Information Technology and Organizational Change - Semantic Scholar

Information Technology and Organizational Change: Causal Structure in Theory and Research Author(s): M. Lynne Markus and Daniel Robey Source: Management Science, Vol. 34, No. 5 (May, 1988), pp. 583-598 Published by: INFORMS Stable URL: http://www.jstor.org/stable/2632080 Accessed: 14-09-2015 14:41 UTC

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MANAGEMENT SCIENCE Vol. 34, No. 5, May 1988

Printedin U.S.A.

INFORMATION TECHNOLOGY AND ORGANIZATIONAL CHANGE: CAUSAL STRUCTURE IN THEORY AND RESEARCH* M. LYNNE MARKUS

AND

DANIEL ROBEY

GraduateSchool of Management,Universityof California,Los Angeles, California90024 College of Business Administration,Florida InternationalUniversity,Miami, Florida 33199 This articleconcernstheoriesaboutwhy and how informationtechnologyaffectsorganizational life. Good theoryguides research,which, when applied,increasesthe likelihoodthat informationtechnologywill be employedwithdesirableconsequencesfor users,organizations, and otherinterestedparties. Butwhatis a goodtheory?Theoriesareoftenevaluatedin termsof theircontent-the specific conceptsused and the human valuesserved.This articleexaminestheoriesin termsof their structures-theorists'assumptionsabout the natureand directionof causalinfluence.Three dimensionsof causalstructureare considered-causal agency,logicalstructure,and level of analysis.Causalagencyrefersto beliefsaboutthe natureof causality:whetherexternalforces causechange,whetherpeopleact purposefullyto accomplishintendedobjectives,or whether changesemergeunpredictablyfrom the interactionof people and events. Logicalstructure refersto the temporalaspectof theory-static versusdynamic-and to the logicalrelationships betweenthe "causes"and the outcomes.Levelof analysisrefersto the entitiesaboutwhichthe theoryposesconceptsand relationships-individuals,groups,organizations,and society. While there are many possiblestructuresfor good theory about the role of information technologyin organizationalchange,only a few of these structurescan be seen in current theorizing.Increasedawarenessof the options,open discussionof theiradvantagesand disadvantages,and explicitcharacterization of futuretheoreticalstatementsin termsof the dimensions and categoriesdiscussedhere should, we believe, promotethe developmentof better theory. (INFORMATIONTECHNOLOGY;ORGANIZATIONCHANGE; CAUSAL STRUCTURE)

Introduction The relationshipbetween informationtechnology and organizationalchange is a centralconcernin the field of InformationSystems(IS). In the 30 yearssince Leavitt and Whisler's(1958) seminalarticle,"Managementin the 1980's,"speculationson the role of informationtechnologyin organizationsand its implicationsfor organizational designhave flourished.Few researchersin the IS field question the importanceof the issue.In an empiricalinvestigationof literaturecitationpatterns,Culnan( 1986)traced the origins of the IS field to Leavitt and Whisler'sarticle and identified"computer impacts"as a clearsubfieldwithin it. Unfortunately,the literatureon informationtechnologyand organizationalchange does not currentlysupport reliablegeneralizationsabout the relationshipsbetween informationtechnologyand organizationalchange.There are severalreasonsfor this. The literaturecontains works by researchersfrom several academic disciplinesand interdisciplinary specialties,includingorganizationaltheory,managementscience,sociology,and computerscience,eachwithits own preferredconceptsand theoreticaland methodologicalbiases.It includesconflictingand uncleardefinitionsand measuresof informationtechnology (Bakopoulos 1985) and organizationalstructure(Fry 1982). Finally, it mixes and crosses units and levels of analysis from the individual, the * Acceptedby Arie Y. Lewin,formerDepartmentalEditor;receivedJune 23, 1986.This paperhas been withthe authors51/2monthsfor 2 revisions. 583 0025- 1909/88/3405/583$0 1.25 Copyright ?) 1988, The Institute of' Management Sciences


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M. LYNNE MARKUS AND DANIEL ROBEY

workgroup,the department,the organization,and society-a practice which leads some observersto fear improperly specified models and ungeneralizablefindings (Freeman 1978, Rousseau 1985).

One importantapproachto solving these problemsis to focus on the substanceof theory, such as concept definition and normative orientation.For example, Kling (1980) has identifiedand defined six theoreticalperspectivesin social analysesand empiricalresearchon computing. These perspectivesare distinguishablefrom each other by their respectivedefinitionsof technologyand social setting,theoreticalconstructs,beliefs about the dynamicsof technicaldiffusion,evaluationsof "good"technology, and ideologiesof the workplace.Kling and Scacchi(1982) have discussedthe differingassumptionsabouttechnology,infrastructure,and the dynamicsof changein versus"web"models. "discrete-entity" Our approachdiffersfrom but complementsKling'sanalysesof theory substance. Instead,we focus on the structureof theory, that is, researchers'conceptionsof the natureand directionof causality.We believethat soundtheoreticalstructure,like good theoreticalsubstance,is necessaryfor better theory. Our purposein this paper is to analyzethe causalstructureof the theoreticalmodels found in the literatureon information technologyand organizationalchange. The causal structure of theoretical models comprises three dimensions: causal agency,logicalstructure,and level of analysis.Causalagencyrefersto beliefsaboutthe nature of causality:whetherexternalforces cause change (the technologicalimperative), whetherpeopleact purposefullyto accomplishintendedobjectives(the organizationalimperative)or whetherchangeemergesfromthe interactionof peopleand events (the emergentperspective).Logicalstructurerefersto the time span of theory (static versusdynamic)and to the hypothesizedrelationshipsbetweenantecedentsand outcomes:whethercausesare relatedto outcomesin an invariant,necessaryand sufficient relationship(variancemodels), or in a recipe of sufficientconditions occurringover time (processmodels). Level of analysisrefersto the entities about which the theory posesconceptsand relationships-individuals,collectives,or both. Thesethreedimensions of causalstructureare shown in Figure 1. Causalstructurecannoteasilybe separatedfrom issuesof theorysubstanceand from variousmethodologicalissues. But a thoroughunderstandingof causal structurerequiresa depthof treatmentnot usuallyfoundin methodologicalcritiques(e.g.,Attewell and Rule 1984;Rice 1980;Robey 1977).Consequently,our focus on causalstructure will exclude several importantconcerns which must also figure prominentlyin the developmentand testingof good theory. CAUSAL AGENCY

