DERIVING AN EMPIRICAL DEVELOPMENT TAXONOMY FOR MANUFACTURING SMES
USING DATA FROM AUSTRALIA’S BUSINESS LONGITUDINAL SURVEY
School of Commerce Research Paper Series: 00-4
ISSN: 1441-3906
Professor Richard G.P. McMahon,
Head, School of Commerce,
The Flinders University of South Australia,
GPO Box 2100,
Adelaide South Australia 5001.
Abstract
This paper substantially extends a pilot study previously undertaken as part of an on-going research effort
to derive, characterise and employ an empirically-based development taxonomy for small and mediumsized 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 age, size and
growth variables to discover if there appear to be any stable development pathways evident in the data.
Each of four 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 interpretation of the cluster analysis solutions. Using the clusters as markers or signposts, three
relatively stable SME development pathways are discernible in the longitudinal panel results. The first is
a low growth pathway apparently leading to the traditional or life-style SME configuration (around 70 per
cent of the panel). The second is a moderate growth pathway possibly leading to the capped growth SME
configuration (around 25 per cent of the panel). And the third is a high growth pathway seemingly leading
to the entrepreneurial SME configuration (around 5 per cent of the panel). Statistical analysis reveals that
differences between the identified SME development pathways in terms of enterprise age, size and
growth variables are highly significant.
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DERIVING A DEVELOPMENT TAXONOMY FOR MANUFACTURING SMEs
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 (1991, 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 and Davies (1990) give a fuller account of the research and policy imperatives for ‘picking winners’
amongst SMEs world-wide.
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 and Hitchens (1988, 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 and Davies (1990, 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 and Zimmer (1994, p. 97) expresses the belief that ‘an operational framework that distinguishes growth
from non-growth small businesses does not exist’.
This paper substantially extends a pilot study previously undertaken by the author (McMahon, 2000) as
part of an on-going 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 objective is to discover 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 the research are presented, followed by conclusions and
recommendations arising from the investigation.
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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 (Marshall, 1890; Penrose, 1952; Penrose, 1959; Rostow, 1960), reliance on
this paradigm in the SME literature is most frequently claimed to date back to Steinmetz (1969). In an often cited
book of readings on the organisational life-cycle, Kimberly and Miles (1980, 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 literatures of economics, business and sociology (Penrose, 1952; Kimberly and Miles, 1980).
Before presenting the findings of their own empirical research, Hanks et al. (1993) 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. (1993, pp. 11-12) observe that:
In recent years, a few empirical studies of the organization life cycle have emerged, providing important
contributions to life-cycle theory (Kazanjian, 1988; Kazanjian and Drazin, 1990; Miller and Friesen,
1984; Smith et al., 1985). 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. (1993) 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. (1985) 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.
(1993) is represented in Figure 1.
After conducting his own critical appraisal of recent research in the field, the present author (McMahon,
1998) believes that some reliance can be placed on both the broad approach and the general features of the
Hanks et al. (1993) 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 (O’Farrell and Hitchens, 1988). Importantly, this model uniquely
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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 (Kazanjian and Drazin,
1989).
The present research does not aspire to directly testing the validity of the Hanks et al. (1993) model with
new data. Rather, it responds to a suggestion for further research made at the close of the Hanks et al. (1993, 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 SME life-cycle 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 (McMahon, 1999). Furthermore, a strong argument
can be made that longitudinal data are inherently appropriate to conceptualising growth and development of
SMEs over time (McMahon, 1999). 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
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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.
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
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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)
released by the ABS on CD-ROM in December, 1999. This CURF contains data on 9,731 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 (Pappas et al., 1990). Over 99 per cent
of all businesses in the Australian manufacturing sector are SMEs according to generally accepted definitions
(Australian Bureau of Statistics, 1996). 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 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 3,411 manufacturing SMEs in the BLS CURF, representing
approximately 35 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 and Godwin (1994, 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 (Freedman and Godwin, 1994;
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Hakim, 1989; Gray, 1992; Hughes and Storey, 1994; Yellow Pages Australia, 1995). 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 2,413
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. (1993)). 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 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. (1995, 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.
