‘Stunted Growth’ in Small Firms:
How to Turn Them Round and Unleash Their Potential
Carmine Bianchi
Department of Management,
Faculty of Political Sciences,
University of Palermo,
Palermo, Italy
bianchi@unip
http://www.unipa.it/~bianchi - www.ced4.it
Graham W. Winch
University of Plymouth Business School,
Plymouth,
United Kingdom
graham.winch@plymouth.ac.uk
Abstract
The phenomenon of ‘dwarf or ‘stunted’ small and micro firms (in Italian nanismo aziendale)
is recognised in the small business literature. These are firms that have survived through
many years, maybe many generations, providing their owners with acceptable returns and
lifestyles, but have remained very small. They might therefore represent potential lost
opportunities for owners and, given the importance of the SME sector, local employment and
economies. A system dynamics model replicating the basic no-growth, cyclical behaviour
attributed to “stunted” SMEs is firstly analysed. Alternative policies arising from different
entrepreneurial views and aimed at changing behaviour to one of stability or steady growth,
are then tested and analysed. In this relatively simple form, the model does link behaviours to
system structure and could support individual entrepreneurs in understanding the reasons for
dwarfism in their firm and the potential for unleashing growth. It could also form the basis
for a more detailed model to support the identification and evaluation of strategic alternatives
in individual firms.
Keywords: Resource-Based View; Strategy Processes; Stunted Growth; Business Dwarfism,
System Dynamics
Introduction
The ‘stunted-growth’ enterprise or ‘dwarf business’ situation (in Italian nanismo aziendale) is
a phenomenon recognised in the small business literature. These terms are applied to small
companies that have survived for many years and may well have provided their owners with a
satisfactory lifestyle (and cannot therefore be judged as unsuccessful), but despite having the
potential to grow into larger companies they have remained very small. Some of the reasons
for this have been examined in a case study analysis of eleven such firms completed by
Bianchi et al. (2003), and include the level of entrepreneur’s inclination to change the
business status quo, and the consistency of strategic business assets.
Companies that under-perform in this way fail to improve the earnings possibilities for their
owners and do not offer the wealth generation and employment opportunities for their local
communities. Given the recognised importance of small firms in local economies and the
failure rates of new firm start-ups and associated difficulties in developing long-term
sustainable businesses, this latter disbenefit is an important loss. A system dynamics model
has been constructed which reflects the strategic resource management and decision-making
identified in Bianchi’s work as typical of such firms. The use of simulation models in this
way enables theories about the reasons for business dwarfism to be tested by creating models
which replicate the behaviour of actual organisations through the generation of ‘reference
behaviours’ - the characteristic shapes of graphs (timepaths) of key variables over time. Once
the possible drivers and decision-making that have lead to dwarfism have been identified,
further experiments with the model can investigate possible changes in a stunted business’
policies or practices which would offer routes to business growth.
Why and how do firms become ‘Stunted’ or ‘Dwarf’ Businesses?
The term business dwarfism (in Italian nanismo aziendale) has been widely adopted in recent
times in the Italian political and socio-economic debate, in order to characterise a kind of
marginal business and entrepreneurial behaviour which seems to eschew growth. Such firms
may well be ‘successful’ in the sense that they have survived over many years, maybe
multiple generations of family ownership, and have been profitable or at least have provided
the owner entrepreneurs with what they consider an adequate quality of life. However, these
firms may well have had significant growth potential, but the owners have unaware or
unconcerned that the firms remained small, or ‘stunted’, and growth potential has not been
realised. ‘Dwarf’ firms are commonly characterised (Russo 1988) as those small and micro
businesses whose structure and management routines have been kept unchanged over several
decades, in terms of: structure (e.g. product portfolio, strategic product positioning, organisation,
number of employees, production capacity, geographical markets), processes, and relational
systems. This views seems to be founded on the premise that such firms are somehow
affected by some ‘structural disease’ and that, therefore, support systems and tax incentives
could be put in place to cure the disease. This might work for owner-entrepreneurs whose
firms are stunted despite their best efforts and, but is unlikely to have any impact for owner-
entrepreneurs who for lifestyle reasons have happily settled for being small — for example as
long as the firm provides reasonable employment for all immediate family members then no
need for growth is recognised.
