Uncertainty

1
Strategic Analysis, 2004/2005 Jan Zika
Word count: 2514
Characterize, using examples, the nature of uncertainties facing firms in their strategic
decision making. Describe, using brief examples, how firms can cope with these
uncertainties.
According to Brown and Eisenhardt (1998), strategy is an answer to two essential questions.
First, where do you want your business to go? Second, how to get there? Parnell et al. (2000)
argue that strategy is created as a response to management uncertainties about competitors,
customers and the environment. Brown and Eisenhardt (1998) conclude that the most
common reason for failure of the traditional strategies in an environment of fast change is the
low predictability of future developments. That raises other, rather complex, questions. What
is the nature of uncertainty or uncertainties in strategic decision making? How can they be
characterized? Finally, how can firms cope with them?
For the purpose of strategic analysis, the Knightian notion of uncertainty seems particularly
relevant. It is used by many researchers, although not always consistently.1
Frank
Knight (1921) distinguishes uncertainty from risk. According to his theory, a decision maker
faces uncertainty if future outcomes will occur with probabilities that cannot be estimated.
Unlike risk, uncertainty involves a situation with unknown probabilities.
Looking at the problem of uncertainty in the light of traditional strategic approaches, Brown
and Eisenhardt (1998) see deficiencies of the traditional tools in their tendency to emphasize
the choice of target and neglect the actual process of reaching it. Courtney et al. (1997) add
that executives tend to view uncertainty in a binary way, i.e. they either assume that future
developments are certain or entirely unpredictable.
Traditional strategies focus on selecting an attractive market segment and deciding on the
right strategic positioning (Porter, 1980), on choosing a specific set of desired competences
(Hamel and Prahalad, 1996), on developing a strategy through gradual learning (Mintzberg et
al., 1998), or on projecting a certain vision of the future events that can be consequently used
as inputs for a cash-flow analysis (e.g., capital investment theory as summarized by
Ansoff (1988)). Although these approaches may be very useful in a relatively stable
environment, Courtney et al. (1997) explain that under uncertainty their influence on strategic
decision making can be marginal, if not dangerous.
Ansoff (1988) outlines the decision making process using the framework of the “adaptive
search method” for strategy formulation. It should be possible, using Ansoff’s framework, to
categorize uncertainty according to the stages of decision making process it relates to. In this
view, one would need to search for the sources of uncertainty related to the formulation of
objectives, choice of goals, evaluation of internal resources and capabilities, appraisal of
outside opportunities, diversification decisions, competitive strategy synthesis and so on. As
this approach depends on the choice of a strategy formulation framework,2
an alternative,
more general view will be explored.
1
See, for example, the scenario valuation models for level 2 uncertainty in Courtney et al. (1997).
2
For example, Parnell et al. (2000) distinguish three general types of uncertainty for the purpose of an empirical
measurement of strategic uncertainty facing a firm. First, the uncertainty about competitors relates to the
managers’ perceptions about current and future rival actions. Second, the uncertainty about customers is linked

2
In the above mentioned model, one may, for example, identify different kinds of uncertainty
stemming from external business environment and these would be merely some of the
elements of the whole set of different types uncertainty. In contrast to this, Courtney et
al. (1997) understand the uncertainty as a general condition, in which the outcomes cannot be
exactly predicted. Their uncertainty is then equivalent to the uncertainty of the business
environment. This notion of the environment uncertainty can be understood as a factor
influencing all stages of the strategic decision making process. In other words, the model does
not distinguish different kinds of uncertainty, but only works with one.
Courtney et al. (1997) then suggest a decomposition of the aggregate uncertainty. They argue,
that in almost any situation, a firm can identify some “clear trends” (e.g., customer market
demographics) and also information that is currently unknown, but can be obtained to some
extent from appropriate research and analysis (e.g., an estimate of price elasticity of demand
for stable products and competitors’ capacity expansion plans). What remains is denoted the
“residual uncertainty” and can be characterized by a level. Four levels of residual uncertainty
were distinguished based on observation of decision making practice.
With level 1 uncertainty, denoted “a clear-enough future”, decision makers can derive a single
prediction of the future which is sufficiently precise for developing a strategy. This does not
imply that the prediction will certainly realize. It only indicates a situation, where the forecast
is narrow enough to point to a single direction. In that case, the residual uncertainty is
irrelevant to strategic decision making. An example of level 1 uncertainty could be the
uncertainty facing an established fast food chain in a relatively new regional market (on the
country level) after its major competitor from other countries decides to enter the market. A
lot of information may not be available at the point of decision, but it is likely to be
obtainable.
Level 2 (“alternate futures”) uncertainty is typically present in situations where the value of a
firm’s strategy strongly depends on a competitor’s unknown strategies or another party’s
actions. The different discrete scenarios are usually known to all players in the market, but the
one that will realize is impossible to predict. At the same time, the elements of strategy would
fundamentally change if the outcome was predictable.
An example of an unknown competitor strategy generating level 2 uncertainty could be an
oligopoly market of particular raw material suppliers. The main factors influencing the
strategy would be major production- and scale-related decisions of a competitor. Conceptually
similar situations can be often identified in regulated industries, such as healthcare,
telecommunications, energy, transport etc., where the future depends on a regulator’s or
legislator’s decision that is often unpredictable, more so if only using legally obtainable
information.3
Level 3 uncertainty (“a range of futures”) can be described by a continuous range, or part of
space, where a limited number of variables define the range, or the dimensions of the notional
space, of potential outcomes. Although it is not possible to identify discrete scenarios, the
variables defining the range are known. This level of uncertainty often faces companies
entering emerging new industries, product markets, or geographical markets.
to respondents’ perceptions of present and prospective customer needs. Third, the uncertainty about the
environment reflects managers’ perceptions of external environment and its changes.
3
Illegal activities that companies may pursue to obtain officially unavailable information or even influence
government and regulator’s decisions are not discussed here, although many researchers and practitioners argue
that not only they exist, but are also significant (Rose-Ackerman, 1999).

