Small and Medium Enterprises (SMEs): Validating Life Cycle Stage Determinants PDF Free Download
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Small and Medium Enterprises (SMEs): Validating Life
Cycle Stage Determinants
Dr Peter N Kiriri
Lecturer in Entrepreneurship and Management
Strathmore University
PO Box 59857, 00200, Nairobi
Kenya
Email: pkiriri@strathmore.edu
Abstract
Over the years studies have examined life cycle stages with a bias towards large organisations
and high-technology firms in growth industries. However, studies on SME life cycles are
limited and most of those available have had a theoretical underpinning in determining life
cycle stages and their determinants. Organisations and business enterprises are presumed to
face different crises and managerial problems and issues at different stages of growth. It is
therefore imperative that in order to address such crises, problems and issues, entrepreneurs
and management should understand how to determine the life cycle stage of their enterprises.
In research conducted in Northern New South Wales, Australia using a sample of 156 small
and medium enterprises, life cycle stage determinants are evaluated in an effort to empirically
validate the determinants as ideal for indicating/deriving enterprise life cycle stages.
Enterprise age, size and rate of growth emerge as ideal life cycle stage determinants.
Keywords: Life cycle stages, stages of growth, stages of development, stage determinants,
Small and Medium Enterprises (SMEs), Australia.
Introduction
Small and medium enterprises (SMEs) form significant sectors in most world economies and
therefore their development is regarded as an important issue for most governments. The
significance of the sector is due to the contribution made to job and wealth creation in the
world economies (Hodgetts & Kuratko 1995). According to the International Labour
Organisation (1986) the sector accounts for over 40 percent of private sector employment. In
Australia, SMEs produce about one-third of total Gross Domestic Product of the economy
(Meredith 2000) and represent about 85.5% of all Australian enterprises including public
trading and general government organisations (Office of Small Business 1999). In the United
States of America, small enterprises employ 57 percent of the private sector workforce and
produce approximately 45 percent of the nation’s gross national product (Helms & Renfrow
1994). Contributions to world economies by SMEs include:
• Providing opportunities for innovation and breeding grounds for new business
ventures (Meredith 1988)
• Producing national output and exports (Perry & Pendleton 1990)
• Acting as a transfer agent from big business to the consumers of products and services
(Perry & Pendleton 1990)
• Helping curb rural-urban migration through decentralisation and employment
generation (Republic of Kenya 1992)
• Creation of competition (amongst small businesses and with large enterprises) and
outlets for entrepreneurial activities (Meredith 1988).
Although SMEs are a vital part of most economies, there has been much concern about
failure. This has led researchers to examine causes of the high SME mortality and suggest
possible solutions to success. For the success of any attempts to prescribe a diagnosis to
SMEs problems there is a need for entrepreneurs to understand problems facing their
enterprises. Different problems will face an enterprise depending on the stage of development
of the enterprise (Vozikis & Glueck 1980; Kazanjian 1984; Terpstra & Olson 1993; Quinn &
Cameron 1983). Research findings, on the other hand, encourage entrepreneurs to determine
the stage of development their firm has reached, so that a strategic disadvantage (advantage)
profile can be drawn based on the problems experienced at various stages (Vozikis & Glueck
1980). In so doing, entrepreneurs have a valid empirical framework that helps them determine
stage of growth of their enterprises and then proceed to address the various problems faced at
the relevant stage.
Authors and academics have engaged in extensive theorizing and conceptualisation on stages
of development theory with very few empirical studies to date. In this light, this study
undertakes to empirically derive a life cycle stage model determinant specifically for small
and medium enterprises. Previous empirical research on life cycle stages have been based on
large organisations or high technology firms (Hanks, Watson, Jansen & Chandler 1993;
Kazanjian & Drazin 1990; Miller & Friesen 1984a; Quinn & Cameron,1983) while very few
have been conducted on small and medium enterprises. However, there abound theoretical
studies on small and medium enterprises life cycles (Churchill 1983: Churchill & Lewis 1983;
Cooper 1979; Scott & Bruce 1987; Vozikis & Glueck 1980; Steinmetz 1969).
Life cycle stages represent different phases in the life of an enterprise. Lack of an explicit
definition of a life cycle stage in the literature lead Hanks et al. (1993) to explore stage
descriptions and derive a life cycle stage definition as a unique configuration of variables
related to organisation context and structure. Life cycle stage models have been referred
using different names by authors with no effort in the literature to distinguish between such
terms as life cycle, growth stages or developmental stages (Hanks et al. 1993). Several authors
have used these terms interchangeably and this research study follows this trend.
Though the models of growth assume that entrepreneurs motives are growth not all
entrepreneurs are for growth. The main reason as to why many do not want growth is
connected to the founders attitudes towards growth and control (Gray 1993). Researchers do
agree that firms do not necessarily have to follow the growth pattern from pre-start to decline.
