If Managing Knowledge is the Solution,
then What's the Problem?

Michael H. Zack
College of Business Administration
Northeastern University
214 Hayden Hall
Boston, MA 02115 USA
(617) 373-4734

©Michael H. Zack, October, 1998
Published in Knowledge Management and Business Model Innovation, Yogesh Malhotra (ed.), Idea Group Publishing, April, 2001


Strategic theories of organizing are grounded in the notion that organizations should configure their internal resources and capabilities to address competitive opportunities and threats. The knowledge-based view suggests that knowledge may be the most enduring and strategic resource. This article presents a taxonomy for describing resources, capabilities and competitive environments in terms of four distinct yet related knowledge processing requirements or "problems", viz., complexity, uncertainty, equivocality, and ambiguity. Each suggests a particular knowledge processing capability. The taxonomy is used to develop finer-grained distinctions regarding knowledge-based theories of the firm and the resource-based concept of inimitability.


Strategic theories of organizing are grounded in the notion that organizations should configure their internal resources and capabilities to address competitive opportunities and threats (Andrews 1971, Ansoff 1965, Barney 1991, Grant 1991, Peteraf 1993, Porter 1985)1. Strategy is represented by the resources and capabilities an organization develops to meet its competitive requirements, and the environments it chooses to participate in given its capabilities. The basic organizing principle is to balance capabilities and requirements.

Knowledge-based views of the firm (e.g., Connor and Prahalad 1996, Grant 1996b, Kogut and Zander 1992, Leibeskind 1996, Spender 1996) suggest that internal resources and capabilities and external opportunities and threats be described in information- and knowledge-based terms. For example, information- processing models of organizations describe the environment in terms such as complexity and uncertainty and the organization's capabilities in terms of its information processing capacity (Driver and Streufert 1969, Galbraith 1973, March and Simon 1958, Thompson 1967, Tushman and Nadler 1978). The prescription is to configure an organization to provide an information processing capacity sufficient to deal with the communication requirements generated by environmental complexity and uncertainty. Information -processing models are useful for understanding firm behavior in uncertain environments, where intellectual resources become strategically more important than property-based resources (Miller and Shamsie 1995).

Organizations, however, are built not only on a foundation of exchanging information, but on creating, sharing, integrating and applying knowledge as well (Demsetz 1988, Grant 1996a, Kogut and Zander 1992, Penrose 1959, Spender 1996, Teece 1980). While information-based models of the firm provide a useful starting point, they fall short of a true knowledge-based view. They assume the exchange of well-formed messages based on shared understanding, rather than recognizing the diversity of knowledge, perspectives, values and interests that may actually exist within a firm. They focus on locating and sharing good answers rather than collectively surfacing and formulating good questions. They do not address how to manage knowledge when the firm doesn't understand its problems, know what questions to ask, or even agree on what it knows.

The knowledge-based view of the firm, on the other hand, recognizes that knowledge is not unidimensional. This perspective, in fact, is based on identifying characteristics of knowledge that make its creation, integration and exchange more appropriate via firms rather than markets (Connor and Prahalad 1996, Grant 1996b, Kogut and Zander 1992, 1996, Magrem 1961, Spender 1996, Teece 1980). For example, knowledge has been differentiated in terms of explicit vs. tacit, individual vs. collective, and common vs. context-specific (Demsetz 1988, Polanyi 1966, Spender 1996). Tacit, collective, context-specific knowledge is difficult to create, transfer, or integrate via markets and thus provides a rationale for firms. The resource- based view similarly suggests that this type of knowledge, if valuable and unique, may provide a competitive advantage because it is less imitable (Barney 1991). Firms, then, are offered as a solution for creating, managing and exploiting tacit, collective, context-specific knowledge. And like information-processing models, organizations would be expected to perform more effectively when their environmental knowledge-processing requirements and internal knowledge-processing capabilities are in balance. A firm's intellectual resources should support that capability today, and its ability to learn should maintain it over time (Lei, Hitt and Bettis 1996).

What is required to advance this view, however, is some discussion of why firms or collectivities process knowledge in the first place, and how various knowledge processing problems, threats or opportunities map to the internal knowledge processing resources and capabilities of the firm. Advancing knowledge-based theories of the firm will require a more fine-grained description of the "knowledge problems" to which firms are offered as a solution.

This paper describes a taxonomy of knowledge problems. It has direct implications for understanding the role of firms vs. markets in dealing with various knowledge-processing problems, for understanding competitive advantage via imitability of knowledge-based resources, and for configuring a firm's organizational resources to provide an appropriate knowledge processing capability. In the following sections, I describe four primary knowledge problems and one derived problem, knowledge- processing load, and suggest how each requires a particular configuration of organizational resources. I expand the framework to address various types of knowledge, show where different literatures have addressed only portions of this knowledge-problem space and propose the taxonomy as a framework for integrating that work. Finally I discuss how this framework applies to broader strategic phenomena such competitive advantage and the theory and management of the firm itself.

