Shared Knowledge and IS Performance

The Contribution ofShared Knowledge toIS GroupPerformance1

By: Kay M. NelsonThe University of KansasAccounting and Information Systems

DivisionSummerfield 315ALawrence, KS 66045U.S.A.knelson @ ukans.edu

Jay G. CoopriderBentley CollegeComputer Information Systems

Department175 Forest StreetWaltham, MA 02154U.S.A.jcooprider@ bentley.edu

Abstract

A major issue facing managers of informationsystems organizations is the increasing pres-sure to demonstrate the business value of thefirm’s investment in information technology.The working relationship between the ISdepartment and other diverse organizationalgroups can have a major contribution toincreasing IS performance. This paperexplores the concept of shared knowledgebetween/S groups and their fine customers asa contributor to IS performance. Shared know/-

Gerry DeSanctis was the accepting senior editor for thispaper.

edge is achieved through the mechanisms ofmutual trust and influence between thesegroups. The relationship of mutual trust, influ-ence, and shared knowledge with IS perfor-mance is tested empirically using path analysisin a study of 86 IS departments. The results ofthis study show that shared knowledge medi-ates the relationship between IS performanceand trust and influence and that increasing lev-els of shared knowledge between IS and linegroups leads to increased IS performance.Recommendations are given for ways man-agers can develop mutual trust and influencebetween these diverse groups and, therefore,achieve higher levels of shared knowledge andIS performance.

Keywords: Shared knowledge, IS group per-formance, trust, influence, IS-user interac-tion

ISRL Categories: AA0101, AL04, DB05,E 10205

Introduction

A major issue facing managers of informationsystems (IS) groups is the increasing pressureto demonstrate the business value of the firm’sinvestment in information technology (IT). Theopportunity for IS groups to be the drivingforce behind business transformation hasnever been greater (Davenport and Short1990; Hammer 1990), yet internal and externalcompetitive pressures (e.g., outsourcing) arethreatening the form and the very existence ofthe internal IS function (Dearden 1987; Lohand Venkatraman 1992). The value of theinvestment in information technology hasremained frequently untapped and largelyunseen in most organizations. To take fulladvantage of the opportunities facilitated by IT,senior managers must integrate the manage-ment of information technology into the variousbusiness departments and functions of the firm(Henderson and Venkatraman 1993;McFarlan, et al. 1983). Improving the relation-ship between IS and line managers has fre-

MIS Quarterly/December 1996 409

Shared Knowledge and IS Performance

quently been suggested as a way to meet thischallenge (Boynton, et al. 1992; Elam 1988;Rockart and Short 1991).

The IS group’s ability to effectively work withdiverse functional groups can be a major factorin both IS and organizational performance(Henderson 1990; Keen 1988; Rockart andShort 1991). This paper develops the conceptof shared knowledge between IS and lineorganizations as a key contributor to IS groupperformance. The building of trust and influ-ence between diverse groups is presented asan important antecedent to achieving cross-functional shared knowledge.

As the business environment becomes moreturbulent and time dependent, organizationalproductivity often depends on an in-depthknowledge of technologies, processes, andpeople -- both in and across diverse functionalareas (Badaracco 1991; Nonkana andJohansson 1985). The interdependenceamong functional groups becomes especiallycritical in complex environments (Pfeffer andSalancik 1978; Schrage 1990; Thompson1967; Weick 1982). Mutual knowledge basesbetween functional groups provide a potentialbridge to organizational productivity (Kraussand Fussel 1990). This is particularly true inthe case of information systems groups andthe line groups they support.

What is unique about shared knowledgebetween IS groups and their customers?Information systems groups are constantlyinvolved in technology transfer processes toline organizations (Cooper and Zmud 1990;Williams and Gibson 1990). A primary respon-sibility of IS groups is to deliver informationtechnology based on requirements of the lineorganization. The need to operate from a com-mon knowledge base begins in the require-ments phase of systems development (Ewersand Vessey 1981), but continues throughmaintenance, support, and eventual deactiva-tion or replacement of the technology(Henderson and Treacy 1986; Jordan andMacheskey 1990). A shared knowledge ofboth this process and the information technolo-gy in question supports and enhances thetransfer of IT from IS to its customer base.

Through this shared knowledge base, barriersto understanding and acceptance between ISand the line are removed (Churchman andSchainblatt 1965; Krauss and Fussell 1990),and both groups increase their ability to worktoward a common goal.

"l"he unit of analysis in this paper is the infor-mation systems group. These groups aredepartments that primarily provide applicationsdevelopment services to a specific businessfunction. Much MIS research in the past hasfocused on the needs and behaviors of usersand user groups (Boland 1987). However, theworld view of IS development groups cangreatly contribute to social and organizationalchange (Willmott etal. 1990)o These IS groupsact as change agents in the organization, andhow they view their relationship to their cus-tomers and the organization as a whole caneffect the way in which they interact(Hirschheim et al. 1987). The degree to whichthe IS group believes it shares knowledge withits line customers can impact its ability to suc-cessfully perform. The objective of using the ISgroup as the unit of analysis is to understandhow this IS view of shared knowledge con-tributes to performance.

This paper uses an organizational behaviorperspective to propose factors that lead toshared knowledge between functional groupswithin an organization. The next section con-ceptualizes shared knowledge by drawing onconcepts of organizational and functionalknowledge. The following section identifies twokey determinants of shared knowledge -- trustand influence -- and proposes a model ofshared knowledge between IS and line groups.A field study for testing the model and a pathanalysis of the study data to validate the modelare described in the sections after that. Thefinal section presents conclusions and futureresearch directions from this work.

Shared Knowledge

Conventional wisdom is that managerial com-rnunication is important. Managers should"manage by walking around" (Peters and

410 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

Waterman 1982) and informal communicationis the means by which organizations function(Sinetar 1988). Of course, communication itself is not enough. The sharing of knowledgeis a different process than managerial commu-nication (Schrage 1990; Sherif and Sherif1953). Shared knowledge goes beyond thebasic informational level (Keen 1988; Swanson1974). There is a need for this deeper form ofinteraction: "One can brief a reluctant managerendlessly without accomplishing anything,unless one comes to realize his hidden resis-tances and strives to bring them up to con-sciousness in some way" (Churchman andSchainblatt 1965, p. B-82).

A first step in going beyond the informationalbriefing stage of the IS-line relationship is tobuild a common language. Shared knowledgemust be expressed in words or symbols thatare common to the social domain of bothgroups (Zeleny 1989). Such a shared lan-guage can facilitate knowledge transfer as wellas create a positive social influence process(Pondy 1978). IS and line managers mustdevelop an appreciation and understanding ofthe other’s environment rather than merelysharing information and translating technicaland procedural terms (Cooprider 1990;Henderson 1990; Swanson 1974). That is,communication is only a means to and facilita-tor of shared knowledge (Bostrom 1989). define shared knowledge as an understand-ing and appreciation among IS and line man-agers for the technologies and processes thataffect their mutual performance.

Appreciation among groups is characterizedby a sensitivity to the frames of reference andinterpretations of the other group. Dent (1991)gives the example of managers appreciatingaccounting. He points out that this appreciaotion, this shared knowledge, is unique betweenany two groups. For example, the appreciationthat exists between an administrative and anaccounting group is different than the appreci-ation between engineering and accounting.This is due to the different environments andlanguages used in administration and engi-neering. Krauss and Fussell (1990) term thisunique shared knowledge a "miniculture"between groups.

Swanson (1974) characterizes appreciationsomewhat differently, emphasizing user’sinvolvement and appreciation of the MIS sys-tem itself, rather than that of the other group.He acknowledges that this "system" apprecia-tion does have an interpersonal element how-ever -- that of mutual support and cooperationbetween managers and MIS staff. Swansondefines appreciation as a manifold of beliefsregarding the object(s) appreciated.

Effective shared knowledge can be viewed asa synergy between groups (Bostrom 1989).This synergy is defined as mutual understand-ing and respect between groups. This repre-sentation of shared knowledge is consistentwith Huber (1991), who differentiates synergyfrom pure information as a new understandingbetween organizational subunits. Appreciationand understanding are the two core elementsof shared knowledge.

