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 Copyright UCT
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Why South African SME engineering managers introduce
business practices: The degree to which chosen business
practices influence firm performance
A Research Proposal
presented to
The Graduate School of Business
University of Cape Town
In partial fulfilment
of the requirements for the
Masters of Business Administration Degree
by
Mark A. Schreiber
09 December 2015
Supervised by: Timothy London
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PLAGIARISM DECLARATION
I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is
one’s own.
I have used a recognised convention for citation and referencing. Each significant
contribution and quotation from the works of other people has been attributed, cited and
referenced.
I certify that this submission is my own work.
I have not allowed and will not allow anyone to copy this essay with the intention of passing
it off as his or her own work
Mark A. Schreiber
31 August 2015
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Acknowledgements
To my supervisor Dr Timothy London, thank you for your swift and detailed feedback. Your
guidance and calm demeanour helped me to remain calm even in the thick of it all, when I
could see no light.
This report would not have been possible had it not been for the prompt response of the
members and administration of SAGI to the survey distribution request. A special thanks goes
out the MarylynnGrant from the SAGI office for her endeavours to ensure that the survey was
distributed promptly.
I would also like to thank my very special and supportive partner Bianca Kramer, my family,
syndicate members and close friends for all of their emotional support, guidance and patience
with me over this trying period. I have been absent for the most part of this last year and they
have all stood by me the entire way. This would not have been possible without each and
every one of your ongoing support. Thank you.
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ABSTRACT
South African SMEs are receiving a lot of attention at the moment and it has been highlighted
at a national level that these firms need to be fostered and grown in order to meet the
country’s needs in terms of the NDP for boosting SAs economy, reducing unemployment and
ultimately spreading equality. SME failure rates are extremely high and many of them do not
last longer than 2 years. Beyond this, those that continue fail to grow, often as a result of a
lack of skilled personnel and management, lack of access to finance and red tape. This study
looks into the South African SMEs in order to determine what the driving force and reasons
are for managers in choosing and implementing business practices. Further, a link between
the implementation of these business practices and firm performance will be investigated.
This population under investigation is the engineering industry in South Africa, who possess
incredible technical skills but in some cases may not have the required management
knowledge and want to grow their companies into larger organisations. A mixed methodology
was used to investigate the reasons for choosing business practices and the links between
implementation of these practices and firm performance. It was discovered in the results that
the majority of the participants were owners of companies that had been in operation for more
than 10 years and generated a revenue of less than R10 million. This raised the question of the
growth intention of these participants. A logistic regression was also run in order to determine
whether there was any link between the firm performance and the ‘business practices’ that the
firm implemented. The results were inconclusive due to insufficient number of participants
and other factors. The study could form as a basis from which to perform more investigations
and glean more generalizable results though the incorporation of a larger more representative
sample.
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TABLE OF CONTENTS
Table of contents ....................................................................................................................... V
List of abbreviations ............................................................................................................... VII
List of figures ........................................................................................................................ VIII
List of tables .......................................................................................................................... VIII
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
a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
A binary multiple regression was also performed on the Financial, HRM, Risk, Operations,
Marketing and Teamwork management practice metrics in order to determine the likely hood
of respondents having a revenue of more or less than R25 million. The results of the
regression were attached in Appendix XI. The ‘-2Log likelihood’ test did not converge in this
case either and therefore did not give a solution. Thus the variance assumption is not fulfilled,
however as a regression model is quite robust the results have been interpreted, but with
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caution. The binomial regression model was not statistically significant with 𝑋𝑋2(8) =
0.000,𝑝𝑝 = 1.000. The model illuminatedthat between 47.8% (Cox & Snell R Squared) and
100% (Nagelkerke R Squared) of the variance in the number of personnel and correctly
classified 100% of the cases.
LIMITATIONS
The results from this research survey may not be robust enough to draw complex conclusions.
This may be due to ill-defined questions, or criteria, and limited responses. Given the
analyses, discussion and conclusion reached it is important to acknowledge the following
limitations.