Knowledge Processing: Four Problems

An organization's environment includes those things it must pay attention to, come to understand, react to, or attempt to influence. These include its competitors, customers, trading partners, technologies, industry associations, government agencies, and economic and social institutions. In the organizational information processing tradition, they are described in terms of the perceived determinacy (or indeterminacy) of their meaning, existing or future states, and relationships to other things. Typically, the environment has been described by its uncertainty, and organizations, in terms of their capability to reduce or tolerate it. However, uncertainty, and related terms such as complexity, dynamism, and ambiguity have been inconsistently defined in the literature (Zack and McKenney 1988).

To address the problem and provide a more coherent and consistent description of the environment in knowledge-based terms, I use the attributes complexity, uncertainty, ambiguity, and equivocality. I argue that each poses a unique knowledge- processing challenge requiring a particular knowledge -processing capability (Table 1), provided by its intellectual and organizational resources. Together those attributes define the knowledge processing requirements of an organization and influence its knowledge-processing behaviors2. Each has been considered separately by some to be the essential organizational or strategic management problem. They do not exist in isolation of one another, however, and taken together, they provide a more complete picture of the challenge in managing organizational knowledge.


Complexity is simply "a large number of parts that interact in a nonsimple way" (Simon 1969, p. 195). It reflects the numerosity and diversity of situational elements (variables, issues, competitors, etc.) and the intricacy of their relationship (Simon 1969, Weaver 1948). Complex situations are not necessarily vague or unpredictable. They may be clearly-de fined or predictable situations whose length or intricacy of procedure or variety of elements and relationships that must be simultaneously considered is too large to easily process. Complex tasks, therefore, have many steps and factors affecting their performance. Complex problems have many potential and interrelated variables, solutions and methods. Complex organizations have a diversity of group members, stakeholders, and relationships (Campbell 1988, Dill 1958, Leblebici and Salancik 1981, Newell, Shaw and Simon 1962, Simon 1958, Wood 1986).

Complexity is not absolute. The number of different elements and relationships that can be considered simultaneously is a function of knowledge processing capacity which is, in turn, a function of what is known and familiar to the organization and its members (Newell, Shaw and Simon 1962, Stabell 1978). Therefore, having superior knowledge can provide competitive advantage because it enables managing greater complexity. The greater the number and variety of a firm's resources and the relationships among them; then the more complex it's capabilities may appear to competitors, the more difficult they are to understand and imitate, and the more sustainable the advantage (Grant 1991, Shoemaker 1990).

The response to complexity is either to increase a firm's capacity to process it or to reduce the level of complexity faced by the firm. Richer knowledge provides an ability to process greater complexity, in a sense allowing an individual or group to "chunk" an issue as a familiar whole rather than dealing with each of its individual parts (Huber 1984, Miller 1956, Stabell 1978). Experiential knowledge in the form of simple rules of thumb (heuristics) helps solve complex problems. Knowledge stored as intricate but familiar procedural routines triggered by recognizable events similarly provides the ability to perform complex group processes (Cyert and March 1963, Nelson and Winter 1982, Pentland and Reuter 1994). Therefore, bringing the appropriate level and variety of knowledge and expertise to bear on a situation helps to reduce or eliminate its complexity.

In the absence of sufficient knowledge and expertise, complexity can be reduced by decomposition, that is, by breaking things into simpler parts. This notion is reflected in division of labor, market segmentation, and pre-manufactured subassemblies. Problems also can be simplified by restructuring or redefining them to resemble something more familiar. Alternatively, limited, simplified views may be enacted by a group or imposed by those in power (Gray, Bougon and Donnellon 1985, Weick 1969). Decomposition is a useful complexity-reduction strategy for inherently decomposable tasks, while cooperating experts and limited views are more appropriate for nondecomposable tasks. Simplification, regardless of how accomplished, requires integrating the differentiated elements before being brought to bear on the output or goal (Driver and Streufert 1969, Huber 1984, Kogut and Zander 1992, Lawrence and Lorsch 1967, Stabell 1978).

Therefore, organizations facing complexity must develop the capability to locate, develop and bring appropriate knowledge, expertise, and skills to bear on those issues, or to restructure their problems, roles and routines to simplify those problems or render them more familiar.