* Technological Imperative

LOGICAL STRUCTURE

* Variance Theory

* Organizational Imperative

LEVEL OF ANALYSIS

* Macro

* Micro

- micro

* Process Theory

- mixed

* Emergent Perspective FIGURE 1.

D)imensions of Causal Structure.


INFORMATION TECHNOLOGY AND ORGANIZATIONAL CHANGE

585

CausalAgency Causalagencyrefersto the analyst'sbeliefsaboutthe identityof the causalagent,the natureof causalaction and the directionof causalinfluenceamong the elementsin a theory.Pfeffer(1982), for example,has identifiedthreeperspectiveson action in organizationaltheory. In the "situationalcontrol"perspective,externalfactorsor events constrainor forcepeople and organizationsto behavein certainways. In the "rational actor"perspective,people and organizationsevaluatealternativecoursesof action and exercisefree rationalchoice. In the "emergent"perspectiveon action, the behaviorof people and organizationsemerges from a dynamic interactionof external circumstancesand internalmotives or interests.' Buildingupon the work of Pfeffer,we have identifiedthree conceptionsof causal agencyin the literatureon informationtechnologyand organizationalchange.We label these: the technologicalimperative,the organizationalimperativeand the emergent perspective.In the technologicalimperative,informationtechnology is viewed as a cause of organizationalchange. In the organizationalimperative,the motives and actions of the designersof information technologies are a cause of organizational change.In the emergentperspective,organizationalchangeemergesfrom an unpredictable interactionbetween informationtechnologyand its human and organizational users. Each of these perspectivesis discussed more fully below and summarizedin Figure2. The Technological Imperative

The essenceof the technologicalimperativeis conveyedby the word "impact."This perspectiveviewstechnologyas an exogenousforcewhich determinesor stronglyconstrainsthe behaviorof individualsand organizations.The technologicalimperativeis consistentwith Pfeffer's(1982) situationalcontrol perspectiveon action in organizations. "In this view, action is seen not as the resultof conscious,foresightfulchoice but as the resultof externalconstraints,demands,or forcesthat the social actor may have little controlover or even cognizanceof" (Pfeffer1982, p. 8). For example,Leavittand Whisler(1958) arguedthat informationtechnologywould alterdramaticallythe shapeof organizationsand the natureof managerialjobs. Organizations would recentralize,levels of middle managementwould disappear,and a top managementelite would emerge.Leavittand Whislerurgedmanagersto preparefor theseinevitableimpactsby developingtheirinternaltechnologicalcapabilitiesand their liaisonsto externaltechnologicalresources. Simon (1977) was less pessimisticthan Leavittand Whislerin his predictionsabout the impactof computers,but no less deterministic.Simon contendedthat computers wouldnot changethe basichierarchicalnatureof organizations,but would recentralize decisionmaking.Line organizationalstructureswould shrinkin size, and the number of levelswoulddecrease.Staffdepartmentswouldincreasein numberand size, making structuresmore complex and requiringmore lateralinteraction. While the technologicalimperativehas a long historyand makessome compelling claims, empiricalresearchhas generatedcontradictoryfindingson almost every dimension of hypothesizedcomputer impact (Robey 1977; Kling 1980; Attewell and Rule 1984). Informationsystems have been found both to enrich and routinizejobs 'Slack (1984) makes similar distinctions among various conceptions of causality in an analysis of communication technologies. "Simple" causality views technology as an independent entity capable of effecting change in social systems. "Symptomatic" causality also presumes technologies to be discrete phenomena, but their effects may be mediated by social forces such as the intentions of rational actors. Slack views both simple and symptomatic conceptions of causality as mechanistic in their basic assumptions. Her descriptions of "expressive"and "structural"causality correspond more closely to Pfeffer'semergent perspective on action in that the distinctions between cause and effect are not sharply drawn.


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Technological

Imperative

Organizational structure

Information Technology

Imperative

Organizational

Purposes\

Organizational Structure

Information

Emergent Perspective

USE INFORMATIONTECHNOLOGY MEANING BEHAVIOR

/P/RPOSI7S

SZ'77/NC

Information Technology FIGURE2. Causal Agency.

