the interactional model: an alternative to the direct...

ERIC B. DENT

THE INTERACTIONAL MODEL: AN ALTERNATIVE TO THEDIRECT CAUSE AND EFFECT CONSTRUCT FOR

MUTUALLY CAUSAL ORGANIZATIONAL PHENOMENA

ABSTRACT. It is time that we in organization sciences develop and implementa new mental model for cause and effect relationships. The dominant model inresearch dates at least to the 1700s and no longer serves the full purposes ofthe social science research problems of the 21st century. Traditionally, researchis “essentially concerned with two-variable problems, linear causal trains, onecause and one effect, or with few variables at the most” (von Bertalanffy, 1968,p. 12). However, the literature is replete with examples of phenomena in whichthe traditional cause and effect construct does not allow for greater understandingand insight into the phenomena. Different conceptions of cause and effect rela-tionships have been developed including producer/product relationships (Ackoff1981), design causality (Argyris and Schon, 1996), and four classes of causalmodels (Schwartz and Ogilvy, 1979). Of interest here is the possibility of mutualcausality, “the assumption that the relationship between two (or more) phenomenais heavily influenced by the presence of feedback loops that are instantaneous, ornearly so” (Dent, 1999). Maturana’s (1998, Maturana and Varela, 1987) work ona new epistemology and ontology provides a foundation for the alternative modelof cause and effect proposed here. This interaction model includes the dynamicsof the traditional X and Y, but adds the structure of X (A), the structure of Y (B),the environment (E), and time (T).

INTRODUCTION1

It is time that we in organization sciences develop and implement anew mental model for cause and effect relationships. The dominantmodel in research dates at least to the 1700s and no longer servesthe full purposes of the social science research problems of the 21stcentury. I am not the first to make this suggestion, but by focusingan article specifically on this subject, I hope to draw more attentionto the question. This article will briefly touch on the foundations ofthe traditional notion of cause and effect, provide examples from theliterature of researchers and theorists who have found this traditional

Foundations of Science 8: 295–314, 2003.© 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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notion ineffective for their study, discuss some alternative framingsof the cause/effect relationship, define mutual causality, introduceMaturana’s work on ontology and epistemology, offer an alternativemodel to replace the traditional cause and effect construct, demon-strate the benefits of this model for two existing research studies,and critique the interactional model.

THE CLASSIC MODEL OF CAUSE AND EFFECT

The notion of causation has always been controversial in philos-ophy. David Hume, in 1748, was perhaps the first person to layout clearly the requirements for causation in the classic model.His three requirements of association, direction of influence, andnonspuriousness have been expanded to include the statement – if Xcauses Y is consistent with the theory proposed, and the theory hasexplained other phenomena, then the theory provides support forthe assertion that X causes Y (Singleton, Straits and Straits, 1993).As long ago as 1948, Russell wrote that even though this elementalform of causality still appears in books, it never takes this form inany advanced science (Russell, 1948, p. 315). Russell provides anumber of qualifiers, for example, omitting the environment as afactor which must be present, in order for such a direct cause andeffect relationship to be asserted.

THE INEFFECTIVENESS OF THE CLASSIC MODEL FORORGANIZATIONAL SCIENCE

The literature is replete with examples of phenomena in which thetraditional cause and effect construct does not allow for greaterunderstanding and insight into the phenomena (Begun, 1994).Several examples, chosen for the wide timeframe they cover andthe wide range of phenomena, are offered here. Researchers havesuggested that personality and environment vary as functions ofeach other (Dill, 1958); that evaluation and empirical analysisare intertwined (Lindblom, 1959); that language and thought arehighly dependent on each other (Engel, 1980); that meaning andcommunity are co-constructive (Drath and Palus, 1994); that indi-vidual and social activity are not separable (Weick, 1995); that

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the arrow points both ways between individual and organizationallearning (Argyris and Schon, 1996); and, that empowerment is amutual interaction of leader and follower (Vaill, 1998, p. 128).

