towards a kernel theory of external knowledge integration for high-tech firms: exploring a failed...

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Towards a kernel theory of external knowledge integration for high-tech firms: Exploring a failed theory test Jeroen Kraaijenbrink , Fons Wijnhoven 1 , Aard Groen 2 University of Twente, School of Management and Governance, P.O. Box 217, 7500AE Enschede, The Netherlands Received 1 August 2005; received in revised form 25 November 2006; accepted 22 December 2006 Abstract Designing information systems (ISs) requires a thorough understanding of the organizational knowledge processes in which these systems are used. Although much is known about internal organizational knowledge processes, the understanding of external knowledge processes is less developed. Hence, this paper reflects an attempt to operationalize and test a model of the process of external knowledge integration (EKI), consisting of an identification, acquisition, and utilization stage. We utilize high-technology based firms from a variety of high-tech categories including nanotechnology based firms since these firms have critical knowledge integration needs. The results of an international survey, with responses of 317 high-tech companies, suggest that not these three EKI- stages, but four organizational effectiveness functions (goal attainment, pattern maintenance, adaptation, and integration) account for most variation in responses. These findings seem to imply that ISs that are to support the EKI-process should be designed according to organizational effectiveness functions rather than to EKI-stages. It is proposed that each organizational effectiveness function imposes different requirements on ISs because users interact differently with IS in each function. © 2007 Elsevier Inc. All rights reserved. Keywords: Knowledge integration; Organizational effectiveness functions; Factor analysis; High-tech SMEs Technological Forecasting & Social Change 74 (2007) 1215 1233 Corresponding author. Tel.: +31 53 489 5443; fax: +31 53 489 2159. E-mail address: [email protected] (J. Kraaijenbrink). 1 Tel.: +31 53 489 3500; fax: +31 53 489 2159. 2 Tel.: +31 53 489 4512; fax: +31 53 489 2159. 0040-1625/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2006.12.003

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Technological Forecasting & Social Change 74 (2007) 1215–1233

Towards a kernel theory of external knowledge integration forhigh-tech firms: Exploring a failed theory test

Jeroen Kraaijenbrink ⁎, Fons Wijnhoven 1, Aard Groen 2

University of Twente, School of Management and Governance,P.O. Box 217, 7500AE Enschede, The Netherlands

Received 1 August 2005; received in revised form 25 November 2006; accepted 22 December 2006

Abstract

Designing information systems (ISs) requires a thorough understanding of the organizational knowledgeprocesses in which these systems are used. Although much is known about internal organizational knowledgeprocesses, the understanding of external knowledge processes is less developed. Hence, this paper reflects anattempt to operationalize and test a model of the process of external knowledge integration (EKI), consisting of anidentification, acquisition, and utilization stage. We utilize high-technology based firms from a variety of high-techcategories including nanotechnology based firms since these firms have critical knowledge integration needs. Theresults of an international survey, with responses of 317 high-tech companies, suggest that not these three EKI-stages, but four organizational effectiveness functions (goal attainment, pattern maintenance, adaptation, andintegration) account for most variation in responses. These findings seem to imply that ISs that are to support theEKI-process should be designed according to organizational effectiveness functions rather than to EKI-stages. It isproposed that each organizational effectiveness function imposes different requirements on ISs because usersinteract differently with IS in each function.© 2007 Elsevier Inc. All rights reserved.

Keywords: Knowledge integration; Organizational effectiveness functions; Factor analysis; High-tech SMEs

⁎ Corresponding author. Tel.: +31 53 489 5443; fax: +31 53 489 2159.E-mail address: [email protected] (J. Kraaijenbrink).