Holmes and Zimmer (1994) consider the link between availability of finance and growth and
hence distinguish Growth Capped from Growth SMEs. In Growth Capped, growth is sought
and the firm develops and implements plans to achieve it. However, growth will only be
financed by additional equity inputs of the existing owners or trading bank debt. Where new
equity from outside sources is not an option, such firms have internal limits to growth. On the
other hand, the latter kind of firms which seek and acquire external capital sources are able to
change and grow. Two critical factors identified by Brown and Kirchhoff (1997) are:
perceived environmental munificence — how benign and exploitable they perceive the
environment to be and resource acquisition self-efficacy — the willingness and ability to
acquire strategic resources. Nonetheless, Gibson (2002) asserts that “the notion that firms may
have a capped growth objective is evident in many areas” and ascribes this behaviour to the
fact that in many micro and small firms, owner-entrepreneurs take actions that suggest they
are concerned with maintaining a stable business, and that growth if it risks loss of stability is
rejected as an objective. Perren (1997), in an empirical study aimed at understanding growth
and non-growth motivations for entrepreneurs defined a number of critical factors, including
= owner’s growth motivation,
= expertise and capability in growth, management
= access to strategic resources,
= demand for products or services.
This author also found that non-growth firms tend to shared a common set of negative
motivations towards growth amongst their owners, and even that these effects are particularly
pronounced in a growing market.
Modelling strategic resource dynamics in dwarf businesses.
Bianchi et al. (2003) suggested a set of possible dwarf business structures based on feedback
thinking and structural diagrams reflecting a resource-based view of the firm (see, for
example, Amit & Schoemaker 1993; Dierickx & Cool, 1989; Warren 2002). In this paper, we describe
the further development of a simple quantitative model (Bianchi & Winch, 2005) based around the
strategic resource structures identified in Bianchi et al. The model is intended to enable light to be shed
on the way that managerial behaviour as captured in the model, including reflections of attitudes and
motivations towards growth achievement or otherwise, impact on company performance. By simulating
small firm behaviour, the critical links can be established between the management of strategic resources
and owner-manager attitudes with stunted growth in a firm so that the phenomenon can be better
understood. At a practical level, entrepreneurs who are unaware of the possibility of these factors
stunting growth might appreciate that growth is an option with possible benefits, and could reinforce the
specific factors in play for those already aware of missed growth opportunities. Finally, the model could
then used to investigate alternate, growth-based behaviours that could lead to improved decision-making.
This use of this form of modelling to support learning and understanding of SME behaviour and to
support entrepreneurs in decision-making and policy formulation is well established by the current
authors as discussed below and by others. (See, for example, the Special issue of the System
Dynamics Review, on Small Medium Enterprises 2002).
The major structure that leads to stunted growth identified by Bianchi and his co-workers
relates to the accumulation of a company’s strategic assets, and specifically the balance
between processes that run such assets down and those that build them up (Bianchi et al.
2003). This is a very basic structure, originally identified by Forrester (1961), and is the key
to organisation success. In this context, the term strategic assets is a catch-all terms and
includes a range of different assets or resources critical to the success of a firm — financial
assets, customer base, knowledge, product range, product and/or service quality. (Figure 1.)
Strategic oo Time to lose
assets loss strategic assets
+
bean Desired
+ AESEES strategic
assets
A GAP
Re-acquisition
of strategic
assets
Figure 1 — Key Structure of Strategic Assets
There are two key loops. Loop B reflects that strategic assets are not immortal, and can be lost
over time through a variety of processes — plant can deteriorate, customers can be lost, quality
relative to competitors falls if they have a better product development programme. Loop A
reflects that the firm probably has a target for the strategic assets, hopefully explicit but
maybe only loosely in mind, and will take remedial action if the assets fall, or fall
significantly, below that target. On its own loop, B will tend to drive assets down to zero,
while loop A will attempt to control the assets at or around the target.
In a firm that is not actively seeking growth and perhaps where the entrepreneur is focused on
day-to-day operations, then resources might be expected to fall away over time (Bianchi
2002). It is most likely that such enterprises do not have a very active programme of replacing
lost assets, so the asset loss could go on for a period of time. Of course, dwarf firms are not
firms that fail, so it is inevitable that at some point the dwarf firm owner-entrepreneur will
recognises that the asset position has deteriorated, maybe to a point where normal operations
are threatened, and will take remedial action. Thus over a period of time dominance switches
between the two loops and an oscillatory pattern of falling and recovering strategic assets will
be observed. This pattern suggests that the firm could survive at modest levels of activity but
would find that breaking out and moving to growth mode requires purposeful changes in
operating policies.
