The institutionalization of computing in complex …jlpweb/papers/the_institutionalization_of...J.L. Perry et at. / Computing in complex organisations tionalization" to refer to the

Informatization and the Public Sector 2 (1992) 47–73Elsevier

The institutionalization of computingin complex organizations

James L. PerryIndiana University, Bloomington, IN, USA

Kenneth L. Kraemer, John Leslie King and Deborah DunkleUniversity of California, Irvine, CA, USA

Received March 1992

Perry, J .L ., Kraemer, K.L., King, J .L . and Dunkle, D., 1992, The institutionalization of computingin complex organizations . Informatization and the Public Sector 2 : 47–73.

Abstract. Complex organizations spend significant resources acquiring new processes and tech-nologies . However, there is no assurance that organizational members will embrace thesetechnologies once they are adopted . Organizational leaders have a strong incentive to ensure thatnew practices persist, and that technological changes become so routine as to be taken forgranted : a process called institutionalization . The research reported here examines the determi-nants of institutionalization of computing in a class of complex organizations—municipal govern-ments in the United States—using longitudinal data from 130 organizations collected in 1975 and1985 . The findings indicate that institutionalization is associated with both internal and externalfactors, but that the strength of internal factors is less prominent than suggested by earlier,cross-sectional studies.

Introduction

Organizations are continuously engaged in replacing existing routines with newroutines for performing organizational tasks (Feldman, 1988) . Among importantroutines are those concerned with use of "core technologies" (Thompson, 1967).In recent decades the opportunities created by information technologies, includ-ing computers, office automation and data communications, have compelled

Correspondence to : K .L . Kraemer, Center for Research on Information Technology and Organiza-tions (CRITO), University of California, Irvine, CA 92717, USA.

0925-5052/92/$05 .00 © 1992 – Elsevier Science Publishers B .V. All rights reserved

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many organizations to make large-scale investments in technology. The processof implementing these new technologies often has forced organizations toreevaluate existing ways of performing tasks (Beniger, 1986 ; Kraemer et al .,1989; Huber, 1990) . Still, organizational leaders confront considerable uncer-tainty about how to merge work behavior and technological requirements, andcannot be sure whether their members will conform to new prescriptions fortheir work behavior when the technology and work prescriptions are imple-mented.

The failure of the organization's members to conform to and embrace thevalues embedded in the technology raises the interesting research question ofwhy even carefully planned technological reforms often do not meet the criticalexpectation of institutionalization, wherein a technology becomes a routine andbackground part of everyday organizational life . More practically, technologicalinnovations that do not become institutionalized cannot be productive assets ofan organization, and failure to achieve institutionalization of innovations canimpose enormous direct, indirect, and opportunity costs on the organization.Thus, there are both academic and practical reasons to investigate the factorsthat influence institutionalization of technology use in organizations.

This study explores the factors associated with successful institutionalizationof computer technology using longitudinal data collected from 130 us municipalgovernments between 1975 and 1985 . The paper begins with conceptual founda-tions drawn from innovation research and institutional theory . These are used toconstruct a set of hypotheses regarding relationships between institutionaliza-tion of technology use as the dependent variable, and a set of environmentaland intra-organizational factors considered as independent variables . The re-sults show that both environmental and intra-organizational factors are associ-ated with institutionalization, though not all factors produced significant results.The results are interpreted in light of the conceptual foundation and qualitativeassessments of information drawn from other sources.

Theoretical framework

The first source for conceptual foundations for the research is the field ofinnovation adoption and diffusion . This field of research is among the broadestin behavioral science, engaging individual, group, organizational and sociallevels of behavior (Rogers, 1983) . Early innovation research focused primarilyon how innovations came to be adopted by individuals and groups, and thusbecame diffused across populations. In recent years, attention has focusedincreasingly on the processes by which innovations come to be abandoned orpermanently established in use . These studies have used different terms todescribe their objectives : Yin (1981) concentrated on "routinization" of innova-tions, Glaser (1981) investigated "durability," while Gasser (1984) studied "per-sistence ." Most recently, Goodman and Steckler (1989) used the term "institu-

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J.L. Perry et at. / Computing in complex organisations

tionalization" to refer to the process by which innovations become accepted aspart of the organization . Although no comprehensive statement has beenwritten to unite these diverse usages of the term, the theme common to all thesedependent variables is that of survival over time.

The convergence of innovation research on the question of survival, persis-tence, and acceptance as part of innovative practices parallels the developmentof the field of institutional theory in organizational sociology (Scott, 1987) . Theconcept behind institutional theory was clearly expressed by Hughes half acentury ago : an institution is a persistent feature of social life that outlasts socialparticipants and survives upheavals in the social order (Hughes, 1939), andinstitutionalization is the process by which a particular feature becomes aninstitution . A feature can be any focus of social inquiry, including practices,rules, and belief systems. There is at present no precise definition of whatfeatures are appropriate for study under the institutional theory rubric . In fact,the area has not gelled intellectually to the point where it serves as a uniformtheoretical base for inquiry . Scott (1987) identified four separate views withininstitutional theory, and called for a more coherent development of the founda-tions for this interesting and emerging field.

A comprehensive comparison and assessment of the various uses of theinstitutionalization concept is needed, but such a work is beyond the scope ofthis paper . Here we discuss a specific aspect of institutionalization arising fromthe innovation research tradition . We build on the contributions of institutionaltheory, however, and our conclusions are constructed in light of that perspec-tive.

Institutionalization of innovative practices

This paper deals with the institutionalization of an innovative practice—the useof administrative computing systems—within individual organizations over time.Our use of the term extends that of Yin's (1981) concept of innovationroutinization, meaning incorporation into regular use . Our use is also close tothe concept used by Kraemer et al . (1987) to describe the routine use ofcomputing innovations in federal agencies . Institutionalization in this paperdescribes the process by which the practice of using an innovation takes its placeamong the established values, norms, and beliefs that have been internalizedamong members of an organization (Kimberly, 1979) . It corresponds directly tothe Goodman et al. (1980) notion that an institutionalized innovation hasdiffused widely, has been subject to socialization efforts to encourage use, andhas been accepted as part of everyday organizational practice.

