not change after becoming entrepreneurs;
the subjects are truthful in their responses to questions;
entrepreneurship can be studied by interviewing individuals who have and have not
become entrepreneurs;
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people’s opinions of themselves are a true reflection of who they are;
people did not change during the interview process; and
candidates responded in a similar manner during the interview and the questionnaire.
3.2.2. Limitations of Research Strategy
According to Johnson and Onwuegbuzie (2004) the weaknesses of the mixed methods research
are as follows:
1. it can be difficult for a single researcher to carry out both qualitative and quantitative
research, especially if two or more approaches are expected to be used concurrently; it
may require a research team;
2. the researcher has to learn about multiple methods and approaches and understand how
to combine them appropriately;
3. methodological purists contend that one should always work within either a qualitative or
a quantitative paradigm;
4. it is more expensive;
5. it is more time-consuming; and
6. some of the details of mixed research remain to be worked out fully by research
methodologists (e.g., problems of paradigm mixing, how to qualitatively analyze
quantitative data, how to interpret conflicting results). (Johnson & Onwuegbuzie, 2007,
p.21).
3.2.3. Justification of Research Strategy
The reason that a mixed research approach is appropriate for this type of research topic lies in
the aim of understanding the factors, within a geographical region, that have been insufficiently
studied. The qualitative section aimed to gain a better understanding of the South African
influencers and was used as a feedback mechanism to adjust the questionnaire that was
distributed nationally. The questionnaire results from the quantitative findings of this report. The
reason for this quantitative study was to analyse a larger sample from which statistical inferences
can be drawn. This approach is a justifiable means for using mixed methods because the
qualitative section assists in developing the instruments for the quantitative section (Bryman &
Bell, 2011). Parker (2007) and Leigh (2012) both used mixed research when investigating
factors influencing/affecting a particular behaviour/outcome, therefore a mixed research
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approach is well within the right direction. Figure 5 shows the research process that will be used
to unpack these factors.
Figure 5: Research process
3.3. Research Design
This research uses a cross-sectional design, which is also known as a social survey design
(Bryman & Bell, 2011). According to Bryman and Bell (2011, p. 53) “A cross-sectional design
entails the collection of data on more than one case (usually a lot more than one) and at a single
point in time in order to collect a body of quantitative or quantifiable data in connection with
two or more variables (usually many more than two), which are then examined to detect patterns
of association”. This explains the methodology chosen for this research that aims to discover the
important factors that enable engineers to become technology entrepreneurs.
This research design is appropriate in comparison to an experimental design because an
experimental design would have to put the sample through changes in variables and monitor the
results. There is neither enough time for this, nor does the topic require this. On the other hand, a
longitudinal design will also consume a great deal of time and will have cost implications.
Furthermore, a case design will not be able to draw results that are applicable to a whole nation.
Additionally, it would be time-consuming to generate enough cases from which solid inferences
can be drawn. Like the other designs, time constraints will not make this possible.
3.4. Phase 1 - Qualitative Research
The first phase of this report involves a qualitative study. This phase had two objectives:
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1. to filter out the many proposed factors that influence engineers to become technology-
based entrepreneurs in South Africa; and
2. to gather information that is specific to South African engineers.
3.4.1. Sampling
The population for this research comprises engineers from South African universities who
practice in South Africa. This population was divided up into two sub-populations. The first are
engineers who had become founding members of South African technology-based start-ups. The
second are engineers who had not become founding members of South African technology-
based start-ups. The aim of studying the first sub-population was to identify the main enabling
factors for starting technology-based firms. Since there was no database that had a record of all
the technology firms and the backgrounds of the founders, this qualitative study used a method
known as snowball sampling, which is a useful method to employ when there is insufficient
information on where to find subjects (Bryman & Bell, 2011).
The aim of studying the second sub-population was to identify the main inhibiting factors for
starting technology-based firms in South Africa. Similarly, the sampling of the second sub-
population was done via snowball sampling. There was an attempt to interview engineers from
different South Africa provinces to make both these samples more representative of the nation.
A minimum sample of ten individuals per sub-population was obtained. Of these samples of ten
subjects, five were black and five were white.
3.4.2. Data Collection
Data was collected using semi-structured interviews based on the initial propositions generated
from the literature review. According to Bryman and Bell (2011), for semi-structured interviews
the researcher normally has an interview guide of the specific topics to be covered and all these
questions will be asked. However, other questions may be added during the interview, as and if
the interviewer receives new information from the interviewee.
Due to time constraints, the interview questions were forwarded to the interviewees by email.
The interview questions are provided in Appendix A herein. The advantage of this method is that
the initial responses are recorded directly from the interviewees and there was a reduction in the
researcher’s inconsistency in phrasing the questions, which could negatively influence the
research. The disadvantage of sending out the interview questions is that the interviewee can
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read all the questions before beginning to answer (Bryman & Bell, 2011). However, this was not
a problem for the type of questions asked in the interviews, because the order of the questions
would not affect the outcome. Additionally, according Bryman and Bell (2011, p. 473),
forwarding the interview guide or schedule “can help strengthen the dependability of the
research”. The researcher followed up these interview questions with telephone calls and/or
emails to confirm the answers received, to ask for more information on vague answers, and to
probe further into newly obtained factors. This email interview is similar to that which was
carried out by Peters and Roberts (1969) where the interviews were conducted via postal mail.
However, there will be greater validity with the confirmatory follow up.
3.4.3. Data Analysis Methods
This research used thematic analysis to analyse the interview data. According to Braun and
Clarke (2006, p. 78), “thematic analysis is a method for identifying, analysing and reporting
patterns (themes) within data”. The manner of conducting thematic analysis varies amongst
business researchers (Bryman & Bell, 2011; Braun & Clarke, 2006). For the purpose of this
research, data analysis is used as a method where the researcher attempts to capture themes from
the answers given by the interviewees. This is similar to that described in Braun and Clarke
(2006), where the process of thematic analysis is described as taking the data from the interview
and analysing it by searching for themes and then naming themes. These themes were also
compared to the factors in the propositions in order to develop the questionnaires for the
quantitative phase.
3.4.4. Limitations
The following limitations of a qualitative study are taken from Bryman and Bell (2011):
1. The research is too subjective. The findings depend on the researchers world-views of
what is important and what is not.
2. They are difficult, if not impossible to replicate. This is more so for unstructured
interviews.
3. They have a problem of generalisation, especially for smaller studies such as that used in
this research report.
4. They lack transparency. It is difficult to establish exactly what the researcher did.
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The majority of these limitations were addressed by the confirming quantitative study that
followed the qualitative study, as well as by the preliminary email data collection.
3.5. Phase 2 – Quantitative Research
The second phase of this research involved a quantitative study. The objective of this phase was
to take the data from the qualitative study in phase 1, alongside the influencers specified in the
literature that had been filtered from the information received in phase 1 and apply it on a larger
sample. This will assist in confirming the results and drawing inferences.
3.5.1. Sampling
The population for the sample are engineering graduates from South African tertiary institutions
who are practicing in South Africa. Similar to the qualitative phase, convenience sampling will
be utilised for this phase since no national contact database of all engineering graduates in the
country is available. According to Bryman and Bell (2011), sampling that is not random
sampling should not be used to draw inferences, however researchers often do so. For example,
Marvel et al. (2007) use incubators to identify technology-based start-ups. Additionally,
according to Marvel et al. (2007), objective and subjective assessments tend to correlate. For this
reason, this report will do the same. However, considering that most of the factors were found in
literature, the size of the sample should be of more importance than the randomness. Bryman
and Bell (2011) suggest that to draw strong inferences from a sample, the number of respondents
must be at least five times the number of questions on the questionnaire. After the phase 1
qualitative study, it was derived that the required sample should comprise 65 technology
entrepreneurs and 60 non-technology entrepreneurs and therefore the target was set at a total of
150 subjects. The reason was that the ratio of technology entrepreneurs was not known
beforehand, and this is one of the research questions of this study.
The sample that consists of engineers was well-suited to an online questionnaire because they
are computer-literate professionals and thus most likely to have access to internet facilities.
3.5.2. Data Collection Methods
Data was collected using an online survey instrument called Survey Monkey. The measurement
uses the likert scale. For this sample, the subjects were asked the extent to which they agreed
with a series of statements. According to Bryman and Bell (2011), the likert scale was designed
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to measure the intensity a respondent feels towards a particular issue. Therefore the quantitative
study will use the 5-point likert scale with the following options:
1 = strongly disagree, 2 = disagree, 3 = neutral 4 = agree and 5 = strongly agree
The questionnaire was an intelligent survey in the sense that, depending on whether or not a
respondent indicated that they are a founding member of a technology-based enterprise, it
channelled itself to the questions for founding members or to those for non-founding members.
The final questionnaire appears in Appendix B.
In an attempt to make the survey representative of the target population, it was distributed via
social media platforms with an emphasis on the engineering and professional platforms.
Additionally, the link to the survey was sent via email to engineers working at various
engineering firms and they were asked, at will, to forward it onto fellow engineers in their
network. Furthermore, alumni offices at universities were contacted to distribute the link to the
survey. In order to increase the number of engineers that have ventured into technology,
technology hubs were also contacted to help with the distribution of this link.
3.5.3. Data Analysis Methods
A combination of descriptive statistics, correlation analysis, and Mann-Whitney U Tests were
used to analyse the data collected. Statistica 11 software was used to perform the statistical
analysis to determine if an investigated factor was a main influence. The following criterion was
used to determine the main factors: The median of the likert scale results of the factor had to be
greater than or equal to 3 (median ≥ 3), indicating that the majority of the sample did not
disagree that it is an influencing factor. At first, the mean was compared to 3, and where it fell
below 3, t-tests were conducted to determine if the statement that the average is at least 3 can be
rejected using a confidence level of α = 0.05. However, after tests for normality, it became
evident that nonparametric, rather than parametric statistical analysis should be used.
Comparisons between African and white ethnic groups were performed to determine if the
factors are the same for both groups. Similar to above, this was initially performed using t-Test
for the Difference in Two Means. However, when it was established that the data was not
normally distributed, this analysis changed to use Mann-Whitney U Tests. The Mann-Whitney U
Tests are the most common nonparametric alternative to t-tests for comparing independent
groups that are not normally distributed (Rubin, 2010). These were done to a two-tailed
significance level of α = 0.05, whereby values less than 0.05 were used to reject that the two
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groups are similar. If there was a difference between the two population groups on some factors,
the medians of the separate population groups for those different factors were analysed to see if
the majority of people did not disagree that it was an inhibitor (median ≥ 3). The factors that
passed were also retained as main inhibitors for that population group.
For the main inhibitors, Spearman’s correlation tests (used for nonparametric data) were done to
determine if there was statistical evidence for relationships amongst them. The confidence level
used to indicate statistical significant correlation was also α = 0.05.
For interest sake, and available in the Appendix E, exploratory factor analysis (EFA) was
performed. Factor analysis is often used with the likert questionnaire (Bryman & Bell, 2011). It
is used to determine if there are clusters in the way that the groups of indicators come together.
3.5.4. Limitations
According to Bryman and Bell (2011, p. 168), a limitation of quantitative measurement is that
the “measurement process possesses an artificial and spurious sense of precision and accuracy”.
Additionally, the analysis of relationships can create “a static view of social life that is
independent of people’s lives” (p. 168). Moreover, “the reliance on instruments and procedures
hinders the connection between research and everyday life” (p. 168). However, this quantitative
study followed a qualitative study which addresses all these concerns.
3.6. Research Criteria
3.6.1. Reliability
According to Bryman and Bell (2011, p. 157), “reliability is fundamentally concerned with
issues of consistency of measures”. There are three main factors when dealing with whether a
measure is reliable or not, namely stability, internal reliability, and inter-observer consistency.
3.6.1.1. Stability
Stability is concerned with whether or not a measure is stable over time. It underpins confidence
that the results will not fluctuate over time (Bryman & Bell, 2011). Due to time constraints this
research did not test for stability. However, the propositions were developed from literature
published over a long time span; therefore it is assumed that the result will be stable.
Additionally, the study from Peters and Roberts (1996) is used; therefore the difference between
that study and the obtained results may show the stability of this research. On the other hand,
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external factors such as changing policies within South Africa may have an effect on the stability
of the results.
3.6.1.2. Internal Reliability
Internal reliability is concerned with the consistency of the indicators (Bryman & Bell, 2011).
The questionnaire that was developed will have as much internal reliability as possible. The
researcher sent it to some of the phase-1 interviewees first, to see if it captures their sentiments.
Additionally, other pilots to engineers that were not part of the phase 1 interviews were used to
provide corrective feedback before the final release. Furthermore, according to Dugard, Todman
and Staines (2010), the usual index of measuring internal consistency of a scale is the
Cronbach’s alpha coefficient. According to Schutte et al. (as cited in Bryman & Bell, 2011) a
Cronbach alpha value of above 0.7 is an indicator of internal consistency. The Cronbach alpha
for the questions addressed to founding members is 0.63, and those addressed to non-founding
members is 0.74, therefore suggesting that the scales used for non-founding members is
internally reliable. Additionally, Peterson (1994) shows that Cronbach alphas are lower for
preliminary research, which is applicable to this study. Therefore, the value of 0.63 for founding
members is a reasonable value for internal reliability.
3.6.1.3. Inter-observer Consistency
This is concerned with the consistency of tasks when subjective judgements need to be used in
the recording of data, translating data, and in instances where more than one observer is involved
in the activities (Bryman & Bell, 2011). For the qualitative section, the initial responses were
collected via email, so there will not be judgement in the initial recording. Additionally, the
author was the only transferor and data was translated using key themes.
3.6.2. Validity
According to Bryman and Bell (2011, p. 42), “validity is concerned with the integrity of the
conclusions that are generated from a piece of research”. There are four main types of validity,
namely measurement, internal, external, and ecological. These will be discussed in this section.
3.6.2.1. Measurement
According to Bryman and Bell (2011, p. 159), measurement validity “refers to the issue of
whether or not an indicator (or set of indicators) that is devised to gauge a concept really
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measures that concept”. For this reason the phase-2 likert questionnaire was initially piloted to
some of interviewees from the phase-1 qualitative study to validate that it measures the factors
that were discovered in this study, and was altered accordingly.
3.6.2.2. Internal Validity
Bryman and Bell (2011, p. 42) state that “internal validity is concerned with the question of
whether a conclusion that incorporates a causal relationship between two variables holds water”.
The large number requirement of the phase 2 questionnaire respondents, as well as the fact that a
similar study has been done before by Peters and Roberts (1969), means that it is likely that the
relationships hold true. Additionally, the phase1 interviews were used as a verification of the
internal validity. Therefore, this research is likely to be internally valid.
3.6.2.3. External Validity
External validity is “concerned with the question of whether the results of a study can be
generalised beyond the specific research context” (Bryman & Bell, 2011, p. 43). The qualitative
study involved a very small sample, therefore it cannot be used to generalise. However, because
attempts were made to make the sample nationally representative, this helps with the external
validity. Additionally, the phase 2 quantitative study target of 150 engineers, although small, can
be used to generalise. Even though this sample is not fully random, other purposeful studies on
entrepreneurs such as that by Marvel et al. (2007) have been used to generalise beyond the
specific research. Furthermore, the research will be conducted nationwide, so it may be used to
generalise.
3.6.2.4. Ecological Validity
Ecological validity is a criterion “concerned with the question of whether or not social scientific
findings are applicable to people’s everyday, natural social settings” (Bryman & Bell, 2011,
p.43). The questions were sent to the interviewees’ emails, the telephone calls and the answering
of the questionnaires was most likely to be attended to at their workplace. This study is of
subjects in a profession, therefore they are in the natural setting where these questions are
applicable. However, according to Bryman and Bell (2011, p. 43), “the unnaturalness of the fact
of having to answer a questionnaire may mean that the findings have limited ecological
validity”. Taking these two points into account means that the ecological validity will be slightly
limited.
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4. PHASE1: QUALITATIVE FINDINGS
4.1. Introduction
This chapter presents the results from the qualitative interviews done in phase 1 of this research.
