SEEKING AN EMPIRICAL DEVELOPMENT TAXONOMY FOR MANUFACTURING SMES
USING DATA FROM AUSTRALIA’S BUSINESS LONGITUDINAL SURVEY
Professor Richard G.P. McMahon,
Head, School of Commerce,
The Flinders University of South Australia.
SCHOOL OF COMMERCE RESEARCH PAPER SERIES: 00-1
ISSN: 1441-3906
Summary
This paper reports on the pilot stage of a proposed research effort to derive, characterise and employ an
empirically-based development taxonomy for small and medium-sized enterprises (SMEs) in the
manufacturing sector using panel data recently made available from Australia’s Business Longitudinal
Survey. Cluster analysis is used with key enterprise size, age and growth variables to discover if there
appear to be any stable development pathways evident in the data. Each of three annual data collections is
separately examined, and then comparisons are made of the resulting cluster analysis outcomes over time.
Descriptive statistics for various enterprise characteristics facilitate initial interpretation of the cluster
analysis solutions. The findings match those of a prior taxonomic study reasonably well; and they provide
general support for a stage-wise SME development pattern. Certain development pathways – subsistence,
capped growth and continued growth in particular – seem to be stable and persistent.
Introduction
Small and medium-sized enterprise (SME) growth and development have, for some time, received considerable
attention from researchers and policy-makers around the world for reasons identified by Turok [1, p. 29] as
follows:
There is considerable interest within the field of small firms policy and research in the identification of
features that distinguish firms which grow from those that stand still or fail. This is thought important if
more selective small firms policies are to be developed. Identifying distinctive features of more and less
successful firms may also provide insights into the factors influencing small firm development and hence
improve understanding of the growth process.
Gibb & Davies [2] give a fuller account of the research and policy imperatives for ‘picking winners’ amongst
SMEs world-wide.
2
This is a frequently investigated domain for researchers in a variety of business-related fields. However,
in many respects, knowledge acquisition has not been cumulative and there is much that is yet to be settled. In
their review of the relevant literature, O’Farrell & Hitchens [3, p. 1380] conclude that ‘At present an adequate
explanatory framework within which to analyse the growth of the small owner-managed manufacturing
enterprise has not been developed’. On the basis of their review, Gibb & Davies [2, p. 26] are of the opinion that
‘The production of such a theory and explanation in the near future is unlikely’. The review of Holmes &
Zimmer [4, p. 97] expresses the belief that ‘an operational framework that distinguishes growth from non-growth
small businesses does not exist’.
This paper reports on the pilot stage of a proposed research effort to derive, characterise and employ an
empirically-based development taxonomy for SMEs in the manufacturing sector using panel data recently made
available from Australia’s Business Longitudinal Survey. The initial objective is to seek for possibly stable
pathways for manufacturing SMEs that, to some helpful degree, reflect their achievement of business growth and
development. The paper proceeds as follows. After briefly considering some background literature that
encourages this investigation, the research method is outlined. Thereafter, the findings of this stage of the
research are presented, followed by tentative conclusions and recommendations that will shape the direction and
specifics of further inquiry.
Background Literature
For many decades it has been very common amongst writers in the area to view SME growth as a series of
phases or stages of development through which the business may pass in an enterprise life-cycle. Having its
origins in the literature of economics [5, 6, 7, 8], reliance on this paradigm in the SME literature is most
frequently claimed to date back to Steinmetz [9]. In an often cited book of readings on the organisational lifecycle, Kimberly & Miles [10, p. ix] draw attention to:
. . . the cyclical quality of organizational existence. Organizations are born, grow, and decline. Sometimes
they reawaken, and sometimes they disappear.
This quotation invokes a biological metaphor for business organisations which has been the source of much
controversy in the literature of economics, business and sociology [6, 10].
Before presenting the findings of their own empirical research, Hanks et al. [11] critically review virtually
all significant prior writing and research on the enterprise life-cycle construct. Commenting on wide differences
in the specifics of prior stages of growth models (particularly inclusion of from 3 to 10 stages), Hanks et al. [11,
pp. 11-12] observe that:
3
In recent years, a few empirical studies of the organization life cycle have emerged, providing important
contributions to life-cycle theory [12, 13, 14, 15]. However, most of these studies have defined growth
stages a priori, using existing conceptualizations. The lack of specificity and empirical rigour in these
typologies may account for unexpected intrastage variance found in some analyses. . . . It may be possible
to address some of these difficulties by deriving taxonomic rather than typological models . . .