(Kling 1978;Bj0rn-Andersen,Eason,and Robey 1986),both centralizeand decentralize authority(Klatzky1970;Whisler1970;Stewart1971;Blau, Falbe,McKinley,and Tracy 1976;Carter1984;Fosterand Flynn 1984;Dawsonand McLaughlin1986),and produce no changeswhere changeswere expected (Robey 1981; Franz, Robey, and Koeblitz 1986). Some investigatorshave proposedthat contingenciesaffectthe relationshipbetween informationtechnology and structuralchange. For example, in a review of studies conductedduringthe 1960s and early 1970s, Robey (1977) observedthat computing appearedto supportan existingdecentralizedstructurein organizationswith uncertain environments.However,in simpleenvironments,computingappearedto strengthena centralizedauthoritystructure.Robey suggestedthat computingtechnologybe viewed as a moderatingvariable,affectingthe strengthof a causal relationshipbetweenenvironmentaluncertaintyand organizationalstructure. Othercontingencieshave also been examined.Leiferand McDonough(1985) controlledfortask routinenessand foundthat departmentsusinga computer-basedsystem weremorecentralized,less complex,and perceivedless environmentaluncertaintythat those not usingthe computer.Klatzky(1970) assertedthat size waspartiallyresponsible for the decentralizationof decision makingthat accompaniedthe use of information technology. Carter(1984) found that organizationalsize moderatedthe relationship



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betweeninformationtechnologyand the structureof newspaperorganizations.Pfeffer and Leblebici(1977) controlledfor both size and environmentalcomplexity in their study of the use of informationtechnology in 38 small manufacturingcompanies. Majchrzakand Mosher(1987) used threedifferentanalysisstrategiesto examine organizationalstructureas a contingencyin the technology/performance relationshipat the individuallevel of analysis. The Organizational Imperative

Whereasthe technologicalimperativearguesthat informationtechnologyconstrains or determineshuman and organizationalbehavior,the organizationalimperativeassumes almost unlimitedchoice over technologicaloptions and almost unlimitedcontrol over the consequences.The organizationalimperativecorrespondsto Pfeffer's "intendedlyrational"perspectiveon action. It assumesthat behaviorsare chosen, that suchchoicesoccuraccordingto a set of consistentpreferences,that choices occurprior to the action itself,and that action is goal directed(Pfeffer1982, p. 6). This perspective holds that humanactorsdesigninformationsystemsto satisfyorganizationalneeds for information.2Thus, informationtechnologyis the dependentvariablein the organizational imperative,causedby the organization'sinformationprocessingneedsand manager'schoices about how to satisfythem. In a widely known versionof this perspective,Galbraith(1977) proposeda number of organizationaldesign alternativesby which organizationscan fill the information processingneeds generatedby uncertainty.Managersmay reducethe need to process informationby managingthe environment,by using slack resources,or by creating self-containedorganizationalunits. Managersmay also increasethe capacityof organizationsto processinformationby developinglateralrelationsand buildinginformation systems. A similar causal argumentis evident in the work of Daft and MacIntosh(1978, 1981).They hypothesizedinformationneeds to varywith task varietyand knowledge aboutthe task and proposeda relationshipbetweeninformationprocessingneeds and use of informationsystems.Unanalyzable,nonroutinetasks requirerich information, capableof conveyingcomplexand equivocalmeanings;facialexpressionsand voice are the media most capableof processingthis rich qualitativeinformation.Concise,computer-basedinformationsystemsaremoreappropriateforsimplerinformationprocessing requirements,accordingto Daft and his colleagues(see Daft and Weick 1984;and Daft and Lengel 1986). Not surprisingly,the normative literatureon information system design evinces considerableoptimism about the degreeof human influenceover the capabilitiesand characteristicsof informationsystems(Olson 1982;Olson and Lucas 1982;Olson and Turner1985). Contextualvariables,which an externalor situationalcontrol perspective mightviewas constraintsor determinants,areviewedin the organizationalimperative as contingenciesthat managersshouldtake into account.Among these contextual variablesareworkunit technology,organizationallevel, environment,decisionmaking style, and uncertainty(Whisler1975;Gordon and Miller 1976;Waterhouseand Tiessen 1978;Ginzberg1980;Olerup 1982;Gorryand Scott Morton 1971). The assumptionof designerdiscretionstandsin sharpcontrastto the externaldeterminism of the technologicalimperative.The organizationalimperativeassumes that systemsdesignerscan managethe impactsof informationsystemsby attendingto both technicaland social concerns(Bj0rn-Andersenet al. 1986;Mumfordand Weir 1979). 2 Theseneedsare sometimesviewedas externallygenerated-derivedfrom uncertaintyoriginatingin the environmentor in work technology(Galbraith1977; Daft and MacIntosh1981; Daft and Lengel 1986; Tushmanand Nadler 1978).


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This view is sharedby managementand organizationtheoristswho see information technologyas a tool for solving organizationalproblems(Child 1984; Huber 1984; Huberand McDaniel 1986). Empiricalsupportfor the organizationalimperativeis limited.Whilestudiesby Daft and MacIntosh(1981), Specht (1986), and Olerup (1982) support the notion that organizationalcharacteristicscorrelatewith information system characteristics,the models of Galbraith(1977) and Tushman and Nadler (1978) have received mixed empiricalsupport(Tushman 1979; Morrow 1981; Penley 1982; Triscariand Leifer 1985;Saundersand Robey 1986). Unfortunately,most of these studies fail to assess designers'intentions and thus cannot be regardedas complete tests of the organizational imperative. The Emergent Perspective

