why south african sme engineering managers...
TRANSCRIPT
Why South African SME engineering managers introduce
business practices: The degree to which chosen business
practices influence firm performance
A Research Proposal
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
Mark A. Schreiber
09 December 2015
Supervised by: Timothy London
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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
Mark A. Schreiber
31 August 2015
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Acknowledgements
To my supervisor Dr Timothy London, thank you for your swift and detailed feedback. Your
guidance and calm demeanour helped me to remain calm even in the thick of it all, when I
could see no light.
This report would not have been possible had it not been for the prompt response of the
members and administration of SAGI to the survey distribution request. A special thanks goes
out the MarylynnGrant from the SAGI office for her endeavours to ensure that the survey was
distributed promptly.
I would also like to thank my very special and supportive partner Bianca Kramer, my family,
syndicate members and close friends for all of their emotional support, guidance and patience
with me over this trying period. I have been absent for the most part of this last year and they
have all stood by me the entire way. This would not have been possible without each and
every one of your ongoing support. Thank you.
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ABSTRACT
South African SMEs are receiving a lot of attention at the moment and it has been highlighted
at a national level that these firms need to be fostered and grown in order to meet the
country’s needs in terms of the NDP for boosting SAs economy, reducing unemployment and
ultimately spreading equality. SME failure rates are extremely high and many of them do not
last longer than 2 years. Beyond this, those that continue fail to grow, often as a result of a
lack of skilled personnel and management, lack of access to finance and red tape. This study
looks into the South African SMEs in order to determine what the driving force and reasons
are for managers in choosing and implementing business practices. Further, a link between
the implementation of these business practices and firm performance will be investigated.
This population under investigation is the engineering industry in South Africa, who possess
incredible technical skills but in some cases may not have the required management
knowledge and want to grow their companies into larger organisations. A mixed methodology
was used to investigate the reasons for choosing business practices and the links between
implementation of these practices and firm performance. It was discovered in the results that
the majority of the participants were owners of companies that had been in operation for more
than 10 years and generated a revenue of less than R10 million. This raised the question of the
growth intention of these participants. A logistic regression was also run in order to determine
whether there was any link between the firm performance and the ‘business practices’ that the
firm implemented. The results were inconclusive due to insufficient number of participants
and other factors. The study could form as a basis from which to perform more investigations
and glean more generalizable results though the incorporation of a larger more representative
sample.
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TABLE OF CONTENTS
Table of contents ....................................................................................................................... V
List of abbreviations ............................................................................................................... VII
List of figures ........................................................................................................................ VIII
List of tables .......................................................................................................................... VIII
Introduction ........................................................................................................................... - 1 -
Research area and problem ................................................................................................ - 1 -
Purpose and significance of research ................................................................................. - 3 -
Research questions and scope............................................................................................ - 5 -
Research assumptions ........................................................................................................ - 5 -
Research ethics .................................................................................................................. - 5 -
Literature review ................................................................................................................... - 6 -
Business practices and performance .................................................................................. - 6 -
Performance Management practices .................................................................................. - 7 -
Strategic planning practices .......................................................................................... - 9 -
Financial management practices ................................................................................. - 13 -
HRM practices ............................................................................................................. - 14 -
Risk management practices ......................................................................................... - 16 -
Operations management practices ............................................................................... - 17 -
Marketing practices ..................................................................................................... - 18 -
Teamwork practices .................................................................................................... - 19 -
Conclusion ....................................................................................................................... - 21 -
Methodology ....................................................................................................................... - 22 -
Research approach and strategy ...................................................................................... - 22 -
Design and Instruments ................................................................................................... - 24 -
Survey Design ............................................................................................................. - 24 -
Pilot Survey ................................................................................................................. - 25 -
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Population, Sampling and Data collection methods ........................................................ - 25 -
Population determination ............................................................................................ - 25 -
Survey sampling .......................................................................................................... - 26 -
Data analysis methods ..................................................................................................... - 28 -
Research Plan ...................................................................................................................... - 30 -
Research findings, analysis and discussion ......................................................................... - 31 -
Research validity and reliability ...................................................................................... - 32 -
Preliminary findings ........................................................................................................ - 33 -
Qualitative Analysis ........................................................................................................ - 37 -
Quantitative Analysis ...................................................................................................... - 39 -
Limitations .......................................................................................................................... - 43 -
Conclusion ........................................................................................................................... - 43 -
Directions for future research .............................................................................................. - 44 -
Bibliography ........................................................................................................................ - 46 -
Appendix I – KPI measurement .......................................................................................... - 52 -
Appendix II – Survey Questions ......................................................................................... - 53 -
Appendix III–Email Distributed to Institutions .................................................................. - 60 -
Appendix IV - Consent form ............................................................................................... - 61 -
Appendix V – Incomplete questionaire breakdown ............................................................ - 62 -
Appendix VI – Reliability test ............................................................................................ - 63 -
Appendix VII – Bar Charts ................................................................................................. - 68 -
Appendix VIII – Frequency table ........................................................................................ - 77 -
Appendix IX – Respondent reasoning ................................................................................ - 81 -
Appendix X – Binomial regression for Number of personnel ............................................ - 84 -
Appendix XI – Binomial regression for firm revenue ........................................................ - 92 -
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LIST OF ABBREVIATIONS
FMP – Financial management practices
HPWS – High performance work system
HR – Human Resources
HRM – Human Resource Management
ISO - International Organisation for Standards
JIT - Just in Time
NDP - National Development Plan
NPC - National Planning Commission
REC - Research Ethics Committee
SA - South Africa
SANSBS - South African National Small Business Strategy
SME – Small to Medium Enterprise
TQM - Total Quality Management
US - United States
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LIST OF FIGURES
Figure 1: Business practices identified .................................................................................. - 3 -
Figure 2: Performance Prism Framework ............................................................................. - 8 -
Figure 3: Domain of business performance ........................................................................ - 10 -
Figure 4: Balanced Scorecard Framework (Harvard business review, 1996) ..................... - 11 -
Figure 5: Seven marketing strategies (Kumar & Petersen, 2005) ....................................... - 19 -
Figure 6: Calculation 1 ........................................................................................................ - 27 -
Figure 7: Age Responses ..................................................................................................... - 33 -
Figure 8: Education Chart ................................................................................................... - 35 -
Figure 9: Time in position vs firm revenue ......................................................................... - 35 -
Figure 10: Time in position vs current position .................................................................. - 37 -
LIST OF TABLES
Table 1: Strategic planning and measurement .................................................................... - 12 -
Table 2: Ratio calculation fields (Zager et al., 2012, p. 379) .............................................. - 14 -
Table 3: SME filtering criteria ............................................................................................ - 23 -
Table 4: Financial performance indicators .......................................................................... - 24 -
Table 5: Variable details ..................................................................................................... - 29 -
Table 6: Gantt Chart ............................................................................................................ - 31 -
Table 7: Respondents Removed .......................................................................................... - 32 -
Table 8: Personnel classification ......................................................................................... - 40 -
Table 9: Number of employees variables ............................................................................ - 41 -
Table 10: Revenue classification ......................................................................................... - 42 -
Table 11: Firm revenue variables ........................................................................................ - 42 -
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INTRODUCTION
Research area and problem
The need to improve business performance and reduce failure rates of South African (SA)
small and medium enterprises (SMEs) partly stems from the South African National Small
Business Strategy (SANSBS) goals. A report conducted by Maas and Herrington (2007)
illustrates the SANSBS goals, which are to promote economic growth, employment creation
and wealth distribution. SMEs have been increasingly recognised as vehicles for economic
and employment growth, as well as a major source of innovation and technological
development (Olawale & Garwe, 2010). Unlike global trends, which result in SMEs
contributing to employment, SAs SMEs are stagnated, resulting in a reduction in employment
creation and turnover. Both measures are extremely important in any economy, with turnover
driving economic growth , and the increase of employment being the catalyst which ensures
social stability (Business Environment Specialists, 2015). In line with this research, the
National Development Plan (NDP) of 2030 (National Planning Commission, 2010) illustrates
the massive shortage of engineers, amongst other professions, in SA. Therefore it is
imperative to ensure that the developing Engineering firms in SA remain sustainable, with
high performance rates in order to grow in the foreseeable future.
SMEs form the basis of many nation’s economic development, however SMEs face many
challenges that require modern management techniques, or 'business practices' in order to
address these challenges (Dumba, 2014, p. 33). Due to such challenges a staggering 75% of
SMEs in SA fail or never become established firms (Olawale & Garwe, 2010). According to
Von Broembesen, Wood and Herrington (2005) in comparison to any of the other Global
Entrepreneurship Monitor (GEM) countries surveyed, in SA it is more likely that a firm will
fail within the first 42 months. Willemse (2010) found that between 50% and 95% of SMEs in
SA fail within the first five years due to issues such as the lack of management skills and the
lack of access to finances.
In order to address the cause of these failures, leaders and managers are always looking for
ways to make improvements to various aspects of how they operate. They do this in order to
maintain the edge against their competitors, and to remain viable. This may be in the form of
rules, methods, frameworks or procedures which the company employs in order to meet their
objectives. These various tangible and intangible components will be referred to as ‘business
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practices’ for the purpose of this study. There has been various research conducted around
the concept of'business practices'; such as moral, ethical, unethical, environmental, and
sustainable business practices, however no clear and specific definition for business practices
have been found(Forsyth, 1992; Fowler & Hope, 2007; Gadenne, Kennedy, & McKeiver,
2008; Sarkis, 1998; Svensson, Wood, & Callaghan, 2010; Tituts & Bradford, 1996). A study
by Mandal and Venta(2008) highlights various measurable elements of 'business practices'
that can be used as indicators to determine whether a 'business practice' is working or not -
these elements include productivity, profitability and innovation. Other than the larger
manufacturing and commercial businesses, there is also a need in the SME sector to reduce
failure rates, as discussed above. Maas and Herrington (2007) found the SMEs’ owner’s level
of education correlated with the businesses capacity to create employment as it develops.
Due to the legacy of apartheid, a lack of sufficient schooling and training has resulted in poor
management capacity in start-up companies in SA, further linking to low levels of
entrepreneurial creation and high failure rates (Von Broembesen et al., 2005). This lack of
basic education and management skills has left many SASME leaders lacking necessary
management skills and knowledge of business practice which is needed to ensure their
businesses perform well and remain viable.
Research conducted by Neneh and van Zyl(2012) focused on the positive effects of 'business
practices' by exploring marketing practices, strategic planning practices, human resources
management (HRM) practices, risk management practices, performance management
practices and teamwork practices, in order to see how their implementation effect business
performance; It was observed that strategic, performance management and marketing
practices dominated when correlated with firm performance. The results of Dumba’s(2014, p.
33) report illustrated that current research has been based on many of the areas identified
above, as well as operational and financial management practices, as a result these practices
have been identified for further investigation. Figure 1, below, displays the consolidated list
of 'business practices', as identified above. There appears to have been large amounts of
research conducted around these specific business practices; however there is limited research
regarding the umbrella term 'business practices'. The sub-business practice sectors identified
in Figure 1 create a platform which can be used to investigate certain areas of a business.
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Figure 1: Business practices identified
The failure rate of SMEs in SA can be attributed largely to the low levels of basic education
(Von Broembesen et al., 2005); management capacity and expertise, the lack of access to
finances (Olawale & Garwe, 2010), and how one understands the influences of business
practice’s on business performance and success(Hsu, Tan, Kannan, & Keong Leong, 2009).
Understanding how management practices can, and are, being implemented to address such
issues, has been speculated to significantly improve the success rate and overall performance
of SMEs in SA.
Purpose and significance of research
SMEs have been a recurrent topic in news for many years and seem to have become more
popular over the past few years. SMEs can be incubators for innovation (Olawale & Garwe,
2010)and are one of the best means to reduce unemployment(Business Environment
Specialists, 2013). In many instances SMEs are said to contribute significantly to an
economies gross domestic product (GDP). On a global scale, SMEs have been found to
account for more than half of the total employment in various countries (University of
Miskolc, 2005) however, as one can see in the research discussed above, the failure rate of
SMEs in Africa, and more specifically SA, is extremely high.
The SA government has set out various plans in their NDP of 2030 to work towards
eliminating specific areas of concern. For example,one of the plans is to improve the
unemployment predicament in SA by reducing the unemployment rate of 24.9% in 2012 to
14% by 2020, and 6% by 2030 (National Planning Commission, 2010). Another area of
concern is the very low level of economic growth, which needs to increase to more than 5.4%
per annum in order to meet the growth objectives. The National Planning Commission (NPC)
suggested that jobs, as well as economic stimulation, that are required will most likely come
•Performance management practices•Strategic planning practices•Financial management practices•Human resource management practices•Risk management practices•Operations management practices•Marketing practices•Teamwork practices
Business practices
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from SMEs and domestic-oriented firms (National Planning Commission, 2010). In the NDP
of 2030 they have highlighted various areas where the facilitation of SME incubation and
growth is an intended outcome. Du Toit and Roodt(2008) highlighted that a large proportion
of the engineers in SA are approaching retirement and with the Accelerated and Shared
Growth Initiative for South Africa (ASGISA) there is large a need for more skilled people to
meet the mandate of this initiative. According to Business Environment Specialists(2013) it
has been proven that SMEs play a vital role in economies all over the world, and various
other studies conducted have also illustrated that SMEs are a key component in job
creation(Amra, Hlatshwayo, & Mcmillan, 2013; Maas & Herrington, 2007; Neneh & van Zyl,
2012). This evidence illustrates how relevant and necessary it is to develop well planned and
successful SMEs in order for enterprises to have the capacity to fulfil the needs required of
them.
