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

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2/17

MANAGEMENT SCIENCE

Vol. 34, No. 5, May 1988

Printedn U.S.A.

INFORMATION TECHNOLOGY

AND ORGANIZATIONAL

CHANGE: CAUSAL STRUCTURE

IN

THEORY

AND RESEARCH*

M. LYNNE MARKUS

AND

DANIEL ROBEY

GraduateSchool of Management, University

of California,Los Angeles, California90024

College of Business Administration,Florida

International

University,Miami, Florida

33199

This

article

concerns heories

about

why

and how information echnologyaffectsorganiza-

tional

life. Good

theory guides research,

which,

when

applied,

ncreases he likelihood

hat

information echnology

will be

employed

with desirable onsequences or users,organizations,

and other nterested

arties.

Butwhat s agood theory?Theories reoftenevaluatedn termsof theircontent-the specific

conceptsused

and

the

human values

served.

This

articleexamines heories

n terms

of their

structures-theorists' ssumptions bout the natureand directionof causal nfluence.Three

dimensionsof causal structure reconsidered-causal agency, ogical structure,

nd

level of

analysis.Causalagencyrefers

o

beliefsabout

the nature

of

causality:

whether xternal orces

cause change,whetherpeople

act

purposefully

o accomplish ntendedobjectives,or whether

changesemerge unpredictablyromthe interactionof people and events. Logicalstructure

refers o the temporal spect

of

theory-static

versus

dynamic-and to

the

logicalrelationships

between he

causes nd the

outcomes.

Levelof analysis efers o the entitiesabout

which

he

theoryposesconcepts

and

relationships-individuals,

roups,organizations,

nd

society.

While

there are many possible structures

or

good theory about the role of information

technology

n

organizational hange,only

a few of these structures an

be

seen in current

theorizing.

ncreased wareness f the

options,

open

discussionof their

advantages

nd

disad-

vantages, nd explicitcharacterizationf future heoretical tatementsn termsof the dimen-

sions

and

categories

discussedhereshould,

we

believe, promotethe development

of

better

theory.

(INFORMATIONTECHNOLOGY;

ORGANIZATIONCHANGE; CAUSALSTRUC-

TURE)

Introduction

The

relationshipbetween information

technology and organizational hangeis a

central

concern

in

the

field of Information

Systems IS).

In the 30

years

since

Leavitt

and Whisler's

1958)

seminal

article, Management

n

the

1980's, peculations

n

the

roleof information echnology n organizations ndits implications ororganizational

design

have flourished.

Few researchers

n

the IS field

question

the

importance

of

the

issue.

In

an empirical nvestigation

f literature itation

patterns,

Culnan

( 1986)

traced

the

origins

of the

IS

field to

Leavitt and Whisler'sarticle

and identified

computer

impacts

as

a

clear subfield

within it.

Unfortunately,

he literatureon information

echnology

and

organizational hange

does

not

currentlysupport

reliable

generalizations

about the

relationships

between

information echnology

and

organizationalhange.

There

are

several

reasons

or this.

The literature

contains works

by

researchers

rom

several academic

disciplines

and

interdisciplinarypecialties, ncludingorganizationalheory, management cience,so-

ciology,

and

computer cience,

each

with

its own

preferred oncepts

and

theoretical

nd

methodologicalbiases.

It

includesconflictingand uncleardefinitionsand measuresof

information echnology (Bakopoulos

1985)

and

organizational

tructure

Fry 1982).

Finally,

it mixes and crosses

units

and levels

of

analysis

from the

individual,

the

*

Acceptedby

Arie Y.

Lewin,

former

DepartmentalEditor;

eceivedJune

23,

1986.

This paperhas

been

with the authors51/2monthsfor

2

revisions.

583

0025- 1909/88/3405/583$0 1.25

Copyright?) 1988, The Institute of'Management Sciences

http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp


3/17

584

M. LYNNE MARKUS

AND DANIEL

ROBEY

workgroup, he department, he organization,and society-a practice which leads

some observers to fear improperly specified models and ungeneralizable indings

(Freeman 1978, Rousseau 1985).

One importantapproach

o

solving these problems s

to

focus on the substance

of

theory, such as concept definition and normative orientation. For example, Kling

(1980) has identifiedand defined

six

theoreticalperspectives

n

social analyses and

empiricalresearch

on

computing.

These

perspectives

are

distinguishable

rom

each

other by

their

respectivedefinitionsof technologyand social setting, theoreticalcon-

structs,beliefs about the dynamics

of

technicaldiffusion,evaluationsof good tech-

nology, and ideologiesof the workplace.Kling and Scacchi(1982) have discussed he

differing ssumptions

about

technology, nfrastructure,

nd

the dynamics

of

change

n

discrete-entity ersus web models.

Ourapproachdiffers

from

but complementsKling's analyses

of

theory substance.

Instead,we focus on the structureof theory, that is, researchers' onceptions of the

natureand directionof causality.We believethat sound theoretical tructure,ike good

theoreticalsubstance, s necessary

or

better theory.

Our

purpose

in

this

paper

is to

analyze

he

causal structure

f

the

theoretical

models found

in

the

literature

n infor-

mation technology

and

organizational hange.