8
Research Findings
Identifying SME Development Pathways
In prior cross-sectional research in this field, efforts have inevitably been made to attach particular meaning to
emerging clusters of SMEs by characterising them as certain phases or stages of development through which
businesses may pass in an enterprise life-cycle. In the present research using longitudinal data, clusters of SMEs
are more appropriately viewed as markers or signposts for apparent development pathways over time. Rather
than the persistence of specific life-cycle stages, the primary concern is with the stability of discernible SME
development pathways. No attempt is made to attribute significance to clusters other than to construe which
development pathways they appear to mark.
On the basis of prior research in the area, particularly Hanks et al. (1993), the following key clustering
variables were selected for use from the BLS CURF:
 Enterprise age – as measured by an ordinal variable reflecting enterprise age in 2-yearly intervals up
to 30 years, with a single category for concerns older than 30 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 available in the BLS CURF. Nevertheless, the available
measure is considered to acceptably approximate an interval variable.
 Enterprise size – as measured by interval variables reflecting 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 interval variables reflecting 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.
To ensure that all SMEs included in the study were truly active or operational, any concerns with no employees
and/or no sales and/or no assets were excluded. In order to deal with differences in scale amongst the clustering
variables, and with the undue influence of outliers, all the variables listed above were standardised and then
observations 4 or more standard deviations from the mean value were removed (Hair et al., 1995). 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;
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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 (Hair et al., 1995; Norusis and SPSS Inc., 1992). Since all
samples employed in this study exceeded 870 cases, with the largest exceeding 1,700 cases, the alternative kmeans 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=1,782). 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 solutions with up to 8 clusters seemed to
be indicated. The full sample was then subjected to k-means cluster analysis using randomly selected seed
points, with 8 clusters being pre-specified. Since Hanks et al. (1993) had arrived at a 6 cluster solution and
McMahon (2000) had arrived at a 7 cluster solution, these possibilities were also examined. Ultimately, a 6
cluster solution was chosen as potentially being the most useful and most amenable to interpretation. This
solution is represented in Figure 2 (while an effort has been made to use enterprise size and enterprise age as
axes, because of space limitations the figure is not drawn strictly to scale). Possible descriptors for pathways
between the clusters are suggested in the figure. The ANOVA significance figures in the first panel of Table 1
suggest that all clustering variables do differ between clusters in the solution. In the first panel of Table 2, 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 4 collections (that is, the first collection for businesses constituting the
on-going longitudinal panel; n=871). Agglomerative hierarchical cluster analysis seemed to indicate solutions
with up to 9 clusters. A 7 cluster solution was ultimately chosen as potentially being the most useful and most
amenable to interpretation. This solution is represented in Figure 3 (as for Figure 2, the figure is not drawn
strictly to scale). The ANOVA significance figures in the second panel of Table 1 suggest that all clustering
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variables do differ between clusters in the solution. In the second panel of Table 2, 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, 1996-97 and 1997-98 BLS
collections including only businesses operating in all 4 collections (that is, the second, third and fourth
collections for businesses constituting the on-going longitudinal panel; n=871 for all three). For these
collections, agglomerative hierarchical cluster analysis seemed to indicate solutions with up to 7 clusters.
Ultimately, 6 cluster solutions were chosen as potentially being the most useful and most amenable to
interpretation. These solutions are represented in Figures 4, 5 and 6 (as for Figures 1 and 2, the figures are not
drawn strictly to scale). The ANOVA significance figures in the third, fourth and fifth panels of Table 1 suggest
that, for all three collections, all clustering variables do differ between clusters in the solutions. In the third,
fourth and fifth panels of Table 2, the results of Kruskal-Wallis one-way analysis of variance tests lead to
rejection, for all three collections, of null hypotheses that median values for all characterisation variables do not
differ between clusters in the solutions.
Bearing in mind the exploratory nature of this study and the acknowledged limitations of the research
method used, the following conclusions might nevertheless be reached at this point:
 While not corresponding in all respects, it would appear that certain features of the 1994-95 crosssectional BLS model (n=1,782) match those of the Hanks et al. (1993) model reasonably well. These
include similar development pathways (see discussion of traditional vs capped growth vs entrepreneurial
SMEs later in the paper ) and the pace of SME development (viewed over 20 or so years). Not
unexpectedly, the enterprise age and size benchmarks for clusters vary somewhat. In judging 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.