Currently SMEs in SA are failing due to a number of challenges - poor business planning, the
need for specific training, insufficient education, limited access to suitable finances, the lack
of skilled labour, governance issues, and poor service delivery(Business Environment
Specialists, 2015). It is important to note that SMEs that endure also face these challenges
(Olawale & Garwe, 2010; Von Broembesen et al., 2005). Therefore it is evident that the
business climate in SA is hostile highlighting the need for government to change their
thinking towards the country’s global competitiveness and how they support SMEs (Business
Environment Specialists, 2015). With government assistance the necessary intervention and
support will be provided which will support SMEs in getting off of the ground and
developing. However, the managers and leaders of SMEs also need to understand what they
can do from their side in order to improve performance and competitiveness in the local and
global marketplace.
By understanding how 'business practices' facilitate the achievement of sustainable growth,
business owners can be assisted in achieving their goals and building better SMEs. In turn,
this will benefit shareholders, as well as deliver to the SA government’s NDP. By
acknowledging the need for improvement in the performance of SMEs, and understanding the
need for reductions in failure rates, this research aims to investigate what the drivers are for
implementing business practices by managers in SA SMEs. Additionally, this research will
consider how the business practices which are implemented impacts on SME performance.
Lastly, this research will contribute to knowledge regarding business practices and business
performance.
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Research questions and scope
This research paper will focus on the drivers for leaders to implement specific business
practices within SA SMEs. It is important to note that this research will be focusing
specifically on the engineering industry. As seen above, the term ‘business practices’ is an
extremely broad term with many different meanings, therefore for the purpose of this study
the term ‘business practices’ will refer to business management practices that fall within the
bounds of the list given in Figure 1 above, as well as the definitions given in the literature
review(Dumba, 2014; Neneh & van Zyl, 2012).
SMEs and start-ups in the SME engineering sector are generally owned and managed by
engineers with inadequate levels of formal academic business or management
training(Karadag, 2015). Therefore they do not always have the necessary skills and
knowledge to build SMEs that will grow into larger more sustainable enterprises. The
question that will be addressed in this research report is: “What are the drivers for leaders of
SA SMEs in selection and use of management practices”. The degree to which these chosen
practices affect firm performance will also be investigated.
Research assumptions
This research is based on the assumption that many engineers in SA SMEs are not aware that
they often use business practices to run their businesses. It can be speculated that various
SMEs do not use such business practices due the lack of knowledge regarding business
practices, which in turn can be linked to the lack of information of such practices in their
engineering coursework during their studies. It can be presumed that the professional
institutions approached for the purpose of this research would be willing to expediently
distribute the survey to their mailing list. Another assumption was that a sufficient number of
responses to the survey would be received to help generalize the findings. Finally, this
research assumed that the respondents would answer the questions honestly and accurately.
Research ethics
This study was conducted within the guidelines of the "Graduate School of Business Ethics in
Research Policy". All participants were made aware of the purpose of the study and were
informed that there is no known risk or danger in participating in this study. The participants
were also informed that the researcher will take all reasonable steps to ensure the responses
remain anonymous.
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Approval was required from all individuals who took place in this study (see the approval
questionnaire in the survey in Appendix IV). Individuals were made aware that they could
withdraw from the study at any time. Approval was also received from the Research Ethics
Committee (REC) for this study. Finally, all participants were given the opportunity to
request a copy of the report on completion of the study.
LITERATURE REVIEW
Business practices and performance
Business practices are the ways in which businesses operate through processes, methods and
techniques which develop in the pursuit of goals which are guided by a company's values
(Neneh & van Zyl, 2012). Achieving an objective can be considered as improved business
performance; however, these objectives are not always tangible or quantifiable. Flynn,
Sakikibara and Schroeder (1995) put it in a slightly more concise manner, highlighting that
practices are inputs that managers and workers action in order to achieve a certain
performance or output.
Performance improvements can only be measured if there is a benchmark to gauge
against(Poister, 2013). When one sets baseline performance goals, then improvements in
outcomes and performance can be monitored through performance management. If baseline
goal metrics are set accurately and made available to workers, then these workers will strive
to meet the set requirements (Eckerson, 2006, p. XIV). It is therefore apparent that for a
SME/company to determine how it is performing, it must have some way of measuring its
performance at a particular point in time.
‘Best’ practices are the practices that should logically produce the best performance (Mandal
& Venta, 2008). Researchers have found a positive relationship between the implementation
of ‘best’ business practices and the performance of businesses (Lau, Zhao, & Xiao, 2004;
Prajogo & Sohal, 2003; Rahman, 2001); Mandal’s (2008) study shows the numerous ways in
which business practices can be established, leading to exemplary business performance, such
as implementation of clear processes and quality requirements and checklists. Kelliher and
Reinl(2009 as cited in Neneh & van Zyl, 2012) stated that in order for SMEs to ensure long-
term survival and success, they should allocate their scarce resources using ‘best’ business
practices to their core business strategies. It is therefore evident that 'best' practices will lead
to a better performance in firms. However, one will not know if a firm is implementing ‘best’
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practices unless they are aware of what types of business practices are being implemented,
and have a means to measure the outcomes of these practices through performance
management.
Performance Management practices
In order to measure performance and decide what might be considered as ‘best’ practices, one
will first need to understand what performance is, and how it is managed. Performance
management is described by Jesuthasan(2013, p. 59) as a “business management tool that
aligns decision making differentially across various roles, reflects the unique aspect of the
business model and culture, and considers the risk profile of the industry”. Jesuthasan(2013)
also states that companies with a good handle on performance management are more likely to
achieve their strategic objectives and improve return on human resource (HR) investments.
Performance management is a strategy that links every activity of a firm, the way in which
this is done varies from business to business. Performance management can aid in bringing
out the inherent nature of employees to want to better themselves and to look good by giving
them an indication of their performance; thus leading to them wanting to better themselves
(Poister, 2013). Strategic practices and their overarching link to all the other practices will be
discussed later.
Neneh and van Zyl(2012) and Poister(2013) highlighted how performance management can
assist managers in sensing problems earlier on, allowing for a swifter response, by seeing
outliers in their performance guidelines for various elements within the process; such as cash
flows and KPI deviations to name a couple. Therefore problems would be highlighted through
the use of metrics compared to benchmarks. Performance management may have a positive
impact on business performance(Poister, 2013), building a culture of self-improvement and
individual responsibility(Taylor & Taylor, 2014). Recorded metrics can form a baseline for
managers to work from and can be used to determine if the changes made will result in
improved or degenerated performance. The improved performance outcomes may be
considered ‘best’ practices until such time as an improved performance is measured.
Poister(2013) found that employees respond well to performance related feedback given on a
regular basis and opportunities for growth, giving them an opportunity to show what they are
capable of. Performance management is a powerful tool for gauging the strengths and
weaknesses in employees, thus giving a point from which training and learning can
begin(Poister, 2013). Rashidi’s(2015) findings illustrated the benefits of performance
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management through improved employee performance and subsequently firm efficiency.
However, Rashidi cautions that the correct performance management system should be
implemented otherwise firm performance will suffer. Self-learning and feedback processes
linked to the employees seem to be crucial in fostering performance improvements. This
feedback and learning loop ensures that employees are continually improving themselves and
their performance which in turn improves firm performance.
A study conducted by Darwish et al. (2013) found that there was no real consensus as to how
to measure operational performance. They did, however, find that “operational performance
can be defined in terms of HR-related outcomes (turnover, job satisfaction, commitment and
others) or organisational outcomes (productivity, quality, service, efficiencies and others)”
(Darwish et al., 2013, p. 3343). This link between HR and Operations Management will be
discussed in their respective sections later on. Statistics New Zealand (2007) identify financial
cost, operational quality, innovation and human resources as performance measures. From
these varying sources it is apparent that performance can be measured through a variety of
lenses.
With that in mind, Neely, Adams and Crowe (2001) break performance down into a
"performance prism framework". This framework consists of five facets, or lenses (Figure 2).
Each of these frameworks is interrelated and looks at the organisation through a different lens.
Figure 2: Performance Prism Framework
The first lens is the stakeholder perspective which seeks to acknowledge the various
stakeholders in the company, and questions what they want. The second lens considers the
1. Stakeholder satisfaction
2. Strategies
3. Processes4. Capabilities
5. Stakeholder Contribution
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businesses strategies, and whether these meet the needs and wants of the stakeholders
identified in the first lens. The third lens considers what processes need to be put in place in
order to meet the refined businesses strategies, and what metrics need to be put in place for
each of these designed processes. The fourth lens looks at the core capabilities within the
company’s personnel, practices, technologies and infrastructure which work together to meet
the process needs. Finally, the fifth lens looks at the contribution of the shareholders to the
needs of the company, espousing that the stakeholder/company interaction is reciprocal; thus
that the stakeholders also the need to contribute to the needs of the company in order for it to
succeed (Neely et al., 2001). Therefore, it is evident that in order for a company to perform
optimally each of these lenses needs to looked at. By considering each lens you are able to
look at each part of your business and build a complete picture of how it is operating and
where improvements or energy are required.
Strategic planning practices
Young (2003 as cited in Neneh & van Zyl, 2012) defines strategic planning as a formal,
flexible process which determines where a company is, and where it wants to go. In line with
this, Skrt and Antoncic(2004) state that the most important parts of strategic planning
practices are having a clear vision and clear objectives. Johnson, Langley, Melin and
Whittington’s (2007) first statement in their book highlights their position that strategy is
something that ‘people do’, as opposed to what researchers historically portrayed strategy is
something that ‘organisations have’. Johnson et al. (2007) highlight that strategy as practice is
not just the overarching strategic idea, but also the activities that develop and enhance
strategies on a day to day basis. These two perspectives consider the overall strategic
direction that the company has chosen, as well as the daily strategies that are developed and
implemented in order to achieve the vision.
Strategic planning focuses on defining a firm’s mid-term objectives and development, as well
as the implementation of plans that allow a firm to meet these objectives(Vargo& Seville,
2011). According to Dincer, Tatoglu, and Glaister(2006) strategic planning in most aspects of
business often do not extend beyond four years Long term strategic planning may be
inappropriate due to the volatility of businesses today, however, it can be helpful to have a
high level strategic vision so that action can be taken if a firm begins to drift too far from the
planned objectives (Vargo & Seville, 2011). Strategic planning practices can be broken into
the following approaches: a planning approach, a competitive position approach, an emergent
or learning approach, a core competence approach, and the learning and knowledge-based
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strategy (Campbell, Stonehouse, & Houston, 2002). Within each of these practices it would
then be beneficial to measure the businesses performance.
Venkatraman and Ramanujam(1986 as cited in McGee, Thomas, & Wilson, 2010) present a
conceptual tool for explaining business performance (Figure 3).The middle ring looks at the
business practices considering internal factors that may impact the firm’s performance. These
internal factors include market position indicators, the rate of new product creation
(innovation), efficiency, product quality, and financial measures, such as return on
investment, equity or earnings per share. These factors fall into different business practices
such as marketing, finances, operations and innovation, which are in line with the strategic
planning framework, therefore reinforcing the interdependent linkages between different
business practices. Based on this, a holistic view of these different performance areas need to
be taken into account in order for a firm to be considered to be performing optimally(McGee
et al., 2010).
Figure 3: Domain of business performance
McGee et al. (2010) refers to the balanced scorecard, linking it to corporate strategy through
four lenses (Figure 4). These lenses question how a business is perceived by shareholders
(financial aspect), how customers perceive the business (market share, brand image and
customer satisfaction), what the business must be good at (quality and processes), and how
the firm can continue to improve (innovation). Doyle (2000 as cited in McGee et al., 2010)
presented a more detailed list of metrics for each of the lenses (Appendix I). Although these
measures form a great basis to start from, it is important that they are aligned with a
company’s objectives and strategies. As it can be seen in Figure 4, each of these lenses
Domain of organizational effectiveness
Domain of financial + operational performance (business performance)
Domain of financial performance
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connects and feed into one another. If any one of the scorecards is out of balance, or not
aligned, the whole system will likely be out of balance and will not perform optimally.
Figure 4: Balanced Scorecard Framework (Harvard business review, 1996)
Eriksen(2008 as cited in Neneh & van Zyl, 2012)and Al-Shammari and Hussein (2007)
established that strategic planning practices can have a positive impact on company
performance. Additionally, quick strategic decision making in consistently evolving
environments is essential to maintain high performance and the competitive edge (Johnson et
al., 2007). Wickham (1998 as cited by Neneh & van Zyl, 2012) points out the benefits of
strategic planning for SME performance, in that it encourages managers to think about
business questions and seek solutions on a continual basis. It has been reasoned by Robinson
and Pearce(1984 as cited by Neneh & van Zyl, 2012) that strategic planning is not always
undertaken by SMEs, as they do not always have the time and resources available despite the
fact that research has shown it to be of some benefit. In addition, research on the impact of
strategic planning on SMEs has not been conclusive, as numerous SMEs did not appear to
plan strategically. Robinson and Pearce (1984 as cited in Neneh & van Zyl, 2012) do however
concede that many of the researchers may have been too stringent in their criteria for strategic
planning, thus excluding evidence of planning in SMEs. A more recent investigation by
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Dincer, Tatoglu and Glaister(2006) revealed that many firms are turning their attention and
time to strategic planning practices.