Hanks et al. [11] see the strength of a taxonomic approach to identifying and specifying stages in an enterprise
life-cycle model as deriving from use of multivariate analysis of empirical data to reveal common patterns and
relationships in the data. They acknowledge only Smith et al. [15] as having previously employed a taxonomic
approach to developing an enterprise life-cycle model, but note that that research had a very small sample size
and various other weaknesses. The taxonomic stages of growth model subsequently described by Hanks et al.
[11] is represented in Figure 1.
After conducting his own critical appraisal of recent research in the field, the present author [16] believes
that some reliance can be placed on both the broad approach and the general features of the Hanks et al. [11]
stages of growth model. As well as overcoming concerns that such models are frequently not empirically based,
it can also be claimed to at least partially answer the most prevalent objections to this type of model that have
appeared in the relevant literature [3]. Importantly, this model uniquely incorporates two disengagement (or
arrested development) configurations that are frequently observed amongst SMEs – the life-style business and
the business electing for capped growth. Furthermore, while predominantly focused upon stages of growth, the
model is sympathetic to, or at least not inconsistent with, an alternative gestalts of growth perspective that has
recently received some support in the literature [17].
The present research does not aspire to directly testing the validity of the Hanks et al. [11] model with
new data. Rather, it responds to a suggestion for further research made at the close of the Hanks et al. [11, p. 24]
paper:
. . . longitudinal studies of the organization life cycle that trace changing organizational configurations
over time are needed. Although the cross-sectional approach taken in this study is suggestive of life-cycle
stages, it is impossible to differentiate between configurations representative of life-cycle stages and those
suggestive of firms simply choosing to do business in different ways. Both historical and repeated
measures designs would provide important insights into patterns of organization growth.
Where attempts have been made to empirically validate stages of growth models, this has hitherto been done
with relatively small and narrowly defined samples, and with cross-sectional data. The need for larger and more
representative samples in SME research is difficult to dispute [18]. Furthermore, a strong argument can be made
that longitudinal data are inherently appropriate to conceptualising growth and development of businesses over
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time [18]. The availability of data from Australia’s Business Longitudinal Survey now provides an opportunity
to meet such research demands.
Research Method
The panel data employed in this research are drawn from the Business Longitudinal Survey (BLS) conducted by
the Australian Bureau of Statistics (ABS) on behalf of the federal government over the financial years 1994-95,
1995-96, 1996-97 and 1997-98. Costing in excess of $4 million, the BLS was designed to provide information
on the growth and performance of Australian employing businesses, and to identify selected economic and
structural characteristics of these businesses.
The ABS Business Register was used as the population frame for the survey, with approximately 13,000
business units being selected for inclusion in the 1994-95 mailing of questionnaires. For the 1995-96 survey, a
sub-sample of the original selections for 1994-95 was chosen, and this was supplemented with a sample of new
business units added to the Business Register during 1995-96. The sample for the 1996-97 survey was again in
two parts. The first formed the longitudinal or continuing part of the sample, comprising all those remaining live
businesses from the 1995-96 survey. The second part comprised a sample of new business units added to the
Business Register during 1996-97. A similar procedure was followed for the 1997-98 survey. Approximately
6,400 business units were surveyed in each of 1995-96, 1996-97 and 1997-98.
All business units in the Australian economy were included within the scope of the BLS except for the
following:
 Non-employing businesses.
 All government enterprises.
 Businesses classified to the following Australian and New Zealand Standard Industrial Classification
(ANZSIC) Divisions:
A – Agriculture, forestry and fishing
D – Electricity, gas and water supply
J – Communication services
M – Government administration and defence
N – Education
O – Health and community services
ANZSIC Sub-Divisions 96 Other services and 97 Private households employing staff, and ANZSIC
Groups 921 Libraries, 922 Museums and 923 Parks and gardens, were also excluded from the BLS.
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The BLS did not employ completely random samples. The original population (for 1994-95) was stratified by
industry and business size, with equal probability sampling methods being employed within strata. Further
stratification by innovation status, exporting status and growth status took place for the 1995-96 survey. The
ABS has calculated a system of weights, reflecting the sample fractions used for each stratum, that can be used
to estimate population parameters from the BLS data.