Our use of the term benefits from three useful notions taken from institu-tional theory (Scott, 1987) . First, the practice of computing is consideredinstitutionalized not when routine use arises due to imposition or directives, norwhen use is the product of specific innovation efforts (although both may play a

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role in institutionalization), but rather when routine use is accompanied by ageneral sentiment among organizational actors that use of computer automationis taken-for-granted in organizational life (Zucker, 1983) . Innovations are insti-tutionalized when they are more in the "background" than in the "foreground ."Second, organizations can arrive at institutionalization of computing use throughdifferent processes, and individual patterns of institutionalization processes canbe assumed to conform to individual organizations' particular needs for legiti-macy, resources, and survival potential at any given time (Meyer and Rowan,1977; DiMaggio and Powell, 1983) . Third, the process of institutionalizationfrequently involves conflict within and across organizations, due to differences inviews about the appropriateness of the innovation, the particular configurationof the innovative practices, and so on (Friedland and Alford, 1987).

These notions from institutional theory are helpful because they lend strengthto an emerging view of the practice of computing use as organizational assimila-tion of complex "packages" of interdependent components (Ellul, 1964 ; Illich,1973; Kraemer et al ., 1981, 1989 ; Kling and Scacchi, 1982) . The package includestechnology (hardware, software, peripherals), technique (procedures, protocols,practices, organizational arrangements), and knowledgeable people (computerspecialists, functional users, and managers) . Moreover, the package is neitherstable nor static; it constantly changes with new technological developments,attrition of key organizational actors, and changes in organizational objectivesand needs.

The "package" view emerged to replace simpler evolutionist concepts inwhich computing innovations are seen as tools with attributes that "lead"organizations to undergo a process of adoption, implementation, and use . Theseearlier concepts are best represented by the "stage" models of Nolan (1973) andGlaser et al . (1983), which obtained considerable currency among academics andpractitioners of organizational computing from the 1970s to mid-1980s . How-ever, widespread failure and abandonment of technically correct systems (Lucas,1975; Cerveny and Clark, 1981), as well as research into the underlying pro-cesses by which computing practices become established (King and Kraemer,1985; Kraemer et al ., 1989), have cast doubt on these models . The emergingview sees technology as an obviously necessary antecedent of technologicalinstitutionalization, but by no means a sufficient condition for institutionaliza-tion. Also required are changes in organizational values, norms, and beliefs . Putsimply, institutionalization has occurred when an innovative practice has becomean organizational routine in the sense described by Feldman (1988), that notonly aids the organization in dealing with rationalizable and predictable tasks,but also allows organizations " . . . to act under conditions that mitigate againstdeliberation and conscious coordination" (Feldman, 1988, p . 4).

Our question is, how does such institutionalization take place in the case ofthe innovative practice of computing use in the complex environment of usmunicipal governments?

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Hypotheses

An organization's decision to adopt an innovation has been found to be stronglyinfluenced by opinion leaders—individuals in interorganizational and organiza-tional networks who are influential in convincing others to be supportive of orantagonistic to stated needs for innovation (Rogers, 1983 ; Leonard-Barton,1985) . Opinion leaders are similarly likely to be significant in the institutional-ization process, since a key factor in both adoption and institutionalization is the"mobilization of bias" (Kraemer et al ., 1981) for or against the reforms impliedby the innovation; a conjecture supported by Yin (1981) and others . Chaiken(1978) and Lawless (1982, 1987) found that failures of innovation institutional-ization could be traced to loss of staffing and budgetary support from keymanagers . Yin's (1981) study of the routinization of innovations in local govern-ment agencies also demonstrated the enabling role of support by top executives,especially through allocation of staffing and budgetary resources for the innova-tion effort . Yin's study further found support for the views of Downs (1967) andNiskanen (1971) that successfully routinized innovations often received supportfrom managers because the innovation provided justification for expandedagency budgets . These influences of top managers on successful routinization,and by extension, institutionalization of innovations, suggest the following hy-pothesis:

Hypothesis 1 . The greater the commitment of top managers to innovative practices,the greater their institutionalization.

In order for innovations to become fully incorporated into organizational life,they must not only receive persistent support from organizational elites, but theymust be accepted by organizational members . Full institutionalization requiresacceptance of and incorporation by organizational members in daily activity(Feldman, 1988) . Goodman et al . (1980) measure institutionalization by theextent of innovative practice within an organization, acceptance of the practiceby organizational members, and the persistence of the practice over time.Widespread performance of new behaviors and acceptance of them as socialfacts are likely to be facilitated by efforts to indoctrinate organizational mem-bers about the new practices . Such indoctrination can include training groups,support groups, and similar methods to draw organizational members to thevalues represented by the technology. The importance of active efforts todevelop grass roots support for institutionalization imply the following hypothe-sis:

Hypothesis 2 . The greater the indoctrination of organizational members to innova-tive practices, the greater the institutionalization of those practices.

Although organizations frequently use persuasion to socialize members to newpractices, the alignment of organizational rewards to encourage acceptance anduse of new practices may also enhance institutionalization . Reward systems may


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affect institutionalization in several different ways (Goodman et al ., 1980;Goodman and Dean, 1982) . It is likely that the provision of different types ofrewards will produce greater institutionalization than reliance on a single type ofreward . Another important factor is discrepancies between expected and actualrewards. Innovations that live up to the expectations of adopters are likely to beself-reinforcing and continue to be used while those that fail to meet expecta-tions are likely to fall into disuse . These arguments suggest the followinghypothesis:

Hypothesis 3 . The greater the organizational reinforcement of innovative practicesthrough rewards, the greater the institutionalization of those practices.

Two alternative strategies confront top managers when implementing an innova-tive practice . One is to make the change incrementally, by beginning in selectedsubunits and expanding from there . This strategy has the advantage of allowinglearning and adjustment to the change, and the making of improvements in theimplementation effort . However, it has the disadvantage of providing an oppor-tunity for opponents of the innovative practice to form coalitions that can blockprogress (Keen, 1981) . Another strategy is to make the change comprehensively,throughout the organization simultaneously . This is expensive and sometimesdifficult, but it has the advantage of constraining the formation of counter-in-novation coalitions (Goodman and Dean, 1982) . Differences in the relative risksof either strategy will depend on the nature of the organization studied . Localgovernments are complex, multi-functional organizations with competing inter-nal interest groups that can mobilize to block initiatives . Thus, we agree withGoodman and Dean (1982) and hypothesize:

Hypothesis 4. The greater the initial diffusion of innovative practices throughoutthe organization, the greater the subsequent institutionalization of those practices.