The simulator is designed to reflect a set of critical interacting assets to establish that this kind
of oscillatory behaviour can be produced by this double loop structure. The model includes
stocks or levels of four important strategic assets — financial assets, the quality of a firm’s
products or services, production capacity (e.g. in terms of human resources and/or machinery)
and the firm’s customer base. Each of these assets has an outflow reflecting loss or
deterioration of the asset and an inflow reflecting that actions can be taken to build them up.
Direct action in terms of product/service R&D and enhancement, more active sales efforts
with promotions, and recruitment or plant acquisition, for example, are feasible for the latter
three assets. However, for the financial assets the in-flow depends on actions in the other
three that lead to increased sales revenues and income. In this model it is assumed that the
owner’s attention would mainly be on the financial assets, and more specifically on expected
dividends, and it is here that the model assumes targets are set and remedial action is triggered
when dividends (based on perceived income) dip to an unacceptable level.
The model thus reflects a management process whereby, if it is decided that the dividends
(based on perceived income) are unacceptably low, the firm will increase activities to enhance
product/service quality, as well as capacity, and will increase its efforts to win new customers
to restore assets to the desired level. This will be likely to improve the income rate and hence
to dividends. Further, the link from Quality to Customer Base also indicates that the model
includes a mechanism that makes it easier to win and retain customers if the firm’s quality is
relatively higher than its competitors. Likewise, both investments in Quality and Capacity
affect the productivity of the firm’s efforts and ability to win new customers through the
Strategic Assets Consistency Index. This variable tries to reflect the extent to which
investments in both sets of strategic assets impacting on commercial policies are balanced
enough to sustain stability or steady growth. In other words, if the firm were to try to foster
commercial policies by mainly investing in only one of the two above strategic assets, this
would result in an unbalanced and unsatisfactory support to its efforts, and therefore could
undermine the customer base. This model also associates costs with the adopted policies
aiming to affect the strategic assets endowment over time in order to pursue the desired level
of dividends. The decision processes are set up in the model to reflect what is believed about
managers in such firms. If the perceived dividends are above or close to their target, then
they will be pretty relaxed and will make no great efforts to match competitors’ quality
jG (Bo) FINANCIAL SUB-SYSTEM
c Aa
Capacty Aquisition Avg wf CSpacity costs Perceived Income
_ *% See
—i oa
\ ~> See
Unit_Commentgl costs ke ‘a x
vidas Kap. Pets
Product we 2) Increase
Unit a »_marg_on_sales Desired Annual. Growth rate
COMMERCIAL SUB-SYSTEM QUALITY SUB-SYSTEM
ecto oe on Capac
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ioe
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Dividends Gap Peta oO
advances or replace lost customers. As the situation deteriorates though they will become
progressively more concerned and increase their actions to recover. The final model is
shown in Figure 2 a-b.
Figure 2-a The Dwarf Business Model
Initial PDT_SQ
Capakit
Product ervice_Quality “°P*X'Y fae
Inje¥4l_Capacity
PDT_SQ_P&g, on_tnitiial Capacity_Pcly_on_Initlial
Avg_PDT_SQ_Pct§,on_Initlial Avg_Capiacity Pctg_on_Initlial
Strategic_Assets/Consistency_Index
“
Effect_of_Strategic_assets_consistency_on_Customer_Acquisition
Figure 2 - b Strategic assets consistency index
Simulations with the Model
Base or Reference Behaviour
A starting point for most system dynamics studies is the identification of a ‘reference
behaviour’, a single variable or set of time series patterns that characterise a system’s basic
behaviour. In their original analysis, Bianchi et al. (2003) identified four different forms of
dwarf business: Bonsai, Rickety, Conservative, and Marginal. They characterised the four
types in these terms:
e Bonsai characterises those “dwarf” firms within which there is an entrepreneurial
spirit that is opened to possible future changes in the business status quo — which
could be kept silent even for a long time, because of a stable relevant context. These
firms also show a harmonious profile, implying a balanced and homogeneous setting
of different subsystems suggesting that change could be achievable.
e Rickety firms, on the other hand, reflect the situation where the entrepreneur’s
inclination to change the business status quo to foster the undertaking of new growth
6
paths is not supported by a consistent set of strategic assets, neither in terms of their
level and mix.
e¢ Conservative firms do not display an inclination to change, and are likely to keep a
relatively stable and consolidated equilibrium condition in their current strategic
assets’ profile.
e Marginal firms are usually in the market because of their ability to exploit contingent
favourable conditions, for example, associated with public financial aids, lack of
competition in very tight market niches, or a very loyal customer base. In terms of
changing environmental conditions, one would expect such firms to be in a
particularly weak position.
