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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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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
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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.
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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.
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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
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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).
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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
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INFORMATION TECHNOLOGY
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 ).
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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
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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
institutionalization,
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
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 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.
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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.
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INFORMATION TECHNOLOGY
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.
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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-
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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.
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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.
References
ATTEWELL,P.
AND
J. RULE, Computing
and
Organizations: What
We
Know and What We Don't Know,
Comm.
ACM,
27
(1984),
1184-1192.
BAKOPOULOS,
J. A. Y., Toward a More Precise Concept of Information Technology,
Proc.SixthInternat.
Conf: Information Systems, Indianapolis, IN, 1985,
17-24.
BARLEY, S. R., Technology
as
an
Occasion
for Structuring:
Evidence
from Observations of CT scanners and
the Social
Order of
Radiology Departments,
Admin. Sci.
Quart.,
31
(1986),
78-108.
BENDIFALLAH,
.
AND
W.
SCACCHI, Understanding
Software
Maintenance
Work, IEEE Trans.Software
Engrg., 13,
1
(1987),
in
press.
BJ0RN-ANDERSEN, .,
K.
EASONAND D.
ROBEY,ManagingComputer mpact:An International tudyof
Management
and
Organization, Ablex, Norwood, NJ,
1986.
BLAU,P. M., C. M. FALBE,
W.
MCKINLEY
AND
P.
K.
TRACY, Technology and Organization in Manufac-
turing, Admin.
Sci.
Quart.,
21
(1976),
20-40.
BOLAND,
RICHARD
., JR., Phenomenology:
A
Preferred
Approach
to
Research on
Information Systems, in
E.
MUmford,
R.
Hirschheim,
G.
Fitzgerald,
and T.
Wood-Harper (Eds.), Research Methods in Infor-
mation Systems, North-Holland,
New
York,
1985.
This content downloaded from 140.119.81.207 on Mon, 14 Sep 2015 14:41:23 UTCAll use subject to JSTOR Terms and Conditions
http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp7/25/2019 The IT Organization and Change
16/17
INFORMATION
TECHNOLOGY
AND
ORGANIZATIONAL CHANGE
597
CARTER,
N.
M., Computerization as a
Predominate
Technology: Its Influence on
the Structure
of
Newspa-
per Organizations, Acad.
Management.,
27
(1984),
247-270.
CHILD, .,
Organization:
Guide oProblemsand
Practice, nd ed., Harper and
Row, London, 1984.
COHEN,
M.
D.,
J. G.
MARCH
AND J. P.
OLSON,
A
Garbage
Can Model of
Organizational Choice,
Admin.
Sci.
Quart.,
17
(1972),
1-25.
COLEMAN,
J. S.,
SocialTheory,SocialResearch, nda Theoryof Action, Amer.J. Sociology,91 (1986),
1309-1335.
CULNAN,
M.
J., The Intellectual
Structure of Management
Information
Systems,
1972-1982:
A
Co-citation
Analysis,
Management ci., 32 (1986),
156-172.
DAFT,R. L.
AND
R. H.
LENGEL, Organizational Information
Requirements,
Media Richness and Structural
Design,
Management Sci., 32 (1986),
554-571.
AND
N.
B.
MACINTOSH,
A New
Approach to the
Design
and
Use of Managemient
Information,
California
ManagementRev.,
21
(1978), 82-92.
AND
- ,
A
Tentative Exploration into
the Amount
and Equivocality of
Information Processing
in Organizational
Work Units,
Admin.Sci. Quart., 6
(1981), 207-224,
AND K. E.
WEICK,
Toward
a
Model of
Organizations as Interpretative
Systems,
Acad.
Management
Rev.,
9 (1984), 284-295.
DAWSON, . AND I.
MCLAUGHLIN,
Computer
Technology and the
Redefinition of
Supervision:
A
Study
of
the
Effects
of
Computerization on Railway
Freight
Supervisors,
J.
Management tudies,
23
(1986),
116-132.
ELLUL, ., The
Technological ociety,Knopf, New
York, 1964.
FOSTER,
L. W. AND D. M.
FLYNN, Management
Information
Technology:
Its Effects
on
Organizational
Form and
Function,
MIS
Quart.,
8
(1984), 229-236.
FRANZ,C.
R.,
D. ROBEYAND R.
R. KOEBLITZ,User
Response to
an Online
Information
System:
A
Field
Experiment,
MIS
Quart.,
10
(1986),
29-42.
FREEMAN, . H., The
Unit of Analysis in
Organizational
Research,
in
M. W.
Meyer
and
Associates,
Environmentsnd
Organizations,ossey-Bass, Inc. San Francisco,
1978.
FRY, L. W.,
Technology-Structure
Research:
Three Critical
Issues,
Acad. Management
., 25,
3
(1982),
532-552.
GALBRAITH,. R., Organization
Design,
Addison-Wesley,
Reading, MA,
1977.
GASSER,L.,
The
Integration
of
Computing and
Routine
Work, ACM
Trans.
Office
nformation
ystems,
4,
3
(1986),
205-225.
GINZBERG,
M.,
An
Organizational
Contingencies
View
of
Accounting and Information
Systems Imple-
mentation,
Accounting,Organizations, nd
Society,
5
(1980),
369-382.
GORDON,
L. A. AND
D.