Keen (1988, p. 52) maintains, "The relation-ship between IS and business managers hasto be one of mutual.understanding -- not ofthe details of each other’s activities, knowl-edge, and skill bases, but of the other’sneeds, constraints, and contribution to anorganizational venture partnership." Simplycommunicating facts is not sufficient. A deep-er level of knowledge must be shared toachieve mutual understanding. This deeperlevel of knowledge is often characterized asorganizational knowledge.

Badaracco (1991, p. 81) describes organiza-tional knowledge as embedded knowledge,which is defined as "knowledge which residesprimarily in specialized relationships amongindividuals and groups and in the particularnorms, attitudes, information flows, and waysof making decisions that shape their dealingswith each other." A lack of this organizationaland cross-functional knowledge may result inlosses of IS performance (Kaiser andSrinivasan 1982). As boundary lines betweenorganizational functions become vaporous(Davenport and Short 1990; Rockart and Short1991), managers struggle to keep themselvesinformed about the technologies, processes,and people that fall outside their primary func-tional area yet contribute to their success. IS

MIS Quarterly/December 1996 411

¯ Shared Know~edge and IS Performance

groups impact nearly every functional group inthe information-intensive organization, butLucas (1984) maintains that functional users informationsystems have very little under-standing of what is involved in the analysis anddesign of information systems. This lack ofknowledge can lead to missed opportunitiesfor line managers to contribute domain knowl-edge at critical points in the design process.However, Newman and Robey (1992) havefound that through design process encountersand joint development episodes, the level ofunderstanding between the IS and user groupscan be improved, reinforcing the need forshared knowledge between these groups.

The day-to-day operations of the business canpresent barriers to IS and line managers takingadvantage of opportunities to share knowl-edge. IS managers are frequently consumedwith keeping pace with rapidly changing tech-nologies and IT processes and can be farremoved from the business functions that theirsystems support (Kaiser and Srinivasan 1982;Newman and Robey 1992). They often seekinformation about the technologies and meth-ods of other functional operations only inresponse to the IS requirements for a specificsupport or design request. The day-to-dayproblems and opportunities of these supportedoperations are often unfamiliar to them(Henderson 1990). IS and line managers oftenspeak different technical and procedural lan-guages (Keen 1988), and as a result they feeldisaffected from one another (Tushman andRomanelli 1983). The operational needs andconstraints presented from one side can beperceived as unreasonable demands and alack of cooperation from the other. The com-monality of organizational goals is often lostdue to a lack of understanding of each others’realities.

When faced with information that is not consis-tent with their own reality, humans experienceinternal conflict, which Festinger (1957) labelscognitive dissonance. The way line managersarticulate their IS design or support needs maybe foreign and inconsistent with the terminolo-gy and methods the IS group uses and under-stands (Keen 1980; 1988). While line man-agers may try to conceptualize and describe

the business requirements of an informationsystem, their counterparts in IS may attempt totranslate without sufficient domain knowledgeto accurately interpret the message and,hence, the actual requirements (Boland 1978;Bostrom 1989; Guinan 1988). The IS managerexperiences an inconsistency between his orher own functional knowledge and the inter-preted line requirements. This can lead to afeeling of distance from both the line managerand the final requirements for the informationsystem (Festinger 1957). This phenomenon often a two-way street, with line personnel alsolacking in knowledge and understanding of thelanguage, technologies, and methods of the ISgroup (Lucas 1984).

By understanding what motivates members ofgroups to seek knowledge and reduce incon-:;istency, it is possible to identify the mecha-nisms that facilitate the sharing of knowledgebetween functional groups. The external inter-actions of groups have patterns similar to theinternal patterns of members of the group(Ancona 1990). In this case, when individualrnembers of the IS group find inconsistenciesbetween their knowledge and that of theircounterparts in the line group, the group itselfdisplays these inconsistencies. As the knowl-edge base, expectations, and realities of eachgroup become more distant from that of theother, lack of cooperation and intergroup con-flicts begin to appear (Sherif 1962). What thenoccurs is the in-group/out-group phenomenon(Sherif et al. 1965), which can exhibit itself an "us against them" group attitude(Bettenhausen 1991). The attainment of orga-nizational goals and mutual productivitybecomes an almost impossible task in the faceof this organizational intergroup conflict. Theabsence of a shared reality between groups is;I critical factor in these dysfunctional groupdynamics. This absence of shared reality maylead to poor group performance, while thepresence of such a shared perception maylead to better performance.

We hypothesize that shared knowledgebetween information systems groups and theirline customers will have a positive impact onthe performance of the IS group.

412 MIS Quarterly/December 1996

Shared Know~edge and I$ Performance

Hypothesis 1: Shared knowledge betweeninformation systems groups and their line cus-tomers, as perceived by the IS organization,lead to improved IS group performance.

Having introduced the concept of sharedknowledge as a contributor to IS performance,we next consider its antecedents in order tomore completely understand this relationship.

Antecedents of SharedKnowledge

Trust

Trust has a major impact in relationshipsbetween organizational groups. Trust isdefined as "a set of expectations shared by allthose in an exchange" (Zucker 1986). Trust an expectation that alleviates the fear thatone’s exchange partner will act opportunistical-ly (Bradach and Eccles 1989). Additionally,trust is a set of expectations that tasks will bereliably accomplished (Sitkin and Roth 1993).Groups work better together in an atmosphereof mutual trust based on mutual commitmentand a stable long-term relationship (Andersonand Weitz 1992). This type of committed, long-term relationship is the foundation for our con-ceptualization of trust. Mutual trust is definedas the expectation shared by the IS and linegroups that they will meet their commitmentsto each other (Dasgupta 1988).

The attainment of mutual trust leads to sharedknowledge. Repeated intergroup exchangecommunications build trust, leading toincreased communications and the eventualsharing of knowledge (Anderson and Narus1990). A study of the relationship betweenmarketing research providers and users,shows that trust is a facilitating factor of otherrelationship processes such as quality of inter-actions and involvement levels (Moorman etal. 1992). By alleviating the fear of the unex-pected and facilitating interactions and involve-ment (Bradach and Eccles 1989), trust encour-ages a climate conducive to the-sharing ofknowledge.

By repeatedly working together to obtain mutu-al goals, groups develop a mutual trust (Sherifand Sherif 1953). By sharing expectations andreducing individual dissonance-inducing fearsamong group members, mutual trust bringsgroups closer together. Empirical evidence ofthis phenomenon is demonstrated in a seriesof controlled studies of camping groups inwhich competing teams develop trust relation-ships -- followed by a sharing of knowledgeon solving a common problem (Sherif 1966).Although it may also seem reasonable thatsharing knowledge might lead to trust, Sherif’swork demonstrates that repeated episodes ofjoint effort and communication leads to trust,which then leads to the sharing of methodsand ideas. Trust -- developed through repeat-ed communication -- is demonstrated to bedifferent from and a determinant of sharedknowledge.

The investment of trust between different orga-nizational groups can be viewed as a leap ofcognitive faith and understanding (Lewis andWeigert 1985). The increases in mutual under-standing brought on by mutual trust result inshared knowledge between groups. Trust alsoleads to appreciation through the commonmanifold belief in the performance of both ofthe groups involved (Swanson 1974). We thushypothesize that mutual trust is a determinantof shared knowledge.

Hypothesis 2: The perception of increasedlevels of mutual trust between the IS and linegroups leads to increased levels of sharedknowledge between these groups.

Mutual trust is conceptualized as the expecta-tion shared by the IS and line groups that theywill meet their commitments to each other.Through a commitment to work toward jointgoals built through repeated periods of com-munication, mutual trust leads to increasedshared knowledge between the groups in thelong term. Having shared knowledge betweengroups may also, in turn, lead to the develop-ment of a greater mutual trust.