The emergentperspectiveholdsthat the uses and consequencesof informationtechnologyemergeunpredictablyfromcomplexsocialinteractions.This perspectivecorrespondsto Pfeffer's"emergent"view of action in organizations:"Becauseparticipation in organizationaldecisionsis both segmentedand discontinuous,becausepreferences developand changeover time, and becausethe interpretationof the resultsof actions -the meaningof history-is often problematic;behaviorcannot be predicteda priori eitherby the intention of individualactorsor by the conditionsof the environment" (1982, p. 9). Kling and Scacchi's(1982) distinctionbetween"discrete-entity"models and "web" models of computingprovidesa useful startingpoint for a discussionof the emergent perspectiveon information technology and organizationalchange. Discrete-entity models conceive of informationtechnologyas a tool with identifiablebenefits,costs, and skill requirements.Like the organizationalimperative,discrete-entitymodels assume that.the goals of designersguide the developmentof computingapplications.By contrast,web models conceive of informationtechnologyas an ensemble of equipment, applications,and techniquesthat carrysocial meanings.The primaryvirtueof web modelsis empiricalfidelity,theirabilityto accountforthe detailsand complexities of actualsituations.Theircharacteristicproblemis analyticalcumbersomeness(1982, p. 10).

Centralconceptsin the emergentperspectivearethe roleof the computinginfrastructure, the interplay of conflicting objectives and preferences,and the operation of nonrationalobjectivesand choice processes.For example,Gasser's(1986) studyof the integrationof computingand routine work examinedthe misalignmentbetween demands of the work setting and the computing resourcesavailable.Gasseridentified strategiesby which organizationalactorscoped with slippagebetweentechnologyand work demands and discussedhow the nature of routine work changed as a result. Ratherthanattributingchangeto actorintentor exogenoustechnology,Gasserfocused on the dynamicinterplayamong actors,context, and technology. In anotherrecent example of an emergentmodel, Barley(1986) studiedthe introduction of computerizedtomography(CT scanners)in radiology.He demonstrated thatthesetechnologiescan alterthe organizationaland occupationalstructureof radiologicalwork.However,"the identicaltechnologiescan occasionsimilardynamicsand yet leadto differentstructuraloutcomes"in differentsettings(1986, p. 105).In Barley's analysis,the scannersoccasionedchange not becauseof their inherentcharacteristics (as the technologicalimperativewould hold), but "becausethey becamesocial objects whose meaningswere defined by the context of their use" (p. 106). The technology presentedan "occasion"for structuralchangebut did not determinewhich of a large varietyof alternativesactuallyemergedfrom the processof structuring. The emergentperspectiveadmitsgreatercomplexityto the issueof causalagencyand This content downloaded from 140.119.81.207 on Mon, 14 Sep 2015 14:41:23 UTC All use subject to JSTOR Terms and Conditions


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to the goal of predictingorganizationalchangesassociatedwith informationtechnology. By refusingto acknowledgea dominant cause of change,emergentmodels differ qualitativelyfrom the deterministiccausal argumentsof the two imperatives.Prediction in the emergentperspectiverequiresdetailedunderstandingof dynamicorganizationalprocessesin additionto knowledgeaboutthe intentionsof actorsand the features of informationtechnology.This addedcomplexitymakesemergentmodels difficultto construct(Bendifallahand Scacchi 1987;Kling 1987). Normative Implications

The threeperspectiveson causalagencypresentedheredifferin theirattributionsof responsibilityfor the outcomes observed.These attributionsimply that particularinterventionswill be more or less efficaciousin producingor increasingthe likelihoodof desirableoutcomes. Consequently,analystsof differentpersuasionscan often be distinguishedas easily (or more so) by their normativestance as by the specificsof their models(Mowshowitz1981). Technologicalimperativeanalysts hold that informationtechnology generallyor some particularconstellationof technologicalfeaturesis responsiblefor "impacts"such as change in organizationalstructure,skill enhancementor deskillingof workers,or change in employment opportunities.Consequently,the policies and remediesproposedby theseanalystscenteron stopping,slowingor acceleratingthe rateof changein informationtechnologyor selectinginformationtechnologieswith particularpackages of features.Organizationalimperativeanalystsattributethe consequencesof information technologyto the choicesand behaviorsof managersand systemdesigners.Consequently, these analysts tend to prescribeimproved design and resource allocation methodsand betterimplementationstrategiesand tactics. Becauseemergentanalysts attributeoutcomesto an unpredictableinteractionof technologicalfeaturesand actors' intentions, their normativeposture is less clear than those consideredabove. Some emergentanalystseschewintervention,arguingthat predictionis impossibleand outcomes areindeterminate;othersadvocate"emancipatory"strategies,such as extensive user participationin the analysis, design, and implementationof informationtechnology. LogicalStructure A second dimensionof theoreticalstructureconcernsthe logical formulationof the theoreticalargument.On this dimension, Mohr (1982, Chapter2) distinguishesbetween varianceand processtheories.The distinctionin theoreticalstructurebetween varianceand processtheoriesis somewhatanalogousto the distinctionbetweencrosssectional and longitudinalresearchmethodologies.Variancetheories are concerned with predictinglevels'of outcome from levels of contemporaneouspredictorvariables; variancetheoriesare concernedwith explaininghow outcomesdevelop over time. Variance and Process Theories

Mohr (1982) explainsthe differencebetweenvariancetheoriesand processtheories in terms of the hypothesizedrelationshipsbetweenlogicalantecedentsand outcomes. These are summarizedin Figure 3. In variancetheories,the precursor(loosely, that which might be referredto as the "cause")is posited as a necessaryand sufficient conditionfor the outcome.3Forexample,in a variancetheorythat hypothesizesuse of informationtechnology as a cause of organizationalcentralization(cf. Leavitt and Thereis a superficialresemblancebetweenvariancetheoriesand regressionmodels,but the two are not identical.Manyregressionmodelscontainvariableswhichpredictthe outcomebut whichare not hypothe(Mohr 1982,Chapter2). sizedto be "causal"in the necessaryand sufficient'sense