However, for the purpose of this study it is assumed that firms will be started and progress
through the stages before getting to the decline stage.
Importance of life cycle stage models for SMEs
Entrepreneurs strive to understand stages of development, in which their enterprises operate,
since growth models are used as diagnostic tools in analysing a firm’s present position and to
plan what will be required as the firm progresses from one stage to the next (growth process)
in the life cycle (Kazanjian & Drazin 1990; Scott & Bruce 1987). Life cycle models can be
used as effective predictive tool for long range planning (Scott & Bruce 1987; Barrie 1974).
As management understands the issues, challenges (current and future) and problems at each
stage, plans and strategies are reviewed to prepare for the future (Churchill & Lewis 1983).
Such a framework helps anticipate key requirements at various points such as time
commitment for owners during the start up period and the need for delegation and changes in
managerial roles when enterprises expand (Churchill & Lewis 1983). Accountants,
consultants, government bodies, funding agencies and other interested parties in the
development of SMEs, use the life cycle framework to diagnose problems and match
solutions for SMEs (Churchill & Lewis 1983; Kiriri 2002). Since transition from one stage to
another requires change accompanied by some crisis, management uses the framework to be
proactive rather than reactive to the changes and crises (Scott & Bruce 1987).
Theoretical considerations
Researchers and academicians who have studied life cycle stages have used various
parameters to determine specific stages. Some of the determinants of stage used include
enterprise age, size, rate of growth, enterprise structure including inherent key management
issues or problems. Researchers have tended to use a combination of the determinants rather
than individual determinants.
Enterprise Size: Size of the enterprise is judged by some to be a robust parameter in
measuring a firm’s life cycle stage (Barrie 1974; Greiner 1972). Size is generally determined
by sales and/or number of employees (Timmons 1994). Enterprises begin as simple firms with
a single product, function and a single region but successfully adapt to multiple regions,
functions and finally multiple products as the enterprise moves from launch to growth,
thereby increasing in size (Galbraith & Nathanson 1979). Barrie (1974) contends that sales
and profits as functions of size are the only ideal factors to determine a life cycle curve since
these data are tangible and meaningful and reflect the fortunes of an enterprise. In comparing
sales and profits of an enterprise over a period of time and plotting the trend, a clear
indication of growth emerges.
Enterprise Age: Another school of thought determines life cycle stages by age. The number
of years an organisation has been in existence determines life cycle stage (Timmons 1994).
However age has been criticised by several researchers as an ideal determinant of life cycle
stage. According to Churchill & Lewis (1983), age of an organisation is unlikely to provide a
valid reflection of life cycle stage on its own. An enterprise as a result of lack of start-up
planning may be in the introduction stage for as long as the enterprise is able to overcome
challenges in this stage and start growing, this may be for up to seven years (Timmons 1994).
On the other hand, a new enterprise in a desired environmental munificence situation (extent
to which an environment can support a new enterprise and enable it to grow and prosper)
grows faster than one faced with a difficult environment (Castrogiovanni 1991). Despite the
two enterprises having started operations at the same time, the former will progress faster than
the latter through growth stages.
The time within each stage cannot be predicted since each stage is faced by different
managerial, economic, socio-political and environmental situations and challenges (Barrie,
1974; Churchill & Lewis 1983; Greiner 1972). Progress to a further stage depends on how an
enterprise copes with situations and challenges inherent in a particular life cycle stage
(Greiner 1972).
Enterprise Rate of Growth: A calculated rate of growth of the enterprise has been used by
some researchers to determine the life cycle stage. It is expected that growth rate would differ
between life cycle stages. This measure has been used in previous research by Hanks et al.
1993; Kazanjian 1984; Kazanjian & Drazin 1990; and McMahon 2000.
Key Enterprise Issues/Problems: In order to determine stages empirically, an analysis of the
key issues or problems faced by the firms can indicate life cycle stage (Kazanjian 1988).
These problems occur sequentially for most ventures and tend to cluster together in such a
way as to define stages that enterprises must pass through to become viable (Kazanjian &
Drazin 1990). Scholars have argued that as firms move through various life cycle stages,
differing problems are addressed, resulting in the need for different management skills,
priorities, and structural configurations (Adizes 1989; Chandler 1962; Greiner 1972;
Kazanjian 1988; Kimberly & Miles 1980; Quinn & Cameron 1983). As it would be expected,
differing size of enterprises experience common problems arising at similar stages in their
development (Churchill & Lewis 1983). Each stage of development in the life cycle therefore
presents a period of transition and unique sets of problems for management (Greiner 1972;
Terpstra & Olson 1993).