Institutionalization requires incorporation of new norms, values, and structureswithin the framework of existing patterns of norms, values, and beliefs (Kim-berly, 1979) . Institutionalization will therefore be affected by the ability oforganization members to control the adaptation process . Member control willhasten the process through which the innovation becomes identified with thevalues of members, and subsequently "owned" by those members (Leonard-Bar-ton, 1985) . Member input to the process of innovation redesign is also likely toenhance member identification and ownership by reducing conflicts betweenexisting and emerging norms, values and structures . Thus:

Hypothesis 5 . The greater the member control over innovative practices, the greaterthe subsequent institutionalization of those practices.

Although prior empirical research (Yin, 1981; Laudon, 1985) indicates thatinternal processes are especially critical for institutionalization, organizationenvironments are also likely to be influential . The environment's effects oninstitutionalization are likely to occur as a result of both coercive and normative

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isomorphic processes (DiMaggio and Powell, 1983 ; Friedland and Alford, 1987).Two institutional features of public organization environments are likely to havesignificant influence on organizational decisionmaking, and, in turn, institution-alization of innovative practices. The first is pluralism, wherein many interestgroups are given access to the policy making process. Pluralism is a mechanismof coercive isomorphism that injects multiple values into organizational deci-sionmaking processes, and stimulates increased competition for resources . Plu-ralism also can disrupt the continuity of organizational leadership, reducingstability and continuity of organizational goals . Pluralism may make it difficult toachieve goal congruence, and by extension, agreement on means for accomplish-ing goals (Selznick, 1948) . Thus:

Hypothesis 6 . The greater the level of community pluralism, the lower the eventualinstitutionalization of any given innovative practice.

Another feature of some local governments is extensive use of "good govern-ment" structural reforms, flowing from the movement that began in the early20th century to reduce graft, corruption, and patronage abuse in cities (Stone etal ., 1941). Such reforms were seen as a check on several negative consequencesof unchecked politicization of governmental processes, and were accomplishedby developing a cadre of politically-protected, professional managers schooled inthe use of modern administrative practices, and reducing partisanship in elec-tion practices . Both the desire to use innovations, and the political protectionafforded by the reformed government structure, increase the likelihood thatinnovation will be pursued vigorously . Thus:

Hypothesis 7. The greater the degree of reformed government practice, the greaterthe institutionalization of any given innovative practice.

An environmental factor likely to affect institutionalization is the populationsize of the government jurisdiction . The mechanism of effect is indirect, throughthe fact that jurisdictions of large population require proportionately largegovernment bureaucracies . Increased size of the bureaucracy brings increasedcontrol and coordination costs, as well as weaker goal congruence, both of whichmake implementation of innovative practices throughout the organization diffi-cult . On the other hand, large bureaucracies are more likely to possess the need,capacity and slack resources necessary for successful application of costly andcomplex innovative practices such as computing . On balance we believe thepresence of available resources, plus the generally centralized decision struc-tures of us municipalities (Kraemer et al ., 1981) suggest that larger bureaucra-cies will have greater success in innovation . Thus:

Hypothesis 8 . The greater the size of the community the greater the institutionaliza-tion of any given innovative practice.

The structures established for carrying out innovation are likely to influence thedegree of institutionalization (Goodman and Dean, 1982) . A most significant


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structural feature involves the assignment of administrative authority for theinnovation . The allocation of authority for advocacy, operation, and promotionof the technology's use can take several forms (King, 1983) . Most organizations,including local governments, initially assigned responsibility for the innovationto functional units such as the finance or administrative services department.Other organizations assigned authority for computing innovation to independentcomputing service departments . The latter strategy is believed to be moreconducive to institutionalization of computing by reducing the intra-departmen-tal goal conflict that occurs when one functional subunit (e .g ., police) mustobtain resources from another functional subunit (e .g ., finance) (Danziger et al .,1982). It is also likely to empower managers of the computing innovation tomold use of the technology to organization-wide norms and values, therebygenerating allegiance to computing reforms among top management (Kraemeret al ., 1989). Thus, it is hypothesized:

Hypothesis 9 . The greater the amount of administrative independence granted tothe unit responsible for new practices, the greater the degree of institutionalizationof those practices.

Methods

Tests of these hypotheses were conducted using data from the URBIS (uRBaninformation systems) Research Project, an ongoing, longitudinal study of com-puting evolution in all us local governments over 50,000 in population . Data forthis study were collected in 1975 and 1985, providing a ten-year interval foranalysis . The ten-year interval between surveys permitted a sufficiently longperiod during which changes in levels of institutionalization would becomeapparent . Data used here were taken from three questionnaires completed byeach government in each panel . Two of the instruments were mailed to dataprocessing managers and one was sent to the chief executive or primarydeputy.The logic of analysis was to use 1975 data about the determinants ofinstitutionalization described above to predict the extent of institutionalizationactually achieved in 1985. In order to test the hypotheses, scores for organiza-tional and environmental predictors, based upon 1975 data, were regressedagainst an institutionalization index based upon 1985 data.

Sample

The sample consisted of 130 organizations for which complete data wereavailable . There are approximately 400 local governments over 50,000 in popula-tion. All were surveyed, and response rates exceeding 70% were obtained inboth 1975 and 1985 . To be used in this study, a respondent organization had tocomplete all necessary questions on all surveys in both 1975 and 1985 . Our

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sample of 129 is approximately 30% of all us cities over 50,000 in population.The sample is smaller than the response for each survey because some citiesresponded to one survey but not the other, some cities with automation in 1985were not automated in 1975, and some cities with 50,000 population in 1985 hadless population in 1975 and therefore were not surveyed.

Measures

Table 1 presents the means, standard deviations, and zero-order correlations forall variables used in the study . The Measurement Appendix presents operationaldetails for the more complex variables.

Dependent variableInstitutionalization was operationalized as a composite of three indicators fromthe 1985 survey . The first was organizational members' knowledge about andpreferences for computing use . This was created from four measures of per-ceived problems with computing use (see Measurement Appendix) . The secondwas the proportion of the organization using computing, measured by anextensiveness index of the proportion of municipal functions using computing.The third was the sophistication of the applications in use, which was a count ofapplications in use according to their information processing task complexity(see Measurement Appendix) . The composite institutionalization index was asum of the standardized scores for each of these three indicators.