• it was found that after the responses were cleaned of incomplete survey results and
had erroneous results removed, the final number of respondents was only 29, which is
well below the required 96 as calculated in the sampling section above
• the fact that the sample data was drawn form only one sector of the industry, as
explained, makes the sample less generalizable to the engineering industry in SA
• Although the sample was drawn from a national database there is a possibility that the
participants were not well distributed geographically and may not represent the
different economic circumstances across SA and many other elements that have not
been considered.
• Only two measures were used to determine firm performance, ‘Firm Revenue’ and
‘Number of Employees’. Given that there were many other methods by which to
measure performance these metrics only represent one aspect of firm performance
• It may also be of interest to consider only SME with a ‘growth intention’ (Wang et al.,
2007).
CONCLUSION
The two questions posed in this research study were 1. “What were the drivers for leaders of
SA SMEs in selection and use of management practices” and 2. “To what degree do these
chosen practices affect firm performance? “. Although a limited number of responses were
received, valuable information could still be gathered from the data.
The results of this study show that managers implement business practices for a number of
reasons. Some of these reasons were – to maintain controls on cash flow, to ensure timely
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delivery of projects, and to ensure that they were seen in the public eye. Some managers
stated that they were not aware what business practices were, while others protested that they
did not have the time or did not need to implement any ‘business practices’ as they were “one
man bands” or too busy with the day to day operations.
This study also revealed that the majority of the participants were owners and had been in the
same company for over 10 years. Again, the majority of these participants companies fell into
the lowest revenue bracket of R0-5 million rand per year. This illustrated the question of
whether these owners were interested in growing their companies or not. As small firms are
being considered in this bracket they may well have grown within the bracket. In this case
further investigation would have to be done to determine whether this posit is true. It was also
acknowledged that most, if not all, of the participants came from the members of SAGI as
none of the other institutions had distributed the survey. As a result these findings were likely
not valid for the rest of the Engineering industry in SA.
In an effort to answer question two, a logistic multiple regression was performed. This
regression resulted in an equation which predicted whether a firm was likely to generate more
than R25 million in revenue, or employ more than 25 personnel given that their summed
scores on each of the ‘business practices’ metrics. Although a result was achieved for each
logistic regression, the results were not all that useful. It was presumed that the reason for the
outcomes not being statistically significant was that the sample size was likely not large
enough. Furthermore, as the majority of the population was suspected of having little, or no,
growth intention there was likely to be little, or no, link between the dependant and
independent variables in this case due to unforeseen circumstances.
DIRECTIONS FOR FUTURE RESEARCH
In order to further explore this research it would be necessary to get responses from a wider
variety of participants operating in the numerous related industries. It would also be
imperative to get more responses overall in order to meet minimum sample size, whilst
ensuring that the participants form a representative sample.
One of the reasons for the small number of responses received in this study was the false
assumption that the institutions would be more open to distributing the survey to their
members. It is thus recommended that these institutions were contacted earlier on, in the
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preparation phase of the research, in order to get them on board before the survey is required
to be distributed.
Another direction that the research could take would be to investigate the growth intention of
the engineering industry in SA and the owner/managers who run these firms. It is believed
that interesting findings could emerge from this study which will shed light on why many
SMEs were not growing and seem to remain stagnant for many years.
Additionally, the survey questions need to be adjusted to include more comprehensive
performance measures. In order to include regression studies these measures were also
required to be captured as continuous values. These changes will be required to improve the
reliability of this questionnaire (Wetcher-Hendricks, 2011).
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APPENDIX I – KPI MEASUREMENT
Financial Lens Measures Consumer Lens
Measures
Internal Lens Measures Innovation and
learning Lens
Return on capital Market share Percent of sales from new
products
New product
development
capabilities
Operating margins Brand image and
awareness
Manufacturing costs R&D core
competencies
Economic value add Customer satisfaction Manufacturing cycle time Technology
capabilities
Cash flow Customer relations Inventory management HR development and
capabilities
Sales growth Customer acquisition Quality indices Improved
manufacturing and
business processes
Supply chain management Improved sales
methods and
techniques
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APPENDIX II – SURVEY QUESTIONS
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APPENDIX III–EMAIL DISTRIBUTED TO INSTITUTIONS
Email Distributed.