The causal structure of theoretical models comprises three dimensions: causal

agency, ogicalstructure, nd

level of

analysis.Causalagencyrefers

o

beliefsabout the

nature of causality:whetherexternal forces cause change (the technological mpera-

tive),

whether

people

act

purposefully

o

accomplish ntendedobjectives the organiza-

tional mperative)

r

whether hangeemerges

rom

the interaction

f

people

and events

(the emergentperspective).Logicalstructure efers o the time span of theory (static

versusdynamic)and to the hypothesized elationshipsbetween antecedentsand out-

comes:whethercausesare related o outcomes

n

an invariant,necessary nd sufficient

relationship variancemodels), or

in

a recipe of sufficientconditions occurringover

time (processmodels). Level of analysisrefers o the entities aboutwhichthe theory

posesconcepts

and

relationships-individuals,collectives,

or

both. These

threedimen-

sions

of

causal

structure

re

shown in

Figure

1.

Causal tructure annot

easily

be

separated

rom

issues

of

theory

substance

and from

various methodological ssues. But a thorough understanding

f

causal structure

re-

quiresa depthof treatmentnotusually ound n methodological ritiques e.g.,Attewell

and

Rule

1984;Rice 1980;Robey 1977). Consequently,

ur

focus

on

causal structure

will

exclude several

important

concerns

which

must also

figure prominently

n

the

development

and

testing

of

good theory.

CAUSAL

LOGICAL

LEVEL F

AGENCY STRUCTURE

ANALYSIS

*

Technological *

Macro

Imperative

*

Variance

Theory

*

Organizational

Imperative *

Micro

-

micro

-

mixed

*

Process

Theory

*

Emergent

Perspective

FIGURE 1.

D)imensionsof Causal Structure.



4/17

INFORMATION

TECHNOLOGY AND

ORGANIZATIONAL CHANGE

585

CausalAgency

Causal

agency

refers

o the analyst'sbeliefs

about

the identity

of

the

causal

agent,

the

natureof causal

action and the directionof causal

influenceamong

the elements in a

theory.Pfeffer 1982), forexample,has identified hreeperspectives n action in orga-

nizational heory.

In the situational ontrol

perspective, xternal

factorsor events

constrainor forcepeople and

organizations

o behave

n

certain

ways.

In

the rational

actor

perspective,

eople

and

organizations

valuatealternative ourses

of action and

exercise ree

rationalchoice. In the emergent

perspectiveon action, the behavior

of

people

and organizationsemerges

from

a

dynamic interaction

of

external

circum-

stances

and internal

motives or interests.'

Building upon

the work of

Pfeffer,we have identified hree

conceptions of causal

agency

n

the literature

n

information

echnologyand organizational

hange.

We

label

these: the technological

mperative,

he

organizational

mperative

and the emergent

perspective. n the technological mperative, nformationtechnologyis viewed as a

cause of

organizational

hange.

In the

organizational mperative,

the motives and

actions of the

designers

of

information

technologies

are

a

cause of

organizational

change.

In

the

emergentperspective,

rganizationalhange

emerges rom

an unpredic-

table

interaction

between

information

echnology

and

its

human and

organizational

users. Each

of

these perspectives

s

discussed more fully

below and summarized

n

Figure

2.

The Technological

Imperative

Theessenceof the technologicalmperatives conveyedby theword impact. This

perspective

iewstechnology

as an

exogenous

orcewhich determines

or

strongly

con-

strains

he

behaviorof individuals

and

organizations.

The technological mperative

s

consistent

with Pfeffer's 1982)

situationalcontrol perspective

on action

in

organiza-

tions.

In

this

view,

action

is

seen

not

as

the

result

of

conscious,foresightful

hoice

but

as

the

result

of external

constraints,demands,

or

forces hat

the social

actor

may

have

little controlover

or

even cognizance

of (Pfeffer1982, p. 8).

Forexample,Leavitt

and

Whisler 1958)

argued hat

information

echnology

would

alter

dramaticallyhe shape

of organizations nd the

natureof managerialobs.Organi-

zations

would

recentralize,

evels

of middle

management

would

disappear,

nd a

top

management

lite wouldemerge. Leavitt

and Whislerurgedmanagers

o

prepare

or

these

nevitable mpactsby

developing

heir

nternal

echnological apabilities

nd

their

liaisons o external echnological

esources.

Simon

(1977)

was less

pessimistic

han

Leavitt

and

Whisler

n

his

predictions

about

the

impact

of

computers,

but

no less deterministic.

Simon

contendedthatcomputers

would not change

he basic hierarchical

atureof organizations,

ut would recentralize

decision

making.Lineorganizational

tructures

would shrink

n

size,

and

the

number

of levelswould decrease.Staff

departments

would increase

n

numberand

size,

making

structures

more

complex

and

requiring

more

lateral

nteraction.

Whilethe technological mperativehas a long historyand makessome compelling

claims, empirical

researchhas

generatedcontradictory

indings

on

almost

every

di-

mension

of

hypothesizedcomputer impact (Robey

1977;Kling 1980;

Attewell

and

Rule

1984).