Organizational variablesAll organizational variables were measured using data from the 1975 study.Indoctrination was measured by two indicators : a simple measure of the percentof the computing budget spent on training, plus a more complex measure of userinvolvement in computing system design based on information system manageranswers to thirteen questions (see Measurement Appendix) . Both indicatorswere seen as reflecting the extent to which management draws organizationmembers into the practice of computing use to familiarize them with thetechnology . Management commitment to the practice was measured by twoindicators: a dummy variable indicating whether there was long-range planningfor computing, and a manager support index based on five Likert-type questionsabout chief executive dedication to computing innovation (see MeasurementAppendix).

Reward allocation was measured with two indicators : chief executives' viewson the incentives provided to middle management to learn about computing;and the views of information systems managers on the extent of chief executives'involvement on six aspects of decision making related to computing . Increasingscores on both indicators indicated use of management rewards for involvementin computing innovation.


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Diffusion of computing technology use was measured by the total number ofcomputer applications in operation in 1975 . Member control over computingactivity was measured by two indicators : an assessment of the means by whichusers evaluate computing services; and a dichotomous measure of the extent towhich overall evaluation of computing services was centralized in top manage-ment, or decentralized in the heads of user departments.

Environmental variablesThree indicators were used to measure key aspects of each municipal govern-ment's environment . Log of total population in '1970 tapped the size of thejurisdiction . An index of reformed structure measured the extent to which thelocal governance system had incorporated electoral and administrative charac-teristics associated with the good government movement . A community plural-ism scale measured the diversity of interests represented within the community.

Structure of the changeA dummy variable indicating whether or not computing was an independentorganizational department reporting to the organization's CEO was used toindicate structure for the innovation.

ControlsThree variables, growth in government employees (1970–1985), growth in popu-lation (1970–1980), and rate of change in government employment (1910–1985),were initially identified to control for trends in the data over time . The twoindicators of employment growth were highly intercorrelated and, therefore,growth in government employees (1970–1985) was dropped from further analy-sis . Growth in population and rate of change in government employment(1970–1985), which tapped different time trends and were moderately corre-lated (r = 0.28), were selected for inclusion in the analysis. One additionalcontrol variable, prior experience with EDP, measured the number of years thegovernment had been involved with computing.

Results

The regression results for the composite institutionalization index are presentedin Table 2 . Regressions for each component of the overall index are presentedin Tables 2a–2c . The procedure used in calculating the regressions was to enterthe control variables first, followed by the structure variable, and finally theenvironmental and organizational predictors . This assured that variations ininstitutionalization attributable to time trends and different structural arrange-ments would be accounted for prior to entering the explanatory variables.

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Table 2Regression analysis of institutionalization

Independentvariables

Beta Standarderror

F (sig .)

Environmental variablesCommunity pluralism -0.15 0 .09 3 .01 (0 .085)Reformed government structure 0 .11 0 .09 1 .45 (0 .213)Log of total population, 1970 0 .28 0 .10 8.35 * * (0 .005)

Organizational variablesCommitment:

Top management support - 0 .15 0 .09 2.99 (0 .087)Long-range plan 0 .02 0 .09 0.05 (0.827)

Indoctrination:Percent data processing budget

spent on training 0 .14 0 .08 2.92 (0.091)User involvement in system design - 0 .07 0 .09 0 .60 (0.440)

Reward allocation:Middle-management incentives to learn 0 .12 0 .08 1 .92 (0.168)Data processing perception of

chief executives interest 0 .14 0 .09 2 .51 (0.116)Diffusion:

Log of total applicationsoperational, 1975 0.01 0.09 0 .01 (0 .937)

Member control:User control of data processing 0.27 0 .09 8 .82 * * (0 .004)Decentralization of service evaluations - 0 .05 0 .09 0 .29 (0 .595)

Structure of the changeIndependent data processing department 0 .17 0 .09 3 .99 * (0.048)

Control variablesGrowth in government

employment, 1970-1985 -0.06 0 .09 0 .45 (0.506)Percent growth in population,

1970-1980 0 .15 0 .09 3 .19 (0.077)Prior experience with DP 0 .04 0 .09 0 .19 (0.664)

Adjusted R 2 = 0 .21F = 3 .14n = 130

As Table 2 indicates, the control variables, rate of government growth,percent growth in population, and prior experience with data processing werenot significant . This verifies that institutionalization was not simply a lineartrend over time, comparable to local population or government size changes, ora product of the period when a municipal government first adopted thetechnology . The dichotomous variable measuring the structure for change wassignificant and positive . This indicates that organizations that located adminis-trative responsibility for computing in an independent unit reporting to the CEO

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Table 2a

Regression analysis of knowledge and preference for computing

Independent Beta Standard F (sig .)variables error

Environmental variablesCommunity pluralism -0.22 0 .10 5 .15 * (0 .025)Reformed government structure 0 .24 0.10 6 .02 * (0 .016)Log of total population, 1970 -0.04 0 .11 0 .17 (0 .684)


Top management support 0 .18 0 .10 3 .52 (0 .063)Long-range plan -0.07 0 .09 0 .60 (0 .441)


spent on training 0 .16 0 .09 3 .28 (0 .072)User involvement in system design 0 .01 0 .10 0 .02 (0 .883)

Reward Allocation:Middle-management incentives

to learn 0 .11 0 .09 1 .52 (0 .221)Data processing perception of

chief executives interests 0 .06 0.10 0.40 (0 .529)

Diffusion:Log of total applications

operational, 1975 - 0 .08 0 .10 0.69 (0 .409)Member control:

User control of data processing 0.03 0 .10 0.07 (0 .792)

Decentralization of serviceevaluations - 0.14 0 .10 2.00 (0 .160)

Structure of the changeIndependent data processing

department 0.17 0 .09 3 .38 (0 .069)

Control variablesGrowth in government employment,

1970-1985 - 0 .00 0 .10 0 .00 (0 .966)Percent growth in population,

1970-1980 - 0 .10 0 .09 1 .07 (0 .304)Prior experience with DP 0 .06 0 .10 0 .38 (0 .539)

Adjusted R 2 = 0 .08F = 1 .65n = 126

experienced higher levels of institutionalization, while those that located respon-sibility in functional units such as finance experienced less institutionalization.Conversely, organizations that assigned computing to an existing administrativeunit such as finance or administration, or to multiple units, achieved a signifi-cantly lower degree of institutionalization.