One example above is briefly elaborated to show the richnessof these cases. At the time Lindblom (1959) wrote (and perhapsamong many policymakers today) the conventional wisdom wasthat Lindblom’s audience of public administrators first determinedthe objectives of the public and then selected or created policyto achieve those objectives. Lindblom argues that “one simultan-eously chooses a policy to attain certain objectives and chooses theobjectives themselves” (p. 82). In other words, “one chooses amongvalues and among policies at one and the same time” (p. 82). Evalu-ation and empirical analysis are so intertwined that their relationshipis not illuminated by the traditional cause and effect construct.

ALTERNATIVE FRAMINGS OF THE CAUSE/EFFECTRELATIONSHIP

Research inadequacies and policy outcomes such as these, togetherwith significant earlier critiques of the linear causal model (Brand,1976; Caws, 1993 and many others), have led to improvementefforts in the understanding of cause and effect relationships. In thissection I will introduce alternative framings suggested by Ackoff(1981), Argyris and Schon (1996), and Schwartz and Ogilvy (1979).Von Bertalanffy (1968) notes that there are a wide field of rela-tionships in which unidirectional causality and models with fewvariables seem appropriate. However, such appropriate settings arenot commonly found in biological, behavioral, and social sciences(Begun, 1994). Yet, the unidirectional model is predominant in thebehavioral sciences (Slife and Williams, 1995, p. 101).

Producer-product

Ackoff (1981, p. 20) writes about a distinction between producer-product and cause-effect. Producer-product is terminology intro-duced by the American philosopher E.A. Singer, Jr. A producer-product relationship exists when X is necessary, but not sufficientto cause Y. Singer uses the example of an acorn and an oak tree.An acorn is necessary to cause an oak tree, but if it is not placed

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in a suitable environment, the acorn will not grow into an oak. Inproducer-product relationships, the producer alone cannot be thecause of the product. There are always other necessary conditions.In the case of the acorn, the necessary conditions are the sunlight,soil, and other environmental conditions.

From the view of producer-product, the environment becomescentral to understanding and explanation. Ackoff (1981) notesthat “the use of the producer-product relationship requires theenvironment to explain everything whereas use of cause-effectrequires the environment to explain nothing. Science based on theproducer-product relationship is environment-ful, not environment-free” (p. 21). Consequently, by definition, any principle offeredabout producer-product relationships must stipulate the conditionsunder which the principle applies. If the principle were to applyin all conditions, then the environmental conditions are not co-producers of the effect.

Design Causality

For many years, Chris Argyris has been identifying several phe-nomena which are related to each other in ways not best describedby traditional cause and effect (Argyris, 1957, 1958). Argyris andSchon (1996) make arguments similar to others presented here,with some additional nuances. The authors begin by noting thatthe purpose of the traditional conception of cause and effect (whichthey refer to as the normal-social-science model of causality) is toestablish “covering laws” for relating variables X and Y, so thatgiven the values of X and the knowledge that X has occurred, Ywill result, independent of any other features of the contexts inwhich X and Y occur. This model centers on the definition of a vari-able, “a named attribute extracted from the complexity of observedphenomena which is treated as essentially the same in whateverlocal context it occurs” (p. 38). This conception stands on theassumption of the constancy of the definition of a variable. Severalresearchers dating back to Follett (1924) have found this assumptionnot to hold with many organizational phenomena. Follett (1924), forexample, suggests that in a presumed causal interaction between twopeople, the nature of the “variable” changes (not just the value of thevariable) with every interaction between the people.


Argyris and Schon (1996) continue by differentiating betweendesign causality and efficient causality. Design causality is definedas “the causal relation that connects an actor’s intention to theaction he or she designs in order to realize that intention” (p. 39).Efficient causality is “the causal connection between an act andits consequences, intended or unintended” (p. 39). Argyris andSchon (1996) intimate that a greater understanding of organizationalphenomena will be achieved by focusing on design causality ratherthan efficient causality. The latter has relatively little utility in prac-tice because of the nature of covering laws which tend to be “precise,quantitative, probabilistic, abstract, and complex” (p. 41). Designcausality is environment-ful in Ackoff’s sense of the term, includingall of the subjective, idiosyncratic, qualitative and other factors ofthe content and context.

