ASSESSING ORGANIZATIONAL KNOWLEDGE CREATION THEORY IN COLLABORATIVE R&D PROJECTS

387

International Journal of Innovation ManagementVol. 6, No. 4 (December 2002) pp. 387–418© Imperial College Press

ASSESSING ORGANIZATIONAL KNOWLEDGE CREATIONTHEORY IN COLLABORATIVE R&D PROJECTS

WILLIAM H. A. JOHNSONBentley College

Management DepartmentWaltham, MA, USA

[email protected]

Received 10 May 2002Revised 6 November 2002

Accepted 10 November 2002

The paper describes research from an intensive study of technological innovation incollaborative research and development (R&D) projects. Specifically, the factors oforganizational knowledge creation presented by Nonaka and Takeuchi are extended intothe inter-organizational realm by examining survey results of 25 collaborative R&D projects.A case study is also presented from a set of six in-depth cases from the study’s populationof projects. It was found that specification of goals and scanning of relevant environmentfactors were significant positive factors in successful technological innovation in thiscontext. In general, the results presented in this paper indicate that inter-organizationalcollaboration in R&D may require different technical knowledge-creating factors or enablingconditions from the single organization situation. Practical and theoretical implicationsregarding the use of such managerial devices in successful technical knowledge creationstrategies are also discussed.

Keywords: Technical knowledge creation; collaboration; innovation; research anddevelopment.

Introduction

Scholars, managers and strategic policy makers have recognized the importanceof knowledge and its creation with greater frequency over the past decade.However, the specific factors associated with the process of knowledge creationin research and development (R&D) are yet to be fully elucidated and examined.

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388 W. H. A. Johnson

Difficulties in this research stream stem from the complexity of such endeavors,which are even more complex in today’s fast-paced innovative efforts requiringmultiple organizational participants (Chen, 1997). Particularly in terms of inter-organizational management, researchers have suggested that we know very littleof the process of knowledge creation (e.g., Coombs and Hull, 1998; Powell,1998). This paper stems from research that utilized Nonaka and Takeuchi’s (1995)theory of organizational knowledge creation as the reference framework. Thisresearch examined collaborative R&D projects, which involved at least threeorganizations from public and private industries collaborating on the developmentof intelligent systems technology. As such, it represents an exploration ofknowledge creation in an inter-organizational setting and extends Nonaka andTakeuchi’s (1995) theory.

Nonaka and Takeuchi’s (1995) theoretical factors are extended to thecollaborative R&D situation because there are theoretical arguments for adifference in their effectiveness in that situation. The implications of such ananalysis are important given the increasing focus on both knowledge creation andcollaboration in the knowledge economy. Ultimately, the strategy utilized ineffective knowledge management might depend heavily on the type of situationin which the knowledge is being created.

This paper provides an empirical exploration of that process by extending thetheory of Nonaka and Takeuchi (1995) to the inter-organizational setting. Itexamines the factors that they suggested enable these processes. Such researchis important given the high rate of failure in collaborative technological innovationprograms, especially in programs involving academic and business partners (cf.Cyert & Goodman, 1997; Lam, 1997). It is particularly important given theevidence that successful initiatives may be very beneficial to both firms andeconomies in general (e.g., Berman, 1990; Studt, 1993).

Knowledge Creation Factors: Conceptual Background

In this section, I define the constructs of the model of knowledge creation basedon Nonaka and Takeuchi (1995). It is important to note that their theory iscomplex. Due to exposition constraints I refer interested readers to the originalwork of Nonaka and Takeuchi (1995) for a complete rendering of the theory’slogic and Johnson (2000) for more on the entire empirical study. Here, I discussthe findings of research looking at similar concepts in the management literatureto devise specific hypotheses to examine using the study’s data. Later, I presenta case study of one project to give the reader an idea of the context examinedand the role knowledge creation factors played in the case project.

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Assessing Organizational Knowledge Creation Theory in Collaborative R&D Projects389

Knowledge and the process of its creation

Knowledge is an elusive concept. For example, it can be so difficult to preciselydefine it such that Grant (1996) preferred to simply use the tautology of “thatwhich is known” (p. 110). It has been defined as “justified true belief ” (Nonaka& Takeuchi, 1995), “information whose validity has been established throughtests of proof ” (Porter-Liebeskind, 1996), and “a set of beliefs held by an individualabout causal relationships among phenomena” (Sanchez, Heene & Thomas,1996: 9). Perkins (1986) described knowledge as “design” framed around fourquestions involving a design’s purpose, structure, model cases, and explanationand evaluation. As this survey demonstrates, tangible measures of knowledgehave been elusive in the management literature; indeed, epistemology, or thephilosophy of human knowledge, is one of the oldest philosophies with thedefinition and nature of knowledge still being debated even after thousands ofyears of human intellectual discourse. What is needed in managerial studies is aproxy for knowledge and its creation. The literature on R&D management oftenfocuses on patent data with a patent denoted as an indication of new technicalknowledge being created (e.g., Almeida, 1996; Mowery, Oxley & Silverman,1996), but there may be biases associated with this measure. Looking only atpatents as a measure of technical knowledge creation ignores the possibility ofknowledge that is not formally codified, for example pre-commercial technicalknowledge, or knowledge not acknowledged by an archaic legal system (Thurow,1997). The concept operationalized later in this paper, while not perfect, is meantto circumvent some of these issues.

The inductive theory of Nonaka and Takeuchi (1995) describes the processesof interplay between explicit and tacit knowledge structures that lead to thecreation of new organizational knowledge. As most management scholars areaware by now, tacit knowledge structures are verbally and often conceptuallyinexpressible (Polanyi, 1966; 1974), but play an important role in knowledgemanagement because much understanding remains “unspoken” (e.g., Boisot, 1995;Hedlund, 1994; Reed & DeFillippi, 1990). This “tacit-explicit” knowledge interplayresults in four “knowledge conversion” processes of socialization (tacit-to-tacit),externalization (tacit-to-explicit), combination (explicit-to-explicit) andinternalization (explicit-to-tacit) (see Nonaka & Takeuchi, 1995, pp. 62–70 for acomplete account of the processes). Specific factors, which Nonaka and Takeuchi(1995) call enabling conditions, support these processes and lead to successfulknowledge creation, as depicted in Fig. 1. The focus of this paper is on therelationship of these factors or enabling conditions to technical knowledge creationin collaborative R&D projects, operationalized as the achievement of technicalobjectives given the potential extent of technological development.