1 Tel.: +31 53 489 3500; fax: +31 53 489 2159.2 Tel.: +31 53 489 4512; fax: +31 53 489 2159.

0040-1625/$ - see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.techfore.2006.12.003

1216 J. Kraaijenbrink et al. / Technological Forecasting & Social Change 74 (2007) 1215–1233

1. Introduction

Designers of information systems (ISs) benefit from a thorough understanding of the processes inwhich these systems are used because these processes strongly affect the user's requirements [1]. With anadequate model of a process, designers can assess which IS design is most likely to be successful for thatparticular process. Conversely, without such a process model, designers run the risk of designing isolatedand underused ISs. Thus, since it enables an assessment of IS designs, an adequate process model servesas a kernel theory for the design of IS [2,3]. For many organizations, a crucial process is that of externalknowledge integration (EKI), which is defined here as the identification, acquisition, and utilization ofexternal knowledge. Since their general lack of internal resources, in particular small and medium sizedenterprises (SMEs) depend heavily on this process. This paper pays attention not only to the issues centralto a small firm but particularly those information issues that are endemic to high-technology based smallfirms. Schumpeter [4] argued that the interface between technology and strategy is the driving forcebehind capitalism, that new production methods were centric to nations and firms search for competitiveadvantage, and that entrepreneurial firms were central to this effort. Hence, high-tech firms play a crucialrole in the developments towards current society. High-tech efforts have been separated by the type oftransformation process [5], by the ability to change the strategic value statement in an industry ordisruptive or sustaining technology [6–8], and by the degree in which a single technology platform can“enable” a variety of products in a variety of industrial setting [4,9]. High-tech firms have the mostextreme need for useful external information since the basis of their company is based on a technologythat must constantly be refreshed. Moreover, their degree of novelty versus competitors is a cornerstone oftheir competitive advantage and their struggle to find a market that can readily adapt their valuestatements is not only critical for their success but also for their survivability.

Whereas the current literature pays much attention to information and knowledge processes that areinternal to organizations, there remains an expressed need to better understand the organizational processesthrough which external knowledge is integrated [10,11]. This paper reflects an attempt to address thislacuna in the literature by empirically analyzing a three-stage model of the EKI process. The modeldistinguishes stages of identification, acquisition, and utilization that each consist of several subprocesses.Our objective is to answer the question as to whether the proposed model of the EKI process is empiricallyvalid in high-tech SMEs, and if so, what are the consequences for the design of ISs that are to support thisprocess. Our main assumption is that subprocesses that go together in practice should be supported by thesame system or by a coherent set of systems because if they support only part of a process, this results inless than optimal performance [12]. Thus, we propose that the EKI process is subdivided in stages ofknowledge identification, acquisition, and utilization, and that each stage requires coherent IS support.

To test this proposition, we conducted an empirical study within a context in which external knowledge ishighly relevant: new product development (NPD) in high-tech SMEs. Particularly here we expect finding thestage model confirmed because SMEs can do only few things at a time because of their limited span ofattention and resources. Consequently, they are likely to concentrate either on identification, acquisition, orutilization of external knowledge. It seems likely, for example, that start-up firms concentrate onidentification because they have to search for partners. Established firms, however, supposedly have anetwork of partners and concentrate on the utilization of knowledge gained from this network.

The paper reports the findings of a survey on the proposed EKImodel among high-tech SMEs. Contrary toour expectations, a confirmatory factor analysis of a data set consisting of 317 observations suggests arejection of the proposed model. Further exploratory analysis seems to corroborate the role of the four

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organizational effectiveness functions as they were outlined by Stein and Zwass [13] and Quinn andRohrbaugh [14]. The paper is organized as follows: TheNext section discusses the research framework, whichis followed by an explanation of the research method. Thereafter we present the results of our study anddiscuss implications for IS research and practice. The paper ends with a conclusion.

2. Analyzing knowledge integration in SMEs

Fig. 1 clarifies our perspective on EKI in high-tech manufacturing SMEs as the process ofidentification, acquisition, and utilization of knowledge from external sources for the NPD-process withinan SME, potentially supported by and interacting with IS.

The focus of the current paper is on the middle part of Fig. 1, that is the EKI process. To measure thisprocess, we have identified subprocesses within each stage and have measured how frequently NPDmanagers in SMEs executed them. The remaining part of Fig. 1 has extensively been studied in severaldisciplines. It has repeatedly been shown that SMEs use mainly knowledge of their customers andsuppliers [15], prefer personal above impersonal sources [16], prefer informal above formal sources [17],and prefer internal above external sources [18]. Moreover, it has been shown that a range of knowledge isneeded during NPD, including market, technological, and organizational knowledge [19] of which mostknowledge is primarily tacit [20]. It has also been shown that the need for knowledge is different invarious NPD stages [21,22].