On a promising note, Wang, Walker and Redmond(2007) found that the better performing
SMEs generally implemented strategic planning practices. SMEs generally do not practice
strategic planning despite the research showing its efficacy (Wang et al., 2007). Operational
planning was found to be more commonly used and beneficial for SMEs than strategic
planning, although there were some positive outcomes related to strategic planning in SMEs
which were found to be present during times of economic need or when change was required
(French, Kelly, & Harrison, 2004). As such, it is evident that all SMEs should at least
consider trying to implement some sort of strategic planning, as required, to improve their
performance.
‘Strategic planning’, ‘domain of business performance’ and ‘balanced scorecard' frameworks
all intertwine(Harvard business review, 1996; McGee et al., 2010; Neely et al., 2001). Table 1
below shows the overlap between the three frameworks and shows how each of the
components fit together in the rows. One can therefore speculate that strategy forms both an
overarching vantage point for a company’s direction and vision as well as a pivotal tool for
planning and performance measurement in each of a business’s domains. These domains
intertwine with the business practices identified in Figure one, and will be used to assess the
performance in each of these practices.
Table 1: Strategic planning and measurement
Strategic planning framework
(Strategic approach)
Domain of business
performance- Internal domain
and finance (Performance impact
factors)
Balanced scorecard framework
(Performance measurement)
Planning approach Financial
Competitive position approach, ie. Market position and customer orientation
Emergent or learning approach, product quality improvement cycle
Internal Business
Core competence approach Efficiency
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Learning, knowledge, growth and innovation
Financial management practices
For an external stakeholder, the financial statements can be seen as the most fundamental
indicator of a company’s performance. Without sound financial management practices
(FMP), a firm is likely to either fail, or at least not operate at an optimal level. Zager et al.
(2012) stated that financial reporting was more often generated for external stakeholders, for
tax or financing institutions, and little or no internal reporting takes place. Karadag(2015)
found that only 11% of business owners or managers analyse their financial statements as part
of their decision making process. This shows us that managers, owners, and leaders alike in
SMEs are often not aware of the financial position of their companies and often do not use the
information at hand to make informed decisions about their company. As financial
management falls under the planning section of the strategic planning framework, it forms
one of the supporting pillars of any business (Table 1). Therefore this lack of awareness and
understanding of the significance of the use of these financials may be a contributor to the
poor performance results of SMEs.
A firm’s financial statementsform a foundation of historical financial position and
performance. These statements present successive periods of financial operations and reveal a
firms level of financial performance, giving insight into how a firm may perform in the near
future (Graham & Winfield, 2012; Lovemore & Brummer, 2003). Lovemore and
Brummer(2003) insist that cash flow is the ‘life blood’ of any firms success. Not having a
firm grip on cash management can very quickly lead to liquidity issues, which is what often
shuts companies down. Liquidity is the ability to settle all short term obligations in cash. For
a SME that is going through a growth period it is imperative that it monitors its liquidity. The
simplest metric for this is cash flow. If a firm’s growth requires more cash outflow than
inflow then it can be detrimental to its liquidity (Madrid-Guijarro, García-Pérez-de-Lema, &
van Auken, 2011). In addition to liquidity there are numerous other financial ratios that can be
calculated. These can be broken down into five different fields;liquidity ratios, leverage
ratios, activity ratios, economy ratios and profitability ratios. These ratio fields are explained
in Table 2 below.
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Table 2: Ratio calculation fields (Zager et al., 2012, p. 379)
RATIO Description of measurement
Liquidity Measures a company’s ability to pay its short term
liabilities
Leverage Measures how much external financing the company is
relying on
Activity Measures how efficiently a company is using its
Assets
Economy Measures the revenue as achieved in relation to
expenses
Profitability Measures the return on invested capital
These ratios provide one method for monitoring a firm’s performance. By expressing a
company’s financial figures in the form of a ratio, they are then able to be compared to other
financial periods as well as to other companies of different sizes (Graham & Winfield,
2012).With financial statements needing to be generated for external stakeholders, there is no
excuse for managers not to utilise the documents to analyse their businesses performance.
However, for reasons discussed before, such as time and knowledge, this information is not
always analysed. Doing so will allow them to pre-empt cash flow or possible performance
issues.
HRM practices
HRM is largely concerned with the learning, knowledge, growth and innovation sector of
Table 1. The learning and knowledge base of your company is reliant on the HRM practices
that you have in place. Armstrong (2012 as cited by Ojokuku, Sajuyigbe, & Ogunwoye, 2014,
p. 2) defines HRM as “as the strategic and coherent approach to the management of an
organization's most valued assets - the people working there, who individually and
collectively contribute to the achievement of the objectives of the business”. Numerous
studies confirm that HRM practices are mainly aimed at managing people effectively
(Armstrong, 2012; Voss & Brettel, 2014; Yeung & Berman, 1997). If this is done correctly it
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is presumed that positive employee outcomes and behaviour will be achieved, which
inevitably leads to positive firm performance and improved competitive advantage and value
creation. This can be described as high-performance. Armstrong (2012) proposes that high
performance work systems (HPWS) are achieved by combining HR practices, which are
complimentary to the work environment in question.
According to McGee et al. (2010) performance management in an organisation is considered
to have an influence on high-performance as it shows that performance is important. If
performance is not earmarked as an important factor in business then it may be overlooked.
The continued management of performance and alignment with firm strategies and other
practices, including HR, is imperative in the pursuit of maintaining a high level of
performance. HR’s role in firm performance is to utilise the human capital within a firm in
order to seek solutions to problems, as well as to manage employee performance issues and
pass them on to management to come up with solutions(McGee et al., 2010).
A longitudinal study in the engineering industry by Boxall and Steeneveld(1999) made key
findings with regard to skilled personnel and management. Some of these findings included
that: 1. successful companies tend to copy each other’s HR strategies, 2. once employees are
identified as major contributors to the company’s revenue streams they are given ownership
in order to lock in commitment, 3. the diversification of skill sets and continual pursuit of
profitable niches drives HR skill development and acquisition, and lastly, 4. especially in
professional services firms, it is imperative that leadership capacity and capability growth is
HRMs most important role (Boxall & Steeneveld, 1999).As such, the performance of a
company is inherently reliant on the performance of the HR department, especially in a
knowledge rich industry such as engineering.
It must be noted that although a considerable amount of research has been done to show a
correlation between HRM practice and firm performance, research has also found that
although there are strong links between HR practices and firm performance this does not
necessarily create a direct cause and effect link (Armstrong, 2012; Darwish et al., 2013;
Neneh & van Zyl, 2012). As there are numerous other causal actions at play in any
environment it is extremely difficult to isolate these factors when trying to determine a causal
effect from an HR practice(Karami, Sahebalzamani, & Sarabi, 2015). What makes predicting
and measuring outcomes even more difficult is that humans are complex beings and will react
in different ways given the same situation. Colbert (2004) presents a framework that was
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developed from 'Complexity' using the living-systems principles. This framework
acknowledges the structured HRM frameworks that have been developed, however it takes
the position that in an environment where complexity prevails a rigid system cannot work and
certain flexibilities need to be introduced. Thus in situations where performance is required in
businesses where human intervention is high a less rigid approach should be considered.
Risk management practices
Risk management is defined by Keizer, Halman and Song (2002) as steps taken to mitigate
risks in a project, after taking the necessary actions to identify this risk. This definition does
not give much insight into what a ‘risk’ is, and how to identify it. According to the
International Organisation for Standards (ISO, 2009) standardised classification ‘risk’ can be
defined as the influence that uncertainty has on the realization of goals. It was reported by
Vargo and Seville (2011) that crises generally present with high risk as well as large
opportunities, which are often handled by two separate practices - crisis management and
strategic planning. As a result opportunities fall through the cracks and may be missed due to
the two aforementioned practices not being met with risk management.
Neneh and van Zyl(2012) found that in various studies the use of risk management practices
led to an increase in firm performance. These risk management practices can be characterised
by the use of insurance, installation of security systems, product diversification and
innovation, professional recruitment, safety, general training and coaching, development of
procedures, and uniform termination processes; to name a few. Implementation of these
countermeasures, training and procedures in the required order and magnitudes is proposed to
improve performance(ISO, 2009; Neneh & van Zyl, 2012).
It is important that SMEs employ the correct risk management practices into their business in
order to gain the possible enhanced performance outcomes. This can be difficult especially
when the firm is operating multiple projects at the same time, which is often the case in
engineering practices(Savci & Kayis, 2006). One method of introducing 'best practices' is to
investigate what other firms or projects are doing successfully and to imitate their methods if
they are working (Savci & Kayis, 2006). Seeing that risk management can evolve from
similar projects, it can also be seen to fall into the learning and knowledge sector from Table
1. Neneh and Van Zyl(2012) reported on various risk management tools from different
authors designed specifically for SMEs. Each of these practices needs to be investigated and
implemented with their merits in mind, as well as the type of outcomes that are required.
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It has been illustrated that risk management may have a positive impact on firm performance
and that various tools can be used for implementing risk management. The effects of each of
these tools will not be investigated, as each business will have its own manner of dealing with
risks. However, what will be investigated is if a business is aware of its risks, and takes
actions to mitigate these risks.
Operations management practices
Operations management is concerned with the areas of a business where value is created. This
is achieved through designing, planning and operationalising value creation processes
(Grutter, 2012). Operation management is often considered from a systems perspective. In
this approach, different systems are recognised - such as natural, mechanical, information and
social systems(Flynn et al., 1995). In each case there are inputs, processes and
outputs(Grutter, 2012). In a good system there is also a feedback loop which informs the
operations team whether what they are doing is working or not. In order to manage the needs
and requirements of operational systems, many operational practices have been developed
such as ‘Just in Time’ (JIT) practices, ‘Total Quality Management’ (TQM) practices, and
Infrastructure practices. Each of these practices has been designed to address different needs
and value requirements within organisations. Infrastructure practices have been cited as all
practices that support both JIT and TQM practices (Flynn et al., 1995).
In seeking to create value for the end user, operations management strives to meet quality
expectations from its customers. As quality is a relative phrase it needs to be benchmarked
against something - customer perceptions, competitors’ products or some other measure
(Meredith & Shafer, 2011). In order to measure different quality metrics, Schonenberger(n.d.
as cited in Meredith & Shafer, 2011) constructed a list of quality metrics which were often
found to be used by customers - conformance to specifications, product performance,
features, quick response, reliability, durability, serviceability, aesthetics and humanity.
Performance in this instance refers to the product or service performance in the eyes of the
customer. Therefore creating value attracts and retains the customer, and these metrics are
likely important value determinants to customers(Foster, Wallin, & Ogden, 2011). Thus, in
the eyes of operations, it might be said that the company is performing well if it is meeting the
customers’ requirements on the relevant metrics above.
The structure of operations can be described by the ‘3S Model’ which breaks operations down
into the strategic, systemic and situational levels. The strategic level (S1) focuses on an
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organisations objectives and goals (strategy), the systemic level (S2) is where systems are put
in place to execute the company’s goals, and the situational level (S3) deals with the
mechanisms, which operationalise the systems which are designed in S2 (Grutter, 2012). The
situational level is the largest component of the three levels and is the level where most of the
value is created for the customer. That does not discount the value creation that takes place at
the higher levels but does point to the area where more time and energy should be spent
overall.
Marketing practices
Marketing like OM looks at business from a customer centric perspective (Jobber & Ellis-
Chadwick, 2013). According to Peter Drucker (n.d. as cited in Warren, 2007, p. 1) “because
the purpose of business is to create and keep customers, it is only two central functions-
marketing and innovation. The basic function of marketing is to attract and retain customers
at a profit”. This is a very simple statement, however, it captures the essence of business in a
clear and concise way, as it is essential for a business to conduct marketing practices (Ghouri,
Khan, & Malik, 2011; Jobber & Ellis-Chadwick, 2013). Ghouri, Khan, Malik and
Razzaq(2011) stressed that a sound marketing strategy improves the competitive edge of the
firm in the market and adds quality to its undertakings when implemented correctly.
There are two distinct perspectives taken when it comes to marketing -1. the customer-centric
perspective, which considers selling customers what they need, and 2. the firm-centric
perspective, which is concerned with selling customers more of what the firm produces
(Bettencourt, Lusch, & Vargo, 2014). Seven marketing strategies were established by Kumar
and Petersen (2005) that can add to a company’s performance and maximise profitability
(Figure 5). From a service perspective, as in engineering, it is important for the firm and
customer to work together to co-create value and in doing so, ensure the desired outcomes are
achieved (Shamir & Eilam, 2005). Tesar, George and Huada(2013) agree that close
cooperation between internal and external aspects of a business's marketing strategy needs to
be in place, highlighting product positioning and location as a key aspect of marketing.