Four Classes of Causal Models

Another view of causation is suggested by Schwartz and Ogilvy(1979) who view causality in terms of four classes of causal models,only one of which closely matches the classical definition. Theirfirst class is direct, linear causality. Probabilities are introduced asa factor in the second class. Cybernetics launched the third classof models which “permit feedback from effects to causes; however,the primary focus is on negative [compensating] feedback” (p. 56).The fourth class is called complex, mutually causal models and isdefined as those relationships which “evolve and change togetherin such a way (with feedback and feedforward) as to make thedistinction between cause and effect meaningless” (Guba, 1985,p. 88).

MUTUAL CAUSALITY

Very few issues being addressed by organizational scientists meetthe conditions of direct cause and effect. Yet, organizational scien-tists are imposing that formulation upon phenomena as if theconditions did prevail. Increasingly, organizational dynamics arefound to be mutually causal. The definition of mutual causality iscarefully chosen here because the term has different meanings inthe literature. In this paper, mutually causal relationships are those

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in which it is impossible to clearly identify discrete steps in a causalchain. This condition, however, violates the traditional requirementsfor a cause and effect relationship, that “events are bounded intocausal chains by two relations: spatio-temporal continuity and statis-tical relevance. Explanation requires the exhibition of such chains”(Kitcher, 1991, p. 321). Perhaps another useful way to think aboutmutual causality is that it is the special case of circular causalitywhen the time lag is effectively reduced to zero. When temporalprecedence can no longer be determined, cause and effect becomejumbled in such a way that traditional research techniques fail tooffer insights into the nature of the relationship between two or morephenomena.

MATURANA’S ONTOLOGY AND EPISTEMOLOGY

One of the reasons for the great limitations of the traditionalcause and effect model is that it is predicated on an epistemo-logy which only narrowly illuminates organizational phenomena oftoday. What is needed is a shift in worldview (Dent, 1999) awayfrom the exclusive dominance of philosophical assumptions suchas objectivity, reductionism, and rationality. Maturana’s work on anew ontology and epistemology holds great promise for a quantumleap in understanding of organizational phenomena. The reader isreferred to Maturana (1998) and Maturana and Varela (1987) for amore complete explanation of his ontology and epistemology.

Maturana begins by strongly suggesting that the assumption of anobjective world is not viable. He offers a simple example to demon-strate the inadequacy of objective reality. People cannot distinguishamong an illusion, a hallucination, or a perception. All three areexperienced identically by human beings. The distinction amongthese three is socially made “through the use of a different exper-ience as a metaexperiential authoritative criterion of distinction,either of the same observer or of somebody else subject to similarrestrictions” (Maturana, 1998, 5:1).

Rather than an objective world, Maturana and Varela (1987)advocate that for humans, reality is subjective because perceptionis interrelated with language to such a degree that “languaging[is] the act of knowing, in the behavioral coordination which is


language, [which] brings forth a world” (p. 234). “Objects arein the process of languaging, consensual coordinations of actionsthat operate as tokens for the consensual coordinations of actionsthat they coordinate. Objects do not pre-exist language” (Maturana,1998, 8:4). Dell (1985) has suggested that a major contribution ofMaturana’s work is the realization that his two major research ques-tions, (1) what takes place in the phenomenon of perception, and(2) what is the organization of the living, are one and the samequestion. Cognition and the process of living are the same biologicalphenomenon.