Data collection in the BLS was achieved through self-administered, structured questionnaires containing
essentially closed questions. Copies of the questionnaires used in each of the four annual collections can be
obtained from the ABS. The questionnaires were piloted prior to their first use, and were then progressively
refined in the light of experience after each collection. As well as on-going questions, each questionnaire also
included once-off questions dealing with certain matters of policy interest to the federal government at the time
of the collections. Various imputation techniques, including matching with other data files available to the ABS,
were employed to deal with any missing data. Because information collected in the BLS was sought under the
authority of the Census and Statistics Act 1905, and thus provision of appropriate responses to the mailed
questionnaires could be legally enforced by the Australian Statistician, response rates were very high by
conventional research standards – typically exceeding 90 per cent.
The specific BLS data used in this pilot study are included in a Confidentialised Unit Record File (CURF)
for the first 3 annual collections, released by the ABS on CD-ROM on 2 July, 1999. This CURF contains data on
9,230 business units employing fewer than 200 persons – broadly representing SMEs in the Australian context.
Restricted industrial classification detail, no geographical indicators, presentation of enterprise age in ranges, and
omission of certain data items obtained in the BLS all help to maintain the confidentiality of unit records.
Furthermore, all financial variables have been subject to perturbation – a process in which values are slightly
varied to provide further confidentiality protection.
This research is concerned only with the manufacturing sector of the BLS CURF. There are two reasons
for this. First, over the last few decades, the performance of the Australian manufacturing sector has been a
major preoccupation of policy-makers and government departments dealing with industry and trade. The sector
has been characterised as non-competitive by international standards, and it is considered to have failed in
countering Australia’s growing trade imbalance with the rest of the world [19]. Over 99 per cent of all businesses
in the Australian manufacturing sector are SMEs according to generally accepted definitions [20]. The second
reason for considering only the manufacturing sector is that is highly probable that cross-industry differences in
the nature of business activities, typical employment per business, capital intensity, etc. could confound findings
6
relating to SME development patterns, and to SME growth and performance more generally. Such influences
are, to a reasonable extent, controlled for by examining a single (albeit broadly defined) industry. There are
3,331 manufacturing SMEs in the BLS CURF, representing approximately 36 per cent of businesses in the file.
Additional focus is provided to this research by considering only manufacturing SMEs legally organised
as proprietary companies. There are a number of reasons for this further narrowing of the unit of analysis. First,
as Freedman & Godwin [21, p. 234] indicate, a particular concern with proprietary companies is not uncommon
amongst SME researchers world-wide:
It would appear that, in so far as the issue is considered at all, the limited liability company is of more
interest to the small business research community than are unincorporated firms; . . . limited liability
companies and entrepreneurship have become equated, or at least associated.
Second, the primary concern in this research is with SME growth and development, and it is more likely that
these will be evident in businesses legally organised as proprietary companies [21, 22, 23, 24, 25]. Third, the
research ultimately aspires to comparing possible groupings of manufacturing SMEs in terms of key financial
performance measures. This becomes problematic if the study sample contains both incorporated and
unincorporated businesses because of the customary procedural difference in accounting for owners’ wages
which are not separately reported in the BLS data.
Proprietary companies are currently of considerable policy significance in Australia. The First Corporate
Law Simplification Act 1995 provides for establishment of one member/director companies where previously a
minimum of two had been required. An outcome of this, and other recent reforms, is a likely reduction in the
proportion of Australian SMEs legally organised as sole proprietorships or partnerships. Incorporation has
become a more feasible option. Thus, this research focuses on the proprietary company as an increasingly more
predominant form of legal organisation for SMEs in Australia. As a consequence, the population for the research
is unlikely to be diminished unacceptably if interest is restricted to proprietary companies. There are 2,374
manufacturing SMEs legally organised as proprietary companies in the BLS CURF, representing approximately
71 per cent of manufacturing SMEs in the file.