Information theory defines uncertainty as lack of enough information to chose from an exhaustive and well-defined set of possible states, even if that set is not complex (Shannon 1949). Decision theory defines uncertainty as the probability or predictability of a set of states, preferred outcomes, and actions to achieve them (Garner 1962, March 1977, Raiffa 1968). Organizational information processing models adopted similar definitions of uncertainty as the lack of enough information to perform a task or the predictability of other activities upon which some task depended (Galbraith 1973, March and Simon 1958).

Uncertainty can range from known sets of states and outcomes, as assumed by information theory, to more subjective and biased assessments of those factors (Kaheneman and Tversky 1982). Thus, situations can be either completely determined ("complete certainty"), defined with known probabilities ("risk"), defined with probabilities estimated at some level of confidence ("subjective uncertainty"), defined but with unknown probabilities ("traditional uncertainty"), or undefined ("complete uncertainty") (Conrath 1967, Gifford et al 1979, Kaheneman and Tversky 1982). In all cases, the interpretive context of the uncertainty is assumed to be well-defined and meaningful. Even complete uncertainty is not meant to imply ambiguity or lack of understanding of the situation. Rather, within some meaningful context, the states, alternatives, and preferences cannot be completely defined because there may be states, etc. of which we are unaware.

Rumelt (1984) considered managing uncertainty to be the essence of strategy. Strategic events such as the emergence of a market, evolution of a technology, or the distribution of information among competitors can all be highly unpredictable. These events provide heterogeneous firms different opportunities to generate windfall rents. Isolating mechanisms help to preserve those newfound advantages. Thus firms must position themselves to capitalize on environmental uncertainty (Rumelt 1984). For example, organizations often deal with strategic uncertainty in a way that reflects classical information theory. According to John Browne, CEO of British Petroleum,

"Given the uncertainty in the world, strategy cannot be about gambling on one possible outcome five or ten years down the road. In my view, strategy is about buying the right options that will give us a shot at competing in the future - that will give us the right to play if we decide we want to when it becomes clearer what the game is about". (Prokesch 1997, p. 168)

A set of "competing hypotheses" about the state of the future in the form of business opportunities is proposed, funded and acted on. As more information is obtained (or eventually as the future reveals itself), it becomes possible to narrow those opportunities to a small number with increasing certainty (Mason and Mitroff 1974) at which point additional investment funds can be better directed.

Uncertainty, then, represents a lack of information, or factual "knowledge about" current and future states, preferences and appropriate actions. Like complexity, uncertainty can be managed by reducing it or increasing the organization's ability to tolerate it. Uncertainty can be tolerated by

  • Using existing situational knowledge to predict, infer, estimate, or assume (possibly by default or omission) values for missing information, with some level of confidence and reliability (Bruner 1973);
  • utilizing resource and information buffers (i.e., slack resources) (Galbraith 1973, Wildavsky 1983);
  • developing an ability to respond quickly and flexibly to unanticipated events (Galbraith 1973, March and Simon 1958, Thompson 1967).

Uncertainty can be reduced by

  • acquiring additional factual information or knowledge about something;
  • acquiring, developing or improving the knowledge and ability to predict, infer or estimate;

To manage uncertainty, then, organizations must develop their intellectual resources and capabilities to predict, infer estimate, and learn. They must develop their structural capabilities, especially their communication networks, to flexibly respond and adapt to the unexpected. They must develop their organizational and technical resources and capabilities to locate, refine, store, and communicate factual knowledge reliably and meaningfully.


In contrast to the assumptions underlying uncertainty and complexity, situations or events are often neither immediately clear nor understandable (Isenberg 1986, McCaskey 1982, Ungson, Braunstein and Hall 1981). Ambiguity represents an inability to interpret or to make sense of something (Machlup 1980, MacKay 1969, Weick 1969)3. Surface ambiguity represents having interpretive knowledge but either not being able to recall or activate it, or activating an inappropriate interpretation, usually because of insufficient informational cues. Deep ambiguity represents a complete lack of interpretive knowledge. Events are perceived as so new and unfamiliar that one cannot even make a vague guess about what is important or about what may happen (Brunnson 1985). This is similar to what Weick calls the collapse of sensemaking. "[P]eople suddenly and deeply feel that the world is no longer a rational, orderly system." (Weick 1993, p. 633). If uncertainty represents not having answers, and complexity represents difficulty in finding them, then ambiguity represents not even being able to formulate the questions. Within the resource-based view of strategy, causal ambiguity as a barrier to imitation has been identified as a key source of competitive advantage (Reed and DeFillippi 1990).