3
For example, a multinational universal bank may want to introduce its small and medium
enterprise banking services to a new country, assuming that another bank has already done it
and the management knows the expansion will be feasible. However, the demand and
prospective market shares in different product areas are hardly predictable, thus hindering a
clear development of strategy. The uncertainty related to an introduction of a new product is
apparent in many high technology companies when they need to decide on the parameters and
performance characteristics of their next product. It is often very difficult to foresee what the
market is going to look like when the product is ready and the rate of return on the investment
is hard to predict.
Finally, on level 4 (“a true ambiguity”), it is impossible to identify discrete scenarios, and it is
not possible even to identify all the variables defining the space of potential outcomes.
Although level 4 uncertainty may be rare and transitory, it does occur.
Consider, for example, a provider of a new online retail payment system. The technological
developments are uncertain as there may be multiple alternative approaches with seemingly
similar potential. Even the technological platforms, where the new system should be
deployed, may not be obvious. Consumer preferences depend on a number of unspecified
economic, social, cultural and technological factors, some of which may be very complex and
unpredictable themselves. For example, consumers’ needs and requirements related to the
payment system will be influenced by developments in all other relevant areas of e-
commerce. At the same time, the regulatory framework for the new scheme may be
incomplete and ambiguous with great chances of virtually unpredictable future revision by the
regulatory authority. All these influences might be combined with network effects and
potential strong competitive pressures from both the new entrants and incumbents in retail
electronic payments and transaction processing.
Searching for factors that influence increasing uncertainty of business environment, one may
need to look at the general social and economic trends. Lippit (1982), Johnson and
Scholes (1997), and Skat-Rørdam (1999) summarized the most important trends. Looking
back into history, the industrial revolution introduced major challenges for strategic analysis.
The other most significant trends are probably technological expansion, globalization and the
transition towards a knowledge society.
These trends resulted in consumers increasingly requiring tailored products. At the same time,
companies face smaller profit windows due to faster product innovation (or shorter product
life-cycle), shorter periods of product exclusivity guaranteed by patent protection, and
decreased sustainability of differentiating advantages caused by the fact that competitors can
quickly copy products or even develop improved alternatives based on them. Issues related to
the intangibility of many new products, such as digital goods, also heighten uncertainty. From
a different perspective, the uncertainty is affected by the economic and public policy, the
complexity of human organizations and the influence of business activity on the natural
environment.
Based on Duncan's (1972) research of conditions of environmental uncertainty,4
Johnson and
Scholes (1997) outline some general suggestions for coping with uncertainty. For simple and
static environments, such as the one facing some raw material suppliers and mass
manufacturing companies, the analysis of historical data is identified as most appropriate,
4
Duncan (1972) identifies the complexity and the dynamic nature of an environment as two main factors
influencing the uncertainty. His findings are derived from empirical research.