Maturana’s assertion poses a tremendous challenge to decadesof organizational research. If reality is not objective and cognitionis a biological phenomenon, then control is ontologically impossible(Dell, 1985, p. 11) and so is the traditional notion of cause and effect.What then do we make of our everyday experience in which manycause-effect situations seem obvious and vivid? An answer lies indistinguishing between traditional cause and effect – what Maturanaand Varela (1987) refer to as “instructive interaction” – and struc-tural coupling. In situations of instructive interaction, for example,a manager can completely determine the behavior of a subordinate.The subordinate is powerless to act in any manner different from themanager’s desire. Most observers will testify that a given leadershipstyle will produce different responses in different subordinates orthat a given form of communication will be interpreted differentlyby different people. “So-called ‘information’ does not and cannotinstruct the behavior of a living system” (Dell, 1985, p. 6). Informa-tion may trigger or select a response but the nature of the responseis primarily determined by the structure.

Structural couplings are interactions between and amongstructurally determined composite unities2 and/or their mediums(Maturana, 1998, 6:3). For example, a manager and a work groupare structurally coupled if the manager’s interactions contribute toeffective performance of a work group. Maturana asserts that theworld is structure determined, which is the “understanding that theoperation of every living system, both in its internal dynamics andin its relational dynamics, depends on its structure” (Ruiz, 1996,p. 286). For example, a manager employing an authoritarian leader-ship style may find that some employees do as the manager wants,

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but that others overtly or subversively defy the authoritarianism.Maturana’s point is that the employees’ reaction depends more oneach person’s structure – beliefs, reasoning, values, experiences, andso forth – than it does on the leader’s action.

Bateson (1972) and others have characterized the foundation ofthe social sciences as hopelessly confused. A dichotomy is clearbetween those who push the social sciences more in the direc-tion of the natural sciences and those who believe that the socialsciences require a foundation fundamentally different from thenatural sciences. Dell (1985) sees Maturana’s (and Bateson’s) workas offering a powerful hypothesis:

We are structure-determined autopoietic unities who operate in structuralcoupling with our medium. In turn, this hypothesis has proved able to generate(and, thereby, to explain) (a) the relation between the organism and its medium;(b) the nature of the structural coupling of organisms to one another; (c) thenature of social systems; (d) the manner in which language arises; (e) the natureof language; (f) the nature of the observer; (g) the manner in which we, asobservers, operate in language, make distinctions, and call forth realities; andthereby, (h) how Maturana himself, as such an observer, has been able to advancethe very generative hypothesis which specifies all of the above, including his ownfunctioning as a human being who makes such distinctions and advances suchhypotheses (p. 17).

Such a hypothesis provides a better foundation for the socialsciences by clarifying the nature of the observer, the nature of cogni-tion, and by contributing a coherent ontology and epistemology.How then can we operationalize cause and effect dynamics usingthis different philosophical foundation?

AN ALTERNATIVE MODEL OF CAUSE AND EFFECT

In organizational phenomena, cause and effect is often asserted insuch a way that person(s) A is taking action(s) X which affectsperson(s) B in such a way that she produces action Y(s). In theshorthand that is adopted, X and Y are often emphasized. Forexample, leadership style affects motivation, hotel characteristicsaffect hotel location, or a strategic planning process affects strategy.The analysis, conclusions, and future research suggested are almostalways limited to a discussion of X’s impact on Y.


I propose a model which has Interaction3 as the unit of analysisand includes X, Y, A, and B, as well as E (the embeddedness orcontext of all this) and T (interaction over time). Maturana seemsto allude to the greatest knowledge coming from understanding theinteraction of X and B, the latter of which plays the primary rolein determining B’s action (which is Y). The resulting interactionof X and Y could conform with any of the classes of Schwartzand Ogilvy’s (1979) categorization. Although Maturana and othersassert the ontological impossibility of Schwartz and Ogilvy’s firstclass, I will leave open the possibility that if the interactions of X, A,B, E, and T are carefully specified, that for all practical purposes, adirect, linear causal explanation may be sufficiently useful, in somesmall number of situations.

The interaction model proposed here is depicted in Figure 1. Ihesitate in including a figure because it is necessarily static andsimplistic and such representations reinforce the linear mindset I amcriticizing here. Figure 1 includes the example of a leader wantingto increase the amount of highly productive work by initiating aprogram of overtime hours for two months.