2.1. Stages in the EKI process

As indicated, EKI is defined as a process with three stages. The internal processes currently associated withknowledge management (KM) we call knowledge utilization. Since external knowledge needs to be acquiredbefore it can be utilized, an essential preceding stage is knowledge acquisition. Similarly, before acquiringexternal knowledge it must first be identified. Acquisition is therefore preceded by a knowledge identificationstage. Based on existing research, a number of subprocesses are identifiedwithin each stage and outlined below.

The identification stage consists of subprocesses involved in locating relevant knowledge outside theorganization. Following literature on information seeking and environmental scanning, this stage is in acontinuous interplay between knowledge seeker and source [23,24], and eventually leading to a‘compromised knowledge need’ between source and seeker [25]. Aguilar [26], Daft and Weick [27], andChoo [23] identify the level of intrusiveness of the seeker as a distinguishing aspect of information seekingbehavior. In his distinction between solicited and unsolicited information, Aguilar also deemed this aspectdistinguishing for the information source. When the levels of intrusiveness of both source and seeker areseen as dichotomies, four identification subprocesses can be distinguished. The first subprocess – highintrusive seeker, low intrusive source– is intentional search. Aguilar refers to this as respectively formal or

Fig. 1. External knowledge integration in its context.

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informal search. In this mode, the seeker actively seeks for knowledge outside the company, for exampleon the Internet, fairs, or in his personal network. The second subprocesses (low-high) is unsolicitedpresentation of knowledge by the source [26]. An example is the dissemination of information on newtechnologies by a source to potential partners. The third subprocess (low–low) is accidental discovery andoccurs, for example, when the seeker browses the Internet without having a particular need for information.This subprocess is similar to what Aguilar has called undirected and conditioned viewing. The fourththeoretically possible subprocess (high–high) is believed to be not relevant within this study, becausedependent on who is most intrusive, it will be similar to intentional search or unasked presentation from theperspective of the seeker. For example, when the seeker is most intrusive (i.e. she finds the source), weexpect that she will not be able to correctly establish whether the source has been intrusive or not.Therefore, this mode is left out for further consideration within this study.

In the acquisition stage, knowledge is transferred from a source to an organization. This transfer cantake several forms, ranging from a document transfer (for explicit knowledge, cf. Nonaka, [28]) tointeractive cooperation (for tacit knowledge) [29]. We base a more fine-grained distinction of acquisitionsubprocesses on several possible carriers of knowledge. Firstly, knowledge that is codifyable can berepresented in written form and transferred in documents or files. Secondly, physical objects can betransferred from the source to the recipient. An example in NPD is reverse engineering of a competitor'sproduct [30]. Thirdly, the people that carry knowledge can be transferred by hiring or employing them.This is common practice in Japanese companies [31]. Fourthly, people can also transfer their knowledgewithout necessarily being employed, for example in the form of courses [32]. Fifthly, when knowledge isembedded in work processes, transfer of knowledge is possible by cooperation between the source andthe recipient, for example by cooperative development. Finally, when knowledge is embedded in thesource organization's structure or culture (cf. [33]), it can be acquired by outsourcing a problem to thesource and staying in contact. Another option is acquiring the source organization. However, since weexpect this to be rare for SMEs because of their small scale, it is left out for further consideration.