It begins by describing the interviewees on a broad scale. The chapter will then describe the
samples’ responses, i.e. the enablers for engineers to become founding members of technology-
based start-ups, as well as new enablers that previously were not explored in this research.
Following this, it will describe the inhibitors for engineers to become founding members of
technology-based enterprises and describe the new inhibitors that were revealed.
4.2. Interviewed Sample
Table 2: Founding members’ description
Interviewee Race Discipline Role in Start-up
FM1 White Electrical Engineer Technology Expert
FM2 White
Electrical Engineer Main Entrepreneur and Technology
Expert
FM3 White
Mechanical Main Entrepreneur and Technology
Expert
FM4 White Mechatronics Technology Expert
FM5 White Mechanical Technology Expert
FM6 Black Electrical Engineer Main Entrepreneur
FM7 Black Electrical and
Computer Technology Expert
FM8 Black Industrial Main Entrepreneur
FM9 Black
Electrical Main Entrepreneur and Technology
Expert
FM10 Black Civil Main Entrepreneur
Twenty engineers were interviewed in total. This consisted of 10 engineers that are or were
founding members of technology-based enterprises (Table 2) and another 10 engineers that were
not part of technology-based enterprises (Table 3). These samples were each further divided into
five black and five white engineers. Understandably, it was difficult to find engineers that were
founding members of technology-based enterprises. Locating them relied on referrals, internet
searches, and advert postings. It was even more difficult to obtain the proportionate sample that
was based on population groups. The majority of the sample resided in either the Western Cape
or Johannesburg; this is due to the ease of locating such a sample. The interviewees ranged from
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being the lead entrepreneur, to being the technical leader in the technology-based start-up. The
industries of the ventures varied immensely, however they were all businesses that would have
required the expertise of engineers.
The following two sections describe the information that was gathered from the 10 founding
members. Representative quotations are used to substantiate the findings.
Table 3: Non-founding members’ description
Interviewee Race Discipline
NFM1 Black Mechanical
NFM2 Black Mechanical
NFM3 Black Civil
NFM4 Black Electrical
NFM5 Black Mechanical
NFM6 White Electrical
NFM7 White Electrical
NFM8 White Civil
NFM9 White Electrical
NFM10 White Mechanical
4.3. Previously Identified Enablers
This section describes the information gathered with respect to the previously identified enablers
that were gathered from the literature review, thus forming part of the initial propositions. For
those enablers that will not be studied further and the reasoning behind these omissions see
Appendix C.
4.3.1. Comfort with Taking Risks
The majority of the interviewees agreed that they did in fact take a risk in getting involved in
their technology-based start-ups. One founding member, a chief technical officer of a start-up
said, “I was willing to take risks at the moment. [I] trusted in the ability of the people that I was
partnering with” (FM1). Further, emphasizing the importance of the diversity of skills of the
founding team, he also said “Some of my colleagues were willing to work together with me,
which helped reduce the risk of being too naive” (FM1).
Another founding member, who was the lead entrepreneur of a start-up, said “I normally focus
more on the vision than the actual risks, normally when I see the opportunity it becomes big
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enough to overcome any potential risks, unless the risk is too visible and apparent” (FM8).
Another founding member said “I was comfortable taking the risks because in the short term, I
only had my salary to worry about – which I could forgo for some time (and very few
overheads)” (FM10). This highlighted how risk is associated with other influencing factors, and
these will be presented later.
4.3.2. Pattern Recognition Ability
The majority of the interviewees agreed that they had high pattern recognition ability. One
entrepreneur stated “I think having an exposure to the industry at an early age in life helps. I was
able to understand the industry in a very deep context of entrepreneurship. I was already
comfortable with how the system worked, how work was procured, how relationships were
formed and various intricacies that would otherwise only come from broad industry exposure
across several firms” (FM10). He also stated that “The tender system had become cut-throat and
was killing the small players in the industry. By the time I graduated, I had done plenty of
research and thought deeply about how I would tackle the problem” (FM10). Another
interviewee stated that “I enjoy thinking through the different sub-system or components that
can be brought together to create a new product or service” (FM6). A typical comment from the
interviewees is summed up in what one of the interviewees stated, “Analytical; I am an
analytical thinker who is always looking for problems to solve” (FM9).
4.3.3. Entrepreneurship Personality
The majority of the interviewees agreed that they have entrepreneurship personalities. One
interviewee said, “I always wanted to start my own business” (FM9), another stated that “The
decision to start my own business was a dream I had held from quite an early age” (FM3) and
yet another said “It’s definitely a personality thing. I started selling sweets and candies at school
at the age of 7” (FM10). In reference to the reason why he established a start-up business, one of
the interviewees said, “I wanted to influence the direction of the company. I wanted to prove to
myself that I could build and run a successful business” (FM2).
When another member of the team, such as a technical expert, instead of the founding member
was the lead entrepreneur, there was usually uncertainty. For example, when asked if he thought
he had an entrepreneurship personality, one interviewee said “Probably not before I joined this
company, but it changed after spending a couple of years here” (FM5), and another interviewee
said “Not as much as some of the people that I know, but yes, I would say that I do” (FM4).
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4.3.4. Social Capital
The majority of the interviewees agreed that a social network is important and that it played a
part in influencing their decision to become a member of a technology-based enterprise.
However, they made distinctions between having a large social network in the field that they
wanted to start their businesses in, and their ability to build a network, i.e. a networking
personality. One interviewee said, “This has more to do with charisma and adaptability. Being
social, speaking the language, fitting in, and making people feel very comfortable in your
company is essential” (FM10). Another interviewee said “Because I am always seeking
opportunities, I meet people who share similar passions and I find myself removed from people
that don’t” (FM8). Moreover, another interviewee said, “I enjoy meeting new people and finding
possibilities for collaboration” (FM4).
Some of the interviewees also found the other founding members through their network, similar
to that found by Stuart and Sorenson (2003). “I was one year into a part-time MSc thesis of
whom one of my supervisors started this business and approached me to become involved”
(FM4). Another said, “Some of my colleagues were willing to work together with me” (FM1),
and another said that “My business partners are also electrical engineers” (FM9). They were all
at the same stage in their careers and shared a common vision. Some of the other founding
members found that it was not just their network that was important, but the networks of their
partners. For example, one engineer said “This business rests on a few other individuals’
networks” (FM4), another said “The success of the business did not really depend on my
network” (FM5), but his partners’ networks helped. However, one interviewee said “It is also
something of an art to move from contacts to actual business appointments” (FM10). Therefore,
emphasising that it is not just about the social capital, but the ability to utilise it.
4.3.5. Work Experience
All the interviewees indicated that the work experience had, in some way, influenced them into
starting a technology-based start-up. One of the engineers said “Being confident through the
experience built up over my career” (FM1) gave him the confidence to make the move.
Similarly, another said, “Perhaps, after acquiring so much experience, I realised a pushing
pressure to practise my own trade” (FM8). Additionally, another interviewee said, “I learnt a
huge amount, lead a sizable team of engineers to create a world-class product” (FM3). Another
founding member stated “I had worked extensively in the Eastern Cape and seen first-hand the
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changes [that] infrastructure development could make to people’s lives” (FM10). Sometimes it
was not the positive things about one’s work experience that caused the shift into becoming a
founding member, but the negative things. For example, one interviewee said “I had recently
worked at a company that was not exactly in my field of expertise and was also extremely badly
run (in my opinion). While I believe that I was good at my job there, I did not gain any
enjoyment from the job” (FM5), and another said “I resigned from another job in the
telecommunications industry as I felt that while the job was challenging and I was gaining great
experience, the technology was not entirely new” (FM4).
4.3.6. Desire for More Money
Most of the interviewees agreed that the desire for more money influenced the choice to become
part of a technology-based start-up. One of the interviews said “I was at an age where I needed
to consider how I would retire, but had not built up any funds. A business opportunity could
offer that” (FM1). While talking about his previous job, another said “We were also expected to
work long hours with no proper compensation. I had decided that by going to a small company I
would be able to control these things myself” (FM5). One of the interviewees, who had worked
at a factory when he was younger, said “I saw that starting and running a similar company could
give me the financial rewards I wanted” (FM3).
Due to their financial circumstances, some of the interviewees were driven by the need for
survival, as opposed to becoming rich. For example, one interviewee said “It was nearly
impossible to find a job in Cape Town” (FM2), another said “I got retrenched at 23” (FM3),
while another said “My father’s business (which I was supposed to take over) had gone
bankrupt, and I had to assist the family in surviving a difficult time. It was the most logical
option at the time” (FM10).
Some of the interviewees were reluctant to state that the desire for money was an influencing
factor. For example, one interviewee said “In some ways, the possibility of being a part of
something that could grow exponentially was a motivator, but as this is a bit of a way off, for
now it is a secondary factor” (FM4), and another said that “There are fantastic opportunities in
this country, not just in order to enrich oneself, but also to make a contribution to society”
(FM7).
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4.3.7. Desire for Work Flexibility
The majority of interviewees specified that the desire for work flexibility triggered the transition
into entrepreneurship. One of the interviewees believed that that he was driven by “The need to
have greater decision-making freedom to consolidate my experience and apply my ideas, rather
than merely following others” (FM1). Another interviewee said that “Having worked in a
multinational environment, I wanted to experience a more agile, flexible workplace” (FM2),
while another interviewee felt that “Part of the attraction was definitely being my own boss. I
wanted to steer my own course and be the master of my own destiny” (FM3). Another
interviewee said, “I prefer to make decisions and implement, I prefer to be independent in all I
do, including working hours. I also prefer to be in charge” (FM8). Furthermore, another
interviewee said, “I started my business to satisfy my need to work in a free flowing
environment” (FM9) and “I was very independent and passionate about my engineering. I
wanted to do things right and was determined to get there. So when the opportunity arose, there
was little hesitation to abandon the corporate world and start my own business” (FM3). The type
of flexibility desired by the interviewees differed. These differences ranged from the desire for
engineering flexibility and the desire for time flexibility, to the desire for flexibility in running a
company.
4.4. New Enablers
4.4.1. Identified Opportunity
The majority of the interviewees made the transition when they identified an opportunity. This is
summed up in what one of the interviews said “when the opportunity arose, there was little
hesitation to abandon the corporate world and start my own business” (F3). However, amongst
the interviewees, some were actively searching for an opportunity while others stumbled across
an opportunity. For example, one of the interviewees felt that they had created the opportunity
and said that “There were few opportunities available in Cape Town, so we created one” (FM1).
Another interviewee said, “The SA m-commerce industry was very new in 2009, with no local
apps. This was an opportunity to be innovator in many sectors” (FM2). Similarly, one
interviewee said, “Perhaps, if you recall the milling industry was regulated and not opened for
commercialisation. With the new government, we have the opportunity to actively compete with
big milling organisations and thus close the monopolist opportunities that existed before” (FM9).
Moreover, another interviewee said “The macro-economic factors were favourable. There was a
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boom in the industry at the time” (F10). These identified opportunities were linked to the self-
perceived pattern recognition abilities of the interviewees.
An example of where an interviewee stumbled across an opportunity was given by one candidate
who said, “I was one year into a part-time MSc thesis ... one of my supervisors started this
business and approached me to become involved” (FM4) On the other hand, some of the
candidates felt that they kept on trying until something happened; their technology-based start-
up was not the first business that they had started.
4.4.2. Exposure to Entrepreneur(s)
Many of the interviewees felt that it was useful having exposure to entrepreneur(s) and that this
made the transition more comfortable. One of the interviewees, while talking about the qualities
needed for entrepreneurship, said, “I've been extremely fortunate as I have grown up surrounded
by people who exude these and other positive qualities” (FM7). Another interviewee said that
“It’s essential to find a ‘political mentor’” (FM10). Another said, “This passion for
experimentation and science was nurtured by my father who always encouraged us to look at
problems and see how we could come up with a solution to solve the problem, but at the same
time understanding the science behind it” (FM6). Another interviewee always knew that he
would take over his father’s business, and therefore had his father as a mentor. One of the
interviewees who wished he had exposure to an entrepreneur said, “It took me years to find a
good business mentor. I believe that supporting young businessmen with advice, support, and
motivation is key to growing the industry” (FM3). This enabler is linked to social capital, as they
can tap into the networks of these mentors.
4.4.3. Job Creation
One theme that arose during the interviews was the need for job creation in South Africa. This
theme was evident in two forms: the need to create a job for one’s self, as stated earlier; and the
second is the need to create jobs for other people in South Africa. Seeing as the former theme
was dealt with under the “need for more money”, this section will deal with the latter. One
entrepreneur said that he was driven by “The desire to do something meaningful that could
contribute to the wellbeing of people in South Africa (through job creation and lessening oil
dependency/pollution)” (FM1). Another said, “One of the things in South Africa that motivates
me personally is there is a real need to create jobs” (FM7) and another stated that he was also
driven by the need for “more businesses that are creating jobs, value, and innovative solutions to
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South Africa's specific challenges” (FM4). Interestingly, one of the interviewees stated that
although the South African situation had driven him to create a job for himself, he has
“contributed more to the country’s overall ability to grow technology-based businesses
employing and training more people to be truly productive” (FM3). This highlights the need for
more founding members to reduce unemployment.
4.4.4. Many Business Opportunities in SA
Another theme that was common amongst the founding members is that they were motivated by
the many business opportunities available in South Africa. One interviewee said that “There are
many opportunities to improve customer service in South Africa” (FM2). Another interviewee
pointed to cost-savings, saying that “The cost of living is relatively low and so one can last much
longer on a certain amount of cash than say, in Silicon Valley. This also means that one can hire
engineers and other skilled people at rates that are much lower than Europe or the States” (FM4).
Another interviewee pointed at an opportunity brought about by the skills shortage, saying,
“There is a shortage of technical expertise in South Africa. This creates [an] opportunity for the
establishment of new businesses, especially in technology” (FM6).
While discussing the founding team, another engineer said, “We all have good technology ideas
that we want to pursue. We believe these ideas would allow us to create more ideas and also
more products” (FM9). In summary, one of the interviewees said that “A crude definition of
business is satisfying someone's need for compensation. In South Africa there are a lot of
unfulfilled needs, and therefore a lot of business opportunities” (FM7). Similar to being
motivated by identifying an opportunity, this enabler shares many attributes with a pattern
recognition personality.
4.4.5. Globalisation
Due to globalisation, businesses can be run from anywhere in the world and can trade with the
whole world. Some of the interviewees felt that being in South Africa had nothing to do with the
reason why they started their businesses. For example, one interviewee said, “I think I would
have pursued this business from anywhere in the world” (FM9), and another said that
“technology businesses are often fairly location agnostic in terms of sales, one can tap into larger
markets worldwide than would be available locally” (FM4). A further description was given by
one interviewee who said that “as large South African firms expand into other African markets
there is the opportunity to expand with them into these markets” (FM7), that businesses do not
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only have to be local. At the reverse end of the spectrum one of the interviewees stated that
“South Africa represents a good opportunity to implement technology solutions already
successful in the rest of the world” (FM7). Thus, globalisation can favour doing business in
South Africa as well. This suggests a relationship between the many business opportunities in
South Africa and globally.
4.5. Previously Identified Inhibitors
This section describes the previously identified inhibitors that will be studied further. For those
that will not be studied further and the reasoning behind their exclusion, see Appendix C.
4.5.1. Risk-averse
Risk aversion was found to be an inhibitor amongst some of the interviewees, however not all of
them were willing to admit to this. Some of the interviewees stated that their current risk
aversion is due to the circumstances or situation in which they find themselves. The focus in this
section will be on the intrinsic personality of the interviewees. The situational/circumstantial
enablers will be discussed in the respective enablers.
One interviewee generalised engineers saying, “Engineers are cautious, and realistic” (NFM6),
while another referred to his own personality by saying, “I’m not a natural risk-taker, rather
preferring to analyse situations” (NFM9). Another interviewee said that “The risks involved in
starting a business don’t seem to be worth it” (NFM2). One of the interviewees expressed
caution, saying, “I need to be sure something will work before starting” (NFM10). Similarly,
another interviewee said, “I am a very considered decision-maker – this may have impacted on
my motivation to start a tech business” (NFM8).
