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Problem Solving in the Management of

Technology and Innovation:

Choosing the Uncertainty-Ambiguity Boundary

Stephan Schrader

William M. Riggs

Robert P. Smith

September, 1991

WP# 3345-91-BPS





Problem Solving in the Management of

Technology and Innovation:

Choosing the Uncertainty- Ambiguity Boundary

Stephan Schrader

William M. Riggs

Robert P. Smith

September, 1991 WP# 3345-91-bps

Please send correspondence to:

Stephan Schrader

Massachusetts Institute of Technology

Alfred P. Sloan School of Management

50 Memorial Drive, E52-553

Cambridge, MA 02139


Tel.: 617-253 5219


I mi

Problem Solving in the Management of Technology and Innovation:
Choosing the Uncertainty-Ambiguity Boundary


Technical problems are solved in an environment of uncertainty and
ambiguity. Most research in technical problem solving has two characteristics in
common: no differentiation between uncertainty and ambiguity occurs, and the
degree of uncertainty and ambiguity is considered exogenous to the problem
solving process.

This paper argues, first, that uncertainty and ambiguity are dissimilar
concepts. Problem solving under ambiguity involves fundamentally different
tasks than problem solving under uncertainty. Consequently, different
organizational structures are appropriate. Second, it is argued that uncertainty
and ambiguity are not exogenously given but at least partially determined in the
problem framing process. In this process, problem solvers select explicitly or
implicitly a specific uncertainty-ambiguity boundary. This boundary demarcates
the areas that the problem solver frames as involving uncertainty from those
that are framed as involving ambiguity. In the paper organizational
consequences of the notion that problem solvers choose an uncertainty-
ambiguity boundary are examined and implications for research on technical
problem solving are discussed.


Research on problem solving, especially on technical problem solving, has
addressed such questions as the effects of ambiguity and /or uncertainty on the
problem solving process (Marples 1961; Sutherland 1977), the interplay between
uncertainty/ ambiguity and organization structure (Marquis and Straight 1965;
Lawrence and Lorsch 1969; Larson and Gobeli 1988), and the need for different
communication channels under different uncertainty /ambiguity regimes
(Tushman 1978; Tushman and Nadler 1980, Allen 1984). Most empirical work on
uncertainty and ambiguity in technical problem solving has two characteristics in
common. First, no explicit distinction between uncertainty and ambiguity is made;
the two concepts are used as if they were interchangeable. Second, uncertainty and
ambiguity are considered exogenously given variables, variables managers must
react to.

In this paper, we first argue that uncertainty and ambiguity are dissimilar
concepts^ and that technical problem solving may involve both uncertainty and
ambiguity. Recognizing the difference between uncertainty and ambiguity is
important since the two concepts relate to different problem solving processes
and thus to different ways of supporting them organizationally. Secondly, we
propose that viewing uncertainty and ambiguity as exogenously given variables
is a misrepresentation of the problem solving process. We argue that one core
task in problem solving is the selection of the uncertainty-ambiguity boundary .
This boundary demarcates the areas that the problem solver frames as involving
uncertainty from those that are framed as involving ambiguity. Thus, we argue.

^ Similar arguments are made by March 1978; McCaskey 1982; Einhorn and Hogarth 1986; Martin
and Meyerson 1988.

levels of uncertainty and annbiguity are not exogenously given, but are the
results of explicit or in^plicit choice.

The notion that uncertainty and ambiguity are selected in the problem
framing process affects our understanding of technical problem solving. Under
conditions of innovation and technological change, it is important to focus
attention on this selection process, since this is the point at which the nature and
potential outcome of the subsequent problem solving process is determined.
Consequently, research on the management of technical innovation should
focus greater attention on the process of determining the uncertainty-ambiguity
boundary. By viewing uncertainty and ambiguity as exogenously given, a crucial
aspect of the problem solving process is neglected. Research results on the
impact of uncertainty and ambiguity on the appropriateness of such aspects as
communication networks (Tushman 1978; Allen 1984), project structure
(Marquis and Straight 1965; Larson and Gobeli 1988) and team composition (Katz
and Allen 1985; Ancona and Caldwell 1989) might need to be reinterpreted in
light of the proposed notion that uncertainty and ambiguity are at least partly
determined in the problem framing process. The observed impact of these
variables on project performance might be due at least partially to a different
choice of the uncertainty-ambiguity boundary for effective projects.