What type of information should be provided about A and B(The squiggle lines between A and X and B and Y are intendedto connote that a person and her actions are not necessarily distinctand discrete)? To the extent that the topic of specifying this infor-mation is addressed today it is under the heading of generalizability,a subject practiced much more narrowly than what is proposed here.Presently, for organizational behavior phenomena, studies typicallyidentify demographic information such as age, gender, educa-tion level, occupation, organizational rank, tenure, marital status,number of children, and salary range (see for example Brockner etal., 1997). Although some of this information is likely relevant forthe structural determination of A or B, what would probably be moreuseful for this field is information about the “interiors” (Wilber,1996) of A and B. For example, although the age of B may be usefulfor understanding how s/he interacts with A who is employing agiven leadership style, it may be much more useful to know someof B’s interior attitudes, opinions, values, and/or philosophies. Thechoice of what aspects of the interior to relate can be informed byprior research. In the example of Figure 1, a sampling of information

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Figure 1. The interactional model.

about A that may be most useful could include: her past experiencesin similar situations, her age, whether she is seen as hard working,whether she is seen as fair, whether she is willing to work overtimealongside her work group, and whether she has other idiosyncraticcredits with the work group.

For the employees (B), useful differentiating information mightinclude: their level of motivation, the amount of family supportthey have for working overtime, how the financial incentive is


valued, whether they desire to advance in the organization, and howcommitted they are to the mission of the organization.

The choice of which environmental considerations to exhibit issimilar to that of A and B. Although demographic information iscommonly provided in many studies, there is no consistent infor-mation typically presented about the environment or context of theresearch. Far too many studies are conducted in settings whichhave no real world counterparts (Argyris and Schon, 1996; Lind-blom, 1959). If the contrived condition or assumption is changed,would the same or similar results hold? Is attention focused in thewrong direction, for example, by studying young college studentsin unrealistic situations such as laying off employees4 (Folger andSkarlicki, 1998). The authors contend that their “approach put theparticipants in the realistic position of having to take conflictingpriorities into account” (p. 81). Are the dynamics of a true layoffeven remotely present when the students may not be in an employ-ment situation (the article describes them as MBA students, butdoesn’t say they are employed), do not have years of day-to-dayhistory together, or do not have the pressures of financial obliga-tions (which may include college tuition for their children, suchas students in the study)? Are these students really concerned thatsomeone they “layoff” may become violent with them? Might they,in a fun way, easily layoff their friends, or, on the other hand, chooseto retain their friends and easily layoff people they do not know well(perhaps these dynamics do match the real world!)?

Mary Parker Follett’s 1924 book, Creative Experience, whichreads like a current book on management and organizations,provides guidance as to what to represent in E,. Although her jargonis different, her writings condemning the construct of linear causeand effect trace many of the arguments her academic descendantsof later generations used. She nicely captures the feel of mutualcausality.

In human relations . . . I never react to you but to you-plus-me; or to be moreaccurate, it is I-plus-you reacting to you-plus-me. “I” can never influence “you”because you have already influenced me . . . Accurately speaking the mattercannot be expressed even by the phrase used above, I-plus-you meeting you-plus-me. It is I plus the-interweaving-between-you-and-me meeting you plus the-interweaving-between-you-and-me, etc., etc. If we were doing it mathematicallywe should work it out to the nth power (pp. 62–63).

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Follett suggests that for all social science research, researchersshould “study the whole a-making; this involves a study of wholeand parts in their active and continuous relation to each other . . . Butthere is an additional point to be considered: environment too is awhole a-making, and the interknitting of these two wholes a-makingcreates the total situation – also a-making” (p. 102).

In the example of Figure 1, possibly important contextual consid-erations could be: whether there is an organizationally-sanctionedability of the leader to punish resistors, whether the organization isin crisis, whether there are norms for generally following leaders,whether there are norms for personal responsibility, whether otherworkers were also asked to work overtime, and whether the requestwas perceived to have been made in a respectful manner.