The utilization stage consists of subprocesses in which obtained knowledge is made accessible, isapplied, and is integrated in the organization. Each of these three subprocesses can take place as a one-time-only static process, or as an ongoing dynamic process, which suggests six subprocesses within thisstage. Providing access on a one-time-only basis, is done by storing knowledge somewhere in theorganization, for example in archives or individual people. The corresponding dynamic subprocess is thatof diffusion. Using the image of a jigsaw puzzle, Galunic and Rodan [34] distinguish two forms ofdiffusion: distribution and dispersion. “A picture on a jigsaw puzzle is distributed when each personreceives a photocopy of the picture. The same image would only be dispersed when each of the pieces isgiven to a different person” (1998: 1198). One-time application of knowledge is the process of putting theobtained knowledge to use in the situation it was needed for. Ongoing application can be referred to asknowledge reuse [35] or exploitation [36]. The integration of knowledge on a one-time-only basis is whatGrant [10] has called direction: codifying tacit knowledge into explicit rules and instructions so that it canbe communicated at low cost throughout the organization ([10]: 379). The second form integration thatGrant gives, is routinization. An organizational routine is “(…) a set of activities (…) routinized to theextent that choice has been simplified by the development of a fixed response to defined stimuli” [37]. Thethree stages and their subprocesses are depicted in Fig. 2.

To identify differences in the EKI process between various types of knowledge that SMEs use, we useda commonly made distinction between technological knowledge (e.g. about materials or productionprocesses), customer/market knowledge (e.g. about demanded quality or functionality), and

Fig. 2. Subprocesses of external knowledge integration.

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organizational knowledge (e.g. about planning or logistics). We assumed and tested that our respondentscould identify the EKI process for each of these types of knowledge.

3. Research method

In researching the framework described above, we followed a two-stage approach consisting of in-depth semi-structured interviews and a large-scale self-administered questionnaire. Both the interviewsand the questionnaire were conducted in Germany, Israel, Netherlands, and Spain as part of a largerEuropean study on EKI. The authors were responsible for overall development, coordination, and analysisas well as for the data collection of the Dutch part of the study.

3.1. Interviews

Based on the frameworks of Figs. 1 and 2, a semi-structured interview scheme was developed in anexpert panel of academics and practitioners. In the four countries, a total of 33 interviews were done withNPD managers. Interviews lasted between one and two-and-half hours. Sampling was based onconvenience, but respondents covered companies of different countries, industries, and sizes.

3.2. Sample

A major challenge was the selection of high-quality address databases for the questionnaire. Since weare not aware of any database that covers the four countries, we had to select four different databases thatallowed selection on similar criteria. Because of their high-quality reputation and similarity, the followingdatabases were selected: Hoppenstedt (Germany), D and A HiTech Information Ltd. (Israel), NationalChamber of Commerce (Netherlands), and AXESOR (Spain). From these databases, we selected astratified random sample of 1306 high-tech manufacturing SMEs. The sample was stratified over country(Germany, Israel, Netherlands, and Spain), size (2–9, 10–49, 50–99, and 100–499 employees), andindustry (industries 24 and 29–35 from the International Standard Industrial Classification). Thesecompanies were contacted by phone, were asked to identify a key informant, received a questionnaire, andwere reminded twice if they did not respond. Although the validity of single-informants research has beendebated, we agree with Kumar, Stern, and Anderson [38] who state that informants are not selected to berepresentative of the members of a studied organization, but because they are supposedly knowledgeableand willing to communicate about the issue being researched. Because smaller companies are less likely

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to have such informants [39], we let companies decide themselves who was the most appropriate personto respond. Hereafter we label these respondents ‘NPD managers’.

A total of 317 NPDmanagers responded, leading to a response rate of 24.3%, which is considerably highfor a randomised sample of SMEs [40,41]. The response rates within each country were: Germany 21.7%,Israel 20.9%, Netherlands 38.2%, and Spain 17.4%. Since we followed the same procedures in each country,the high response rate in theNetherlandswas surprising. One possible explanation is thatDutch governmentspay relatively much attention in their policies to the acquisition and use of external knowledge by SMEs.During the interviews, the interest of NPDmanagers in our study also seemed higher in the Netherlands thanin the other countries. The profile of the responding companies and individuals is given in Table 1. Acomparison (T-test and Mann-Whitney test) of respondents with non-respondents showed no significantdifferences on industry. However, regarding company size, companies with 10–49 employees wererelatively underrepresented in the response set, while companies with over 100 employees were relativelyoverrepresented. Moreover, younger companies were relatively underrepresented, while older companieswere overrepresented. A comparison of early and late respondents on all variables in this study showedhowever no significant differences ( pb0.05). Thus, substantial non-response bias seems unlikely.