The resolution of one set of problems or key management issues leads to the emergence of
another set of problems and key issues which means no problem ever completely disappears,
some become more salient than others. Failure to address the issues by adapting the firm’s
systems and processes leads to growth crises (Adizes 1989; Greiner 1972) and ‘growing
pains’ (Flamholtz 1990) which ultimately result to stalling or thwarting the growth process
(Hanks et al. 1993). The dominant set of management issues that the firm faces at any point in
time are the key issues which occupy management’s attention, and define the life cycle stage
a venture is in and the critical contingencies that must be solved (Kazanjian & Drazin 1990).
For this study, after a conclusive literature review and analysis of issues facing most
enterprises, a list of 25 key business issues were arrived at. The 25 issues incorporate 17 of
those developed by Kazanjian (1988). Since the study by Kazanjian did not address the pre-
start and decline/regeneration phases the addition eight issues included did address issues
facing most businesses in these two stages of growth.
Enterprise Structural dimensions: Based upon Chandler’s (1962) thesis that enterprises
develop structural patterns in response to common growth and market challenges, authors
have built their model determinants from this (Hanks et al. 1993). Enterprise structural
dimensions have been identified as enterprise structural form, formalisation, centralisation,
vertical differentiation and number of enterprise levels (Hanks et al. 1993). Enterprise
structures and key management issues influence each other and give rise to a small number of
extremely common configurations representing common structures, common scenarios of
strategy-making in context and even common developmental or transitional sequences (Hanks
et al. 1993; Miller & Friesen 1984a; Miller & Friesen 1984b). Thus, life cycle stages can be
determined and characterised on the basis of the configurations of variables related to
structure and context (Hanks et al. 1993).
Number of stages
Authors and researchers have suggested various numbers of stages that a business may go
through (Churchill & Lewis 1983; Hanks et al. 1993). The number of stages generally varies
between 3 and 5 and at time extends to 10 or beyond (Stanworth & Curran 1976). Kazanjian
(1984) noted some twenty different life cycle stages or development models with different
numbers of stages and enterprise elements addressed. Greiner (1972), Churchill & Lewis
(1983), Miller & Friesen (1984a) Scott & Bruce (1987) and Galbraith (1982) suggest five
stages of growth in their models. Barrie (1974), Kazanjian (1984), Kazanjian (1988), and
Quinn & Cameron (1983) suggest four stages while other researchers like Cooper (1979) and
Smith, Mitchel & Summer (1985) suggest three stages. Flamholtz (1986) suggested a seven-
stage model and Adizes (1989) had a model made up of ten life cycle stages.
Ideal stage determinants
There seems to be no agreed method of determining the developmental stages of an
enterprise, as researchers have tended to support that which suits their area of study and that
which helps answer the questions asked by a researcher (Galbraith & Nathanson 1979). Each
of the determinants has a weakness and as such there is no absolute determinant (Churchill &
Lewis 1983). Barrie (1974) suggests the use of hindsight rather than a current vision of the
situation to determine where the company is, by predicting the next stage and working
backwards. Kiriri (2002) in a literature review on stage determinants found that the most
common stage determinants by most researchers and stage theorists were age, size, rate of
growth and key management issues in the business.
Kazanjian (1984) suggests most of the life cycle stage models primarily provide a description
of structure while very few models focus on non-structural characteristics of the firm at
different stages. In criticising the models, Kazanjian (1984, pp145) notes:
‘In almost all the cases though, the stages tend to be defined in terms of internal
characteristics, resulting in a tautology of sorts whereby stages define internal
characteristics, which define stages. They provide little understanding as to why those
characteristics emerge. Is stage of growth simply a descriptive concept or does it
represent something more? Secondly, many models are inflexible and offered in a
universalistic fashion such that all organisations must proceed through all stages in
sequence. Finally, the work related to stage of growth has been overwhelmingly
conceptual with few if any empirical studies to date’.
However, one common characteristic that emerges from most of the models is that firms pass
through a sequence of stages whereby they face a myriad of problems and challenges and
progression to the next stage highly dependent on how successful the firm was in addressing
the issues in the previous stage. All stages of growth models can be seen as what Starbuck
(1971) termed “metamorphosis models”, in that they describe problems that firms in differing
circumstances, are likely to encounter and the corresponding organisational forms likely to
result since, as the problems change, an organisation alters its form accordingly (Kazanjian
1988). Greiner (1972) explicitly viewed the growth of organisations as a series of evolutions
and revolutions precipitated by crises related to leadership, control and coordination. Table 1
captures and presents a comparison of the life cycle stage models, numbers of stages and
stage determinants by different researchers as adapted and modified from Hanks et al. (1993).
Based on a literature review, this study developed a theoretical model, which considered the
shortcomings of other models and took into consideration the nature and operations of SMEs.
The theoretical model derived takes cognisant note of the pre-start stage and decline/renewal
stage of an enterprise, which are often omitted by researchers. The pre-start stage is the basic
foundation on which any enterprise is made. The exclusion of the decline stage can aptly be
attributed to two characteristics of organisational decline according to Hanks et al. (1993).