4.5.2. Low Social Capital
Low social capital was identified as an inhibitor amongst the interviewees. However, there are
two different ways that this is signified. The first is the low ability to build a social
capital/network and the other is the current social capital/network that the person has. One of the
interviewees said, “I’m not a people’s person. My emotional intelligence is too weak. I will hurt
the feelings of the people” (NFM2). On the opposite side of the spectrum, another interviewee
said, “I am a very social person and I have the network which I use sometimes” (NFM2). He
also said that although he does not have a network in the field he works in; his networks in other
fields can help him get the network that he needs. Others felt that they do not have the network
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to find a team to help them start up. For example, one interviewee said, “But I’ve yet to meet the
right people at the right time who are willing to start a business” (NFM6). Some of the
interviewees stated that they do not have the political network or political connections needed,
however this will be presented as a separate inhibitor, because it is specific to the South African
environment.
4.5.3. Lack of Interest in Entrepreneurship
Some of the interviewees were not interested in entrepreneurship or becoming a founding
member of a technology-based start-up. One interviewee said, “It has never crossed my mind to
even start a business” (NFM2). For some of the candidates it was just the lack of desire to start a
business in the technology/engineering field. For example, one interviewee said, “I am creative
and people-focussed – I have never had a specific desire or interest in starting a tech company”
(NFM8). Some of the reasons given for the lack of interest were associated with some of the
other inhibitors; however these will be discussed in their respective sections.
4.5.4. Lack of Funding
A commonly stated inhibitor was lack of funding. One of the interviewees, a civil engineer, said,
“The environment does not promote SMMEs. Say I wanted R4million to buy a building to
renovate, who will sponsor this?” (NFM3). Another interviewee said that “Starting a business
will require some investment which I just do not have” (NFM5). Similarly, when asked why he
has not started a technology-based start-up one interviewee replied that he did “Not having
enough capital to get the business off the ground” (NFM7). Another interviewee said he was
“Not aware of government incentives to help fledgling businesses” (NFM10). With respect to
funding, one of the candidates said that “most BEE projects I’ve been involved in, the payments
have been slowly processed and I just don’t have the patience to wait for 3-6 months to be paid
after having been put under pressure and having had to work long hours to deliver on the
projects on time” (NFM1). Some of the other candidates felt the need to state why they could not
fund their businesses, stating reasons such as having too many responsibilities and the lack of
political connections. These reasons will be presented in their respective sections.
4.5.5. Job Comfort
Job comfort was a commonly agreed inhibitor to becoming a founding member of a start-up
business. However, the reasons can be divided into two main categories: The first reason is that
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those individuals really liked their job, and did not see themselves leaving their jobs; and the
second reason is that some individuals are comfortable receiving a guaranteed salary. One of the
interviewees said, “I prefer working in a multi-product development environment where I’m
able to change subjects regularly” (NFM1) rather than starting a technology-based business. One
of the interviewees works for a state owned enterprise and stated that it has a monopoly in
telecommunications, saying, “The company always introduces new technology that we must
study” (NFM4), this sufficiently fulfils his comfort as an engineer. While another stated that he
is “passionate about maintenance engineering” (NFM2), and therefore he does not see himself
leaving his job.
In terms of being comfortable with the salary he receives, one interviewee said, “I am
comfortable with my income. I can easily live a comfortable life while having a healthy social
life, which is both balanced and fruitful” (NFM2). Similarly, another interviewee stated that “It’s
a guaranteed salary. You don’t have to ‘hustle hard’ like business people. It’s a fixed contract
salary which increases annually. Like they say in business, your salary is not guaranteed”
(NFM4). Furthermore, another interviewee said, “I only started looking at starting a business
when I was retrenched, however when I got a job that I liked, the idea just frizzled away”
(NFM6).
4.5.6. Lack of Business Skills
Interviewees mentioned a lack of business skills as a reason for not becoming a founding
member of a technology-based-enterprise. One of the interviewees said, “Managing cash flow is
not known to be in an engineer’s skill set” (NFM6). Another interviewee said, “I have not yet
had relevant exposure to business, and running a business” (NFM9), but added that he would be
registering for a business course in the coming year to rectify this. A reference to the engineering
curriculum was given by one interviewee, who said, “My undergraduate engineering curriculum
did not equip me with any skills ... I feel like I do not have the necessary business skills that will
allow me to determine if a business idea is feasible” (NFM5). However, some of the candidates
felt that this was not the main driving reason since some of them had business training while
others felt that they could learn the necessary business skills on the job. For example, one
interviewee said, “I feel I could start a tech-based company, but it would be difficult as I have no
business experience” (NFM10).
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4.6. New Inhibitors
4.6.1. Lack of Opportunities
The lack of opportunities or an idea was common amongst those who were interviewed. Most
interviewees felt that given the opportunity they would have started a business. One interviewee
stated that he lacked innovative ideas. Another interviewee said, “I have not been exposed to a
viable opportunity. No ideas worth pursuing” (NFM10). Additionally, another interviewee said,
“I have not come across a tech start-up that I felt excited to be a part of” (NFM8). Some of the
interviewees felt that there are no opportunities in their areas of specialisation. For instance, one
interviewee said, “Civil eng[ineering] has so many specialisations and in each specialisation
there are lots of job and work opportunities” (NFM1). On the other hand, another interviewee
stated the following as an inhibitor, “Not having the skill set required in an area where it might
be possible to easily start a company” (NFM7). One of the engineers stated that because of his
priorities he has not had the time to develop an idea. In contrast, some of the interviewees felt
that they had ideas but because of other inhibitors such as lack of funding and lack of business
skills, they are unable to implement their ideas.
4.6.2. Many Responsibilities
A significant number of the engineers stated the factor of having many responsibilities as a
reason for not becoming a member of a technology-based enterprise. This inhibitor is associated
with not being able to take the risk; however, it is not a personality issue but a
situational/circumstantial issue. It is also related to the reason why some individuals are
comfortable in their job where they receive a guaranteed consistent income, as well as not
having the funds to start a business. An interviewee said, “I have a family to support. The risks
involved in starting a business don’t seem to be worth it” (NFM2). Similarly, another
interviewee said, “Because of my lifestyle and family needs, I require a fixed monthly income to
cover my living expenses” (NFM5).
An interesting direction taken by this inhibitor was that some of the interviewees made a
reference to this inhibitor being because of the South African culture. For example, one
interviewee, in reference to the South African culture, said that “getting into debt straight out of
varsity makes it difficult to do anything other than working for a fixed income” (NFM5).
Additionally, another interviewee said that “few people [that are] aware of the situation will
attempt a start-up if they have responsibilities (a bond, dependants, etc.)” (NFM6). Another
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echoed the above statement, saying, “The transition to starting a new business is difficult
financially – particularly at my stage of life where I have 3 kids at school, etc.” (NFM8).
4.6.3. Lack of Political Connections
As stated earlier, the lack of political connections was stated as a reason as to why some of the
interviewees did not become founding members of a start-up enterprise. The interviewees stated
that political connections are a requirement of doing business in South Africa. One interviewee
said, “Most people that succeed in South African businesses are politically connected
individuals” (NFM2). Similarly, another interviewee said, “In order to do well in business today,
you either have to be politically connected or be born [into] a wealthy family” (NFM4).
Furthermore, when speaking about getting tenders, one interviewee said, “it’s impossible to
secure such contracts unless one is ready to bribe their way up” (NFM1). However in contrast,
when speaking about political connections, one interviewee posited that political connectedness
may play a part in some businesses, but he feels that social networking is more important than
being politically connected.
4.6.4. Lack of ECSA Registration
Not being professionally registered with ECSA was cited as a reason for not getting into
entrepreneurship for some of the unregistered interviewees. One of the interviewees said,
“without this it’s impossible to sign-off on any major engineering project” (NFM1), and if he
was to start a business he would have to employ a registered professional and they are
expensive. One interviewee said, “In the civil engineering industry a professional registration
accreditation is pivotal for clients to consider doing business with the entrepreneur” (NFM3). In
contrast, some of the interviewees stated that they do not see the absence of accreditation as a
barrier to becoming involved in a technology-based business. One interviewee said, “I don't feel
that ECSA registration is necessary to start many types of technology-based businesses”
(NFM10).
4.6.5. Labour Laws and Unions
The lack of desire to deal with the unions and the labour laws was stated as a reason for not
starting a technology-based enterprise in South Africa. One of the interviewees said, “The laws
that the unions come up with make life very difficult for the average South African
businessperson, I believe” (NFM2), and “To penetrate the business market in South Africa is
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probably very easy, but there are too many labour laws that make it hard for most business
personnel to start an engineering company” (NFM2). Another interviewee said that “South
African skills are expensive compared to countries like India, where skilled labour is more
readily available” (NFM6). When discussing labour, one engineer said, “It can be summarised as
follows: Easy out – Easy in, Difficult out – Difficult in” (NFM7). For him to ensure that a
business survives in these economic conditions, a company must be allowed to downscale easily
and quickly, and the labour laws (specifically in South Africa) prevent that from happening.
On the other side of the spectrum, one of the interviewees said that “most start-up tech
companies begin with a single person or group of friends and thus would not have to worry too
much about labour laws or unions” (NFM10). Additionally, when talking about labour laws and
unions, another interviewee said, “This is a factor, but not something that would stop me from
starting something if I was really interested in the technology and believed it could give an
excellent return on investment” (NFM8).
4.7. Summary of Results
Most of the studied influencers were mentioned by at least one person. However, because it is
difficult to get a good response to questionnaires there is a limitation to the amount of questions
that can be asked. The qualitative interviews gave a good understanding of how the influencing
factors interrelate. It was also decided that in order to get a statistically relevant response rate
after a few pilots, questions relating to EE or BEE were removed because the feedback was that
these questions deterred people from completing the questionnaire (see Appendix C for
qualitative findings of influencers not studied further).
The table below summarises the results of the enabling factors study.
Table 4: Table showing the results of qualitative study for enabling factors
Previously Identified Newly Identified Not studied further
Comfort with taking risks Identified opportunity Salary ceiling and scarce jobs for
white engineers
Pattern recognition ability Influence of mentor(s) The business opportunities
brought about by B-BEE for
black engineers
Social Capital Networking ability Postgraduate education
Work experience Job creation Being part of a small firm
Desire for more money Many business opportunities High job turnover
Desire for work flexibility Globalisation Perceived support of new
businesses
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The table below presents the summary of the inhibiting factors.
Table 5: Table showing the results of qualitative study for inhibiting factors
Previously Identified Newly Identified Not studied further
Risk-averse Lack of opportunities Lack of time to develop an idea
Low social capital Low networking ability Lack of facilities to develop an
idea
Lack of interest in
entrepreneurship
Many responsibilities The high salaries and job
opportunities created by B-BEE
Lack of funding Lack of political connections B-BEE as a barrier for white
engineers
Job comfort Lack of ECSA registration
Lack of business skills Labour laws and unions
4.8. Final Propositions
This section describes the final propositions of the research after the qualitative study.
4.8.1. Proposition 1: Factors Enabling Engineers to Become a Part of Technology-
based Start-ups
The main factors enabling engineers to become a part of technology-based start-ups are:
E1: Comfort in taking risks
E2: Pattern recognition ability
E3: Entrepreneurship personality
E4: Low networking ability
E5: Work experience
E6: Desire for more money
E7: Low social capital
E8: Desire for work flexibility
E9: Identified opportunity
E10: Exposure to entrepreneur(s)
E11: Desire to create jobs in SA
E12: Many business opportunities in SA
E13: Globalisation
4.8.2. Proposition 2: Factors Inhibiting Engineers to Become a Part of Technology-
based Start-ups
The main factors inhibiting engineers to become a part of technology-based start-ups are:
I1: Risk-averse
I2: Low networking ability
I3: Lack of interest in entrepreneurship
I4: Low social capital
I5: Lack of funding
I6: Job comfort
I7: Lack of business skills
I8: Lack of opportunities
I9: Many responsibilities
I10: Lack of political connections
I11: Lack of ECSA registration
I12: Labour laws and unions
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5. PHASE2: QUANTITATIVE FINDINGS
5.1. Introduction
This chapter presents the quantitative results that were acquired using an online survey. The
chapter begins by describing the sample, and then uses descriptive statistics to present the results
of the demographic and engineering background questions for the entire sample. Following that,
descriptive statistics, Mann-Whitney U Tests, and correlation analysis’ are used to describe and
analyse the two divisions of the sample, namely founding members and non-founding members.
5.2. Descriptive Statistics of the Sample
These results were captured over a period of four weeks using an online questionnaire. The total
sample was limited to 150 engineers.
5.2.1. Sample Geographical Distribution
The participants were asked which province they resided in and the results are presented in the
figure below. It is evident that the majority of the respondents resided in the Western Cape and
Gauteng, with a small proportion coming from the other provinces.
Figure 6: Geographical spread of the sample
1% 1% 4%
42%
1%2%3%1%
45%
Province
Eastern Cape
Free State
Mpumalanga
Gauteng
Northern Cape
North West
Kwazulu Natal
Limpopo
Western Cape
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5.2.2. Sample Population Groups
Figure 7: Sample population groups
The participants were asked to indicate their closest population group. These population group
categorisations were taken from Statistics South Africa (2012), who refer to the different
population groups as African, Indian/Asian, Coloured, and white. (Previously in this research
report, the term ‘black’ referred to Africans, Indians/Asians, and Coloureds). As can be seen in
Figure 7, the majority of the respondents were white or African. The Indian/Asian and Coloured
groups made up 10% and 7% respectively. Therefore, a comparison between Africans and
whites will provide useful insight.
5.2.3. Sample Gender
Figure 8: Gender of the sample
42%
10%7%
41%
Population Groups
African
Indian/Asian
Coloured
White
87%
13%
Gender
Male
Female
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The participants were also asked to specify their gender. As shown in Figure 8, the majority of
the sample was male and 13% were female. This is expected since engineering in South Africa is
a male-dominated field.
5.2.4. Sample Business Education
The participants were asked to indicate if they have any formal business education and were
given examples of certificates, diplomas, and degrees. As shown in Figure 9, the majority of the
sample did not have a business-related education; however, a significant size of the sample
(39%) indicated that they did have a business-related education.
Figure 9: Proportion of sample with a business education
5.2.5. Sample Engineering Disciplines
Figure 10: Sample engineering disciplines
The participants were asked to indicate their closest engineering discipline and were given the
option of ‘Other’ to specify if their discipline did not fit into any of the categories. The ‘Other’
39%
61%
Business Education
Have a formal businesseducation
Do not have a formalbusiness education
0.679.33 8.00
39.33
4.00
24.67
8.67 5.33
0
10
20
30
40
50
Engineering Discipline
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category consisted of telecommunications, process engineering, and a mixture of fields. As
shown in Figure 10, the majority of the sample came from electrical engineering and mechanical
engineering environments.
5.2.6. ECSA Professional Registration Status
Figure 11: Sample proportion of ECSA professionals
The participants were asked to specify if they were registered with ECSA as professional
engineers, with examples of Pr Eng and Pr Tech given. As shown in Figure 12, the majority of
the sample respondents were not registered ECSA professionals, however 30% of the sample
indicated that they were registered ECSA professionals.
5.2.7. Number of Years since Graduation
Figure 12: Number of years since graduation
30%
70%
ECSA Registered Professionals
Registered
Not Registered
0
10
20
30
40
50
60
0 - 3 Years 4 - 10 Years 11-20 Years 20+ Years
Per
cen
tage
(%
)
Years
Number of Years Since Graduation
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The participants were asked to indicate how many years it had been since they had graduated as
an engineer. As shown in Figure 12, the majority of the sample had graduated between 4-10
years prior to the interview.