“To whom it may concern:
I would like to request your assistance in distributing my survey link to your registered
professionals so that I can get responses from as diverse an engineering population as
possible.
I am a Professional Land Surveyor, registered with PLATO in South Africa. I am busy
completing a study that aims to research what the drivers are for SME
managers/owners/leaders within the South African Engineering sector to implement business
practices. This study forms part of the data collection process for my MBA Thesis, based out
of UCT's Graduate School of Business. The responses are important to the outcome of this
study, and will be treated in the strictest confidence.
The survey should take about fifteen minutes to complete. The anonymous results will be
used to draw conclusions and in answering this research question.
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Mkt_CanFindKeepCust 261.95 732.712 .507 .893
Mkt_MngLoyalty 261.64 737.290 .435 .894
Mkt_MktFinDeptComm 262.32 747.656 .336 .895
Mkt_ConsMktShare 262.00 753.905 .151 .896
Team_ValTool 261.05 754.807 .188 .896
Team_Encourage 261.18 757.108 .126 .896
Team_OpenComm 261.05 757.093 .146 .896
Team_CombDecisionMaking 261.73 757.255 .094 .897
Team_NotTooMuch 261.77 756.184 .124 .897
Team_ForComplexTasks 261.23 745.422 .433 .895
Team_RecognCoreCompet 261.32 752.608 .220 .896
Team_KeepMoraleHigh 261.64 752.242 .291 .896
Team_DontChange 261.77 755.327 .142 .896
Team_BalanceAttributes 261.91 757.039 .105 .897
Team_RecognStrengWeak 261.45 746.831 .344 .895
Team_AwareOfGrpThink 262.09 746.563 .299 .895
Scale Statistics
Mean Variance Std. Deviation N of Items
265.36 762.719 27.617 73
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APPENDIX VII – BAR CHARTS
Bar Chart
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APPENDIX VIII – FREQUENCY TABLE
Age
Frequency Percent Valid Percent Cumulative Percent
Valid 40-50 years 8 26.7 26.7 26.7
50-65 years 20 66.7 66.7 93.3
Other 2 6.7 6.7 100.0
Total 30 100.0 100.0
Position
Frequency Percent Valid Percent Cumulative Percent
Valid Owner 21 70.0 70.0 70.0
Manager 2 6.7 6.7 76.7
MD 2 6.7 6.7 83.3
CEO 2 6.7 6.7 90.0
Other 3 10.0 10.0 100.0
Total 30 100.0 100.0
Industry
Frequency Percent Valid Percent Cumulative Percent
Valid Civils 2 6.7 6.7 6.7
Construction 7 23.3 23.3 30.0
Industrial 1 3.3 3.3 33.3
Engineering 6 20.0 20.0 53.3
Other 14 46.7 46.7 100.0
Total 30 100.0 100.0
Education
Frequency Percent Valid Percent Cumulative Percent
Valid Diploma 10 33.3 33.3 33.3
Undergraduate 4 13.3 13.3 46.7
Honours 11 36.7 36.7 83.3
Post graduate 3 10.0 10.0 93.3
Masters 2 6.7 6.7 100.0
Total 30 100.0 100.0
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Mng_training
Frequency Percent Valid Percent Cumulative Percent
Valid No 20 66.7 66.7 66.7
Yes 10 33.3 33.3 100.0
Total 30 100.0 100.0
TimeInPos
Frequency Percent Valid Percent Cumulative Percent
Valid 0-2 years 1 3.3 3.6 3.6
2-5 years 2 6.7 7.1 10.7
5-10 years 2 6.7 7.1 17.9
10 years or more 23 76.7 82.1 100.0
Total 28 93.3 100.0 Missing System 2 6.7 Total 30 100.0
Bus_Life
Frequency Percent Valid Percent Cumulative Percent
Valid 0-2 years 1 3.3 3.4 3.4
2-5 years 1 3.3 3.4 6.9
5-10 years 2 6.7 6.9 13.8
10 years or more 25 83.3 86.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
No_Empl
Frequency Percent Valid Percent Cumulative Percent
Valid 0-5 9 30.0 32.1 32.1