4
because the change is predictable. In dynamic environments, the decision makers need to
consider the future as well as the past. They may do that either intuitively or through
employing structured analytical and decision making mechanisms, such as the scenario
planning. To cope with complex environments, firms may utilize decentralization or leverage
organizational learning.
Hamel and Prahalad (1989) advocate the use of “strategic intent”. Strategic intent could be
briefly described as a long-term vision of future achievement. Strategic intent is formed by an
idea of a firm’s ultimate purpose and projects a desired leadership position. It sets a target that
is perceived as worthwhile by all employees and deserves personal effort and commitment.
An example of strategic intents from the past could be the Canon’s “beat Xerox”, Coca Cola’s
“put a Coke within arm’s reach of every consumer in the world” or McDonald’s “dominate
the global food service industry”.
Skat-Rørdam (1999) criticizes a blind focus on Porter’s generic strategies under uncertainty
and fast change. Instead, he suggests that companies need to strive for the continuous creation
of new advantages. Skat-Rørdam (1999) further develops an “opportunity approach” as an
alternative to traditional strategic planning.
Courtney et al. (1997) suggest a use of traditional strategic approaches for uncertainty of
level 1; decision analysis, option valuation models and game theory applications for level 2;
and latent-demand research, technology forecasting and scenario planning for level 3. For true
ambiguity (level 4) they recommend to research analogies and patterns, and to apply
nonlinear dynamic models. Courtney et al. (1997) consequently develop portfolios of
“actions” (these are “big bets”, “options” and “no-regret moves”)5
that correspond to a
specific level of uncertainty and a firm’s “posture” (i.e. “shaping”, “adapting” or “reserving a
right to play”).6
The Microsoft Network (MSN) initiative illustrates the “actions” approach. As a level 2
shaper, Microsoft originally employed a big bet strategy by investing in the development of
its own proprietary standard. The choice was understandable considering the level 2 dilemma
(proprietary versus open standard), the level 3 problems of projecting consumer demand for
network services and the assumption that it was not possible to make money on a service
where the access is free. When the subsequent developments proved Microsoft wrong, it
repositioned itself into an adapter and refocused MSN around the Internet. This was possible
due to a set of options, e.g., the “semicoherent strategic direction” as described by Brown and
Eisenhardt (1998), and no-regret moves like the employment of engineers with general-
programming skills and the thorough analyses of customer data.
Similarly to the actions approach, Brown and Eisenhardt (1998) build their strategic
framework on three distinct tactics: (1) reaction to change, e.g., releasing a new product in
response to a competitor’s move; (2) anticipation of change, e.g., lining up appropriate
resources for the future; and (3) leadership of change, e.g., creating new technologies,
elevating industry standards and influencing customer expectations. Brown and
Eisenhardt (1998) show, using the example of Intel Corporation, that one company can
5
Big bets are large commitments which will result in large gain or large losses. Options are intended to secure
big bets in case of an undesired outcome. No-regret moves are actions that will pay-off under all conditions.
6
Shapers create new opportunities. Adapters react to the opportunities provided by the existing market structure
and its expected future evolution. Reserving a right to play is a special form of adaptive strategy relevant only to
level 3 and 4 uncertainty, which encompasses making incremental investments to put a company in a privileged
position in the future.

5
successfully combine the mentioned approaches. Furthermore, they develop a “semicoherent
strategic direction” in search for an overall strategic goal and advocate continuous change,
improvisation, time pacing and leadership as means of achieving it.
Eisenhardt and Sull (2001) recommend that companies jump into chaotic markets, rather than
trying to avoid uncertainty. Given the high pace of change in today’s competitive
environment, Eisenhardt and Sull (2001) stress that companies should focus on key strategic
processes and the creation of “simple rules” that shape those processes, rather then
elaborating complicated strategies.7
They argue that complicated strategies cannot sufficiently
match the complexity of the real world. Furthermore, they tend to be inert. Eisenhardt and
Sull (2001) point out that not all rules make effective simple rules, and some key
considerations are the optimal number of rules and the process of their creation.
Eisenhardt and Sull (2001) demonstrate the principle of simple rules using the example of
Autodesk, a global leader in digital design software for building, manufacturing,
infrastructure, digital media, and location services. In the mid-1990s, Autodesk dominated
most of its target markets. The saturated mature market translated into a slow growth rate for
the company. The management saw the opportunities of leveraging Autodesk’s technological
expertise in wireless communications, the Internet, imaging and global positioning. However,
they were not sure which of the areas were the most promising. Given the uncertainty about
future technological developments and demand, the management implemented a simple-rule
strategy of radical shortening of the product development schedule. The new rule changed the
pace, scale and strategic logic, and created space for new opportunities.
Some simple-rule strategies are consistent with other theoretical approaches, such as the
Mintzberg’s functional strategy model (Mintzberg et al., 1998). For example, a product
innovation rule, which says that engineers can work on a project of their choice for a portion
of working hours, can be viewed as a design strategy. The same would be true about setting
boundaries through several basic rules and leaving everything else up to the discretion of
engineers.
Uncertainty is an increasingly important factor in strategic analysis. As many researchers and
practitioners found out, traditional analytical tools, which try to predict the future, seldom
provide sufficient capability to account for the complexity of a highly uncertain environment.
As a result, various frameworks incorporating uncertainty into strategic decision-making have
emerged. One of the first problems that come up when designing such frameworks is the
definition and classification of uncertainty. An interesting approach is provided by Courtney
et al. (1997), who view uncertainty as a general condition, which influences all stages of the
strategic decision making process, and which can be characterized by levels of intensity. Most
factors that have increased uncertainty today are somehow related to technological expansion,
globalization and the transition towards a knowledge society.
To make sense of an uncertain business environment, firms can develop a strategic intent,
employ Skat-Rørdam’s (1999) opportunity approach, use a portfolio of actions as suggested
by Courtney et al. (1997), create simple rules, or follow a semicoherent strategic direction
described by Brown and Eisenhardt (1998).
7
The “simple rules” can be divided into five broad categories: how-to rules, boundary rules, priority rules,
timing rules and exit rules. (Eisenhardt and Sull, 2001)