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The original theory of Nonaka and Takeuchi (1995) was developed studyingnew product development programs, which were similar in nature to mostcollaborative R&D projects that have as a major objective the creation of a newtechnological product or process. One major distinction between their researchand the research described here is that in the latter, the empirical examinationwas within an inter-organizational setting and utilized a more deductive approachbasing the operationalization of constructs on the Nonaka and Takeuchi (1995)framework. This seemed appropriate as Nonaka and Takeuchi (1995) suggest thattheir theory will also apply to such inter-organizational settings. It is hoped thatan examination of the theory in such a setting will help extend our understandingof organizational knowledge creation in general.

Goal Specificity

Autonomy(Individual Project

Members)

Autonomy(Project

Management)

Creative Chaos

Redundancy inKnowledge Sets

EnvironmentalScanning

TechnicalKnowledge Creation

H1(+)

H2a(+/-)

H2b(+)

H3(-)

H4(+/-)

H5(+)

Fig. 1. Hypotheses regarding enabling conditions of technical knowledge creation in the collaborativeproject (based on Nonaka and Takeuchi, 1995).

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The Enabling Conditions

Intention and goal specificity

Nonaka and Takeuchi (1995) argue that intention is necessary for successfulknowledge creation in a single organization situation. Indeed, intention, definedas “an organization’s attentiveness to its goals”, has long been a factor for corporateoptimization in the traditions of economics, organization behavior and strategicdevelopment (cf. Shubik, 1961; Cyert & March, 1963; Hamel & Prahalad, 1989).Goal and task specificity is a distinct measure of the organization’s attentivenessto its goals with the more specific or explicitly defined intention, the moreexplicitly defined the tasks necessary to complete the project successfully. Thereis empirical evidence that group-level intention in the form of goal specificationis positively related to performance (Erez & Somech 1996; Leary-Kelly,Martocchio & Frink, 1994). For example, in a study of collaborations betweenGTE laboratories and university researchers, Cukor (1992) found that successfulcollaboration required well-defined, realistic, and relevant objectives. However,some empirical research regarding goal setting and performance has beeninconclusive. Buller and Bell (1986) found only a small performance effect;though of those improvements that did occur, more were found in the goal-settingcondition than in the team-building condition.

According to Judge, Fryxell and Dooley (1997), the most innovative R&Dunits function as goal-directed communities rather than as typical bureaucraticdepartments. This might be because specifying goals helps to coordinate actionsacross various organizational units (Cyert & March, 1963; Thompson, 1967) andalso helps to determine the final success of any venture by acting as cues towarda level of success (Motowidlo, Loehr & Dunnette 1978). Even improvisationrequires adherence to a general sense of intention in order to be effective, eventhough final implementations may be unforeseeable when plans are actually made(Moorman & Miner, 1998). Because goals act to coordinate action it is expectedthat goal specificity will be positively related to successful technical knowledgecreation in the collaborative situation.

Hypothesis 1: As the level of goal specificity in a collaborative projectincreases, technical knowledge creation will increase.

Autonomy

Autonomy refers to “the ability of an entity to govern its own affairs”. Nonakaand Takeuchi (1995) argue that the autonomy of individuals within an organizationis necessary for successful knowledge creation in a single organization situation.

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It has been suggested that autonomy is highly important to creativity (Judge,Fryxell & Dooley, 1997; Shalley, 1995; Zhou, 1998). It may also be intimatelytied to the previous construct of goal specification, particularly if the latter isassociated with control (Feldman, 1989). In fact, autonomy is likely to be“constrained” to some extent in most situations. Even if one has the autonomyto make strategic choices (e.g., Child 1972; Child, 1997), one is still constrainedby the amount of resources at one’s disposal and the pressures of institutionalizingforces (Oliver, 1991; Osborn & Hagedoorn, 1997). Researchers have suggestedthat there are many dialectical processes (e.g., between freedom and control,differentiation and integration, etc.) at work in managing technological invention(cf. Fryxell, 1990; Bahrami, 1992; Judge, Fryxell & Dooley, 1997). In suchcases, goal specification might be seen to set the boundaries upon which themeasurement of goal attainment is based and autonomy refers to the choices oftasks, methods and management structures and processes utilized to attain thepre-set goals.

In collaborative projects, autonomy may be exhibited at two levels. Therefore,this study examined the autonomy of individuals within the project and theautonomy of the project itself (i.e., from the participant organizations). Individualautonomy is potentially important because it fits the knowledge creation modelin which individuals are the initial holders of valuable tacit knowledge. In sucha case, it is reasonable to suggest that individuals be free to develop their ideaswith some degree of autonomy. Project level autonomy may also be importantbecause of the inter-organizational nature of the collaborative project.

Individual autonomy

Individual autonomy is a traditional element of organizational job design models(Hackman & Oldham, 1976; Keidel, Bell & Lewis 1994). The empirical evidencelinking individual autonomy with performance has been mixed. In a study of 49collaborative R&D projects, Hakanson (1993) found that managerial autonomyimproved project success. However, Souder (1974) found that output of R&Dlaboratories was not correlated with the level of an individual researcher’s freedom.This evidence suggests that autonomy may be more prevalent and beneficial atthe managerial level than the operational level. While many researchers contendthat autonomy is good for creativity, most would suggest that a balance is necessarybetween a structure that is too rigid and one that is too loose. For instance, interms of international R&D structures, Gassman and Zedtwitz (1999) found thatthere has been a trend both towards higher autonomy of multinational subsidiariesand also re-centralization, or a focused effort on integrating strategy across units.

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In examining self-directed work teams, Janz (1999) suggested that autonomymight not be as important as cooperative learning factors to achieving improvedwork outcomes.

Complex, novel projects necessitate greater levels of knowledge creation thanless complex and more traditional projects for successful completion becausethere is a greater gap between what is already known and what must be knownfor success. In order to spur the creativity necessary for solving the technicalproblems at hand, high levels of individual autonomy will be expected toaccompany successful complex projects. In general, individual autonomy shouldlead to greater levels of learning because of the latitude given the individual toexplore various avenues of discovery. However, many explorations are notconducive to the successful completion of the project (i.e., the meeting of thetechnological objectives). Thus, autonomy may be a “double-edged sword” inR&D management. While it may lead to knowledge creation in general, theactual knowledge created may be such as to limit its practicality. As individualautonomy increases, it is likely to become more disassociated from thecollaborative knowledge creation efforts of the project such that the initial positiveeffects of individual autonomy are lost and increasing levels result in negativereturns. A curvilinear inverted U relationship is expected.

Hypothesis 2a: Individual autonomy of project members will becurvilinearly associated with technical knowledge creation such that lowand high levels of individual autonomy correspond with low levels oftechnical knowledge creation, and moderate levels of individual autonomycorrespond with high levels of technical knowledge creation.

