Why South African SME engineering managers...

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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Why South African SMEs managers introduce business practices

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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


Observed

Predicted Revenue_Binary

Percentage Correct <R25m >R25m

Step 1 Revenue_Binary <R25m 27 0 100.0

>R25m 0 3 100.0



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



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 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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BIBLIOGRAPHY

Al-Shammari, H. a, & Hussein, R. T. (2007). Strategic planning-firm performance linkage: empirical investigation from an emergent market perspective. Advances in Competitiveness Research, 15(1&2), 15–26.

Amra, R., Hlatshwayo, A., & Mcmillan, L. (2013). SMME Employment in South Africa, (September), 25–27.

Armstrong, M. (2012). Armstrong’s handbook of human resource management practice (12th Editi.). Kogan Page Limited.

Belbin, R. M. (1981). Management teams: Why they succeed or fail. Johannesburg: William Heinmann Ltd. doi:434-90127-X

Bettencourt, L. a, Lusch, R. F., & Vargo, S. L. (2014). A service lens on value creation: Marketing’s role in achieving strategic advantage. Califiornia Management Review, 57(1), 44–67. doi:10.1525/cmr.2014.57.1.44

Boxall, P., & Steeneveld, M. (1999). Human resource strategy and competitive advantage: a longitudinal study of engineering consultancies. Journal of Management Studies, 36(4), 443–463. doi:Doi 10.1111/1467-6486.00144

Business Environment Specialists. (2013). Developing a new path for SMEs in South Africa: Reassessing for growth. Alert (Vol. 1).

Business Environment Specialists. (2015). SME sustainability and growth should be an obsession for job creation in South Africa. Alert (Vol. 1).

Campbell, D., Stonehouse, G., & Houston, B. (2002). Business strategy: An introduction (Second.). London and New York: Routledge.

Colbert, B. a. (2004). The complex resource-based view: Implications for theory and practice in strategic human resource management. The Academy of Management Review, 29(3), 341. doi:10.2307/20159047

Crawford, E. R., & Lepine, J. A. (2013). A configural theory of team processes: Accounting for the structure of taskwork and teamwork. Academy of Management Review, 38(1), 32–48. doi:10.5465/amr.2011.0206

Creative research systems. (2015). Sample Size Calculator. Retrieved November 22, 2015, from http://www.surveysystem.com/sscalc.htm

Darwish, T. K., Singh, S., & Mohamed, a. F. (2013). The role of strategic HR practices in organisational effectiveness: an empirical investigation in the country of Jordan. The International Journal of Human Resource Management, 24(17), 3343–3362. doi:10.1080/09585192.2013.775174

Dincer, O., Tatoglu, E., & Glaister, K. W. (2006). The strategic planning process: evidence from Turkish firms. Management Research News, 29(4), 206–219. doi:10.1108/1409170610665068

du Toit, R., & Roodt, J. (2008). Engineering professionals: Crucial key to development and growth in South Africa.

Dumba, E. (2014). An evaluation of the management practices of micro and small enterprises (MSEs) in Zimbabwe : A case study of the manufacturing MSEs in Masvingo Urban. International Journal of Innovation Research in Management, 5(3), 31–41.

Copyright UCT


- 47 - | P a g e

Eckerson, W. (2006). Performance dashboards: Measuring, monitoring, and managing your business. New Jersey: John Wiley & Sons, Inc.

Flynn, B. B., Sakakibara, S., & Schroeder, R. G. (1995). Relationship between JIT and TQM: Practices and performance. Academy of Management Journal, 38(5), 1325–1360. doi:10.2307/256860

Forsyth, D. R. (1992). Judging the morality of business practices: The influence of personal moral philosophies. Journal of Business Ethics, 11(5-6), 461–470. doi:10.1007/BF00870557

Foster, S. T., Wallin, C., & Ogden, J. (2011). Towards a better understanding of supply chain quality management practices. International Journal of Production Research, 49(8), 2285–2300. doi:10.1080/00207541003733791

Fowler, S. J., & Hope, C. (2007). Incorporating sustainable business practices into company strategy. Business Strategy and the Environment, 16(1), 26–38. doi:10.1002/bse.462

French, S. J., Kelly, S. J., & Harrison, J. L. (2004). The role of strategic planning in the performance of small, professional service firms: A research note. Journal of Management Development, 23(8), 765–776. doi:10.1108/02621710410549611

Gadenne, D. L., Kennedy, J., & McKeiver, C. (2008). An empirical study of environmental awareness and practices in SMEs. Journal of Business Ethics, 84(1), 45–63. doi:10.1007/s10551-008-9672-9

Ghouri, A. M., Khan, N. U. R., & Malik, M. A. Marketing practices of textile business and firm’s performance: A case of Pakistan. , 2 Euro Economica (2011).