5.3. The Proportion of Engineering Graduates that have been involved as Founding
Members of a Technology-based Start-Up
Figure 13: Proportion of founding members in sample
The participants were asked to indicate whether or not they were founding members of
technology-based start-ups. Only 32 respondents were founding members of technology-based
start-ups and 118 were non-founding members, as shown in Figure 13. The proportion of the
sample that are founding members of technology-based enterprises is 21%. Due to the difficulty
experienced in the qualitative phase of locating founding members of technology-based
enterprises, and the difficulty to get these participants to spare time because of their extremely
busy schedules, it was expected that they would constitute a smaller fraction of the questionnaire
respondents. Additionally, it is expected that founding members are only a small percentage of
the engineering population.
5.4. The Main Factors that Enable Engineers to be Founding Members of Technology-
based Start-Ups in South Africa
This section presents the results of the portion of the sample that indicated that they were
founding members of technology-based start-ups.
21%
79%
Founding Members
Founding members
Non-Founding members
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5.4.1. Summary of Enablers Data
Table 6 lists the results of the quantitative study in ranking order of average agreement. As can
be seen, the median was above 3 for all, indicating that the majority of the sub-sample agreed
that these are the main enablers to becoming a founding member. In some cases the mean and
median differed a lot, therefore further justifying the use of non-parametric methods for analysis.
For example, the mean was less than 3 for social capital, however the median is not less than 3,
therefore indicating that the majority did not disagree that this was a main enabler. Therefore, all
the studied enablers are classified main enablers.
Table 6: Quantitative results of enablers in ranking order
No.
Enabler N Mean Med Min Max SD
1 E3 Entrepreneurship personality 32 4.31 4.5 2 5 0.86
2 E9 Identified opportunity 32 4.28 4.0 2 5 0.73
3 E1 Comfort in taking risks 32 4.03 4.0 2 5 0.74
4 E2 Pattern recognition ability 32 3.94 4.0 2 5 0.76
5 E4 Networking ability 32 3.84 4.0 2 5 0.85
5 E5 Work experience 32 3.84 4.0 1 5 0.95
7 E12 Many opportunities in SA 32 3.69 4.0 1 5 0.90
8 E10 Exposure to entrepreneurs(s) 32 3.50 4.0 1 5 1.05
9 E6 Desire for more money 32 3.47 4.0 1 5 1.08
9 E8 Desire for work flexibility 32 3.47 3.5 2 5 1.11
9 E13 Globalisation 32 3.47 3.0 1 5 0.92
12 E11 Desire to create jobs in SA 32 3.22 3.0 2 5 0.87
13 E7 Social capital 32 2.75 3.0 1 5 0.95
A brief examination of the data in the table gives the indication that in general what could be
considered personality attributes (i.e. E1, E2, E3 and E4, excluding outlier E9) were the most
influencing enablers for the founding members to become involved in a technology-based start-
up. This is followed by those enablers that can be considered as emanating from the individual’s
current situation at the time (i.e. E5, E6, E7, E8, E10 and outlier E9), and lastly by the factors
that are specific to being in South Africa (i.e. E11, E12 and E13). The least enabling factor is
social capital (E7); however, networking ability (E4), which translates to the ability to build
social capital, was a much stronger enabling factor.
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5.4.2. Comparisons of Inhibitors between Africans and Whites
As mentioned earlier, South Africa is a new democracy and therefore there is an interest in
determining whether or not race affects the influencers. This is supported by Urban (2006, p.
182) who states that in South Africa “much is left unexplained with regards to the effects of
culture on entrepreneurship”. Only Africans and whites will be compared, because there were
insufficient responses to compare the other two population groups. Comparing the responses
using the Mann-Whitney U test reveals significant differences between Africans and whites for
the rated agreements for enablers, namely entrepreneurship personality (E3) and networking
ability (E4). Africans rated these enablers higher than Whites (4.63, 4.11 vs. 3.73, 3.36).
However, the majority of both ethnic groups agreed that these were enablers (median ≥ 3);
therefore this was not investigated further. See Appendix D for test results.
5.5. Relationships between Factors that Enable Engineers to be Founding Members of
Technology-based Start-Ups
A correlation analysis was done to determine the relationships between the inhibitors. Table 7
below displays Spearman’s correlations, (for a full matrix see Appendix D). The pairs for which
there is statistical evidence of correlation are denoted by an asterisk. According to the results,
there is statistical evidence that the following variables are correlated:
Comfort in taking risk (E1) is correlated to Entrepreneurship personality (E3) and to
Desire for more money (E6).
Entrepreneurship personality (E3) is also correlated to Networking ability (E4) and to
Desire to create more jobs in SA (E11).
Social capital (E7) is correlated to Exposure to entrepreneur(s) (E10).
Desire to create jobs in SA (E11) also correlated to Globalisation (E13).
These relationships will be explored further in the discussion in Chapter 6.
The sample of 32 is theoretically too small to do a factor analysis. However, for the sake of
interest, an exploratory factor analysis (EFA) was conducted to explore if there are constructs
within the data; this is provided in Appendix E.
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Table 7: Spearman’s correlations for related main enablers
E1 E3 E4 E6 E7 E10 E11 E13
E1 1.00 0.37* 0.31 0.42* -0.01 -0.06 0.23 0.20
E3 0.37* 1.00 0.48** 0.26 -0.01 0.30 0.39* 0.06
E4 0.31 0.48** 1.00 0.15 0.00 0.04 0.26 0.16
E6 0.42* 0.26 0.15 1.00 -0.04 -0.17 -0.01 -0.05
E7 -0.01 -0.01 0.00 -0.04 1.00 0.50** 0.19 0.17
E10 -0.06 0.30 0.04 -0.17 0.50** 1.00 0.32 0.15
E11 0.23 0.39* 0.26 -0.01 0.19 0.32 1.00 0.53**
E13 0.20 0.06 0.16 -0.05 0.17 0.15 0.53** 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=32
5.6. The Main Factors that Inhibit Engineers to be Founding Members of Technology-
based Start-Ups in South Africa
This section presents the results of the portion of the sample that indicated that they were not
founding members of technology-based start-ups.
5.6.1. Summary of Inhibitors Data
Table 8 lists the results of the quantitative study in ranking order of average agreement. The
factors not meeting the test criteria of being classified a main factor (those with median < 3) are
highlighted. As can be seen, in contrast to the results for enablers, seven of the inhibitors were
not classified as main inhibitors for the purpose of this study. A brief examination of the patterns
of the data in the table gives the indication that generally the most inhibiting factors are caused
by what could be considered situational attributes, brought about by the circumstances that the
individuals were in at the time (i.e. I5, I6, I7, I8, I9, and I10). These are followed by the
inhibitors that are specific to being in the South African environment (i.e. I10, I11 and I12) and
closely followed by those inhibiting factors that can be considered personality attributes (i.e. I1,
I2 and I3).
A higher proportion of the founding members agreed to the enablers than did the non-founding
member who agreed to the inhibitors. The reason may have been that the enablers are slightly
more pleasant things to hear about oneself, and/or it only takes fewer inhibitors per person to
prevent them from becoming a founding member. The latter can be substantiated by the fact that
only a small proportion of the sub-sample agreed that they experienced a lack of interest in
entrepreneurship.
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Table 8: Quantitative results of inhibitors in ranking order
No. Code Inhibitor N Mean Med Min Max SD
1 I5 Lack of funding 118 3.45 4.0 1 5 1.08
2 I8 Lack of identified opportunity 118 3.33 4.0 1 5 1.23
3 I9 Many responsibilities 118 2.93 3.0 1 5 1.19
4 I6 Job comfort 118 2.90 3.0 1 5 1.21
5 I7 Lack of business skills 118 2.78 2.5 1 5 1.16
6 I4 Low social capital 118 2.65 2.0 1 5 1.14
7 I10 Lack of political connections 118 2.60 3.0 1 5 1.06
8 I12 Labour laws and Unions 118 2.48 2.0 1 5 1.07
9 I2 Low networking ability 118 2.40 2.0 1 5 1.06
10 I1 Risk-averse 118 2.39 2.0 1 5 1.13
11 I11 Lack of ECSA registration 118 1.88 2.0 1 5 0.84
12 I3 Lack of interest in entrepreneurship 118 1.78 2.0 1 4 0.82
The two factors, namely a lack of entrepreneurship education and a lack of ECSA registration,
were used as independent variables to determine if they would moderate the results. Mann-
Whitney U tests were used to verify any significant difference in the rated agreement of the
inhibitor between the two parts of the samples. A significance level of α = 0.05 (two tailed tests),
was used to reject that the portions are equal (see Appendix D for full table).
Table 9: Results of Mann-Whitney U test for no business education vs. business education
Inhibitors
No Business
Education
Business
Education 'U' Stat Sig.
N M N M
I7 Lack of business skills 73 3.05 45 2.33 1086.5 0.002
As expected, the result is that having no formal business education made a difference to the rated
agreement of the lack of business skills factor (Table 9), and not having ECSA professional
registration made a difference to the rated agreement for the lack of ECSA registration (Table
11).
Table 10: Results of Mann-Whitney U test for non-ECSA registered vs. ECSA registered
Inhibitors
Not ECSA
Registered
ECSA
Registered 'U' Stat Sig.
N M N M
I11 Lack of ECSA registration 85 1.85 33 1.64 1055.0 0.037
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Table 11 below shows the extracted responses for those that indicated that they do not have a
business education and those that indicated that they do not have ECSA professional registration.
The result is that lack of business skills factor (I7’) has a median of 3 for the sample, which
indicated that they do not have a formal business education, and therefore it is not rejected as a
main inhibitor for those without a business education. This confirms the results of Peters and
Roberts (1969). However, the median of the lack of ECSA registration (I11’) still stays below 3,
also suggesting that the majority of those without ECSA registration do not feel that it is an
inhibitor.
Table 11: Filtered results of ECSA registered and those with business education
Inhibitor N Mean Med Min Max SD
I7’ Lack of business skills 73 3.05 3 1 5 1.19
I11’ Lack of ECSA registration 85 1.85 2 1 5 0.75
5.6.2. Comparisons of inhibitors between Africans and whites
This section aims to determine if there are significant differences between Africans and whites
for the rated agreements for the inhibiting factors. Mann-Whitney U tests were used for this. The
number of Africans to whites in the sample only differed by five people, making this study
useful. Table 12 shows the results and the factors where there is a significant statistical
difference between the two population groups.
Table 12: Results of Mann-Whitney U test comparing Africans to whites
Inhibitors
African White 'U' Stat Sig.
N M N M
I1 Risk-averse 45 1.98 50 2.66 756.0 0.0056
I2 Low networking ability 45 2.11 50 2.56 837.0 0.0317
I3 Lack of interest in entrepreneurship 45 1.47 50 1.88 840.5 0.0335
I4 Low social capital 45 2.24 50 2.86 766.5 0.0071
I5 Lack of funding 45 3.31 50 3.52 1012.0 0.4035
I6 Job comfort 45 2.38 50 3.24 683.5 0.0009
I7 Lack of business skills 45 2.60 50 2.82 995.5 0.3360
I8 Lack of identified opportunity 45 3.18 50 3.28 1081.0 0.7469
I9 Many responsibilities 45 2.76 50 3.10 945.5 0.1818
I10 Lack of political connections 45 2.24 50 2.62 875.5 0.0627
I11 Lack of ECSA registration 45 1.82 50 1.84 1118.5 0.9615
I12 Labour laws/Unions 45 1.98 50 2.86 627.0 0.0002
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The rated agreement for six out of the 12 inhibitors was found to be significantly different
between the two population groups. Due to these differences, the data pertaining to each
population group was analysed to see if some of these inhibitors might be main inhibitors for one
of the population groups. Table 13 and Table 14 show the result (“A” and “W” used to indicate
where the subjects are African or white)
Table 13: Inhibitor results for African non-founding members
Code Inhibitor N Med Mean Min Max SD
IA1 Risk-averse 45 2.0 1.98 1 5 1.01
IA2 Low networking ability 45 2.0 2.11 1 5 1.05
IA3 Lack of interest in entrepreneurship 45 1.0 1.47 1 3 0.59
IA4 Low social capital 45 2.0 2.24 1 5 1.11
IA6 Job comfort 45 2.0 2.38 1 4 1.13
IA12 Labour laws/Unions 45 2.0 1.98 1 4 0.89
Table 14: Inhibitor results for white non-founding members
Code Inhibitor N Med Mean Min Max SD
IW1 Risk-averse 50 2.5 2.66 1 4 1.17
IW2 Low networking ability 50 2.0 2.56 1 5 1.05
IW3 Lack of interest in entrepreneurship 50 2.0 1.88 1 4 0.90
IW4 Low social capital 50 3.0 2.86 1 5 1.09
IW6 Job comfort 50 3.5 3.24 1 5 1.15
IW12 Labour laws/Unions 50 3.0 2.86 1 5 1.09
The results are that for white engineers, the factor of low social capital (IW4) and the labour
laws and unions factor (IW12) are main inhibitors to becoming a founding member (median ≥3).
Additionally, although not rejected previously for the full sample, job comfort (IA6) was not a
main inhibitor for African engineers, whilst it remained a main inhibitor for white engineers
(IW6).
5.7. Relationships between Factors that Inhibit Engineers to be Founding Members of
Technology-based Start-Ups
A correlation analysis was done to determine the relationships between the inhibitors. Table 15
displays Spearman’s correlations between all the enablers (see Appendix D for full matrix). The
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correlations for which there is statistical evidence of correlation are marked by an asterisk.
According to the results, there is statistical evidence that the following variables are correlated:
Lack of funding (I5) vs. Many responsibilities (I9).
Lack of funding (I5) vs. Lack of political connections (I10).
Job comfort (I6) vs. Many responsibilities (I9).
Job comfort(IW6) vs. Labour laws and unions (IW12)
Low social capital (IW4) vs. Lack of business skills (IW7)
Low social capital (IW4) vs. Lack of political connections (IW10).
Labour laws and unions (IW12) vs. Many responsibilities (IW9).
Labour laws and unions (IW12) vs. Lack of political connections (IW10).
Table 15: Spearman’s correlations amongst main inhibitors
I5 I6 I8 I9 I10
I5 1.00 -0.07 -0.10 0.20* 0.35***
I6 -0.07 1.00 0.11 0.30*** 0.15
I8 -0.10 0.11 1.00 -0.11 -0.02
I9 0.20* 0.30** -0.11 1.00 0.16
I10 0.35** 0.15 -0.02 0.16 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=118
Table 16: Spearman’s correlations for inhibitors for those without business skills
I5 I6 I7’ I8 I9 I10
I7’ 0.02 0.01 1.00 0.07 0.18 0.16
Note: *p<0.05, p<0.01**, p< 0.001***, N=73
Table 17: Pearson’s correlations amongst main inhibitors specific to White engineers
IW4 IW5 IW6 IW7 IW8 IW9 IW10 IW12
IW4 1.00 0.16 0.10 0.46*** 0.26 0.41** 0.37** 0.27
IW6 0.10 -0.24 1.00 -0.12 0.20 0.29* -0.03 0.36*
IW12 0.27 0.12 0.36* -0.02 0.03 0.34* 0.32* 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=50
For interest’s sake, EFA on the total sample was conducted to determine if there are constructs
within the data; this is provided in the Appendix E.
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5.8. Summary
The main influencers for engineers to become a founding member of a start-up enterprise in
South Africa are displayed in Table 18. The analysis on how this research meets the research
criteria of a quantitative research was provided in section 3.6.
Table 18: Summary of main influencing factors
Enablers
Inhibitors
E3 Entrepreneurship personality I5 Lack of funding
E9 Identified opportunity I8 Lack of identified opportunity
E1 Comfort in taking risks I9 Many responsibilities
E2 Pattern recognition ability I6 Job comfort
E4 Networking ability I10 Lack of political connections
E5 Work experience I7’ Lack of business skills
E12 Many opportunities in SA IW4 Low social capital
E10 Exposure to entrepreneurs(s) IW12 Labour laws and unions
E6 Desire for more money
E8 Desire for work flexibility E13 Globalisation
E11 Desire to create jobs in SA
E7 Social Capital
The summary of the factors that have a relationship is displayed in Table 19.