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THEORY VARIANCE

PROCESSTHEORY

ROLEOF TIME

Static

Longitudinal

DEFINITION

Tht cause Is necessary and sufficient for the outcome

Causation consists of necessary conditions In sequence; chance and random events play a role

ASSUMPTIONS

Outcome will Invariablyoccur when necessary and sufficient conditions are present

Outcomes may not occur (even when conditions are present)

ELEMENTS

Variables

Discrete outcomes

FORM LOGICAL

IfX, then Y; if more X,then more Y

Ifnot X, then not Y; cannot be extended to "moreX'or "moreyU

FIGURE3. Logical Structure.

Whisler 1958), centralizationis expectedalwaysto occur wheneverinformationtechnology is used (where"always"is definedin terms of statisticalconfidencelevels). In processtheories,the precursoris assumedinsufficientto "cause"the outcome, but is held to be merelynecessaryfor it to occur. In general,necessaryconditions alone cannot constitute a satisfactorytheory. For example, while water may be necessaryfor the growth of plants, it is not sufficient; therefore,it cannot be consideredthe cause of plant growth. Mohr has observed, however,that necessaryconditionscan comprisea satisfactorycausalexplanationwhen they are combined in a "recipethat stringsthem togetherin such a way as to tell the story of how [the outcome] occurs wheneverit does occur" (1-982,p. 37). In short, outcomesare (partially)predictablefrom a knowledgeof process,not from the level of predictorvariables. A good example of a processtheory is the "garbage-can"theory of organizational choice (Cohen, Marchand Olson 1972). In this model, decisions result from the (almost) randomcollisionsof participants,choice opportunities,solutions,and decisions. Many diffusionof innovationtheoriesare processtheories,at least implicitly.Barley's study (1986) of CT scanning in radiologyalso meets the requirementsof a process theory. Markus(1984) proposeda processtheory for explaininguser resistanceto information systems.Includedas a necessaryconditionfor userresistancein this "recipe"is the introductionof an informationsystem with featuresdifferingfrom the featuresof the organizationalsetting.This necessarycondition is not believedsufficientto ensurethe occurrenceof resistance,but it is believedto be necessary.Consequently,this process theoryrecognizesthat resistancemay not alwaysoccur, even when the necessarycondition of "differingfeatures"is present.In any given case, resistancemay not occur for severalpossiblereasons:peoplemay like the changesembodiedin the system;they may



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be too apathetic to resist; or they may find ways to circumvent the changes the system implies. More accurate predictions are only possible when these additional ingredients of the setting are known and their temporal relationships to each other are understood. Another example of a process theory of information technology is the work of Zmud and Apple (1988) on optical scanning technology in supermarkets. Zmud and Apple distinguish between the routinization of an innovation, defined as the accommodation of an organization's governance system to the innovation, and its institutionalization, defined as the organization's achievement of higher levels of use and benefits from the innovation. They argue persuasively that these two concepts are related in a necessary but not sufficient fashion. Routinization is necessary for institutionalization, but institutionalization is not certain to occur when an innovation is routinized. Variance theories, then, differ from process theories in their assumptions about the relationship between antecedents and outcomes. Variance theories posit an invariant relationship between causes and effects when the contingent conditions obtain. Process theories assert that the outcome can happen only under these conditions, but that the outcome may also fail to happen.4 -Variance and process theories also differ in their conceptualization of outcomes and precursors. In variance theories, these constructs are usually conceptualized as variables: entities which can take on a range of values. This practice allows the prediction of the full range of values of the outcome variable. For example, if the use of information technology is necessary and sufficient for organizational centralization, then increased use of information technology should lead to greater centralization. In process theories, however, outcomes are not conceived as variables that can take on a range of values, but rather as discrete or discontinuous phenomena, that might be called "changes of state." Process theories cannot be extended, as variance theories can, to explain or predict what happens when there is "more" of a precursor variable. Thus, if a process theory specifies that certain conditions are sufficient to cause user resistance, it does not follow that more of these conditions will mean more resistance. To illustrate, in their study of the adoption of supermarket scanners, Zmud and Apple (1988) identified three distinct levels of institutionalization, each accompanied by a unique ideology of the organizational role served by scanners: to enforce retail unit worker discipline, to possess the capability of better managerial information, and to provide better managerial information. While a "higher level" of institutionalization includes the "lower level" states, the levels are better conceived as qualitatively different outcomes than as varying degrees of a single dimension. Mohr (1982) believes that variance and process theories can "peacefully coexist," but that the distinctions between them should not be blurred in an attempt to gain the advantages of both within a single theoretical approach. He offers three reasons for this position. First, for any variance theory, it is always possible to specify mechanisms that intervene between antecedents and outcomes. But, because variance theories posit sufficiency, the inclusion of intervening variables does not improve prediction of the outcome variable. In short, intervening variables are redundant, unless one is "puzzled about the means of bridging the gap between one included phenomenon and another" (p. 43). Second, process theories can easily bog down under the imposition of conditions thought to increase the likelihood of the outcome (pp. 61-65). Third, while agreeing that process theories and variance theories may mutually inform each other, Mohr concludes that "odd bits and pieces of research results cannot be integrated or interchanged from one theoretical type to the other; the effort produces confusion and ' While contingencytheoriesdeal with necessaryconditions, they are not processtheories.They are variancetheories,becausethe conditionsthey specify are both necessaryand sufficientfor the outcome to occur.