 While not identical, the 1994-95 cross-sectional BLS model (n=1,782) corresponds acceptably with the
first BLS panel model (for 1994-95; n=871). The latter sample is, of course, a sub-set of the former
sample. Most importantly, both samples suggest the existence of three main development pathways – low,
moderate and high growth. This gives some confidence in the first of the four models derived for the
longitudinal panel.
 Further support for the existence of stable SME development pathways is gained by comparing the first
BLS panel model (for 1994-95; n=871) with subsequent panel models for 1995-96 (n=871), 1996-97
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(n=871) and 1997-98 (n=871) which are derived for exactly the same businesses. As time elapses, the
identified development pathways – low, moderate and high growth – 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 SME configurations.
In the following sub-section of the paper, attempts are made to characterise, and to distinguish between, the
apparent SME development pathways identified above. Descriptive statistics for various enterprise
characteristics are used for this purpose. The resulting depiction facilitates interpretation of the nature and
significance of the development pathways.
Characterising SME Development Pathways
The first step in characterisation of the three apparent SME development pathways in the panel data was to
construe the clusters for each collection as markers or signposts for the pathways, as follows (using Figures 3, 4,
5 and 6):
 For 1994-95 (n=871) – Cluster 7 (n=364) and Cluster 4 (n=204) mark the low growth pathway; Cluster 2
(n=16), Cluster 5 (n=156) and Cluster 6 (n=81) mark the moderate growth pathway; and Cluster 1 (n=40)
marks the high growth pathway.
 For 1995-96 (n=871) – Cluster 2 (n=358) and Cluster 4 (n=206) mark the low growth pathway; Cluster 6
(n=83), Cluster 5 (n=72) and Cluster 3 (n=91) mark the moderate growth pathway; and Cluster 1 (n=61)
marks the high growth pathway.
 For 1996-97 (n=871) – Cluster 1 (n=338) and Cluster 5 (n=207) mark the low growth pathway; Cluster 4
(n=105), Cluster 6 (n=109) and Cluster 3 (n=74) mark the moderate growth pathway; and Cluster 2
(n=38) marks the high growth pathway.
 For 1997-98 (n=871) – Cluster 6 (n=265), Cluster 5 (n=159) and Cluster 4 (n=198) mark the low growth
pathway; Cluster 3 (n=121) and Cluster 1 (n=81) mark the moderate growth pathway; and Cluster 2
(n=47) marks the high growth pathway.
The only cluster not so far assigned to a development pathway is Cluster 3 (n=10) in the data for 1994-95. The
difficulty here is that, in 1994-95, all three pathways appear to commence in Cluster 3. The approach taken was
to retrospectively assign businesses in Cluster 3 to pathways in 1994-95 according to which pathway they
appeared to lie on in 1995-96. Ultimately then, for each year of the panel data, each business is seen as being on
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one only of the three SME development pathways identified. Relative frequency distributions for each pathway
in each year are presented in Table 3.
The second step in characterisation of the three apparent SME development pathways in the panel data
was to construe a single dominant pathway for each business over the four years of data, as follows:
 Businesses lying on the same pathway in all four years were simply assigned to the pathway concerned.
These businesses account for 52.4 per cent (n=456) of the panel sample (n=871).
 Businesses lying on the same pathway in three of the four years (that is, only changed pathways once)
were assigned to the dominant pathway. These businesses, together with those that did not change
pathways at all, account for 88.6 per cent (n=772) of the panel sample (n=871).
 Businesses that changed pathways twice in the four years were assigned to a dominant pathway using
researcher judgement. These businesses account for just 11.4 per cent (n=99) of the panel sample
(n=871). Details of the assignment rules applied consistently to these businesses are available from the
author. By way of example, a business on the same pathway in the last two years of the panel data was
assigned to that pathway regardless of its position in the first two years. It is acknowledged that such rules
could be seen as somewhat arbitrary. However, given the small proportion of businesses for which
researcher judgement had to be exercised in this manner, it is believed that the overall findings of the
research have not been unduly compromised.
Note that, since there are only three development pathways identified in the research, there could be no
businesses in the panel data that lay on different pathways in all four years. The relative frequency distribution
for each dominant pathway over all four years in the panel data is presented at the bottom of Table 3. The low
growth development pathway appears to account for around 70 per cent of SMEs in the panel. The moderate
growth pathway seems to be followed by around 25 per cent of the panel. And around 5 per cent of the panel
look to lie on the high growth pathway, which is in accord with the observed rarity of substantial growth
amongst SMEs world-wide (McMahon et al., 1993).