Information

ystems

have been

found both to

enrich and

routinize

obs

'Slack (1984)

makes

similar distinctions among

various conceptions

of

causality

in an analysis of

commu-

nication

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's

emergent

perspective

on action

in

that the

distinctions between

cause

and effect are not sharply

drawn.



5/17

586 M. LYNNE MARKUS AND DANIEL

ROBEY

Technological Imperative

Information Organizational

Technology

structure

Organizational

Imperative

Purposes\

Organizational

Information Structure

Emergent Perspective

INFORMATION

ECHNOLOGYSE

MEANING

BEHAVIOR

/P/RPOSI7S

SZ'77/NC

Information

Technology

FIGURE

.

Causal Agency.

(Kling 1978; Bj0rn-Andersen, ason,

and

Robey 1986),

both centralizeand decentra-

lize

authority Klatzky 1970;

Whisler

1970;

Stewart

1971;Blau, Falbe,McKinley,

and

Tracy 1976;

Carter

1984;

Foster

and

Flynn 1984;

Dawson and

McLaughlin1986),

and

produce

no

changes

where

changes

were

expected (Robey 1981; Franz, Robey,

and

Koeblitz

1986).

Some

investigators

ave

proposed

hat

contingencies

affect he

relationship

etween

information

technology

and structural

change.

For

example,

in a

review of

studies

conducted

during

the

1

960s

and

early 1970s, Robey (1977)

observed

hat

computing

appeared

o

support

an

existing

decentralized tructure

n

organizations

with

uncertain

environments.

However,

n

simple environments, omputingappeared

o

strengthen

centralized

authority

tructure.

Robey suggested

hat

computing echnology

be viewed

as

a

moderatingvariable,affecting

he

strength

of

a

causal

relationship

betweenenvi-

ronmentaluncertainty

nd

organizational

tructure.

Othercontingencies

have also been examined.

Leiferand

McDonough (1985)

con-

trolled

or

task routineness

nd

found

that

departments sing

a

computer-basedystem

were

more

centralized,

ess

complex,

and

perceived

ess

environmental

uncertainty

hat

those not

using

he

computer.Klatzky 1970)

asserted

hat

size

was

partially esponsible

for the decentralization

f decision

making

that

accompanied

he use

of

information

technology. Carter 1984) found that organizational ize moderated he relationship



6/17

INFORMATION TECHNOLOGY AND

ORGANIZATIONAL CHANGE 587

between nformation

echnologyand the structureof newspaper

rganizations.Pfeffer

and

Leblebici 1977) controlledfor both size

and environmental

complexity

in

their

study of the use of

informationtechnology

in

38 small

manufacturing ompanies.

Majchrzak nd Mosher 1987) used three

differentanalysisstrategies o examine orga-

nizational tructure s a contingency n the technology/performanceelationship t the

individual evel of analysis.

The Organizational

Imperative

Whereashetechnological

mperativeargues

hat

information

echnology

constrains

or determineshuman and

organizationalbehavior,

the

organizational

mperative

as-

sumes almost unlimitedchoice over

technological

options

and almost unlimitedcon-

trol over the

consequences.

The

organizational

mperativecorresponds

o Pfeffer's

intendedly

ational

perspective n action.

It

assumes

hat behaviorsare

chosen,

that

such choicesoccuraccordingo a set of consistentpreferences,hatchoices occurprior

to the

action

itself,

and that action is

goal

directed

Pfeffer1982, p. 6).

This

perspective

holds that human

actors

design

nformation

ystems

o

satisfyorganizational

eeds

for

information.2Thus,

information

echnology

s the

dependent

ariable

n

the

organiza-

tional

imperative,

aused

by

the

organization's

nformation

processing

needs and

man-

ager's

choices

about how to

satisfy

hem.

In

a

widely known

versionof this perspective,Galbraith 1977)

proposeda number

of

organizationaldesign alternativesby which

organizations an

fill

the information

processingneeds generated

by uncertainty.Managersmay reducethe

need to process

information

by managing

he

environment,by

using slack resources,

or by creating

self-contained

rganizational

nits.

Managersmayalsoincrease he

capacityof organi-

zations o

process nformation

by developing ateral

relationsandbuilding nformation

systems.

A

similar causal argument

s evident

in

the

work of Daft and MacIntosh(1978,

1981).

They hypothesizednformationneeds to

vary with task variety

and knowledge

about the task

and

proposed

a

relationship

between nformation

processingneeds

and

use of information

ystems.Unanalyzable,nonroutine

asks require

rich

information,

capable

of

conveyingcomplex

and

equivocalmeanings;

acial

expressions nd

voice

are

the media

most

capable

of

processing

his rich

qualitative

nformation.

Concise,

com-

puter-basednformation ystemsaremoreappropriateorsimpler nformationprocess-

ing

requirements, ccording

o

Daft and his

colleagues see

Daft and Weick

1984;

and

Daft and

Lengel 1986).

Not

surprisingly,

he

normative literature

on

information

system design evinces

considerable

ptimism

about the

degree

of human

influence

over

the

capabilities

and

characteristics f information

ystems(Olson

1982;

Olson and Lucas

1982;

Olson and

Turner

1985).

Contextual

variables,

which

an

external

or

situational

control

perspec-

tive

might

view as constraints

r

determinants,

reviewed

n

the

organizationalmpera-

tive as

contingencies

hat

managers

hould

take

into account.