A final aspect of the model is the inclusion of time (T). Manyresearchers have discovered that what is called “cause” and what iscalled “effect” often depends on the snapshot of time in which thedynamic is observed. We join with Zaheer, Albert and Zaheer (1999)in calling for the specification of the relevant time scale, which theyargue is as critical as the specification of the unit of analysis. Zaheer,Albert and Zaheer (1999) suggest that the time periods which needto be specified are the existence, observation, recording, aggrega-tion, and validity intervals. Not only does time scale play a role inthe outcomes of a study, it also may change the meaning of conceptsor relationships between data.

Sastry (1997) provides an example of how organizational dyna-mics can change “direction” over time. A change effort, forexample, could show a positive effect on an organization if assessedat one point in time, but a negative effect if assessed at a later time.In the example of Figure 1, one view is that the leader’s action iscausing a change in the work groups’ behavior. Another view is thatthe work groups’ behavior (perhaps they have not been producingenough) caused a change in the leader’s action. Consequently, inthis case it might be important to know: has there been a pattern ofsuch requests (is this the first time or the nth time?), is this requesthappening early or late in the work group’s formation, and what hasbeen the past performance of the group?


ADVANTAGES OF THE PROPOSED MODEL

The interactional model proposed here is more complex than theclassic model of cause and effect. In order for a model which is morecomplex to be accepted when simpler alternatives are available, thecomplex model must “work” in situations in which the simple modeldoes not. In this section, I will offer one detailed example of theinteractional model providing insight in a situation in which theclassic cause and effect model would produce a “wrong” finding.For several years, Business Week magazine has been conductingresearch in order to rank the top business schools in the country.BW takes the “customer’s” point of view and asks students andthe companies that hire them to rate their experiences with thebusiness schools. The simple cause and effect explanation is thatgood MBA programs (X) will result in good opinions by studentsand companies (Y) which will be reflected in the rankings. The1998 study discusses a difficulty in this simple X-Y formulation.“After an investigation lasting several months, BW determined thatsome students at five schools tried to ‘game’ the system by inflatingtheir responses on the student portion of the Business Week survey”(Reingold and Habal, 1998, p. 94). The reporters suggest that thestudents “took [the rankings] too seriously.”

A different perspective is that the research designers overlookedthe fact that they were studying a phenomenon which included“knowing subjects” (B). These students were aware that their inputwould largely determine the ranking of their school. Some studentsdecided to “artificially” increase their responses because a higherranking in the polls is more important for their career entry thantheir immediate educational experience is. In general, the classiccause-effect construct considers the “target” group to be affected(B) as passive elements of the causal system. What the students havedone in this case, and people often do in similar cases, is to take anactive role in the process, thereby altering the system under investi-gation. The researchers and reporters felt that this action affectedthe “integrity” of the data. Deans at four of the five schools said thatthe student actions reflect the positive enthusiasm that the studentshave for the schools. In order to “discount” this “enthusiasm,” theresearchers eliminated some responses and gave greater weight to1994 and 1996 studies.

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Researchers have to go to great lengths, many of them ques-tionable, to maintain the simple X-Y formulation. For example, in1998, they happened to discover the active role of the students.By giving greater weight to the 1994 and 1996 studies, theresearchers are assuming that no such action took place then. Theyare also assuming that such action was limited to the five schoolsthey discovered. Furthermore, BW offered no possibility that therecruiters, who are also surveyed, took an active role in the process.Is it possible that a recruiter would rate more highly the schoolsof the employees s/he hired, enabling some self-justification by therecruiters to their bosses?

The BW study wanted to ignore the context or environmental(E) factors that are present in such a situation. Such factors wouldinclude the student culture in each school, the student-administratorrelationships, the role that the findings play in the hiring process,and others. MIT’s Sloan School dropped six places from theprevious study, suggest the authors, largely for reasons that could bedescribed as contextual. One reason offered is that the students weresending a message to the administrators about their dissatisfactionwith not being able to take a course offered by a popular professor.A second reason offered in the article is that recruiters rated theschool lower because they were “spurned” when students acceptedentrepreneurial jobs rather than signing up with more establishedcorporations.