MIS Quartedy/December 1996 413

Shared Knowledge and IS Performance

Influence

Organizational groups engaged in joint workare often d~pendent upon each other for theachievement of goals (Sherif 1962). One of theconsequences of this dependence is the cre-ation of influence relationships (Anderson andNarus 1990). Without this mutual influence,mutually interdependent tasks can becomedecoupled, and conflict between groups canresult (House 1991). The ability of a group accomplish its goals can be limited by its abili-ty to influence other groups in the organization(Kanter 1983; Pfeffer and Salancik 1978). Oneway influence is developed is through the lawof reciprocity (Cohen and Bradford 1989).People expect payback for contributions to anexchange. The perception of reciprocal bene-fits leads to mutual influence and success infuture group exchanges. It is in this positiveway that we define mutual influence as theability of groups to affect the key policies anddecisions of each other.

Social communication and social influenceprocesses are interwoven with the processesof knowledge creation and dissonance reduc-tion (Festinger 1957). By seeking social sup-port for ideas, individuals and groups seek toeither influence others into accepting theseideas or be influenced by others’ ideas andattitudes. This influence process is necessaryfor achieving mutual understanding betweengroups (Churchman and Schainblatt 1965).Through this social influence mechanism, cog-nitive elements are exchanged betweengroups -- leading to shared knowledge.

The frequency of information exchangebetween buyer-seller groups is positively relat-ed to the level of group influence (Boyle et al.1992). The sharing of knowledge is not limitedto simple information exchange, but is relatedto the influence developed between groups asa result of more frequent and in-depth commu-nication. By depending on each other for thejoint accomplishment of goals, expectations,needs, and knowledge are shared acrossgroups. One way influence is developed isthrough the law of reciprocity (Cohen andBradford 1989). People expect payback forcontributions to an exchange. The perception

of reciprocal benefits leads to mutual influenceand success in future group exchanges. Wetherefore hypothesize that:

Hypothesis 3: The perception of increasedlevels of mutual influence between IS and linegroups leads to increased levels of sharedknowledge between these groups.

Mutual influence has been conceptualized asthe ability of groups to affect the key policiesand decisions of each other. These influenceprocesses result in increased levels of appreci-ation and understanding of each others’ workenvironment and accomplishments throughmutual policy making and decision making,leading again to shared knowledge. As in thecase of mutual trust, it is conceivable that once=shared knowledge is achieved, it may result inhigher levels of mutual influence betweengroups.

Figure 1 presents the completed model ofshared knowledge. This model illustrates twoimportant aspects of shared knowledge. First,mutual trust and influence are presented asantecedents of shared knowledge. Second,shared knowledge is presented as a fullymediating variable between mutual trust andinfluence -- leading to IS group performance.

Communication is an antecedent of mutualtrust and influence. These constructs, althoughdistinct in nature, .are closely linked to eachother. The establishment of a history of com-=munications in the context of quality interac-tions impacts trust, while the frequency of¯ these communications in the context of socialmechanisms leads to influence. It is likely thatsome interaction between mutual trust andinfluence is a result of communication quality,and frequency. In the discussion of shared’knowledge, we stated that communication is,only a means to and a facilitator of sharedknowledge. That is, repeated and frequentcommunications contribute to IS performancethrough the development of mutual trust andinfluence leading to shared knowledge. As IS.and line groups move beyond simple commu-nications to understanding and appreciating¯ the expectations, realities, and methods of,each other, the benefits of these dynamics are

414 MIS Quarterly/December 1996

Shared Knowledge and IS Performance

Figure 1. A Model of Shared Knkowledge

seen in IS group performance. In this way,shared knowledge acts as a mediating vari-able between mutual trust and influence andIS performance.

the involved parties have attained some levelof mutual understanding. Thus, influenceleads to shared knowledge -- which precurs-es performance.

A mutual appreciation of and attractivenesstoward another group or individual is an inte-gral component of shared cognition (Festinger1957; Sherif 1966). A series of experiments oncompeting groups showed that contactbetween groups was not in itself sufficient tomotivate the groups to achieve common goals(Sherif 1966). Only through repeated coopera-tion between groups is trust developed, andthis trust leads to an increased seeking ofinformation about the other group -- resultingin shared knowledge being desired and built.This sharing of knowledge is needed forgroups to achieve superordinate goals that arebeneficial to both groups. These experimentsreinforce the role of shared knowledge as amediating variable in the relationship betweentrust and performance. Similarly, Churchmanand Schainblatt (1965) maintain that (1) influ-ence is necessary to achieve mutual under-standing between groups, and (2) successfulimplementation of projects presuppose that

Hypothesis 4: Shared knowledge acts as amediating variable between mutual trust andinfluence and IS performance.

Using this model of the contribution of sharedknowledge to IS performance, the next sectiondiscusses the research design, and the follow-ing section presents the analysis for a test ofthis model and the four hypotheses presentedabove.

Research Design

Data to test the model and hypotheses weredrawn from a cross-sectional field study of 132IS departments and their line customers inseven firms (Cooprider 1990). Seven organiza-tions were chosen for this study and representthe pharmaceuticals, insurance, gas and oil,

MIS Quarterly~December 1996 415

Shared Knowledge and IS Performance

consumer goods, computer manufacturing,and automotive industries.

The level of analysis of the study is the ISorganization, since the intent of the study is toexplain the behavior and attitudes of the ISorganization rather than those of individuals.Participating organizations were asked, there-.fore, to identify distinct IS organizations (i.e.,IS units with a specific management structurein place) serving a single client organization. Inaddition, each question in the questionnairewas customized to include the names of thespecific IS organization and its correspondingline client.

The IS organizations surveyed represent dis-tinct organizational units that serve a well-defined line-client organization. These IS orga-nizations were chosen on the basis that theirprimary service provided was applicationdevelopment. These organizations supportone or more software applications serving asingle line domain function, each questionnaire.administered was customized for the particularIS organization being surveyed.

While it would have been ideal for the sake ofexternal validity to randomly choose compa-nies, partnerships, and individuals to partici-pate, it was not possible in this study. In thedata collection, all companies agreeing to par-ticipate were included -- a convenience sam-ple. Because of this, there is a possible selec-tion bias that cannot be entirely discounted.Table 1 describes the industries and number

of IS organizations studied for each of the,companies participating in the study. Thenature of the sample selection process’focused on maintaining internal validity, sincethe broad range of organization and industrytypes made it unlikely that unmonitored expla-nations would cause effects in all of the targetorganizations. However, since there was not arandom sampling of respondents (for example,two companies accounted for 59% of the par-ticipating IS organizations), the generalizabilityof the results across all firms is necessarily lim-ited due to the possibility of selection bias.

.Study respondents were chosen based on aIkey-informant methodology (Phillips andBagozzi 1986). For each IS organization,respondents included first level IS personneland management from within the IS and linegroups. Completed surveys were receivedfrom a total of 86 groups for a 65% responserate. This sample size is generally accepted asbeing sufficient to perform path analysis(Kerlinger and Pedhauser 1973).

Measurement of organizational characteristicsrequires research methods different from thoseused for measuring the characteristics of indi-viduals (Seidler 1974), and a key-informantmethodology is a frequently adoptedapproach. The dependent variable -- IS groupperformance -- was collected from "stakehold-ers" in each firm, usually senior IS or functionalmanagement. There are many reasons forusing this approach. First, a large body of liter-ature exists that highlights the substantial

Table 1. Study Participation by Organization

Company

ABCDEFG

Industry Type

PharmaceuticalsInsuranceOil & GasConsumer GoodsComputer ManufacturingInsuranceAutomotive

Number of ISOrganizations

Identified

1013375

331717

Totals: 132

Number of ISOrganizationsParticipating

40262

251712

86 (65%)

416 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

problems involved in measuring IS perfor-mance (ICIT Research Team #2 1988;Kemerer 1989). It is typically not possible tofind objective (e.g., accounting) measures thatcan be gathered and used consistently acrossa range of organizations such as those partici-pating in this study. Venkatraman andRamanujam (1987) suggest that perceptualassessments of performance provided byknowledgeable managers have a high level ofconvergence with objective performance mea-sures. We therefore conclude that using man-agerial ratings is a suitable method for gather-ing performance data for this study. It shouldbe noted, however, that such an approach isnot without its weaknesses. Our specific oper-ationalization, for example, includes indicatorsof systems quality and efficiency. There areclearly other indicators that might representother aspects of IS performance (e.g., effec-tiveness). Future studies should clearlyexplore a broader conceptualization and oper-ationalization of IS performance (DeLone andMcLean 1992).