However, for all four groups, they suggest a common reference behaviour - a no-growth
situation with fluctuations around and just below what might be considered their target for
strategic assets. They presented this summary in graphical form, a simplified version of
which appears here as Figure 3.
°
3
Z 2 BONSAI RICKETY
z |g 5
z |i i
B 3 3
5 5
sy TIME TIME
Z,
a CONSERVATIVE MARGINAL
is}
2 g F
Zz g a
g g Fd
5 @ £
< 5 3
Z | & 5
oy a TIME ‘TIME
is}
a High Low
STRATEGIC ASSETS CONSISTENCY
Figure 3 Characterisation of different forms of dwarf or stunted business with common
reference behaviour
The model described above, and parameterised with reasonable values was simulated over a
longish period of time, 600 months (50 years). This time was chosen simply to reflect that
dwarf firms do survive for extended period of time, and it was also considered long enough to
permit oscillatory behaviour to be easily identified.
This model, as the original model (Bianchi & Winch 2005), does generate the cyclical
behaviour expected for this kind of firm - consistent with the general behaviour described in
Figure 3 and observed in the original case study firms. The periodicity of the cycles is a
function of the assumed delays and decay rates in the example model, while the amplification
was deduced as a function of the attitudes and strength of response by the simulated firm
when managing product/service, as well capacity development, and efforts to win and retain
customers. Should the functions that represent these latter factors be changed then the
amplification changed accordingly, for example, a less relaxed reaction to perceived financial
resource gap being below the target results in smaller oscillations closer to the target figures.
The more reactive response portrayed by the model suggests that the oscillations are destined
to die out over time. This might be the case if the operating environment were perfectly
constant over the period; however, perturbations, or changes in the environment are likely to
trigger further oscillations.
Possible scenarios for a ‘stunted firm’ to move into growth mode
The new version of the model (as in Figure 2a & b) was run with a variety of scenarios that
reflected different possible futures for a dwarf firm to break out of its stunted growth
situation. Six are presented here to indicate the outputs obtained and to consider their
implications.
Scenario 1:
A first scenario shows again how the observed system structurally generates oscillations in
the strategic assets values over time, around the initial value (Figure 4).
PARAMETER RUN 1 RUN 2 RUN 3
Customer base loss reaction 12 months 20 months 4 months
time
Product Service/Quality loss 1 month 1 month 1 month
reaction time
Capacity loss reaction time 1 month 1 month 1 month
Desired Annual Growth rate 0% 0% 0%
Competitors’ Product 100% 100% 100%
Service/Quality
Table 1 - Scenario 1
Such oscillations can be amplified over decades due to a more reactive commercial policy
aimed to recover lost customers in a short period of time. On the other hand, a more relaxed
commercial policy tends to reduce oscillations. However, also in this case (run 2) oscillations
tend to increase in the long run. Another indicator showing that the policy underlying run 2
can be preferable to the two others is related to the “strategic assets consistency index”. This
is a synthetic parameter showing the extent to which capacity and product service/quality " are
uniformly adapted and improved over time, as a consequence of the firm’s investment policy
enhanced by the need to adapt average (perceived) dividends to desired levels.
" Such index does not include the customer base, though it is also an important strategic asset, since it is in turn
affected by capacity and product service/quality.
2 4 a / \. :
7 a § sh J \/
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a 08 i i
go :
§ oss " 5
Sos fl ; :
NV NE
Hens
Equity
eae
serainancy ie
a
——
Months
Figure 4 Scenario 1 behaviours
Scenario 2:
A second scenario shows again an unstable system, characterised by fluctuations in the
strategic assets values, generated by the entrepreneur’s policies.