In this paper we first discuss the distinction between uncertainty and
ambiguity. We then describe how uncertainty and ambiguity affect differently
problem perception and appropriate solution strategies. Next, we propose that
problem solvers, whether individuals or groups, have at least some control over
the level of uncertainty and ambiguity in technical problem solving. They
choose the uncertainty-ambiguity boundary. This proposition conflicts directly
with the predominant concept that a problem can be characterized by specific
uncertainty and ambiguity levels. Subsequently, we discuss the organizational

consequences of the notion that the uncertainty-ambiguity boundary is the result
of a choice process, and we demonstrate how this idea changes our perception of
how research on technical problem solving should be designed, conducted, and


The concepts of uncertainty and ambiguity have been defined in a number
of ways in the organizational literature, depending on the nature of the research
question being addressed. In this section we will briefly review these definitions,
and then offer definitions which we find appropriate for discussing problem
solving in a technological environment.

Information theorists define uncertainty most abstractly: "the uncertainty
of an event is the logarithm of the number of possible outcomes the event can
have...." (Garner 1962, p. 19). Decision theorists define uncertainty more broadly,
as the situation where possible future outcomes are known, but where the
probability distribution is unknown, or at best known subjectively (e.g. Luce and
Raiffa 1957; Owen 1982). (Decision theorists also define the concept of risk as a
special case of uncertainty; that is, uncertainty with known probabilities, e.g.
Shubik 1982. We argue below that in technical problem solving no situations
with objectively known probability distributions exist.)

Organizational researchers have built on the above definitions,
broadening them to fit the organizational context. At the broadest level
uncertainty is defined in organization theory as a lack of clarity of information, a
lack of knowledge of causal relationships, and a lack of timely, definitive
feedback (Lav^ence and Lorsch 1969). This coincides with early definitions of
uncertainty provided by researchers on the psychology of problem solving (e.g.

Miller and Frick 1949), as derived from the mathematical theory of
communication (Shannon and Weaver 1949). In both lines of research,
uncertainty is viewed as stemming from a paucity of information.

In an effort to develop specific measures of uncertainty in the context of
organization research, Duncan (1972) operationalizes uncertainty as containing
three components:

"(1) the lack of information regarding environmental factors associated
with a given decision-making situation, (2) not knowing the outcome of a
specific decision in terms of how much the organization would lose if the
decision were incorrect, and (3) inability to assign probabilities with any
degree of confidence with regard to how environmental factors are going
to affect the success or failure of the decision unit in performing its

The first two of these components focuses on the lack of information, in a
manner similar to the broad definition of Lawrence and Lorsch (1969). The third
component is similar to the narrower definitions such as proposed by
information theorists and decision theorists, but emphasizes that participants
assign probabilities to outcomes subjectively, leaving doubt as to the accuracy of
these probabilities.

A common thread running through these definitions is that in each case
uncertainty relates to a lack of information. Consequently, if problem solvers
wish to reduce uncertainty, they must gather information about possible

Several authors, however, argue that management decision making
frequently is not well described by models of decision making under uncertainty
(e.g. Conrath 1967; March 1978; McCaskey 1982; Daft and Lengel 1986; Einhorn
and Hogarth 1986; Gimpl 1986; Martin and Meyerson 1988). They propose that

often possible future outcomes are not well defined, and that there may be
conflict with regard to what these will or should be. These authors therefore
maintain that decision making and problem solving are often carried out under
conditions of ambiguity, rather than uncertainty, where ambiguity is defined as
lack of clarity regarding the relevant variables and relationships (Martin and
Meyerson 1988, p. 112).i

As many as twelve sources of ambiguity faced by business managers have
been suggested (McCaskey 1982). However, three basic causes of ambiguity seem
to capture the fundamental issues (Kosnik 1986; Meyerson and Martin 1987):
confusion caused by ignorance, unpredictability resulting from unknown futvire
states, and contradictions due to paradox or irreconcilable conflicts. To illustrate
how these three factors could be associated with a technological choice problem,
corisider the problem of choosing a high capacity storage device for a new
computer. If the remainder of the computer design is not yet fully defined, the
engineer will be ignorant of the proper characteristics for the device; the
information necessary to determine his or her iriformational needs does not yet
exist. Such ignorance produces ambiguity about the choice. Further, suppose
that each of the available devices offers different adaptability for future changes.
It may not be possible to predict which characteristics will be needed in the
future, because that is a complex and unknown function of user needs,
application software requirements and hardware capability. This unpredictability
of future directions produces ambiguity. Finally, suppose that device memory
capacity and device access speed are both desirable dimensions of merit, and that

^ Ambiguity relates directly to Daft and Lengel's (1986) notion of equivocality, wfiich they define as
"...ambiguity, the existence of multiple and conflicting interpretations about an organizational

one can be maximized only at the cost of the other. This need to decide between
desired characteristics leads to conflict, which also produces ambiguity.

In sum, ambiguity is seen as resulting from a lack of clarity about choices,
stemming from needed information which does not yet exist, needed knowledge
of future conditions which cannot be predicted, and conflict over choices which
must be made that cannot be reconciled for technological or other reasons.