5-10 10 33.3 35.7 67.9
10-25 5 16.7 17.9 85.7
25-50 3 10.0 10.7 96.4
50-100 1 3.3 3.6 100.0
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Total 28 93.3 100.0 Missing System 2 6.7 Total 30 100.0
Revenue
Frequency Percent Valid Percent Cumulative Percent
Valid R0-5m 20 66.7 66.7 66.7
R5-10m 5 16.7 16.7 83.3
R10-25m 2 6.7 6.7 90.0
R25-50m 1 3.3 3.3 93.3
>R100m 2 6.7 6.7 100.0
Total 30 100.0 100.0
Use_FIN
Frequency Percent Valid Percent Cumulative Percent
Valid No 4 13.3 13.8 13.8
Yes 25 83.3 86.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_HRM
Frequency Percent Valid Percent Cumulative Percent
Valid No 12 40.0 41.4 41.4
Yes 17 56.7 58.6 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Mkt
Frequency Percent Valid Percent Cumulative Percent
Valid No 16 53.3 55.2 55.2
Yes 13 43.3 44.8 100.0
Total 29 96.7 100.0
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Missing System 1 3.3 Total 30 100.0
Use_Ops
Frequency Percent Valid Percent Cumulative Percent
Valid No 10 33.3 34.5 34.5
Yes 19 63.3 65.5 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Risk
Frequency Percent Valid Percent Cumulative Percent
Valid No 13 43.3 44.8 44.8
Yes 16 53.3 55.2 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Strat
Frequency Percent Valid Percent Cumulative Percent
Valid No 11 36.7 37.9 37.9
Yes 18 60.0 62.1 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
Use_Team
Frequency Percent Valid Percent Cumulative Percent
Valid No 10 33.3 34.5 34.5
Yes 19 63.3 65.5 100.0
Total 29 96.7 100.0 Missing System 1 3.3 Total 30 100.0
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APPENDIX IX – RESPONDENT REASONING
Use_Reason Rank_Reason
Too small too small
Small Land Surveying practice Not necessary
All the above are important to success Marketing creates awareness, operations generate cashflow and finance keeps the business going
Too small a team - use project management Small team - use project management
I am hands on Without sound financial management there is no business.Human resource is important for running the entity.Strategizing give direction
Any input neglected negatively affects the organisations performance
Any input neglected negatively affects the organisations performance
We are a big Company and all practices are actually being implemented if not by me then by someone within our group. We are a big Construction & Engineering group.
It is all about utilising the best team for a certain project and allocating those personnel to that project and managing my survey teams.
Work is mostly on a tender basis, so marketing is limited to making sure our clients get good quality data and prefer using our services. This helps in situations where quoted prices are very close.
Cash-flow and cash resources are criticalWe manage operations carefully to make sure all our gear and field staff are fully utilized all the time. A strong, motivated and positive attitude team is what it all rests on.
I just started up. Am a very small practice. 1 man with 1 assistant.
Strategic- position yourself , prepare for where you see you are heading.Financial- money will enable you to purchase equipment, appoint someone, etc. Team management - close to operations, where you can delegate you should delegate
I recognise the need for training but haven't managed to create time yet as we are in a growth phase
based on order of stress
? ?