PARAMETER RUN 1 RUN2 RUN 3
Customer base loss reaction 24 months 24 months 24 months
time
Product Service/Quality loss 24 months 24 months 1 month
reaction time
Capacity loss reaction time 1 month 24 months 24 months
Desired Annual Growth rate 0% 0% 0%
Competitors’ Product 100% 100% 100%
Service/Quality
Table 2 Scenario 2
However, as shown by the first run, such fluctuations are substantially reduced if the adopted
policy is focused on a continued update of capacity and a less relaxed response to the loss of
customers and product service/quality. The policy related to the third run cannot be
considered as robust, since it generates wider oscillations in the strategic assets endowment of
the firm. In fact, it implies that the aggressive push towards an improvement of the business
capability in product service/quality is not supported by a consistent endowment of capacity
resources (e.g. in terms of people or machinery). Also the second run shows an inconsistent
behaviour in the strategic assets endowment over time. As a matter of fact, a less relaxed
policy in upgrading both capacity and product service/quality does not allow the firm to
sustain a stable commercial policy.
It is worth remarking that the above dynamics, and related implications in terms of robust
policy making, could be different, if another set of hypotheses were to be adopted in the
model parameters setting, particularly concerning:
= the policies adopted by decision makers in upgrading both product quality/service and
capacity, as well as in customer acquisition, in response to a perceived gap in
dividends;
= the effect of product service/quality and capacity on customers acquisition/loss;
= the unit (commercial, capacity and product quality/service) costs related to the
acquisition of the above strategic assets.
Such costs in turn affect (together with sales unit contribution margins) the income rate,
which determines both equity and perceived dividends over time.
Customer base
2s H
° 100 20 300 a0 0 oto obo
Months Months
Product/Service Quality
Perceived Dividends
PR EERE
———
RPI =
Equity
consistency index
Strategic assets
8
8
Figure 5 Scenario 2 Behaviours
Scenario 3:
A third scenario aims to test the robustness of growth strategies. Three different sets of
policies related to the desired annual growth rate (in terms of desired increase in dividends)
are tested and matched with the setting of parameters related to the first run of the previous
scenario.
PARAMETER RUN 1 RUN2 RUN 3
Customer base loss reaction 24 months 24 months 24 months
time
Product Service/Quality loss 24 months 24 months 24 months
reaction time
Capacity loss reaction time 1 month 1 month 1 month
Desired Annual Growth rate 60% 65% 55%
Competitors’ Product 100% 100% 100%
Service/Quality
Table 3 Scenario 3
The results portrayed in Figure 6 show that a 65% desired growth rate (run 2), according to
the hypotheses embodied in the model, could generate a growth, which is sustainable in the
long run. On the contrary, a more relaxed growth rate would either level off (run 1) or even
generate more undesirable oscillations in the endowment of strategic assets. The simulation
also remarks that pursuing a 65% growth rate would imply for the dwarf business the need to
substantially increase its own strategic assets. This could also mean to pursue a policy of
gradual introduction of managers from outside the business owning family.
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Figure 6 Scenario 3 Behaviours
It is also worth observing that, although a 65% annual growth rate may seem significant in
absolute terms, it might not be the case in relative terms if matched to the specific reality of a
dwarf business, aiming to increase its own structure and processes to a wider dimension,
which not necessarily will imply the fact of becoming a medium firm.
Scenario 4:
A fourth scenario tests the 65% growth rate policy, with the two other policies which were
previously discarded when discussing simulation 2. In particular, the policy related to the
run 2 of simulation 3 is compared with two other policies, both implying a 24 months
customer base loss reaction time and that after the 300" month (i.e. the 25" year) a 65%
growth rate is pursued. However, the second policy also implies a less relaxed approach in
recovering losses in both product service/quality and capacity. Instead, the third policy tests a
short reaction time to losses in service/quality and a long time to restore capacity. Results are
more counterintuitive than the previous ones.
PARAMETER RUN 1 RUN2 RUN 3
Customer base loss reaction 24 months 24 months 24 months
time
Product Service/Quality loss 24 months 24 months 1 month
reaction time
Capacity loss reaction time 1 month 24 months 24 months
Desired Annual Growth rate 65% 65% 65%
Competitors’ Product 100% 100% 100%
Service/Quality
Table 4 Scenario 4
As a matter of fact, run 3 shows a more robust growth pattern than the others. Consequently,
although the third run of scenario 2 showed that if a non-growth policy (aimed to keep stable
the system) is adopted, a prompt reaction time to restore product service/quality and a smooth
time to restore capacity generate wider oscillations, a growth scenario context suggests that
such policy is the most robust one. Nevertheless, the first bottom-left graph illustrating
simulation behaviours suggests that such “best” policy implies a lower level of consistency in
the adaptation of the strategic assets endowment, if compared to the other two policies. In
particular, under this point of view, the policy associated with the first run is still the most
preferable.