Finally, considerations of time do not enter into the classic X-Ymodel. BW would like readers to believe that each bi-annual surveyis a free-standing event, that the 1994 results don’t influence the1996 results which don’t influence the 1998 results. This isn’t oftenthe case when knowing subjects are involved because the peoplein the system “learn” over time. People know what the past resultswere, know what the implications of those results were in severaldimensions, and factor that accordingly into their input.

RESEARCH CRITIQUED USING THE PROPOSED MODEL

A second method for showing the value of a model is to consider thismodel relative to a typical article published in what is commonlyrecognized as a high quality journal. Such an article usually adds


to the organizational knowledge by reporting a finding, such as,“the optimal profile for high-performing groups includes important,moderate task conflicts, no relationship conflicts, little or no proce-dural conflict, with norms that task conflict is acceptable and resolv-able and with little negative emotionality” (Jehn, 1997, p. 552). X,the supposed cause, is carefully specified. Y, the alleged effect (highperformance) is also mentioned. The conclusions do not mentionA (who/how the cause was produced), B (the nature of the groupwhich will be high performing), E (the environment in which thisgroup works), or T(how all these interactions evolve over time).

Many may suggest that specifying A wouldn’t necessarily bethe researcher’s job in this situation, so I will not argue that pointhere. The article provides some data about the groups in the study(we are told the function they perform in a household- goods-moving organization), but makes no effort to describe the structuraldynamics of the group so that others may judge the likelihood ofthe effect generated by these groups also being generated by othergroups. Likewise, there is little or no information about the environ-ment in which these groups operate, and what role environmentalfeatures play in the claim. How often do they experience first-orderand second-order change? Is the environment highly contextual-ized? These and many other environmental characteristics couldbe specified in a more complete interactional model. Several Tquestions are also raised by the claim above. For example, “norelationship conflicts” could easily be the “effect” and “high perfor-mance,” the “cause.” No effort was made to describe the “initialkick” condition, so at the time the snapshot of data was collectedand correlations were calculated, the directionality is uncertain.

Consider as a second possibility the relationship between thenorm “conflict is acceptable and resolvable” and performance.Suppose the relationship is different after Jehn’s snapshot. Perhapsthis norm contributes modestly to performance for a time, but thenthe more pronounced dynamic becomes that sustained high levels ofperformance generate this norm.

RELIABILITY, VALIDITY, AND ROBUSTNESS

Because the interactional model subsumes the traditional model, thegood news is that all measures of reliability and validity from the

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traditional model remain relevant. There cannot be, for example, asituation in which cause and effect was not found in the traditionalmodel, but it would be found in the interactional model. The inter-actional model adds the ability to have a measure of robustness.If Y results from X regardless of A, B, E, and T, the outcome ismaximally robust (Juarrero, 1999). The relationship between X andY is time-dependent if the relationship differs at time T1 and T2.

As it is further developed, the interactional model will bestrengthened when universal principles for explicating A, B, X, Y,E, and T have been formulated. The general question is how to fullyspecify these six variables. Perhaps T should be, at least, denotedat an initial time, some intermediate time, and some end time. Suchuniversal principles for the six variables are not apparent at this timesince they seem to vary from situation to situation.

MODEL CRITIQUE AND FUTURE RESEARCH

The proposal made here for a reconception of the traditional cause-effect relationship is necessarily crude and perhaps does not fullyaddress the implications of Maturana’s ontology and epistemology.The traditional model has over 250 years of inertia and institution-alization. A different model must overcome the tendency in the fieldto “over apply” Ockham’s razor (Scott, 1995, p. 2) and strain tofit data into simple, existing (and misleading) models rather thanseek more elaborate, accurate models (Harman, 1998, pp. 100–101).In shifting paradigms, it is difficult to move from a highly refinedmodel, even if it is no longer effective, to a coarse model (Kuhn,1970). If this model is on the right track other theorists will need tosuggest elaborations and/or refinements.