The principal research instrument for this studyasked a series of questions about characteris-tics of the IS-line relationship. These charac-teristics were evaluated by determining a con-sensus of the respondents from each IS orga-nization. This consensus was determined bytaking the mean of answers from all respon-dents. Homogeneity of variance tests wereperformed to insure that there were no signifi-cant differences across raters. Such anapproach assumes equal reliability amonginformants, which is unlikely to be completelyjustified in practice since some informants maybe more knowledgeable, less biased, etc.(Seidler 1974). However, there was no reasonto suspect a systematic bias among respon-dents, and it was felt that combining responseswould provide measures containing lessunique variance since aggregated valueswould be less affected by idiosyncraticresponses of specific individuals. The mean,standard deviation, and range across all indi-vidual respondents for each indicator in thestudy as well as for each construct are listed inthe Appendix.

Construct measurement

The study was conducted in two phases(Cooprider 1990). In phase one, measuresand collection instruments were developed.The first step in the measurement develop-ment process was to identify an initial set ofmeasurement items as candidates for lateruse in the construct scales. The managementand marketing literature contain constructsand definitions of these indicators, and theoperationalizations and measures found inthese reference disciplines are not specific tothe IS-line relationship. Rather than adoptthese broader measures, this study has usedfield-driven constructs based on existing theo-ry to capture the elements of mutual trust andinfluence between IS groups and their linecustomers.

It is important to anchor survey questions inthe domain.being assessed as completely aspossible (Silk and Kalwani 1982). In our study,first, candidate indicators were derived frompublished research articles that discussed orattempted to measure similar constructs.Second, candidate indicators were generatedfrom a content analysis of a series of 28 inter-views with executives managing organizational"partnership-style" relationships (Henderson1990). It was critical for the indicators generat-ed to be meaningful for (1) the constructs interest, (2) the specific IS context, and (3)each of the organizations to be studied.Therefore, from the candidate indicators, apilot questionnaire was created and testedusing two to six managers from five organiza-tions (not participating in Phase Two of thestudy). Following the completion of this pilotinstrument, each respondent was debriefed todetermine if any questions were confusing or ifthe terminology used related in a meaningfulway to the concepts they were intended tomeasure. All evidence from the pilot studiesand executive interviews suggested that theseindicators tapped the respondents’ view of thetheoretical constructs.

Two types of measures are used to assess theorganizational characteristics of shared knowl-edge, trust, and influence. The first type is ageneral measure. Each informant is asked to

MIS Quarterly~December 1996 417

Shared Knowledge and IS Performance

assess the overall level of interaction for aspecific characteristic of a particular relation-ship. For’example, one question might askrespondents to evaluate "the level of apprecia-tion that the IS organization and the line orga-nization have for each other’s accomplish-ments." The second type of measure is a mul-tiplicative or interaction measure. Each infor-mant is asked to assess separately the role ofIS and the line for each characteristic. Forexample, the questionnaire might contain thefollowing two statements: "the level of appreci-ation that the line organization has for theaccomplishments of the IS organization," and"the level of appreciation that the IS organiza-tion has for the accomplishments of the lineorganization." Using the conceptualization of fitas interaction (Venkatra.man 1989), we opera-tionalize this measure as "IS role * line role,"multiplying the two responses together. Theactual indicators for each construct appear inthe Appendix.

There are a number of advantages to thismeasurement scheme. The two types of mea-sures (general and multiplicative) can thought of as different methods, from aCampbell and Fiske (1959) perspective. Usingmeasures in this way provides a stronger testof the validity of the measurement schemethan would be possible if only one type ofmeasure was used for each indicator. That is,the extent to which these two kinds of indica-tors agree provides a much stronger test ofvalidity than would be possible if only one orthe other type of indicator was used. Further,using both types of measures balances possi-ble threats to validity inherent in either typealone. For example, the general assessmentsrequire a complex set of summarizations andinterpretations by respondents, leading topotential error due to the large cognitive bur-den such assessments place on key infor-mants (Silk and Kalwani 1982).

The questions used for the multiplicativeassessment, however, are very specific aboutthe role and characteristic of interest, placing amuch smaller cognitive burden on respon-dents. Similarly, the operationalization of themultiplicative indicators as "IS Role * LineRole" is one of several possible operationaliza-

tions of interaction (Venkatraman 1989). Thegeneral questions, on the other hand, are~firect assessments of the fit relationship inquestion. To the extent that these two very dif-ferent types of indicators show convergent anddiscriminant validity in their measurement ofthe constructs in question, we can have ahigher level of confidence about the validity ofthe measures.

IS group performance is conceptualized in twoparts; operational performance and serviceperformance. These dimensions of perfor-rnance capture two different aspects of IS per-formance; the "inward" operational activities ofproduction and development, and the "out-ward" service activities of customer service(Berger 1988; Cooprider 1990). Operationalperformance is therefore operationalized asthe quality of the IS group’s work product, theability of the IS group to meet its organizationalcommitments, and the ability of the IS organi-zation to meet its goals. Service performanceis operationalized as the ability of the IS groupto react quickly to line needs, its responsive-hess to the line group, and the contribution theIS group has made to the line group’s successin meeting its strategic goals.

Participating firms were each asked to selecttwo stakeholders to fill out a measurementinstrument to assess IS performance. These.,itakeholders were required to be knowledge-able about the performance of the IS organiza-tion in its relationship with its line customer. Toprevent a common method bias, the chosenstakeholders could not have filled out the origi-nal relationship questionnaire. Stakeholdersconsisted of both IS and line managers, with 80respondents representing the line and 90respondents representing IS. Altogether, asshown in Table 1, questionnaires were receivedfrom team members and stakeholders for 86 ofthe 132 identified IS organizations (65%).

The Appendix provides Chronbach’s alpha foreach of the four constructs measured in thestudy. All alphas are well above the acceptablerange for empirical studies of this type(Nunnally 1967), with the smallest being .84.We therefore conclude that the measures arereliable. To assess convergent and discrimi-

418 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

nant validity, the correlation matrix for al.I 10indicators is shown in Table 2. This matrixshows all correlations within constructs to behigher than any correlations across constructs,implying convergent and discriminant validityof the constructs (Campbell and Fiske 1959).

Data Analysis

The model of shared knowledge displayed inFigure 1 is tested empirically using path analy-sis. Path analysis was chosen as the analytictechnique in this study because it assessescausal relationships (Kerlinger and Pedhazur1973; Wright 1971). It is a regression-basedtechnique that permits the testing of causalmodels using cross-sectional data (Baroudi1985). Normalized path coefficients (betas) used to determine the strength and direction ofcausal paths or relations. These betas repre-

sent the fraction of the standard deviation ofthe dependent variable for which the indepen-dent or mediating variable is responsible(Kerlinger and Pedhazur 1973).

In order to assess the validity of our model ofshared knowledge, a series of alternative pathmodels were tested. The first of these is a the-oretical model of shared knowledge, and itappears in Figure 2.

To assess the Figure 2 model, a hierarchicalregression was performed by first examiningthe relationship between shared knowledgeand performance, and then between sharedknowledge and trust and influence. Figure 2shows the results of this analysis, with allbetas large and statistically significant n sup-porting the proposed model. Specifically,Hypotheses 1-3 are directly supported by thesignificance of paths I, II, and III, respectively.It should be noted that the constructs of

R2= .63; F = 101.34; p < .01.