The principal analytical procedure used in this research is exploratory cluster analysis (as employed by
Hanks et al. [11]). This is a multivariate statistical technique for developing meaningful clusters or groupings of
cases. The aim is to objectively classify cases into a small number of mutually exclusive groups on the basis of
similarities amongst values for certain clustering variables selected by the researcher. The groups should exhibit
high internal (within-cluster) homogeneity and high external (between-cluster) heterogeneity. The groups are not
7
predefined, but are derived from the ‘natural’ structure of the data. Subsequent profiling using characteristic or
demographic variables facilitates interpretation of the nature of the groups.
As Hair et al. [26, p. 435] point out, ‘Cluster analysis is not a statistical technique where parameters for a
sample are assessed as possibly representative of a population’. Rather, cluster analysis is typically an
exploratory technique that requires minimal distributional assumptions regarding clustering variables.
Nevertheless, issues such as the representativeness of the sample, differences in scale amongst clustering
variables, the undue influence of outliers, and multicollinearity amongst clustering variables are important
considerations.
Research Findings
On the basis of prior research in the area, particularly Hanks et al. [11], the following key clustering variables
were selected for use from the BLS CURF for the first 3 annual collections (1994-95, 1995-96, 1996-97):
 Enterprise age – as measured by an ordinal variable with 5 categories (less than 2 years, 2 to 5 years, 5
to 10 years, 10 to 20 years, and more than 20 years). Cluster analysis normally requires clustering
variables to be measured on at least an interval scale. However, for reasons of confidentiality, such an
age measure is not to be available in BLS CURFs. This problem will be ameliorated in subsequent
stages of this research, because the 4-year BLS CURF to be released early in 2000 will include
enterprise age measured in 2-yearly intervals up to 30 years.
 Enterprise size – as measured by total employment and annual sales. To avoid problems of
multicollinearity amongst clustering variables, total assets was not used as a size measure during
cluster analysis. However, it was used in ex post characterisation of clusters.
 Enterprise growth rate – as measured by annual employment growth and annual sales growth. It was
not possible to estimate annual growth rate in total assets for the first collection (1994-95), and so this
variable was not used in the cluster analyses. In order not to exclude new starts in the first collection,
employment and sales growth estimates were calculated for all collections as follows (1994-95 growth
in employment used as an example):
Employment growth, 1994-95 = (1995 Employment – 1994 Employment) x 100 per cent
1995 Employment
While acknowledged to be unusual, this form of growth measure was used by Hanks et al. [11] for the
same reason. Clearly, caution should be exercised when interpreting such measures.
8
In order to deal with differences in scale amongst the clustering variables, and with the undue influence of
outliers, all these variables were first standardised and then observations 5 or more standard deviations from the
mean value were removed. Given the mixed use of both point-of-time and flow variables in the cluster analyses,
it was not possible to use the system of sample weights included in the BLS CURF. This undoubtedly introduces
some bias to the samples employed; but, given the exploratory aspirations of the study and the size and coverage
of the samples, this is not considered to be a serious problem.
Because of the heavy computational and storage demands on personal computer memory, agglomerative
hierarchical cluster analysis (that is, the CLUSTER procedure in SPSS for Windows) is not recommended for
use with samples containing 200 or more cases [26, 27]. Since all samples employed in this study exceeded
1,000 cases, with the largest exceeding 2,000 cases, the alternative k-means cluster analysis was primarily used
(that is, the QUICK CLUSTER procedure in SPSS for Windows). This non-hierarchical procedure is based on
nearest centroid sorting with squared Euclidean distance being the similarity measure.
The analysis began by examining data from the 1994-95 BLS collection including all operating
businesses in that collection (n=2,162 after removing outliers). Sub-samples were subjected to agglomerative
hierarchical cluster analysis to get a feel for the likely number of clusters needed to represent the data. For the
reason given in the previous paragraph, this procedure was extremely slow; but eventually a 7 cluster solution
seemed to be indicated. The full sample was then subjected to k-means cluster analysis using randomly selected
seed points, with 7 clusters being pre-specified. Since Hanks et al. [11] had arrived at a 6 cluster solution, this
possibility was also examined. Ultimately, the 7 cluster solution was chosen as potentially being the more useful
and more amenable to interpretation. This solution is represented in Figure 2. Possible descriptors for some
pathways between the clusters are suggested in the figure. The ANOVA significance figures in the first panel of
Figure 6 suggest that all clustering variables do differ between clusters in the solution. In the first panel of Figure
7, the results of Kruskal-Wallis one-way analysis of variance tests lead to rejection of null hypotheses that
median values for all characterisation variables (enterprise age, total employment, annual sales, total assets,
annual employment growth, and annual sales growth) do not differ between clusters in the solution.