Project autonomy

Research has shown that autonomy and differentiation from the rest ofthe organization is often useful in R&D project management (Gwynne, 1997).It is a recommended managerial practice at many innovative companies(Kimura & Tezuka, 1992; Single & Spurgeon, 1996). Recently, Tatikonda andRosenthal (2000) found project management autonomy to be one factor positivelyassociated with project execution success. Higher project autonomy should beassociated with higher project performance, manifested as technical knowledgecreation.

Hypothesis 2b: As the level of project autonomy in a collaborative projectincreases, technical knowledge creation will increase.

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Creative chaos

According to Nonaka and Takeuchi (1995), creative chaos is a complex managerialintervention that exists to “stimulate interaction between the organization and theexternal environment” (p. 78). It is chaos generated intentionally by managementto stimulate artificial fluctuation, which refers to the environmental forces thatcreate breakdowns in the routines, habits or cognitive frameworks of organizationalmembers and may lead to the re-framing of events necessary for positiveinnovation. In the original prescriptive model the intervention is characterized bythe intentional creation of ambiguity and crisis. No empirical research exists thatexplicitly examines the concept as proposed by Nonaka and Takeuchi (1995), butthere is some work that has looked at related concepts such as managing on theedge of chaos (e.g., Brown & Eisenhardt, 1997; 1998) and the inherent chaosunderlying the early beginnings of some consortiums (Browning, Beyer & Shetler,1995). The major difference in the concepts is that the former is purposefullyintroduced to induce creative thinking while the latter exists naturally by virtueof context.

As with autonomy, creative chaos may have detrimental effects at either toohigh or too low of a level. Too much chaos is likely to lead to panic and loss ofintegration of effort, while too little may not provide the necessary stimulationfor creative problem solving. However, this is not likely to be the case in thecollaborative situation where ambiguity and crisis may lead to dissension amongproject members. In the single organization such interventions may be positivelyreinforced by the overall strategic intent of the firm, held together by the glue ofthe commitment of members to the organization with which they strongly identify.However, in collaborative projects there may be less of a strong identificationbetween various project members such that intentional ambiguity and crisis havea distinct negative effect. In collaborative R&D projects, such chaos is likely tohave a negative relationship to knowledge creation.

Hypothesis 3: As the level of creative chaos in a collaborative projectincreases, technical knowledge creation will decrease.

Redundancy in knowledge sets

Nonaka and Takeuchi (1995) argue that redundancy in information and systemsis necessary for successful knowledge creation in a single organization situation.Redundancy in the collaborative project occurs in the duplication and overlap ofelements within a project. The fundamental type of redundancy in organizationalknowledge creation is manifest in the overlap of the knowledge and skill setsalready held by individuals. One example of a general knowledge set is the

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working use of a language, which allows individuals to communicate. In general,sharing the same language leads individuals to better understand each other andbuilds a sense of community (Lane & Lubatkin, 1998; Brown & Duguid, 1991).When project members’ individual knowledge sets overlap so as to increase thetransfer of knowledge, further knowledge creation should be facilitated. Thisshould be similar in both single organization and inter-organizational situations.However, if the overlap between individuals’ knowledge sets is very high, therewill be little new knowledge to exchange, and knowledge creation from theoverlap will be lessened. That is, too little overlap results in a lack of integrationand too much in a lack of differentiation. In fact, Nonaka and Takeuchi (1995)suggest that high levels of redundancy are associated with participants having toomuch information with which to cope.

Hypothesis 4: Redundancy in knowledge sets of project members will becurvilinearly associated with technical knowledge creation such that lowand high levels of redundancy in knowledge sets correspond with lowlevels of technical knowledge creation, and moderate levels of individualautonomy correspond with high levels of technical knowledge creation.

Requisite variety and environmental scanning

Requisite variety is a concept from cybernetic systems and control theory thatrefers to an organization’s matching of its internal diversity with the variety andcomplexity of the environment in order to deal with challenges posed by theenvironment (Nonaka & Takeuchi, 1995, p. 82). This concept is manifest invariety-handling structures, which may be of even greater importance forcollaborative R&D projects because they operate in environments that are muchmore complex than traditional single organization situations (Chen, 1997). Ascollaborative R&D projects involve more than one organization a requisitestructural variety is often present in the form of increasing number and diversityof organizational participants and this may provide some variety-handling ability.

Environmental scanning is an example of a variety-handling mechanismavailable for use by management in all types of collaborative R&D situations. Itis defined as “the process of actively measuring various aspects of the environmentfor purposes of strategically responding to environmental change”. It representsa means of developing the structures for interpreting and controlling the interfacebetween the internal and external environments (e.g., Daft & Weick, 1984; Ghoshal,1988) such that obtaining information on several aspects of specific environmentalsectors facilitates alignment between competitive strategies and environments(Beal, 2000). One study found that chief executives primarily scan the competition,

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customer, regulatory, and technological sectors of the environment (Auster andChoo, 1994).

There is empirical support for the importance of environmental scanning,particularly in relation to strategic performance (Sim & Teoh, 1997). Milliken(1990) found empirical support for Daft and Weick’s (1984) proposition thatorganizational adaptation involves three distinct tasks of scanning, interpreting,and learning. From a qualitative perceptive, Yasai-Ardekani & Nystrom (1996)found that organizations with effective scanning systems aligned their scanningdesigns with the requirements of their context, while organizations with ineffectivescanning systems typically failed to exhibit the requisite level of alignment betweencontext and scanning design.

Following the dictum that the more you know the more you can know, themore sources of input into any knowledge creating endeavor, the greater theability to create new knowledge. A trade-off is likely to come in the form ofincreasing costs associated with increased scanning. From a developmentperspective, however, the greater the use of scanning the more one can “control”the internal project development to suit the external development of the rest ofthe world. As environmental scanning is increased, generally it will result in agreater ability to match internal development with environmental changes andknowledge, resulting in increased knowledge creation. Looked at it inversely, themore complex a project’s technological innovation is, the greater the technicalknowledge creation necessary and the greater the magnitude of scanning and thenumber of environmental elements that are actively scanned and monitored bymanagement in a collaborative project.

Hypothesis 5: As the magnitude of scanning and the number ofenvironmental elements that are actively scanned and monitored bymanagement in a collaborative project increases, technical knowledgecreation will increase.

Methods

Research sample

This study addresses the factors of knowledge creation at the collaborative R&Dproject level. As such, key informants consisted of the project leaders of suchprojects and individuals associated with the management of the project at anexecutive level. Those other than the project manager were usually senior scientistsand managers who had working knowledge of management at the project leveland who provided a means to triangulate measures for some projects.