The resolution of ambiguity occurs by reframing a situation to something meaningful, by acquiring contextual or explanatory knowledge either from others or by learning and experience, or by having an interpretation externally imposed by others. Surface- level ambiguity may require only a small amount of additional factual information or prompting to provide sufficient orientation to recall or trigger the appropriate contextual knowledge. Resolving deep ambiguity where the knowledge is not available at all, on the other hand, cannot be resolved by gathering more facts. It typically requires cycles of interpretation, explanation and social ratification. Tentative hypotheses are created and a search begins for new knowledge or interpretation to test the hypotheses. Hypotheses are iteratively created until some plausible explanation emerges, providing the context within which to interpret the ambiguous information or event and to take action (Schank 1987, Weick 1969).

The key organizational capability is to provide for rich, interactive face-to-face conversation among a socially familiar yet intellectually diverse set of individuals. British Petroleum, for example, has done this by breaking up its huge organization into 90 autonomous business units to encourage within unit interaction, and by creating peer groups among units around common problems to encourage cross-unit interaction (Prokesch 1997). These interactions foster the development of expertise and advice networks which can be called on when needed.


Equivocality refers to multiple meanings for or interpretations of the same thing (Aristotle 350BC, Daft and Macintosh 1981, Machlup 1980, Weick 1969). Each interpretation is individually unambiguous (although possibly inappropriate or erroneous), but collectively they differ and may be mutually exclusive or in conflict. Equivocality also describes situations where there is agreement on a set of descriptive criteria (e.g., desirable market/ undesirable market) but disagreement either on their boundaries (e.g., the point at which markets go from being desirable to undesirable) or on their application to a particular situation (e.g., whether a particular market is desirable or undesirable).

The management of equivocality, viewed as the coordination of meaning among organization members, is considered fundamental to organizing (Gray, Bougon and Donnellon 1985). Each individual, having a unique, tacitly known set of experiences, values, and knowledge, will tend to interpret events differently (Weick 1969). Although consensual meanings do develop within practice communities, they are dynamic and tend towards divergence over time unless managed. For example, in cases where tacitly held process knowledge cannot be clearly articulated or unambiguously communicated to another person, multiple interpretations of how something should be done emerge, often resulting in no clearly unique best approach to a process (Bohn 1986). Today's popular movement towards sharing best practices in organizations therefore may be constrained by the ability to meaningfully explicate those practices. Equivocality also may result from unreliable or conflicting information sources, noisy channels, differing or ambiguous goals and preferences, vague roles and responsibilities, or disparate political interests allowing room for different interpretation (Aldrich 1979, Astley et al 1982, Gray, Bougon and Donnellon 1985, March 1977, McCaskey 1982, Weiner 1961).

Equivocality requires cycles of interpretation, interactive discussion, and negotiation to converge on a definition of reality (McCaskey 1982, Mintzberg, Raisinghani and Theoret 1976, Weick 1969). However, overly precise or coherent policies, rules and procedures for coordinating or imposing interpretation may misrepresent the contradiction, confusion, or diversity of views inherent in a situation (Daft and Wiginton 1979, March 1977, Weick 1969). Equivocality may be usefully sustained so as to avoid premature closure, maintain commitment, and address conflicting goals (Cyert and March 1963, Hackman and Walton 1986, March 1977, Quinn 1980). Ultimately, coordinated action may still be possible if it appropriately satisfies the range of interpretations (Donnellon, Gray and Bougon 1986).

Thus equivocality, representing multiple clear meanings, requires either cycles of face-to-face interpretation and negotiation to converge on one meaning, agreement on action that addresses multiple meanings, or externally imposed meaning.

Relationship Among the Four Problems

The four primary knowledge problems can be distinguished by the nature of the knowledge being processed, and whether the requirement is to acquire more knowledge or to place restrictions on what exists (Figure 1).

The first distinction is that of processing factual "knowledge-about", which is more akin to the notion of information, vs. the richer, more complex knowledge structures that support interpreting that information and building inferences and explanations about how and why things work (Kogut and Zander 1992). Information processing is associated with managing uncertain and complex situations within some agreed-on and meaningful knowledge context, and is amenable to analysis, manipulation and communication of facts. Processing knowledge is associated with resolving or managing ambiguous and equivocal situations, requiring interpreting, creating, sharing, and negotiating meaning. This distinction is similar to that between convergent and divergent problems proposed by Schumacher (1977) and cited by Cameron (1986).

"Convergent problems deal with distinct, precise, quantifiable, logical ideas that are amenable to empirical investigation. Convergent problems are solvable problems, and as they are studied more rigorously and precisely, answers tend to converge into a single accepted solution. Divergent problems, on the other hand, are problems that are not easily quantifiable or verifiable and that seem not to have a single solution. The more rigorously and precisely they are studied, the more the solutions tend to diverge, or become contradictory and opposite."