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Figure 5: Seven marketing strategies (Kumar & Petersen, 2005)
In order for a company to stay in business it needs to be both efficient (does things right) and
effective (does the right things); if either of these areas are not completed correctly the
company will not operate optimally and therefore performance will suffer (Jobber & Ellis-
Chadwick, 2013). Jobber and Ellis-Chadwick (2013) linked marketing with firm performance.
Their research illustrates a substantial correlation between marketing and business
performance. Firms in the commodity business generally result in high performance whether
the business had a low or high market orientation, however if the business showed a medium
market orientation, low performance was measured. Non-commodity businesses showed
increased performance measures in a linear fashion when moving market orientation from
low to high. When considering using marketing to improve firm performance the identified
marketing strategies in Figure five need to be developed to meet the market characteristics
found to be associated with high performance companies (Jobber & Ellis-Chadwick, 2013;
Lado, Duque, & Alvarez Bassi, 2013). Due consideration also needs to be taken as to whether
the company is in the commodity or non-commodity business. Therefore, in the case of
engineering firms, they are mainly non-commodity driven, however they do often work for
commodity driven firms which will also play a role in their marketing decision.
Teamwork practices
A comprehensive teamwork definition was compiled by Xyrichis and Ream (2008, p. 238)
which is summarised as follows: a dynamic process involving two or more complimentary
persons, sharing common goals and working together through physical and mental exertion to
assess, plan and implement various tasks. Neneh and van Zyl(2012) establish, through various
studies, that teamwork is crucial in business and entails working in collaboration within a
group, each bringing their own set of skills to the table. It is important for people in an
• Customer selection• Customer communication and contact• Sending the right messages at the right time• Manage multiple channels• Manage high cost customers• Finding and keeping the right customers• Managing loyalty and profitability
Marketing strategies
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organisation to understand the significance of collaboration and working together. Teamwork
has a strong link to HRM in that it is a powerful tool for tapping into the vast knowledge and
skills of the employees (Sewell, 2005 as cited in Xyrichis & Ream, 2008).
Some benefits for individuals found to be associated with teamwork include: reduced stress
levels, motivation, increased morale and self-confidence (Gueldenzoph Snyder, 2008;
Xyrichis & Ream, 2008). Businesses that took on teamwork also showed improved individual
performance, innovation and problem solving abilities, all of which improved company
performance (Neneh & van Zyl, 2012). However not all teams are successful.
In Belbin’s (1981) research on teamwork the following factors were found to be contributors
in unsuccessful teams - ‘team morale’ was found to be a marginal factor in team success,
while mental ability of the team members was seen to be critical; ‘team role reversal’ was
also found to upset the team with some teammates not being comfortable when their roles
were altered. Belbin’s (1981) and Kane (1975 as cited in McCallin, 2001) surmised that
successful teams relied on various factors such as 1. chairperson’s attributes, 2. a balanced
spread of team member attributes, 3. well placed responsibilities to enable an individual’s
capabilities and 4. openness to readjusting the team if imbalance is recognised. An important
lesson is that one must know what each of their team member’s strengths and weaknesses are
so that he/she can extract the most value out of each individual, and the team as a
collective(Crawford & Lepine, 2013).
Parker (2008, pp. 135–146) described four stages that teams go through from inception. Stage
one is ‘forming’, which may happen when new teams are constructed, team leaders are
changed, or when the team’s purpose is significantly altered. The next stage is ‘storming’,
which sometimes entails disagreements between various team members on certain points and
can lead to sub-groups being formed. It is noted that teams which fail to go through this stage
do not learn to deal with their differences. The third stage is ‘norming’, which signifies the
emergence of a team from the storming stage. Here a team reflects and learns from the
outcomes of their storming and sets guidelines for all identified team interactions and
processes. Hackman (as cited in Xyrichis & Ream, 2008) proposed three stages - 1. Inputs,
which deal with the composition of the group and tasks. - 2. Processes that are made up of
communication styles and project organization - 3. And outputs, where if the first two
elements are managed correctly the team and firm goals should be met. The team becomes
more cohesive and are open to each-others opinions and ideas(Parker, 2008).
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Working in a team can often lead to groupthink (Janus, 1972 as cited in Parker, 2008;
Woodfield & Kennie, 2008) which may inhibit diverse thinking and new ideas. According to
Whyte (1989) Groupthink is then members of a team override their natural instinct to
consider alternative courses of action in order to appease the teams consensus. The last stage
is ‘performing’; when a team reaches this stage they are able to periodically assess their own
performance and align their expectations and deliverables with improved goals. Complacency
can set in, thus it is important to make sure that the team keeps current, reassesses bad habits,
and does not let standards slip. Parker (2008) gives guidelines for managing and fostering
high performing teams, which can result in improved firm performance. These frameworks
can also be used to assess what stages teams are in organisations and if teams are operating
efficiently the company’s performance might be linked in part to this.
Consideration should also be given to the possibility of too much teamwork. Crawford and
Lepine(2013) found that too much teamwork can lead to reductions in innovation and
decreases in morale. They suggested that depending on the complexity of the task at hand, it
may be beneficial for a team to limit the amount of teamwork required, in order to maintain
synchronicity and preserve autonomy for less complex tasks. Too much teamwork can also
lead to more emphasis being put on relationships rather than on the task at hand (Ross, 2005).
Therefore, teamwork needs to be balanced rather than becoming a crutch for managers.
Teamwork is important when the task is complex, however, individuals’ core capabilities
need to be utilised individually when the opportunity presents itself, in order to enable the
individual members to perform efficiently (Crawford & Lepine, 2013; Ross, 2005).
Conclusion
There are copious amounts of reports which state that the implementation of a specific
business practice will result in improved firm performance. Research has shown that there is a
strong correlation between the identified business practices. Therefore, these practices cannot
be implemented in isolation without considering the other practices, or the impact that each of
them will have on each other.
Each business practice plays a significant part in a business’s optimal performance, and each
will have a different impact depending on a variety of factors, such as the environment,
industry or the firm in question. The performance prism framework gives a holistic
breakdown of the frames through which to view the firm’s performance. There are numerous
other models which focus on each of the frames proposed. Used in conjunction with the
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measurement areas identified in Table 1, required metrics can be strategized to manage
performance. Performance measures are important to each of the business practices identified.
Once this framework has been implemented and the outcomes are mapped out, the business
practices described can be reviewed to address the performance areas established.
Overarching all of these practices is the concept of ‘strategic planning’. This is concerned
with each of the other practices and should align the development of each of these practices
with the long term vision, as well as the short to medium term objectives of the company. As
discussed in the introduction, many SMEs develop due to the need for a specific service and
as a means to provide income. This need is believed to shield the owner/manager from
constructing a longer term strategy. Once this long term vision is realised it is espoused that
the interrelated business practice strategies will beginning to fall in line, through learning, in
order to deliver the vision.
It is proposed that a little of each practice will have to be implemented and built on over time.
In doing so, the business will reap from the knowledge based rewards associated with each
practice. Over time more tools from each practice can be integrated as knowledge and
understanding develop. In this way not all the business owners and managers time is usurped
by the implementation of models and measure of data, but rather through the use of business
practices they build an understanding, seeking knowledge and reaping the rewards of ever
improving performance.
METHODOLOGY
Research approach and strategy
This study used the snowball sampling method and cross-sectional approach (Wetcher-
Hendricks, 2011). Both qualitative and quantitative methodological approaches were used
with the majority of the results being in the form of quantitative research. A questionnaire
survey was designed to gather the data in line with the drivers for leaders of SA SMEs in
selecting and using management practices. The questionnaire also queried the implementation
of key metrics for each management practice to be used in order to investigate a potential link
between the use of business practices and firm performance. The questionnaire was designed
as an online survey (see Appendix II) and was emailed to the participating institutions (see
Appendix III).
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The questionnaire included two sections – section one incorporated a combination of open
and closed ended questions. These questions covered the basic demographic information
needed in order to identify the participant’s industry sector, business size and educational
data. In addition, the ranking, preference and terms of use for each practice (strategic planning
practices, financial management practices, HRM practices, risk management practices,
operations management practices, marketing practices and teamwork practices) were
questioned. In this specific section open ended questions were used to elicit reasons for each
participant’s specific choices. Each question was designed to attempt to determine whether
SME managers implement any of the identified business practices in their businesses or not,
and, if so, what their order of preference is. These questions were drawn from literature on
these practices. The question design is explained in more detail in the ‘Research Design’
section below.
Section two of the questionnaire contained questions relating to metrics for the measurement
of performance in SMEs. These questions were drawn from literature regarding such
practices, extracting key performance metrics identified in the literature. Statements were
developed that correlated with these metrics in order to create questions using the Likert
scale. These metrics are given below and was discussed in the ‘Research Design’ section
below.
The criteria for the SME selection was based on the National Small Business Amendment Act
26 Schedule (RSA, 2003). In this schedule, businesses are classified as SMEs based on
revenue and the number of employee ranges. As Engineers operate in various industrial
sectors, a median classification of the different sector classifications was adopted. Therefore
the following criteria in Table 3, was determined for the SME classification for the purpose of
this study. In order to be considered as a SME the companies had to meet both of the criteria
below.
Table 3: SME filtering criteria
Number of Employees Revenue
5-100 R0.50m-R100m
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Design and Instruments
Survey Design
The survey questions were designed to gather anonymous information from the respondents
(see Appendix II for the full survey). Firstly, demographic data was gathered to identify the
respondent’s age, level of education, and current position in their work. The company data of
each participant was determined by querying the company’s revenue, the industry that it
operates in, and the number of current and full time employees in the business. The last two
questions in section one asked participants to indicate whether they used the specified
business practices and to rank them in the order of importance specific to each participant. An
open ended question gave the participants the opportunity to elaborate on the reasons for their
choice. This part of the survey was designed in order to answer the ‘why managers implement
business practices’ aspect of this study.
Section two of the questionnaire employed a six-point Likert scale (1 = Very Rarely, 2 =
Rarely, 3 = Neutral, 4 = Often, 5 = Very Often, and 0 = Don’t Know). This would measure
the attitudes of the participants linked to the statements in each of the business practice
questions. A five to seven point Likert scale is generally adequate for most organisational
type, closed ended, surveys (Tharenou, Donohue, & Cooper, 2007). After the survey results
were received, it was decided to incorporate the ‘don’t know’ responses into the ‘neutral’
section for two reasons. - 1. To reduce the scale to five points to bring it in line with the
research above; 2. To avoid dragging the overall average down for the responses. In this case
it was speculated that the ‘don’t know’ responses would have the same effect as the ‘neutral’
responses. The augmented questions in section two was therefore reviewed into a five-point
Likert scale (1 = Very Rarely, 2 = Rarely, 3 = Neutral/Don’t Know, 4 = Often, and 5 = Very
Often). In this section the strategic performance indicators were enquired for each participant
with regard to each of the identified business practices. This was achieved by extracting key
indicators identified in the literature review and formulating questions around each of these
indicators. The indicators identified are given in Table four as seen below.
Table 4: Financial performance indicators
Practices Indicators
Financial
performance
Interest cover, Ability to cover short term liabilities, Comfortable debt levels, Can borrow
more if required, Reliance on external financing, Is not operating at full capacity, Not
operating far under capacity, Manages to cover fixed and variable costs, Balances fixed and
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variable costs actively, Return on equity.
HRM
performance
High performance driven, Performance reviews, Aligns performance requirements with
business goals
Risk
performance
Identify risk steps, Mitigate risk steps, Use insurance, Maintain cash reserves, Security
systems in place, Diversify product offering, Professional recruitment, Safety training,
General training and coaching, Have procedures for processes, Follows uniform termination
process, Applies appropriate Risk Management practice,
Operations
performance
Consider inputs, processes and outputs, Considers improvement of outputs, Awareness of
Ops tools, Considers customer needs in Ops, Competitor benchmarking, Customer needs
benchmarking, Consider quality measures: (performance, conformance to specs., features,
response rate, reliability, durability, serviceability, aesthetics, & humanity)
Marketing
performance
Customer retention and acquisition,Have marketing budgetand strategy, Knowing customer
segment to target, Customer communication, Knowing when to communicate with
customers, Diversity of marketing channels, Managing loyalty, Communication between
finance and marketing depts., Consider market share as performance indicator.
Teamwork
performance
Acknowledge power of teamwork, Encourage collaboration, Encourage open
communication, Encourage combined decision making, Knowing when to and not to use
teamwork, Consider individuals core competencies, Aim to keep team morale up, Try not to
mix teams up, Balance team attributes, Aware of group think.
Pilot Survey
The survey questions were initially circulated to a small sample of industry practitioners that
fit the criteria for the survey. Their responses and feedback were used to make adjustments to
the survey to ensure that the questions were clear and unambiguous. Some minor changes
were made to the wording and grammar and scale; no major changes were required.
Population, Sampling and Data collection methods
Population determination
This study was designed to focus on business owners and managers in the Engineering
industry in SA. A study by du Toit and Roodt(2008) reported that SA had 473 Engineers for
every one million citizens. The SA population stands at approximately 53.65 million
(Worldmeters, 2015). Given that the average employment growth for Engineers in SA was
5.9% between 1996 and 2005 (du Toit & Roodt, 2008), and assuming a conservative 5%
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growth per year, between 2008 and 2015, in the number of Engineers per million citizens,
then 665 Engineers per million citizens would be in the workplace today. That would place
the current Engineering population in SA in the region of 35,677 Engineers in 2015. Not all
Engineers are mangers however. Valiris & Glykas (2004) proposed that between three and
eight employees per manager was an appropriate number, depending on the standardisation of
the work being performed. For the purpose of determining a suitable sample size for this
survey a figure of five employees per manager was chosen, resulting in an Engineering
management population of 7135.