3.3. Questionnaire

For operationalization it is important to regard validity, reliability, and practicality, of which the last isconcerned “(…) with a wide range of factors of economy, convenience, and interpretability” [42]. Inparticular in SMEs, practicality is important, because managers are usually overloaded with their dailysurvival and have little time to fill out questionnaires (cf. [43]). Illustrative is a remark of one participantof our study: he at times receives up to ten questionnaires a week, of which some are obligatory. For thedevelopment of the questionnaire we preferred using existing scales because of their proven validity and

Table 1Profile of respondents and their companies

Industry % Year of foundation %

24 Chemicals and chemical products 10.7 Before 1965 13.129 Machinery and equipment n.e.c. 28.4 1966–1980 13.130 Office machinery and computers 11.7 1981–1990 18.031 Electrical machinery and apparatus n.e.c. 4.1 1991–1995 14.632 Radio, TV and communication equipment 19.9 1996–1998 15.533 Medical, precision and optical instruments 12.6 1999–2001 16.234 Motor vehicles, trailers and semi-trailers 5.0 Missing 9.535 Other transport equipment 3.2 (after 2001 excluded)Missing 4.4

# of employees Total On R&D Position of respondent %

2–9 14.3% 58.5% Director/general manager 29.910–49 28.7% 23.2% Manager/head R&D 37.850–99 16.5% 5.2% Manager/head marketing 14.3N=100 35.1% 3.4% Other 12.8Missing 5.5% 9.8% Missing 5.2Mean 89.5 14.8

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reliability. However, a search in 500+ relevant journal articles and the ISWorld MIS Survey Instrumentsdatabase yielded no scales that concern EKI processes. What we found, were, for example, scales oncapabilities and outcomes [44,45], on IT support [46], on learning [47], or on institutionalisation ofknowledge transfer activities [48]. Moreover, the scales we found were rather lengthy lists of items,limiting the practicality of the questionnaire. Therefore, as an alternative approach, we developed a newquestionnaire in close interaction with respondents. Based on the interviews, a draft English questionnairewas developed and discussed in an expert panel of fifteen academics and practitioners. Consequently, theclarity and validity of this draft questionnaire was tested with three to four potential respondents in eachcountry. After improving the questionnaire it was tested again in a similar way before it was doubleblindly translated in the four national languages. The translated versions were also tested and transformedinto an online questionnaire, which was finally tested again.

The pretests of the questionnaire showed the need for simplifications. With respect to the types ofknowledge there were some difficulties. Respondents clearly recognized the technological and customer/market category, but found the category ‘organizational’ ambiguous. Moreover, they refused to fill out thesame questionnaire for three categories of knowledge. Consequently, the category ‘organizationalknowledge’ was omitted. With respect to the identification stage, we initially had distinguished differenttypes of sources (supplier or customer) that provided information. The pretests showed however that thisdistinction was too specific. Regarding the acquisition stage, we initially also included ‘talking to the source’as a means of acquisition. However, this was seen as so obvious that it even annoyed some respondents. Thedifference between direction and routinization and between direction and diffusion in the utilization stagewas not clear to the respondents. Also after an explanation of the difference, they indicated that this differencewas too subtle. Moreover, it turned out that explanations and instruction within the questionnaire weresimply not read. Consequently, direction and routinizationwere combined in one subprocess: internalization.A final modification was the replacement of the term ‘diffusion’ by ‘dissemination’ because respondentswere more familiar with this second term.

The final English questionnaire is included in the Appendix. The question numbers correspond to thesubprocess numbers in Fig. 2. For each of the subprocesses respondents were asked about the frequency ofexecuting that subprocess for technological as well as for customer/market knowledge. A single balanced5-point Likert-type scale was used, with only the two extremes given to the respondent. We used theextremes ‘never’ and ‘always’ because the pretests showed that extremes that were less strong (e.g. ‘hardly’and ‘very often’) did not sufficiently cover the range of likely responses. Amore fine-grained scale than the5-point scale was indicated as being too subtle. In addition to the questions on the subprocesses thequestionnaire contained also a number of control variables to get a profile of the respondents.