Table 19: Summary of correlations amongst the main influencing factors
Enabler Inhibitors
Comfort in taking risks vs. Desire for
more money (β =0.42, p <0.05)
Lack of funding vs. Many responsibilities (β =
0.20, p < 0.05)
Comfort in taking risks vs.
Entrepreneurship personality (β =0.37,
p <0.05)
Lack of funding vs. Lack of political connections
(β = 0.35, p < 0.001).
Entrepreneurship personality vs.
Networking ability (β =0.48, p < 0.01)
Job comfort vs. Many responsibilities (β =0.30, p
< 0.01)
Entrepreneurship personality vs. Desire
to create jobs in SA (β = 0.39, p < 0.05)
Job comfort (IW6) vs. Labour laws and unions
(IW12) (β = 0.36 p < 0.05)
Social Capital vs. Exposure to
Entrepreneurs(s) (β =0.50, p < 0.01)
Low social capital (IW4) vs. Lack of business
skills (IW7) (β =0.46, p < 0.001)
Desire to create jobs in SA vs.
Globalisation (β = 0.53, p < 0.01)
Low social capital (IW4) vs. Many
responsibilities (IW9) (β =0.41 p < 0.01)
Low social capital (IW4) vs. Lack of political
connections (IW10) (β = 0.37, p < 0.01)
Labour laws and unions (IW12) vs. Many
responsibilities (IW9) (β = 0.34, p < 0.05)
Labour laws and unions’ (IW12) vs. Lack of
political connections (IW10) (β = 0.32, p < 0.05)
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6. DISCUSSION OF FINDINGS
This chapter discusses the results presented in both the qualitative and the quantitative findings.
It aims to analyse these results further, as well as to merge them in order to answer the research
questions. Additional discussions of enablers not studied further can be founding in Appendix C.
Relationships amongst enablers and inhibitors, as suggested by the findings, are shown in Figure
14 and Figure 15 respectively.
6.1. Discussion on the Main Factors that Enable Engineers to be Founding Members of
Technology-based Start-Ups in South Africa
Figure 14: Relationships amongst enabling factors
The data suggests that 13 investigated factors are main enablers to becoming a founding member
of a technology-based start-up in South Africa. The highest ranked enabler was found to be
having an entrepreneurship personality. During the qualitative study, a common theme was that
most of the founding members have always wanted to start their own business, or have been
involved in some start-up business in the past. There was only uncertainty about whether the
engineer had an entrepreneurship personality in the situation that the founding member was not
the lead entrepreneur (see section 4.5.1). Consequently, they had to depend on the
entrepreneurship personalities of the other founding members. This enabler was found to be
highly correlated to the networking ability factor (jointly ranked 5th) (β =0.48, p < 0.01), which
is understandable, because an entrepreneur must be able to grow their business and networking
ability is what we associate with entrepreneurs. For example, Foley and O’Connor (2013, p. 277)
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define social networking as the part of social capital “that has a specific purpose for accessing
entrepreneurship resources”.
Additionally, the data suggests that being motivated by having an entrepreneurship personality is
correlated to the desire to create jobs in SA (ranked 12th) (β = 0.39, p < 0.05). This result is
supported by Benzing and Chu (2009) who studied the motivators for entrepreneurs in other
African countries and found that some were motivated by job creation for both themselves and
for others. This coincides with the main target of many policy-makers of creating more jobs by
using entrepreneurship as a vehicle (Steyaert & Katz, 2004). Fortunately, this appears to be a
trait of entrepreneurs in South Africa. However, one cannot help but wonder if the entrepreneurs
are not just mimicking what society expects of them, rather than what really motivates them.
Furthermore, being motivated by the desire to create more jobs in SA is also highly correlated to
being motivated by globalisation (jointly ranked 9th) (β = 0.53, p < 0.01). This can be explained
by the fact that to create more jobs, the economy must grow. For the economy to grow, we have
to trade with other nations. Globalisation has made the world smaller, and allows us to trade with
other countries more easily. The qualitative interviews revealed that for some founding members
being in South Africa had no influence on the transition to becoming an entrepreneur. With
globalisation, an entrepreneur can run a business from anywhere in the world. Consequently,
globalisation was an enabler for some of the founding members as they were not limited to the
South African market and were able to easily move their businesses to another country if need
be. However, most businesses in South Africa deal with the government, therefore globalisation
may not have an influence on all entrepreneurs, hence the lower ranking of this factor.
The low ranking of the desire for more money enabler (jointly ranked 9th) was not surprising
because it was usually the last enabler that was revealed during the qualitative interviews, and it
usually only came after a bit of probing. Some entrepreneurs are not driven by money, but by the
need to achieve (Mitchell & Mickel, 1999; Lee & Tsang, 2001). This achievement is displayed
by material items that are bought with money. This is supported by a statement from one of the
interviewees in the qualitative study who said, “money is an indicator of the success of the
venture; I am more excited about growing the business and creating jobs” (FM6).
On the other hand, the desire for more money was found to be correlated to higher ranked
variables namely, comfort in taking risks (ranked 3rd) (β =0.42, p <0.05). As was discussed
briefly earlier, the comfort in taking risks referred to the personality of the person. However, the
risk-taking propensity of individuals can be moderated by the situation/circumstance in which
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they find themselves. Therefore, a possible explanation of the correlation is that the desire for
more money creates a personality type that is willing to do anything, including taking risks to get
that money. Mitchell and Mickel (1999) also found that there is a relationship between people
who are desirous for money and risk-taking behaviour.
The factor of comfort in taking risks was also found to be correlated with being motivated by
entrepreneurship personality (β =0.37, p <0.05). This is not surprising, as this was confirmed in
the literature and the interviews conducted. Conversely, risk aversion was rejected as a main
inhibitor to becoming a founding member. This suggests that it may not be the personality of the
non-founding members that inhibits them from becoming a founding member. Therefore, if they
were to become a founding member in the future, they would also indicate that they were
comfortable taking risks. Additionally, as discussed by Palich and Bagby (1995), studies like this
reflect the opinions of the individuals about their own propensity to take risks. Furthermore, as
mentioned earlier, it has been suggested that the risk-taking behaviour of entrepreneurs does not
differ from that of non-entrepreneurs (Palich & Bagby, 1995; Wu & Knott, 2006). This research
supports this claim.
The factor of social capital (ranked 13th) is the least influencing of the main enablers to become a
founding member of a technology-based start-up business. This was surprising, since it is
constantly mentioned in literature as a motivator. The data suggests that it is less influencing
than the factor of networking ability (jointly ranked 5th) that was presented earlier. As indicated
previously, social capital has to do with the having access to a network that can make a business
succeed. Hence a possible reason for this result may be that it is less pleasant to hear that you
started a business because of the network that you have. As shown in the qualitative study of
non-founding members, the strength of social capital (connections) is linked to political
connections. Most people do not want to be considered as starting a business because of the
political connections that they have or because of whom they know. Additionally, De Carolis,
Litzky and Eddleston (2009), who investigate why social capital affects the progress of new
venture creation by some entrepreneurs more than others, state that it’s not only the social
network but also the relational capital that makes businesses successful.
However, being motivated by social capital is strongly correlated to the factor of exposure to
entrepreneurs(s) (ranked the 8th) (β =0.50, p < 0.01). A possible explanation is that the
entrepreneur(s) that the person is exposed to become(s) the social capital that influences the
person to become a founding member. This suggests that the element of social capital that is
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most enabling is the motivational influence of other entrepreneurs, rather than a network as a
channel to make a business succeed.
The highly influencing factor of having identified an opportunity (ranked 2nd) was an obvious
enabler for those subjects who were looking for an opportunity. However, as discovered in the
qualitative interviews, some of the subjects were not looking for an opportunity, but were
presented with an opportunity that they then seized (see section 4.4.1). This is another isolated
enabler in the sense that there is no statistical evidence that it is correlated to any other studied
enabler. This is in contrast to the findings by Bhagavatula, Efring, van Tilburg and van de Bunt
(2010) who find that it is related to social capital. On the other hand, interestingly the factor of
Lack of identified opportunity ranks highly amongst the inhibitors (2nd). This will be explored in
the discussion of inhibitors in the next section.
Another isolated enabler is having pattern recognition ability (ranked 4th). However, during the
interviews it seemed that it was related to believing that there are many business opportunities in
South Africa. Similarly, being motivated by the many business opportunities in SA (ranked 7th)
was not found to be correlated to any of the other enablers.
There was no statistical evidence that work experience (jointly ranked 5th) was correlated to any
of the other main enablers. This is in contrast to the findings by Lee and Wong (2004) who relate
prior managerial experiences to the desire for more money (see section 2.6.2.2). The reason
could be that the majority of the respondents, as was found in the qualitative interviews, had
wanted to become entrepreneurs from a young age, even before their work experience.
The last isolated enabler is the desire for work flexibility (jointly ranked 9th). This is in contrast
to the study by Zanakisi et al. (2012) who found that independence and work flexibility was
more motivating than the desire for more money. Additionally, this was an interesting result
because in the qualitative interviews the majority of the sample mentioned this enabler.
However, a possible explanation can be deduced from some of the other interviewees who were
pushed into becoming a founding member by their individual circumstances. Thus, it can be seen
that this factor was not necessarily a driver for them in such a context. Moreover, these results
come from the founding members after they were already founding members. Consequently, the
amount of hard work and lack of flexibility that they experience as founding members could
have slightly swayed them. Furthermore, this enabler is most likely to create lifestyle
entrepreneurs, rather than those that are focused on creating big businesses that employ many
people.
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There were no significant differences between the African and white population groups in terms
of the main enablers; however the African population group on average agreed more to
entrepreneurship personality and networking ability as enablers.
6.2. Discussion on the Main Factors that Inhibit Engineers to be Founding Members of
Technology-based Start-Ups in South Africa
Figure 15: Relationships amongst inhibiting factors
The quantitative analysis retained only eight main inhibiting factors. A brief examination of the
rejected main inhibitors reveals a pattern that the majority of these inhibiting factors have to do
with the personality attributes of the individuals. The remaining inhibitors relate to the situation
that the non-founding members find themselves in. This is in contrast to the determined main
enabling factors, where the highest ranked factors can be linked to the personality attributes of
the engineer. This section discusses the main factors that inhibit engineers to be founding
members of technology-based start-ups.
The factor of lack of funding was the most prominent inhibitor to becoming a founding member
of a technology-based start-up business. This was closely followed by the lack of identified
opportunity (ranked 2nd), and this will be discussed later. Analysing this in comparison to the
least inhibiting and rejected factor, namely the lack of interest in entrepreneurship, reveals a
possible explanation that the majority of the sample would like to be a founding member of a
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technology-based start-up business but do not have the funds to do so. This is consistent with the
results of Urban (2006) who found that generally South Africans are motivated to become
entrepreneurs. Conversely, from the qualitative interviews, a consistent theme was that the
founding members did not believe that there is funding for businesses and proceeded without it
(see Appendix C). This may indicate a difference between founding members and non-founding
members that needs to be investigated further.
Additionally, the lack of funding was found to be correlated to many responsibilities (ranked 3rd)
(β = 0.20, p < 0.05). As deduced from the qualitative study, the reasoning behind this is that
when one has many financial commitments, one cannot afford to fund a new venture. Therefore,
without external funding they cannot form a start-up business. This is in contrast to the study
conducted by Lee and Wong (2004), who argue that a person’s views of their financial
constraints do not influence their desire to become entrepreneurs. Additionally, this correlation
may be indirectly explained by its relationship/correlation between the factors of many
responsibilities and job comfort (ranked 4th) (β =0.36, p < 0.05). As is evident in the qualitative
study, one of the reasons why some engineers said that they were comfortable in their job was
because their job provided a consistent income allowing them to manage their responsibilities. It
was therefore too risky for them to leave their job to start a new venture. Although risk aversion
was rejected as a main inhibitor, the subjects were not willing to take the risks because of the
circumstances that they are in, and not because they had risk-averse personality. As mentioned
earlier, taking risks is associated with the opportunity costs.
Furthermore, lack of funding was found to be correlated to lack of political connections (ranked
7th) (β = 0.35, p < 0.001). This can be explained by an examination of the qualitative interviews
where some of the respondents felt that without political connections they would not get access
to funding. The quantitative study supports this claim over a greater number of people.
There were statistically significant differences in six out of 12 of the means of the inhibiting
forces between the African and white population groups. Thus, this further illustrates that studies
on South Africans cannot be generalised, as the studies depend on a population group. The
factors of low social capital and labour laws were retained as main influencing factors because
they were influencing factors for white engineers. For this population group low social capital
(ranked 6th) was found to be correlated to the lack of political connections (ranked 7th) (β = 0.37,
p < 0.01). An explanation is that political connections can be considered to be the social capital
(network) needed to make a business succeed. It is possible that this is more of an inhibiting
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factor for white engineers because South African businesses mostly deal with government;
therefore, business and political networks are intertwined. Perhaps white engineers feel
disadvantaged when having to play on a political frontier that is African-dominated.
For white engineers, low social capital was also correlated to being inhibited by many
responsibilities (β =0.41 p < 0.01). The relationship between these two is unclear and needs
further exploration. A possible reason may be that with many responsibilities one is unable to
have the time to build the social capital. On the other hand, low social capital is also relatively
highly correlated to the lack of business skills (β =0.46, p < 0.001). A reason for this may be that
building social capital is associated with being a businessperson or an entrepreneur, as
mentioned previously in section 6.1. Therefore, feeling that one is inhibited by social capital
may be because of their lack of business skills.
For the white engineers, job comfort was found to be correlated to the labour laws and unions (β
= 0.36 p < 0.05). Interestingly, when examining African engineers alone, labour laws and unions
were not a main inhibitor, although they were and still are an inhibitor for some. According to
Klapper, Laeven and Rajan (2006), the costs of complying with such regulations can inhibit new
firm entry. Consequently, this may inhibit entrepreneurship. The above authors suggest that
developing countries could benefit from reducing these regulations substantially. However, the
authors also admit that there could be deeper political and economic interests underpinning such
regulations. This is certainly the case in South Africa, which is a new democracy attempting to
reduce the income disparity between ethnic groups. Therefore, this effect must be weighed
against the other goals of the regulations. Furthermore, being inhibited by labour laws and
unions (ranked 8th) was found to be correlated to the lack of political connections (β = 0.32, p <
0.05). The reason behind this correlation may be that the labour law is considered a political
issue. Additionally, the respondents may feel that with political connections it would be easier to
manage the unions’ labour laws. Alternatively because these are uniquely South African
elements, the same person would be inhibited by both these factors.
Finally, for white engineers, having many responsibilities is found to be correlated to being
inhibited by labour laws and unions (β = 0.34, p < 0.05). The relationship between these two is
unclear and needs further exploration. A possible explanation is that having many
responsibilities deters the engineer from taking the risk of starting a business and having to deal
with labour laws and unions. Alternatively, the link could be in respect of the relationship that
job comfort has to being inhibited by labour laws and unions.
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It is evident from the discussion that the factor of having many responsibilities is at some stage
connected to all but one of the main inhibitors. Therefore, this suggests that this should be a
main focus of policy-makers who wish to reduce the barriers to entrepreneurship. One of the
founding members that were interviewed, (FM3), suggested that this barrier may be common
amongst engineers in South Africa because of their structured way of thinking caused by the
bureaucratic paperwork in South African engineering companies. Consequently, when they think
they have too many responsibilities, they immediately associate it with not being able to start a
business. On the other hand, one solution is to increase the financial education of engineers. A
study by Manyama (2007) suggests that the South African debt culture and lack of savings can
be reduced by financial education. However, the author also suggests that this needs to start at a
low education level, i.e. from primary school. Financial education can help to reduce the
inhibitors to becoming a founding member in South Africa and increase access to savings’
products and should thus be made compulsory in the education system.