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Table 2bRegression analysis of extensiveness of computing


Environmental variablesCommunity pluralism - 0 .13 0 .08 2 .39 (0.125)Reformed government structure 0.08 0.08 0 .80 (0.374)Log of total population, 1970 0.27 0.09 8 .39 * * (0.004)


Top management support - 0 .25 0 .08 8 .99 * (0 .003)Long-range plan 0 .02 0 .08 0 .06 (0 .800)


spent on training 0 .13 0 .08 2 .64 (0 .107)User involvement in system design - 0 .06 0 .09 0 .45 (0 .502)

Reward Allocation:Middle-management incentives

to learn 0 .10 0 .08 1 .50 (0 .223)Data processing perception of

chief executives interests 0 .10 0 .08 1 .42 (0 .236)Diffusion:

Log of total applicationsoperational, 1975 0 .06 0 .09 0.47 (0 .495)

Member Control:User control of data processing 0 .30 0 .09 12.33 * * (0 .001)Decentralization of service

evaluations 0 .02 0 .08 0.03 (0 .858)


department 0 .14 0 .08 2.90 (0 .091)


1970-1985 -0.09 0 .09 1 .17 (0.281)Percent growth in population,

1970-1980 0 .16 0 .08 4.12 * (0 .045)Prior experience with DP 0.04 0.08 0.23 (0 .631)

Adjusted R 2 = 0 .28F = 4 .19n = 130

Two environmental or organizational predictors were significant : the log oftotal population in 1970, and member control of computing . The other predic-tors, commitment, indoctrination, reward allocation, and diffusion, were notsignificant.

The regressions for the three components of the institutionalization index,presented in Tables 2a, 2b and 2c, revealed the significance of several variables

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Table 2cRegression analysis of sophistication of computing applications


Environmental variablesCommunity pluralism -0.04 0 .08 0 .29 (0.594)Reformed government structure - 0.05 0 .08 0 .37 (0.544)Log of total population, 1970 0.41 0 .09 20 .47 * * (0.000)


Top management support - 0 .23 0 .08 8 .12 * * (0 .005)Long-range plan 0 .10 0 .08 1 .46 (0 .229)


spent on training 0 .03 0 .08 0 .14 (0 .712)User involvement in system design -0.08 0 .09 0.82 (0 .369)

Reward allocation:Middle-management incentivesto learn 0 .06 0 .08 0 .56 (0 .457)Data processing perception of

chief executives interests 0 .14 0 .08 3 .17 (0.078)Diffusion:

Log of total applicationsoperational, 1975 - 0 .01 0.09 0 .02 (0 .890)

Member Control:User control of data processing 0 .26 0 .08 9 .68 * * (0 .002)Decentralization of service

evaluations 0 .02 0 .08 0 .06 (0 .811)


department 0 .09 0 .08 1 .30 (0 .257)


1970-1985 0 .04 0 .09 0 .20 (0 .654)Percent growth in population,

1970-1980 0 .25 0 .08 9 .51 * * (0 .003)Prior experience with DP - 0 .00 0 .08 0 .00 (0 .958)

Adjusted R 2 = 0 .31F = 4 .63n = 130

that did not attain significance for the overall index . Two aspects of theinstitutional environment, community pluralism and reformed government struc-ture, were significant determinants of knowledge and preference for computing.Three variables, log of total population, 1970, top management support, andpercent growth in population, 1970-1980, were significant in the regressions forboth extensiveness and sophistication of computing . The significance of the


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Table 3Relative contribution of organizational and environmental predictors of institutionalization

R 2 change F (sig .)

Organizational predictors:Diffusion 0 .000 0 .01 (0 .937)Commitment 0 .018 1 .50 (0 .227)Reward allocation 0 .025 2 .07 (0 .131)Indoctrination 0 .021 1 .69 (0.189)Member control 0 .055 4 .46 * * (0.014)

Environmental predictors 0 .059 3 .22 * (0 .025)

control variable, percent growth in population, indicates a linear trend for theseindicators of institutionalization.

The explanatory power of the environmental, diffusion, commitment, rewardallocation, indoctrination, and member control variables was tested by compar-ing their relative contributions to the regression . These comparisons are pro-vided in Table 3 . The block of environmental variables explain a significant part

Table 3aRelative contribution of organizational and environmental predators of knowledge and preferencefor computing


Organizational predictors:Diffusion 0.005 0 .69 (0 .409)Commitment 0.027 1 .84 (0 .164)Reward allocation 0.013 0 .89 (0 .412)Indoctrination 0.024 1 .64 (0 .200)Member control 0.015 1 .01 (0 .369)

Environmental Predictors 0 .103 4 .63 * * (0 .004)

Table 3bRelative contribution of organizational and environmental predictors of extensiveness of comput-ing


Organizational predictors:Diffusion 0 .003 0 .47 (0 .495)Commitment 0 .051 4 .56 ** (0 .013)Reward allocation 0 .015 1 .36 (0 .260)Indoctrination 0 .017 1 .49 (0 .229)Member control 0 .069 6 .25 ** (0 .003)

Environmental predictors 0 .050 3 .01 * (0 .033)

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Table 3cRelative contribution of organizational and environmental predictors of sophistication of applica-tions of computing


Organizational predictors:Diffusion 0 .00 0 .02 (0 .890)Commitment 0 .046 4 .28 ** (0 .016)Reward allocation 0 .019 1 .78 (0 .174)Indoctrination 0 .005 0 .46 (0 .632)Member control 0 .053 4 .94 * * (0 .009)

Environmental predictors 0.127 7 .93 * (0 .000)

of the change in R 2 in each of the four regression equations . Among theorganizational predictors, only the member control block is significant for thecomposite index of institutionalization.

Discussion

The analysis provided only moderate support for the hypotheses . The environ-mental predictors and structure of the change were significantly related to theoverall institutionalization index, but among the five organizational predictors,only member control was significant . Commitment, indoctrination, reward allo-cation, and diffusion were not significant in the regression for the compositeinstitutionalization index.