The first is the impact of organisational structure and systems being far less predictable than
changes associated with growth. Secondly the onset of decline may actually occur at any stage
of the organisation (Hanks 1990; Miller and Friesen 1984a; Scott & Bruce 1987). Whetten
(1980) attributes this to the dominance of a growth paradigm among researchers and the
assumption of conditions of expansion. As an example, Whetten argues that there are very
few academic courses focusing on management in declining enterprises or under crisis
conditions. Model stages are pre-start, birth, growth, maturity and decline/renewal.
Model No. of stages Determinant
Adizes 1989) 10 Age;
Size;
Transitional problems
Churchill & Lewis (1983) 5 Age;
Size;
Growth rate;
Major strategies
Flamholtz (1986) 7 Age;
Size;
Growth rate;
Critical development tasks
Galbraith (1982) 5 Age;
Size;
Growth rate;
Tasks
Greiner (1972) 5 Age;
Size;
Growth rate;
Management focus
Kazanjian (1988) 4 Age;
Size;
Growth rate;
Dominant management problems
Miller & Friesen (1984b) 5 Age;
Number of employees;
Sales growth;
Size (relative to competitors;
Concentration of ownership;
Environmental situation;
Strategy variables
Smith, Mitchell & Summer
(1985) 3 Age;
Size (sales);
Size (Employees);
Growth rate;
Top management priorities
Quinn & Cameron (1983) 4 Age;
Size;
Criteria of organisational effectiveness
Scott & Bruce (1987) 5 Age;
Size;
Growth rate;
Industry stage;
Key issues
Table 1: Stage models, number of stages and determinants. Source: Adapted and modified from Hanks
et al. (1993)
Research questions
The primary objective of this study was to empirically validate determinants of life cycle
stages of an enterprise by providing a framework to which entrepreneurs and others could use
to classify enterprises so that various issues pertinent to each stage can be planned. In this
regard, the following research questions were explored:
Research Question 1: Does the key management issues or problems an enterprise face
determine the enterprise stage of development?
Research Question 2: Does the age of the enterprise (regardless whether the present owner
bought the enterprise as a going concern or established the enterprise) indicate the enterprise
stage of development?
Research Question 3: Does the size of an enterprise (in terms of total number of employees
and annual sales turnover) determine the enterprise stage of development?
Research Question 4: Can the enterprise rate of growth (growth in sales/employees current
year compared to previous year) indicate the enterprise stage of development?
Methodology
To validate the life cycle stage determinants, a quantitative research design was applied. This
research design was appropriate in describing and determining SME life cycle stages. The
research selected a sample from SMEs in the northern region of New South Wales, Australia
who consult with or are registered with Business Enterprise Centres (BECs). For the purpose
of the study, the following Australian Bureau of Statistics (ABS) definition of SMEs was used
(ABS 1999):
Micro enterprise: Those small enterprises employing fewer than 5 Full Time Equivalent
(FTE) persons;
Small enterprise: Those enterprises, which are not subsidiaries of another company and are
neither public company, unincorporated cooperatives nor incorporated associations and
employ less than 20 Full Time Equivalent (FTE) persons and;
Medium enterprise: Those enterprises that are not small businesses, but employ less than
200 people.
Ten (10) BECs out of 50 in the region of study were selected to provide the study sample of
350 SMEs. To select the sample, non-probability sampling was used, as there was need to
have sample units representing SMEs from the five life cycles stages. Convenience sampling,
as a nonprobability sampling technique was ideal as the method assisted in obtaining the most
convenient sample units for study. This technique helped in ensuring that the study sample
was a representation of firms in all stages of growth. For example, in order to ensure
representation of enterprises in the pre start-up stage, the sample was selected from those
firms that had not begun operations but had consulted with BECs for their start-up process.
Though convenience sampling provides a convenient and most readily accessible subjects,
this form of sampling has the greatest risk of bias. Due to the fact that subjects tend to be self-
selecting, this form of sampling is the weakest in terms of generalizability.
A pre-tested questionnaire was sent by mail to the respondents. Mail survey has been found
(compared to other methods) to be relatively cheap, providing much information very quickly
without the problems of interviewer bias and variability inherent in face-to-face techniques,
assuring respondent anonymity in gathering sensitive information, and allows data
verification where specific data are requested (Forsgren 1989). 350 questionnaires were sent
to respondents in the area of study through the BECs. The respondents answered the questions
and returned the answered questionnaires directly to the research centre. A total of 156
useable questionnaires were returned which indicated an acceptable response rate (44.6%).
The 156 returned questionnaires were analysed using standard statistical procedures. Idealised
statistical investigation includes three stages which include data preparation, preliminary and
descriptive analysis and analysis of relationships (Kervin 1992; Chatfield 1988). Initially a
descriptive sample characteristic is provided before classifying the enterprises in different life
cycle stages with further analysis to validate life cycle stages.