Moreover, for white engineers, the lack of political connections was correlated to three main
inhibitors, namely low social capital, lack of funding, and labour laws and unions. This suggests
that the South African political-business relations are also responsible for inhibiting the majority
of white engineers and some African engineers from becoming founding members of
technology-based start-ups. This can be supported by Southall (2004, p. 326) when considering
the South African political-business climate, who states that “with the state being so centrally
involved in the task of class creation, the political connections enjoyed by individual capitalists
become crucial in pulling down official loans, decisions and favours, with outright corruption a
not uncommon outcome”.
Lastly, as mentioned previously, the lack of identified opportunity (ranked 2nd), is the second
most influencing inhibitor. This factor is not correlated to any of the other inhibitors.
Interestingly, coming across an opportunity was the highest rated enabler for entrepreneurship,
indicating that if the majority of the sample were to come across an opportunity they would
become founding members of technology-based start-ups. Policy-makers can assist by
identifying opportunities and making these known to engineers within the country on a
continuous basis.
In summary, this discussion indicates that the main focus for policy-makers should be to address
the following inhibitors: lack of funding, lack of opportunity, many responsibilities, lack of
political connections, and labour laws and unions. Helping with these inhibitors should reduce
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the barriers to creating more founding members of technology-based start-ups, and subsequently
help to reduce unemployment in South Africa.
6.3. Limitations and Constraints
6.3.1. Sample of Founding Members
The 32 subjects’ sample of founding members was small, and therefore not ideal to draw strong
statistical inferences from the results. However, they were sufficient to provide an indication of
inferences. The difficulty with eliciting sufficient responses from founding members is related to
getting many results from a questionnaire that is only directed to a limited population, namely
South African engineers. Additionally, it was expected that founding members make up a small
proportion of engineering graduates.
6.3.2. Sampling Technique
The snowball sampling technique was necessary in order to retrieve results only from engineers.
The reason is that there was no specific database available that contains the addresses of all
engineers in the country, and therefore a random sampling technique could not be used. Thus,
although the results are indicative, ideally they should not be generalised to the entire
engineering population. However, as mentioned previously, researchers often draw inferences
from a non-random sample study, and this research does the same.
6.3.3. Access to Sample
Access to alumni databases from tertiary institutions was refused because of anti-spamming
policies or policies limiting the number of emails that may be sent to alumni per year. Similarly,
some social media groups restricted the posting of surveys.
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7. RESEARCH CONCLUSIONS
This research describes the main factors that influence engineers to become founding members
of technology-based start-up businesses in South Africa. The qualitative phase helps to achieve a
realistic impression of the issues at hand as opposed to only dealing with numbers in a purely
quantitative study. The quantitative phase helps verify the themes acquired from the qualitative
study across a larger sample. This research also highlights relationships between the factors, and
suggests possible explanations using both literature and new data collected from the research.
The findings can assist policy-makers and academic institutions to facilitate efforts to reduce the
inhibitors to becoming a founding member of a start-up business and thus hopefully promote
more entrepreneurs in the field of technology.
In conclusion to this research report, the research questions presented in the introduction are
answered, using the results from the findings and a discussion of findings.
7.1. Research Question 1: What proportion of engineering graduates has been involved as
founding members of technology-based start-ups?
Of the studied sample, 21% of engineers have become founding members of technology-based
start-ups. Therefore, the inference is that 21% of the engineering graduates have become a
founding member of a technology-based start-up business.
7.2. Research Question 2: What are the main factors that enable engineers to be part of the
founding team of technology-based start-ups?
According to the analysed results, the main factors that motivate engineers to become a part of
technology-based start-ups are:
Comfort in taking risks
Pattern recognition ability
Entrepreneurship personality
Networking ability
Work experience
Desire for more money
Social capital
Desire for work flexibility
Identified opportunity
Exposure to entrepreneur(s)
Desire to create jobs in SA
Many business opportunities in SA
Globalisation
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7.3. Research Question 3: What are the main factors that inhibit engineers from being a
part of a founding team of technology-based start-ups?
According to the analysed results, the main factors that hinder engineers from becoming a part of
technology-based start-ups are:
Lack of social capital (for white
engineers)
Lack of funding
Job comfort
Lack of business skills
Lack of opportunities
Many responsibilities
Lack of political connections
Labour laws or unions (for white
engineers)
7.4. Research Question 4: Are there relationships amongst the main factors that influence
engineers to become part of a founding team of technology-based start-ups in South
Africa?
The relationships are shown in the table below.
Table 20: Summary of relationships amongst influencing factors
Enabler Inhibitors
Comfort in taking risks to Desire for more
money Lack of funding to Many responsibilities
Comfort in taking risks to Entrepreneurship
personality
Lack of funding to Lack of political
connections
Entrepreneurship personality to Networking
ability Job comfort to Many responsibilities
Entrepreneurship personality to Desire to
create jobs in SA
Job comfort to Labour laws and unions (for
white engineers)
Social Capital to Exposure to
Entrepreneurs(s)
Low social capital to Lack of business skills
(for white engineers)
Desire to create jobs in SA to Globalisation Low social capital vs. Many responsibilities
(for white engineers)
Low social capital to Lack of political
connections (for white engineers)
Labour laws and unions to Many
responsibilities (for white engineers)
Labour laws and unions to Lack of political
connections (for white engineers)
The theories from the discussion of findings of how these are related are presented in Figure 14
and Figure 15.
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7.5. Research Question 5: What are the factors that influence engineers to be a part of
technology-based start-ups that are unique to the South African environment?
The enabling factors unique to the South African environment are:
a desire to create jobs in South Africa; and
the availability of many business opportunities in South Africa.
The inhibiting factors unique to the South African environment are:
many responsibilities;
labour laws and unions; and
lack of political connections.
7.6. Research Question 6: Are there issue or factors that could be further emphasised in
the academic programmes that would promote increased entrepreneurship from its
graduates?
The factors investigated in this research that can be influenced by the academic programmes are:
lack of business skills; and
many responsibilities
These can be addressed by both business education and personal finance education as a
compulsory element of the curriculum to reduce the financial commitments that engineers
expose themselves to after leaving university.
7.7. Contributions to Research
The research identified that engineers in South Africa are inhibited from becoming founding
members of technology-based start-ups because of the situations that they find themselves in,
rather than because of their personalities. On the other hand, those that have become founding
members rated attributes linked to personality as the most influencing reasons. Additionally, it
identified that the factors that inhibit entrepreneurship differ for white and African engineers.
This is significant, because according to Urban (2006, p. 182), in South Africa “much is left
unexplained with regards to the effects of culture on entrepreneurship”.
Moreover, the research identified relationships between the main factors that influence engineers
to becoming founding members. It showed that most of those engineers who do not have a
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formal business education were inhibited by their lack of business skills. It also showed that
having many responsibilities (e.g. financial commitments) has the most amount of relation to
other inhibitors for not becoming a founding member of a technology-based start-up. Moreover,
the research showed that the lack of political connections was an inhibitor that was related to
three main inhibitors for white engineers.
Furthermore, the research showed that the main focus for policy-makers should be on making
funding available and known, helping engineers to identify opportunities, and providing personal
finance education. Lastly, it also highlights how South African labour laws and unions as well as
lack of political connections inhibit entrepreneurship.
7.8. Implications for Policy-Makers
The implications for policy-makers are that in order to promote more entrepreneurship amongst
engineers, the focus must be on the following issues. Firstly, focus must be on making funding
available and known. Secondly, the focus must be on making engineers aware of opportunities
for becoming founding members in technology-based start-ups. Thirdly, focus must be on
assisting with personal financial education to reduce the likelihood of engineers taking on too
many responsibilities too soon. The savings problem does not only affect economic growth by
putting the country in debt, but it also influences it indirectly by inhibiting entrepreneurship.
Fourthly, focus should be on reducing the inhibiting effects labour laws and unions have on
start-ups organisations. Lastly policy-makers should aim to reduce the effects that political
connections have on starting a business in South Africa.
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8. FUTURE RESEARCH DIRECTIONS
This section describes the directions that future research can take. This research has opened up
the doors for many directions on future research.
8.1. The Influence of BEE in the Transition to becoming a Founding Member of a
Technology-based Start-up Business
For a qualitative study with a lot more time, the influence of BEE and EE on the entire
population can be investigated further. There must also be a tie-in to the inhibitor of the lack of
political connections. This research identified that some effects can be pursued further (see
Appendix C).
8.2. Difference in Perceptions of Founding Members and Non-founding Members as to
what Influences the Transition to Becoming a Founding Member of a Technology-
based Start-up Business
Research on further comparisons between founding members and non-founding members can be
done for a more quantitative research. This study can pose questions regarding the opinions of
what are both enabling and inhibiting factors for engineers to becoming founding members. The
difference between that and what was studied in this research project is that firstly, it is the
opinion of the sample that will be gathered, and not necessarily what the situation was for them.
Secondly, the same questions should be posed to both founding members and non-founding
members. The findings of such research could possibly reveal whether or not, on average, there
is a difference between perceptions of the factors that influence the transition to becoming
founding members of technology-based start-ups in South Africa.
8.3. Study to verify relationships amongst factors engineers to transition into becoming a
founding member of a start-up
This study will use the relationships found in Figure 14 and Figure 15 to create a survey to ask
engineers to what extent they agree with those relationships. This will further verify the
relationships amongst the factors.
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APPENDIX A – QUALITATIVE INTERVIEW GUIDES
Qualitative interview guide for founding members
Introduction
This study aims to determine the factors that influence engineers that studied at South African
tertiary institutions and are practicing in South Africa to decide to become a founding member of
technology-based start-up business in South Africa. This interview aims to identify the things
about the individual’s personality, the situation that the individual was in and the things about
the South African environment that influenced the decision to become involved in starting a
business.
Terms
Technology-based start-up: A new firm/business established by the founding members that
deals with technology or in the field of engineering including all parts of the product/technology
development chain e.g. engineering consultancies, design, production, maintenance etc.
Background
Name:
Contact number:
Please indicate your population group (African, White, Indian/Asian, Coloured):
Do you fall under EE? (Yes/No):
Gender (Male/Female):
Degree(s)/Diploma(s) and Universities:
Graduation date(s):
Business name:
Business industry:
Business location/address:
Business start-up year:
Position in business at start-up:
Current position in business:
Would you like to be kept anonymous (Yes/No):
Would you like a copy of the findings of this research report (Yes/No):
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This interview consists of three questions presented on the next three pages.
Question 1a/3: What is it about your personality that contributed towards you being involved in
starting a business? Please list 3 or more (the more the better) and freely expand and elaborate
on them.
Question 1b/3: Why did you choose a technology-based business?
Question 2/3: What was happening or had happened in your life at the moment that you decided
to become involved in a start-up that motivated you to start this technology-based start-up?
Please list 3 or more (the more the better) and freely expand and elaborate on them.
Question 3a/3: What are the things about the South African environment that motivated you to
start your own business? For example; B-BBEE or other policies, particular industry culture etc.
Regardless of whether it is the good things about these that is a driver or the bad things about
these is a driver. Please list 3 or more (the more the better) and freely expand and elaborate on
them.
Question 3b/3: Do any of these factors from 3a have a relation to the fact that the business is in
technology sector?
Qualitative interview guide for non-founding members
Introduction
This study aims to determine the factors that influence engineers that studied at South African
tertiary institutions and are practicing in South Africa to decide to become a founding member of
technology-based start-up business in South Africa. This interview aims to identify the things
about the individual’s personality, the situation that the individual is in and the things about the
South African environment that influenced the decision not to become involved in the start-up
this far.
Terms
Technology-based start-up: A new firm/business established by the founding members that
deals with technology or in the field of engineering including all parts of the product/technology
development chain e.g. engineering consultancies, design, production, maintenance etc.
Background
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Name:
Contact number:
Please indicate your population group (African, White, Indian, Coloured etc):
Do you fall under EE (Yes/No):
Gender (Male/Female):
Degree(s)/Diploma(s) and Universities:
Graduation date(s):
Company name:
Company industry:
Company start-up year:
Current position in company:
Would you like to be kept anonymous (Yes/No):
Would you like a copy of the findings of this research report (Yes/No):
This interview consists of three questions presented on the next three pages.
Question 1/3: What is it about your personality that has prevented you from being involved in
starting a technology-based business? Please list 3 or more (the more the better) and freely
expand and elaborate on them.
Question 2/3: What situations are you in that are preventing you from becoming involved in a
technology-based start-up? Please list 3 or more (the more the better) and freely expand and
elaborate on them.
Question 3/3: What are the things about the South African environment that are barriers for you
to starting a technology-based business? For example; B-BBEE or other policies, particular
industry culture etc. Regardless of whether it is the good things about these that is a barrier or
the bad things about these is a barrier. Please list 3 or more (the more the better) and freely
expand and elaborate on them.
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APPENDIX B – ONLINE QUESTIONNAIRE
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For founding members
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For non-founding members
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APPENDIX C – QUALITATIVE FINDINGS
This section presents the results of the qualitative study that were not presented in Section 4.
Previously Identified Enablers not studied further
This section describes what was found to be influencing enablers that were not studied further.
Salary ceiling and scarce job for white engineers brought about by the
implementation of BEE
It was gathered from the interviews that this remains a sensitive topic in South Africa. One of the
interviewees said that he had heard some people speak about how there is a ‘salary ceiling’ in
the workplace and how it is a driver out of their respective organisations, but this was not an
influencer for him (FM1). Some organisations have lost a lot of skill caused by white engineers
leaving their jobs. However, this was not because of a salary ceiling but because they have
identified an opportunity to make more money by contracting themselves to these organisations
(FM1). However, these are one-man contractors and not necessarily founding companies that
will create jobs (FM1). Another interviewee stated that in Cape Town, “many foreigners end up
in start-ups for lack of other options” (FM2). Even though he was not a citizen he provided
insight.
One of the interviewees said: “When I did end up back in SA in 2001, I had very little option but
to start my own business. As a 31-year-old white male I was persona non gratia” (FM3). Others
felt that although they have heard others speak of this enabler, it was not an enabling factor for
them. One interviewee said: “There were not really any B-BBEE factors in my career so far. I
am lucky to be in an industry where success seems to be measured by skill/experience more than
any other factors” (FM5). However, he admitted that he found issues when it came to applying
for bursaries as he was not of the right population group. Another interviewee said:
“Interestingly I have not been turned away from a job yet for EE reasons. I think there are too
few electrical engineers in general” (FM4).
Due to the sensitivities of this topic it was decided not to use this influencer in the questionnaire,
as it could have reduced the number of respondents and it could have reduced the opportunity for
getting meaningful insights into the other enablers.
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The business opportunities brought by B-BBEE for black/EE engineers
As with the previous section, this enabler also brings about a sensitive topic in South Africa.
Initial reaction amongst the black engineers was that this is not an enabler. However, after some
conversation, some admitted that it may play an influencing role. For example, one interviewee
said: “for black business it’s pretty difficult to get any work from a private business which is
mostly white dominated in SA” (FM10). However, the same interviewee also said: “BBBEE in
practice has little to do with getting work. There are wholly white-owned large firms that have a
BBEEE level rating of 1, so there is no real advantage to being black” (FM10). Although this
may have been an exaggeration, he described it further by stating that it is easy for white owned
firms to get the required B-BBEE rating through recruiting employees that fall into the EE
designated groups or through giving shares to qualified candidates or companies. Consequently,
white engineers are not necessarily disadvantaged and black engineers do not necessarily have
an advantage.
Another interviewee stated that his business has nothing to do with B-BBEE and that this does
not play a part in his business (FM8). This was similar to another interviewee who later
eventually admitted that his company has received a few tenders in the past saying “We have
won a few tenders in the past but I would like to think that this was because of our competence
and not B-BBEE status. Tenders are not the main focus of our business” (FM9). Similarly
another one of the interviewees said: “B-BBEE has created opportunities for some, but we as a
technology business have not been able to benefit from it, even though we were a black owned
business” (FM6), for his type of business, function is the biggest criteria for their clients, so B-
BBEE status does not play a role.