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The final research sample consisted of 25 collaborative R&D projects involvingthe creation and development of novel intelligent systems, which consist ofartificial intelligence, computer and robotic technologies. Examples are a digitalmicroscope capable of analyzing a living sample utilizing multiple spectroscopictests and a lumber grading system based on non-evasive measuring techniques.The latter is described in more detail later in the paper. The projects were allpartially funded by a Canadian consortium called PRECARN that sponsors researchinto intelligent systems technology being developed for commercial applications.Each project had at least three organizational participants of three distinct types:a university or government laboratory, an industrial developer of the technologybeing created and a potential end user of the technology. While the study primarilyfocused on the project level, individual participants could be classified as eitherscientists or engineers (Allen, 1977).

The PRECARN consortium’s management provided a list of project managers,including the project leader, for each of 32 projects that had been sponsored upto the date of initial research. The entire research project included six in-depthcase studies, a cross-sectional survey of the 25 projects and observation by theresearcher of some project meetings and prototypes. Prior to survey development,I conducted 21 in-depth interviews with managers from each organization involvedin six different projects. Those interviews formed the basis for the questionnairedevelopment as well as preliminary theory development, extending Nonaka andTakeuchi’s (1995) model into the collaborative setting. All measures in the surveywere new and developed based on the terminology of Nonaka and Takeuchi’s(1995) inductive model in order to stay as close as possible to their theoreticalconstructs’ definitions. After initial testing on consortium level management andsimilar informants from outside of the sample, the self-administered surveyswere then sent to the project managers and managers from the other organizationsthat actively participated in the management of the 32 collaborative projectssponsored since 1990. Two of these projects ended in 1995 and were eliminatedfrom the eligible sample due to problems in contacting informants and potentialmemory biases. All other projects had been completed recently, with the oldestbeing 18 months since completion. Two other projects were eliminated becauseof severe managerial problems, leading to work stoppages and cancellation byconsortium management. After eliminating these four projects, the survey obtainedan 89% response rate by project. For eleven of the projects (44%), multipleinformants responded.

The inter-rater reliability (using Cronbachs alpha) for the projects with multipleinformants averaged a reasonable 0.76 (s.d. = 0.09; range = 0.62–0.90). Theaverage investment for the projects was $US 1,495,000 (s.d. = 1,360,000;range = 420,000–6,298,000). The average number of organizational participants

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was 4.7 (s.d. = 1.7; range = 3–9); however, usually a few individuals of onlysome participants were active in actually managing the project. Post survey analysisof non-response bias suggested that only some respondents had knowledge of theentire project-level management processes and therefore could complete the entiresurvey. I also surveyed PRECARN administrators to test against common measurebias. The results of that survey are discussed in relation to the findings from theself-administered survey.

Measures

The appendix summarizes the scales used in the survey. The technical knowledgecreation score is an index created from the survey data for each project based onthe potential extent of technological innovation and the achievement oftechnological objectives scales described below. It was determined by multiplyingthe potential extent of technological innovation (i.e., the gap in knowledge) bythe percentage of actual achievement of technological objectives (i.e., the amountof the gap that was filled). Due to the use of the 7-point scale, the equation iscalculated as:

Technical knowledge creation = Potential extent of technological innovation ×[(Achievement of technological objectives − 1)/6]

It is useful to view potential extent of technological innovation as an ex antemeasure that describes the potential knowledge creation necessary for successfultechnological innovation, whereas the knowledge creation measure represents aproxy for the actual knowledge that was created. All of the constructs are describedbelow.

The concept of the Potential Extent of Technological Innovation was used todevelop a measure of technical knowledge creation. In examining technicalknowledge creation within organizations, it is instructive to take into account thenewness of the development. The most commonly used classification oftechnological innovation has been the radical versus incremental dichotomy(Kamm, 1987; Tornatzky & Fleischer, 1990). I expand on this concept such thatthe newer the idea, the more radical the technology and the greater the scope ofdevelopment, the greater the overall potential extent of the technologicaldevelopment (See the Appendix for survey items). The importance of the conceptis that it characterizes the potential knowledge creation of the project as anamount of knowledge that must be created by the project team for successfultechnological invention. It is a proxy for the difference between what is knownat the beginning of the project and what must be known at the end for projectsuccess. It is therefore useful in operationalizing, and then estimating, the measure

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of technological knowledge creation because knowledge creation can beapproximated as the amount of this potential that has actually been realized.Furthermore, the generic nature of the construct makes it possible to compareacross entities from various industries and sectors. Potential extent of technologicalinnovation was measured using a seven-item scale consisting of items designedto probe for the newness of the technological innovation as well as the scope.The coefficient alpha (Cronbachs alpha) for the seven-item scale was 0.89. Alphasof between 0.65 and 0.90 are generally considered good.

Achievement of technological objectives was measured utilizing two itemsthat asked informants to rate on a 7-point scale whether the project had achievedits goals. The coefficient alpha for the two-item scale was 0.70.

Goal specificity was assessed using a six-item scale that tapped into thegeneral use of intention and, specifically, goal specification in the project’smanagement. The coefficient alpha for the six-item scale was 0.82. Individualautonomy and project autonomy were eventually measured using single items.Originally, a number of items assessed autonomy but subsequent analysis suggestedthat items could be classified according to whether there was a potential forautonomous behavior and whether autonomous behavior was actually practiced.The items concerning actual autonomous behavior were utilized here. Creativechaos measures were developed utilizing the concept as described by Nonakaand Takeuchi (1995) that equates intentional ambiguity and crisis with stimulatinginnovation. The coefficient alpha for the two-item scale was 0.89. Redundancyof knowledge sets consisted of a three-item scale with a coefficient alpha of 0.67.Environmental scanning was assessed using a five-item scale that tapped intomanagement’s activities in scanning the project’s external environment. Thecoefficient alpha for the five-item scale was 0.87. The items of the construct aretheoretically linked by the fact that each represents scanning an element of theenvironment. It appeared that when a project measured one element more actively,it tended to measure them all more actively. However, closer examination didreveal contingencies, for example, with medically related projects scoring higheron measuring the regulatory environment than others.

Finally, I performed Z-score transformations on the data to avoid the influenceof collinearity between independent variables in testing for the curvilinearrelationships (Aiken & West, 1991).

Because of the small “n” of the survey research, it is useful to share some ofthe qualitative data from the case studies and observations of the researcher inorder to help clarify the findings within the context observed. For this reason, Ihave included pertinent qualitative data that help in analyzing the survey resultsand provide some sense of construct and internal validity (Yin, 1989).