Figure 2. A Theoritical Model of Shared Knowledge

MIS Quarterly~December 1996 419

Table 2. MTMM Correlation Matrix for Construct Indicators

Shared Shared Shared IS ISTrust1 Trust2 Influence1 Influence2 Influence3 Know,1 Know.2 Know.3 Perform.1 Perform.2

Trust1Trust2Infl.1Infl.2Infl3SK!SK2SK3ISPerflISPerf2

1.000.780.410.320.320.430.620.550.310.13

0.781.000.620.580.510.590.720.680.230.14

0.410.621.000.690.7113 R1

0.620.630.07-0.06

0.320.580.691.000.790.570.630.500.230.17

0.320.510.710.791.000.590.580.440.220.21

0.430.590,610.570.59

0.810.730.140.11

0.620.720.620.630.580.8!1.000.790.300.23

0.550.680.630.500.440,730.791.000.300.21

0.310.230.070.230.220,140.300.301.000.80

0.130.14

-0.060.170.210.110.230.210.801.00

Shared Know~edge and IS Performance

shared knowledge, mutual trust, and mutualinfluence were measured on a single instru-ment, while performance was measured on aseparate instrument, which could have con-tributed to the lower r value between sharedknowledge and performance. This resultedbecause the separate instruments were filledout by different respondents at different levelsof the organization, resulting in possibly differ-ent contextual conditions (Kerlinger andPedhazur 1973).

A series of alternate models were evaluatednext to attempt to further validate the modelshown in Figure 2 (Blalock 1971). Specifically,in order to test Hypothesis 4 (shared knowl-edge as a mediating variable), three alternatemodels were tested. The first two alternates (A& B) each eliminated one of the independentvariables -- trust or influence -- and treatedshared knowledge as an independent ratherthan a mediating variable. These two modelsand the results of their assessment are shownin Figures 3 and 4, respectively.

For both of these models, neither determinant(trust or influence) was significantly related IS performance, providing support forHypothesis 4’s contention that mutual trust andinfluence do not have a direct effect on perfor-

mance but rather only an effect through themediation of shared knowledge. As a final testof Hypothesis 4, IS performance wasregressed on trust, influence, and sharedknowledge as a group -- as shown in Figure5. The results of this assessment eliminate thepotential paths between trust and performanceand influence and performance (since they arenon-significant) (Kerlinger and Pedhazur1973). In addition, the regression’s F-statisticis not significant, and comparing this with theresults of the Figure 2 model also supportsHypothesis 4, implying further that sharedknowledge acts as a mediating variablebetween mutual trust and influence and ISperformance.

A final confirmatory test of the Figure 2 modelwas performed by reconstructing the originalcorrelation coefficients between variables(Baroudi 1985; Kerlinger and Pedhazur 1973).Any discrepancies between the original correla-tion coefficients and the reconstructed coeffi-cients greater than .05 was reason to reject thecausal path model. Correlations were recon-structed by adding the direct and indirect effectsof the path using the following equations:

rmt,sk = bsk, mt + bsk,mi*rmt,mi

~ = .04 (p = .82)~ 13 = .30 (p = .06)

R2 = .07; F = 3.75; p = .03.

Figure 3. Alternative Model A:Trust and Shared Knowledge as Independent Variables

MIS Quarterly~December 1996 421

Shared Know~edge and IS Performance

J] = .03 (p = .84) ,~,~,~ _,,J~ 13 = .30 (p = .06)

R2 = .07; F = 3.75; p = .03.

Figure 4. Alternative Model B:Influence and Shared Knowledge as Independent Variables

!~ ~ r = .19\~Mutual ~ ( Shared ""~

R2 = .07; F = 2.50; p = .06.

Figure 5. Alternative Model C:Influence, Trust and Shared Knowledge as Independent Variables

422 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

rmi.sk = bsk,mi + bsk, mt*rrnt,mi

rsk.isp = bisp.mt*rrnt,sk + bisp,mi*rmi,sk + bisp, sk

where ra,b is the correlation between a and b,bs,b is the beta for the direct path between a

¯ and b, sk is shared knowledge, mt is mutualtrust, mi is mutual influence, and isp is IS per-formance¯ Table 3 shows the results of solvingthese equations.

Since the difference between the reconstruct-ed correlations and the original correlationswas in all cases less than .05, the modelpassed the test of reconstructed correlations¯

Finally, analysis of variance procedures andBartlett-Box F tests were performed on eachvariable to test for homogeneity of varianceamong raters. The ANOVA for all variables wassignificant, indicating intergroup agreement¯The results of the Bartlett-Box F were insignifi-cant (.05 level) for the variables of trust, influ-ence, and shared knowledge, supporting thehypothesis that the individual differences werenot significantly greater across certain groupsthan others (Glass and Hopkins 1984). TheBartlett-Box F test could not be run for the per-formance variable because of the large numberof responses with a zero variance¯ Table 4shows the results of this analysis.

Discussion of Results

The path analysis discussed in the previoussection shows that our proposed model bestdescribes the causal relationships betweenmutual trust, influence, shared knowledge, andIS group performance. The insignificant betavalues obtained when removing either mutualtrust or mutual influence from the model indi-cates that both variables are necessary toachieve shared knowledge. Since there is evi-dence in the literature that trust and mutualinfluence directly influence performance whenshared knowledge is not used as a variable(Henderson and Lee 1992) this relationshipwas tested in alternative models A and B.These models eliminate the possibility thateither of these variables, along with sharedknowledge, has a direct effect on IS perfor-mance, supporting Hypothesis 4. The role ofshared knowledge as a mediating variable issupported by comparing the Figure 5 modelwith our proposed (Figure 2) model. Low andinsignificant betas in Figure 5 confirm that theoriginal "trimmed" model (one which does notinclude all possible paths) explains the mostcausality among the variables. The analysisfurther supports Hypotheses 1o3 that mutualtrust and influence are not direct causalantecedents of IS performance, but rather actthrough shared knowledge.

Table 3. Recon~tructed Correlations for Path Model

Original Reconstructed Direct IndirectPath Correlation Correlation Effect Effect

SK--> P 0.27 0¯27 0.27 0.00T--> SK 0¯75 0.77 0.58 0.19I --> SK 0.63 0.63 0¯30 0.33

Table 4. Analysis of Variance and Bartlett - Box F Results

VariableInfluenceTrustShared

KnowledgePerformance

ANOVA F

1.602.57

2.158.36

ANOVASignificance

.002.000

.000.000

Bartlett-Box F

1.00.966

1.20not applicable

Bartlett-Box FSignificance

.476

.583

.085not applicable

MIS Quarterly/De’cember 1996 423

Shared Knowledge and IS Performance

The use of path analysis does not preclude thepossibility that reverse or reciprocal causalityexists between constructs (Bagozzi, et al.1991). It could be argued that a greater level ofshared knowledge between groups could leadto easier development of mutual trust andinfluence. Prior research (Sherif 1962, 1966;Sherif and Sherif 1953; Wheeless 1978)shows that mutual trust and influence are pre-cursors to the development of informationsharing and shared knowledge. However,once shared knowledge is achieved, it is pos-sible that it could, in return, lead to increasesin mutual trust and influence. Such a relation-ship could be tested longitudinally. The pathanalysis also indicates a strong relationshipbetween mutual trust and mutual influence,although not as strong as the individual vari-ables relationship to shared knowledge.. Thisrelationship between trust and influence alsowarrants further investigation.

In summary, the path analysis supportsHypotheses 1-4 and the proposed sharedknowledge model. The results indicate thatmutual trust and influence between IS groupsand their line customers lead to increased lev-els of shared knowledge. This shared knowl-edge, in turn, is a positive contributor to ISgroup performance.

Conclusions and FutureResearch

This model of shared knowledge’s contributionto IS group performance has implications forboth researchers and managers. We proposethat IS and line groups have the opportunity todevelop mutual trust and influence throughrepeated periods of positive communication,social interaction, and goal attainment. Theseattributes lead to the groups’ increased attrac-tiveness to each other and an increase inshared information regarding problems,processes, and opportunities. This sharing ofinformation leads to the sharing of technicaland organizational knowledge. When sharedknowledge occurs, the IS and line obtain amore complete understanding and apprecia-

tion of each others’ reality. Shared knowledgeplays a mediating role in the achievement of ISgroup performance through mutual trust andinfluence.