Similar to the previous section, although with difficulty that this factor was found to be an
enabler, it will not be investigated further in the qualitative study because it may deter subjects
from completing the questionnaire
Non-Influencing Previously Identified Enablers
Postgraduate education
The majority of the interviewees did not have a postgraduate education in engineering.
Postgraduate education within engineering was not found to be an enabler to move into
entrepreneurship. For those that had one, the influence was only in a similar way to any other
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undergraduate degree. For example, one interviewee said: “Yes, my degree was in the renewable
sector and I was looking for an opportunity to use it” (FM5). The other interviewee- when
talking about doing a post graduate degree said: “actually [it/ a postgraduate degree] could have
been considered as a hindrance as I am still pursuing a masters and this business interest
competes for my time and attention” (FM4). The others already felt that they were planning on
starting a business without the postgraduate education, or had already started one while doing
one or before even studying further. None of the interviewees were influenced by the fact that
they were doing or had done a postgraduate degree in engineering. However, for some of the
interviewees, a postgraduate degree in other non-engineering fields such as project finance or
business administration helped the transition into entrepreneurship; however, they were already
planning to make the transition before these degrees.
Being part of a small firm
The majority of the interviewees did not come from small firms, thus this was not an influencing
factor. Only one of the interviewees felt that he had a great work experience by being involved
in a small firm that he was seeking the same when he became a founding member of a start-up
(FM5). However, it was agreed that this type of influence is covered under the ‘previous work
experience’ enabler. On the other hand, there was one interviewee who was influenced to
become a founding member because he wanted to be a part of a small firm where he could play a
more influential role.
High Job turnover
None of the interviewees had a significant job turnover. However they did feel that it is not high
job turnover that would be an influencing factor, but the work experience while doing so. Thus,
this influencer is covered under the ‘previous work experience’ enabler.
Perceived support of new businesses
This enabler was not particularly supported amongst the interviewees. Only one said: “as a small
country, the access to government is quite good” (FM1). However, him and his partners “later
found that this was actually a disadvantage because there was change in the government
departments and the organisations could not easily establish long-term strategy” (FM1). Another
one of the interviewees’ businesses was funded by the innovation fund but they could not use
this funding when they spun the business off from the university (FM7). One of the
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entrepreneurs said: “I could never get the kind of financial backing I required to really get on my
feet” (FM3). Others felt that they never really considered funding. For example one interviewee
said: “I never depended on any funding schemes, in fact I thought such never existed or was too
mismanaged to assist right business ideas” (FM8). Another when talking about funding said that
there was none available, and that “you just have to be smart about how you structure your
business and the cost structures at the early stages” (FM10).
Non-Influencing Previously Identified Inhibitors
Lack of time to develop an idea
The lack of time to start a technology-based enterprise was not seen as an inhibitor for the
majority. One engineer said “Time is a factor; however, I tend to give time to things that I am
interested in and not having a specific” (NFM8). Another interviewee said: “Perhaps
rescheduling and managing my time other way will enable me to develop the idea of business”
(NFM4). One said that he does not have time as he is finishing off his masters. However, it was
generally agreed that lack of time is just an excuse and if they really wanted to do something
they would make the time for it.
Lack of facilities to develop an idea
Lack of facilities was not really seen as an inhibitor. In favour of this inhibitor one engineer said:
“I stay in a small flat and don't have the space or money at the moment to develop a workshop
space with useful tools and equipment” (NFM10). This could be solved by funding. However,
one engineer stated that “Not at all, facilities are available” (NFM4). Another interviewee said:
“I would not see myself doing the development but rather outsourcing this – so this is not a
constraint” (NFM8). Furthermore, another interviewee said: “equipment has been coming down
and engineers could share; some expensive radio equipment” (NFM6). This inhibitor this is
something that is highly influenced by the other investigated factors and is not really an issue.
The high salaries and job opportunities brought by B-BBEE is for black/EE
engineers.
This was a sensitive topic amongst the black engineers. Most agreed that their jobs paid well as
was presented in the job comfort inhibitor. However, there was disagreement that this was
specific to black engineers and because of B-BBEE. One interviewee stated that some white
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engineers even get paid more because of the living expenses brought about by the expensive
areas that they live in.
The BEE as a barrier for white engineers to start their business
The interview questions were similar for all candidates so this issue was investigated as well.
One engineer stated that “While BEE is a barrier; I feel that it’s mainly an administrative barrier,
as I believe any business should be investing in training and mentoring human capital – both for
the benefit of the country and for the business” (NFM6). Another stated that “Sometimes it
implies being forced to employ people who aren’t suitable for your company. This could result
in your company being uncompetitive compared to companies outside of SA who don’t have to
do this” (NFM7) additionally, he also stated that “As is the case for B-BBEE, the intension
behind these laws is good, but it accomplishes the opposite from what government is trying to
achieve” (NFM7).
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APPENDIX D – QUANTITATIVE FINDINGS
Table 21: Mann-Whitney U Tests for enablers for Africans vs. Whites
Enablers
African White 'U' Stat Sig.
N M N M
E1 Comfort in taking risks 19 4.00 11 4.09 103.0 0.9662
E2 Pattern recognition ability 19 4.05 11 3.73 77.0 0.2497
E3 Entrepreneurship personality 19 4.63 11 3.73 53.0 0.0264
E4 Networking ability 19 4.11 11 3.36 56.5 0.0374
E5 Work experience 19 3.89 11 3.64 97.5 0.7670
E6 Desire for more money 19 3.53 11 3.45 102.0 0.9326
E7 Social capital 19 2.47 11 3.09 66.0 0.1026
E8 Desire for work flexibility 19 3.58 11 3.55 103.5 0.9662
E9 Identified opportunity 19 4.37 11 4.09 78.5 0.2680
E10 Exposure to entrepreneur 19 3.42 11 3.64 98.0 0.7995
E11 Desire to create jobs in SA 19 3.37 11 3.00 81.5 0.3279
E12 Many business opportunities in SA 19 3.84 11 3.45 70.5 0.1454
E13 Globalisation 19 3.37 11 3.55 93.5 0.6412
Table 22: Mann-Whitney U Tests for no business education vs. business education
Inhibitors
No Business
Education
Business
Education 'U' Stat Sig.
N M N M
I1 Risk-averse 73 2.48 45 2.24 1464.0 0.325
I2 Low networking ability 73 2.47 45 2.29 1487.5 0.392
I3 Lack of interest in entrepreneurship 73 1.77 45 1.80 1571.5 0.695
I4 Low social capital 73 2.75 45 2.49 1442.0 0.269
I5 Lack of funding 73 3.45 45 3.44 1638.0 0.982
I6 Job comfort 73 2.88 45 2.93 1596.0 0.800
I7 Lack of business skills 73 3.05 45 2.33 1086.5 0.002
I8 Lack of identified opportunity 73 3.18 45 3.58 1338.5 0.092
I9 Many responsibilities 73 3.04 45 2.76 1425.5 0.230
I10 Lack of political connections 73 2.52 45 2.73 1470.5 0.342
I11 Lack of ECSA registration 73 1.79 45 2.02 1442.5 0.269
I12 Labour laws/Unions 73 2.45 45 2.53 1567.5 0.679
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Table 23: Mann-Whitney U Tests for Non-ESCA registered vs. ECSA registered
Inhibitors
Not ECSA
Registered
ECSA
Registered 'U' Stat Sig.
N M N M
I1 Risk-averse 85 2.08 33 2.70 1124.0 0.096
I2 Low networking ability 85 2.18 33 2.58 1221.5 0.279
I3 Lack of interest in entrepreneurship 85 1.73 33 1.73 1379.5 0.891
I4 Low social capital 85 2.33 33 2.85 1236.5 0.321
I5 Lack of funding 85 3.13 33 3.61 1228.0 0.298
I6 Job comfort 85 2.78 33 2.76 1272.0 0.438
I7 Lack of business skills 85 2.51 33 3.00 1203.5 0.234
I8 Lack of identified opportunity 85 3.24 33 3.30 1361.5 0.807
I9 Many responsibilities 85 2.58 33 3.24 1129.5 0.102
I10 Lack of political connections 85 2.26 33 2.45 1224.5 0.287
I11 Lack of ECSA registration 85 1.85 33 1.64 1055.0 0.037
I12 Labour laws/Unions 85 2.25 33 2.39 1292.0 0.511
Table 24: Mann-Whitney U Tests for inhibitors for Africans vs. Whites
Inhibitors
African White 'U' Stat Sig.
N M N M
I1 Risk-averse 45 1.98 50 2.66 756.0 0.0056
I2 Low networking ability 45 2.11 50 2.56 837.0 0.0317
I3 Lack of interest in entrepreneurship 45 1.47 50 1.88 840.5 0.0335
I4 Low social capital 45 2.24 50 2.86 766.5 0.0071
I5 Lack of funding 45 3.31 50 3.52 1012.0 0.4035
I6 Job comfort 45 2.38 50 3.24 683.5 0.0009
I7 Lack of business skills 45 2.6 50 2.82 995.5 0.3360
I8 Lack of identified opportunity 45 3.18 50 3.28 1081.0 0.7469
I9 Many responsibilities 45 2.76 50 3.1 945.5 0.1818
I10 Lack of political connections 45 2.24 50 2.62 875.5 0.0627
I11 Lack of ECSA registration 45 1.82 50 1.84 1118.5 0.9615
I12 Labour laws/Unions 45 1.98 50 2.86 627.0 0.0002
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Table 25: Full table of inhibitors amongst Africans
Code Inhibitor N Med Mean Min Max SD
IA1 Risk-averse 45 2.0 1.98 1 5 1.01
IA2 Low networking ability 45 2.0 2.11 1 5 1.05
IA3 Lack of interest in entrepreneurship 45 1.0 1.47 1 3 0.59
IA4 Low social capital 45 2.0 2.24 1 5 1.11
IA5 Lack of funding 45 4.0 3.31 1 5 1.14
IA6 Job comfort 45 2.0 2.38 1 4 1.13
IA7 Lack of business skills 45 2.0 2.60 1 5 1.27
IA8 Lack of identified opportunity 45 4.0 3.18 1 5 1.30
IA9 Many responsibilities 45 3.0 2.76 1 5 1.19
IA10 Lack of political connections 45 2.0 2.24 1 5 1.00
IA11 Lack of ECSA registration 45 2.0 1.82 1 4 0.78
IA12 Labour laws/Unions 45 2.0 1.98 1 4 0.89
Table 26: Full table of Inhibitors for White engineers
Code Inhibitor N Med Mean Min Max SD
IW1 Risk-averse 50 2.5 2.66 1 4 1.17
IW2 Low networking ability 50 2.0 2.56 1 5 1.05
IW3 Lack of interest in entrepreneurship 50 2.0 1.88 1 4 0.90
IW4 Low social capital 50 3.0 2.86 1 5 1.09
IW5 Lack of funding 50 4.0 3.52 1 5 1.09
IW6 Job comfort 50 3.5 3.24 1 5 1.15
IW7 Lack of business skills 50 2.5 2.82 1 5 1.10
IW8 Lack of identified opportunity 50 4.0 3.28 1 5 1.23
IW9 Many responsibilities 50 3.0 3.10 1 5 1.18
IW10 Lack of political connections 50 3.0 2.62 1 5 0.99
IW11 Lack of ECSA registration 50 2.0 1.84 1 5 0.82
IW12 Labour laws/Unions 50 3.0 2.86 1 5 1.09
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Table 27: Full spearman’s correlation matrix for enablers
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13
E1 1.00 0.01 0.37* 0.31 0.15 0.42* -0.01 0.18 -0.07 -0.06 0.23 -0.08 0.20
E2 0.01 1.00 0.31 0.23 0.18 -0.19 0.01 0.31 -0.02 0.16 0.33 -0.08 0.07
E3 0.37* 0.31 1.00 0.48** 0.16 0.26 -0.01 0.00 0.18 0.30 0.39* 0.13 0.06
E4 0.31 0.23 0.48** 1.00 -0.02 0.15 0.00 0.11 0.16 0.04 0.26 -0.01 0.16
E5 0.15 0.18 0.16 -0.02 1.00 0.25 0.02 -0.03 -0.09 -0.23 -0.12 0.14 -0.20
E6 0.42* -0.19 0.26 0.15 0.25 1.00 -0.04 0.18 0.09 -0.17 -0.01 0.28 -0.05
E7 -0.01 0.01 -0.01 0.00 0.02 -0.04 1.00 0.00 -0.15 0.50** 0.19 0.04 0.17
E8 0.18 0.31 0.00 0.11 -0.03 0.18 0.00 1.00 -0.01 -0.25 0.29 0.09 0.30
E9 -0.07 -0.02 0.18 0.16 -0.09 0.09 -0.15 -0.01 1.00 0.19 0.20 0.14 -0.16
E10 -0.06 0.16 0.30 0.04 -0.23 -0.17 0.50** -0.25 0.19 1.00 0.32 0.22 0.15
E11 0.23 0.33 0.39* 0.26 -0.12 -0.01 0.19 0.29 0.20 0.32 1.00 0.27 0.53**
E12 -0.08 -0.08 0.13 -0.01 0.14 0.28 0.04 0.09 0.14 0.22 0.27 1.00 0.10
E13 0.20 0.07 0.06 0.16 -0.20 -0.05 0.17 0.30 -0.16 0.15 0.53** 0.10 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=32
Table 28: Full spearman’s correlation matrix for inhibitors
I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12
I1 1.00 0.50*** 0.43*** 0.27** 0.06 0.32*** 0.16 0.05 0.28** 0.21* 0.11 0.28**
I2 0.50*** 1.00 0.32*** 0.55*** -0.02 0.31*** 0.32*** 0.24** 0.14 0.23* 0.14 0.31***
I3 0.43*** 0.32*** 1.00 0.23* -0.03 0.37*** 0.12 0.17 0.19* 0.27** 0.15 0.24**
I4 0.27** 0.55*** 0.23* 1.00 0.33** 0.18* 0.43*** 0.18* 0.29** 0.54*** 0.35*** 0.36***
I5 0.06 -0.02 -0.03 0.33*** 1.00 -0.07 0.08 -0.10 0.20* 0.35*** 0.08 0.12
I6 0.32*** 0.31*** 0.37*** 0.18* -0.07 1.00 0.06 0.11 0.30*** 0.15 0.16 0.43***
I7 0.16 0.32*** 0.12 0.43*** 0.08 0.06 1.00 0.04 0.14 0.22* 0.22* 0.15
I8 0.05 0.24** 0.17 0.18* -0.10 0.11 0.04 1.00 -0.11 -0.02 -0.03 0.08
I9 0.28** 0.14 0.19* 0.29** 0.20* 0.30** 0.14 -0.11 1.00 0.16 0.23* 0.20*
I10 0.21* 0.23* 0.27** 0.54*** 0.35** 0.15 0.22* -0.02 0.16 1.00 0.42*** 0.40***
I11 0.11 0.14 0.15 0.35*** 0.08 0.16 0.22* -0.03 0.23* 0.42** 1.00 0.47***
I12 0.28** 0.31*** 0.24** 0.36*** 0.12 0.43*** 0.15 0.08 0.20* 0.40** 0.47*** 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=118
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Table 29: Full spearman’s correlation matrix for inhibitors for White engineers
IW1 IW2 IW3 IW4 IW5 IW6 IW7 IW8 IW9 IW10 IW11 IW12
IW1 1.00 0.47*** 0.43** 0.41** 0.08 0.25 0.20 0.06 0.36* 0.24 0.15 0.22
IW2 0.47*** 1.00 0.42** 0.76*** -0.03 0.15 0.49*** 0.20 0.27 0.28* 0.24 0.15
IW3 0.43** 0.42** 1.00 0.38** 0.02 0.35* 0.21 0.20 0.20 0.28* 0.15 0.23
IW4 0.41** 0.76*** 0.38** 1.00 0.16 0.10 0.46*** 0.26 0.41** 0.37** 0.31* 0.27
IW5 0.08 -0.03 0.02 0.16 1.00 -0.24 0.14 -0.22 0.31* 0.28* 0.18 0.12
IW6 0.25 0.15 0.35* 0.10 -0.24 1.00 -0.12 0.20 0.29* -0.03 0.10 0.36*
IW7 0.20 0.49*** 0.21 0.46*** 0.14 -0.12 1.00 0.13 0.24 0.23 0.07 -0.02
IW8 0.06 0.20 0.20 0.26 -0.22 0.20 0.13 1.00 -0.04 -0.03 -0.10 0.03
IW9 0.36* 0.27 0.20 0.4*1* 0.31* 0.29* 0.24 -0.04 1.00 0.09 0.30* 0.34*
IW10 0.24 0.28 0.28* 0.37** 0.28* -0.03 0.23 -0.03 0.09 1.00 0.32* 0.32*
IW11 0.15 0.24 0.15 0.31* 0.18 0.10 0.07 -0.10 0.30* 0.32* 1.00 0.41**
IW12 0.22 0.15 0.23 0.27 0.12 0.36* -0.02 0.03 0.34* 0.32* 0.41** 1.00
Note: *p<0.05, p<0.01**, p< 0.001***, N=50
Table 30: Descriptive statistics for enablers
Mean N Med Mode Min Max Std.Dev. Variance Range Skewness Kurtosis
E1 4.03 32 4.00 4.00 2.00 5.00 0.74 0.55 3.00 2.24 129.00
E2 3.94 32 4.00 4.00 2.00 5.00 0.76 0.58 3.00 0.03 126.00
E3 4.31 32 4.50 5.00 2.00 5.00 0.86 0.74 3.00 1.49 138.00
E4 3.84 32 4.00 4.00 2.00 5.00 0.85 0.72 3.00 -0.29 123.00
E5 3.84 32 4.00 4.00 1.00 5.00 0.95 0.91 4.00 1.58 123.00
E6 3.47 32 4.00 4.00 1.00 5.00 1.08 1.16 4.00 -0.58 111.00
E7 2.75 32 3.00 3.00 1.00 5.00 0.95 0.90 4.00 -0.01 88.00
E8 3.47 32 3.50 4.00 2.00 5.00 1.11 1.22 3.00 -1.31 111.00
E9 4.28 32 4.00 4.00 2.00 5.00 0.73 0.53 3.00 1.69 137.00
E10 3.50 32 4.00 4.00 1.00 5.00 1.05 1.10 4.00 -0.35 112.00
E11 3.22 32 3.00 3.00 2.00 5.00 0.87 0.76 3.00 -0.63 103.00
E12 3.69 32 4.00 4.00 1.00 5.00 0.90 0.80 4.00 1.67 118.00
E13 3.47 32 3.00 3.00 1.00 5.00 0.92 0.84 4.00 0.59 111.00
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Table 31: Descriptive statistics of Inhibitors
Mean N Med Mode Min Maxx Std.Dev. Var Range Skewness Kurtosis
I1 2.39 118 2.00 2.00 1.00 5.00 1.13 1.28 4.00 0.44 -0.86
I2 2.40 118 2.00 2.00 1.00 5.00 1.06 1.11 4.00 0.47 -0.70
I3 1.78 118 2.00 2.00 1.00 4.00 0.82 0.67 3.00 1.10 1.06
I4 2.65 118 2.00 2.00 1.00 5.00 1.14 1.31 4.00 0.33 -0.79
I5 3.45 118 4.00 4.00 1.00 5.00 1.08 1.17 4.00 -0.44 -0.58
I6 2.90 118 3.00 4.00 1.00 5.00 1.21 1.46 4.00 -0.07 -1.15
I7 2.78 118 2.50 2.00 1.00 5.00 1.16 1.35 4.00 0.24 -1.01
I8 3.33 118 4.00 4.00 1.00 5.00 1.23 1.52 4.00 -0.30 -1.12
I9 2.93 118 3.00 2.00 1.00 5.00 1.19 1.41 4.00 0.04 -0.99
I10 2.60 118 3.00 3.00 1.00 5.00 1.06 1.11 4.00 0.24 -0.46
I11 1.88 118 2.00 2.00 1.00 5.00 0.84 0.70 4.00 1.29 2.56
I12 2.48 118 2.00 2.00 1.00 5.00 1.07 1.14 4.00 0.47 -0.54
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APPENDIX E – FACTOR ANALYSIS OF INFLUENCING FACTORS
This appendix presents the results of factor analysis that was performed on the influencing
factors identified from the qualitative interview, but using the results from the quantitative study
questionnaire.