Median or mean values for certain characterisation variables (enterprise age, total employment, annual
sales, total assets, compound annual employment growth, and compound annual sales growth) for the three
identified SME development pathways are presented in Table 4. All values shown are entirely consistent with
the existence of low, moderate and high growth development pathways amongst SMEs in the panel. When
assessing the implications of these values, it should be recalled that the BLS CURF is restricted to business units
employing fewer than 200 persons. Thus, enterprise size is constrained at the larger end of the scale. At the
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smaller end, being manufacturing SMEs, they could be expected to have more employees than concerns in other
industrial sectors such as service or trading. In Table 5, 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 development pathways. Differences between the identified SME development pathways in terms
of enterprise age, size and growth variables are highly significant in a statistical sense.
Conclusions and Recommendations
Further scholarly inquiry certainly seems to be encouraged by this multivariate analysis of data from Australia’s
Business Longitudinal Survey to derive an empirically-based development taxonomy for manufacturing SMEs.
Three relatively stable development pathways are discernible in the longitudinal panel results – low, moderate
and high growth. Highly significant differences between the identified SME development pathways in terms of
key enterprise age, size and growth variables underpin confidence in this development taxonomy. Furthermore, it
would appear that the development pathways and the pace of SME development in the present research match
reasonably well with those in earlier research of a similar nature undertaken by Hanks et al. (1993). Both
development models seem to lead towards the same range of SME configurations that are widely recognised in
the relevant research literature (McMahon et al., 1993):
 Traditional or life-style SMEs – following the low growth development pathway, these concerns
generally have few, if any, growth aspirations. They principally exist to provide their owner-managers
with a source of employment and income. Furthermore, they are frequently operated in a manner
consistent with the life-style aspirations of their owner-managers. The present research suggests that after
approximately 15 years such SMEs would have fewer than 20 employees, sales less than $3 million per
annum, total assets below $2 million, little or no employment growth, and sales growth up to 5 per cent
per annum.
 Capped growth SMEs – following the moderate growth development pathway, these concerns generally
have modest growth aspirations. Bounds to growth could be externally imposed by the nature of their
competitive environment; or may be intrinsic given the nature of their operations. Frequently though,
growth is deliberately capped by owner-managers to a rate that limits dependence upon external financing
– thus minimising surrender of control and accountability obligations this support would normally bring.
The present research suggests that after approximately 15 years such SMEs would have fewer than 100
employees, sales around $10 million per annum, total assets less than $10 million, employment growth up
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to 3 per cent per annum, and sales growth as much as 10 per cent per annum. At the current stage of this
research, it is not possible to discern whether the restricted growth of these SMEs is externally imposed,
intrinsic to their operations, or elective on the part of their owner-mangers.
 Entrepreneurial SMEs – following the high growth development pathway, these concerns generally have
ambitious growth aspirations. They are most often associated with entrepreneurial aptitude, technical and
commercial innovation, international outlook, and other business qualities that could see them eventually
become large enterprises. The present research suggests that after approximately 15 years such SMEs
would have over 100 employees, sales around $30 million per annum, total assets more than $20 million,
employment growth exceeding 5 per cent per annum, and sales growth greater than 10 per cent per
annum.
The fact that these common SME configurations are recognised in this research lends further plausibility to the
empirically-based development taxonomy described.
The following recommendations are made to shape the direction and specifics of further research effort to
characterise and employ the empirically-based development taxonomy for Australian manufacturing SMEs
presented in this paper:
 Using the SME development taxonomy data now available for learning purposes, employ ordinal
polytomous logistic regression to construct a model that would allow manufacturing SMEs to be reliably
classified as to their most likely future development pathway on the basis of measured values for key
enterprise age, size and growth variables. Other characteristic variables included in the BLS CURF could
also be added to such a model to enhance its reliability.
 Using the excellent financial data provided in the BLS CURF, produce financial profiles for
manufacturing SMEs on different development pathways employing conventional measures (such as
financial ratios) reflecting financial position and performance. A major focus here would be linkages
between growth and performance in manufacturing SMEs. It should also be possible to examine
connections between growth and experience of certain financial problems such as overtrading, liquidity
crises and inappropriate financing.