Shared knowledge positively relates to the per-formance of the IS organization. This researchcontributes to an overall conceptual under-standing of the nature and importance ofknowledge as an organizational performancefactor. From a theoretical perspective, theseresults imply that communication by itself isnot sufficient for knowledge sharing.Communication can: contribute to mutual trustand mutual influence, and it is these factorsthat lead to the sharing of knowledge thatimpacts IS group performance. By identifyingtrust and influence as the determinants ofshared knowledge, the relationship betweenthe IS and line groups is characterized as acomplex interaction beyond that of mere com-munication. The interaction of mutual trust andin, fluence is an interesting phenomenon war-ranting future research.

This study examines a large sample of IS-linerelationships in a range of firms. While therneasurements used have demonstrated sta-tistical, convergent, and discriminant validity,issues of concern remain. Although stake-holder assessments of performance havebeen found to have a high level of conver-gence with objective measures (Venkatramanand Ramanujam 1987), it is clear we havetaken a relatively narrow approach to IS per-formance. As was mentioned earlier, futurestudies in this area should include a morecomprehensive conceptualization and opera-tionalization of IS performance that betterreflects its multi-dimensional nature (DeLoneand McLean 1992). In addition, the data pre-sented are cross-sectional. The developmentof mutual trust and influence leading toshared knowledge is an ongoing phenome-non. These constructs were measured at astatic point in time rather than as they devel-oped, thus losing some richness of explanato-ry power. An ethnographic study of sharedknowledge between diverse organizationalgroups would be an alternative method ofcapturing this richness.

424 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

From a managerial perspective, the identifica-tion of mutual trust and influence between ISand line groups as determinants of sharedknowledge implies the need to provide oppor-tunities for these qualities to be developed. Bydefining shared knowledge as an understand-ing and appreciation among cross-functionalmanagers for the technologies and processesthat affect their mutual performance, it is notimplied that the groups need to be able to per-form each other’s jobs. However, IS and linemanagers should be provided opportunities tosocially interact and communicate about theirwork. These opportunities could be activitiessuch as joint training on interdependent tasks,joint planning sessions, and formation ofcross-functional teams (Henderson 1990)oThese activities can lead to improved IS groupperformance by providing a greater under-standing and appreciation of the constraintsand environment of each group. One exampleof these activities is repeated interactionsbetween groups that result in predictable andrecognizable patterns of behavior (Gulati1995). Strategic rotation -- the temporarymovement of managers from one group toanother (Nonaka 1994) --is another activitythat can lead to mutual trust and influence.

Future research indicated by this studyincludes looking at changes in trust, influence,and shared knowledge levels over time andthe relationship of those changes to IS perfor-mance. The possibly of reciprocal relationshipsamong the variables once shared knowledgeis established can be investigated. Changes inperformance can be assessed against anyinterventions used, such as the repeated inter-actions or strategic rotation techniquesdescribed above. Mutual trust, mutual influ-ence, and shared knowledge should also bestudied with respect to their impact on the per-formance of the line group as well as IS.Finally, the model can be used as a theoreticallens to examine similar organizational relation-ships. An example of such a relationship mightbe R&D and manufacturing groups. It is hopedthat the model of shared knowledge providedin this paper will provide insight to managers ina variety of organizational contexts in thefuture.

Acknowledgements

The authors would like to thank Bill Glick, JohnHenderson, and a set of anonymous reviewersfor their helpful suggestions concerning thispaper.

References

Ancona, D.G. "Outward Bound: Strategies forTeam Survival in an Organization,"Academy of Management Journal (33:2),June 1990, pp. 334-365.

Anderson, J.C. and Narus, J.A. "A Model ofDistributor Firm and Manufacturer FirmWorking Partnerships," Journal ofMarketing (54:1), January 1990, pp. 42-59.

Anderson, E. and Weitz, B. "The Use ofPledges to Build and Sustain Commitmentin Distribution Channels," Journal ofMarketing Research (29:1), February 1992,pp. 18-35.

Badaracco, J. The Knowledge Link: How FirmsCompete through Strategic Alliances,Harvard Business School Press, Boston,MA, 1991.

Bagozzi, R. P., Yi, Y., and Phillips, L. W."Assessing Construct Validity inOrganizational Research," AdministrativeScience Quarterly (36:3), September 1991,pp. 421-458.

Baroudi, J.J. ’qhe Impact of Role Variables onIS Personnel Work Attitudes andIntentions," MIS Quarterly (9:4), December1985.

Berger, P. "Selecting Enterprise - LevelMeasures of IT Value," in MeasuringBusiness Value of Information Techno-logies, ICIT Press, Washington, D. C.,1988.

Bettenhausen, K.L. "Five Years of GroupsResearch: What We Have Learned andWhat Needs to be Addressed," Journal ofManagement (17:2), June 1991, pp.345-381.

Blalock, H.M. "Four Variable Causal Modelsand Partial Correlations," in Causal Modelsin the Social Sciences, H.M. Blalock (ed.),Aldine Publishing Company, Chicago, IL,1971, pp.18-32.

MIS Quarterly~December 1996 425

Shared Know~edge and IS Performance

Boland, R.J. "The Process and Product ofSystem Design," Management Science(24:9), May 1978, pp. 887-898.

Boland, R. J., Jr. "The Information ofInformation Systems," in Critical Issues inInformation Systems Research, R. J.Boland and R. A. Hirschheim (eds.), JohnWiley, New York, 1987.

Bostrom, R.P. "Successful Application ofCommunication Techniques to Improve theSystems Development Process,"Information & Management (16), 1989, pp.279-295.

Boyle, B., Dwyer, R., Robicheaux, R.A., andSimpson, J.T. "Influence Strategies inMarketing Channels: Measures and Use inDifferent Relationship Structures," Journalof Marketing Research (29), November1992, pp. 462-471.

Boynton, A.C., Jacobs, G.C., and Zmud,R.W."Whose Responsibility is IT Management?"Sloan Management Review (33:4), Summer1992, pp. 32-38.

Bradach, J.L. and Eccles, R.G. "Price,Authority, and Trust: From Ideal Types toPlural Forms," in Annual Review ofSociology (15), W.R. Scott and Jo Blake(eds.), 1989, pp.97-118.

Campbell, D.T. and Fiske, D.W. "Convergentand Discriminant Validation by theMultitrait-Multimethod Matrix," Psychologi-cal Bulletin (56:2), 1959, pp. 81-105.

Churchman, C.W. and Schainblatt, A.H. "TheResearcher and the Manager: A Dialectic ofImplementation," Management Science(11:4), February 1965, pp. B69-B87.

Cohen, A.R. and Bradford, D.L. "InfluenceWithout Authority: The Use of Alliances,Reciprocity, and Exchange to AccomplishWork," Organizational Dynamics (17:3),Winter 1989, pp. 4-18.

Cooper, R.B. and Zmud, R.W. "InformationTechnology Implementation Research: ATechnological Diffusion Approach,"Management Science (36:2), February1990, pp. 123-139.

Cooprider, J.G. Partnership Between Line andIS Managers: A Management Model,unpublished dissertation, MIT Sloan Schoolof Management, Cambridge, MA, 1990.

Dasgupta, P. "Trust as a Commodity," in Trust:Making and Breaking Cooperative

Relations, D. Gambetta (ed.), BlaekwellPublishing, New York, 1988, pp. 47-72.

Davenport, T.H. and Short, J.E. "The NewIndustrial Engineering: InformationTechnology and Business ProcessRedesign," Sloan Management Review,Summer 1990, pp. 11-27.

Dearden, J. "The Withering Away of the ISOrganization," Sloan Management Review,Summer 1987, pp. 87-91.

r)eLone, W.H. and McLean, E.R. "InformationSystems Success: The Quest for theDependent Variable," Information SystemsResearch (3:1), March 1992, pp. 60-95.

r)ent, J.F. "Accounting and OrganizationalCultures: A Field Study of the Emergenceof a New Organizational Reality,"Accounting, Organizations, and Society(16:8), 1991, pp.705-732.

Elam, J. J. "Establishing Cooperative ExternalRelationships," in Transforming the ISOrganization, J. J. Elam, M. J. Ginzberg, P.G. W. Keen, and R. W. Zmud (eds.), ICITPress, Washington D. C., 1988, pp. 55-82.