Survival of company Survival of company
MOST WORK OF SHORT TERM BASIS REWUIRING DEDICATED ATTENTION AND SOLUTION TO TECHNICAL, LOGISTAL AND FINACIAL CONSTRAINTS
COMPLETING PROMJECTS AT HAND CRITICAL TO SUSTAINING CASH FLOW, REQUIRING FOCUSING OF HUMAN AND EAUIPMENT RESCOURCES
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These are essential to survival They are the most essential if you survival horizon is close
little risk People are the most important
Owner Owner
That's what it takes to run a practice Cash flow
makes good sense makes good sense
Standard business practices Our business is based on sound financial decision - rest follows that strategy
I haven't seen the business practice book spoken of Stress avoidance
Have to in order to be the most competitive we can be
These things work for me and they help me to become extremely competitive
? ?
Owner To survive and experience in the industry.
Being a one man business it is not necessary Need to know my finances and marketing is by good practice and word of mouth
Xx Xx
I do not believe that any business can survive or grow without implementing these business practises
1.Problems with cash flow can cripple an otherwise sound business2.Without marketing the company could lose its relevance in the market place.3.In the survey business team synergy is an inherent ingredient for successfully completed large projects.
Financial, marketing management and operations management is important to me. I am basically a one-man business, therefore human resource and team management does not really apply for me. I apply risk management when I quote for a job or when I take on a job, where I assess whether I can complete the job in time or whether I can actually do the job.
Financial management determines your success in life, marketing management determines what kind of jobs you are being offered and team management leads to satisfied employees.
It helps to run the business effectively Cashflow is the most important. If you do not market you do not get new business. Your operations must run smoothly and therefore cost effective.
Financial management –Cashflow control is the most crucial aspect.Operations management - to minimise stress due to deadlines conflicting.risk management- to minimise redoing jobs.Strategic management - to determine branching into new technologies.Team management- how operations and risk management is implemented.
withoutcashflow you cannot survive. risk management reduces re-doing of activities which reduces errors due to repetition of tasks. team management is important to limit the frustration due to hurry -up and wait situations.
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APPENDIX X – BINOMIAL REGRESSION FOR NUMBER OF PERSONNEL
Logistic Regression
Notes
Output Created 06-DEC-2015 10:33:47
Comments Input Data /Users/sherianne/Downloads/Final Data
20151130_Condensed_Binary.sav
Active Dataset DataSet4
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 30
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing
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Syntax LOGISTIC REGRESSION VARIABLES
NoPpl_Binary
/METHOD=ENTER
Fin_SumHRM_SumRIsk_SumOps_SumMk
t_SumTeam_Sum
/CLASSPLOT
/CASEWISE OUTLIER(2)
/PRINT=GOODFIT CI(95)
/CRITERIA=PIN(0.05) POUT(0.10)
ITERATE(20) CUT(0.5).
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.00
[DataSet4]
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 28 93.3
Missing Cases 2 6.7
Total 30 100.0
Unselected Cases 0 .0
Total 30 100.0
a. If weight is in effect, see classification table for the total number of cases.
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Dependent Variable Encoding
Original Value Internal Value
<25 people 0
>25 people 1
Block 0: Beginning Block
Classification Tablea,b
Observed
Predicted NoPpl_Binary
Percentage Correct <25 people >25 people
Step 0 NoPpl_Binary <25 people 24 0 100.0
>25 people 4 0 .0
Overall Percentage 85.7
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
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Step 0 Constant -1.792 .540 11.007 1 .001 .167
Variables not in the Equation
Score df Sig.
Step 0 Variables Fin_Sum 1.919 1 .166
HRM_Sum 3.005 1 .083
RIsk_Sum 2.697 1 .101
Ops_Sum 1.481 1 .224
Mkt_Sum 1.913 1 .167
Team_Sum 1.629 1 .202
Overall Statistics 4.012 6 .675
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 22.966 6 .001
Block 22.966 6 .001
Model 22.966 6 .001
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Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 .000a .560 1.000
a. Estimation terminated at iteration number 20 because maximum iterations has been
reached. Final solution cannot be found.