Among

these

contextual

variables

rework

unit

technology,organizational

evel, environment,

decision

making

style,

and

uncertainty Whisler

1975;

Gordon and Miller

1976;

Waterhouse

nd

Ties-

sen

1978;Ginzberg1980;Olerup 1982;Gorry

and Scott Morton

1971).

The

assumption

of

designer

discretion tands

n

sharp

contrast o the

externaldeter-

minism of

the

technological

mperative.

The

organizationalmperativeassumes that

systems

designers

an

manage

he

impacts

of information

ystemsby

attending

o both

technical

and

social concerns

(Bj0rn-Andersen

t al.

1986;Mumford

and Weir

1979).

2

These needs

are sometimesviewedas externally enerated-derived rom

uncertainty

riginating

n

the

environmentor

in

work

technology Galbraith1977;

Daft and

MacIntosh

1981;

Daft

and

Lengel 1986;

TushmanandNadler 1978).



7/17

588 M.

LYNNE

MARKUS

AND DANIEL ROBEY

This view is sharedby managementand organization

heoristswho see information

technology as a tool

for

solving organizationalproblems

(Child 1984;

Huber

1984;

Huberand McDaniel 1986).

Empirical upport or the organizationalmperative s

limited.While studiesbyDaft

and MacIntosh(1981), Specht (1986), and Olerup (1982) supportthe notion that

organizational haracteristics orrelate

with information

system characteristics,

he

models of Galbraith 1977) and Tushman

and Nadler

(1978)

have received

mixed

empiricalsupport (Tushman 1979;

Morrow

1981;

Penley 1982;

Triscari

and

Leifer

1985; Saundersand Robey 1986). Unfortunately,

most of these studies fail to assess

designers'ntentions and

thus

cannot be

regarded

as

complete

tests

of the

organiza-

tionalimperative.

The Emergent Perspective

The emergentperspectiveholdsthatthe usesandconsequencesof information ech-

nologyemergeunpredictablyrom complex social interactions.

This perspective orre-

sponds

to Pfeffer's

emergent

iew

of action

in

organizations:

Because

participation

in organizational ecisions s both segmentedand discontinuous,becausepreferences

develop and changeover time,

and because he

interpretation

f

the

results

of actions

-the

meaning

of

history-is

often

problematic;

ehaviorcannot

be

predicted

a

priori

either by

the intention of individualactors or

by

the conditions

of the environment

(1982,

p.

9).

Kling and Scacchi's 1982) distinction

between discrete-entitymodels and web

models of computingprovides

a

useful startingpoint

for a

discussion

of

the

emergent

perspectiveon informationtechnologyand organizationalchange. Discrete-entity

models conceive of

information

echnology

as a tool

with

identifiable

benefits,costs,

and skill

requirements.

Like the

organizationalmperative,

discrete-entity

models as-

sume that. he

goals

of

designersguide

the

development

of

computingapplications.By

contrast,

web models conceive

of information

technology

as an

ensemble

of

equip-

ment, applications,

and

techniques

hat

carry

social

meanings.

The

primary

virtue of

web models s empirical idelity, heirability

o

account

for the detailsand complexities

of

actualsituations.

Their

characteristic

roblem

s

analytical

cumbersomeness

1982,

p. 10).

Central onceptsintheemergentperspective retherole of thecomputingnfrastruc-

ture,

the

interplay

of

conflicting objectives

and

preferences,

and

the

operation

of

nonrational bjectivesand choice processes.For example,

Gasser's 1986) study of the

integrationof computingand routine work examined the

misalignmentbetween de-

mands of the

work

setting

and the

computing

resourcesavailable.Gasser identified

strategiesby

which

organizational

ctors

coped

with

slippage

between

echnology

and

work demands

and

discussed how the nature of routine

work

changed as

a result.

Rather

han

attributing hange o actor ntent or exogenous

echnology,Gasser ocused

on

thedynamic nterplayamong actors,context,

and

technology.

In anotherrecent example of an emergentmodel, Barley 1986) studied the intro-

duction of

computerized omography CT scanners)

n

radiology.

He demonstrated

that

these

technologies

an

alter

he

organizational

nd

occupational

tructure f radio-

logical

work.

However, the identical echnologiescan occasion

similar

dynamics

and

yet

lead to different tructural

utcomes

n

different

ettings 1986, p. 105).

In

Barley's

analysis,

he

scannersoccasionedchange not because

of their

inherent

characteristics

(as

the

technological mperative

would

hold),but because hey became

social

objects

whose meaningswere defined by the context of their use

(p. 106). The technology

presentedan occasion or structural hange but did

not determinewhich

of a

large

varietyof alternatives ctuallyemerged rom the process

of structuring.

The emergentperspective dmitsgreater omplexity o the issue of causalagencyand



8/17


AND ORGANIZATIONAL

CHANGE

589

to the goal

of predictingorganizational

hangesassociatedwith information

echnol-

ogy. By refusing o

acknowledge

a dominant cause of change,

emergentmodels

differ

qualitativelyrom the

deterministic

ausalargumentsof

the two imperatives.Predic-

tion

in

the

emergentperspective

equiresdetailedunderstanding

f

dynamicorganiza-

tionalprocessesn addition o knowledgeaboutthe intentionsof actorsand the features

of

information

echnology.