Non-Founding Members
This factor analysis excludes Factors: Lack of ECSA registration and Lack of business skills
because these are moderated by having ECSA registration and by having a formal business
education and thus are not applicable to the whole 118 non-founding members.
A plot of Eigen value plot is also known as a scree plot. It is a useful manner for determining
how many factors are present within a data set (Dugard, Todman & Staines, 2010). The rule is to
identify the region where there is a change (decline) of the curve. The corresponding number of
Eigen values at this region will indicate the number of factors to search for. However the
researcher must make sure that the factor must make some sense (Dugard, Todman & Staines,
2010). The scree plot was done using the varimax rotation method for factor analysis.
According to (Dugard, Todman & Staines, 2010), when determining the number of factors to
extract, “a sudden change in the slope is often a useful guide” (p. 188). In the diagram below the
plot indicates that there are two factors within the data set.
Plot of Eigenvalues
1 2 3 4 5 6 7 8 9 10
Number of Eigenvalues
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Valu
e
Figure 16: Scree plot for inhibitors
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Eigenvalues (Non-Technopreneurs FA data 150) Extraction: Principal components
Eigenvalue % Total - variance Cumulative - Eigenvalue Cumulative - %
1 3.043059 30.43059 3.04306 30.4306
2 1.542199 15.42199 4.58526 45.8526
3 1.128633 11.28633 5.71389 57.1389
4 0.931255 9.31255 6.64515 66.4515
5 0.806391 8.06391 7.45154 74.5154
6 0.751960 7.51960 8.20350 82.0350
7 0.628935 6.28935 8.83243 88.3243
8 0.494587 4.94587 9.32702 93.2702
9 0.403027 4.03027 9.73005 97.3005
10 0.269954 2.69954 10.00000 100.0000
According to the table above two Eigen values explain 46% of the variance.
Doing factor analysis with a varimax rotation to put more emphasis on the factors and suppress
the non factors gives the table below. The inhibitors highest correlated to the factor are
highlighted.
Factor Loadings (Varimax normalized) (Non-Technopreneurs FA data 150) Extraction: Principal components
Factor - 1 Factor - 2
I1 0.628643 0.188055
I2 0.702925 0.178323
I3 0.641106 0.131644
I4 0.367588 0.686212
I5 -0.224495 0.759624
I6 0.667430 0.101551
I8 0.479767 -0.246951
I9 0.215198 0.475409
I10 0.128253 0.754317
I12 0.469957 0.376106
Expl.Var 2.445089 2.140170
Prp.Totl 0.244509 0.214017
Description of Factor 1
In order of correlation strength Factor 1 appears to contain: Lack of networking ability (I2), Job
comfort (I6), Lack of interest in entrepreneurship (I3), Risk aversion (I1), Lack of identified
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opportunity (I8) and labour laws (I12). These items point towards a personality based inhibitor
construct.
Description of Factor 2
In order of correlation strength Factor 1 appears to contain: Lack of funding (I5), Lack of
political connections (I10), Lack of social network (I4) and Many responsibilities (I9). These
items point towards a situational-based inhibitor construct
Item analysis
After performing item analysis on the factors that belong to each factor independently
For factor 1 I8 was deleted to get the Crobach alpha to its maximum of 0.7. The results of factor
1 are presented below. No further deletions will increase the Cronbach alpha.
Factor 1 reliability analysis
Summary for scale: Mean=11.9492 Std.Dv.=3.58736 Valid N:118 (Non-Technopreneurs FA data 150) Cronbach alpha: .699702 Standardized alpha: .703449 Average inter-item corr.: .323973
Mean if - deleted Var. if - deleted StDv. if - deleted Itm-Totl - Correl. Alpha if - deleted
I1 9.55932 8.331226 2.886386 0.484961 0.637840
I2 9.55085 8.772839 2.961898 0.463213 0.647363
I3 10.16949 9.801782 3.130780 0.450035 0.659418
I6 9.05085 7.963516 2.821970 0.493285 0.635114
I12 9.46610 9.028513 3.004748 0.406895 0.670777
For factor 2 I9 was deleted to get the Crobach alpha to its maximum of 0.68. The results of
factor 1 are presented below. No further deletions will increase the Cronbach alpha.
Factor 2 reliability analysis
Summary for scale: Mean=8.70339 Std.Dv.=2.55975 Valid N:118 (Non-Technopreneurs FA data 150) Cronbach alpha: .677762 Standardized alpha: .678175 Average inter-item corr.: .415903
Mean if - deleted Var. if - deleted StDv. if - deleted Itm-Totl - Correl. Alpha if - deleted
I4 6.050848 3.099109 1.760429 0.525004 0.537171
I5 5.254237 3.647228 1.909772 0.409584 0.684704
I10 6.101695 3.311692 1.819806 0.544154 0.516048
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Factor Loadings (Varimax normalized) (Non-Technopreneurs FA data 150) Extraction: Principal components (Marked loadings are >.500000)
Factor - 1 Factor - 2
I1 0.691269 0.066367
I2 0.685468 0.191785
I3 0.668801 0.066333
I4 0.365361 0.731542
I5 -0.163927 0.768784
I6 0.718438 -0.043101
I10 0.193364 0.786322
I12 0.528657 0.323952
Expl.Var 2.388395 1.896873
Prp.Totl 0.298549 0.237109
The loadings are much stronger now.
Factor 1 - Personality based inhibitors
In order of correlation strength, Factor 1 appears to contain:’ Lack of networking ability’ (I2),
‘Job comfort’ (I6), ‘Lack of interest in entrepreneurship’ (I3), ‘Risk-averse’ (I1) and ‘labour
laws and unions’ (I12).
‘Labour laws and unions’ (I12) can be removed from this factor as it also loads heavily on the
second factor. Perhaps not wanting o deal with the labour laws and unions is both related to the
personality and the situation the individual finds themselves in.
Factor 2 – Situational based inhibitors
In order of correlation strength Factor 2 appears to contain: ‘Lack of funding’ (I5), ‘Lack of
political connections’ (I10), and ‘Lack of social capital’ (I4).
Perhaps this suggests that ‘Identified opportunity’ (I8) and ‘Many responsibilities’ (I9) do not
load on the two factors and are isolated inhibitors. They were also ranked 2nd and 3rd most
inhibiting factors.
Founding Members
Although sample size of 32 falls short of the 65 target for factor analysis, however it was done
for interest sake. Below is the scree plot.
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Plot of Eigenvalues
1 2 3 4 5 6 7 8 9 10 11 12 13
Number of Eigenvalues
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Valu
e
Figure 17: Scree plot for enablers
According to the table below, two Eigen values explain 36% of the variance.
Eigenvalues (Technopreneurs) Extraction: Principal components
Eigenvalue % Total - variance Cumulative - Eigenvalue Cumulative - %
1 2.721438 20.93414 2.72144 20.9341
2 1.964203 15.10926 4.68564 36.0434
3 1.714939 13.19184 6.40058 49.2352
4 1.498282 11.52525 7.89886 60.7605
5 1.264716 9.72858 9.16358 70.4891
6 1.113705 8.56696 10.27728 79.0560
7 0.715669 5.50514 10.99295 84.5612
8 0.547624 4.21249 11.54057 88.7737
9 0.444600 3.42000 11.98518 92.1937
10 0.333562 2.56586 12.31874 94.7595
11 0.291892 2.24532 12.61063 97.0048
12 0.214353 1.64887 12.82498 98.6537
13 0.175018 1.34629 13.00000 100.0000
Doing factor analysis with a varimax rotation to put more emphasis on the factors and suppress
the non-factors gives the table below. The highest correlation was used to identify the values that
contributed most to this factor.
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Factor Loadings (Varimax normalized) (Technopreneurs) Extraction: Principal components
Factor - 1 Factor - 2
E1 0.662030 0.041479
E2 0.427018 0.215340
E3 0.588664 0.303898
E4 0.579213 0.162290
E5 0.438861 -0.314994
E6 0.544293 -0.343529
E7 -0.010543 0.639303
E8 0.503207 -0.099572
E9 -0.018069 0.226601
E10 -0.128531 0.831604
E11 0.491425 0.650083
E12 0.032114 0.250764
E13 0.288684 0.516778
Description of Factor 1
In order of correlation strength Factor 1 appears to contain: ‘Comfort taking risk’ (E1),
‘Entrepreneurship personality’ (E3), ‘Networking ability’ (E4), ‘Desire for more money’ (E6),
‘Desire for work flexibility’ (E8), ‘Work experience’ (E5), ‘Pattern recognition Ability’ (E2).
The majority of these enablers have to do with the personality of the individual, especially the
strongly correlated ones..
Description of Factor 2 – Situational-based enabler
In order of correlation strength Factor 1 appears to contain: Social Capital (E7), Exposure to
entrepreneurs (E10), Desire to create jobs in SA (E11) and Globalisation (E13). The ‘Many
business opportunities in SA’ (E12) and ‘Identified opportunity’ (I9) have a very low correlation
to this factor. The majority of these enablers have to do with the situation of the individual
especially the strongly correlated ones.
Item Analysis
After performing item analysis on the factors that belong to each factor independently
For factor 1, E2 was deleted to get the Crobach alpha to its maximum 0.61. The results of factor
1 are presented below. No further deletions will increase the Cronbach alpha. The low reliability
measure is mostly due to the small number of respondents.
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Factor 1 reliability analysis
Summary for scale: Mean=19.5000 Std.Dv.=2.82843 Valid N:32 (Cronbach alpha: .613659 Standardized alpha: .622137 Average inter-item corr.: .256616
Mean if - deleted Var. if - deleted StDv. if - deleted Itm-Totl - Correl. Alpha if - deleted
E1 15.46875 5.874024 2.423639 0.381243 0.558160
E3 15.18750 5.277343 2.297247 0.452511 0.517148
E4 15.65625 5.600586 2.366556 0.368939 0.559372
E5 15.65625 5.663086 2.379724 0.269628 0.612060
E6 16.03125 4.780274 2.186384 0.398124 0.546408
For factor 2, E8 was deleted was deleted to get the Crobach alpha to its maximum of 0.69. The
results of factor 2 are presented below. No further deletions will increase the Cronbach alpha.
Factor 2 reliability analysis
Summary for scale: Mean=12.9375 Std.Dv.=2.72311 Valid N:32 Cronbach alpha: .686968 Standardized alpha: .688180 Average inter-item corr.: .365595
Mean if - deleted Var. if - deleted StDv. if - deleted Itm-Totl - Correl. Alpha if - deleted
E7 10.18750 4.277344 2.068174 0.524979 0.585617
E10 9.43750 4.121093 2.030048 0.477892 0.619194
E11 9.71875 4.764648 2.182807 0.450843 0.634556
E13 9.46875 4.686524 2.164838 0.432150 0.645135
Factor Loadings (Varimax normalized) Extraction: Principal components
Factor - 1 Factor - 2
E1 0.155287 0.649663
E3 0.209099 0.720611
E4 0.161195 0.665675
E5 -0.388446 0.446316
E6 -0.361747 0.618166
E7 0.680247 0.011411
E10 0.746893 -0.048698
E11 0.640092 0.411359
E13 0.651757 0.165320
Factor 1 – Personality-based enablers
The items that fall under this construct in order of strength of correlation are: Entrepreneurship
personality (E3), Networking Ability (E4), Comfort in taking risks (E1), Desire for more money
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(E6), previous work experience (E5). This more clearly demonstrates the personality of the
entrepreneur.
Factor 2 – Situational-based enabler
The items that fall under this construct are: Exposure to Entrepreneurs (E10), Social Capital
(E7), Globalisation (E13) and Desire to create jobs in South Africa (E11). This more clear
demonstrates that these have to do with the situational enablers.