6
Bibliography
AMRAN, M. and KULATILAKA, N. (1999). “Uncertainty: the new rules for strategy.” Journal of business
strategy, Vol. 20, Iss. 3. Bradford: Emerald Group Publishing Ltd. ISSN 0275 6668.
ANSOFF, I. (1988). “Corporate strategy.” Revised edition. Harmondsworth: Penguin Books.
BORDIA, P. et al. (2004). “Uncertainty during organizational change: types, consequences, and management
strategies.” Journal of business and psychology, Vol. 18, Iss. 4. Dordrecht: Kluwer Academic Publishing.
ISSN 0889 3268.
BROWN, S. and EISENHARDT, K. (1998). “Competing on the edge: strategy as structured chaos.”
Boston: Harvard Business School Press.
COURTNEY, H. et al. (1997). “Strategy Under Uncertainty.” Harvard Business Review, Vol. 75, Iss. 6.
Watertown: HBS Publishing. ISSN 0017 8012.
DRUCKER, P. (1993). “Post-capitalist society.” Oxford: Butterworth-Heinemann Ltd.
DUNCAN, R. (1972). “Characteristics of Organizational Environments and Perceived Environmental
Uncertainty.” Administrative Science Quarterly, Vol. 17, Iss. 3. Ithaca: Cornell University. ISSN 0001 8392.
EISENHARDT, K. and SULL, D. (2001). “Strategy as simple rules.” Harvard Business Review, Vol. 79, Iss. 1,
pp. 106–117. Watertown: HBS Publishing. ISSN 0017 8012.
HAMEL, G. and PRAHALAD, C. (1989). “Strategic intent.” Harvard Business Review, Vol. 67, Iss. 3.
Watertown: HBS Publishing. ISSN 0017 8012.
HAMEL, G. and PRAHALAD, C. (1996). “Competing for the future.” Boston: Harvard Business School Press.
HAO MA (2003). “To win without fighting: an integrative framework.” Management Decision, Vol. 41,
pp. 72–84. Bradford: Emerald Group Publishing Ltd. ISSN 0025 1747. Available online from Emerald Fulltext.
JOHNSON, G. and SCHOLES, K. (1997). “Exploring corporate strategy.” 4th
edition. London: Prentice-Hall.
KNIGHT, F. (1921). “Risk, uncertainty, and profit.” Boston: Hart, Schaffner & Marx. Available online at
http://www.econlib.org/library/Knight/knRUP.html (retrieved 25/11/2004).
LIPPITT, G. (1982). “Organizational renewal: a holistic approach to organization development.” 2nd
edition.
Englewood Cliffs: Prentice-Hall.
MINTZBERG, H. et al. (1998). “The strategy process.” London: Prentice-Hall.
PARNELL, J. et al. (2000). “Strategy as a response to organizational uncertainty: an alternative perspective on
the strategy-performance relationship.” Management Decision, No. 8, Vol. 38, pp. 520–530. Bradford: Emerald
Group Publishing Ltd. ISSN 0025 1747. Available online from Emerald Fulltext.
PORTER, M. (1980). “Competitive strategy: techniques for analyzing industries and competitors.”
New York: Free Press.
ROSE-ACKERMAN, S. (1999). “Corruption and government: causes, consequences and reform.”
Cambridge: Cambridge University Press.
SKAT-RØRDAM, P. (1999). “Changing strategic direction.” Copenhagen: Copenhagen Business School Press.
STACEY, R. (2000). “Strategic management and organisational dynamics: the challenge of complexity.”
3rd
edition. Harlow: Pearson Education.

Uncertainty

More Related Content

What's hot

Similar to Uncertainty

Uncertainty