 Take advantage of the numerous opportunities that now exist for comparing and contrasting
manufacturing SMEs on different development pathways in terms of their demographic characteristics,
ownership structures, business aspirations, management practices, industrial arrangements, training
15
commitment, international orientation, innovation potential, networking activities, etc., etc. A major focus
here would be identification of correlates with SME success in growth and performance terms.
Clearly, it is also possible to extend the methods employed in this research to SMEs in other industrial sectors,
such as service and trading, so that a range of industry-specific development taxonomies become available for
use. The potential benefits of this and the other lines of inquiry identified above to SME scholarship, policymaking, support and management are readily apparent.
Acknowledgments
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.
Responsibility for interpretation of the findings lies solely with the author.
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18
Table 1: K-MEANS CLUSTER ANALYSIS ANOVAs
Cluster
Annual
Standardised
Collection
Variable
Error
Mean
Mean
Univariate
Square
df
Square
df
F Ratio
Significance
246.540
5
0.302
1776
816.578
0.000
196.103
5
0.237
1776
826.392
0.000
87.750
5
0.152
1776
576.985
0.000
24.195
5
0.110
1776
220.043
0.000
23.994
5
0.043
1776
562.977
0.000
110.431
6
0.233
864
473.535
0.000
94.499
6
0.178
864
529.682
0.000
33.406
6
0.126
864
264.591
0.000
11.471
6
0.116
864
98.849
0.000
9.435
6
0.033
864
289.259
0.000
124.609
5
0.276
865
451.750
0.000
95.210
5
0.258
865
368.800
0.000
40.017
5
0.176
865
227.906
0.000
37.048
5
0.293
865
126.369
0.000
58.940
5
0.395
865
149.373
0.000
Z Score
Enterprise Age
Z Score
Total Employment
1994-95
(n=1,782)
Z Score
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
Z Score
Enterprise Age
Z Score
Total Employment
1994-95
Z Score
(n=871)
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
Z Score
Enterprise Age
Z Score
Total Employment
1995-96
Z Score
(n=871)
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
19
Table 1 (Continued): K-MEANS CLUSTER ANALYSIS ANOVAs
Cluster
Annual
Standardised
Collection
Variable
Error
Mean
Mean
Univariate
Square
df
Square
df
F Ratio
Significance
126.638
5
0.263
865
480.725
0.000
107.443
5
0.191
865
562.539
0.000
52.375
5
0.129
865
405.053
0.000
51.346
5
0.352
865
146.036
0.000
2.677
5
0.248
865
10.792
0.000
125.431
5
0.269
865
467.078
0.000
118.732
5
0.166
865
713.703
0.000
51.218
5
0.150
865
341.691
0.000
4.282
5
0.236
865
18.123
0.000
42.069
5
0.408
865
103.109
0.000
Z Score
Enterprise Age
Z Score
Total Employment
1996-97
Z Score
(n=871)
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
Z Score
Enterprise Age
Z Score
Total Employment
1997-98
Z Score
(n=871)
Annual Sales
Z Score
Annual Employment
Growth
Z Score
Annual Sales
Growth
20
Table 2: 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=1,782)
Statistic
df
Significance
1262.609
810.266
717.380
656.942
270.555
146.377
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
590.702
607.234
537.221
493.634
62.733
65.594
6
6
6
6
6
6
0.000
0.000
0.000
0.000
0.000
0.000
567.429
396.264
342.781
316.240
269.031
255.945
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
557.679
515.476
470.551
430.941
301.177
49.904
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
561.021
553.359
487.205
442.274
80.407
360.007
5
5
5
5
5
5
0.000
0.000
0.000
0.000
0.000
0.000
KruskalWallis