An implication of this is that the level of risk implied by the policy reflected in run 3 is much
higher than the one related to run 1. Such risk is due to the possibility that the firm could find
difficulties in increasing the endowment of its own strategic assets according to the time path
and priorities which can better support the high growth strategy. In particular for a dwarf
business wishing to grow this could mean a significant risk, since it will have to face a sharp
discontinuity with the past, that could imply unexpected delays due to different kinds of
difficulties (e.g. contrasts in the business owning family, lack of image to attract qualified
managers and other resources). The above thoughts could suggest the opportunity of
undertaking a more relaxed and cautious policy, such as the one associated with run 2.
1 I
Months
‘Customer base
3
n
i:
:
IN
Months Months
\\
\
\
x y X ve 4 ; —s ‘3.000.000 ae
" [2 a * 2,000.06 Pail
J a oo
3 rs ae ao te wo wo ¢ Cr a a)
Months Months
Figure 7 Scenario 4 Behaviours
Scenario 5:
A fifth scenario tests the three growth rate policies analysed in the previous one, in the light of
a new context: an increase of rivals’ competitiveness in which product service/quality level is
increased from 100% (parity) to 150% (competitive advantage) from the 300" month. Results
confirm the analysis previously done. In this case, furthermore, the levels of product
service/quality and capacity are higher and more stable than in the previous ones. Similar
remarks can be referred to the strategic assets consistency index.
PARAMETER RUN 1 RUN 2 RUN 3
Customer base loss reaction 24 months 24 months 24 months
time
Product Service/Quality loss 24 months 24 months 1 month
reaction time
Capacity loss reaction time 1 month 24 months 24 months
Desired Annual Growth rate 65% 65% 65%
Competitors’ Product 150% 150% 150%
Service/Quality
Table 5 Scenario 5
A possible explanation of this is that responding to threats arising from competitors’ policies
can result in a factor stabilising the business system. This can allow a dwarf business to either
pursue a more balanced strategy aimed to keeping unchanged the status quo, or to grow ina
non-turbulent manner.
—_——
‘Customer base:
Capacity
a
Months Months
2 so ||. Zz
8B on B asec 1
3 oe a r; oO
2 —— |5 ON i i,
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Months Months
i. —
Pra com
HE. a
be A 2B so0n00
£3" &
£2 p——2——? || ove.
= . Se
Months Months
Figure 8 Scenario 5 Behaviours
Scenario 6:
A sixth scenario, finally, shows a pattern of crisis, associated to a policy aimed to disembark
from investments, to restore the level of strategic assets. Such a policy has been previously
applied especially to what we have called marginal firms. In this case, if competitors keep
their product service/quality stable over time, the firm will be able to remain alive also for a
long time, although its own strategic assets behaviour shows a declining process.
PARAMETER RUN 1 RUN 2
Customer base loss reaction 24 months 24 months
time
Product Service/Quality loss 24 months 24 months
reaction time
Capacity loss reaction time 1 month 1 month
Desired Annual Growth rate 0% 0%
Competitors’ Product 100% 150%
Service/Quality
Table 6 Scenario 6
On the other hand, if — after the 300" month — competitors were to increase their level of
quality/service to customers, in this case the firm would show a much sharper declining path
in its strategic assets, leading to sudden crisis. It is worth remarking that in this case the only
strategic asset showing a relatively stable pattern is capacity, despite the off-loading policy
adopted by the firm. The reason for this is that a prompt capacity loss reaction time is
adopted, leading to a weak capacity acquisition, aimed to overcome the sharp reduction of
dividends caused by unsatisfactory competitive and financial results.
BL ee,
J
|
a _———
i * = © «© = | ¢ = = os « o =
Months enths
Product/Service Quality
Perceived Dividends
J Neer ~—
z _ ee
= a Pe
Figure 9 Scenario 6 Behaviours
The simulations described and discussed above confirm that the application of simple changes
in owner-manager attitudes could potentially enable a stunted firm to break out into growth,
but also to gradually generate structural instability or even crisis, with the changing set of
relevant external variables, such as those related to competitors’ strategies, a further factor.