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Results: Survey of Project Participants andPRECARN Administrators

Table 1 provides descriptive statistics and Spearman rank correlation coefficientsfor the responses from each of the 25 projects. Goal specificity, individualautonomy and environmental scanning are all correlated with technical knowledgecreation. As goal specificity and environmental scanning were also correlatedwith each other, I checked for collinearity effects by doing a partial correlationanalysis of each variable with technical knowledge creation while controlling forthe other variable. The analysis showed goal specificity still tentatively correlated(r = 0.37; p < 0.08) and environmental scanning still correlated (r = 0.49;p < 0.02) with technical knowledge creation.

Table 2 provides descriptive statistics and Spearman rank correlationcoefficients for the responses from projects and a survey administered toPRECARN administration (managers responsible for overseeing the funding ofparticular projects). Because this survey could only capture data from 22 projects,new data based on only these 22 projects is given for enabling conditions. Meansand relationships are similar. What is interesting is the notably negative, though

Table 1. Means, standard deviations and correlations based on evaluations by project management.a

Variable Mean s.d. 1 2 3 4 5 6

ENABLING CONDITIONS

1. Goal Specificity 5.44 0.882. Individual Autonomy 3.43 1.81−0.38†

3. Project Autonomy 4.23 1.44 0.11 0.234. Creative Chaos 2.09 1.28−0.4* 0.14 −0.295. Redundancy in Knowledge Sets 3.41 1.16 0.02−0.07 −0.05 0.226. Environmental Scanning 4.48 1.43 0.42*−0.31 −0.02 −0.14 0.12

TECHNICAL OUTCOME

7. Technical Knowledge 4.24 1.50 0.46*−0.41* 0.16 −0.09 0.14 0.64***Creation Score

ELEMENTS OF TECHNICALKNOWLEDGE CREATION

8. Potential extent of 5.52 0.980.51** −0.37† 0.14 −0.10 0.35† 0.64***Tech. Innovation

9. Achievement of 5.50 1.16 0.26 −0.30 0.11 −0.00 −0.04 0.47*Tech. Objectives

aN = 25 Projects (Self reporting).†p < .10; *p < .05; **p < .01; ***p < .001.

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not statistically powerful, relationship now seen between goal specificity andtechnical knowledge creation as measured from PRECARN administration data.This is discussed later.

Regression analyses

Table 3 presents the hierarchical regression models used to test the hypotheses.The first model tested Hypothesis 1. There was a strong positive linear relationship(beta = 0.64, p < 0.001) that was highly significant (adjusted R2 = 0.39,p < 0.001). Thus, Hypothesis 1 was supported in terms of a linear relationshipbetween goal specificity and technical knowledge creation.

Hypothesis 2a was only partially and tentatively supported, as individualautonomy appeared to have a weak negative relationship with increasing knowledgecreation. Models 2 and 3 tested for the linear and curvilinear components of therelationship between individual autonomy and technical knowledge creation. Theresults suggest that there is a tentative negative linear relationship between

Table 2. Means, standard deviations and correlations based on evaluations by PRECARNadministrators.b

Variable Mean s.d. 1 2 3 4 5 6

ENABLING CONDITIONS

1. Goal Specificity 5.44 0.912. Individual Autonomy 3.30 1.72−0.353. Project Autonomy 4.21 1.43 0.22 0.154. Creative Chaos 2.15 1.31−0.40† 0.13 −0.39†

5. Redundancy in Knowledge Sets 3.46 1.20 0.03−0.03 −0.04 0.226. Environmental Scanning 4.51 1.43 0.35† −0.23 0.10 −0.12 0.09

TECHNICAL OUTCOME

7. Technical Knowledge Creation 3.97 1.29−0.42† 0.07 −0.38† 0.23 −0.03 0.43*Score (PRECARN)

ELEMENTS OF TECHNICALKNOWLEDGE CREATION

8. Potential extent of Tech. 5.77 0.75 0.20−0.16 0.10 −0.12 0.09 0.57**Innovation (PRECARN)

9. Achievement of Tech. 5.111.12 −0.56** 0.14 −0.33 0.24−0.15 0.26Objectives (PRECARN)

bN = 22 Projects (PRECARN administration reporting where indicated).†p < .10; *p < .05; **p < .01; **p < .001.

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Table 3. Results of regression analyses of project factors on knowledge creation based on evaluations by project management.a

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Goal Specificity 0.64*** 0.29 0.34†

Individual Autonomy −0.35† −0.41† −0.21Individual Autonomy squared 0.18 0.08Project Autonomy 0.25 0.30Creative Chaos −0.02 0.15Redundancy in Knowledge Sets 0.15 0.12 0.03Redundancy in Knowledge Sets squared 0.07 −0.03Environmental Scanning 0.70*** 0.46* 0.48*

R2 0.42 0.13 0.15 0.06 0.00 0.02 0.03 0.48 0.66 0.55Adjusted R2 0.39 0.09 0.08 0.02 −0.04 −0.02 −0.06 0.46 0.48 0.51F-Statistic 16.29 3.28 1.99 1.53 0.01 0.51 0.29 21.51 3.81 13.61Probability 0.00 0.08 0.16 0.23 0.91 0.48 0.75 0.00 0.01 0.00

aN = 25 Projects. All coefficients are standardized.†p < .10; *p < .05; **p < .01; ***p < .001.

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individual autonomy and knowledge creation (beta = −0.35, p < 0.10) but themodel is not strong (adjusted R2 = 0.09, p < 0.08). A curvilinear effect could notbe determined. Hypothesis 2b, positively relating project autonomy to knowledgecreation, was not supported by Model 4.

Hypothesis 3, which predicted a negative relationship between creative chaosand technical knowledge creation, was not supported by Model 5. Models 6and 7 did not support Hypothesis 4, which predicted a curvilinear relationshipbetween redundancy in knowledge sets and technical knowledge creation.Hypothesis 5 was strongly supported by Model 8 (adjusted R2 = 0.46, p < 0.001)with a strong positive linear relationship found between environmental scanningand technical knowledge creation (beta = 0.70, p < 0.001).

Models 9 and 10 are multiple regression models. The results suggest thatenvironmental scanning is the most powerful antecedent of technical knowledgecreation. For example, when a stepwise regression technique was utilized to seeif the model was correctly specified all factors dropped out of the model exceptenvironmental scanning and the significant relationship found in Model 8 resulted.

A Case Study: Intelligent Lumber Grading System (ILGS) Project

The following case study is presented to help the reader understand the type oftechnological innovation and processes involved in the projects studied. I choseto describe the particular project here because it is a relatively straightforwardexample of the development of an intelligent systems technology. (Please seeJohnson (2000) for in-depth descriptions of all six cases, plus one pre-case, thesurvey analysis and the study as a whole.)