1994-95
Statistic
(n=871)
df
Significance
KruskalWallis
1995-96
Statistic
(n=871)
df
Significance
KruskalWallis
1996-97
Statistic
(n=871)
df
Significance
KruskalWallis
1997-98
Statistic
(n=871)
df
Significance
21
Table 3: RELATIVE FREQUENCY DISTRIBUTIONS FOR SME DEVELOPMENT PATHWAYS
Low
Moderate
High
Annual
Frequency
Growth
Growth
Growth
Collection
Measure
Pathway
Pathway
Pathway
1994-95
Total
Frequency
576
255
40
871
Per Cent
66.1
29.3
4.6
100.0
Per Cent
64.6
95.4
100.0
Frequency
564
246
61
871
Per Cent
64.8
28.2
7.0
100.0
Per Cent
64.8
93.0
100.0
Frequency
545
288
38
871
Per Cent
62.6
33.1
4.4
100.0
Per Cent
62.6
95.6
100.0
Frequency
622
202
47
871
Per Cent
71.4
23.2
5.4
100.0
Per Cent
71.4
94.6
100.0
Frequency
629
203
39
871
Per Cent
72.2
23.3
4.5
100.0
72.2
95.5
100.0
Cumulative
1995-96
Cumulative
1996-97
Cumulative
1997-98
Cumulative
1994-98
Cumulative
Per Cent
22
Table 4: CHARACTERISATION VARIABLES FOR SME DEVELOPMENT PATHWAYS
Low
Moderate
High
Characterisation
Growth
Growth
Growth
Variable
Pathway
Pathway
Pathway
Enterprise age median
for 1997-98 (years)
12 to 14
16 to 18
12 to 14
16.9
64.7
123.5
2.5
11.0
30.5
1.6
7.2
22.9
-0.2
2.4
6.6
5.3
9.3
10.4
Total employment mean
for 1997-98 (persons)
Sales mean
for 1997-98 ($million per annum)
Total assets mean
for 1997-98 ($million)
Compound employment growth
for 1994-95 to 1997-98 (% per annum)
Compound sales growth
for 1994-95 to 1997-98 (% per annum)
23
Table 5: KRUSKAL-WALLIS TESTS FOR SME DEVELOPMENT PATHWAY
CHARACTERISATION VARIABLES
Compound
Compound
Annual
Annual
Test
Enterprise
Total
Annual
Total
Employment
Sales
Details
Age
Employment
Sales
Assets
Growth
Growth
KruskalWallis
Statistic
df
Significance
15.634
475.407
418.986
357.454
21.121
17.746
2
2
2
2
2
2
0.000
0.000
0.000
0.000
0.000
0.000
24
Figure 1: Hanks et al. (1993) 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
25
Figure 2: Business Longitudinal Survey 1994-95 Enterprise Life-Cycle Model 1 (n=1,782)
ENTERPRISE AGE (YEARS)
CLUSTER 4 (n=25)
Enterprise age median:
2 to 4 years
Total employment mean:
13.5 persons
Sales mean:
$2.8 million p.a.
Total assets mean:
$1.2 million
Employment growth mean:
18.6% p.a.
Sales growth mean:
395.7% p.a.
CLUSTER 3 (n=771)
Enterprise age median:
6 to 8 years
Total employment mean:
16.3 persons
Sales mean:
$2.2 million p.a.
Total assets mean:
$1.4 million
Employment growth mean:
0.0% p.a.
Sales growth mean:
14.7% p.a.
MODERATE
GROWTH?
LOW GROWTH?
CLUSTER 2 (n=82)
Enterprise age median:
6 to 8 years
Total employment mean:
16.7 persons
Sales mean:
$1.7 million p.a.
Total assets mean:
$1.1 million
Employment growth mean:
70.5% p.a.
Sales growth mean:
37.0% p.a.
HIGH GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 1 (n=544)
Enterprise age median:
18 to 20 years
Total employment mean:
15.9 persons
Sales mean:
$1.9 million p.a.
Total assets mean:
$1.1 million
Employment growth mean:
-1.0% p.a.
Sales growth mean:
6.7% p.a.
MODERATE GROWTH?
CLUSTER 5 (n=123)
Enterprise age median:
8 to 10 years
Total employment mean:
97.7 persons
Sales mean:
$19.8 million p.a.
Total assets mean:
$13.3 million
Employment growth mean:
7.1% p.a.
Sales growth mean:
25.3% p.a.
CLUSTER 6 (n=237)
Enterprise age median:
24 to 26 years
Total employment mean:
60.3 persons
Sales mean:
$7.8 million p.a.
Total assets mean:
$5.3 million
Employment growth mean:
5.5% p.a.
Sales growth mean:
11.8% p.a.
26
Figure 3: Business Longitudinal Survey 1994-95 Enterprise Life-Cycle Model 2 (n=871)
ENTERPRISE AGE (YEARS)
LOW GROWTH?