This addedcomplexity

makesemergent

models difficult

o

construct Bendifallah

nd Scacchi1987;

Kling 1987).

Normative Implications

The

three

perspectives

n causalagencypresented

here differ n theirattributions

f

responsibility

or the outcomes

observed.These

attributions mply

that particularn-

terventions

will

be

more or

less

efficacious

n

producing

or

increasing

he likelihood

of

desirable

outcomes.

Consequently,

analysts

of differentpersuasions

an often be dis-

tinguishedas easily(or moreso) by their normativestance as by the specificsof their

models(Mowshowitz

1981).

Technological

mperative analysts

hold that information

technologygenerally

or

some

particular

onstellationof

technological

eatures s responsible

or impacts uch

as

change

in

organizational

tructure, kill

enhancementor deskilling

of workers,

or

change

in

employment

opportunities.

Consequently, he

policiesand remedies

pro-

posedby

these analystscenteron

stopping, lowingor accelerating

he

rateof change n

information

echnologyor selecting

nformation

echnologies

with

particular

ackages

of

features.

Organizational

mperative

analystsattribute

he consequences

of informa-

tion

technology

o the choicesand

behaviors

of managers

nd

systemdesigners.

Conse-

quently, these analysts tend to prescribe mproved design and resourceallocation

methods

and betterimplementation

trategies

nd tactics.Because

emergentanalysts

attribute utcomes

o

an

unpredictable

nteraction

of

technological

eatures

nd

actors'

intentions,

their normative posture

is

less

clear than

those

considered

above.

Some

emergentanalysts

eschew intervention,arguing hat prediction

s impossible

and

out-

comes are

indeterminate;

thersadvocate emancipatory

trategies,

uch

as

extensive

user

participation

n

the

analysis,

design,

and

implementation

of

informationtech-

nology.

LogicalStructure

A

second dimension

of

theoretical

tructure

oncerns

the

logical

formulation

of the

theoretical

argument.

On

this

dimension,

Mohr

(1982,

Chapter2) distinguishes

be-

tween variance

and

process

theories.

The distinction

n

theoretical

tructure

between

variance

and process heories s

somewhat

analogous o

the

distinction

between

cross-

sectional and

longitudinal

research

methodologies.

Variance

theories are

concerned

with predicting

evels of outcome

from

levels

of contemporaneous redictor

variables;

variance

heoriesare

concernedwith

explaininghow outcomesdevelop

over

time.

Variance and Process

Theories

Mohr

(1982) explains

the

differencebetween

variance

heoriesand

process

heories

in terms

of the

hypothesized

elationships

between

ogical

antecedents

and

outcomes.

These are

summarized

n

Figure

3.

In variance

heories,

the

precursor loosely,

that

which

might

be referred

o

as

the

cause )

s

posited

as a

necessary

and

sufficient

condition

for

the outcome.3

For

example,

n a

variance

heory

that

hypothesizes

use of

information

technology

as a cause

of

organizational

entralization

cf.

Leavitt

and

There s a superficial

esemblance

etween

variance

heories

and regression

models,

but the two

are not

identical.

Many regression

models

contain

variableswhichpredict

he outcome

but which

arenot

hypothe-

sizedto be causal n the necessary ndsufficient'senseMohr 1982,Chapter ).



9/17

590

M. LYNNE MARKUS AND DANIEL ROBEY

VARIANCE

HEORY PROCESSTHEORY

ROLEOF TIME Static Longitudinal

DEFINITION Tht

cause Is Causation consists

necessary

and of necessary

sufficient conditions In

for the

sequence;

chance

outcome

and random events

play

a

role

ASSUMPTIONS Outcome will

Outcomes

may not

Invariablyccur occur (even when

when

necessary conditions are

and

sufficient

present)

conditions

are

present

ELEMENTS Variables Discrete outcomes

LOGICAL

ORM

If

X,

then

Y;

If not

X,

then

not

Y;

if

more X, then

cannot be

extended

more

Y

to

moreX'or

more

yU

FIGURE . Logical Structure.

Whisler 1958), centralization s expected

alwaysto occur whenever nformation ech-

nology is used (where always

s defined

n

terms of statistical

confidence

evels).

In

processtheories,the precursor s assumed nsufficient o cause he outcome,

but is

held to be

merelynecessary

or

it

to

occur.

In

general,necessary

conditions alone cannot constitute a

satisfactory

heory.

For

example,

while

water

may

be

necessary

or the

growth

of

plants,

it is

not

sufficient;

therefore, t cannot be consideredthe cause of plant growth. Mohr has observed,

however,

hat

necessary

onditions

can

comprise

a

satisfactory ausal

explanation

when

they

are combined

in

a

recipe

hat

strings

hem

together

n

such

a

way

as

to

tell

the

story

of

how

[the outcome]

occurs

whenever

t

does occur

(1-982,p. 37).

In

short,

outcomes

are

(partially)predictable

rom a

knowledgeof process,

not from

the level

of

predictorvariables.

A

good example

of a

process

theory is

the

garbage-can heory of organizational

choice

(Cohen,

Marchand Olson

1972).