The ILGS project involved close cooperation of CAE Newnes Ltd. and variouslumber-oriented engineering departments of the University of British Columbia(UBC) in Canada. A third partner, Landmark Truss & Lumber, acted as the betasite tester. The major technical objective of the study was to develop an intelligent,high-performance system for the automatic grading of lumber products. Themajor advantage of the new technology was improved grading accuracy comingfrom an increase in the accuracy of strength and stiffness predictions of lumbersamples. Essentially, the project consisted of taking some existing scanningtechnologies with newer scanning technologies and integrating them into a gradingsystem augmented by newer and more powerful artificial intelligence predictionand correlation engines. The base technologies were:

i. XLG (x-ray) scanner — predicts strength of lumber based on wood density;ii. Microwave Grading — measures wood grain angle, another factor in

determining wood strength;

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iii. Laser Profile Sub-system — measures the geometric properties of the wood;iv. Functional Approximation Technology — the AI predictor engine used in

assigning the statistical correlation of wood properties to wood strength; and,v. Geometrical Knot Modeling — a model of the effects of knots in the board

on wood strength.

The concept of utilizing non-destructive testing of lumber products andcorrelation methodologies to predict wood properties, primarily strength, hadbeen around for at least 30 years, with the x-ray component of testing systemsdeveloped about ten years ago. The problem of increasing the accuracy of thesesystems was the greatest impetus to the project. A major technical achievementwas to integrate the scanning technologies so as to increase the accuracy of theprediction. This required a large volume of data so that the major obstacle beyondaccuracy was the speed with which the predictor algorithms developed couldanalyze the disparate inputs of data and accurately predict the grading for a pieceof wood. Eventually, the commercial advantage would be more high-grade woodthat could be sold at higher prices.

Like most PRECARN projects ILGS objectives were specified at the beginningof the project. As evidence of the importance of intention, two of the informantsinterviewed referred to the concept of intention in managing the project beforebeing prompted by the researcher. For example the project leader stated, “We hada good idea of what we wanted to do and how we wanted to do it. I would havesaid a lot of the answers weren’t completely there. But we had a clear idea ofwhat we wanted to do. The objectives were very clear”.

Autonomy was also alluded to prior to prompting by the researcher. All of theinformants felt that autonomy might be important but that it should be temperedby control and focus. The project leader stated, when asked of autonomy’simportance to knowledge creation, “I think that definitely makes sense becausewe expect UBC to work the problems they’re responsible for and if they havesome difficulties then we’ll discuss it. But they’re on the hook to find the solution”.

As with many of the cases studied, managers felt that autonomy had to be tiedto responsibility and the ability to accomplish the tasks assigned. Generally, themore experience and skills participants demonstrated, the more autonomy theyhad. But this autonomy was always tied back to the original objectives and tasksnecessary to accomplish the goals of the project.

From a project autonomy perspective, the project was able to survive a buy-out of the original company, Newnes by CAE Inc. in 1998, during the course ofthe project, suggesting that it was able to maintain its own autonomy. However,this was only after demonstrating to the new owners the potential of thetechnological development.

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Also, as in most of the cases, tension existed such that project managementnever felt it wise to intentionally create chaos. However, one informant believedthat chaos or tension could help drive the management of the project but that itmight depend on the context. Interestingly, the informant referred to the intra-organizational situation when mentioning its importance:

“Some people think that, based on the individual, some people are betterunder pressure. I would say I work better under pressure, if you want togive me a deadline — give me a problem. Is it intentional? I think it’shealthy for a business and for a manager to intentionally put their employeesunder pressure to get something done. I think it’s instructive.”

All of the participants in the project had experience in lumber-relatedtechnologies and the lumber industry so that redundancy in basic knowledge, ora common language, did exist in the project. However, in terms of tasks andspecific technologies, the project was less redundant. In fact, when asked aboutredundancy within the project, one informant stated, “There’s certainly no overlaphere. Not here”.

However this lack of redundancy may only be due to the project managementcontext under examination and is most evident in relation to tasks rather thanknowledge sets. For example, the project leader referred to the redundantknowledge set of some of the participants but clearly stated that redundancy intasks within the project was avoided:

“There is a lot of overlap between some of the work that UBC has doneand work that we’ve done; parallel paths, doing very similar things. We’remore on the production side and they’re more on the research side. I wouldsay that there has been a lot of that. But in the project we separate that outbecause we don’t want to be doing the same things. We get more efficiencywhen we break things up and have separate objectives for separate parties.”

Project management paid a great deal of attention to the environment beyondthe mere technical factors under development. As the academic lead researcherstated, “We’ve had to look at a lot of factors such as economic issues,manufacturing issues, and differences between educational organizations andmanufacturing organizations”. As with PRECARN projects in general, multipleorganizational participants provided a certain requisite variety to the project.

The ILGS case demonstrates qualitatively that, of all of the enabling conditions,intention and environmental scanning appeared to be utilized most strongly. Otherconditions, like creative chaos, were seen as good concepts but not used in theproject. It was also examined that the learning that took place within the project

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surpassed merely meeting project objectives. Although it is less tangible andharder to identify, the importance of spin-off knowledge from technologicalinnovation (long acknowledged but less studied in the technology and innovationliterature) was evident in the ILGS project. A major problem associated withproject knowledge creation as opposed to organizational situations in general,however, is the fact that all projects terminate. For the knowledge creation spiralto continue, new knowledge must be transplanted from the project to otherorganizational outlets. This is captured in a quote of one informant regarding thesuccess of the project:

“To date, it’s been successful. I mean, we thought initially this would be:‘Here’s three technologies put them to work in your product and thanksvery much, we’re out of here.’ I never knew things would come out whichare quite exciting and one of the issues now is how can we keep everybodyhappy, about these new issues, and willing and able to work on them in thefuture beyond this PRECARN project to get all the benefits of the projectin the long run.”

Discussion

The results of this empirical exploration suggest that goal specificity andenvironmental scanning are significant factors in managing successful collaborativeR&D projects. This lends some support to the extension of Nonaka and Takeuchi’s(1995) model of organizational knowledge creation to the inter-organizationalrealm. This is important given the dearth of empirical verification of the theory.While contributing to our understanding of the phenomenon, most studiesconnected to the theory have been based on small case-based research and havenot attempted to operationalize the theory’s constructs (Inkpen & Dinur, 1998;Kidd, 1998; Rumizen, M. C. 1998). It is thus important for future research toverify the theory and its constructs in various contexts of knowledge creationefforts.