Sample enterprise characteristics
The analysis presented in Table 2 shows enterprise sample characteristics. Most of the SMEs
in the study were limited liability companies (40.4 percent), while partnership and sole
proprietorship represented 32.1 percent and 22.4 percent. Most of the enterprises had been in
business for more than 5 years (75.6 percent). The industry breakdown indicates an emphasis
towards wholesale/retail business at 45.5 percent, with service enterprises representing 30.1
percent and manufacturing and construction enterprises 14.1 percent and 5.8 percent
respectively. Approximately 81.4 percent of the enterprises had less than 10 employees.
Annual sales turnover levels were fairly evenly distributed with 17.3 percent reporting less
than A$ 100,000; 19.2 percent, between A$100,000 and A$ 300,000; 18.6 percent between
A$300,000 and A$ 500,000; and 39.7 percent over A$500,000.
Type of ownership
Sole Proprietorship 35 22.4
Limited Liability Company 63 40.4
Partnership 50 32.1
Corporation 8 5.1
Business age
Under 5 years 33 21.2
6-10 28 17.9
11-18 34 21.8
19-30 28 17.9
Over 31 28 17.9
No response 5 3.2
Type of industry
Construction 9 5.8
Services 47 30.1
Manufacturing 22 14.1
Wholesale/Retail 71 45.5
Other 7 4.5
No of employees
Under 5 92 59.0
6-10 35 22.4
11-20 19 12.2
Over 21 11 7.1
Sales turnover
Less than A$ 100,000 27 17.3
A$ 100,000 - A$ 300, 000 30 19.2
A$ 300,000 - A$ 500,000 29 18.6
Over A$ 500,000 62 39.7
No response 8 5.1
Table 2: Sample enterprise characteristics (n = 156). Source: Developed for this study.
Measurements
In order to address the research questions of this study, research analyses was performed in
two stages. Stage 1 set out to develop a base for theory building. The theory building process
sought to categorise respondents into different stages before addressing the research
questions. Stage 2 evaluated the measures developed from the literature review to validate the
outcome of stage 1 thereby addressing the research questions.
Stage 1 – Developing a base for theory building
Opinions of the respondents were sought on the present life cycle stage of their enterprises.
Two questions were asked on stages. One question provided a list of the stages and
respondents were asked to select a stage representing their enterprise. A graphical
representation of stages was presented for easier comprehension. The second question
required respondents to select from 5 descriptive statements, the one that best described their
current situation (self-categorisation as used by Kazanjian 1984). Each statement represented
a specific life cycle stage. The five life cycle stages were pre-start, launch, growth, maturity
and decline. To ensure that all the stages were represented in the sample of study, a deliberate
move in sampling was carried out to include potential entrepreneurs who had consulted with
BECs and attended workshops on how to start a business but were yet to begin business
operations.
Life cycle stage classification
In analysing the results, respondents were grouped into five life cycle stages. To validate the
selection, responses were cross-tabulated against sales turnover, business age and number of
employees’ previous year and current year. In Table 3, a classification of enterprises into life
cycle stages indicating the self-categorisation and the new classification after cross tabulation
is presented.
Description Self
classification New
classification Percent of new
classification
Pre-start 4 4 2.6
Launch 13 11 7.1
Growth 57 62 39.7
Maturity 72 69 44.2
Decline/regeneration 10 10 6.4
Total 156 156 100
Table 3: Life cycle stages respondent enterprises classification. Source: Developed for this study.
Stage 2: Validation of life cycle determinants
Key enterprise issues
Respondents were presented with 25 key enterprise issues facing their enterprises and were
required to rate them according to their perceived importance in their current enterprise on a
scale of 1-7. It was envisaged that the particular key issues faced at a given time would help
define a firm’s position in an enterprises life cycle (Kazanjian 1984). The scale used was
adapted from Kazanjian (1984) and modified for the current research. Seven new variables
were included to the 18 in Kazanjian’s scale in order to represent the five stages of growth
under review in this study. By classifying the enterprises into life cycle stages it was expected
that particular key enterprise issues would be more prevalent in some stages than others.
Enterprise age
The age of the business (in years) was established through a question asking the respondents
to indicate how long the enterprise had been operating. Number of years was regardless of
whether the owner had bought the enterprise as a going concern or had begun ‘from scratch’.
The rationale behind wanting to know the total number of years the business has been in
existence (regardless of ownership) was because age helps determine the stage in the business
life cycle and if respondents reported only how long they had owned/operated the business
disregarding prior years (when they did not own the business), the age given might not be a
clear indication of where the enterprise lies in the life cycle.