The four problems may also be distinguished by the notion of restrictive vs. acquisitive processing. Complexity and equivocality require restrictive processing to limit, impose, or enact structure and meaning, the first on factual information and the second on diverse viewpoints or interpretations. Uncertainty requires the acquisition (or generation by inference) of factual information, while ambiguity requires the acquisition of knowledge or interpretive frames. Thus restrictive processing is generally internally focused (work with or make sense of what information and knowledge you already have), while acquisitive processing requires external (to the individual, group or organization) search for more information or knowledge.

The four knowledge problems can be ordered by the extent of determinacy in interpreting or defining a situation, event or process (Figure 2). The most "wicked" of the problems is ambiguity, having no framework or means to interpret or define something. Equivocality, representing multiple interpretive frames and definitions, is slightly less wicked. Each, however, similarly involves managing and processing knowledge. More tractable is the case of uncertainty, where one unique interpretation has been defined, although only with some degree of confidence or predictability. Finally, even a single interpretation defined with certainty may still require considering many elements and linkages, representing complexity. Ultimately, certain simplicity represents one clear, confidently predictable interpretation that is familiar enough to chunk as a small number of elements. It should be noted that these states are not mutually exclusive. For example, once an ambiguous situation has been interpreted, it may reveal itself to be uncertain, complex or both. However, based on extensive field observations 4, the four problems do exhibit a patterned sequence. Meaning must be established and sufficiently negotiated prior to acting on uncertainty and complexity. Ambiguity, if perceived, must be resolved first, often leading to equivocality as multiple interpretations emerge. Resolving equivocality creates a shared context for dealing with uncertainty or complexity, and ongoing systematic learning.

Processing Load

Together, the four primary knowledge-processing problems create a derived problem call load. Load traditionally represents the information processing demand on an individual, group or organization, relative to its capacity to process that information. Overload refers to processing demands that exceed capabilities, at which point performance degrades (Driver and Streufert 1969, Garner 1962, Meier 1963, Miller 1977, Schneider 1987, Shannon 1949). Strategy, from an information processing perspective, is a matter of avoiding overload by balancing a firm's internal information processing capabilities to the information processing load generated by its competitive environment and position.

A baseload amount of communication and knowledge processing is required even for a steady-state organization. Beyond that, load is driven by changes in perceptions, interpretations and expectations about goals, states of the world, resources, capabilities, and appropriate actions. Load, then, reflects what the contingency theory literature has typically referred to as dynamism (Duncan 1972), volatility (Leblebici and Salancik 1981, Tosi, Aldag and Storey 1973) or turbulence (Huber 1984). However, it is not change per se that is of concern, but change relative to the four knowledge problems. For example, change could be simple, involving only a few factors or relations changing one-at- a-time in a known sequence, or it could be complex, involving many factors and relationships simultaneously. Change could be highly predictable (what might be called variation) or highly uncertain (what might be called volatility or turbulence). Similarly change could be ambiguously or equivocally perceived and interpreted.

Load, from a knowledge perspective then, is the amount of knowledge processing that a firm must perform within some time interval to manage complexity, uncertainty, equivocality and ambiguity to perform its tasks and execute its strategy, as well as to adapt to change and maintain the organization itself. Overload occurs when the organization is unable to perform the amount of processing required because that amount is too great, given the time and resources available. The challenge is for the organization and its members to develop sufficient intellectual resources and processing capabilities to manage or reduce equivo cality, ambiguity, complexity, and uncertainty. Alternately, the organization may manage the knowledge environment generating that load (for example, by reducing the number of customers, serving more stable markets, or taking on simpler or more familiar tasks) to bring it into balance with its capabilities. Strategicaly, organizations must maintain a balance between overload and underload, in that overload reduces performance effectiveness by exceeding capabilities while underload reduces performance effectiveness by a lack of challenging experiences, stimulation for learning, and inefficient use of resources (Hedberg 1981, Tushman and Nadler 1978).

Extending the Taxonomy to Different Types of Knowledge

There are four types of organizational knowledge, each of which uniquely contributes to the healthy functioning of an organization.