CLUSTER 3 (n=10)
Enterprise age median:
2 to 4 years
Total employment mean:
22.3 persons
Sales mean:
$5.0 million p.a.
Total assets mean:
$1.6 million
Employment growth mean:
14.5% p.a.
Sales growth mean:
565.8% p.a.
CLUSTER 7 (n=364)
Enterprise age median:
6 to 8 years
Total employment mean:
11.4 persons
Sales mean:
$1.3 million p.a.
Total assets mean:
$0.8 million
Employment growth mean:
6.0% p.a.
Sales growth mean:
22.5% p.a.
CLUSTER 2 (n=16)
Enterprise age median:
8 to 10 years
Total employment mean:
18.1 persons
Sales mean:
$2.8 million p.a.
Total assets mean:
$1.4 million
Employment growth mean:
124.7% p.a.
Sales growth mean:
39.5% p.a.
CLUSTER 4 (n=204)
Enterprise age median:
24 to 26 years
Total employment mean:
25.9 persons
Sales mean:
$3.5 million p.a.
Total assets mean:
$2.3 million
Employment growth mean:
2.0% p.a.
Sales growth mean:
8.8% p.a.
MODERATE
GROWTH?
MODERATE
GROWTH?
HIGH
GROWTH?
LOW GROWTH?
CLUSTER 1 (n=40)
Enterprise age median:
6 to 8 years
Total employment mean:
112.4 persons
Sales mean:
$24.5 million p.a.
Total assets mean:
$18.0 million
Employment growth mean:
12.3% p.a.
Sales growth mean:
26.1% p.a.
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 5 (n=156)
Enterprise age mode:
10 to 12 years
Total employment mean:
48.9 persons
Sales mean:
$7.2 million p.a.
Total assets mean:
$4.6 million
Employment growth mean:
6.8% p.a.
Sales growth mean:
16.0% p.a.
MODERATE GROWTH?
CLUSTER 6 (n=81)
Enterprise age median:
28 to 30 years
Total employment mean:
88.9 persons
Sales mean:
$12.7 million p.a.
Total assets mean:
$8.7 million
Employment growth mean:
4.8% p.a.
Sales growth mean:
17.7% p.a.
27
Figure 4: Business Longitudinal Survey 1995-96 Enterprise Life-Cycle Model (n=871)
ENTERPRISE AGE (YEARS)
LOW GROWTH?
MODERATE
GROWTH?
HIGH GROWTH?
CLUSTER 2 (n=358)
Enterprise age median:
8 to 10 years
Total employment mean:
17.9 persons
Sales mean:
$2.6 million p.a.
Total assets mean:
$1.6 million
Employment growth mean:
-10.1% p.a.
Sales growth mean:
-3.5% p.a.
CLUSTER 6 (n=83)
Enterprise age median:
8 to 10 years
Total employment mean:
22.2 persons
Sales mean:
$2.5 million p.a.
Total assets mean:
$1.4 million
Employment growth mean:
48.5% p.a.
Sales growth mean:
11.4% p.a.
CLUSTER 1 (n=61)
Enterprise age median:
8 to 10 years
Total employment mean:
100.8 persons
Sales mean:
$22.4 million p.a.
Total assets mean:
$15.4 million
Employment growth mean:
4.3% p.a.
Sales growth mean:
13.6% p.a.
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
LOW GROWTH?
CLUSTER 5 (n=72)
Enterprise age median:
10 to 12 years
Total employment mean:
21.4 persons
Sales mean:
$3.8 million p.a.
Total assets mean:
$2.5 million
Employment growth mean:
10.8% p.a.
Sales growth mean:
56.9% p.a.
CLUSTER 4 (n=206)
Enterprise age median:
24 to 26 years
Total employment mean:
25.6 persons
Sales mean:
$3.6 million p.a.
Total assets mean:
$2.3 million
Employment growth mean:
-4.0% p.a.
Sales growth mean:
0.0% p.a.
MODERATE GROWTH?
CLUSTER 3 (n=91)
Enterprise age median:
28 to 30 years
Total employment mean:
83.9 persons
Sales mean:
$12.6 million p.a.
Total assets mean:
$9.3 million
Employment growth mean:
2.8% p.a.