Graham, M., & Winfield, J. (2012). Understanding financial statements (Second edi.). Mega Digital.

Grutter, A. (2012). Introduction to operations management: A strategic approach (First edit.). Pearson Education South Africa (Pty) Ltd.

Gueldenzoph Snyder, L. (2008). Teaching teams about teamwork: Preparation, practice, and performance review. Business Communication Quarterly, 72(1), 74–79. doi:10.1177/1080569908330372

Harvard business review. (1996). Balanced scorecard framework. Harvard Business Review. Retrieved August 22, 2015, from http://www.google.co.za/imgres?imgurl=http://www.maaw.info/images/BalancedScorecardFramework.gif&imgrefurl=http://management-class.com/courseware/shift/dsm.ppt&h=480&w=640&tbnid=94NgSJqyjFuAIM:&docid=SF9UZESSapYzPM&ei=vXjYVdbAKIq27gaG6ZbIDA&tbm=isch&ved=0

Hsu, C.-C., Tan, K. C., Kannan, V. R., & Keong Leong, G. (2009). Supply chain management practices as a mediator of the relationship between operations capability and firm performance. International Journal of Production Research, 47(3), 835–855. doi:10.1080/00207540701452142

ISO. (2009). ISO 31000:2009 Risk management: principles and guidelines. Retrieved from http://www.npcse.co.th/pdf/iso31000/ISO_FDIS_31000_(E).pdf

Jesuthasan, R. (2013). Performance management as a business discipline: Aligning talent with strategic priorities. People & Strategy, 36(2), 58–71.

Jobber, D., & Ellis-Chadwick, F. (2013). Principles and practices of marketing (Seventh ed.).

Copyright UCT


- 48 - | P a g e

McGraw-Hill Higher Education. doi:9780077140007

Johnson, G., Langley, A., Melin, L., & Whittington, R. (2007). Strategy as practice: Research directions and resources. Cambridge University Press.

Karadag, H. (2015). Financial management challenges in small and medium-sized enterprises: A strategic management approach. Emerging Markets Journal, 5(1), 26. doi:10.5195/emaj.2015.67

Karami, A., Sahebalzamani, S., & Sarabi, B. (2015). The influence of HR practices on business strategy and firm performance: The case of banking industry in Iran. The IUP Journal of Management Research, XVI(1), 30–53.

Keizer, J. a., Halman, J. I. M., & Song, M. (2002). From experience: Applying the risk diagnosing methodology. Journal of Product Innovation Management, 19(3), 213–232. doi:10.1016/S0737-6782(02)00138-8

Kumar, V., & Petersen, J. a. (2005). Using a customer-level marketing strategy to enhance firm performance: A review of theoretical and empirical evidence. Journal of the Academy of Marketing Science, 33(4), 504–519. doi:10.1177/0092070305275857

Lado, N., Duque, L. C., & Alvarez Bassi, D. (2013). Current marketing practices and market orientation in the context of an emerging economy: The case of Uruguay. Journal of Small Business Management, 51(4), 602–616. doi:10.1111/jsbm.12007

Lau, R. S. M., Zhao, X., & Xiao, M. (2004). Assessing quality management in China with MBNQA criteria. International Journal of Quality & Reliability Management, 21(7), 699–713. doi:10.1108/02656710410549064

Lovemore, F., & Brummer, L. (2003). The ABC of financial management: An introduction to financial management and analysis (Second edi.). Van Schaik Publishers.

Maas, G., & Herrington, M. (2007). Global Entrepreneurship Monitor South African Report 2006, (August).

Madrid-Guijarro, A., García-Pérez-de-Lema, D., & van Auken, H. (2011). An analysis of non-financial factors associated with financial distress. Entrepreneurship & Regional Development, 23(3-4), 159–186. doi:10.1080/08985620903233911

Mandal, P., & Venta, E. (2008). Business practices and performance in US manufacturing companies: An empirical investigation. International Journal of Business Excellence, 1(1/2), 141. doi:10.1504/IJBEX.2008.017571

Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and Statistical Research Methods : From Hypothesis to Results. Somerset: Wiley.