Survey sampling
A representative sample is one which mirrors, or has the proportionate characteristics to the
survey population (Wetcher-Hendricks, 2011). In determining the target representative
sample size, an alpha of 0.05 was used, as there have been limited studies over this broad a
spectrum of business practices. This was also done to ensure that there was a reasonably small
possibility of making a type I or II error (Martin & Bridgmon, 2012). One may speculate that
an 0.01 alpha would be better, however there have not been many studies conducted in this
area as there is little data to determine what errors may be expected. Using the ‘Creative
Research System’ online sample size calculator, a sample size of 94 was calculated. This was
achieved using an alpha of 0.05, the estimated population size of 7,135, as calculated above,
and confidence interval of ±10 (Creative research systems, 2015). The results can be seen
below in Figure 8 below.
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Figure 6: Calculation 1
The sample size of 95 was selected as the minimum required number of responses for the
survey to be generalisable. In addition it was considered important to ensure that the sample
was gathered in a fashion that was representative of the geographical, socio-economic, and
racial representation of the population. This was not addressed. As this was not distributed
directly to the required participants, it was anticipated that some of the participants may not
necessarily fit into the specific definition of an SME, or possibly not work for an SME, and
potentially not be in a management or leadership role. For this reason, information relating to
the firm size and current position was included in the survey in order to ensure that non-
conforming participants could be removed from the final dataset. This information included
‘firm revenue’, ‘number of employees’, and ‘current position’. A number of professional
bodies, industry representatives and other related institutions were approached and invited to
distribute the link to the online survey to their mailing list. This survey was distributed by
sending a link to Engineering industry institution administrators, who then distributed this via
email to their databases. These institutions included the South African Geomatics Institution
(SAGI), the South Africa Council for Professional and Technical Surveyors (PLATO),
Engineering Council of South Africa (ECSA), South African Institute of Mechanical
Engineers (SAIMechE), South African Institute of Electrical Engineers (SAIEE), and South
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African Institute of Civil Engineers (SAICE). The aim of this particular distribution was to
reach as many managers, leaders and owners of SMEs in the SA Engineering industry as
possible (these institutions were also listed above in the opening section). These institutions
were not guaranteed to have information regarding the participant’s position and job details,
and the survey included demographic and job related questions in order for the desired sample
to be extracted from the responses in the analysis after collection. Therefore specific samples
had to be extracted in order to ensure that these samples focused on managers, leaders or
business owners who currently run SMEs, and Engineering enterprises in SA.
In order to distribute the survey link, the aforementioned institutions were contacted via email
with an email asking for their assistance in distributing the survey to their members. A
confirmation from SAGI was received three days later that the board had approved the survey
to be distributed via their mailing list. No response was received from the other institutions
within the first week. At this point, each institution was contacted via telephone and asked to
give information of the correct person to contact with regard to the needs of this study.
Another round of emails was sent out to the new addresses, but no response was received.
After a further round of emails and phone calls, SAIEE responded and indicated that they
were busy with a study of their own and would not be able to assist until their survey had
been concluded. No definitive answer was received from any of the other institutions. At this
point, 63 responses had been received on the survey platform. Most, if not all of them, were
likely to be from the SAGI database as no confirmation was received from the other
institutions. It is acknowledged that this will have an impact on the external validity of the
survey (Tharenou et al., 2007).
When the survey was closed after four weeks, the final count was 72 responses out of an
approximate 2500 members at SAGI. This reduced sample of respondents could potentially
be less diverse and might reduce the generalisability of the results to the Engineering industry,
especially as the sample responses are suspected to be from only one institution.
Data analysis methods
The survey was designed with two main sections which was used in conjunction in order to
draw data and conclusions combining qualitative and quantitative analysis.
The open ended responses were coded in order to determine if there are any themes that
emerged from these responses. The size of the companies were compared to the number of
practices used, considering whether the participants had received management training, as
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well as their education background, in order to determine if there is a link between certain
business practices, or the number of business practices implemented and the these other
inputs.
The statistical data analysis package SPSS was used to analyse the collected quantitative data
in order to determine if any correlations or dependence exists between the use of business
practices and firm performance. The results from section one and two were presented
separately to give insight into the general usage of practices in SMEs, and reported
performance in the different measurement areas identified. The short open ended questions
were expected to give insight from the business representatives explaining as to why their
companies have chosen to implement certain selected business practices. These two sections
will then be cross-referenced to investigate if there is any correlation between certain business
practices and the associated performances reported. The resulting data was presented in a
graphical manner (where relevant) which best represents the results and therefore they could
be easily read and interpreted. Interpretation and explanation of the results was drawn from
these representations. Inferential statistics were also used in order to draw more conclusions
from the results of this study.
The independent variables in this study were setup as the inputs in SPSS. The dependent
variables were input as targets in SPSS. See Table five.
Table 5: Variable details
Name Measure Role
ResponseID Nominal Input Age Ordinal Input Position Nominal Input Industry Nominal Input Education Nominal Input Mng_training Nominal Input TimeInPos Ordinal Input Bus_Life Ordinal Input NoPpl_Binary Scale Target No_Empl Ordinal Input Revenue_Binary Scale Target Revenue Ordinal Input Use_FIN Nominal Input Use_HRM Nominal Input Use_Mkt Nominal Input Use_Ops Nominal Input Use_Risk Nominal Input Use_Strat Nominal Input Use_Team Nominal Input Rank_FIN Scale Input
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Rank_HRM Scale Input Rank_Mkt Scale Input Rank_Ops Scale Input Rank_Risk Scale Input Rank_Strat Scale Input Rank_Team Scale Input Fin_Sum Scale Input HRM_Sum Scale Input Risk_Sum Scale Input Ops_Sum Scale Input Mkt_Sum Scale Input Team_Sum Scale Input
Only two dependant variables were identified in this study. These were the performance
factors, being ‘firm revenue’ and ‘number of employees’. All of the other variables were
considered to independent.
RESEARCH PLAN
The plan depicted in the Gantt Chart (Table six) below was proposed for the purposes of this
study. The deliverable and planned dates were considered to be achievable at the time of
producing this plan.
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Table 6: Gantt Chart
RESEARCH FINDINGS, ANALYSIS AND DISCUSSION
On closing the survey 72 responses were recorded. This was significantly less than the sample
size that was originally required for this research to potentially be generalised to the
engineering industry in SA. The data does however still hold some value in its findings. All
72 of the participants completed the permission question affirmatively. However, 20 of the 72
did not proceed beyond this point. It is speculated that they were either distracted or decided
that they no longer wanted to participate in the survey. Of the 72 responses received there
were a number of incomplete forms. The breakdown of these incomplete forms can be found
in Appendix V. Of all the responses 30 were found to be usable for the statistical analysis and
In order to ensure that the participants were made up of the required SME owners, managers
and leaders the filter criteria for SMEs was first checked. It was found that all of the
participants companies employed less than 100 personnel; however two of the participants
companies had annual revenues of more than R100 million. Although this did not form a
large part of the participant pool it was outside of the criteria stipulated in the methodology
for SMEs.
In order to run statistical analysis on the dataset a large number of the incomplete forms were
removed. This was done in a progressive fashion - in stage one all the participants that did not
proceed beyond the permissions form were removed. In the second stage all participants who
did not proceed beyond the revenue question were removed, except for 32334. This was done
to ensure that there were at least 30 responses. In accordance with the ‘central limit theorem’
the larger the sample number, the closer the frequency distribution tends towards being
normally distributed (Martin & Bridgmon, 2012).The number 30, although random, is large
enough that the dataset will begin to appear more normally distributed (Wetcher-Hendricks,
2011). See the stages and respondents removed below in Table 7.
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Table 7: Respondents Removed
Stage Respondents removed
1 3204, 3205, 32223, 32228, 32241, 32243, 32244, 32259, 32261, 32263, 32272, 32275, 32276,
32283, 32291-32294, 32299, 32301
2 32203, 32208-32210, 32212-32214, 32216, 32217, 32221, 32229-32231, 32233, 32246, 32247,
32260, 32266, 32279, 32282, 32287, 32300
Research validity and reliability
When one is attempting to get generalizable survey results one must ensure that a
representative sample is attained. In obtaining a representative sample both the population and
the sampling methods need to be accurately demonstrated (Wetcher-Hendricks, 2011). In this
study the population has been described in detail, however the exact figures and percentages
of the different disciplines that make up this population were not presented. The sampling
methodology explained in detailed how the paper was distributed between numerous
engineering institutions in order to try and acquire a well distributed sample of engineers
across SA. Unfortunately due to lack of interest from all but one of the institutions, an overall
representative sample was not achieved. However, a representative sample of the SAGI
engineers could have been achieved.
A reliability test was run on the data in SPSS ( see Appendix VI). Only 22 of the 30
participants that were input were accepted as valid by the software, and the remaining eight
were excluded from the computation. The resulting Cronbach’s Alpha of 0.896 for all 73
items suggests relatively high internal consistency and reliability. Further down in the report
is a table of ‘Item-Total Statistics’. The main items of interest in this table were the
‘Correlated Item-Total Correlation’ and ‘Alpha if Item removed’.
The ‘Correlated Item-Total Correlation’ indicates how well items go together and whether the
items were measuring the same object. As one can see from the table, many of the items were
close to zero, and in some cases negative. Under normal circumstances when developing a
new scale these items would be reconsidered, or removed, from the scale. However in this
case there were very few responses therefore the items were not removed. As a result these
responses can not be used and additional samples should be sought in future research.
The ‘Alpha if Item removed’ column indicates what the ‘Cronbach’s Alpha’ for all items
would be if a particular item was removed. Ideally, if the Alpha for all items would increase
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then an item should be removed. In line with the reasoning in the previous paragraph, all the
items were left in the database for further analysis despite removal of some resulting in an
increased Alpha for all the items.
Preliminary findings
It is interesting to note that all of the respondents were above 30 years old. This is not that
surprising as managers and leaders were the target of this study. However this indicates that
there were not very many young owner/managers in this segment of the industry that
responded. 66.7% of the participants were between the ages of 50 and 65, and 93.4% of the
participants were between the ages of 40 and 65. 6.7% of the participants selected the ‘Other’
option. It is unlikely that the participants would be younger than 20, which was the lowest
option offered in the questionnaire, so it is assumed that the participants were older than 65
years of age or did not want to answer the question. Additional results were presented in
Figure 9 below. Any graphs and frequency charts which have not been mentioned in this
section can be found in Appendices VII and VIII.
Figure 7: Age Responses
Question five, regarding the industry in which the participants operated, resulted in 46.7% of
the participants selecting the ‘Other’ option. It is suspected that this was a result of the
participants not operating solely in the given industry, or not identifying with the given
industries. The participants who selected ‘Other’, specified answers such as:
“Land survey - cuts across all sectors listed”
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“Professional Land Surveying & Town Planning consultants”
“Cadastral, Legal, Town Planning, Engineering surveys”
“Mine cadastral engineering”
“Cadastral land surveying”
“Education”
These were the general themes that arose from the ‘Other’ participants’ responses. Overall,
38% of the participants appeared to operate in the survey industry in some form or another.
This was not surprising as SAGI, which represents surveyors, was the only institution
approached that confirmed they had distributed the survey link to their database.
On the education front, all of the participants had attained some form of tertiary education.
This was made up with the majority of participants, 36.7%, having ‘Honours’, followed by
33.3% having ‘Diplomas’, 13.3% having ‘Undergraduates’, 16.7% having ‘Masters’ and
16.7% having ‘Postgraduate’ degrees.. This is further depicted in Figure 10.
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Figure 8: Education Chart
The sample showed that 66.7% of all the participants have not received any management
training. Furthermore, 82.1% and 86.2% had been in their current position, and their current
companies have been in business, respectively, for more than 10 years. Company sizes were
considered in terms of the number of employees and their revenue. The results indicated that
67.9% of the companies employed less than 10 personnel and 66.7% generated less than R5
million per year. A staggering 83.3% of these companies generated less than R10 million per
year. Looking at the company ownership, size and longevity overall it was noted that it would
be interesting to see how many of these long lasting companies owner/managers had received
management training.
One should note that 93.4% of the participants were over the age of 40, and many of these
participants had not received any management training (63.64%). As presented above, of the
participants who had been in their current positions for more than 10 years, 68.18% generated
a revenue of less than R5 million per annum and 81.82% generated less than R10 million per
annum, (see Figure 11). The statistics for the companies that had been in existence for more
than ten years were similar; with 62.5% and 79.17% of participant companies earning less
than R5 million and R10 million. Also of interest is that the shape of the firm revenue for
respondents who had, versus those who had not, received management training. There is no
notable difference and this is attributed possibly to the growth intention of the owners.
Figure 9: Time in position vs firm revenue
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This was an interesting finding and leads to question the growth intention of these
managers/owners. Perhaps they were not interested in growing and thus measuring the use of
management practices, perhaps their influence on firm performance will not have any bearing
on revenue of other similar performance measures. This was cited as one of the factors in the
introduction to this report. The participants’ companies performance needs may be different
to conventional performance measures in that they only wish to cover their monthly expenses
and draw a liveable salary. These claims were evidently not supported by evidence, but could
perhaps be explored in future research.