In this

model,

decisions result

from the

(al-

most)

random

collisions

of

participants,

hoice

opportunities, olutions,

and decisions.

Many

diffusion

of innovationtheories

are

process heories,

at least

implicitly.

Barley's

study (1986)

of CT

scanning

in

radiology

also meets the

requirements

of a

process

theory.

Markus

1984) proposed

a process

heory

for

explaining

user resistance o

informa-

tion

systems.

Included

as

a

necessary

ondition

for

user

resistance

n

this recipe

s the

introduction

of

an informationsystem

with

features

differing

rom

the featuresof the

organizational etting.

This

necessary

ondition is not

believedsufficient o

ensure he

occurrence

of

resistance,

but

it

is

believed

to

be

necessary.Consequently,

his

process

theory recognizes

hat resistance

may

not

alwaysoccur,

even

when the

necessary on-

dition of

differing

eatures

s

present.

n

any given case,

resistance

may

not

occur

for

severalpossiblereasons:people maylike the changesembodied n the system; hey may



10/17

INFORMATION

TECHNOLOGY AND ORGANIZATIONAL CHANGE 591

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


but insti-

tutionalization 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 vari-

ables:

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

resis-

tance,

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


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 condi-

tions

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

contingency

heories deal with

necessaryconditions, they

are not

process

theories. They

are

variance

heories,

because

the

conditionsthey

specify

are both

necessary

and

sufficient

or

the

outcome

to occur.



11/17

592

M.

LYNNE MARKUS

AND

DANIEL

ROBEY

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).

Relationship

betweenCausal

Agency

and

Logical

Structure

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

organiza-

tional

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 self-

sustaining 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.

Advantages f

Process

Theories

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

con-

fidence

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

predictabil-

ity of social phenomena

that characterizes the

technological

and

organizational

impera-

tives.

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

mecha-

nism by which subsequent stages come about.



12/17


AND ORGANIZATIONAL

CHANGE 593

research.By contrast,

some criticsof variance heory research

ake a strongantipositi-

vist stance, arguing hat predictionof human action

(such as the use of information

systems) s not a

legitimatepursuit.6

Thus, while empirical

processresearch

ypicallyreveals hatthings are more compli-

cated han variance heoryrepresentshemto be, such researchhouldnot be dismissed

as isolatedstories

or

illustrative

ases

only. With care, findingscan be generalized

o

other-

ettings,

and predictions an be tested n laterresearch.

The advantage f process

theories

s

that these predictionsmay correspondmore faithfully

o actual events

in

organizationshan

do the typicalpredictionsof variance ormulations.

In summary,

we

believe that process

theories are useful preciselybecause,

while

recognizing nd accepting

he complexityof causalrelationships,

hey do not abandon

the goalsof generalizabilitynd prediction.

By acceptinga

more limited definition

of

prediction,one in which the analyst s

able to say only that the outcome is likely

(but

notcertain)undersome conditionsandunlikelyunderothers,process heoristsmaybe

able to

accumulate

and consolidate indings

about the

relationship

between

nforma-

tion technologyand organizational hange.

Level of Analysis

The specific theories

and researchstudies discussed

in this paper concern

three

different

ypes

of entities,

or levels of

analysis: ndividuals,

organizations,

nd

society.

Questions

about

the

appropriate

evel

of

analysis

have

been

widely

debated

n

the

social

sciencesgenerally,

but have

rarelybeen

explicitlydiscussed

within those research

om-

munitiesconcernedwith the causesand consequencesof information echnology n

organizations.

The debatecenterson two issues:problemsof

inference

and

ideological

biases(Pfeffer1982).

Problemsof

inferencearisewhen

concepts

are definedand data

arecollected

at levels

of

analysis nappropriate

or the

theoretical

propositions

being

examined.

For

example,

researchersnterested

n organizational oals often collect

data

on

the goals

of

key

individuals.When

inferencesdrawn rom these data referonly

to organizational oals,

levels of analysis

have been confused.Avoiding such inference

problemsrequires he

researcher

to bound

the

organization

n

such a

way

that observed

units

are

unambig-

uouslyseparable

rom

each otherand

from

theirenvironments

n

both

space

and time

(Freeman1978, p. 336).

Ideologicalbiasesoriginate

n

the orientations

of different

disciplinary roups Rous-

seau

1985).

The

customary

division of

levels of

analysis

into macro-level

and

'micro-level

heories

reflect

disciplinary

boundaries,

each

with

its

favored

rescarch

questions,acceptable

methodologies,

and conventions

for

reporting

esults.

The

con-

cepts

in

macro-level

heories are

properties

of

large-scale

collectives

(organizations,

populations,

societies);

his level

of

analysis

is

favoredby

macrosociologists,

macro-

economists,

and

evolutionary

heorists.

The

concepts

n

micro-level

heories

are

prop-

ertiesof

individuals

and

small

groups;

his

level

of

analysis

s favored

by

social

psychol-

ogistsand microeconomists.

Macro-leveland

micro-level

proponents

end to

prefer

different

causal structures.

Macro

sociological

heory typicallyexplains phenomena by

referrng only

to

macro-

6

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 interven-

tion

to alleviate current conditions.