Subsequently, a similar analysis was undertaken examining data from the 1994-95 BLS collection
including only businesses operating in all 3 collections (that is, the first collection for businesses constituting the
on-going longitudinal panel; n=1,189 after removing outliers). Again, agglomerative hierarchical cluster analysis
seemed to indicate a 7 cluster solution. However, a 6 cluster solution was ultimately chosen as potentially being
the more useful and more amenable to interpretation. This solution is represented in Figure 3. The ANOVA
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significance figures in the second panel of Figure 6 suggest that all clustering variables do differ between
clusters in the solution. In the second panel of Figure 7, the results of Kruskal-Wallis one-way analysis of
variance tests lead to rejection of null hypotheses that median values for all characterisation variables do not
differ between clusters in the solution.
Finally, similar analyses were undertaken examining data from the 1995-96 and 1996-97 BLS collections
including only businesses operating in all 3 collections (that is, the second and third collections for businesses
constituting the on-going longitudinal panel; n=1,186 and n=1,186 after removing outliers). For the 1995-96
collection, agglomerative hierarchical cluster analysis seemed to indicate a 7 cluster solution. However, a 6
cluster solution was ultimately chosen as potentially being the more useful and more amenable to interpretation.
For the 1996-97 collection, agglomerative hierarchical cluster analysis seemed to indicate a 5 cluster solution.
However, a 4 cluster solution was ultimately chosen as potentially being the more useful and more amenable to
interpretation. These solutions are represented in Figures 4 and 5. For the 1995-96 collection, the ANOVA
significance figures in the third panel of Figure 6 suggest that all clustering variables do differ between clusters
in the solution. For the 1996-97 collection, the ANOVA significance figures in the fourth panel of Figure 6
suggest that clustering variables other than annual sales growth do differ between clusters in the solution. In the
third and fourth panels of Figure 7, the results of Kruskal-Wallis one-way analysis of variance tests lead to
rejection of null hypotheses that median values for all characterisation variables do not differ between clusters in
the solutions.
Conclusions and Recommendations
Bearing in mind the exploratory nature of this pilot study, the acknowledged limitations of the research method
used, and that it has not yet been possible to employ the full 4-year BLS CURF with its improved enterprise age
measure, the following tentative conclusions might nevertheless be reached at this stage:
 While not corresponding in all respects, it would appear that certain features of the first BLS 1994-95
enterprise life-cycle model (n=2,162) match those of the Hanks et al. [11] model reasonably well. These
include a discernible stage-wise development pattern (see below), approximately the same number of
stages, and the apparent existence of disengagement stages such as life-style and capped growth.
Furthermore, the pace of SME development – viewed over 20 or so years – seems similar in the two
models. Not unexpectedly, the enterprise age and size benchmarks for stages vary somewhat. In judging
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these models against each other, it should be borne in mind that the BLS model is derived from a much
larger and broadly more representative manufacturing SME sample.
 Support for a stage-wise SME development pattern is gained by comparing the second BLS 1994-95
model (n=1,189) with the models for 1995-96 (n=1,186) and 1996-97 (n=1,186) which are derived for
exactly the same businesses. As time elapses, fewer stages are evident, but certain development pathways
– subsistence, capped growth and continued growth in particular – seem to persist. Clusters at the left of
Figures 3, 4 and 5 tend to disappear and there appears to be a rightward drift in the models towards
roughly the same range of configurations. Moreover, the stability of the ultimate configurations seems to
be suggested by the diminished significance of differences in employment and sales growth measures
between clusters – but not so for enterprise size measures – as the businesses mature.
Thus, further scholarly inquiry certainly seems to be encouraged by this preliminary multivariate analysis of
empirical data derived from Australia’s BLS to reveal common development patterns amongst manufacturing
SMEs.
Pending informed feedback on this paper, the following recommendations are made to shape the direction
and specifics of further research effort to derive, characterise and employ an empirically-based development
taxonomy for Australian manufacturing SMEs legally organised as proprietary companies:
 To facilitate interpretation of the development models produced, use of the unusual employment and sales
growth measures underpinning this study should be reconsidered – even if this means a reduction in
sample sizes because of missing growth observations.