Sales growth mean:
6.0% p.a.
28
Figure 5: Business Longitudinal Survey 1996-97 Enterprise Life-Cycle Model (n=871)
ENTERPRISE AGE (YEARS)
LOW GROWTH?
MODERATE GROWTH?
CLUSTER 1 (n=338)
Enterprise age median:
8 to 10 years
Total employment mean:
12.7 persons
Sales mean:
$1.7 million p.a.
Total assets mean:
$1.1 million
Employment growth mean:
-8.8% p.a.
Sales growth mean:
0.8% p.a.
CLUSTER 4 (n=105)
Enterprise age median:
8 to 10 years
Total employment mean:
22.6 persons
Sales mean:
$2.5 million p.a.
Total assets mean:
$1.3 million
Employment growth mean:
45.3% p.a.
Sales growth mean:
17.5% p.a.
LOW GROWTH?
MODERATE
GROWTH?
CLUSTER 6 (n=109)
Enterprise age median:
10 to 12 years
Total employment mean:
55.9 persons
Sales mean:
$10.3 million p.a.
Total assets mean:
$6.7 million
Employment growth mean:
-1.5% p.a.
Sales growth mean:
6.9% p.a.
HIGH GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 5 (n=207)
Enterprise age median:
26 to 28years
Total employment mean:
25.0 persons
Sales mean:
$3.6 million p.a.
Total assets mean:
$2.7 million
Employment growth mean:
-3.1% p.a.
Sales growth mean:
-2.0% p.a.
MODERATE GROWTH?
CLUSTER 2 (n=38)
Enterprise age median:
12 to 14 years
Total employment mean:
123.3 persons
Sales mean:
$30.0 million p.a.
Total assets mean:
$21.6 million
Employment growth mean:
10.2% p.a.
Sales growth mean:
10.1% p.a.
CLUSTER 3 (n=74)
Enterprise age median:
28 to 30 years
Total employment mean:
81.2 persons
Sales mean:
$12.9 million p.a.
Total assets mean:
$9.0 million
Employment growth mean:
3.9% p.a.
Sales growth mean:
7.3% p.a..
29
Figure 6: Business Longitudinal Survey 1997-98 Enterprise Life-Cycle Model (n=871)
ENTERPRISE AGE (YEARS)
LOW GROWTH?
LOW GROWTH?
MODERATE GROWTH?
ENTERPRISE SIZE
(EMPLOYMENT, SALES, TOTAL ASSETS)
CLUSTER 6 (n=265)
Enterprise age median:
10 to 12 years
Total employment mean:
12.9 persons
Sales mean:
$1.6 million p.a.
Total assets mean:
$1.0 million
Employment growth mean:
-5.0% p.a.
Sales growth mean:
-8.4% p.a.
CLUSTER 5 (n=159)
Enterprise age median:
10 to 12 years
Total employment mean:
14.2 persons
Sales mean:
$2.2 million p.a.
Total assets mean:
$1.1 million
Employment growth mean:
13.9% p.a.
Sales growth mean:
30.3% p.a.
CLUSTER 3 (n=121)
Enterprise age median:
10 to 12 years
Total employment mean:
54.3 persons
Sales mean:
$10.2 million p.a.
Total assets mean:
$6.3 million
Employment growth mean:
4.3% p.a.
Sales growth mean:
6.4% p.a.
HIGH GROWTH?
LOW GROWTH?
LOW GROWTH?
CLUSTER 4 (n=198)
Enterprise age median:
26 to 28years
Total employment mean:
22.7 persons
Sales mean:
$3.5 million p.a.
Total assets mean:
$2.4 million
Employment growth mean:
-2.1% p.a.
Sales growth mean:
1.4% p.a.
MODERATE GROWTH?
CLUSTER 2 (n=47)
Enterprise age median:
18 to 20 years
Total employment mean:
127.9 persons
Sales mean:
$28.5 million p.a.
Total assets mean:
$20.9 million
Employment growth mean:
13.0% p.a.
Sales growth mean:
9.5% p.a.
CLUSTER 1 (n=81)
Enterprise age median:
more than 30 years
Total employment mean:
71.9 persons
Sales mean:
$11.4 million p.a.
Total assets mean:
$8.1 million
Employment growth mean:
1.6% p.a.
Sales growth mean:
3.4% p.a..