In the cross sectional survey analysis presented here, for example, the otherfactors of the organizational knowledge creation model were not significant factorsin managing successful knowledge creation in collaborative R&D projects of thistype. This might be the case for a number of reasons. First, the study specificallyexamined an inter-organizational setting as opposed to the intra-organizational,which Nonaka and Takeuchi (1995) used primarily in developing their theoreticalframework. This suggests that the utilization of knowledge creating managerialfactors might be contingent on the scenario being studied. As discussed below,the theoretical basis for contingencies due to organizational setting (i.e., between

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intra- and inter- organizational settings) is compelling. A second reason for lackof significance in some factors may stem from the fact that the study lookedspecifically at collaborative R&D projects that, by their very design, specified apriori goals in the initial project proposal. It is possible that important knowledgewas created that was not specified and thus dropped out of the “empirical net”.This bias is seen in the high scores and little variance about the mean for goalspecificity. However, the significant relationship found between goal specificityand knowledge creation is even more compelling given this fact. Furthermore,the finding is in line with previous research that also found a positive relationshipbetween goal specificity and performance (Leary-Kelly, Martocchio & Frink,1994; Mento, Steel & Karren, 1987; Motowidlo, Loehr & Dunnette, 1978; Tubbs,1986).

The significant negative correlations between goal specificity and bothindividual autonomy and creative chaos depicted in Table 1 provide some evidencethat there is a trade-off between these various factors in managing knowledgecreation in collaborative projects. Logically, as goal specification rises, individualautonomy is lessened. The trade-off may explain the partial findings relating toindividual autonomy; however, it is preliminary to speculate on this relationship.While the idea of an interaction effect is compelling, stronger data is needed thatwould allow for the analysis of interactions that specifically focus on the trade-offs between these factors.

The specific operationalization of independent autonomy might also influencethis relationship. The item used in this study might be interpreted by respondentsas indicating a lack of commitment on the part of project members. However, theitem was used because it specifically recognized the actual use of autonomousaction as opposed to the potential for autonomous action. As one informantstated in the preliminary qualitative research, “By autonomy, I mean that eachgroup’s project inside the team can proceed in parallel and doesn’t have to waitfor the other guy.” The same informant later tied autonomy to communicationwithin the project, “It was more a question of communication. You could say thatif you don’t have communication then you have autonomy. If you have severalgroups that are supposed to be part of a larger group moving off in the wrongdirection — [that’s autonomy]”. Other qualitative data from the larger study inwhich the survey was conducted indicated that autonomy was sometimes usefulin managing a project but that its actual use depends on the previous experienceof the project member. For example, one manager stated, “It depends on theteam. If you have senior people they have to be able to do their own thing. If youhave junior people, they need and appreciate more supervision. It really dependson the makeup of the team”. See also the ILGS project case study earlier. Giventheir complex nature, autonomy and the flip side of integrating the collaborative

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work through goal specification need greater focus in further studies of innovation.I speak to this later in the discussion.

The lack of support for the negative relationship of creative chaos andknowledge creation probably stems from a low usage of this managerialintervention, at least as it was operationalized in the study. The very low average(2.09 on a 7-point scale) depicted in Table 1 demonstrates that the practice wasnot generally used by most projects’ management. In fact, the initial interviewsprior to survey development showed some anecdotal evidence that the concept,if used at all, was detrimental to a project’s successful management. As oneproject leader interviewed had stated, “I think we had enough embedded tensionin the project that I don’t need to create anything on top of that!” Another projectleader echoed similar sentiments, “Well, crisis happens. You start off usuallywith an optimistic schedule and it never quite works out as well as you want itto. You’re always under the gun to get finished a lot of the things. I don’t thinkI’d purposely create more chaos because there’s enough of it already!” As such,the lack of a significant relationship is in line with the general theoretical stance,taken in the initial model development section of the paper, that creative chaosis detrimental (or insignificant) in inter-organizational knowledge creation.

The lack of support for a relationship between knowledge creation andredundancy in knowledge sets may be a result of the sample studied. Manycollaborative R&D projects are designed to eliminate unnecessary duplication.Previous interviews also confirmed the propensity of management to eschewredundancy of specialized knowledge. In fact, a major purpose of collaborativeR&D is the integration of complementary competencies. Very basic knowledgesets, such as an understanding of computer programming languages, were oftenpervasive in the sample studied. Like language, in general, these allow participantsto communicate effectively with one another. However, in the projects examinedmore specific skills and knowledge were generally economized on and were notduplicated across the project. Only three of the projects had redundancy scoresgreater than five on the seven-point scale and even with these three projects theredundancy score average was only 3.41, suggesting a strong ceiling effect.

The strong support for environmental scanning confirms that knowing one’senvironment has beneficial effects on the ability to create new knowledge. Theconcept may also have a strong relationship with the absorptive capacity of theproject, as the mere scanning of information without the ability to absorb is notuseful. The increased use of scanning mechanisms and the ability to benefit fromthem is indicative of an increased absorptive capacity (Cohen & Levinthal, 1990;Fiol, 1996).

Finally, observing Tables 1 and 2, it is interesting to compare the self-reportingdata with evaluation from outside the project by PRECARN administration. Goal

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specificity and environmental scanning provide the most interesting comparison.When outside evaluation is used, goal specificity actually has a negative effecton technical knowledge creation! Closer look at the components of this measurereveal that the primary effect is from the achievement of technical objectives asopposed to the potential extent of technological innovation seen in the self-reporting data. When looking at things internally, more complex and radicalprojects are associated with greater goal specification. When looking at thingsthrough the eye of external evaluators, projects with more specific goals are lesslikely to achieve them! Goal specificity is a double-edged sword. It allows theexplication of specific objectives for internal development but at the same timeallows others to hold the project to its original objective. The more specific thoseinitial objectives, the more likely their non-achievement will be detected byexternal evaluators. Environmental scanning is significant for both self-reportingand external evaluation but the highly significant support for the potential extentof technological innovation scores for both suggests that the concept is mostassociated with project complexity and radicalness. This emphasizes the need forscanning in new, complex projects.

Study limitations

Some limitations of the study should be noted. The first concerns thegeneralizability of the sample. Like most social science research, the sample maynot be generalized to all inter-organizational settings. Further research needs toexamine other inter-organizational as well as intra-organizational settings todetermine an appropriate knowledge creation model that is contingent on context.Nonaka and Takeuchi (1995) proposed their theory to be relevant to the inter-organizational setting (p. 197) and this study has provided an empirical examinationof an inter-organizational setting of specific collaborative R&D projects. Assuch, the research provides for theoretical generalization, in which “previouslydeveloped theory is used as a template with which to compare the empiricalresults” (Yin, 1989, p. 38).