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stagnation-the frustration of theory. Sorting the two out and keeping them separate, however, produces clarity and the basis of progress" (1982, pp. 67-68).

RelationshipbetweenCausalAgencyand LogicalStructure At first glance, it may appear that all imperative theories are variance theories and all emergent theories are process theories. Slack (1984) and Mohr (1982) appear to support this observation. Indeed, we find it hard to imagine how emergent theories could effectively be cast as variance models. However, both variance and process models are available to analysts from the perspective of either the technological or the organizational imperative. An example of a technological imperative process model should suffice to make the point. An early formulation of the technological imperative can be seen in the work of Ellul (1964) and Winner (1977). Ellul argued that technology creates social changes which reach far beyond its original applications. Once developed, technology follows a selfsustaining evolutionary path with the dynamic that whatever can be developed must be developed. Thus, techniques carry in themselves the seeds of new applications. Winner extended Ellul's theory by noting the role of supporting infrastructures, such as the huge power plant required to supply electricity for small appliances. These supporting infrastructures institutionalize and perpetuate the technologies they were originally created to support. Initially, the inevitability of technological impact-the hallmark of the technological imperative-appears to be the salient feature of Ellul's and Winner's arguments. Yet a long historical perspective reveals many technologies that have been abandoned with.out trace or consequence. What seems to make the difference for a technology to have an extensive and enduring impact is the formation of an infrastructure, which, once established, perpetuates itself and institutionalizes the use of the technology. In process theory terms, the development of the infrastructure is a necessary condition (but not necessary and sufficient) for social changes to occur.

Advantagesof ProcessTheories To say that process models are possible does not provide a convincing rationale for their use. While we do not argue that process models are superior to variance models, process models have been unjustly neglected in favor of the more common variance theories.5 Analysts should consider the following advantages of process theories when formulating the logical structure of their theories. By their very structure, variance theories posit an invariant relationship between antecedents and outcomes. This assumption may simply be too stringent for social phenomena. Put differently, if a behavioral outcome occurs only some of the times when its antecedents are present, then it may not be possible to establish an invariant relationship;between the antecedents and outcome, even with generous statistical confidence levels. As Sutherland has put it, "not all real-world phenomena will ultimately become deterministic if we spend enough time analyzing them." (1973, p. 145) In circumstances like these, process theories may enable researchers to find patterns in empirical data that variance theorists might miiss. Process theories have another advantage. While they retain the empirical fidelity of the emergent perspective, they also preserve the belief in the regularity and predictability of social phenomena that characterizes the technological and organizational imperatives. Prediction of patterned regularities over time is one of the goals of process theory I Some of this neglect may stem from the disrepute of information systems "stage" models. Mohr (1982) describes stage models as incomplete process models, because they generally lack specification of the mechanism by which subsequent stages come about.



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research.By contrast,some criticsof variancetheory researchtake a strongantipositivist stance, arguingthat predictionof human action (such as the use of information systems)is not a legitimatepursuit.6 Thus,while empiricalprocessresearchtypicallyrevealsthat thingsare more complicatedthanvariancetheoryrepresentsthem to be, such researchshouldnot be dismissed as isolatedstories or illustrativecases only. With care, findingscan be generalizedto other-settings,and predictionscan be testedin laterresearch.The advantageof process theoriesis that these predictionsmay correspondmore faithfullyto actual events in organizationsthan do the typicalpredictionsof varianceformulations. In summary, we believe that process theories are useful preciselybecause, while recognizingand acceptingthe complexityof causalrelationships,they do not abandon the goals of generalizabilityand prediction.By acceptinga more limited definitionof prediction,one in which the analystis able to say only that the outcome is likely (but not certain)undersome conditionsand unlikelyunderothers,processtheoristsmay be able to accumulateand consolidatefindingsabout the relationshipbetweeninformation technologyand organizationalchange. Level of Analysis The specific theories and researchstudies discussed in this paper concern three differenttypes of entities,or levels of analysis:individuals,organizations,and society. Questionsaboutthe appropriatelevel of analysishavebeen widelydebatedin the social sciencesgenerally,but have rarelybeen explicitlydiscussedwithin those researchcommunities concernedwith the causes and consequencesof informationtechnologyin organizations.The debatecenterson two issues:problemsof inferenceand ideological biases(Pfeffer1982). Problemsof inferencearisewhen conceptsaredefinedand dataarecollectedat levels of analysisinappropriatefor the theoreticalpropositionsbeingexamined.For example, researchersinterestedin organizationalgoals often collect data on the goals of key individuals.When inferencesdrawnfromthese data referonly to organizationalgoals, levels of analysishave been confused.Avoidingsuch inferenceproblemsrequiresthe researcher"to bound the organizationin such a way that observedunits are unambiguouslyseparablefromeach otherand fromtheirenvironmentsin both spaceand time" (Freeman1978, p. 336). Ideologicalbiasesoriginatein the orientationsof differentdisciplinarygroups(Rousseau 1985). The customary division of levels of analysis into "macro-level" and "'micro-level"theories reflectdisciplinaryboundaries,each with its favoredrescarch questions,acceptablemethodologies,and conventionsfor reportingresults.The concepts in macro-leveltheories are propertiesof large-scalecollectives (organizations, populations,societies);this level of analysis is favoredby macrosociologists,macroeconomists,and evolutionarytheorists.The conceptsin micro-leveltheoriesare propertiesof individualsand smallgroups;this level of analysisis favoredby socialpsychologistsand microeconomists. Macro-leveland micro-levelproponentstend to preferdifferentcausal structures. Macro sociologicaltheory typicallyexplains phenomenaby referrng only to macro6 For example, Boland (1985) argues for a phenomenology of information science in which researchers "read the interaction during system design and use it in order to interpret the significance and potential meanings they hold" (p. 196). Hirschheim and Klein (1985; Klein and Hirschheim 1983; Hirschheim 1985) argue for a "consequentialist" perspective, based on hermeneutic analysis. Phenomenology and hermeneutics strive for subjective "understanding," and view prediction as an illegitimate goal. Lyytinen and Klein (1985) take a critical theory perspective toward information system research. In critical theory, the aim is intervention to alleviate current conditions. Prediction is irrelevant to critical theorists, since outcomes are assumed to be under the control of actors.