In examining age variable, the data attained nonnormal distribution. Since the data was to be
used for multivariate analysis it was a requirement that the data be transformed to normal
distribution (Hair et al. 1995; Stevens 1996). Age data was positively skewed and the most
effective transformation of positively skewed distributions is by taking logarithms (log) of the
variable (Hair et al. 1995). The log of age (AGElog) was calculated and used to achieve a
normal distribution so that the data could be used for further analysis. The log of this measure
minimises the effect of skewness in the distribution (Blau & Schoenherr 1971; Khandwalla
1977).
Enterprise size
The size of a business may be determined by either the total number of employees, total assets
or annual sales turnover. Preliminary investigations revealed that very few SMEs would
reveal their “worth” in terms of total assets while some owners did not know the value of their
business assets. For this reason a question on total assets was omitted from the questionnaire.
Total annual sales (as at 2000) and total employees (as at 2001) were used as a measure of
enterprise size. As for age data, the total number of employees’ data, was poorly distributed
(positively skewed) and as a result the log of total number employees (TOTEMPlog) was
used to transform the data. As for the age distribution above, the log helps minimise the effect
of skewness in the distribution of the total number of employees (Blau & Schoenherr 1971;
Khandwalla 1977).
Enterprise growth rate
The growth rate measure reflects an enterprise growth for the enterprise’s recent year of
performance. The growth rate is measured by annual employment growth, growth in total
assets and annual sales growth. As indicated, total assets figures were not collected in this
research while it was not possible to collect data on sales for 1999 and 2000. Small business
owners are averse to reporting actual sales figures and it was thought that any information on
sales would not have been a valid reflection of reality.
As Hanks et al. (1993) found, enterprise structural response is more closely related to number
of employees than sales and the disclosure rates of respondents was higher for employment
figures than sales figures. A question was asked of respondents to choose a range/category
that fitted their annual sales turnover for 2000, this however could not be used for computing
the rate of growth. The growth rate was therefore calculated using self reported employment
data, based on the following formula (Hanks et al. 1993):
Employee growth index = (Total Employees 2001 – Total Employees 2000)
Total Employees 2001
The above formula differs from the traditional formula expressed as:
Employee growth index = (Total Employees 2001 – Total Employees 2000)
Total Employees 2000
The difference in the two formulae is that the first portrays the change in employment as a
proportion of the current year (2001) employment, while the second portrays the change as a
proportion of the previous (2000) year’s employment. The first was used as it allows the
retention of new firms, which were not operating in 2000 in the analysis where the second
does not. The first formula while being acknowledged to be “unusual” was used by Hanks et
al. (1994) and McMahon (2000).
Results and discussion
Key enterprise issues
The respondents were presented with 25 key enterprise issues facing their enterprises and
were required to rate them according to perceived importance in their current enterprises on a
scale of 1-7. By classifying the enterprise into life cycle stages it was expected that particular
key enterprise issues would be prevalent in some stages more than others. The scale used to
collect data on key enterprise issues was tested on its reliability using Cronbach alpha as this
method requires only a single test administration and provides a unique estimate of reliability
for given test administration (Cronbach 1951; Carmines & Zeller 1979) attaining an
acceptable alpha of .9014.
A multivariate analysis of variance (MANOVA) was performed inputting the rates on all 25
variables and groupings on the life cycle stages. The expectation from the analysis was that
there would be marked differences in mean ratings on the 25 key enterprise issues by the
respondents at different life cycle stages. Preliminary assumption testing was conducted to
check for normality and homogeneity of variance-covariance matrices. The data was found fit
for multivariate analysis.
The analysis resulted in an overall significant finding of F = 1.293 and P < .067 suggesting
that there was no significant differences in key business issues ratings across the life cycle
stages. The respondents in the different life cycle stages therefore did not rate key enterprise
issues differently. It would however been expected that there would have been a difference in
means rating between the different life cycle stages on the 25 key enterprise issues (see
appendix for the key business issues means at different life cycle stages).
Salient variables
Investigation was conducted on the determination of life cycle stage by using age, size in
sales and number of employees and growth rate as salient variables. The variables helps
assess the construct validity of the life cycle stage grouping derived in Table 3 by
investigating the relationship between life cycle stages and the independent variables
(Kazanjian 1988).
The salient variables have been supported by various authors as determinants of life cycle
stages (Scott & Bruce 1987; Churchill & Lewis 1983; Timmons 1994). In analysing the
variables, correlation analysis was performed to show the relationship between life cycle stage
and the salient variables. Further, a one-way multivariate analysis of variance was conducted
to test whether there were any significant differences between variables and life cycle stages.