MILLER,
A
Contingency
Framework for the
Design
of
Accounting
Information
Systems,
Accounting,
Organizations,
nd
Society,
1
(1976),
59-69.
GORRY,
G. A. AND
M.
S.
SCOTT
MORTON, A Framework for
Management
Information Systems,
Sloan
Management
Rev.,
13
(1971), 55-70.
HIRSCHHEIM,
. A.,
Office
Automation: Socialand
Organizational erspective,Wiley,
New York, 1985.
ANDH. K. KLEIN, Fundamental Issues of Decision Support Systems: A Consequentialist Perspec-
tive,
Decision
Support ystems,
1
(1985), 5-24.
HUBER,
G.
P.,
The
Nature
and
Design
of
Post-industrial Organizations,
Management
Sci.,
30
(1984),
928-95 1.
ANDR. R.
MCDANIEL, R.,
Exploiting Information
Technologies to
Design More Effective
Organiza-
tions,
In
M.
Jarke
(Ed.),
Managers,Micros
andMainframes,Wiley,
New
York, 1986.
KLATZKY,
.
R.,
Automation, Size, and the Locus
of Decision
Making:
The
Cascade
Effect, J.
Business,
3
(1970),
141-151.
KLEIN,
H. K.
AND R. A.
HIRSCHHEIM,Issues
and Approaches to
Appraising
Technological Change
in the
Office:
A
Consequentialist
Perspective,
Office:Technologynd
People,
2
(1983), 15-42.
KLING,R.,
The
Impacts of
Computing on the Work
of
Managers, Data Analysts, and
Clerks, Public Policy
Research Organization, University of California, Irvine, 1978.
Social
Analyses
of
Computing:
Theoretical
Perspectives
in
Recent
Empirical
Research,
Computing
Surveys,
12
(1980),
61-110.
,
Defining
the
Boundaries of
Computing Across
Complex
Organizations,
in
R. Boland
and R.
Hirschheim
(Eds.),
Critical ssues in
Information
ystemsResearch,
ohn
Wiley, New York,
1987.
AND
W.
SCACCHI,
The Web
of
Computing: Computer
Technology
as
Social
Organization, Ad,
vances
n Computers,1,
Academic Press, New York,
1982.
LEAVITT,
.
J.,
ANDT.
L.
WHISLER,
Management
in
the
1980s,
Harvard
BusinessRev.,36 (1958),
41-48.
LEIFER,R.,
AND
E.
F.
McDONOUGH
II, Computerization
as a
Predominant
Technology Effecting Work
Unit
Structure, Proc.Sixth Annual
Conf
Information
ystems,
1985,
238-248.
LYYTINEN,
K.
AND
H.
K.
KLEIN,
The
Critical
Theory of
Jurgen
Habermas
as a Basis for
a
Theory
of
Information
Systems,
in
E. Mumford, R. Hirschheim,
G. Fitzgerald,
and
T. Wood-Harper
(Eds.),
ResearchMethodsn Informationystems,
North-Holland, New York, 1985.
This content downloaded from 140.119.81.207 on Mon, 14 Sep 2015 14:41:23 UTCAll use subject to JSTOR Terms and Conditions
http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp7/25/2019 The IT Organization and Change
17/17
598 M.
LYNNE
MARKUS AND DANIEL
ROBEY
MAJCHRZAK, A. AND P. MOSHER, Technological Change
and Structural Contingency Theory, unpublished
paper, 1987.
MARKUS,M. L.,
Systems n Organizations: ugs andFeatures,Pitman, Marshfield, MA, 1984.
MOHR,L. B., ExplainingOrganizational ehavior, ossey-Bass, San Francisco, 1982.
MORROW, P.
C., Work Related Communication, Environmental Uncertainty, and Subunit Effectiveness: A
Second Look
at
the Information
Processing
Approach
to
Subunit
Communication,
Acad.
Manage-
mentJ.,
24
(1981), 851-858.
MOWSHOWITZ,
., On Approaches to the Study of Social Issues
in
Computing,
Comm.ACM,
24 (1981),
146-155.
MUMFORD, . AND M.
WEIR, Computer ystems n
Work
Design-The ETHICSMethod,Wiley,
New
York,
1979.
OLERUP, A.,
A
ContextualFrameworkor Computerizednformation ystems,Nyt
Nordisk
Forlag
Arnold
Busck, Copenhagen, 1982.
OLSON,
M.
H.,
New Information
Technology
and
Organizational
Culture,
MIS
Quart., (1982),
71-92.
AND H.
C. LUCAS,R.,
The
Impact
of Office
Automation
on the
Organization:
Some
Implications
for
Research
and Practice, Comm.
ACM,
25
(1982),
838-847.
AND
J. A. TURNER, Rethinking OfficeAutomation,
Proc.SixthAnnualConf. nformation ystems,
(1985),
259-269.
PENLEY,
L. E., An Investigation
of the Information
Processing
Framework of
Organizational
Communica-
tion,
HumanCommunication
es.,
8
(1982),
348-365.
PFEFFER,
J.,
Organizations nd OrganizationTheory,Pitman,Marshfield,MA, 1982.
AND H.
LEBLEBICI,
InformationTechnology
and
Organizational Structure,
Pacific
Sociological
Rev.,
20
(1977), 241-261.
RICE,R. E., The Impacts of Computer-Mediated
Organizational
and
Interpersonal Comm