  • Declarative knowledge, or knowledge about, refers to the ability to recognize and classify concepts, things and states of the world. It can be represented as a hierarchical classification scheme (Bobrow and Norman 1975) such as that underlying the Dewey decimal system, the charting of genus and species of life forms, a bill of materials, or a table of contents. Effective communication and sharing of knowledge requires the members of an organization to agree on the labels, categories and distinctions used to represent the things important to the organization (Rogers and Kincaid 1981, von Krogh and Roos 1995).
  • Procedural knowledge, or knowledge how, refers to the understanding of an appropriate sequence of events or the ability to perform a particular set of actions (Gioia and Poole 1984). This may include organizational ceremonies and rituals as well as everyday operating procedures and routines (Cohen and Bacdayan 1994). Procedural knowledge can be represented as ordered sequences of events associated with particular roles and relations. Shared procedural knowledge enables efficient coordinated action to take place.
  • Causal knowledge, or knowledge why, refers to an understanding of why something occurs, for example, the factors influencing product quality or customer satisfaction. Causal knowledge can be formally represented by describing the causal links among a set of factors (Schank 1975, Weick and Bougnon 1986), but more often is less formally represented as organizational stories (Schank 1990). Shared stories provide a means for organizations to develop consensus about why particular actions should be taken or how best to achieve some goal (Boje 1991).
  • Relational knowledge refers to an understanding of the relationships among or between these types of knowledge. For example, learning and innovation is often the result of creating or modifying relationships among existing and seemingly disparate concepts and ideas. Applied to organizations, firm performance is strongly related to knowledge of how the resources and competences of the firm relate to one another (Black and Boal 1994, Penrose 1959, Spender 1996). Developing new products and markets is often the result of recombining existing resources and competences rather than acquiring new ones (Grant 1996a, Schumpeter 1934), and failures are similarly the result of not understanding how those resources relate. A particularly useful form of relational knowledge is understanding how the human resources of the firm relate to one another - that is, the social and communication networks of the firm through which knowledge is transferred or shared (Krackhardt and Hanson 1993).

These types of knowledge form a hierarchy. Organizations cannot exist without some definition of the classifications, distinctions and labels used to communicate and make sense of the world (Von Krogh and Roos 1995) - its declarative knowledge. They next must have knowledge of how to perform their work and engage in collective behavior (procedural knowledge), leading to deeper knowledge of why things occur and which actions to take (causal knowledge). Relational knowledge of how the entire organizational system is internally and externally interconnected is the highest form of knowledge. The four types of knowledge described above can be juxtaposed against the four knowledge problems (Table 2). This framework provides more specific guidance for strategically managing organizational knowledge. Each cell represents a somewhat unique knowledge management challenge requiring a particular knowledge processing capability.

A range of literature has addressed particular cells of this matrix. For example, evolutionary and resource-based economics (Nelson and Winter 1982, Reed and DeFillipi 1990, Rumelt 1984) have focused on causal ambiguity, knowledge-based economics (Romer 1992) on declarative complexity, decision sciences (Conrath 1967, Raiffa 1968) on declarative and causal uncertainty, linguistics (Akmajian, Demers and Harnish 1984) on declarative equivocality (which they term semantic or lexical ambiguity), and the problem solving literature (Newell, Shaw and Simon 1962) primarily on relational complexity.

These knowledge problems as illustrated here, however, are not isolated cells, but rather pieces of a broader knowledge management puzzle requiring an integrated view for its management. Organizations are simultaneously managing the progression from ignorance to truth across several knowledge types and domains. These challenges range in their tractability and solvability. At one extreme is the case of managing well- defined and agreed on (although possibly complex) labels and facts, amenable to codification and manipulation via predictable roles, routines and use of information technology. At the other extreme is perhaps the most challenging yet highest-value knowledge problem of managing ambiguous, tacit, systemic, relational knowledge (Black and Boal 1997, Boisot 1995, Winter 1987). Each cell poses a unique challenge and opportunity. The matrix offers a useful starting place for mapping them and the associated knowledge resources of an organization.


Following the strategic management literature, organizations were described as having capabilities that should be matched to the requirements of their competitive environment to sustain effective performance. Taking a knowledge-based view, organizations were characterized as knowledge processors having a particular set of intellectual resources and knowledge-processing and learning capabilities. The environment was characterized as posing four knowledge problems, namely, complexity, uncertainty, ambiguity and equivocality. Together, these problems generate a particular knowledge-processing load for an organization. To the extent that capabilities and load are in balance, the organ ization should perform more effectively. To the extent that load exceeds the capability to deal with it, the organization experiences overload, and to maintain performance effectiveness must eventually take action to reduce its load or increase its knowledge processing capabilities. To the extent that capability exceeds load, the organization may, over time, experience lower performance and may either reduce capabilities or, if appropriate, act to increase knowledge-processing load.

Ambiguity and equivocality require rich face-to-face interaction to create or negotiate meaning or at least consensus on action. Uncertainty requires the ability to get information to where needed when needed, the knowledge and skills to infer or estimate in the face of missing information, the capability to be responsive or, when all else fails, physical and intellectual resource buffers. Complexity requires the capability to bring expertise to bear, the capability to consider more related factors simultaneously, or to simplify by decomposing the problem or the organization into simpler parts.