Ewers, J. and Vessey, I. "The SystemsDevelopment Dilemma: A ProgrammingPerspective," MIS Quarterly (5:2), June1981, pp.33-45.

F;estinger, L. A ~’heory of CognitiveDissonance, Stanford University Press,Stanford, CA, 1957.

Glass, G. V. and Hopkins, K. D. StatisticalMethods in Education and Psychology,Allyn and Bacon, Needham Heights, MA,1984.

Guinan, P.J. Patterns of Excellence for ISProfessionals: An Analysis ofCommunication Behavior, ICIT Press,Washington, D.C., 1988.

Gulati, R. "Does Familiarity Breed Trust? TheImplications of Repeated Ties forContractual Choice in Alliances," TheAcademy of Management Journal (28:1),February 1995, pp. 85-112.

Hammer, M. "Reengineering Work: Don’tAutomate, Obliterate," Harvard BusinessReview, July-August 1990, pp. 104-114.

Henderson, J.C. "Plugging Into StrategicPartnerships: The Critical IS Connection,"Sloan Management Review (31:3), Spring1990.

426 MIS Quarterly/December 1996

Shared Knowledge and IS Performance

Henderson, J.C. and Treacy, M.E. "ManagingEnd-User Computing for CompetitiveAdvantage," Sloan Management ReviewWinter 1986, pp. 3-14.

Henderson,J.C. and Lee, S. "Managing I/SDesign Teams: A Control TheoriesPerspective," Management Science (38:6),1992, pp. 757-777.

Henderson, J.C. and Venkatraman, N."Strategic Alignment: LeveragingInformation Technology for TransformingOrganizations," IBM Systems Journal(32:1), 1993, pp. 4-16.

Hirschheim, R., Klein, H., and Newman, M. "ASocial Action Perspective of InformationSystems Development," Proceedings of theEighth International Conference onInformation Systems, Pittsburgh, PA,December 6-9, 1987, pp. 45-56.

House, R.J. "The Distribution and Exercise ofPower in Complex Organizations: A MesoTheory," Leadership Quarterly (2:1 ), 1991,pp. 23-58.

Huber George Po "Organizational Learning:The Contributing Processes and theLiteratures," Organization Science (2:1),February 1991, pp. 88-114.

ICIT Research Study Team #2. MeasuringBusiness Value of Information Techno-logies, ICIT Press, Washington D.C., 1988.

Jordan, E.W. and Macheskey, J.J. SystemsDevelopment, PWS-Kent, Boston, MA,1990.

Kaiser, K. and Srinivasan, A. "User-AnalystDifferences: An Empirical Investigation ofAttitudes Related to SystemsDevelopment," Academy of ManagementJournal (25:3), 1982, pp. 630-646.

Kanter, R.M. The Change Masters, Simon andSchuster, New York, 1983.

Keen, P.G.W. "MIS Research: ReferenceDisciplines and a Cumulative Tradition,"Proceedings of the First InternationalConference on Information Systems,December 8-10, 1980, Philadelphia, PA,pp. 9-18.

Keen, P.G.W. "Relationship of SeniorManagement and the IS Organization," inTransforming the IS Organization, J.J.Elam, M.J. Ginzberg, P.G.W. Keen, andR.W. Zmud (eds.), ICIT Press, Washington,D.C., 1988.

Kemerer, C.F. "An Agenda for Reseamh in theManagerial Evaluation of Computer-AidedSoftware Engineering (CASE) ToolImpacts," in Proceedings of the Twenty-Second Annual Hawaii InternationalConference on System Science, Honolulu,HI, January 3~-6, 1989, pp. 219-228.

Kerlinger, F.N. and Pedhazur, E.J. MultipleRegression in Behavioral Research, Holt,Rinehart, and Whinston, Inc., New York,1973, pp. 305-333.

Krauss, R.M. and Fussell, S.R. "MutualKnowledge and CommunicationsEffectiveness," in Intellectual Teamwork:Social and Technological Foundations ofCooperative Work, J. Calegher, R.E. Kraut,and C. Egido (eds.), Lawrence Erlbaum andAssociates, Hillsdale, N J, 1990, pp.111-145.

Lewis, J.D. and Weigert, A. "Trust As a SocialReality," Social Forces (63:4), June 1985,pp. 967-985.

Loh, L. and Venkatraman, N. "Determinants ofInformation Technology Outsourcing: ACross-Sectional. Analysis," Journal ofManagement Information Systems (9:1),Summer 1992, pp. 7-24.

Lucas, H. C. "Organization Power and theInformation Services Department,"Communications of the ACM (27:1),January 1984, pp. 58-65.

McFarlan, F.W., McKenney, J. L., and Pyburn,P. "The Information Archipelago, Plotting aCourse," Harvard Business Review,March-April 1983, p. 130.

Moorman, C., Zaltman, G., and Deshpande, R."Relationships Between Providers andUsers of Market Research: The Dynamicsof Trust Within and Between Organi-zations," Journal of Marketing Research(29), August 1992, pp. 314-328.

Newman, M. and Robey, D. "A Social ProcessModel of User-Analyst Relationships," MISQuarterly (16:2), June 1992, pp. 249-266.

Nonaka, I. "A Dynamic Theory ofOrganizational Knowledge Creation,"Organization Science (5:1), February 1994,pp. 14-37.

Nonaka, I. and Johansson, J.K. "JapaneseManagement: What About the ’Hard’Skills?" Academy of Management Review(10:2), June 1985, pp.181-191.

MIS Quarterly/December 1996 427

Shared Knowledge and IS Performance

Nunnally, J.C. Psychometric Theory, McGraw-Hill, New York, 1967.

Peters, T.J. and Waterman, R.H. Jr. In Searchof Excellence: Lessons from America’sBest-Run Companies, Warner Books, NewYork, 1982.

Pfeffer, J. and Salancik, G.R. The ExternalControl of Organizations, Harper and Row,New York, 1978.

Phillips, L.W. and Bagozzi, R.P. "OnMeasuring the Organizational Properties ofDistribution Channels: MethodologicalIssues in the Use of Key Informants," inResearch in Marketing, VoL 8, JAI Press,Greenwich, CT, 1986, pp. 313-369.

Pondy, L. R. "Leadership is a LanguageGame," in Leadership: Where Else Can WeGo? M.W. McCall and M.Mo Lombardo(eds.), Duke University Press, Durham, NC,1978, pp. 87-99.

Rockart, J.F. and Short, J.E. "The NetworkedOrganization and the Management ofInterdependence," in The Corporation of the1990’s: Information Technology andOrganizational Transformation, M.S. ScottMorton (ed.), Oxford University Press,Oxford, 1991, pp. 189-219.

Schrage, M. Shared Minds: The NewTechnologies of Collaboration, RandomHouse, New York, 1990.

Seidler, J. "On Using Informants: A Techniquefor Collecting Quantitative Data andControlling for Measurement Error inOrganizational Analysis," AmericanSociological Review (38), December 1974,pp. 816-831.

Sherif, M. (ed.). Intergroup Relations andLeadership, John Wiley and Sons, Inc.,New York, 1962.

Sherif, M. In Common Predicament: SocialPsychology of Intergroup Conflict andCooperation, Houghten Mifflin Company,Boston, 1966.

Sherif, M. and Sherif, C.W. Groups inHarmony and Tension, Harper andBrothers, New York, 1953.

Sherif, C.W., Sherif, M., and Nebergall, R.E.Attitude and Attitude Change, W.B.Saunders Company, Philadelphia, 1965.

Silk, A. and Kalwani, M. "Measuring Influencein Organizational Purchase Decisions,"

Journal of Marketing Research (19), 1982,pp. 165-181.

Sinetar, M. "The Informal Discussion Group --A Powerful Agent for Change," SloanManagement Review (29:3), Spring 1988,pp. 61-66.

Sitkin, S. and Roth, N.L. "Exploring the LimitedEffectiveness of Legalistic ’Remedies’ forTrust]Distrust," Organization Science (4:3),August 1993, pp. 367-392.

Swanson, E.B. "Management InformationSystems: Appreciation and Involvement,"Management Science (21:2), October 1974,pp. 1.78-188.