Further evidence to support the above statement is given in Figure 12 below which depicts the
percentage breakdown of the participants who have been in their current position for more
than 10 years. It is apparent, from the data, that 72.7% of the participants were owners of their
companies with 59.06% generating less than R5 million in revenue per year. The question
that is begged here is; are they not able to grow if they don’t want to?
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
No management training W. management training
More than 10 years
Chart of time in position vs firm revenue
R0-5m
R5-10m
R10-25m
R25-50m
>R100m
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Figure 10: Time in position vs current position
Qualitative Analysis
Firstly looking at the use of ‘business practices’ amongst participants, we see that the
participants top three management practices used were FM at 86.2% and teamwork and
operations management at 65.5% usage response rate each (see Appendix VIII). Some of the
feedback received was from participants who stipulated that they had used these three
practices, and others, were as seen below. The full list of responses is attached in Appendix
XII:
“That's what it takes to run a practice”
“All the above are important to success”
“Any input neglected negatively affects the organisations performance”
“Financial management –Cashflow control is the most crucial aspect.Operations
management - to minimise stress due to deadlines conflicting.Risk management- to minimise
redoing jobs.Strategic management - to determine branching into new technologies.Team
management- how operations and risk management is implemented”
“I do not believe that any business can survive or grow without implementing these business
practises”
It is apparent that all the participants have different perspectives and reasons for
implementing the practices that they do. However, the general consensus is that they were
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
R0-5m R5-10m R10-25m R25-50m >R100m
More than 10 years
Time in position & revenue vs current position
Owner
Manager
MD
CEO
Other
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necessary in order for a business to operate smoothly and efficiently. Financial management
appeared to be the most popular practice amongst the participants. Many of the participants
stated that the reason that they did not implement certain ‘business practices’ was due to the
fact that:
“I haven't seen the business practice book spoken of”
“being a one man business it is not necessary”
The research shows that the use of any, and all, ‘business practices’ is important to any
business. However, the amount of time and effort dedicated to business practices were likely
linked to the growth and complexity of the undertaken business operations. Furthermore some
participants who did not indicate any use of the listed business practices stated the following
as their reasoning:
“I recognise the need for training but haven't managed to create time yet as we are in a
growth phase”
“I just started up. Am a very small practice”
” Too small”
It is not surprising to hear from owners and managers that they were not aware of what
business practices were, or stating that they do not have the time to consider business
practices. This was suspected in the build-up to this study and is interesting to find that it may
be true, at least for a few of the participants. This confirms that many engineers do not have
specific training on developing and running businesses, they also do not see the value in
making time to implement business practices in smaller businesses. Another interesting
finding is that all of the participants who use none of the mentioned practices were over the
age of 40.
The preferential ranking of ‘business practices’ by each participant were, much like the
practices use selection, ‘financial management’, ‘teamwork’, and ‘operations management’
practices. Some of the reasons for selecting these practices were given below:
“withoutcashflow you cannot survive. risk management reduces re-doing of activities which
reduces errors due to repetition of tasks. team management is important to limit the
frustration due to hurry -up and wait situations.”
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“Without sound financial management there is no business.Human resource is important for
running the entity.Strategizing give direction”
“Cash-flow and cash resources are critical. We manage operations carefully to make sure all
our gear and field staff are fully utilized all the time. A strong, motivated and positive
attitude team is what it all rests on.”
“Strategic- position yourself, prepare for where you see you are heading.Financial- money
will enable you to purchase equipment, appoint someone, etc. Team management - close to
operations, where you can delegate you should delegate”
“1.Problems with cash flow can cripple an otherwise sound business2.Without marketing the
company could lose its relevance in the market place.3.In the survey business team synergy is
an inherent ingredient for successfully completed large projects.”
Overall marketing practices have been ranked lowest by the participants. It was apparent from
some of the responses that a few of the questions were not clear. This was additionally
noticed as some responses have the same ranking for various practices, while a different
ranking was required for each of the seven practices between the number one and seven. A
few of the participants also used the ranking in the reverse order. This would have an effect
on the results which needed to be considered when reporting on the findings.
Quantitative Analysis
Quantitative analysis was used to determine whether there was a link between the use of
'business practices' and ‘firm performance’. This was performed by running a multiple
regression of the metrics for each of the 'business practices'.
The number of personnel and firm revenue were collected, using bins and not continuous
values as required, for a regression to be performed. It was then decided that the variables
would be converted into binary variables. Logistic regression has become more popular in
management research, as it is more flexible and does not rely on distributional assumptions
(Tharenou et al., 2007). The lower three values of the personnel were placed into ‘0 = <25’
personnel and the upper 3 values into ‘1 = >25’ personnel; whilst the lower three values of the
firm revenue into ‘0 = <R25m’ personnel and the upper 3 values into ‘1 = >R25m’ personnel.
In addition, the value of the different metrics for each 'business practice' were summed to
create one metric for each of the queried 'business practices'.
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When testing the assumptions for the logistic regression the following results were achieved -
1. The dependant variables had been converted into binaries and were thus correct for both
regressions; 2. The logistic regression calculates the probability of the dependant variable is =
1, therefore all interpretation needs to take this into account. In this case the two dependant
variables tested were whether the number of people employed was greater than 25, or if the
firm revenue was greater than R25 million in the two regressions that were run; 3. The
measurements must not be interdependent; 4. A relatively large sample size is required. The
sample size in this study was not particularly large, however it was considered large enough
to conduct the test for some indicatory results.
The results from the classification table (see Table 8) for the number of employees regression
correctly predicted all of the outcomes. This is not considered to be that likely, and one would
expect that some outcomes would be incorrectly positioned in a regression as there should be
some error. This could be attributed to the small sample size used in the calculation of this
regression or some other factor.
Table 8: Personnel classification
Classification Tablea
Observed
Predicted NoPpl_Binary
Percentage Correct <25 people >25 people
Step 1 NoPpl_Binary <25 people 24 0 100.0
>25 people 0 4 100.0
Overall Percentage 100.0
a. The cut value is .500
The variables in the equation for the regression performed on the number of people that a
company might employ were given in Table 9 below. It is apparent that none of the variables
were statistically significant as they were not less than the chosen confidence level of 0.05.
This could be as a result of the regression not having sufficient responses. The odds ratios
also do not make sense in this case as 100 percent correctness is not likely. Therefore, no
reasonable predictions can be made from these results.
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Table 9: Number of employee’s variables
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for EX
Lower U
Step 1a Fin_Sum 53.020 1129.562 .002 1 .963 1.06E+23 .000
HRM_Sum 119.091 2459.552 .002 1 .961 5.257E+51 .000
RIsk_Sum -4.129 153.801 .001 1 .979 .016 .000 1.32
Ops_Sum -6.966 388.853 .000 1 .986 .001 .000
Mkt_Sum 24.127 536.472 .002 1 .964 30089347357.88 .000
Team_Sum -22.407 551.766 .002 1 .968 .000 .000
Constant -3432.839 71220.665 .002 1 .962 .000
a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
A binary multiple regression was performed on the Financial, HRM, Risk, Operations,
Marketing and Teamwork management practice metric in order to determine the likelihood of
respondents employing more or less than 25 personnel. The results of the regression were
attached in Appendix X. The ‘-2Log likelihood’ test did not converge and therefore did not
give a solution. Thus, the variance assumption is not fulfilled, however as a regression model
is quite robust the results have been interpreted, but with caution. The binomial regression
model was not statistically significant with 𝑋𝑋2(5) = 0.000,𝑝𝑝 = 1.000. The model explained
between 56% (Cox & Snell R Squared) and 100% (Nagelkerke R Squared) of the variance in
the number of personnel and correctly classified 100% of the cases.
The results from the classification table (Table 10) for the number of employee’s regression
correctly predicted all of the outcomes. This is not considered to be that likely and one would
expect that some outcomes would be incorrectly positioned in a regression as there should be
some error. This could be attributed to the small sample size used in the calculation of this
regression or some other factor.
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Table 10: Revenue classification
Classification Tablea
Observed
Predicted Revenue_Binary
Percentage Correct <R25m >R25m
Step 1 Revenue_Binary <R25m 27 0 100.0
>R25m 0 3 100.0
Overall Percentage 100.0
a. The cut value is .500
The variables in the equation for the regression performed on the number of people that a
company might employ were given in Table 11 below. It is apparent that none of the variables
were statistically significant as they were not less than the chosen confidence level of 0.05.
This could be as a result of the regression not having sufficient responses, or a number of
other factors. The odds ratios also do not make sense in this case. Therefore, no reasonable
predictions can be made from these results.
Table 11: Firm revenue variables
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Step 1a Fin_Sum -28.248 1978.228 .000 1 .989 .000 .000 .
HRM_Sum 17.769 1341.236 .000 1 .989 52129932.790 .000 .
RIsk_Sum 12.679 914.828 .000 1 .989 320818.079 .000 .
Ops_Sum 11.848 874.002 .000 1 .989 139751.846 .000 .
Mkt_Sum -12.477 944.421 .000 1 .989 .000 .000 .
Team_Sum -3.874 386.061 .000 1 .992 .021 .000 .
Constant -18.290 4681.942 .000 1 .997 .000
a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
A binary multiple regression was also performed on the Financial, HRM, Risk, Operations,
Marketing and Teamwork management practice metrics in order to determine the likely hood
of respondents having a revenue of more or less than R25 million. The results of the
regression were attached in Appendix XI. The ‘-2Log likelihood’ test did not converge in this
case either and therefore did not give a solution. Thus the variance assumption is not fulfilled,
however as a regression model is quite robust the results have been interpreted, but with
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caution. The binomial regression model was not statistically significant with 𝑋𝑋2(8) =
0.000,𝑝𝑝 = 1.000. The model illuminatedthat between 47.8% (Cox & Snell R Squared) and
100% (Nagelkerke R Squared) of the variance in the number of personnel and correctly
classified 100% of the cases.
LIMITATIONS
The results from this research survey may not be robust enough to draw complex conclusions.
This may be due to ill-defined questions, or criteria, and limited responses. Given the
analyses, discussion and conclusion reached it is important to acknowledge the following
limitations.
• it was found that after the responses were cleaned of incomplete survey results and
had erroneous results removed, the final number of respondents was only 29, which is
well below the required 96 as calculated in the sampling section above
• the fact that the sample data was drawn form only one sector of the industry, as
explained, makes the sample less generalizable to the engineering industry in SA
• Although the sample was drawn from a national database there is a possibility that the
participants were not well distributed geographically and may not represent the
different economic circumstances across SA and many other elements that have not
been considered.
• Only two measures were used to determine firm performance, ‘Firm Revenue’ and
‘Number of Employees’. Given that there were many other methods by which to
measure performance these metrics only represent one aspect of firm performance
• It may also be of interest to consider only SME with a ‘growth intention’ (Wang et al.,
2007).
CONCLUSION
The two questions posed in this research study were 1. “What were the drivers for leaders of
SA SMEs in selection and use of management practices” and 2. “To what degree do these
chosen practices affect firm performance? “. Although a limited number of responses were
received, valuable information could still be gathered from the data.
The results of this study show that managers implement business practices for a number of
reasons. Some of these reasons were – to maintain controls on cash flow, to ensure timely
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delivery of projects, and to ensure that they were seen in the public eye. Some managers
stated that they were not aware what business practices were, while others protested that they
did not have the time or did not need to implement any ‘business practices’ as they were “one
man bands” or too busy with the day to day operations.
This study also revealed that the majority of the participants were owners and had been in the
same company for over 10 years. Again, the majority of these participants companies fell into
the lowest revenue bracket of R0-5 million rand per year. This illustrated the question of
whether these owners were interested in growing their companies or not. As small firms are
being considered in this bracket they may well have grown within the bracket. In this case
further investigation would have to be done to determine whether this posit is true. It was also
acknowledged that most, if not all, of the participants came from the members of SAGI as
none of the other institutions had distributed the survey. As a result these findings were likely
not valid for the rest of the Engineering industry in SA.
In an effort to answer question two, a logistic multiple regression was performed. This
regression resulted in an equation which predicted whether a firm was likely to generate more
than R25 million in revenue, or employ more than 25 personnel given that their summed
scores on each of the ‘business practices’ metrics. Although a result was achieved for each
logistic regression, the results were not all that useful. It was presumed that the reason for the
outcomes not being statistically significant was that the sample size was likely not large
enough. Furthermore, as the majority of the population was suspected of having little, or no,
growth intention there was likely to be little, or no, link between the dependant and
independent variables in this case due to unforeseen circumstances.
DIRECTIONS FOR FUTURE RESEARCH
In order to further explore this research it would be necessary to get responses from a wider
variety of participants operating in the numerous related industries. It would also be
imperative to get more responses overall in order to meet minimum sample size, whilst
ensuring that the participants form a representative sample.
One of the reasons for the small number of responses received in this study was the false
assumption that the institutions would be more open to distributing the survey to their
members. It is thus recommended that these institutions were contacted earlier on, in the
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preparation phase of the research, in order to get them on board before the survey is required
to be distributed.