Prediction is

irrelevant

to

critical

theorists,

since

outcomes are assumed

to

be under the control of actors.



13/17

594

M.

LYNNE MARKUS

AND DANIEL

ROBEY

levelconcepts.Stinchcombe 1968),

for example,discusses

hree

types of causal struc-

tures-demographic, functional,

and historicist-which

explain

social

phenomena

without

introducingsuch concepts as individual attitudes,

intentions, motives and

choices.

Proponentsof macro-levelanalysisargue hat it is a productive trategy or generat-

ing

falsifiable, arsimonious,and readilycomprehensible

xplanations or behavior

(Pfeffer

1982, p. 256). Criticsarguethat macro-level heoriessuffer

rom data insuffi-

ciency,

failure o explainhow macro

relationships

ome

about,

and the

need to assume

the existenceof a social system as a

startingpoint (Coleman

1986).

An

exampleof a proposition rom

macro-level heory

s Leavittand

Whisler's 1958)

claim that the

introductionof information echnology

causes

change

n

organizational

structure.

This propositioncontains no concepts

about the individualswho

populate

the

organization,design

the information

echnology,

use the information

echnology,

and so on. As

part

of a variance

heory,

his

statement

does

not

requireany

articulation

of the

individualbehaviors

and

processes

by

which the outcomeoccurs.

By contrast,

a

correspondingmicro-level

construction of

Leavitt and Whisler's

proposition might

include

ndividuals' hoices to use information echnology,

changed

coordination

pat-

terns

n

the work

unit,

and

managers'

needs to

maintain

control.

Proponentsof

micro-levelanalysisargue

that

only people

can

act;

collective

bodies

are

incapableof action. Further,

social

collectives

consist of

individuals,

and

macro

concepts

ike

organizational

tructure

re

permissible nly

when

it

is

possible

o

ground

them

in

the individualbehaviorsand

the

micro-level

events and

processes

hat

com-

prise them

(Pfeffer 1982). Critics

of the micro level

of analysis accuse

it

of logical

fallacy,confusingthe questionof causalitywith the assertionof an answer,reducing

social

phenomenato biological

phenomena,

and reliance

on

concepts

that

logically

reside

n

the heads of people (Pfeffer

1982, p. 22).

In

contrast o

our

cautionagainst

mixingprocess

and variance

heory,

we

believe hat

mixing

evels

of

analysismay

be useful

n

research

nd

theory

on information echnol-

ogy

and

organizationalchange.

In

defense of mixed-level

theory,

Rousseau

(1985,

1986)

asserts hat technologies

uch

as officeautomationare

neitherstrictlymicro nor

macro

in

character.

She believes

that mixed-level

research

hould

abound

in

an

inter-

disciplinary

ield where mixed-level phenomena

are the

inevitable

subject

of

study

(1985; pp.

2-3).

That it

does not

is a

disturbing ommentaryon

the

power

of

disci-

pline-based esearch roups.

In

one of

the

few

systematicattempts

o address he level

of

analysis

ssue for

studies

of

computing,Kling (1987) proposes

criteria or establishing he

analytic

boundaries

f

computer

applications.

He

argues hat

populations,equipment,spatial

and

temporal

elementsshouldbe included

within

the

boundaries

of an

analysis

when

these

elements

eitherconstrainactors nvolved n the

specificapplication

or are taken nto

account

by

the actors

in

constructing

heir

actions.

These

criteria

expand

the

focal

situation to

include

arger ocial arenas,

hus

creating

a

need for mixed-levelresearch

trategies.

Coleman

(1986) proposes

one such mixed-level

trategy:

not to

remainat the

mac-

rosocial evel but to move downto the level of individualactionsand backup again'' p.

1322).

An

example

of

a

mixed-level

heory

of

information

echnology

and

structure

is

seen

in

Barley's 1986)

work. The

introduction

of

a

new

computer-based

echnology

into

a work

setting macro-level)

ffects

he

skills

and

competencies

of the

people

n

the

work unit

(micro-level).

Interactions

among people

at

different evels

of

skill

create

patterns

of

seeking

and

giving

advice

(micro-level).Ultimately,

hese

patterns

become

institutionalized

s

formal

organizational

tructure

macro-level).

Barley's nsights

on

the

relationship

between

technology

and structure

depend

on

moving carefully

across

levels of analysis.

The role that mixed-level heory

gives to human purposeand intention s

consistent

with Stinchcombe's onception of technologyas a descriptionof the causalconnec-



14/17

INFORMATION TECHNOLOGY AND ORGANIZATIONAL CHANGE

595

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 method-

ologically individualist (Coleman 1986). Consequently, a mixed-level strategyremains

vulnerable to the criticisms of macro-level proponents mentioned above.

In summary, theorists and researchers who study the relationship between informa-

tion

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.

Summary

and Conclusions

Social theoriesembodyresearchers'onceptionsof causality.

In

this paper

we have

discussed he causalstructures ound

in

theoriesabout

the

relationship

between

nfor-

mation technologyand organizational hange.

We have

evaluated heoryand research

in

the field by focusingour discussionon three dimensions

of causal

structure: ausal

agency, ogicalstructure,

nd level

of analysis.