Table 4 shows the results of a pairwise correlation of enterprise age, size (sales/number of
employees) and growth rate with life cycle stage using Spearman rank order correlation
coefficient
AGElog Size
(sales) TOTEMPlog Growth
rate Life cycle
stage
AGElog
Sig. 1.000
. 0.483**
.000 0.290**
.000 -0.173*
.044 0.451**
.000
Size (sales)
Sig. 1.000
. 0.711**
.000 -0.111
.202 0.335**
.000
TOTEMlog
Sig. 1.000
. 0.152
.075 0.039
.647
Growth rate
Sig. 1.000
. -0.362**
.001
Life cycle stage
Sig. 1.000
.
*Correlation is significant at the .05 level (2-tailed)
**Correlation is significant at the .01 level (2-tailed)
Where: AGElog = log of age of business in years
TOTEMlog = log of number of employees current year (2001)
Table 4: Salient variables and life cycle stage correlation. Source: Developed for this study.
From the analysis in Table 4, it is evident that stage is strongly positively correlated with age
of an enterprise (r= .451, p< .000) indicating that age might help determine the life cycle stage
and enterprises in operation for many years might be expected to be in later life cycle stages.
On the positive relationship between stage and size in sales (r= .335, p< .000) it can be
concluded that as firms progress through stages, their size increases and thus size can also be
used to determine life cycle stage. The rate of growth was negatively correlated with life cycle
stage (r= -. 362, p< .001), which suggests that enterprises may have a decrease in rate of
growth as they move through the life cycle stages. There was no correlation between life
cycle stages and the total number of employees.
On the other hand, age is strongly correlated with size (r= .483, p< .000) suggesting that the
older an enterprise is the higher the sales turnover. Age is also positively correlated with the
number of employees (r= .290, p< .000), which means the older the enterprise the more the
number of employees engaged. Size in sales was strongly correlated with total employees (r=
.711, p< .000) indicating that as the size of a business increases the number of employees
increase as well. The rate of growth is not correlated with other salient variables of age, size
and total employees meaning that there is no relationship between the other salient variables
and rate of growth.
In order to further an understanding of the salient variables and life cycle stage, a MANOVA
of the salient variables and life cycle stage was calculated. The data was initially assessed for
multivariate assumptions. However, the growth rate variable was found to be violating the
multivariate assumption of equality of variance and therefore it was necessary to review the
alpha level for growth rate variable (Tabachnick & Fidell 2001). The revised alpha level for
the growth rate variable was .01 instead of the conventional .05. Due to the small number of
respondents at the pre-start stage (4 respondents), this stage was omitted in the MANOVA
analysis.
The multivariate analysis results of the four variables revealed that overall, there were
significant differences between the life cycle stages and the independent variables (F = 3.956,
P< .000). A further analysis was undertaken to investigate which individual variables had
significant differences. The analysis revealed that AGElog (age), size (sales) and growth rate
(at the adjusted alpha level of .01) achieved significant levels between them and the life cycle
stages. There was no significant level achieved between TOTEMlog (number of employees)
and life cycle stages. Table 5 presents the salient variables means and MANOVA analysis.
Life cycle
stage Birth Growth Maturity Decline/
Renewal F= P<
Overall 3.956 .000
AGElog 0.565 1.105 1.309 1.366 6.736 .000
Size (sales) 1.667 2.737 3.281 2.444 4.096 .008
TOTEMPlog 0.519 0.829 0.838 0.726 0.825 .482
Growth rate -0.067 0.152 -0.032 0.004 6.704 .000
Where: AGElog = natural log of age of business in years
TOTEMlog = natural log of number of employees current year (2001)
Table 5: Life cycle stage and salient variables MANOVA. Source: Developed for this study
To determine the specific life cycle stage with significant results with the variables, a post-hoc
comparison based on Bonferroni tests was conducted for the three variables with significant
results (Pallant, 2001). The result shows that the significant difference in means was only
between certain stages and AGElog and growth rate. The post-hoc tests indicated that for size
variable there was no significant difference with any life cycle stage. The significance
difference in means on AGElog was between birth stage and maturity stage (P= .005); launch
stage and decline stage (P= .008); and growth and maturity stages (P= .016). In growth rate
there was a significant difference between the growth stage and maturity stage and as such
enterprises in growth and maturity had significant differences in their rate of growth. The
means of the variables and life cycle stages in Table 5 were plotted and presented in Figure 1
below to present a clearer picture of how the salient variables behaved.
In analysing the determinants of life cycle stage, it was apparent that not all measures
evaluated supported the life cycle stage grouping. Age, size (in sales turnover) and rate of
growth had significant results between them and life cycle stage supporting the model by
behaving in an expected manner as the business moves from one life cycle stage to another.
On the other hand number of employees and key business issues did not achieve significant
results providing less support to the model of growth postulates or determinants. The findings
give support for validity of the life cycle classification determinants providing a clear picture
indicating an expected behaviour of the determinants through the enterprise life cycle stage
classification.