Another direction that the research could take would be to investigate the growth intention of
the engineering industry in SA and the owner/managers who run these firms. It is believed
that interesting findings could emerge from this study which will shed light on why many
SMEs were not growing and seem to remain stagnant for many years.
Additionally, the survey questions need to be adjusted to include more comprehensive
performance measures. In order to include regression studies these measures were also
required to be captured as continuous values. These changes will be required to improve the
reliability of this questionnaire (Wetcher-Hendricks, 2011).
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APPENDIX I – KPI MEASUREMENT
Financial Lens Measures Consumer Lens
Measures
Internal Lens Measures Innovation and
learning Lens
Return on capital Market share Percent of sales from new
products
New product
development
capabilities
Operating margins Brand image and
awareness
Manufacturing costs R&D core
competencies
Economic value add Customer satisfaction Manufacturing cycle time Technology
capabilities
Cash flow Customer relations Inventory management HR development and
capabilities
Sales growth Customer acquisition Quality indices Improved
manufacturing and
business processes
Supply chain management Improved sales
methods and
techniques
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APPENDIX II – SURVEY QUESTIONS
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APPENDIX III–EMAIL DISTRIBUTED TO INSTITUTIONS
Email Distributed.
“To whom it may concern:
I would like to request your assistance in distributing my survey link to your registered
professionals so that I can get responses from as diverse an engineering population as
possible.
I am a Professional Land Surveyor, registered with PLATO in South Africa. I am busy
completing a study that aims to research what the drivers are for SME
managers/owners/leaders within the South African Engineering sector to implement business
practices. This study forms part of the data collection process for my MBA Thesis, based out
of UCT's Graduate School of Business. The responses are important to the outcome of this
study, and will be treated in the strictest confidence.
The survey should take about fifteen minutes to complete. The anonymous results will be
used to draw conclusions and in answering this research question.
http://gsblive.uct.ac.za/SurveyNetAD/TakeSurvey.aspx?SurveyID=74KMmmm3
Your assistance will be greatly appreciated. Please let me know if you require any further
information. Can I also request that if you do distribute the survey to please let me know
so that I can acknowledge your participation in this distribution. This survey will close in
16 days.
Regards,
Mark Schreiber
PLS1288, PHS1198”
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APPENDIX IV - CONSENT FORM
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APPENDIX V – INCOMPLETE QUESTIONAIRE BREAKDOWN
No of Respondents Questions Skipped
20 2-6,8
21 7
23 9
22 12
24 10
25 11
42 13-15
45 17-19
46 20-21
47 22
65 4
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APPENDIX VI – RELIABILITY TEST
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases Valid 22 73.3
Excludeda 8 26.7
Total 30 100.0
a. Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha N of Items
.896 73
Item Statistics
Mean Std. Deviation N
Rank_FIN 5.59 2.085 22
Rank_HRM 3.95 1.704 22
Rank_Mkt 3.36 1.649 22
Rank_Ops 4.18 2.108 22
Rank_Risk 3.73 1.579 22
Rank_Strat 3.95 1.939 22
Rank_Team 4.73 1.830 22
Fin_InterstCover 3.59 .908 22
Fin_ShortTLiabCover 3.95 .844 22
Fin_ExtFinComfort 3.27 .883 22
Fin_CanRaiseLoan 2.73 1.202 22
Fin_ReliesOnExFin 2.36 1.255 22
Fin_ExtraCapacity 2.64 1.255 22
Fin_NotLargExCapacity 3.18 1.259 22
Fin_CanCoverFixedVarExp 3.68 .995 22
Fin_TrytoBalFixVarCosts 3.68 .780 22
Fin_GenGoodROE 3.64 1.093 22
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Fin_GenSimilarROE 3.45 .963 22
HRM_ClearHighPerfMsg 4.59 .666 22
HRM_ClearPerfImp 4.45 .963 22
HRM_RegPerfRev 3.32 1.041 22
HRM_AlignPerfWBusGoals 3.41 1.260 22
Risk_IdentRisk 3.59 .908 22
Risk_MitigateRisk 3.55 1.011 22
Risk_UseInsur 4.14 .990 22
Risk_MaintSufCash 3.50 1.012 22
Risk_HasSecurity 3.45 .912 22
Risk_ProdDevInovat 3.59 1.182 22
Risk_UseProfRecruitm 2.09 1.269 22
Risk_SafetyTraining 2.95 1.397 22
Risk_GenTrainRegularly 3.41 1.054 22
Risk_HasProcedures 3.64 1.002 22
Risk_HasTerminationProced
ure 3.00 1.414 22
Risk_HaveRskMngSyst 3.14 1.283 22
Ops_InProcOut 3.00 1.069 22
Ops_ConsiderImprove 3.36 .902 22
Ops_AwareOfTools 2.82 1.296 22
Ops_ConsiderCustNeeds 4.23 .752 22
Ops_BenchmarkProdsToCo
mp 3.64 1.177 22
Ops_BenchmarkProdsToCu
stNds 3.77 .922 22
Ops_ConsiderPerform 3.82 .664 22
Ops_ConsiderConformtoSp
ecs 4.27 .767 22
Ops_ConsiderFeatures 3.82 .733 22
Ops_ConsiderSpeedOfServi
ce 4.05 .950 22
Ops_ConsiderReliability 4.50 .673 22
Ops_ConsiderDurability 4.32 .716 22
Ops_ConsiderServicability 4.09 .811 22
Ops_ConsiderAesthetics 3.27 1.032 22
Ops_ConsiderHumanity 2.95 1.090 22
Mkt_CustRetention 4.55 .739 22
Mkt_NewCust 4.27 .767 22
Mkt_Budget 2.55 1.101 22
Mkt_HaveStrat 2.73 1.162 22
Mkt_KnowWhatCustAreTgtd 3.64 .953 22
Mkt_KnowHowToTgt 3.59 1.141 22
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Mkt_KnowWhenToCommun 3.59 1.141 22
Mkt_UseMultMktChannels 3.32 1.211 22
Mkt_CanFindKeepCust 3.41 1.054 22
Mkt_MngLoyalty 3.73 1.032 22
Mkt_MktFinDeptComm 3.05 .785 22
Mkt_ConsMktShare 3.36 .953 22
Team_ValTool 4.32 .716 22
Team_Encourage 4.18 .733 22
Team_OpenComm 4.32 .646 22
Team_CombDecisionMakin
g 3.64 .902 22
Team_NotTooMuch 3.59 .854 22
Team_ForComplexTasks 4.14 .710 22
Team_RecognCoreCompet 4.05 .785 22
Team_KeepMoraleHigh 3.73 .631 22
Team_DontChange 3.59 .854 22
Team_BalanceAttributes 3.45 .858 22
Team_RecognStrengWeak 3.91 .811 22
Team_AwareOfGrpThink 3.27 .935 22
Item-Total Statistics
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
Rank_FIN 259.77 750.755 .067 .901
Rank_HRM 261.41 709.587 .553 .892
Rank_Mkt 262.00 722.667 .421 .894
Rank_Ops 261.18 695.965 .560 .892
Rank_Risk 261.64 737.576 .264 .896
Rank_Strat 261.41 732.634 .251 .897
Rank_Team 260.64 706.719 .541 .892
Fin_InterstCover 261.77 747.422 .291 .895
Fin_ShortTLiabCover 261.41 745.777 .352 .895
Fin_ExtFinComfort 262.09 755.706 .128 .897
Fin_CanRaiseLoan 262.64 750.909 .157 .897
Fin_ReliesOnExFin 263.00 774.000 -.184 .900
Fin_ExtraCapacity 262.73 758.779 .034 .898
Fin_NotLargExCapacity 262.18 758.442 .039 .898
Fin_CanCoverFixedVarExp 261.68 734.799 .499 .894
Fin_TrytoBalFixVarCosts 261.68 742.608 .459 .894
Fin_GenGoodROE 261.73 742.303 .323 .895
Fin_GenSimilarROE 261.91 738.753 .440 .894
HRM_ClearHighPerfMsg 260.77 744.660 .485 .894
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HRM_ClearPerfImp 260.91 755.325 .122 .897
HRM_RegPerfRev 262.05 736.712 .441 .894
HRM_AlignPerfWBusGoals 261.95 742.331 .274 .896
Risk_IdentRisk 261.77 735.803 .530 .893
Risk_MitigateRisk 261.82 731.489 .553 .893
Risk_UseInsur 261.23 736.279 .474 .894
Risk_MaintSufCash 261.86 734.885 .489 .894
Risk_HasSecurity 261.91 746.753 .304 .895
Risk_ProdDevInovat 261.77 736.660 .385 .894
Risk_UseProfRecruitm 263.27 775.255 -.200 .901
Risk_SafetyTraining 262.41 744.253 .217 .896
Risk_GenTrainRegularly 261.95 733.379 .495 .893
Risk_HasProcedures 261.73 725.827 .665 .892
Risk_HasTerminationProced
ure 262.36 736.338 .318 .895
Risk_HaveRskMngSyst 262.23 730.184 .445 .894
Ops_InProcOut 262.36 738.147 .403 .894
Ops_ConsiderImprove 262.00 744.857 .346 .895
Ops_AwareOfTools 262.55 744.355 .236 .896
Ops_ConsiderCustNeeds 261.14 743.742 .449 .894
Ops_BenchmarkProdsToCo
mp 261.73 746.684 .228 .896
Ops_BenchmarkProdsToCus
tNds 261.59 738.825 .460 .894
Ops_ConsiderPerform 261.55 748.736 .372 .895
Ops_ConsiderConformtoSpe
cs 261.09 737.801 .584 .893
Ops_ConsiderFeatures 261.55 752.736 .235 .896
Ops_ConsiderSpeedOfServic
e 261.32 729.561 .628 .893
Ops_ConsiderReliability 260.86 741.933 .555 .894
Ops_ConsiderDurability 261.05 756.522 .144 .896
Ops_ConsiderServicability 261.27 744.589 .395 .895
Ops_ConsiderAesthetics 262.09 749.610 .213 .896
Ops_ConsiderHumanity 262.41 732.539 .491 .893
Mkt_CustRetention 260.82 745.584 .411 .895
Mkt_NewCust 261.09 752.944 .218 .896
Mkt_Budget 262.82 748.727 .212 .896
Mkt_HaveStrat 262.64 748.814 .197 .896
Mkt_KnowWhatCustAreTgtd 261.73 729.827 .621 .893
Mkt_KnowHowToTgt 261.77 729.898 .511 .893
Mkt_KnowWhenToCommun 261.77 734.565 .434 .894
Mkt_UseMultMktChannels 262.05 738.807 .341 .895
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Mkt_CanFindKeepCust 261.95 732.712 .507 .893
Mkt_MngLoyalty 261.64 737.290 .435 .894
Mkt_MktFinDeptComm 262.32 747.656 .336 .895
Mkt_ConsMktShare 262.00 753.905 .151 .896
Team_ValTool 261.05 754.807 .188 .896
Team_Encourage 261.18 757.108 .126 .896
Team_OpenComm 261.05 757.093 .146 .896
Team_CombDecisionMaking 261.73 757.255 .094 .897
Team_NotTooMuch 261.77 756.184 .124 .897
Team_ForComplexTasks 261.23 745.422 .433 .895
Team_RecognCoreCompet 261.32 752.608 .220 .896
Team_KeepMoraleHigh 261.64 752.242 .291 .896
Team_DontChange 261.77 755.327 .142 .896
Team_BalanceAttributes 261.91 757.039 .105 .897
Team_RecognStrengWeak 261.45 746.831 .344 .895
Team_AwareOfGrpThink 262.09 746.563 .299 .895
Scale Statistics
Mean Variance Std. Deviation N of Items
265.36 762.719 27.617 73
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APPENDIX VII – BAR CHARTS
Bar Chart
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APPENDIX VIII – FREQUENCY TABLE
Age
Frequency Percent Valid Percent Cumulative Percent
Valid 40-50 years 8 26.7 26.7 26.7
50-65 years 20 66.7 66.7 93.3
Other 2 6.7 6.7 100.0
Total 30 100.0 100.0
Position
Frequency Percent Valid Percent Cumulative Percent
Valid Owner 21 70.0 70.0 70.0
Manager 2 6.7 6.7 76.7
MD 2 6.7 6.7 83.3
CEO 2 6.7 6.7 90.0
Other 3 10.0 10.0 100.0
Total 30 100.0 100.0
Industry
Frequency Percent Valid Percent Cumulative Percent
Valid Civils 2 6.7 6.7 6.7
Construction 7 23.3 23.3 30.0
Industrial 1 3.3 3.3 33.3
Engineering 6 20.0 20.0 53.3
Other 14 46.7 46.7 100.0
Total 30 100.0 100.0
Education
Frequency Percent Valid Percent Cumulative Percent
Valid Diploma 10 33.3 33.3 33.3
Undergraduate 4 13.3 13.3 46.7
Honours 11 36.7 36.7 83.3
Post graduate 3 10.0 10.0 93.3
Masters 2 6.7 6.7 100.0
Total 30 100.0 100.0
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Mng_training
Frequency Percent Valid Percent Cumulative Percent
Valid No 20 66.7 66.7 66.7
Yes 10 33.3 33.3 100.0
Total 30 100.0 100.0
TimeInPos
Frequency Percent Valid Percent Cumulative Percent
Valid 0-2 years 1 3.3 3.6 3.6
2-5 years 2 6.7 7.1 10.7
5-10 years 2 6.7 7.1 17.9
10 years or more 23 76.7 82.1 100.0
Total 28 93.3 100.0 Missing System 2 6.7 Total 30 100.0
Bus_Life
Frequency Percent Valid Percent Cumulative Percent
Valid 0-2 years 1 3.3 3.4 3.4
2-5 years 1 3.3 3.4 6.9
5-10 years 2 6.7 6.9 13.8
10 years or more 25 83.3 86.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
No_Empl
Frequency Percent Valid Percent Cumulative Percent
Valid 0-5 9 30.0 32.1 32.1
5-10 10 33.3 35.7 67.9
10-25 5 16.7 17.9 85.7
25-50 3 10.0 10.7 96.4
50-100 1 3.3 3.6 100.0
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Total 28 93.3 100.0 Missing System 2 6.7 Total 30 100.0
Revenue
Frequency Percent Valid Percent Cumulative Percent
Valid R0-5m 20 66.7 66.7 66.7
R5-10m 5 16.7 16.7 83.3
R10-25m 2 6.7 6.7 90.0
R25-50m 1 3.3 3.3 93.3
>R100m 2 6.7 6.7 100.0
Total 30 100.0 100.0
Use_FIN
Frequency Percent Valid Percent Cumulative Percent
Valid No 4 13.3 13.8 13.8
Yes 25 83.3 86.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_HRM
Frequency Percent Valid Percent Cumulative Percent
Valid No 12 40.0 41.4 41.4
Yes 17 56.7 58.6 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Mkt
Frequency Percent Valid Percent Cumulative Percent
Valid No 16 53.3 55.2 55.2
Yes 13 43.3 44.8 100.0
Total 29 96.7 100.0
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Missing System 1 3.3 Total 30 100.0
Use_Ops
Frequency Percent Valid Percent Cumulative Percent
Valid No 10 33.3 34.5 34.5
Yes 19 63.3 65.5 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Risk
Frequency Percent Valid Percent Cumulative Percent
Valid No 13 43.3 44.8 44.8
Yes 16 53.3 55.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Strat
Frequency Percent Valid Percent Cumulative Percent
Valid No 11 36.7 37.9 37.9
Yes 18 60.0 62.1 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Team
Frequency Percent Valid Percent Cumulative Percent
Valid No 10 33.3 34.5 34.5
Yes 19 63.3 65.5 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
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APPENDIX IX – RESPONDENT REASONING
Use_Reason Rank_Reason
Too small too small
Small Land Surveying practice Not necessary
All the above are important to success Marketing creates awareness, operations generate cashflow and finance keeps the business going
Too small a team - use project management Small team - use project management
I am hands on Without sound financial management there is no business.Human resource is important for running the entity.Strategizing give direction
Any input neglected negatively affects the organisations performance
Any input neglected negatively affects the organisations performance
We are a big Company and all practices are actually being implemented if not by me then by someone within our group. We are a big Construction & Engineering group.