In general, we can conclude that institutionalization of computing innovationsis a function of both environmental and internal factors . While the environmen-tal factors show up strongly in this analysis, the managerially controllableinternal factors of member control and structure of the change were significantas well . The surprise was that the other organizational factors were not signifi-cant . Earlier, historically-oriented case study research of computing innovationover time by Laudon (1985) and Kraemer et al . (1989) suggested that environ-mental stimuli had a significant effect on organizational decisions to adoptcomputing practices, but that organizational factors under control of managersplayed by far the greatest role in establishing computing use as a routine,embedded, institutionalized organizational practice . We explore the likely rec-onciliation of the current findings with those studies below.

First, we discuss the implications of the significant predictors . The analysessuggest that the ability of the organization to focus its institutionalization effortsis important to success . In keeping with Hypothesis 6, community pluralismappears to influence institutionalization of innovative practices by alteringmember preferences for the technology . Increasing community interest groupactivity appears to reduce the ability to inculcate new habits, norms, and values


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J. L . Perry et al. / Computing in complex organisations

within the organization . This is also in keeping with Meyer's (1979) finding thatambiguity and indeterminateness in decision making in public bureaucraciesincrease with greater societal (as opposed to internal) determination of organi-zational goals and practices.

Pluralism is a broad concept, applicable both with respect to an organization'srelations with its larger community, and with respect to intra-organizationalunits and the larger organizational ecology . Thus we also found, in keeping withHypothesis 9, that increased administrative independence was associated withincreased institutionalization . This suggests provision of a means for focusingtop-level, independent attention on the needs of the innovation and institution-alization process reduced the potentially crippling effects of pluralism within theorganization . Creation of an independent computing service provider not onlyremoves the institutionalization process from parochial views of one or a fewfunctional departments, but it creates a locus of dedicated and focused advo-cacy, possibly extending to missionary zeal, for use of the innovation . Forexample, Danziger et al . (1982) found that such organizational units engaged inactive promotion of the technology, rapid expansion of computer applications,and moralization about the potential of the technology to bring about organiza-tional change. We raise a note of caution here, however . Under some circum-stances, the creation of the independent computing services department pro-duces "empire building" behavior in which widespread organizational use ofcomputing is directly tied to the bureaucratic welfare of the computing servicesdepartment. These "skill bureaucracies," to use Danziger et al .'s (1982) expres-sion, seek to secure and sustain their independence from both top managementand the user community by maintaining a monopoly on expertise even whileexpanding by encouraging the use of computing throughout the organization.Regardless of whether the overall welfare of the organization is best served byestablishment of computing skill bureaucracies, it seems likely that such self-in-terested focus can play a role in successful institutionalization of innovations.

Our findings also suggest that focused efforts to promote computing use andavoid the divisive aspects of internal pluralism are not sufficient to produceinstitutionalization of computing use . True institutionalization arises when orga-nizational members incorporate computing use as routine, "background" activ-ity. This requires, at least to some extent, diffusion of real control overcomputing use throughout the organization. In keeping with Hypothesis 5,member control of computing innovation was a significant factor in institutional-ization. Such control facilitates member acceptance of the innovation as anintegral part of the organization's activities, and creates a distributed cadre ofbelievers who will help to bring more reluctant organizational participants along.Member control provides feedback from the user community to the computingservice providers (and their superiors) about the character of systems and theconsequences of their use . In this way, the creation of an independent comput-ing department and maintenance of member control may be consonant, in thatan organization-wide independent support unit is likely to be more willing than

64



a narrowly-focused functional unit to extend opportunities for widespreadmember control.

Summarizing these findings, institutionalization of computing use is influ-enced by the organization's ability to focus its efforts in the absence of pressurefrom multiple external and internal interest groups, while maintaining a coher-ent program of innovation promotion . Establishment of an internal, indepen-dent service provision unit helps with both objectives . However, to obtainwidespread acceptance of the innovation among organizational members, it isnecessary to make the executive authority of the independent service provisionunit accountable by extending member control over evaluation of the serviceunit's performance . The issue is not as simple as executive vs . pluralisticapproaches, but rather the maintenance of a balance of executive authority forsome activities and pluralistic processes for others.

We return now to discussion of the failure of the other organizationalpredictors to reach significance in the regressions. The failure of diffusion toappear as a significant predictor is surprising because earlier cross-sectionalanalyses of the 1975 IRais data showed those organizations with the mostdiffused use of the technology to be the most sophisticated in use as well(Kraemer et al ., 1981) . It stands to reason that a combination of extensive andsophisticated use would bode well for downstream institutionalization. However,two factors that cannot be accounted for in this analysis might intervene . First,diffusion is significantly dependent on the character of the technology itself, andimproving price-performance characteristics of computing during the decadebetween 1975 and 1985 could well have altered organizational predispositions toinvest further in computing innovations . Second, diffusion itself is a componentof the larger question of institutionalization, and is subject to the influence offactors such as indoctrination, commitment, and so on . Recent case studies in aselected set of the cities included in the 1975 study have shown that some of the"early adopters" of computing in 1975 fell into the middle of the population by1985, and some "late adopters" surged ahead during this period (Kraemer et al .,1989). These findings suggest that comprehensive change strategies may havebeen less efficacious than incremental change strategies, but we were unable todetect this distinction with the way diffusion was measured. Whether the lack ofsignificance of the diffusion variable is due to problems in measurement or inthe precision of our theoretical constructs, it is apparent that both requireadditional attention in future research.

The lack of significance of the other three variables, commitment, indoctrina-tion, and reward allocation is more puzzling . For purposes of assessment, wecan combine these variables by noting that they are all "top–down" factorsinvolving the top management . Top management commits to the innovation,dedicates the organization's resources to indoctrination mechanisms, and ex-tends suitable rewards to middle managers for promoting the innovation . Thereis little doubt that these factors do matter at some level . For example, it seemshard to imagine how computing innovations could ever be institutionalized if top


65


managers withheld their support or resources, or if they punished middlemanagers for use of the technology . At minimum, top management musttolerate the innovative practices.

From another perspective, however, it is not clear that active top manage-ment support is essential or even conducive to institutionalization. Dutton andKraemer (1979) and Perry and Kraemer (1977) found the role of municipal chiefexecutives not to be uniformly positive with respect to implementing computinginnovations . Executives often set unrealistic expectations and priorities forcomputing . They also tended to allocate resources for computing use in waysthat produced political advantage even if those ways were suboptimal to theoverall organization . Further, it was not uncommon for chief executives tosupport questionable projects simply because of faith in technology rather thanknowledge of the issues . Thus, active chief executive attention to computing mayplay a mixed role in institutionalization.

