A Behavioral Model of Diversification and Performance
in a Mature Industry
John D. W. Morecroft
London Business School
Sussex Place, Regent's Park
London NW1 4SA UK
email: j.morecroft@Ibs.lon.ac.uk
Introduction
Although system dynamics has long been applied to strategic business problems, there has
been surprisingly little published work dealing with the topic of diversification and multi-
business firms. Widely cited business models in the field typically deal with dynamics at the
level of a single business or else at the level of an industry strategic group. Nevertheless,
diversified firms are numerous and have been studied closely by academics working in the area
of corporate strategy. Much debate surrounds the question of whether (and under what
circumstances) diversified firms can outperform firms that focus on a single core business.
Researchers in this area have principally concerned themselves with statistical analyses of the
link between financial performance and portfolio relatedness (Markides and Williamson 1994,
Robins and Wiersema 1995).
By contrast, this paper uses behavioral modeling and simulation to explore the link between
financial performance and managerial perceptions influencing investment. Two system
dynamics models are used to compare the fortunes and performance of two firms (A and B)
facing an identical scenario in their traditional core industry, under identical starting conditions.
Firm A focuses strictly on its core business while firm B diversifies. Simulations show how
the firms' relative performance depends on the optimism, persistence and foresight of
investment policy.
A Glimpse at the Motivation for Diversification
The managerial motivation for diversification often stems from dissatisfaction with the
performance of the current business or the realization that opportunities for improved
performance may exist in other areas of business in which the firm does not compete (see
Penrose 1959 chapter VII, for a classic discussion of the economics of diversification).
Central to management decision making is the judgement of whether existing markets are
relatively less attractive (profitable) than new markets for any new investment the firm wants to
undertake.
The tire industry in the 1980s provides a good example of an industry that came to be viewed
by its own executives as unattractive, thereby prompting a wave of diversification (Ginsberg
1995, Ginsberg and Morecroft 1995). Between 1975 and 1985 total tire demand fluctuated in
the range 160 to 210 million tires per year. There was very little growth. Severe capacity
shortages and surpluses developed, caused by long lead times on capacity expansion and an
overhang of old technology bias-ply capacity held by producers unwilling to exit the industry.
The result was damaging peaks in rivalry that depressed prices and spoiled firm profitability.
The persistence of such adverse trading conditions over many years (coupled with corporate
mindsets shaped by the pessimism of the post oil-shock economy) was sufficient to cause
leading producers such as Uniroyal, Goodrich and Goodyear to curtail investment in the core
tire business and look for new businesses in which to invest.
Two models examine the aggregate policies that control investment in such situations.
Model of Firm A, the Focusser
Figure 1 shows the investment policy of firm A and the feedback loops in which it is
embedded. Firm A, an imaginary tire maker, is assumed to focus strictly on the core business,
rather like Goodyear in the 1970s and early 1980s. The firm invests according to
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management's perception of return on the core business relative to an agreed benchmark return,
as shown in bold on the right of the figure. The better the performance the more investment.
If return exceeds the benchmark then resources in the core business grow (providing that the
rate of investment exceeds the rate of resource depreciation). Conversely, if return is below the
benchmark then the core business is starved of investment and resources fall. Return itself
depends on a variety of factors that reflect the feedback consequences of past investment
decisions and industry conditions. These additional factors are shown in the remainder of
figure 1. There is an important balancing loop that results from the direct connection between
resources and return. As resources grow then ceteris paribus return relative to those resources
tends to fall. On the other hand, as resources fall, return tends to rise. The balancing loop
represents management's efforts to keep return in line with the agreed benchmark. There is a
long term reinforcing loop linking resources to tire attractiveness and to tire sales. Greater
investment in core resources can lead to a better product, increased market share and more
sales. Finally, overall industry conditions play through in their effect on total tire sales and
industry rivalry.
OEM Tire Demand
Tre Capacity ——|| Total Tire —
{ Demand
~~
No i
Rivalry {
~ \
Pwo CTB Tire Sales
CTB Market Share SS Pee \
AK f . Lk. investment in Core
s, © Tro Businoss
NN.
Benchmark Return
Tire Resource
Depreciation
Figure 1: Investment in the Core Business by Firm A, The Focusser
Model of Firm B, the Diversifier
Figure 2 shows the investment policy of firm B as it diversifies into non-core business. Firm
B still runs its core business (represented by the box at the top of the figure labelled ‘core tire
business & market’), but also invests in the non core when its financial performance exceeds
the core, rather like Goodyear in the mid 1980s. At the heart of the map is a reinforcing loop,
shown in bold, connecting investment to assets in the non-tire business. This loop will
generate growth in assets providing that the performance of the non-tire business is judged as
superior to the core - a relative judgement. However, the dilemma facing managers is that they
don't know for certain how well the new business will perform when it is integrated into the
diversified portfolio. Instead they must make do with a judgement of expected performance that
blends their initial performance assumption (as originally foreseen at the time of diversification)
and reported performance. The blend will differ from company to company depending on the
optimism, persistence and foresight of the management team. Moreover, the reported
performance of the non-tire business can be distorted in the short to medium term by disruption
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caused by heavy new demands on management. The faster the rate of diversification, the
greater the disruption. The same temporary disruption can also upset the performance of the
core business, thereby further confusing the judgement of relative performance.