Because Nonaka and Takeuchi’s (1995) theory is process-oriented, especiallywith regard to the knowledge conversion processes, a longitudinal research designwould be ideal. However, the ability to do longitudinal research on Nonaka andTakeuchi’s (1995) knowledge conversion processes and tracking tacit knowledgechanges is limited because by their very definition tacit knowledge sets areimmeasurable. Thus, the processes may be observable only after the fact, whichmakes the cross sectional design of the survey research somewhat necessary. Infact, this problem is inherent to all research on innovation because successfulinnovation can only be recognized ex post after it has materialized and been

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evaluated by performance criteria. Before its introduction any innovation is oneof potential only. The same is true of identifying knowledge that is not tied toperformance measures. Like tacit knowledge, it may exist, but is limited in beingstudied contemporaneously because it cannot, by definition, be measured in itsnatural state. The limitation of studying process phenomena using cross sectionaland delayed data should be seen as one of necessity, at least presently, due to thenatural limitations of empirical inference in the field. Similarly, an experimentresearch design would be ideal but in reality is prohibitively expensive. Thus, atthis point the data can only be interpreted as correlational, not causal, thoughtheory and logic suggest probable causality.

A final limitation is the size of the survey sample. However, given the lownumber of data points the strong findings confirming goal specificity andenvironmental scanning are even more compelling evidence of a robust relationshipbetween these factors and technical knowledge creation. Common method biasis also a potential limitation. This was mitigated to some extent by comparingself-reporting with external evaluations of projects and by the use of multiplerespondents when possible, case studies produced before the survey research andobservations of project prototypes and other outcomes by the researcher. Ultimately,similar methodologies in other settings will add to our understanding of anystrong contingencies.

Effect of the research on application and theory development

As a practical matter, the results demonstrate the importance of specifying goalsand of utilizing environmental scanning mechanisms in collaborative projects —two concepts that have been fundamental in the general strategic managementliterature. Thus, concepts such as planning and strategic intent have great relevancenot only to strategy making and implementation but also to technical knowledgecreation in general.

Theoretically, the results suggest that the factors of technical knowledge creationare different for collaborative R&D projects as opposed to R&D projects in asingle organization. However, more theoretical development and empirical testingis necessary to determine the antecedent factors of knowledge creation and theircontingencies in a multitude of contexts. The significant findings regarding goalspecification agree with those of Cukor (1992) and Judge, Fryxell, and Dooley(1997) and are in line with much of the literature on goal specification and groupperformance. The significant findings regarding environmental scanning also agreewith much of the literature on strategic management. While previous studieshave shown support for the importance of environmental scanning to strategicperformance (Sim & Teoh, 1997), the findings here suggest that they are also

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important to successful technical knowledge creation in some collaborative R&Dprojects. Implications for technological innovation are that these factors inparticular are potential antecedents of performance.

Conclusion

The results reported here show that there is a need for greater awareness andunderstanding of the contexts in which various managerial factors that enable thecreation of new technical knowledge are most effective. In extending the workof Nonaka and Takeuchi (1995), which focused primarily at the intra-organizationallevel, this research showed that only some of these factors were significant at theinter-organizational level of collaborative R&D projects. The theoretical andempirical extensions of Nonaka and Takeuchi (1995) provided in this paperdemonstrate that while the underlying fundamental processes of knowledge creationmay be universal their enabling factors are contingent on context (in this case,between the single organization R&D projects in Nonaka and Takeuchi’s (1995)research and the collaborative R&D projects in this paper). This suggests thatmore development is needed in explicating a complete, contingency theory oforganizational knowledge creation, especially with regard to technologicalinnovation. New studies will be beneficial given the importance of knowledgecreation in terms of strategy, technological innovation and organizational renewalto many organizations. Furthermore, the application of such research will be ofgreat benefit to the management of all types of innovative endeavors.

Acknowledgements

I’d like to thank David Johnston, Jim Lyttle, John Medcof, Ann Miner andChristine Oliver for their help in the research and thoughts leading up to thispaper. I’d also like to thank an anonymous reviewer for suggestions that ultimatelyimproved the paper.

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Appendix

Constructs and items used in the survey analysis (Survey utilized 7-pt Likert scale ranging fromCompletely Disagree to Completely Agree):

Dependent Variable: Potential extent of Technological Innovation

• To the best of our knowledge, the concept behind the project’s innovation is new.• The technology provides for business opportunities not previously possible.• To the best of our knowledge, no other group has such technology.• The technology provides a brand-new functional capability unavailable previously.• The technological solution represents a discontinuity in current technological capabilities.• The technology is very different from that which existed before in the field.• The technology is best described as a system versus a component technology.

Dependent Variable: Achievement of Technological Objectives

• Overall, the project was successful in meeting most of its original goals.• All of the original technological objectives were met.

Independent Variable: Goal Specificity

• There were specific, well-defined goals established at the beginning of the project.• A major managerial undertaking was the establishment of project goals.• The initial goals of the project helped to define the majority of the tasks that needed to be

accomplished in the project.• One definite intention of the project was the creation of new knowledge.• The project’s initial goals helped drive the innovation in the project.• Without making the initial intent of the project explicit, innovation would not be possible.

Independent Variable: Individual Autonomy

• Within the project, individuals often went beyond the boundaries of their assigned tasks to workon whatever problem task they desired.

Independent Variable: Project Autonomy

• The project’s management was autonomous, in that the management of the organizations makingup the project team didn’t govern its policies and procedures.

Independent Variable: Creative Chaos

• Management intentionally created at least one artificial crisis within the project to stimulateinnovation.

• To stimulate innovation, management intentionally introduced ambiguity regarding tasks and theirallocation within the project.

Independent Variable: Redundancy in Knowledge Sets

• If the project lost one of its participant members the original goals could still be achieved withknowledge held by the other participants.

• The participants had too much information to deal with from the project.

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• There was a fair amount of intentionally built-in overlap in information between projectparticipants.

Independent Variable: Environmental Scanning

• Project management actively measured the market for technology.• Project management actively measured the industry aspects of the external environment (e.g.,

potential competitors).• Project management actively measured aspects of government regulation.• Project management actively measured political aspects of the external environment (e.g., ethical

implications of technology).• Project management actively measured social aspects of the external environment (e.g.,

environmental implications of technology).

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Documents

ASSESSING ORGANIZATIONAL KNOWLEDGE CREATION THEORY IN COLLABORATIVE R&D PROJECTS