 Further inquiry should employ the 4-year BLS CURF to be released early in 2000. First, this will include
an improved enterprise age measure that may enhance the validity and specificity of development models
produced. Second, an extra year of observations for the on-going longitudinal panel should make these
models more dependable and informative.
 Further inquiry should seek to employ truly longitudinal methods of analysis, rather than repeated crosssectional methods. There is no readily available longitudinal counterpart to cluster analysis, other than
relaxing independence requirements and using pooled data from the various collections. However, there
are longitudinal methods for assessing the significance of differences between clusters in successive
collections. Other, less common, possibilities for longitudinal analysis should also be explored.
Acknowledgments
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The permission of the Australian Statistician to use confidentialised data from the federal government’s Business
Longitudinal Survey, and to publish findings based on analysis of that data, is gratefully acknowledged.
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Figure 1: Hanks et al. (11) Enterprise Life-Cycle Model
DEVELOPMENT
STAGES
DISENGAGEMENT
STAGES
START-UP
Mean number
of employees: 6.46 persons
Mean annual
sales revenues: US$0.27 million
Mean age: 4.29 years
LIFE-STYLE
Mean number
of employees: 7.00 persons
Mean annual
sales revenues: US$0.41 million
EXPANSION
Mean number
of employees: 23.64 persons
Mean age: 18.71 years
Mean annual
sales revenues: US$1.40 million
Mean age: 7.36 years
CAPPED GROWTH
Mean number
of employees: 24.65 persons
Mean annual
sales revenues: US$2.05 million
MATURITY
Mean number
of employees: 62.76 persons
Mean annual
sales revenues: US$3.71 million
Mean age: 6.66 years
DIVERSIFICATION
Mean number
of employees: 495.40 persons
Mean annual
sales revenues: US$45.76 million
Mean age: 16.20 years
Mean age: 12.65 years
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Figure 2: Business Longitudinal Survey 1994-95 Enterprise Life-Cycle Model 1 (n=2,162)
ENTERPRISE AGE (YEARS)
CLUSTER 2 (n=384)
Enterprise age mode: 2 to 5 years
Total employment mean: 10.2 persons
Sales mean: $1.1 million p.a.
Total assets mean: $0.8 million
Employment growth mean: 23.4% p.a.
Sales growth mean: 39.5% p.a.
SUBSISTENCE?
CE
CLUSTER 7 (n=441)
Enterprise age mode: 5 to 10 years
Total employment mean: 12.3 persons
Sales mean: $1.5 million p.a.
Total assets mean: $0.8 million
Employment growth mean: -3.9% p.a.
Sales growth mean: 8.1% p.a.
CLUSTER 5 (n=713)
Enterprise age mode: 10 to 20 years
Total employment mean: 14.7 persons
Sales mean: $1.7 million p.a.
Total assets mean: $1.1 million
Employment growth mean: 1.1% p.a.
Sales growth mean: 10.1% p.a.
LIFE-STYLE?
EARLY GROWTH?
CLUSTER 6 (n=330)
Enterprise age mode: more than 20 years
Total employment mean: 53.4 persons
Sales mean: $7.3 million p.a.
Total assets mean: $4.5 million
Employment growth mean: 5.1% p.a.
Sales growth mean: 16.1% p.a.
CLUSTER 4 (n=168)
Enterprise age mode: 5 to 10 years
Total employment mean: 57.8 persons
Sales mean: $9.3 million p.a.
Total assets mean: $6.4 million
Employment growth mean: 21.4% p.a.
Sales growth mean: 35.8% p.a.
CAPPED GROWTH?
CONTINUED GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 3 (n=109)
Enterprise age mode: more than 20 years
Total employment mean: 114.4 persons
Sales mean: $16.2 million p.a.
Total assets mean: $11.6 million
Employment growth mean: 6.0% p.a.
Sales growth mean: 22.0% p.a.
CLUSTER 1 (n=47)
Enterprise age mode: more than 20 years
Total employment mean: 116.2 persons
Sales mean: $39.7 million p.a.
Total assets mean: $32.3 million
Employment growth mean: 8.2% p.a.