CORE TIRE
BUSINESS
& MARKET
Performance of Coro
Tie Business
Minimum A
asa ge \
Se a \
“> Diversification Management's initial \
7 Wego J Performance Assumption .
ff Performance / \
/ a \ / \
Desired Growth Expected Pertormance
vA of Non Tire Business
/ \ |
Investment in TT i
Non Tire Business i: H
Re Return {
oN en Non Tp Business — /
& - J . /
True Rat /
Me 7 on Non Tire Business /
Disruption from
Diversification ~~. ao
Assets in sain ie ae
Non Tire Business ~———~"
Gy SN. Resources in
8 Core Tire Business
‘Asset Depreciation
Figure 2: Investment in the Non Core Business by Firm B, The Diversifier
The overall feedback model of diversification as depicted in figures 1 and 2 embodies four
specific assumptions about managerial behavior that can influence the relation between
diversification and performance:
1. Diversification takes place when existing markets become relatively less profitable for any
new investment the firm's managers want to undertake - a Penrosian view.
2. Relative performance (core versus non core) depends on a complex managerial judgement
that compares a perception of the performance of the core business with a perception about the
future possible performance of the targeted non-core business(es).
3. Management's perception of the future possible performance of the non-core business
depends on a blend of optimism, persistence and foresight.
4. The faster the rate of diversification, the lower the performance of both the core and non
core businesses due to disruption.
Design of Simulation Experiments on Diversification
There is not the space in this short conference paper to present a full set of simulations that
explore the implications of the behavioral model described above. Instead I will outline the
simulation experiments conducted so far and report a few of the findings.
Experiment 1: Firm A Faces a Drop in Industry Demand
In Experiment 1 Firm A , the focusser, faces a 20 percent drop in industry demand, starting in
year 2 of the simulation and extending to year 20. The benchmark return on the core business
is initially 6 percent per annum and by definition firm A has no option to diversify. The
simulation reveals clearly the sensitivity of firm revenue and profit to recessions in a mature
capital intensive industry with high exit barriers. The simulation also shows a trade-off
between market share and return that is characteristic of single business firms in mature
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industries. In order to maintain high returns in a declining market it is necessary to squeeze
resources, which tends to lower market share.
Expériment 2: Firm B Faces a Drop in Industry Demand and Finds a Superior Investment
Opportunity vers; — _
Firm B, the diversifier, faces an identical 20 percent drop in industry demand. But
management diversify (by acquisition then organic growth) when return on the core falls below
a threshhold of 4 percent per annum. The benchmark return on the core business is initially 6
percent per annum. The expected return on the non core business is set at 8 percent per year
and is assumed to be an accurate estimate of the true underlying return. Under these conditions
there is steady growth in the size of the non core business. Meanwhile the core business loses
share by comparison with firm A, the.focusser. This share loss comes partly from the
disruption caused by diversification and partly from extra downsizing of the core business in
an effort to meet the return target. Interestingly, the overall performance (return) on firm B is
almost identical to firm A, even though firm B's non core business has the potential to
outperform the core. Drifting performance standards in the core coupled with disruption from
growth of the non core conspire to nullify performance advantages from diversification, or at
least defer the advantages for many years. Only in the case where the non core has a very large
and sustainable return advantage over the core (say 5 percent or more) are early relative
performance advantages likely to accrue.
Experiment 3: Firm B Faces a Drop in Industry Demand and is Lured into Diversification by
Over Optimism
Once more Firm B, the diversifier, faces a 20 percent decline in industry demand with the
option to diversify. In this case however, management's initial performance assumption for
the non core is much too optimistic at 12 percent per annum rather than the ‘true’ underlying
return of 6 percent. The true return of 6 percent is deliberately chosen to be no better than the
average expected from the core. Under these conditions, there is excessive growth of the non
core. Investment in the non core is four times as high as it would have been had the initial
performance assumption been accurate. The core business is squeezed more than necessary
causing loss of market share. Meanwhile, overall returns from the whole adventure are
virtually identical to firm A.
Areas for Further Work
The existing model views the diversifying firm as a simple binary investor, choosing whether
to favour investment in the core or non core business, based on relative financial performance.
The policy implications of this view need to be drawn from further simulations. More work is
needed to relate the binary investor view (and its policy implications) to established strategy
work on performance and relatedness in diversification. The experiments conducted so far
make no assumption one way or the other about the relatedness of the core and non core
businesses, though the two are assumed to be coupled through the disruption effect.
References
Ginsberg A. (1995), "Transformation of the US Tire Industry", Case Study, Stern School of
Business, New York University.
Ginsberg A. and Morecroft J.D.W. (1995), "Weaving Systems Thinking into the Case
Method: An Application to Corporate Strategy Analysis", working paper W-95-2, System
Dynamics Group, London Business School.
Markides C.C, and Williamson P.J. (1994) "Related Diversification, Core Competencies and
Corporate Performance", Strategic Management Journal, vol 15, special issue, pp 149 - 165.
Penrose E. (1959) The Theory of the Growth of the Firm, Basil Blackwell, London.
Robins J. and Wiersema M.F. (1995) "A Resource Based Approach to the Multi-Business
Firm: Empirical Analysis of Portfolio Interrelationships and Corporate Financial Performance”,
Strategic Management Journal, vol 16, no. 4, pp 277 - 299.
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