Thompson, J.D. Organizations in Action,McGraw-Hill, Inc., New York, 1967.

Tushman, M. and Romanelli, E. "Uncertainty,Social Location and Influence in DecisionMaking: A Sociometric Analysis,"Management Science (29:1), January 1983,pp. 12-23.

Venkatraman, N. "The Concept of Fit inStrategy Research: Toward Verbal andStatistical Correspondence," The Academyof Management Review (14:3), 1989, pp.423-444.

Ventkatraman, No and Ramanujam, V."Measurement of Business EconomicPerformance: An Examination of MethodConvergence," Journal of Management(13:1), Spring 1987, pp. 109-122.

Weick, K.E. "Management of OrganizationalChange among Loosely CoupledElements," in Change in Organizations,P.S. Goodman and Associates (eds.),Jossey-Bass, San Francisco, 1982, pp.375-408.

Wheeless, L.R. "A Follow-up Study of theRelationships Among Trust, Disclosure, andInterpersonal Solidarity," HumanCommunication Research (4:2), 1978, pp.143--157.

Williams, F. and Gibson, D.V. (eds.).Technology Transfer: A CommunicationPerspective, Sage Publications, NewburyPark, CA, 1990.

Willmott, H., Mouritsen, J., Flensburg, P., andElkjaer, B. "Systems Developers;Preoccupations, Knowledge, and Power,"Proceedings of the Eleventh InternationalConference on Information Systems,

428 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

Copenhagen, Denmark, December 16-18,1990, pp. 257-264.

Wright, S. "Path Coefficients and PathRegressions: Alternative or ComplementaryConcepts?" in Causal Models in the SocialSciences, H.M. Blalock (ed.), AldinePublishing Company, Chicago, 1971, pp.101-114.

Zeleny, M. "Knowledge as a New Form of cap-ital: Part 1. Division and Reintegration ofKnowledge," Human Systems Management(8:1), 1989, pp. 45-58.

Zucker, L. "Production of Trust: InstitutionalSources of Economic Structure,1840-1920," in Research in OrganizationalBehavior (8), B.M. Staw and L.L.Cummings (eds.), 1986, pp. 53-111.

About the Authors

Key M. Nelson is an assistant professor ofinformation systems at the Graduate School of

Business at the University of Kansas. Herresearch interests include organizationalknowledge, flexibility and change, and informa-tion systems’ performance measurement. Sheis a recent doctoral graduate of the Universityof Texas at Austin and has published severalmajor conference papers.

Jay G. Cooprider is an associate professor ofinformation systems in the ComputerInformation Systems Department at BentleyCollege. He received his Ph.D. from the SloanSchool of Management at the MassachusettsInstitute of Technology. His reseamh interestsinclude managing knowledge and partnershipin information systems organizations and theimpact of advanced technologies on organiza-tional performance. He has published papersin the Journal of Management InformationSystems, Information Systems Research, andthe IBM Systems Journal.

MIS Quarterly/December 1996 429

Shared Knowledge and IS Performance

Appendix

Construct Measurement

A. Characteristics of Independent and Mediating Variables

[Taken From Relationship Questionnaire Given To IS Organizations]

Please characterize the general working relationship that currently exists between the [IS orga-nization] and the [line/functional organization].

Note: Items in brackets were customized to reflect the exact names of the participating organizationsand functional groups.

Scale used to measure constructs:

1 2 3 4 5 6 7

Extremely Weak Moderately About Moderately Strong- ExtremelyWeak Weak Average Strong Strong

SHARED KNOWLEDGE (Cronbach’s Alpha = .91)

Shared Knowledge Indicator 1: Multiplicative Assessment:

The product of the responses for the following:

1. The level of understanding of the [line organization] for the work environment (problems, tasks,roles, etc.) of the [IS organization] is: (mean=3.70, s.d.=1.47, range=7.00).

2. The level of understanding of the [IS organization] for the work environment (problems, tasks, roles,etc.) of the [line organization] is: (mean=4.38, s.d.=l.37, range=7.00).

Shared Knowledge Indicator 2: Multiplicative Assessment:

The product of responses for the following:

1. The level of appreciation that the [line organization] has for the accomplishments of the [IS organi-zation] is: (mean=4.16, s.d.---1.55, range=7.00).

2. The level of appreciation that the [IS organization] has for the accomplishments of the [line organi-zation] is: (mean=4.30, s.d.=1.31, range=7.00).

Shared Knowledge Indicator 3: General Assessment:

The level of appreciation that the [IS organization] and the [line organization] have for each other’saccomplishments is: (mean=4.40, s.d.=1.35, range=7.00).

Shared Knowledge Construct:

The mean of the above indicators:(mean=13.43, s.d.=6.68, range=35.00)

430 MIS Quarterly~December 1996

Shared Knowledge and IS Performance

MUTUAL TRUST (Cronbach’s Alpha = .84)

Mutual Trust Indicator 1 : General Assessment:

The level of trust that exists between the [IS organization] and the [line organization] is: (mean=4.58,s.d.=l.40, range=7.00).

Mutual Trust Indicator 2: Calculated Assessment:

The product of the responses for the following:

1. The reputation of the [line organization] for meeting its commitments to the [IS organization] is:(mean=4.11, s.d.=l.33, range=7.00).

2. The reputation of the [IS organization] for meeting its commitments to the [line organization] is:(mean=4.83, s.d.=1.39, range=7.00).

Mutual Trust Construct:

The mean of the above indicators:(mean=12.93, s.d.=6.29, range=34.83)

MUTUAL INFLUENCE (Cronbach’s Alpha = .90)

Mutual Influence Indicator 1: General Assessment of Interaction:

The average of responses for the following:

1. In general, the level of influence that members of the [IS organization] and the [line organization]have on each other’s key decisions and policies is: (mean=4.27, s.d.=1.31, range=6.50).

2. In general, the ability of members of the [IS organization] and the [line organization] to affect eachother’s key decisions and policies is: (mean=4.55, s.d.=1.25, range=6.50).

Mutual Influence Indicator 2: Calculated Assessment of Interaction:

The product of responses for the following:

1. In general, the level of influence that members of the [line organization] have on key decisions andpolicies of the [IS organization] is: (mean=4.58, s.d.=1.48, range=7.00).

2. In general, the level of influence that members of the [IS organization] have on key decisions andpolicies of the [line organization] is: (mean=3.64, s.d.=l.45, range=7.00).

Mutual Influence Indicator 3: Calculated Assessment of Interaction:

The product of responses for the following:

1. In general, the ability of members of the [line organization] to affect key policies and decisions of the[IS organization] is: (mean=4.46, s.d.=1.47, range=7.00).

2. In general, the ability of members of the [IS organization] to affect key policies and decisions of the[line organization] is: (mean=3.64, s.d.=1.46, range=7.00).

Mutual Influence Construct:

The mean of the above indicators:(mean=12. 4 7, s.d.=5. 45, range=28.00)

MIS Quarterly~December 1996 431

Shared Knowledge and IS Performance

B. The Dependent Variable

[Taken From Performance Questionnaire Given To Organizational Stakeholders]

The following questions ask you to compare the [IS organization] to other such IS organizationunits. In relation to other comparable units you have observed, how does the [IS organization]rate on the following?

Note: Items in brackets were customized to reflect the exact names of the participating organizationsand functional groups.

Scale used to measure constructs:

1 2 3 4 5 6 7Non- Very Weak About Strong Very Extremely

Existent Weak Average Strong Strong

IS PERFORMANCE (Cronbach’s Alpha = .89)

IS Performance Indicator 1: General Assessment:

In general, the quality of the work produced for the [line organization] by the [IS organization] is:(mean=5.09, s.d.=0.98, range=3.50).

IS Performance Indicator 2: General Assessment:

In general, the efficiency of the [IS organization] in performing its work for the [line organization] is:(mean=4.82, s.d.=1.05, range=5.00).

Performance Construct:

The mean of the above indicators:(mean=4.94, s.d.=0.97, range=5.00)

432 MIS Quarterly~December 1996