Chapter XIV

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Approaches to Causal


A Proposal

Robert F. Otondo

The University of Memphis, USA


Comparing, contrasting, and collectivizing causal maps provides a useful way for

extending representations of individual-level cognitions to an organization-level of

analysis. Carrying out these processes can be tricky, however, because the terms used

to denote nodes within causal maps are often so terse that important nuances and

meanings critical to linking or distinguishing the espoused beliefs of multiple individuals

may not be faithfully represented. Previous efforts in causal map research are extended

by representing these linguistic and semantic nuances in associative, categorical, or

other cognitive maps, then using those maps to link related elements of causal maps.

These multiple types of cognitive maps are then integrated in a logical view (i.e., class

and object structures) of a graph-theoretic, object-oriented design.


Causal maps represent the network of causal relations embedded in an individual’s

explicit statements, and as such provide an explicit representation of the deep-rooted

cognitive maps of individuals (Huff, 1990; Nelson, Nadkarni, Narayanan & Ghods, 2000).

While causal maps provide a concise representation of an individual’s beliefs and

assumptions about causality, that conciseness often fails to represent important nuances

in complex beliefs and assumptions that are crucial to extending individual causal

maps to an organization-level of analysis. These nuances in word use can lead to the same

term being used to represent different ideas, different terms being used to represent

similar ideas, or a host of words changing and emerging within a vocabulary over time

as individuals share beliefs. These patterns of word use create difficulties in the

comparison of individual causal maps, and suggest that while a causal map may be

sufficiently “rich” to represent causal beliefs at the individual level of analysis, collections

of individual causal maps do not adequately represent the richness of the problem

space of social causal cognition.

The purpose of this chapter is to provide frameworks for representing important nuances

in language use during social causal cognition, and to embed those frameworks in groupand

organization-level causal maps. These goals will be accomplished through two

objectives. First, an overall strategy for mitigating the representational limitations of

causal maps will be presented. This strategy augments collections of causal maps with

other representations of the cognitive, communicative, and behavioral aspects of

knowledge sharing. This family of representations, collectively called cognitive maps,

is a general class of physical representations of thoughts and beliefs that can represent

individual assertions, or those elicited from a group (Huff, 1990; Montazemi & Conrath,

1986). Cognitive maps can provide a rich resource for comparing, contrasting, or

collectivizing large numbers of causal maps. Causal maps are only one sub-class of

cognitive maps. Other sub-classes of cognitive maps include such representations as

categorical maps that focus on relationships of similarity and associative maps that

represent frequencies and changes in word use (Huff, 1990).

The second objective of the chapter is to provide a design for a tool that can seamlessly

acquire, store, and manipulate multiple cognitive maps. This is a tall order because

augmenting causal maps with various types of cognitive maps would significantly

increase the computational complexity of processing causal maps. Computer-based

information systems are a likely candidate for this tool because they have been

successfully used in the past for problem spaces of similar complexity.

The chapter is organized as follows. First, difficulties of using causal maps at the social

level of analysis are examined. Second, alternative high-level designs for a computerbased

tool that are commensurate to the characteristics of cognitive mapping are

proposed and discussed. This discussion is then extended to a more detailed description

of data and functional elements necessary for the proposed computer-based tool. Finally,

conclusions, limitations, and potential applications are discussed.

Representing Causal Beliefs at Social

Levels of Analysis

Causal maps were originally designed to represent an individual’s beliefs about causal

relationships between entities in the real world. There are several reasons why these

representations of individual cognition might contribute to our understanding of social

cognition. Perhaps the simplest reason is that a comparison of causal maps from different

individuals is useful for identifying similarities and differences in causal beliefs from

across an organization, thus providing measures of an organization’s cognitive homogeneity

(Laukkanen, 1994). Causal maps are also useful for documenting changes in coworkers’

causal beliefs over time, therefore providing a means for analyzing the processes

of belief sharing and organizational learning (Langfield-Smith, 1992). A third

reason lies in the hope of identifying feedback loops and organization-wide effects,

which could help mitigate “vicious circles” and unintended effects (Morecroft, 1988;

Senge, 1990; Senge & Sterman, 1992; Eden, Ackermann & Cropper, 1992). These various

processes for using individual causal maps to represent and understand social causal

cognition are called, for the purposes of this chapter, social causal mapping.