The knowledge-problem framework applies not only to managing particular problems, tasks or events, but also lends insight into the nature of competitive advantage and knowledge-based theories of the firm.


The resource-based view of the firm suggests that sustainable competition is based, in part, on resources and capabilities that are inimitable (Barney 1991, Lippman and Rumelt 1982, Rumelt 1984). Imitation requires understanding a set of factors that include the resources being used or required, potential resource substitutes, how those resources combine to provide a capability, what that capability is, how that capability is combined with other capabilities to provide advantage, and how to implement it. Each of these elements is subject to the four knowledge problems, and that framework lends a finer-grained insight into the nature of imitability.

Complexity suggests that these factors are hard to understand and imitate because of the numerosity and diversity of resources and the intricacy and tight coupling of their relationships (Grant 1991). This represents complex imitability. Uncertainty suggests that relationships among resources, capabilities, and outcomes maybe meaningful but difficult to observe as there is a stochastic element that the observing firm may not understand, or of which they are unaware. Therefore the imitator requires either knowledge in the form of a stochastic model by which to predict those relationships (which represents knowledge that even the focal firm might have difficulty developing), or more information in the form of a large number of observations of those relationships on which to base predictions. Stochastic relationships among resources, capabilities and performance therefore will represent a stronger isolating mechanism than if those relations were determined (Lippman and Rumelt 1982, Rumelt 1984). This type of inimitability represents uncertain imitability. Ambiguity suggests that potential imitators are unable to place the resources, capabilities or relationships within a meaningful framework. The precise reasons for success or failure cannot be determined, even after the event has occurred, and it is impossible to produce an unambiguous list of the factors of production, much less measure their marginal contributions (Rumelt 1984). Even if they could describe the resources, capabilities and relationships, they would not be able to make sense of them. This type of inimitability represents ambiguous imitability. Likewise, equivocal imitability would obtain where a potential imitator experiences various and difficult to reconcile interpretations of what a competitor is doing, the resources and capabilities needed, or their relationships.

Competitors' indeterminate perceptions of a firm's resources, capabilities and their relationships can provide isolating mechanisms. However, these same indeterminacies, if perceived within the firm, give it no clear knowledge advantage over its competitors (Peteraf 1993), and may hamper effective performance and learning. Firms, therefore, have an incentive to resolve these four knowledge problems for themselves, while attempting to maintain and promote those knowledge barriers vis-a- vis competitors. In general then, sustainable comparative advantage from inimitability can result from having superior intellectual resources and capabilities such that the firm's capabilities are perceived as relatively more complex, uncertain, equivocal, or ambiguous to a firm's competitors than to the firm itself.

Knowledge-based Theories of the Firm

The four-problem framework similarly lends insight into knowledge-based views of the firm. Several authors have used market failure to develop a knowledge-based theory of the firm (Conner and Prahalad 1996, Demsetz 1988, Grant 1996b, Kogut and Zander 1992, 1996, Leibeskind 1996, Malmgren 1961, Teece 1980). Markets incur knowledge-based transaction costs because of bounded rationality (Conner and Prahalad 1996, Willimason's 1975). Willimason's (1975) definition comprises two components (Conner and Prahalad 1996). First is the limited capacity to process the scope and diversity of information necessary to support market transactions. This represents what I have defined as the complexity and uncertainty of otherwise well-defined contracted behaviors. Second, individuals are bounded by their inability to articulate particular types of (tacit) knowledge or feelings in ways that permit them to be understood by others. This reflects the ambiguity or equivocality inherent in executing and monitoring market contracts. Thus, even if people did not behave with guile as assumed by Williamson, there still may be frictions associated with the exchange of knowledge in the market (Conner and Prahalad 1996, Kogut and Zander 1996). Honest persons may disagree on the best course of action or the division of gains. They may make different judgements, hold different expectations or formulate different interpretations given the same information (Conner and Prahalad 1996), all reflecting equivocality.

Each of the four knowledge problems raises a distinct issue that must be identified and remedied (at some cost) to enable effective market contracts. Complex transactions suggest decomposing contracts into simpler parts or contracting only in known and familiar situations. Uncertain transactions suggest shortening the duration of the contract, establishing acceptable bands of performance variation, introducing buffers (at some agreed to cost-sharing), establishing contingencies and effective environmental monitoring systems, or contracting only under more predictable circumstances. Equivocal transactions require negotiating the meaning of terms and procedures before negotiating the contract, or agreeing to a set of behaviors that explicitly addresses all meanings and interpretations. Ambiguity requires establishing a context for interpreting the meaning of all terms and actions before contracting. Markets would be expected to fail in situations where transactions cannot be unambiguously specified. The key is to describe the knowledge profile of the transaction along these four characteristics to better identify the sources of knowledge-based transaction costs and the advantages of within-firm knowledge processing.