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 .000 5 1.000
Contingency Table for Hosmer and Lemeshow Test
NoPpl_Binary = <25 people NoPpl_Binary = >25 people
Total Observed Expected Observed Expected
Step 1 1 10 10.000 0 .000 10
2 3 3.000 0 .000 3
3 3 3.000 0 .000 3
4 3 3.000 0 .000 3
5 3 3.000 0 .000 3
6 2 2.000 1 1.000 3
7 0 .000 3 3.000 3
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a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
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Step number: 1 Observed Groups and Predicted Probabilities 32 + + I I I I F I I R 24 +< + E I< I Q I< I U I< I E 16 +< + N I< I C I< I Y I< I 8 +< + I< I I<>I I<>I Predicted ---------+---------+---------+---------+---------+---------+---------+---------+---------+---------- Prob: 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Group: <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Predicted Probability is of Membership for >25 people The Cut Value is .50 Symbols: < - <25 people > - >25 people Each Symbol Represents 2 Cases.
CasewiseLista
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a. The casewise plot
is not produced
because no outliers
were found.
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APPENDIX XI – BINOMIAL REGRESSION FOR FIRM REVENUE
Logistic Regression
Notes
Output Created 06-DEC-2015 10:36:53
Comments Input Data /Users/sherianne/Downloads/Final Data
20151130_Condensed_Binary.sav
Active Dataset DataSet4
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 30
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing
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Syntax LOGISTIC REGRESSION VARIABLES
Revenue_Binary
/METHOD=ENTER
Fin_SumHRM_SumRIsk_SumOps_SumMk
t_SumTeam_Sum
/CLASSPLOT
/CASEWISE OUTLIER(2)
/PRINT=GOODFIT CI(95)
/CRITERIA=PIN(0.05) POUT(0.10)
ITERATE(20) CUT(0.5).
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.00
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 30 100.0
Missing Cases 0 .0
Total 30 100.0
Unselected Cases 0 .0
Total 30 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
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<R25m 0
>R25m 1
Block 0: Beginning Block
Classification Tablea,b
Observed
Predicted Revenue_Binary
Percentage Correct <R25m >R25m
Step 0 Revenue_Binary <R25m 27 0 100.0
>R25m 3 0 .0
Overall Percentage 90.0
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -2.197 .609 13.035 1 .000 .111
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Variables not in the Equation
Score df Sig.
Step 0 Variables Fin_Sum .304 1 .582
HRM_Sum 1.591 1 .207
RIsk_Sum 1.321 1 .250
Ops_Sum 1.059 1 .303
Mkt_Sum .048 1 .827
Team_Sum .963 1 .326
Overall Statistics 7.339 6 .291
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 19.505 6 .003
Block 19.505 6 .003
Model 19.505 6 .003
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
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1 .000a .478 1.000
a. Estimation terminated at iteration number 20 because maximum iterations has been
reached. Final solution cannot be found.
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 .000 8 1.000
Contingency Table for Hosmer and Lemeshow Test
Revenue_Binary = <R25m Revenue_Binary = >R25m
Total Observed Expected Observed Expected
Step 1 1 3 3.000 0 .000 3
2 3 3.000 0 .000 3
3 3 3.000 0 .000 3
4 3 3.000 0 .000 3
5 3 3.000 0 .000 3
6 3 3.000 0 .000 3
7 2 2.000 0 .000 2
8 4 4.000 0 .000 4
9 3 3.000 0 .000 3
10 0 .000 3 3.000 3
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a. Variable(s) entered on step 1: Fin_Sum, HRM_Sum, RIsk_Sum, Ops_Sum, Mkt_Sum, Team_Sum.
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Step number: 1 Observed Groups and Predicted Probabilities 32 + + I I I< I F I< I R 24 +< + E I< I Q I< I U I< I E 16 +< + N I< I C I< I Y I< I 8 +< + I< I I<>I I<>I Predicted ---------+---------+---------+---------+---------+---------+---------+---------+---------+---------- Prob: 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Group: <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Predicted Probability is of Membership for >R25m The Cut Value is .50 Symbols: < - <R25m > - >R25m Each Symbol Represents 2 Cases.
CasewiseLista
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