They also suggest that the kind of stable asset situation beneficial for sustained growth would
require the move to a more reactive attitude to strategic asset management than is perhaps
typical in dwarf businesses. Of course, the simulator is only a simple reflection of the
selected key assets and it can in no way point to specific actions to achieve quality or capacity
improvement and build the customer base. However, by demonstrating that growth objectives
might be feasible, it should stimulate and encourage the owner-manager with perhaps other
family stakeholders to take a more detailed look at the practical options.
Discussion and Conclusions
The dwarf or stunted growth firm, or nanismo aziendale, is identified in the small-medium
enterprise literature as a significant phenomenon, though it is an area that is not well
researched and knowledge of how and why such firms evolve is thin. Further, despite the fact
that the SME sector has been identified as a key factor for employment generation and local
economy revitalisation in many regions, very little has also been done to investigate
sustainable policies to unleash the potential of stunted firms. Firms in such circumstances
might be failing to maximise the income and wealth creation potential for the owners and
mean that the local economy and employment prospects are also constrained. Of course it is
also recognised that in many cases the owners are not unhappy with this situation and the firm
has provided a satisfactory income and lifestyle, maybe through a number of generations of
family ownership. However, as the case-studies in Bianchi et al. (2003) also pointed out,
there may be changing environmental conditions or changes in ownership that demand that a
firm move into a growth mode.
In practical terms therefore, there are potentially three challenges facing the owners of a dwarf
firm:
= Enlightening owner-managers that their firms may be in a stunted growth situation and
that there could consequently be opportunities for moving the firms forward. (Though,
of course, with the rider that individual owners might not wish to embark on that
path).
= Assisting owner managers who might wish to break out in understanding why the
constraint has historically occurred, in identifying the possible constraining
mechanism(s), and where there is potential to break out by modifying strategic targets
and attitudes.
= Identifying and evaluating specific actions and strategic alternatives for achieving
growth in the key strategic assets.
The model described in this paper is directed towards the first two of these roles, the last
would require a more detailed model calibrated to a specific firm.
Although only a simple model, it does include sufficient detail to enable it to replicate the
expected cyclical behaviour of dwarf firms, and to reflect the structures that lead to the ebb
and flow of strategic assets. It can also show how the firm is constrained from achieving any
form of sustained, stable growth in a way that is likely to be meaningful to entrepreneurs.
This paper has described a set of scenarios showing how it could be used to examine
particular changes that might enable a firm to achieve growth in the future. Thus, it is argued,
this paper demonstrates the potential of using a simulator such as this in the first two roles.
For the simulator to be fully functional as an insight model, it could benefit from even further
refinement, in particular the further enhancement of the model by the addition of additional
mechanisms to represent the management of other strategic assets that might be relevant for a
wider range of firms, for example knowledge-base, company image, or network contacts, and
the related analysis of the behavioural impacts. Such a generic model could then be used with
owner-managers, maybe in a collective situation like a workshop or small business
organisation event, initially to raise the issue of whether they are all achieving the growth
potential of their firms and to highlight the constraining factors. A further role for this
simulator would be to support the efforts of economic development agencies, small-firm
training and advisory organisations, and consultants who support SME development.
An entrepreneur who might want a more detailed model tailored to his/her own firm’s
circumstances could be provided with this option. This could be achieved through two paths.
The first would be through the commissioning of the tailoring of this base model into a
company-specific model by the addition of mechanism appropriate to its products/services,
distribution channels, target markets, access to incentives and support, and so on, and the
parameterisation of the model to its own data. The second, and probably more economical,
16
route could be through the development of a generic model which can be easily parameterised
to an individual firm through an easy to use interface. As mentioned earlier, this latter
concept has been shown feasible and effective in other research (Winch 2000; Winch and
Arthur 2002).
In summary, these results add significantly to the understanding of this particular aspect of
growth dynamics in small firms. A type of model has been described which could assist
policy makers and small firm support agencies in identifying strategies for support initiatives
and training that will help small business avoid the pitfalls that lead to dwarfism and achieve
their growth potential. More directly, the simulator, in its present form or in a more refined
version, could be used to help small companies understand why their stunted nature means
they might be missing opportunities for enhancing the owners’ revenues and net worth.
Further, it could suggest possible ways forward to release them from the factors that are
constraining their growth. For younger firms, it might help them understand how to avoid
falling into this situation in the first place.
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- System Dynamics Review, 18:3, 2002
