Goal Attainment Functions

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Goal attainment functions support planning and control. Goal attainment functions “help

organizational actors frame and identify goal states in the context of the organizational

past, store goal states, formulate strategies for achieving goal states, evaluate progress

in the direction of goal states, suggest alternatives based on the evaluations, update goal

states based on new information, and store annotated histories” (Stein & Zwass, 1995).

Planning and control methods address relationships between the strategic, tactical, and

operational variables that constitute the organization’s vertical and horizontal dimensions.

Accordingly, planning and control methods would match the objects and relationships

among the cognitive maps of individuals of differing organizational levels. Such

patterns link managerial knowledge with multiple operational domains, thus facilitating

planning and control over a broad spectrum of organizational activities. As with adaptive

functions, graph-theoretic databases are more capable of constructing these transitive

vertical associations than relational databases.

Pattern Maintenance Functions

Pattern maintenance functions refer to those functions that help preserve and develop

human resources over time (Stein & Zwass, 1995). Accordingly, these methods can

generate a variety of inputs to the firm’s human resources system. Perhaps some of the

most important pattern maintenance functions would be those that track the added value

of an individual’s contributions. These functions could help answer such questions as

how often the individual’s cognitive maps have been accessed, how many times

contributors have met with other employees to elaborate upon their contributions, and

the extent to which an individual’s causal maps have influenced organizational decisionmaking

and sense-making activities. In this way, the social causal mapping system could

help ensure that contributors are fairly rewarded. These reports would also be beneficial

in identifying important causal maps that could be incorporated into the firm’s employee

training activities. Since the causal maps would be drawn from both IS personnel and their

clients, the pattern maintenance functions within a social causal mapping system could

help support the careers of both sets of employees.

Mnemonic, integrative, adaptive, goal attainment, and pattern maintenance functions go

beyond the simple mechanistic linking of cognitive maps, i.e., the “aggregation” of causal

maps (Bougon, 1992). When embedded with suggestions, rules, and procedures for

understanding distributed and social cognition—such as those that ask questions

challenging assumptions, or treating “human meaning” as subjective and interpretive

rather than as unproblematic, predefined, and prepackaged (Boland et al., 1994)—these

OMIS functions support the continuous social negotiation and enactment of the

organization’s social system (i.e., Bougon’s 1992 concept of “congregation”). In this

way, a computer-based social causal mapping system can serve as an important tool for

research, practice, and organizational policy making.

Constituent Processes of OMIS Functions

Stein and Zwass’ OMIS functions give a high-level description of some of the methods

required of a causal mapping system. These methods can be decomposed into more

fundamental processes. Research in organizational learning has long relied upon graph

theory as a means of analyzing such phenomena as the cumulative effects of paths and

feedback cycles and the emergent properties of causal maps (Forrester, 1961; Axelrod,

1976). These include the relationship between changes in individual cognitive maps and

changes in organizational stability (Axelrod, 1976; Fiol & Lyles, 1985), and how reorganization

or reengineering changes cognitive maps within the organization (Fiol & Lyles,

1985; Huber, 1991).

Concatenations of node-arc-node segments should be processable in such a way as to

identify salient features of the organization’s knowledge terrain. One such process is

traversal. Traversal requires that elementary node-arc-node components be assembled

into transitive “paths.” Here, “transitive” is used in its mathematical sense (i.e., a→b→c),

not in its temporal sense. The transitive nature of organizational knowledge cannot be

easily represented in set theory. However, rules taking the form “if A1 and A2 and ... An

are true, then B is true” (Ullman, 1988) can be used to calculate transitive closures. Such

statements, called Horn clauses, can be applied when organizational memory is represented

in a logical rather than a relational form. This logical form, called predicates, allows

the representation of knowledge as functions mapping arguments to TRUE and FALSE

values. This representation permits the identification of paths between nodes, and can

identify relevant data for computing overall effects of the paths (e.g., x→y→z

= x→z; x→y−→z = x−→z).

Traversal is often accomplished via iterative functions that typically use a relatively

simple algorithm to step repetitively across node-arc-node segments. Iterative traversal

functions can discover long chains of node-arc-node segments—something that is

virtually impossible for causal beliefs represented in set-theoretic relational databases.

However, care must be taken in the analysis of cyclic paths to avoid infinite loops.

Therefore, more sophisticated algorithms—such as those that identify the existence of

cyclic paths, or the shortest available path (e.g., Dijkstra, 1959)—should be applied.

Traversing a collection of causal maps can help a user identify characteristics of

concatenated causal relationships between important organizational variables. For

example, locating and resolving equivocalities—the existence of contradictory beliefs—

is an important form of organizational learning (Weick, 1979). Equivocalities may exist in

a number of forms. The simplest involves a direct contradiction between two causal

beliefs (e.g., x→y, x−→y). Another more complex example involves contradictory

paths of concatenated causal beliefs (e.g., x→u→y, x→v−→y).

A third example can use categorical maps to locate equivocalities: the causal beliefs

x→y and x−→z are equivocal if there exists a categorical relation between y and

z such that y = z.

Methods that encode traversal algorithms offer a means for integrating causal knowledge

(represented by causal maps) with multiple vocabularies, patterns of word use, assumptions,

decision models, and other aspects of reasoning represented by categorical,

associative, and other cognitive maps. This integration of multiple types of representations

thus increases the depth of knowledge and semantic richness of a social causal

mapping system. This increased depth of knowledge may also allow users to compare

their causal beliefs to information contained in other organizational knowledge repositories.

The usefulness of this last approach can be demonstrated in the following

comparison of individual beliefs and empirical reports. Suppose that a particular belief

x→y is held by a wide number of organization members. Suppose also that the

opposing belief x−→y is not held by any organization member, but that an empirical

report generated from the organization’s data mining programs supports the interpretation

x−→y. The contradictory interpretation x−→y embodied within the empirical

report is an example of causal associations generated from non-human, organizational


The creation of these molecular organizational knowledge structures composed of

human- and computer-generated components does not guarantee organizational learning

will occur. However, it can help support organizational learning in at least two ways.

First, it supplies useful raw materials for organizational learning processes, such as

conflicting causal beliefs (i.e., equivocalities), undesirable feedback loops (e.g. “vicious

circles”), and unintended effects (e.g., a change in one node may lead to an unwanted

change in another). Second, it identifies individuals who can participate in social

processes in which they may elaborate upon the richer context of that knowledge. That

is, individuals who have contributed to the causal mapping system can be called upon

at a later date to explain or elaborate their causal assertions.