Social Causal Mapping Across Diverse Vocabularies

While comparing, contrasting, and collectivizing causal maps can play important roles

in understanding social cognition, these processes are often problematic because the

terms, words, and phrases elicited from subjects revealing their causal beliefs can be

difficult to match. Unlike the systems dynamics models of engineered physical systems

that reflect modules interconnected via well-defined interfaces described in more-or-less

standardized nomenclatures (e.g., Forrester, 1961), collections of causal maps are

representations of individual cognitive belief systems that typically reflect a wide variety

of experiential, cultural, contextual, and procedural knowledge domains and related

vocabularies. This complexity is especially evident in knowledge management systems,

and is embodied within Davenport and Prusak’s (1998) differentiation of knowledge (i.e.,

“a fluid mix of framed experience, values, contextual information, and expert insight that

provides a framework for evaluating and incorporating new experiences and information”

from data (i.e., “a set of discrete, objective facts about events”) and information (i.e., data

that “informs,” that “makes a difference”). These differences, Davenport and Prusak

argue, require that knowledge management projects encompass a wider set of behavioral

factors, including the motivation of trust, communication, encouragement, and rewards.

Comparing, contrasting, and collectivizing diverse causal maps under these circumstances

is typically complex, especially when the terms used in the nodes of causal maps

are terse distillations of complex beliefs. Past research has usually addressed this

complexity in one of two ways. One approach unifies the interpretations and terms

embedded within causal maps into a “standard” vocabulary in which one word is chosen

to represent a group or category of synonyms. An example of this approach is displayed

in Figure 1. That example is based on the ways an organization can mitigate risk, and

expanded into how those perceptions might be conceptualized and asserted by two

individuals. Such mental concepts are represented as nodes, which typically bear the

name of that item or concept (e.g., “risk” and “loss of data”). Causal relationships

between nodes are represented by solid arcs in which the node at the “tail” of the arc is

a determinant of the node at the “head” of the arc. Arcs representing causal relationships

are typically valued as “+” or “-” to denote whether the arc represents a directly or

inversely proportional relationship, respectively. Arcs representing categorical relation346

ships do not need plus and minus signs. For the purposes of this chapter, categorical

relationships are represented by two-headed dashed arcs __ that signify the nodes

so linked are co-members of a set of synonyms. Figure 1 contains two sets of synonymous

terms: one set refers to hazards of organizational life (i.e., risk, settlement costs, and loss

of data), while the other refers to the protection of information systems (IS) (i.e., IS

security procedures, “acceptable IS use” training, and updating firewalls).

Another method for comparing, contrasting, and collectivizing diverse causal beliefs

relies on multiple group interview sessions. In this approach, all participants meet

together at one or more times, and one or more collective causal maps are drawn up to

represent the “products of [their] collective cognitions” (Langfield-Smith, 1992). Thus,

a collective causal map can document single or multiple group interviews. The degree of

shared beliefs is often represented with dotted and hatched areas (Figure 2). Causal

beliefs elicited from individuals within these group meetings can be conceptualized as

sets, in which each individual’s causal map is represented as a distinct set. Each

individual causal map is then represented within a circle of a Venn diagram. Shared beliefs

among group participants are represented as intersections between the circles of

individual causal map sets (Figure 2). Multiple collective causal maps, each documenting

one group session, can show changes in the use and understanding of specific terms as

well as in general levels of shared beliefs. This approach to collective causal mapping

places the responsibility of establishing word meanings on participants, not researchers.

Figure 1. Integrated causal and categorial maps


Causal relationship

Categorical relationship

IS security

procedures risk





virus attacks

loss of data

“acceptable IS

use” training litigation — settlement



— +

Individual “A”

Individual “B”

Another approach to collectivizing raw individual maps relies upon generalized terms,

typically drawn from academic theory or disciplines, as a basis for interpreting and/or

translating idiosyncratic causal assertions into a common language (e.g., Narayanan &

Fahey, 1990; Laukkanen, 1994). While useful, such references and interpretations should

be recorded to maintain the integrity of the original causal assertions and to document

interpretation, translation, and learning processes over time.

Representing Diverse Vocabularies in Social Causal