Sales growth mean: 27.2% p.a.
15
Figure 3: Business Longitudinal Survey 1994-95 Enterprise Life-Cycle Model 2 (n=1,189)
ENTERPRISE AGE (YEARS)
CLUSTER 6 (n=138)
Enterprise age mode: 2 to 5 years
Total employment mean: 16.5 persons
Sales mean: $2.2 million p.a.
Total assets mean: $1.9 million
Employment growth mean: 39.9% p.a.
Sales growth mean: 77.4% p.a.
SUBSISTENCE?
LIFE-STYLE?
CE
EARLY GROWTH?
CLUSTER 1 (n=361)
Enterprise age mode: 5 to 10 years
Total employment mean: 17.2 persons
Sales mean: $2.3 million p.a.
Total assets mean: $1.5 million
Employment growth mean: 2.0% p.a.
Sales growth mean: 7.2% p.a.
CLUSTER 5 (n=95)
Enterprise age mode: 5 to 10 years
Total employment mean: 78.7 persons
Sales mean: $12.6 million p.a.
Total assets mean: $7.6 million
Employment growth mean: 17.8% p.a.
Sales growth mean: 52.3% p.a.
CLUSTER 2 (n=446)
Enterprise age mode: 10 to 20 years
Total employment mean: 24.2 persons
Sales mean: $3.2 million p.a.
Total assets mean: $2.1 million
Employment growth mean: 1.4% p.a.
Sales growth mean: 7.1% p.a.
CAPPED GROWTH?
CONTINUED GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 1 (n=47)
Enterprise age mode: more than 20 years
Total employment mean: 97.5 persons
Sales mean: $13.8 million p.a.
Total assets mean: $9.8 million
Employment growth mean: 4.9% p.a.
Sales growth mean: 6.3% p.a.
CLUSTER 3 (n=38)
Enterprise age mode: more than 20 years
Total employment mean: 113.2 persons
Sales mean: $44.3 million p.a.
Total assets mean: $39.7 million
Employment growth mean: 10.3% p.a.
Sales growth mean: 26.3% p.a.
16
Figure 4: Business Longitudinal Survey 1995-96 Enterprise Life-Cycle Model (n=1,186)
ENTERPRISE AGE (YEARS)
CLUSTER 1 (n=62)
Enterprise age mode: 5 to 10 years
Total employment mean: 14.8 persons
Sales mean: $2.5 million p.a.
Total assets mean: $1.4 million
Employment growth mean: -101.1% p.a.
Sales growth mean: -38.0% p.a.
CLUSTER 6 (n=414)
Enterprise age mode: 5 to 10 years
Total employment mean: 19.0 persons
Sales mean: $2.8 million p.a.
Total assets mean: $2.0 million
Employment growth mean: 1.9% p.a.
Sales growth mean: 5.3% p.a.
SUBSISTENCE?
CE
CLUSTER 5 (n=396)
Enterprise age mode: 10 to 20 years
Total employment mean: 18.0 persons
Sales mean: $2.5 million p.a.
Total assets mean: $1.6 million
Employment growth mean: 0.0% p.a.
Sales growth mean: 0.0% p.a.
LIFE-STYLE?
CAPPED GROWTH?
CLUSTER 3 (n=89)
Enterprise age mode: 5 to 10 years
Total employment mean: 114.0 persons
Sales mean: $17.0 million p.a.
Total assets mean: $11.8 million
Employment growth mean: 5.9% p.a.
Sales growth mean: 6.9% p.a.
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CONTINUED GROWTH?
CLUSTER 4 (n=179)
Enterprise age mode: more than 20 years
Total employment mean: 61.4 persons
Sales mean: $9.0 million p.a.
Total assets mean: $6.2 million
Employment growth mean: 0.5% p.a.
Sales growth mean: 5.1% p.a.
CLUSTER 2 (n=46)
Enterprise age mode: more than 20 years
Total employment mean: 100.3 persons
Sales mean: $46.5 million p.a.
Total assets mean: $34.9 million
Employment growth mean: -0.5% p.a.
Sales growth mean: 7.5% p.a.
17
Figure 5: Business Longitudinal Survey 1996-97 Enterprise Life-Cycle Model (n=1,186)
ENTERPRISE AGE (YEARS)
CLUSTER 1 (n=404)
Enterprise age mode: 5 to 10 years
Total employment mean: 18.4 persons
Sales mean: $2.7 million p.a.