The lack of significance for the top management support variable may alsoinvolve limitations of the measure used in the present study . The role of topmanagement support is likely to be more complex and subtle than the measuresfrom the 1975 data can address . Careful study of the histories of computing inseven municipal-governments by Kraemer et al . (1989) revealed critical roles formanagers in the character and success of computing initiatives . How can wereconcile these differences?

First, the term "top management" is imprecise. The 1975 survey measuredonly chief executive attitudes, but top management consists of more than theCEO . In fact, while CEO support was occasionally critical in the histories of theseven case study cities, the most influential senior managers were usually one ortwo steps down at the assistant city manager and department head level . The1975 survey probably aimed too high in attempting to identify the role ofmanagerial influence on institutionalization . It would be ideal to check thisconjecture using the 1975 data set, but data for the key variables are onlyavailable from the chief executive level . One cannot go back and ask thequestions that have subsequently proven to be important in later analysis . This isan obvious but seldom articulated limitation with longitudinal, empirical re-search in the social sciences.

Second, the study of seven organizational histories revealed that managerialactions that influenced institutionalization of computing did not manifest them-selves as clear-cut behaviors waiting to be turned into measurable variables.Certainly, some factors such as managerial knowledge of the issues and sensitiv-ity to the organizational and political realities of the situation proved importantin every case . But it is very difficult to measure such managerial attributesremotely via self-administered instruments, or by any other means, for thatmatter (Feldman, 1988).

Thus, we find it somewhat surprising that the organizational predictors ofcommitment, indoctrination and reward allocation were not significant . How-ever, we believe our results reflect more on the character of our 1975 data

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collection instruments than on the sensibility of the ideas we were testing.Indeed, despite extensive efforts to make the 1975 instruments as sound aspossible (cf Kraemer et al ., 1976, 1981), so little was known about computinginnovations in vivo that shortcomings in some of the measures derived from theinstruments were inevitable . Discussion of these shortcomings can be found inseveral places, including Kraemer and King (1981), King and Kraemer (1985),and Kraemer et al . (1989). Nevertheless, as Medawar (1982) suggests, the way ofscience includes both learning about the world and learning about how to learn.The obvious call in following this research is to learn from our results, improveand expand our measures of the organizational factors that might play a role ininstitutionalization, and to apply them in future longitudinal research.

Measurement appendix

Institutionalization indexThis index was calculated by summing the Z-scores of three indexes: (1)knowledge about and preference for computing, (2) extensiveness of computing,and (3) sophistication of computing . Descriptions of the three indexes areprovided below. The intercorrelations of the three indexes:

knowledge/

extensiveness

sophisticationpreference

knowledge/preference

1 .00

0.21

0 .13extensiveness

1 .00

0 .87sophistication

1 .00

Coefficient alpha = 0 .67.

Knowledge about and preference for computing index (coefficient alpha = 0 .80)was computed by obtaining the mean response to four Likert-type items in theData Processing Manager Survey, 1985 . Four response categories were pro-vided: not a problem, at times a problem, often a problem, very often a problem.The four items are : (1) EDP lacks acceptance from top government officials, (2)EDP lacks acceptance from major department heads, (3) EDP lacks acceptancefrom staff of user departments, and (4) User departments are not knowledge-able about EDP . Scores ranged from a low of 1 .50 to a high of 4.00 (mean = 3 .14,s .d . = 0.57) . The average score was then converted into a Z-score.

Extensiveness of computing index was computed by calculating the proportion of34 specified functional areas that were automated in the city . Scores rangedfrom a low of 0 .06 to a high of 0.88 (mean = 0 .44, s .d . = 0.15) . A functional areawas considered automated if there were at least two applications automated inthat area. The proportion was then converted into a Z-score.


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Sophistication of computing index is the sum of all applications automated in thecity with each application first weighted by degree of information task complex-ity of the applications. All applications listed in the Data Processing Manager'sSurvey, 1985 were scored with respect to the level of information task complex-ity involved in the application . Information task complexity weights ranged froma low of 1 to high of 5 ; 1 = recordkeeping (activities which primarily involved theentry, updating, and storage of data), 2 = calculating/ printing (activities whichprimarily involve sorting, calculating, and printing of stored data to producedspecific operational outputs), 3 = a record-restructuring (activities which involvereorganization, reaggregation, and/or analysis of data), 4 = sophisticated ana-lytic (activities which utilize sophisticated visual, mathematical, simulation orother analytical methods to examine data), and 5 = process control (activitieswhich approximate a cybernetic system ; data about the state of the system arecontinually monitored and fed back to a human or automatic controller whichsteers the system toward a performance standard) . Scores ranged from a low of37 to a high of 510 (mean = 177 .70, s .d . = 94.73). The total weighted score wasthen converted to a Z-score.

Pluralism of community influence scaleScale constructed by computing the average degree of influence (1 = not influ-ential ; 2 = somewhat influential, 3 = quite influential, 4 = extremely influential)over 16 community groups that were rated by chief executives in 1975 (coeffi-cient alpha = 0.80) . Scores ranged from a low of 1 .25 to a high of 3 .13. Thegroups included: Democratic Party, Republican Party, newspapers, bar associa-tion, local medical groups, labor unions, minority groups such as Blacks, Chi-canos, Puerto Ricans, other ethnic groups, neighborhood groups, church lead-ers, chamber of commerce, industrial leaders, building and real estate people,bankers and executives of other financial institutions, good government organi-zations such as the League of Women voters and citizens's leagues, andenvironmental/ ecology groups.

Reformed government structure indexThe measure was constructed by summing three indicators of progressivereforms in local governments: type of government structure, electoral systemand election laws. Government structure was scored as 2 = council-managersystem, 1 .5 = mayor-council with chief administrative officer and 1 = all others;electoral system was scored as 2 = at-large elections, 1 .5 = mixed and 1 = ward;non-partisan election laws was scored as 2 = nonpartisan ; 1.2 = local partiesonly and 1 = partisan . Scores on this index range from a low of 1 to a high of 2.Intercorrelation matrix for these items is:

governmentstructure

electoralsystem

electionlaw

government structure 1 .00 0 .29 0 .26electoral system 1 .00 0 .14election law 1 .00

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Log of total population, 1970Measure is the log of the total population as recorded in the 1970 census.Values on this variable range from a low of 4 .7 to a high of 6 .29.