The factors associated with ambiguity differ significantly from the accepted
cause of uncertainty, i.e. the lack of information. None of these factors is, in fact,
susceptible to amelioration through information gathering alone. Because the
causes of uncertainty and ambiguity are different, organizational responses to
uncertain and ambiguous situations differ as well. For example. Daft and Lengel
(1986) argue that in situations of ambiguity, information media of greater
richness (for example, face-to-face communication as opposed to written reports)
are needed than in situations of uncertainty.

Mental Models and the Distinction Between Uncertainty and Ambiguity

What is different between a situation that is characterized by a lack of
information (uncertainty) and a situation characterized by a lack of clarity
(ambiguity)? We propose that characteristics of the mental models used by
problem solvers can help to distinguish more clearly between ambiguity and
uncertainty and to determine organizational consequences of this distinction.
The typical mental models are quite different in these two situations. This
difference has considerable ramifications for how problems are solved and for
how to manage the problem solving process.

Mental models guide individuals' behaviors, especially their problem
solving behavior (Mintzberg 1976; Brief and Downey 1983; Simon 1987; Clement

1989). "In effect, managers (like everyone else) use their information to build
mental models of their world, which are implicit synthesized apprehensions of
how their organizations and environments function. Then, whenever an action
is contemplated, the manager can simulate the outcome using his implicit
model" (Mintzberg 1976, p. 54). Mental models determine what is relevant for
understanding a specific phenomenon or for solving a problem. A well defined
mental model implicitly predetermines the relevant solution space to a problem
(Clement 1989).

When facing a problem, problem solvers might feel that they know what
to do, what specific information to look for, and what results to strive for. In this
case, the problem solvers have mental models available to them that they
consider adequate for the problem. This model demarcates the boundaries of the
problem and identifies the specific tasks necessary to solve the problem.

Alternatively, problem solvers might think they do not yet have a "good
grip" on the problem. This would imply an inability to decide on the problem
scope, to define the tasks involved, to discriminate relevant from irrelevant
inputs, or to identify the desired outcome. In other words, no mental model for
problem structuring is available to the problem solver that is perceived as
adequate. Problem solvers must find or create an appropriate model before
problem solving activity can begin.

The first situation characterizes problem solving under uncertainty . The
uncertainty is created by the problem solver not yet knowing the precise
characteristics of the outcome of the problem solving process. If the outcome
were known a priori, this would not be a case of problem solving. But the
problem solver has a dear understanding of the problem and possesses a mental
model that guides the problem solving process. The problem solving process

involves specifying the precise values of the variables of the mental model. The
informational needs are well defined.

The second situation characterizes problem solving under ambiguity .
Ambiguity exists because the problem solver does not yet know the precise
structure of the problem and consequently of the problem solving process. The
problem solver does not have a mental model available that is considered
adequate to guide problem solving behavior. The problem solver must first
determine a mental model to guide problem solving behavior.

According to this distinction, uncertainty implies that the problem solver
has a mental model to work within and (explicitly or implicitly) considers this
model to be sufficient for dealing with the problem. A sales manager planning
next month's sales activities might use a problem solving rule for predicting
sales volume, specifying that next month's sales will equal this month's sales
plus or minus five percent. No ambiguity exists in regard to which variables to
consider, however, uncertainty exists as to the exact values. Similarly, an
engineer calculating the friction of a surface might apply a standard formula to
his specific problem. He knows that the formula is rigorously applicable only to
idealized problems, and therefore is likely to provide only an approximate value.
However, as long as the engineer considers the method to be sufficient to obtain
a value which approximates the true value within acceptable limits, he is
solving the problem under uncertainty.

In the case of ambiguity, the problem solver does not have a model
available that he or she considers adequate to the problem. For example, the
sales manager might have the problem of forecasting sales for a new product
line. The old forecasting method might be considered inadequate because there
are no relevant past sales and trends from which to extrapolate. Thus, our sales
manager perceives the need to determine another way to forecast sales. This


implies identifying the variables that might be relevant and determining the
functional relationships. In other words, he needs to develop a model that he
considers appropriate to the problem. Likewise, an engineer might be faced with
the problem of estimating the friction to be expected in an automobile engine
piston assembly incorporating new ceramic parts. The new materials are
thought not to be amenable to the approximation methods used for more
traditional materials and the engineer does not yet know how to take these new
factors into consideration. He faces ambiguity since he does not have a model
available that helps him determine relevant variables and which prescribes the
problem solving steps to take. Alternatively, ambiguity might exist because
several conflicting friction models are available and no criterion is available for
deciding among them.

In the context of organizational problem solving, ambiguity frequently
arises because different members of the organization may use different,
conflicting models. Only if this conflict is somehow resolved does the annbiguity
give way to uncertainty.