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level concepts.Stinchcombe(1968), for example,discussesthreetypes of causalstructures-demographic, functional, and historicist-which explain social phenomena without introducingsuch concepts as individual attitudes, intentions, motives and choices. Proponentsof macro-levelanalysisarguethat it is a productivestrategyfor generating "falsifiable,parsimonious,and readilycomprehensibleexplanationsfor behavior" (Pfeffer1982, p. 256). Criticsarguethat macro-leveltheoriessufferfrom data insufficiency, failureto explainhow macrorelationshipscome about,and the needto assume the existenceof a social systemas a startingpoint (Coleman 1986). An exampleof a propositionfrommacro-leveltheoryis Leavittand Whisler's(1958) claim that the introductionof informationtechnologycauseschangein organizational structure.This propositioncontains no concepts about the individualswho populate the organization,design the informationtechnology,use the informationtechnology, and so on. As partof a variancetheory,this statementdoes not requireany articulation of the individualbehaviorsand processesby whichthe outcome occurs.By contrast,a correspondingmicro-levelconstructionof Leavitt and Whisler'sproposition might includeindividuals'choices to use informationtechnology,changedcoordinationpatternsin the workunit, and managers'needsto maintaincontrol. Proponentsof micro-levelanalysisarguethat only people can act; collectivebodies are incapableof action. Further,social collectivesconsist of individuals,and macro conceptslike organizationalstructurearepermissibleonly when it is possibleto ground them in the individualbehaviorsand the micro-levelevents and processesthat comprise them (Pfeffer 1982). Critics of the micro level of analysis accuse it of logical fallacy,confusingthe question of causalitywith the assertionof an answer,reducing social phenomenato biologicalphenomena,and relianceon concepts "that logically residein the headsof people"(Pfeffer1982, p. 22). In contrastto our cautionagainstmixingprocessand variancetheory,we believethat mixinglevels of analysismay be usefulin researchand theoryon informationtechnology and organizationalchange. In defense of mixed-leveltheory, Rousseau (1985, 1986)assertsthat technologiessuch as officeautomationare neitherstrictlymicro nor macro in character.She believesthat mixed-levelresearchshouldaboundin an interdisciplinaryfield where mixed-level phenomena are the inevitable subject of study (1985; pp. 2-3). That it does not is a disturbingcommentaryon the power of discipline-basedresearchgroups. In one of the few systematicattemptsto addressthe level of analysisissue for studies of computing,Kling(1987) proposescriteriafor establishingthe analyticboundariesof computerapplications.He arguesthat populations,equipment,spatialand temporal elementsshouldbe includedwithinthe boundariesof an analysiswhen theseelements eitherconstrainactorsinvolvedin the specificapplicationor are takeninto accountby the actors in constructingtheir actions. These criteriaexpand the focal situationto includelargersocial arenas,thus creatinga need for mixed-levelresearchstrategies. Coleman(1986) proposesone such mixed-levelstrategy:"not to remainat the macrosociallevelbut to move down to the level of individualactionsand backup again''(p. 1322).An exampleof a mixed-leveltheory of informationtechnologyand structure.is seen in Barley's(1986) work. The introductionof a new computer-basedtechnology into a worksetting(macro-level)affectsthe skillsand competenciesof the peoplein the work unit (micro-level).Interactionsamong people at differentlevels of skill create patternsof seekingand givingadvice (micro-level).Ultimately,these patternsbecome institutionalizedas formalorganizationalstructure(macro-level).Barley'sinsightson the relationshipbetweentechnologyand structuredepend on moving carefullyacross levels of analysis. The role that mixed-leveltheorygives to humanpurposeand intentionis consistent with Stinchcombe'sconception of technologyas "a descriptionof the causalconnecThis content downloaded from 140.119.81.207 on Mon, 14 Sep 2015 14:41:23 UTC All use subject to JSTOR Terms and Conditions


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tion between the ends people have and what they have to do to achieve those ends" (1983; p. 122). Both macro-level theory and much micro-level research tend to ignore human intentions.7 While the mixed-level strategy preserves macro-level concepts, it grounds these concepts in individual purposes and behavior and so remains "methodologically individualist" (Coleman 1986). Consequently, a mixed-level strategy remains vulnerable to the criticisms of macro-level proponents mentioned above. In summary, theorists and researchers who study the relationship between information technology and organizational change have given little explicit attention in their writing to the choice of an appropriate level of analysis -macro, micro or mixed. Choice of any level is subject to criticism by proponents of the others, but researchers will be better able to respond to these criticisms after deliberate and thoughtful choice of the appropriate level of analysis for their own work.