It is all about utilising the best team for a certain project and allocating those personnel to that project and managing my survey teams.
Work is mostly on a tender basis, so marketing is limited to making sure our clients get good quality data and prefer using our services. This helps in situations where quoted prices are very close.
Cash-flow and cash resources are criticalWe manage operations carefully to make sure all our gear and field staff are fully utilized all the time. A strong, motivated and positive attitude team is what it all rests on.
I just started up. Am a very small practice. 1 man with 1 assistant.
Strategic- position yourself , prepare for where you see you are heading.Financial- money will enable you to purchase equipment, appoint someone, etc. Team management - close to operations, where you can delegate you should delegate
I recognise the need for training but haven't managed to create time yet as we are in a growth phase
based on order of stress
? ?
Survival of company Survival of company
MOST WORK OF SHORT TERM BASIS REWUIRING DEDICATED ATTENTION AND SOLUTION TO TECHNICAL, LOGISTAL AND FINACIAL CONSTRAINTS
COMPLETING PROMJECTS AT HAND CRITICAL TO SUSTAINING CASH FLOW, REQUIRING FOCUSING OF HUMAN AND EAUIPMENT RESCOURCES
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These are essential to survival They are the most essential if you survival horizon is close
little risk People are the most important
Owner Owner
That's what it takes to run a practice Cash flow
makes good sense makes good sense
Standard business practices Our business is based on sound financial decision - rest follows that strategy
I haven't seen the business practice book spoken of Stress avoidance
Have to in order to be the most competitive we can be
These things work for me and they help me to become extremely competitive
? ?
Owner To survive and experience in the industry.
Being a one man business it is not necessary Need to know my finances and marketing is by good practice and word of mouth
Xx Xx
I do not believe that any business can survive or grow without implementing these business practises
1.Problems with cash flow can cripple an otherwise sound business2.Without marketing the company could lose its relevance in the market place.3.In the survey business team synergy is an inherent ingredient for successfully completed large projects.
Financial, marketing management and operations management is important to me. I am basically a one-man business, therefore human resource and team management does not really apply for me. I apply risk management when I quote for a job or when I take on a job, where I assess whether I can complete the job in time or whether I can actually do the job.
Financial management determines your success in life, marketing management determines what kind of jobs you are being offered and team management leads to satisfied employees.
It helps to run the business effectively Cashflow is the most important. If you do not market you do not get new business. Your operations must run smoothly and therefore cost effective.
Financial management –Cashflow control is the most crucial aspect.Operations management - to minimise stress due to deadlines conflicting.risk management- to minimise redoing jobs.Strategic management - to determine branching into new technologies.Team management- how operations and risk management is implemented.
withoutcashflow you cannot survive. risk management reduces re-doing of activities which reduces errors due to repetition of tasks. team management is important to limit the frustration due to hurry -up and wait situations.
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APPENDIX X – BINOMIAL REGRESSION FOR NUMBER OF PERSONNEL
Logistic Regression
Notes
Output Created 06-DEC-2015 10:33:47
Comments Input Data /Users/sherianne/Downloads/Final Data
20151130_Condensed_Binary.sav
Active Dataset DataSet4
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 30
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing
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Syntax LOGISTIC REGRESSION VARIABLES
NoPpl_Binary
/METHOD=ENTER
Fin_SumHRM_SumRIsk_SumOps_SumMk
t_SumTeam_Sum
/CLASSPLOT
/CASEWISE OUTLIER(2)
/PRINT=GOODFIT CI(95)
/CRITERIA=PIN(0.05) POUT(0.10)
ITERATE(20) CUT(0.5).
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.00
[DataSet4]
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 28 93.3
Missing Cases 2 6.7
Total 30 100.0
Unselected Cases 0 .0
Total 30 100.0
a. If weight is in effect, see classification table for the total number of cases.
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Dependent Variable Encoding
Original Value Internal Value
<25 people 0
>25 people 1
Block 0: Beginning Block
Classification Tablea,b
Observed
Predicted NoPpl_Binary
Percentage Correct <25 people >25 people
Step 0 NoPpl_Binary <25 people 24 0 100.0
>25 people 4 0 .0
Overall Percentage 85.7
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
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Step 0 Constant -1.792 .540 11.007 1 .001 .167
Variables not in the Equation
Score df Sig.
Step 0 Variables Fin_Sum 1.919 1 .166
HRM_Sum 3.005 1 .083
RIsk_Sum 2.697 1 .101
Ops_Sum 1.481 1 .224
Mkt_Sum 1.913 1 .167
Team_Sum 1.629 1 .202
Overall Statistics 4.012 6 .675
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 22.966 6 .001
Block 22.966 6 .001
Model 22.966 6 .001
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Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 .000a .560 1.000
a. Estimation terminated at iteration number 20 because maximum iterations has been
reached. Final solution cannot be found.
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 .000 5 1.000
Contingency Table for Hosmer and Lemeshow Test
NoPpl_Binary = <25 people NoPpl_Binary = >25 people
Total Observed Expected Observed Expected
Step 1 1 10 10.000 0 .000 10
2 3 3.000 0 .000 3
3 3 3.000 0 .000 3
4 3 3.000 0 .000 3
5 3 3.000 0 .000 3
6 2 2.000 1 1.000 3
7 0 .000 3 3.000 3
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Classification Tablea
Observed
Predicted NoPpl_Binary
Percentage Correct <25 people >25 people
Step 1 NoPpl_Binary <25 people 24 0 100.0
>25 people 0 4 100.0
Overall Percentage 100.0
a. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Step 1a Fin_Sum 53.020 1129.562 .002 1 .963 1.06E+23 .000 .
HRM_Sum 119.091 2459.552 .002 1 .961 5.257E+51 .000 .
RIsk_Sum -4.129 153.801 .001 1 .979 .016 .000 1.325E+129
Ops_Sum -6.966 388.853 .000 1 .986 .001 .000 .
Mkt_Sum 24.127 536.472 .002 1 .964 30089347357.88 .000 .
Team_Sum -22.407 551.766 .002 1 .968 .000 .000 .
Constant -3432.839 71220.665 .002 1 .962 .000
a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
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Step number: 1 Observed Groups and Predicted Probabilities 32 + + I I I I F I I R 24 +< + E I< I Q I< I U I< I E 16 +< + N I< I C I< I Y I< I 8 +< + I< I I<>I I<>I Predicted ---------+---------+---------+---------+---------+---------+---------+---------+---------+---------- Prob: 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Group: <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Predicted Probability is of Membership for >25 people The Cut Value is .50 Symbols: < - <25 people > - >25 people Each Symbol Represents 2 Cases.
CasewiseLista
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a. The casewise plot
is not produced
because no outliers
were found.
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APPENDIX XI – BINOMIAL REGRESSION FOR FIRM REVENUE
Logistic Regression
Notes
Output Created 06-DEC-2015 10:36:53
Comments Input Data /Users/sherianne/Downloads/Final Data
20151130_Condensed_Binary.sav
Active Dataset DataSet4
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 30
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing
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Syntax LOGISTIC REGRESSION VARIABLES
Revenue_Binary
/METHOD=ENTER
Fin_SumHRM_SumRIsk_SumOps_SumMk
t_SumTeam_Sum
/CLASSPLOT
/CASEWISE OUTLIER(2)
/PRINT=GOODFIT CI(95)
/CRITERIA=PIN(0.05) POUT(0.10)
ITERATE(20) CUT(0.5).
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.00
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 30 100.0
Missing Cases 0 .0
Total 30 100.0
Unselected Cases 0 .0
Total 30 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
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<R25m 0
>R25m 1
Block 0: Beginning Block
Classification Tablea,b
Observed
Predicted Revenue_Binary
Percentage Correct <R25m >R25m
Step 0 Revenue_Binary <R25m 27 0 100.0
>R25m 3 0 .0
Overall Percentage 90.0
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -2.197 .609 13.035 1 .000 .111
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Variables not in the Equation
Score df Sig.
Step 0 Variables Fin_Sum .304 1 .582
HRM_Sum 1.591 1 .207
RIsk_Sum 1.321 1 .250
Ops_Sum 1.059 1 .303
Mkt_Sum .048 1 .827
Team_Sum .963 1 .326
Overall Statistics 7.339 6 .291
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 19.505 6 .003
Block 19.505 6 .003
Model 19.505 6 .003
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
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1 .000a .478 1.000
a. Estimation terminated at iteration number 20 because maximum iterations has been
reached. Final solution cannot be found.
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 .000 8 1.000
Contingency Table for Hosmer and Lemeshow Test
Revenue_Binary = <R25m Revenue_Binary = >R25m
Total Observed Expected Observed Expected
Step 1 1 3 3.000 0 .000 3
2 3 3.000 0 .000 3
3 3 3.000 0 .000 3
4 3 3.000 0 .000 3
5 3 3.000 0 .000 3
6 3 3.000 0 .000 3
7 2 2.000 0 .000 2
8 4 4.000 0 .000 4
9 3 3.000 0 .000 3
10 0 .000 3 3.000 3
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Classification Tablea
Observed
Predicted Revenue_Binary
Percentage Correct <R25m >R25m
Step 1 Revenue_Binary <R25m 27 0 100.0
>R25m 0 3 100.0
Overall Percentage 100.0
a. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Step 1a Fin_Sum -28.248 1978.228 .000 1 .989 .000 .000 .
HRM_Sum 17.769 1341.236 .000 1 .989 52129932.790 .000 .
RIsk_Sum 12.679 914.828 .000 1 .989 320818.079 .000 .
Ops_Sum 11.848 874.002 .000 1 .989 139751.846 .000 .
Mkt_Sum -12.477 944.421 .000 1 .989 .000 .000 .
Team_Sum -3.874 386.061 .000 1 .992 .021 .000 .
Constant -18.290 4681.942 .000 1 .997 .000
a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
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Step number: 1 Observed Groups and Predicted Probabilities 32 + + I I I< I F I< I R 24 +< + E I< I Q I< I U I< I E 16 +< + N I< I C I< I Y I< I 8 +< + I< I I<>I I<>I Predicted ---------+---------+---------+---------+---------+---------+---------+---------+---------+---------- Prob: 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Group: <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Predicted Probability is of Membership for >R25m The Cut Value is .50 Symbols: < - <R25m > - >R25m Each Symbol Represents 2 Cases.
CasewiseLista
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a. The casewise plot
is not produced
because no outliers
were found.
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