The Difference Between Uncertainty Reduction and Ambiguity Reduction

Problem solving is frequently characterized as a process of uncertainty
and/or ambiguity reduction (e.g. Sutherland 1977). It follows from the
discussion of uncertainty and ambiguity provided above that the processes of
uncertainty reduction and ambiguity reduction must be quite distinct and
qualitatively different in structure, content and approach.

Uncertainty reduction is the process of gathering information relevant to
the variables defined within one's mental model. The problem solver has a
model that he or she considers adequate to the problem. This model corresponds


to an integrated conception of all relevant factors and their functional
relationships. Problem solving involves gathering information relevant to this
model and integrating this information according to the assumed functional
relationships. In short, one knows what one doesn't know and one tries to
reduce or eUminate this lack of knowledge.

Reduction of ambiguity is the process by which a model considered to be
appropriate to the problem is found or built. Ambiguity, as we have seen, is the
state of not knowing what the relevant variables and their functional
relationships are — it is lack of clarity in a problem situation. Constructing a
model to specify the relevant variables and the relationships betw^een them is a
creative process requiring rethinking of inputs, processes and outputs. Thus,
ambiguity reduction is inherently less structured, less amenable to organization
and management, and less predictable than uncertainty reduction. One does not
know what one doesn't know (but should know), and one seeks a model to
define this.

The difference between these two processes implies that they involve
different tasks. In the case of uncertainty reduction the key tasks are information
gathering and integration. In the case of ambiguity reduction, the tasks are
model building, negotiation, problem framing, evaluating and reframing, and
model testing.

Also implicit in the difference is that the two processes will be different to
manage. With uncertainty, one can manage the content of the problem solving
process, since one knows the tasks involved (e.g. Was the experiment run as
specified?) In the case of ambiguity, one can manage only the process (e.g. Has
the software engineer explored options which will speed up the program?)

Similarly, the difference between uncertainty reduction and ambiguity
reduction leads to different control measures. In the case of uncertainty


reduction, content oriented control measures can be used. In the case of
ambiguity reduction, only process or output oriented control measures are


We suggest that no a priori criterion exists for determining fully whether a
situation contains uncertainty or ambiguity. Most of the discussion of
uncertainty and ambiguity found in the literature has the assumption in
common that the uncertainty and/or ambiguity in a given situation is
exogenously given. We propose that, contrary to this assumption, uncertainty
and ambiguity are not exogenous to the problem solver, but rather that the
relevant levels of uncertainty and ambiguity are at least partially determined in
the problem framing process.

The prediction of heads or tails in a coin toss will serve to illustrate this
proposition. The problem of tossing a coin and predicting the outcome is usually
regarded as a problem of known risk, i.e. of a known probability distribution. But
is this necessarily the case? The person tossing a coin might assume that heads
and tails are equally likely — he is not able, however, to know this with
certainty. Or the person might assume that heads and tails occur in a fixed but
still unknown ratio and may decide to use Bayes' theorem to reach a better
estimate of this ratio. In this case, the problem solver would frame the problem
as one of uncertainty. He knows the relevant variables (occurrences of head and
tails in trial tosses) and thus can collect information regarding these variables.
But another alternative exists: the player may reject the proposition that the
game is fair or even that the ratio is constant. He could decide to attempt to
determine factors that affect the outcome distribution. He might not know.


however, which variables are likely to influence the outcome. Ambiguity exists
regarding the problem structure. He may investigate whether the coin is bent or
has any physical defect which produces a bias. He may experiment to determine
whether the way the coin is thrown has an influence on the outcome. Or he
may consider the possibility that the coin might sometimes land on its edge, thus
introducing another outcome. In other words, the player (or problem solver)
must choose the scope of the problem and thereby the levels of uncertainty and
ambiguity involved.

Two conclusions can be drawn from this example:

(1) The traditional distinction between risk (known probability
distribution) and uncertainty (unknown or subjective probability distribution) is
not helpful in the context of problem solving. The decision maker never knows
with certainty if an objective probability distribution exists and what the precise
characteristics of this distribution are. He can at best estimate those
characteristics — in other words, he has to dedde on what to consider a useful
representation of reality.

(2) The scope of the problem and the range of potential outcomes are
selected in the problem framing process. This conflicts with the traditional view
that the level of uncertainty and ambiguity are objective characteristics of a given
problem. As the example shows, the problem solver has at least some control
over determination of the levels of uncertainty and ambiguity that will be

The last conclusion has particular relevance in technological problem
solving, since the technical scope and the characteristics to expect of the outcome
(for example, which technologies to use in development and what performance
to expect of a new product) are not known a priori . Organizations seem often to
be inclined to frame problems as problems in uncertainty rather than ambiguity.


thereby limiting the possible solutions to the ones that fit within existing mental
models (Schon 1967; Henderson and Clark 1990). Conversely, if the organization

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Online LibraryStephan SchraderProblem solving in the management of technology and innovation--choosing the uncertainty-ambiguity boundary → online text (page 1 of 3)