Prior experience with DP

Prior experience with DP is operationalized as the total number of years that acity has had computing. Scores range from a low of less than one year (0) to ahigh of 20 years.

Top management support scaleThe scale is constructed by computing the mean of the chief executive responsesin 1975 to five Likert-type items (coefficient alpha = 0 .70) . Responses to theitems were scored as 0 = strongly disagree, 25 = disagree, 50 = undecided, 75 =agree, 100 = strongly agree . The items in this scale include: (1) the computer isan essential tool in the day-to-day operations of this government ; (2) in thefuture, the computer will become much more essential in the day-to-dayoperations of this government; (3) for the most part, computers have clearlyincreased the speed and ease of performance of government operations wherethey have been applied; (4) the elected legislative body here is generallyfavorable to expanding the use of computers and data processing ; and (5) thedepartment heads are generally favorable to expanding the use of computersand data processing . Scores for this scale range from a low of 45 to a high of100.

Long-range planThis dichotomous variable (1 = no, 2 = yes) is obtained from the responses ofthe data processing managers in 1975 in each city to the question : does thisinstallation have a long range (two years or more) EDP plan. In those cities inwhich there was more than one data processing installation, the city-levelmeasure was obtained by calculating the mean response across all data process-ing managers in the city who responded to the Data Processing ManagersSurvey, 1975. Scores range from a low of 1 to a high of 2.

Percent data processing budget spent on trainingThis variable was computed using the responses to two items in the DataProcessing Managers Questionnaire, 1975 : total expenses associated with train-ing and education of programmers and analysts for the current year divided bythe total expenditures budgeted for the current year . In those cities in whichthere was more than one data processing installation, the city-level measure wasobtained by first summing all budget expenditures by installation and thencomputing the percent spent on training . Scores ranged from a low of 0% to ahigh of 5 .38%.


69


User involvement in system design scaleThis scale was computed by calculating the mean response of data processingmanagers to four items in the Data Processing Managers Questionnaire, 1975.Responses to the items were scored as : 1 = never, 2 = seldom, 3 = often, and4 = always. The four items were designed to tap four dimensions of userinvolvement : adoption, design, development, and evaluation . The followingitems were used to represent these four dimensions : (1) adoption : the frequencywith which users initiate major changes of EDP applications (such as changingthe flow of information, the input or output); (2) design : the frequency withwhich users work as a member of a technical group in designing an application;(3) development : the frequency with which users provide test data for anapplication ; and (4) evaluation : the frequency with which users formally evaluateapplications they use . Average scores ranged from a low of 1 (never) to a high of3 .75. Coefficient alpha = 0 .75.

Middle-management incentives to learnThis measure is a single item taken from the chief executive's response to thefollowing question on the Chief Executive Survey, 1975 : Middle managementshould be provided incentives either by pay, promotion ; or free instruction toencourage them to learn more about computers and data processing . Responsecategories include : 1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree,and 5 = strongly agree. Scores on this variable range from a low of 2 to a high of5.

Data processing perception of chief executives interest scaleThis scale was computed by calculating the mean response of data processingmanagers to six items in the Data Processing Managers Questionnaire, 1975.Dichotomous response categories in which 0 = no and 1 = yes were used foreach of the items. Data processing mangers were asked to respond to whetherthe chief executive was involved in : (1) providing a major input into whether ornot a new set of EDP applications will be adopted ; (2) has the authority forsetting application priorities ; (3) approves budget requests for new computermainframes and systems ; (4) approves requests for new peripheral equipment inuser departments; (5) is primarily responsible for evaluating the services pro-vided by the installation ; and (6) must approve major reorganizations such aschanging the departmental status or location of EDP, or consolidating severalindependent EDP units . Scale scores ranged from a low of 0 to a high of 1.Coefficient alpha = 0 .74.

Log of total applications operational, 1975This measure is the log of the total count of applications that were operationalin the city in 1975 . Data Processing Managers completed the Local GovernmentComputer Applications Questionnaire, 1975 which provided an inventory of 254

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computer applications and files divided into 33 functional areas of local govern-ment. They were asked to indicate which of the 254 computer applications andfiles were operational in their installation . Scores ranged from a low of 0 to ahigh of 4 .78.

User control of data processing scaleThis scale was computed by calculating the mean responses of data processingmanagers to two items in the Data Processing Installation Questionnaire, 1975.Response categories include : 1 = never, 2 = seldom, 3 = often, 4 = always . Exactwording of the items are: What is the frequency with which users of your dataprocessing unit (1) sit on a policy board overseeing the . computer unit, and (2)complete questionnaires or evaluation forms on their satisfaction with dataprocessing services? Scores range from a low of 1 to a high of 4 . Coefficientalpha = 0 .62.

Decentralization of service evaluationThis dichotomous single item scale is based on the responses of data processingmanagers to the following question : Who is primarily responsible for evaluatingthe services provided by the installation? The following response items wereprovided: data processing manager, department head over data processing, userdepartment heads, chief executive official, local legislative body, inter-depart-mental board or steering committee, inter-governmental board or steeringcommittee, or other . If "user department heads" or "inter-governmental Boardor steering committee" was selected, the city was scored as "decentralized",otherwise, the city was scored as "centralized" . Under conditions of multipleinstallations within a city, the individual installation scores were mean averagedto obtain the city-level score . Scores range from 1 (centralized) to 2 (decentral-ized).

Independent data processing departmentCities were scored a "1" if there was a single installation within the city and theinstallation was an independent data processing department under the chiefexecutive. All other organizational arrangements, as well as all cities with morethan one installation within the city were scored a "0".

Growth in government employment, 1970—1985The variable was computed as the rate of change in the number of employeesper 1000 population in 1980 with the number of employees per 1000 populationin 1985 . Scores range from a low of — 0.67 to a high of 1 .21.

Percent growth in population, 1970—1980The variable was computed as a percent rate of change in the total populationbetween 1970 and 1980.


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The institutionalization of computing in complex …jlpweb/papers/the_institutionalization_of...J.L. Perry et at. / Computing in complex organisations tionalization" to refer to the