Management of the Firm

The four-problem framework also lends insight into the evolution of organizational management generally. Organizations have historically been structured and managed to deal with complexity. The competitive environment has been assumed to be known, describable, and relatively stable and predictable (Tsoukas 1994). The problem has been to identify the structure that best suits the complexity reflected in the organization's competitive strategy (Chandler 1962) and implement the appropriate routines (Cyert and March 1963). If the organization can legitimately take a relatively simple view of its competitive landscape (e.g., a narrow product-line firm), then a functional organization may suffice. As product line complexity increases, product divisions emerge to map those product lines onto simpler organizational subunits with greater focus, and operating routines are similarly realigned (Skinner 1974). As the market becomes more geographically or demographically complex, market- oriented divisions may similarly emerge. As the environment becomes even more complex, the structure of the organization may become so as well, for example, in the form of matrix organizations. The essence of mapping the structure to the environment is to keep communication patterns and operating routines simple and relatively contained. If organized appropriately, most communication and coordination will be within each organizational unit, and only a small amount will be required across units. Managing complexity, therefore, can be though of as an operational model of organizing. That is, analyze the marketplace, chose the firm's competitive position, design and build the appropriate organization, and put it to work. Manufacture products and push them through the supply chain. And if markets are underserved or growing in stable ways, this may suffice as strategy. And this is the situation in which many companies found themselves once their environment started to become less predictable and certain.

Uncertainty, requiring the ability to dynamically move information to where needed when needed in unanticipated ways, places a huge strain on the capacity of a hierarchically decomposed organization designed for managing complexity. Hierarchy assumes that the cross-unit communication load will not be great. However, even those companies that have taken more organic forms so as to better deal with uncertain environments, still assume that the world is known and that it is known consistently and coherently across the organization. The problem is only to get enough information about the world to describe it today or predict its state tomorrow. This is the assumption underlying the sense-and-respond models of management (Heackel 1995, Haeckel and Nolan 1993). If the firm can get enough information about the actual behavior of its products, customers and competitors, it can determine how to best supply and dominate the market. Managing uncertainty underlies quick response, continuous replenishment, agile manufacturing and other approaches to supply chain management. This model has also driven the information technology industry of late in the form of, for example, customer support systems, electronic commerce, data warehouses, total quality management, and data mining technologies. The management of uncertainty can be thought of as a control model of management. Set a performance goal, predict and measure interim outcomes, and perform mid-course corrections. In the case where an organization can obtain maximal information in a minimal time period and can reconfigure itself with little or no set-up time and costs, the need for planning theoretically goes away as uncertainty becomes fully tolerable.

Both sense-and-respond and make-and-sell models suffer from assuming a known or knowable world. For example, mining customer feedback information requires knowing what questions to ask, how to interpret and share the results, and what actions to take. Doing this well requires the ability to learn over time and to share that learning with others in the organization. Learning and sharing, when the assumption is that the world is neither clearly known nor knowledge easily communicated, requires new approaches and organizational forms that encourage rich interaction among those who need to create, share and integrate knowledge rather than information (Brown and Duguid 1991, Quinn, Andersen and Finkelstein 1996). Sense and respond must give way to experiment and enact.

Organizations have begun to move from managing based on what they think they know, to discovering what they actually know. What will be more difficult is accepting that there are things about which they don't know and acting accordingly. The challenges of complexity and uncertainty have not gone away. However, in today's dynamic competitive environment, they have been augmented with problems of equivocality and ambiguity. The taxonomy of knowledge problems presented here is an attempt to refine our thinking about knowledge, the problems it poses, the opportunities it creates, and the approaches required to create, manage and exploit it. The truly knowledge-based firm must maintain the capability to handle the entire scope of knowledge problems.


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1. Even counter-arguments to the so-called design school of strategic choice such as Mintzberg (1990, 1991) posit that particular organizational forms provide particular capabilities.

2. Knowledge processing behaviors are also influenced by social factors such as culture and power, whose discussion is beyond the scope of this paper.

3. Ambiguity traditionally has referred to the semantically ambiguous situation of multiple possible meanings for a text (Minsky 1968). I distinguish, however, between the cases of multiple clear meanings and complete lack of meaning. Based on information theory (Shannon 1949), I refer to multiplicity of meaning as equivocality (Aristotle 350BC, Dretske 1981), and lack of meaning as ambiguity.

4. Those organizations represent a wide range of industries including financial services, software, publishing, consumer and industrial goods manufacturing, professional services, and retailing.