McCallin, A. (2001). Interdisciplinary practice--a matter of teamwork: an integrated literature review. Journal of Clinical Nursing, 10(4), 419–428. doi:10.1046/j.1365-2702.2001.00495.x

McGee, J., Thomas, H., & Wilson, D. (2010). Strategy: Analysis and practice (second edi.). McGraw-Hill Education.

Meredith, J. R., & Shafer, S. M. (2011). Operations management (Fourth Edi.). John Wiley & Sons, Inc.

Mujahid Ghouri, A., Rehman Khan, N. U., Abdul Malik, M., & Razzaq, A. (2011). Marketing practices and their effects on firm’s performance: Findings from small and medium sized

Copyright UCT


- 49 - | P a g e

catering and restaurants in Karachi. International Journal of Business and Management, 6(5), 251–259. doi:10.5539/ijbm.v6n5p251

National Planning Commission. (2010). National Development Plan (2030): Our future - make it work: Executive summary. doi:ISBN: 978-0-621-41180-5

Neely, A., Adams, C., & Crowe, P. (2001). The performance prism in practice. Measuring Business Excellence, 5(2), 6–13. doi:10.1108/13683040110385142

Neneh, N. B., & van Zyl, J. H. (2012). Achieving optimal business performance through business practices: evidence from SMEs in selected areas in South Africa. Southern African Business Review, 16(3), 118–144.

Ojokuku, R. M., Sajuyigbe, A. S., & Ogunwoye, A. B. (2014). Human resource management practices and small scale business performance: Evidence from Osun State, South Western Nigeria. Journal of Emerging Trends in Economics Management Sciences, 5(7), 1–6.

Olawale, F., & Garwe, D. (2010). Obstacles to the growth of new SMEs in South Africa : A principal component analysis approach. African Journal of Business Management, 4(5), 729–738.

Parker, G. M. (2008). Team players and teamwork: New strategies for developing successful collaberation (Second edi.). John Wiley and sons, Inc.

Poister, T. (2013). Does performance management lead to better outcomes? Evidence from the US public transit industry. Public Administration …, xx, 1–12. doi:10.1111/puar.12076.Does

Prajogo, D. I., & Sohal, A. S. (2003). The relationship between TQM practices, quality performance, and innovation performance: An empirical examination. International Journal of Quality & Reliability Management, 20(8), 901–918. doi:10.1108/02656710310493625

Rahman, S.-U. (2001). Total quality management practices and business outcome: Evidence from small and medium enterprises in Western Australia. Total Quality Management, 12(2), 201–210. doi:10.1080/09544120120011424

Rashidi, R. (2015). A review of performance management system. International Journal of Academic Research, 7(1), 210–215. doi:10.7813/2075-4124.2015/7-1/B.36

Ross, J. A. (2005). Team camaraderie: Can you have too much? Harvard Management Update, 3–5.

RSA. National small business amendment act 26 (2003).

Sarkis, J. (1998). Evaluating environmentally conscious business practices. European Journal of Operational Research, 107(1), 159–174. doi:10.1016/S0377-2217(97)00160-4

Savci, S., & Kayis, B. (2006). Knowledge elicitation for risk mapping in concurrent engineering projects. International Journal of Production Research, 44, 1–17. doi:10.1080/00207540500445321

Shamir, B., & Eilam, G. (2005). “What’s your story?” A life-stories approach to authentic leadership development. The Leadership Quarterly, 16(3), 395–417. doi:10.1016/j.leaqua.2005.03.005

Skrt, B., & Antoncic, B. (2004). Strategic planning and small firm growth: An empirical

Copyright UCT


- 50 - | P a g e

examination. Managing Global Transitions, 2(2), 107–122. doi:10.1007/s10464-011-9453-y

Statistics New Zealand. (2007). Business practices and performance: Data from the business operations survey.

Svensson, G., Wood, G., & Callaghan, M. (2010). A corporate model of sustainable business practices: An ethical perspective. Journal of World Business, 45(4), 336–345. doi:10.1016/j.jwb.2009.08.005

Taylor, A., & Taylor, M. (2014). Factors influencing effective implementation of performance measurement systems in small and medium-sized enterprises and large firms: a perspective from Contingency Theory. International Journal of Production Research, 52(3), 847–866. doi:10.1080/00207543.2013.842023

Tesar, G., & Huada (Eds’), J. (2013). Marketing management and strategy: An African casebook. Routledge.