Total assets mean: $1.9 million
Employment growth mean: -3.5% p.a.
Sales growth mean: -854.6% p.a.
CLUSTER 4 (n=519)
Enterprise age mode: 10 to 20 years
Total employment mean: 20.0 persons
Sales mean: $2.8 million p.a.
Total assets mean: $1.9 million
Employment growth mean: -8.1% p.a.
Sales growth mean: -571.4% p.a.
CAPPED GROWTH?
CONTINUED GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
SUBSISTENCE?
CE
CLUSTER 3 (n=194)
Enterprise age mode: more than 20 years
Total employment mean: 77.0 persons
Sales mean: $12.7 million p.a.
Total assets mean: $8.6 million
Employment growth mean: 0.3% p.a.
Sales growth mean: 3.0% p.a.
CLUSTER 2 (n=69)
Enterprise age mode: more than 20 years
Total employment mean: 128.9 persons
Sales mean: $38.7 million p.a.
Total assets mean: $30.1 million
Employment growth mean: 1.8% p.a.
Sales growth mean: -0.5% p.a.
18
19
18
Figure 6: K-MEANS CLUSTER ANALYSIS ANOVAs
Cluster
Annual
Standardised
Collection
Variable
Error
Mean
Mean
Univariate
Square
df
Square
df
F Ratio
Significance
289.685
6
0.195
2155
1482.592
0.000
150.683
6
0.096
2155
1568.038
0.000
272.760
6
0.179
2155
1519.836
0.000
8.743
6
0.226
2155
38.763
0.000
5.735
6
0.153
2155
37.458
0.000
152.820
5
0.359
1183
425.630
0.000
156.574
5
0.293
1183
534.066
0.000
111.514
5
0.136
1183
821.907
0.000
27.977
5
0.517
1183
54.111
0.000
86.658
5
0.463
1183
186.993
0.000
153.925
5
0.349
1180
441.147
0.000
170.969
5
0.245
1180
698.192
0.000
136.111
5
0.128
1180
1062.720
0.000
41.106
5
0.213
1180
193.210
0.000
6.696
5
0.231
1180
28.970
0.000
Z Score
Enterprise Age
Z Score
Total Employment
1994-95
(n=2,162)
Z Score
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
Z Score
Enterprise Age
Z Score
Total Employment
1994-95
(n=1,189)
Z Score
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
Z Score
Enterprise Age
Z Score
Total Employment
1995-96
(n=1,186)
Z Score
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
19
Figure 6 (Continued): K-MEANS CLUSTER ANALYSIS ANOVAs
Cluster
Annual
Standardised
Collection
Variable
Error
Mean
Mean
Univariate
Square
df
Square
df
F Ratio
Significance
258.362
3
0.345
1182
748.927
0.000
285.531
3
0.243
1182
1172.997
0.000
222.339
3
0.195
1182
1142.519
0.000
0.403
3
0.121
1182
3.320
0.019
0.001
3
0.004
1182
0.286
0.836
Z Score
Enterprise Age
Z Score
Total Employment
1996-97
(n=1,186)
Z Score
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
20
Figure 7: KRUSKAL-WALLIS TESTS FOR CLUSTER CHARACTERISATION VARIABLES
Annual
Annual
Annual
Test
Enterprise
Total
Annual
Total
Employment
Sales
Collection
Details
Age
Employment
Sales
Assets
Growth
Growth
KruskalWallis
1994-95
(n=2,162)
Statistic
df
Significance
1750.806
1365.673
1210.178
1058.357
150.282
183.789
6
6
6
6
6
6
0.000
0.000
0.000
0.000
0.000
0.000
804.243
595.215
525.983
472.751
137.886
387.468
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
809.915
660.335
562.352
514.641
184.616
62.218
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
788.827
602.650
535.058
481.634
11.557
10.274
3
3
3
3
3
3
0.000
0.000
0.000
0.000
0.009
0.016
KruskalWallis
1994-95
(n=1,189)
Statistic
df
Significance
KruskalWallis
1995-96
(n=1,186)
Statistic
df
Significance
KruskalWallis
1996-97
(n=1,186)
Statistic
df
Significance