Causal

agency

refers o

analysts'assumptions

about the

identity

of the

causal

agent

and the directionof causal nfluence.Much of our thinkingabout nformation echnol-

ogy's consequences

in

organizations

has

been

guided by

theories with

fairly simple

notions of causal agency. The technological mperativeviews technology as causal

agent,and

the

organizationalmperative

iews

humanbeings

as

agentsof socialchange.

A

third conception of causal agency, the emergentperspective,attributes ausality o

complex

indeterminant nteractionsbetween

technology

and human

actors

n

organi-

zations.Central o

the

emergentperspective

s

the social

meaning

ascribed o informa-

tion

technology.This perspective

accounts for

conflicting

research

indings

about

im-

pacts by demonstrating he differentmeaningsthat

the

same technology acquires

n

different ocial

settings.

Logicalstructure

n

theoryrefers o the natureof the relationshipbetweenelements

identifiedas antecedentsand those identifiedas outcomes.

In

variance heories,ante-

cedentsareconceivedas necessary nd sufficient onditions or the outcomesto occur.

In

process heories,

antecedents

are

necessary

but not

necessary

and sufficient.

Process

theories

depend heavily

on the

specification

of

temporal

relations

among

theoretical

elements,

much

like a

recipe strings ogether ngredients

or

a

finishedmeal. Process

theories

have

lower

aspirations

bout

explained ariance,

but

provide

richer

xplana-

tions of how and

why

the outcomes

occur when

they

do occur.

Finally, evel of analysisdistinguishes

more or less inclusive

entities

as the

focus

of

analysis.

The macro evel of

analysis

ocuseson societiesand

formalorganizations;he

micro level addresses

ndividualsand small

groups.

Causalstructures or

macro

levels

of

analysisexplain

social

phenomena

without

using

the constructs

of individuals'

men-

tal

processes,

and

many

micro

analysis gnore

human

purposes

and intentions. Since

theoriesabout information

echnology

in

organizations

re

difficult o confine natu-

rally to one

level

of analysis,

mixed levels

of analysis

have

become attractive o

re-

7Coleman (1986)

traces the popularity

of

micro-level

theories of

social phenomena

to the development of

statistical survey research

methods

in

the

1

940s. 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.



15/17

596

M. LYNNE MARKUSAND DANIEL

ROBEY

searchers

in

this

field.

By consciously mixing

levels of

analysis,

researcherscan

explore

the

dynamic

interplay among individuals,

technology, and larger

social

structures.

Itis no secret hat

research n information

echnology

and

organizational

hangehas

producedconflictingresultsand few reliablegeneralizations.By carefullyconsidering

each

of the dimensions

of causalstructure iscussed

n

this

paper,

researchershould

be

able to

constructsounder

theories

to

guide

more

fruitful

research.

While

attending

o

researchdesign,

sampling,

measurement,

tatistical

analysis,

and other research

ech-

niques will also improve

the state of the

art,

we

believe that

the more

fundamental

issues of theoryconstruction

must be addressed irst.When

assumptions

about

causal

agency, logical

structure,

and levels of

analysis

are

addressed

explicitly,

subsequent

decisions

about

research

trategy

and

technique

will

be better nformed.For

example,

ethnographyappears

better suited

to

emergent process

researchat

mixed

levels of

analysis handoessurveyresearch.Too often,we fear,researchmethodsareelectedfor

reasons

other than

their

utility

in

serving

a

particular

heoretical

approach.

We cannot exclusively

endorse

any single

combination

of the dimensions

we have

discussed

as the correct

ausal

structure

or

research

n

information

ystems.

All

can

serve the

goals

of

interesting

discovery

and

rigorous esting

to which

researchers

or-

mally aspire.

However,

some causalstructures arebetter

when evaluated

against

crite-

ria

of simplicity

and

parsimony;

thersfarebetterwhen

evaluated or

empirical

idel-

ity, the ability

to

mirror

aithfully

he

phenomenaunderstudy. We find little

balance

within our

field

between

these two sets of criteria or

evaluating heories.Much

of the

research n resistance

o

systems,growth

of the

information

ystems

unction

n

organi-

zations;systemsimplementation,and the like, has adoptedvariance heoryformula-

tions of

logical

structure

and an

imperativeconception

of causal

agency.

We suspect

that

greater

use

of theoretical

tructureswhich

emphasize

empirical idelity

will

stimu-

late more and better

researchon these

phenomena.

In

conclusion,

careful

examinationof causalstructuress

a productive xercise

n

any

field.Researchers

nterested

n

the

consequences

of

information

echnology

or

organi-

zationsshould

make

clearand consciouschoices

regarding

he causal

structures f their

theories.

These choices

are at least as

important

as more technical

research

ssues,

perhaps

more so.

The discussion

of

causal structure

n

this

paper

should facilitate

choice and criticalthinkingboth for researchers nd for those who apply research

findings.8

8

We extend our

thanks

o the

many people,

too

many

to list

here,

who

have

given us helpfuladviceon

earlierdraftsof this

paper.

We

especially

hankJohn

King,

Rob

Kling,

Ron

Rice,

Jon

Turner,

and several

anonymous

eviewers

who

have commented

xtensively

n severalof our drafts.

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