Summaryand Conclusions Social theoriesembody researchers'conceptionsof causality.In this paperwe have discussedthe causal structuresfound in theoriesabout the relationshipbetweeninformationtechnologyand organizationalchange.We have evaluatedtheoryand research in the field by focusingour discussionon three dimensionsof causal structure:causal agency,logicalstructure,and level of analysis. Causalagencyrefersto analysts'assumptionsabout the identityof the causalagent and the directionof causalinfluence.Muchof our thinkingaboutinformationtechnology's consequences in organizations has been guided by theories with fairly simple

notions of causal agency. The technologicalimperativeviews technology as causal agent,and the organizationalimperativeviewshumanbeingsas agentsof socialchange. A third conception of causalagency,the emergentperspective,attributescausalityto complex indeterminantinteractionsbetweentechnologyand human actorsin organizations.Centralto the emergentperspectiveis the social meaningascribedto information technology.This perspectiveaccountsfor conflictingresearchfindingsabout impacts by demonstratingthe differentmeaningsthat the same technologyacquiresin differentsocial settings. Logicalstructurein theoryrefersto the natureof the relationshipbetweenelements identifiedas antecedentsand those identifiedas outcomes. In variancetheories,antecedentsare conceivedas necessaryand sufficientconditionsfor the outcomesto occur. In processtheories,antecedentsare necessarybut not necessaryand sufficient.Process theoriesdepend heavily on the specificationof temporalrelationsamong theoretical elements, much like a recipe stringstogetheringredientsfor a finishedmeal. Process theorieshaveloweraspirationsabout"explainedvariance,"but providericherexplanations of how and why the outcomesoccurwhen they do occur. Finally,level of analysisdistinguishesmore or less inclusiveentities as the focus of analysis.The macrolevel of analysisfocuseson societiesand formalorganizations;the micro level addressesindividualsand small groups.Causalstructuresfor macro levels of analysisexplainsocialphenomenawithoutusingthe constructsof individuals'mental processes,and many micro analysisignorehuman purposesand intentions.Since theoriesabout informationtechnologyin organizationsare difficultto confine naturally to one level of analysis, mixed levels of analysishave become attractiveto re7Coleman (1986) traces the popularity of micro-level theories of social phenomena to the development of statistical survey research methods in the 1940s. Inferences drawn from statistical associations within survey data formed the basis for causal explanations of behavior, but the methods had little natural affinity for the intentions or purposes of individuals. The causes of human behavior were theorized as either characteristics of the individual or characteristics of the individual's environment, without recourse to individual purpose or intention (1986, p. 1314). Coleman rejects macro-level theory, particularly because it attributes purpose or intention to collectives, but he regretsthe tendency of much micro-level research to ignore individual purpose or intention.



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searchers in this field. By consciously mixing levels of analysis, researcherscan explore the dynamic interplay among individuals, technology, and larger social structures. It is no secretthat researchon informationtechnologyand organizationalchangehas producedconflictingresultsand few reliablegeneralizations.By carefullyconsidering each of the dimensionsof causalstructurediscussedin this paper,researchersshouldbe able to constructsoundertheoriesto guide more fruitfulresearch.While attendingto researchdesign, sampling,measurement,statisticalanalysis,and other researchtechniques will also improve the state of the art, we believe that the more fundamental issuesof theoryconstructionmust be addressedfirst.When assumptionsabout causal agency, logical structure,and levels of analysis are addressedexplicitly, subsequent decisionsabout researchstrategyand techniquewill be betterinformed.For example, ethnographyappearsbetter suited to emergent process researchat mixed levels of analysisthan does surveyresearch.Too often, we fear,researchmethodsareelectedfor reasonsotherthan their utility in servinga particulartheoreticalapproach. We cannot exclusivelyendorse any single combinationof the dimensionswe have discussedas the "correct"causalstructurefor researchin informationsystems.All can servethe goals of interestingdiscoveryand rigoroustestingto which researchersnormallyaspire.However,some causalstructuresfarebetterwhen evaluatedagainstcriteria of simplicityand parsimony;othersfarebetterwhen evaluatedfor "empiricalfidelity," the abilityto mirrorfaithfullythe phenomenaunderstudy.We find little balance within our fieldbetweenthese two sets of criteriafor evaluatingtheories.Much of the researchon resistanceto systems,growthof the informationsystemsfunctionin organizations;systemsimplementation,and the like, has adoptedvariancetheory formulations of logical structureand an imperativeconception of causal agency. We suspect that greateruse of theoreticalstructureswhichemphasizeempiricalfidelitywill stimulate more and betterresearchon these phenomena. In conclusion,carefulexaminationof causalstructuresis a productiveexercisein any field.Researchersinterestedin the consequencesof informationtechnologyfor organizationsshouldmakeclearand consciouschoicesregardingthe causalstructuresof their theories. These choices are at least as important as more technical researchissues, perhapsmore so. The discussion of causal structurein this paper should facilitate choice and critical thinking both for researchersand for those who apply research findings.8 8 We extend our thanksto the many people,too many to list here,who have given us helpfuladviceon earlierdraftsof this paper.We especiallythankJohn King, Rob Kling,Ron Rice, Jon Turner,and several anonymousreviewerswho havecommentedextensivelyon severalof our drafts.

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