-1
0
1
2
3
4
Birth Growth Maturity Decline
AGElog Size(sales) TOTEMPlog Growth rate
Figure 1: Salient variables and life cycle stages. Source: Developed for this study
There was overlapping dominance of key business issues between the different life cycle
stages and therefore key business issues could not aid in defining position in a particular life
cycle stage. Some key business issues were important in most of the life cycle stages. This
lends support to the conceptual argument offered by some researchers that in reality, stage
descriptions such as key business issues are unrealistically streamlined and stages overlap
(Norman 1977; Kazanjian 1988).
However, stage description by age, size, number of employees and growth rate have been
supported by researchers as determinants of life cycle stage (Kazanjian 1988; McMahon
2000; Hanks et al. 1993) and so does this study. Age, size (in sales) and growth rate have
significant results at the .05 level. Age seems to increase with each subsequent stage as the
business moves from one stage to the other. An analysis of the size measured by sales shows
that as an enterprise moves from birth to maturity there is a sustained growth in sales and
thereafter a decline in sales in decline stage. The total number of employees has also a
sustained increase in growth, stabilisation in maturity and an eventual decline in the decline
stage. However, MANOVA analysis indicates no significant difference between total number
of employees and stages. The rate of growth variable displays a negative growth rate in birth
stage followed by a rise in the growth stage and another negative period in maturity stage and
a slight increase in the decline stage. The slight decline could also be attributed to efforts of
regeneration by firms at this life cycle stage.
Conclusion
The importance of understanding life cycle stage of an enterprise by entrepreneurs,
academicians, consultants and all those involved in the development of small enterprises is
paramount to be able to give advise and assist, based on sound judgement of the current
situation facing the business enterprise. For effective diagnosis of problems facing an
enterprise there is need to determine the stage at which the enterprise is in. This study has
empirically validated the determinants that can be used to classify enterprises into various life
cycle stages.
A limitation of the current study is that it provides only a cross-sectional view of enterprises
at their current life cycle stages and has therefore used a ‘snapshot picture’. The ultimate of
the life cycle determinants would be derived through a longitudinal study using a high number
of respondents and possibly a clustering analysis. However, the analysis presented provides
valid insights into life cycle stage growth patterns for SMEs.
In summary, it appears that enterprises would follow a predictable pattern that can be related
to the enterprise age, size in sales and number of employees and rate of growth. The results of
the study displayed a clear pattern of the developmental process defined by the stage
determinants. It is however recommended that, in order to optimally determine the life cycle
stage, the determinants should be used together as opposed to deriving a conclusion from a
single determinant.
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Appendix
Key enterprise issues means at life cycle stages
Mean
Key Enterprise Issues Pre-
start Birth Growth Maturity Decline /
Renewal
1 Developing new products and or services 5.25 5.64 5.34 5.33 5.50
2 Securing financial resources and backing 4.50 3.55 3.41 3.43 4.40
3 Acquiring key outside advisers and board members 3.25 2.91 2.55 2.06 1.80
4 Product support or customer service 6.25 6.27 5.86 5.97 6.30
5 Attracting capable personnel 4.00 3.82 5.50 4.86 4.40
6 Increasing business facilities and /or space 2.25 2.64 4.50 3.67 4.30
7 Developing a network reliable vendors and suppliers 6.25 5.18 4.91 5.46 4.60
8 Increasing product / service volume due to demand
increase 5.25 5.82 5.86 5.44 5.20
9 Meeting sales targets 4.25 4.64 5.32 5.14 6.20
10 Having a management team with depth and talent 3.75 4.27 5.25 5.29 4.60
11 Controlling costs 6.00 6.00 5.87 6.17 6.30
12 Definition of organisational roles / responsibilities
/
p
olicies 5.50 4.82 4.57 4.49 5.00
13 Upgrading computers/technology and MIS 1.25 4.73 4.57 4.24 4.40
14 Attaining profitability or market share 6.50 6.18 5.66 5.70 6.10
15 Entering into new market regions 5.00 4.82 4.66 4.21 4.30
16 Administrative burden and red tape 3.50 4.45 3.95 4.25 4.50
17 Developing of financial and internal control systems 4.00 4.82 4.32 4.41 4.90
18 Establishing a strong products / market segments
p
osition 4.50 6.36 5.36 5.21 5.20
19 Feasibility studies on business opportunities 5.25 4.45 3.71 3.71 4.00
20 Having / considering departments in the business
1.50 2.45 3.04 2.92 2.60
21 Delegating responsibilities and setting supervisors
roles 1.50 3.36 3.96 3.67 3.80
22 Reducing number of employees and or business
assets 1.25 2.18 1.80 2.57 2.60
23 Management succession 1.00 3.00 3.41 2.89 4.00
24 Downsizing the organisation 1.00 1.73 1.30 2.05 2.20
25 Divesting unprofitable products / units 1.00 2.27 3.64 4.11 4.90