This model does address many of the concerns listed in thisarticle. The inclusion and prominent role of the interactions of (E) isconsistent with Ackoff’s (1981) call for an environment-ful model.It is also responsive to Follett’s beliefs about interactions, althoughthe model is more static than taking changes to the nth degreeas she theorizes. Argyris and Schon (1996) also take issue withthe assumption of the constancy of the definition of the variable.Although the model does include a time dimension, which is a stepin the right direction, it does not offer a robust way of handling


changes in the definitions (structures in Maturana’s terms) of thevariables (composite unities).

The model is sensitive to Argyris and Schon’s (1996) call forincluding the actor’s intention, because such an intention is certainlypart of specifying the actor’s structure. The model can also be usedto identify the “initial kick” (or what chaos theorists often refer toas SDIC – sensitivity dependence on initial conditions). However,if the starting point is not included in the timeframe in which themodel is used, that information is lost. Still, by focusing on mutuallycausal possibilities, researchers using this model will be less likelyto assert causality in a single direction when the dynamic observedis better explained with knowledge of the initial kick (or conditions).

System dynamics is a field in which a great deal of progresshas been made in specifying the environment and representinginteractions over time (Meadows and Robinson, 1985). This workis especially important because it has developed new tools forobserving and modeling phenomena. Traditional statistical methodssuch as regression and factor analysis are built upon the assumptionof linearity. The work in system dynamics alone, however, is notenough. That discipline makes philosophical assumptions incon-sistent with the ontology and epistemology recommended here.System dynamicists often assume objective observation, and do notallow for knowing subjects or self-organization (Dent, 2001).

The model proposed here is substantially more complicated thanthat of traditional cause and effect as it is commonly practicedtoday. Yet, even crude research which is conducted this way willadvance the field further than the sophisticated mathematical tech-niques in use today which rest upon a philosophical foundationwhich leads to dead ends, and misleads to conclusions which arehighly questionable.

In writing an article like this, I am trying to be structurallycoupled with you, the reader. I (A) have written an article (X) whichyou (B) are reading and my intention is that you will find meritin the interactional model (Y). All the considerations of the modelsuggested apply here. The examples I have offered, or the style inwhich I have written affect how you perceive the content. (B) factorspossibly include your academic discipline, your set of experiences,your own research and others. The acceptability of such a modelin the community of scholars is a contextual (E) factor, as would

312 ERIC B. DENT

be its complexity relative to the simpler, classic model. A time (T)factor is that the model being proposed here is happening centuriesafter the classic model. Consequently, a huge “investment” exists inthe prevailing paradigm, and it has all of the advantages of inertiaand incumbency. It will be interesting to see how all of these factorsinterrelate as this alternative model receives consideration.

NOTES

1. I especially thank Peter Vaill for his invaluable critique of versions ofthis paper. Special thanks also go to William Sparks, Nancy Dixon, StuartUmpleby, Chris Cosans, and Alicia Juarrero.

2. Maturana distinguishes between simple unities and composite unities. Asimple unity is constituted only as a whole. A composite unity allows furtherdistinctions which an observer can decompose into components.

3. An anonymous reviewer has made an important observation. He or she notesthat the terms “mutual causality” and “interaction” have implications inthe traditional cause and effect model and by using those terms the modelproposed unnecessarily carries with it that baggage. This stage of modeldevelopment is the proverbial spot between a rock and a hard place. An alter-native is to use terms such as “mereological” or “historically and contextuallyconstrained” (Juarrero, 1999). Yet, these terms are not widely understood andseem to present the greater of two evils. Consequently, the terms “mutualcausality” and “interaction” will be used here and the reader is encouraged tokeep in mind the way they are used in this paper, rather than historically.

4. In fairness to the authors, their paper did include other research designs.

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