Tharenou, P., Donohue, R., & Cooper, B. (2007). Management research methods. Cambridge University Press.

Tituts, P. A., & Bradford, J. L. (1996). Reflections on consumer sophistication and its impact on ethical business practice on JSTOR. The Journal of Consumer Affairs, 30(1), 170–194.

University of Miskolc. (2005). The economic role of SMEs in world economy, especially in Europe. European Integrated Studies, 4(1), 3–12.

Valiris, G., & Glykas, M. (2004). Business analysis metrics for business process redesign. Business Process Management Journal, 10(4), 445–480. doi:10.1108/14637150410548100

Vargo, J., & Seville, E. (2011). Crisis strategic planning for SMEs: finding the silver lining. International Journal of Production Research, 49(18), 5619–5635. doi:10.1080/00207543.2011.563902

Von Broembesen, M., Wood, E., & Herrington, M. (2005). Global entrepreneurship monitor: South African report 2006. Retrieved August 29, 2015, from http://www.gbs.nct.ac.za/gbswebb/userfiles/gemsouthafrica2000pdf

Voss, U., & Brettel, M. (2014). The Effectiveness of Management Control in Small Firms: Perspectives from Resource Dependence Theory. Journal of Small Business Management, 52(3), 569–587. doi:10.1111/jsbm.12050

Wang, C., Walker, E., & Redmond, J. (2007). Explaining the lack of Strategic planning in SMEs: The importance of owner motivation. International Journal of Organisational Behaviour, 12(1), 1–16.

Warren, M. (2007). Peter Drucker - one of the world’s most respected thinkers on management and society. Grey Matter Strategy and Innovation, (March), 1.

Wetcher-Hendricks, D. (2011). Analysing quantitative data. New Jersey: John Wiley and sons, Inc.

Whyte, G. (1989). Groupthink reconsidered. Academy of Management Review, 14(1), 40–56. doi:10.5465/AMR.1989.4279001

Willimse, J. (2010). SME failure statistics. The Forum SA. Retrieved March 16, 2015, from

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http://www.theforumsa.co.za/forums/showthread.php/7808-SME-Failure-Statistics

Woodfield, S., & Kennie, T. (2008). “Teamwork” or “Working as a team”? The theory and practice of top team working in UK higher education. Higher Education Quarterly, 62(4), 397–415. doi:10.1111/j.1468-2273.2008.00399.x

Worldmeters. (2015). South Africa Population. Population. Retrieved November 22, 2015, from http://www.worldometers.info/world-population/south-africa-population/

Xyrichis, A., & Ream, E. (2008). Teamwork: A concept analysis. Journal of Advanced Nursing, 61(2), 232–241. doi:10.1111/j.1365-2648.2007.04496.x

Yeung, A. K., & Berman, B. (1997). Adding value through human resources: Reorienting human resource measurement to drive business performance. Human Resource Management, 36(3), 321–335. doi:10.1002/(SICI)1099-050X(199723)36:3<321::AID-HRM4>3.0.CO;2-Y

Zager, K., Sacer, I. M., & Decman, N. (2012). Financial ratios as an evaluation instrument of business quality in small and medium-sized enterprises. International Journal of Management Cases, 14(4), 373–385. doi:10.5848/APBJ.2012.00112

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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


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


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


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


Valid No 20 66.7 66.7 66.7

Yes 10 33.3 33.3 100.0

Total 30 100.0 100.0

TimeInPos


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


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


No_Empl


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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Revenue


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


Valid No 4 13.3 13.8 13.8

Yes 25 83.3 86.2 100.0


Use_HRM


Valid No 12 40.0 41.4 41.4

Yes 17 56.7 58.6 100.0


Use_Mkt


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


Valid No 10 33.3 34.5 34.5

Yes 19 63.3 65.5 100.0


Use_Risk


Valid No 13 43.3 44.8 44.8

Yes 16 53.3 55.2 100.0


Use_Strat


Valid No 11 36.7 37.9 37.9

Yes 18 60.0 62.1 100.0


Use_Team


Valid No 10 33.3 34.5 34.5

Yes 19 63.3 65.5 100.0


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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]


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




>25 people 4 0 .0


a. Constant is included in the model.

b. The cut value is .500



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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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Observed




>25 people 0 4 100.0





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


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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


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




>R25m 3 0 .0


a. Constant is included in the model.

b. The cut value is .500



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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Observed




>R25m 0 3 100.0





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


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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.

Copyright UCT

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