3.4. Validity and reliability measures

Although it cannot be tested statistically, the results of the careful procedures during pretesting havegiven us confidence that the content validity of the questionnaire is satisfying. This can be judged in theAppendix. To test construct validity of the several models, confirmatory factor analysis was used, usingLISREL 8.30. Goodness of Fit (GFI), Adjusted Goodness of Fit (AGFI) and pwere used as measures of fit.Moreover, we used SPSS 10.0 for exploratory factor analysis, using principal components analysis as theextraction technique and varimax as the method of rotation. Convergent validity was evaluated 1) byidentifying the smallest ‘within stage’ correlation and test it whether it is significant; and 2) by testingwhether all item-to-total correlations are positive and significant. Using the MTMM approach,

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discriminant validity was tested for each subprocess by counting the number of times it correlated morehighly with a subprocess of another stage than with subprocesses within its stage [49]. Campbell and Fiske[50] suggest determining whether this number is higher than half of all the comparisons. As measurementsof reliability we used Cronbach's alpha and inter-item correlation within the subprocesses.

4. Results

Means, standard deviations, and correlations between subprocesses are given in Table 2, which is anefficient representation of two normal correlation tables, one for customer/market knowledge (N≈220,normal font), and one for technological knowledge (N≈270, italics). The second and third rows givemeans and standard deviations for technological knowledge, the second and third column for customer/market knowledge. Rather than giving variations, the diagonal provides correlations between identicalsubprocesses for both types of knowledge (in bold).

4.1. Factor analysis

A confirmatory factor analysis of the three-stage model of Fig. 2 for technological knowledge, did notresult in a fit, even after 1000 iterations. For customer/market knowledge there was reached a fit, but onlypoor (GFI=.907, AGFI=.868, χ2 =157.51 at 74 df, p=.00000). The suggested modifications did also notsubstantially reduce χ2, which lead to a rejection of the full stage model. As shown in Table 2, mostcorrelations for DISCOVER are negative (though mostly insignificant), while correlations for virtually allthe other subprocesses is positive. Because of this observation, the three-stage model was also tested withDISCOVER excluded. This model also showed a poor fit for both technological knowledge (GFI=.904,AGFI=.859, χ2 =185.87 at 62 df, p=.00000) and customer/market knowledge (GFI=.914, AGFI=.873,χ2 =135.04 at 62 df, p=.00000). Again the three-stage model was rejected. A test of the parallel model withall subprocesses separate (tested including and excluding DISCOVER) did also only result in a very poorfitting model. The best fitting model (customer/market knowledge, DISCOVER excluded) scoredGFI=.914, AGFI=.873, χ2 =177.08 at 65 df, and p=.00000. Consequently, we can conclude that the three-stage model does not fit the data and should therefore be rejected. Therefore, no further analyses were doneon this model. Alternatively, we continued our analysis to find alternative factors that explain the significantcorrelations of Table 2. This has changed the nature of this study from theory testing to exploration.

Hence, we continued our analysis with an exploratory factor analysis. The Kaiser–Meyer–Olkin (KMO)measure of sampling adequacy, which indicates the proportion of variance that is common variance, i.e.which might be caused by underlying factors, was .717 (valuesN .6 are regarded acceptable). Withoutspecifying the number of factors, four factors with eigenvaluesN1 emerged from the data. Together, thesefactors explain 50.38% of total variance. Table 3 shows the factor loadings greater than .3 (loadingsb .5 inparentheses). A factor analysis with unweighted least squares and maximum likelihood instead of principalcomponents yielded the same factors, but with lower factor loadings.

Since there are only two subprocesses with factor loadings above .3 on more than one factor fortechnological knowledge, and most factor loadings are greater than .6, there seem to be four factors in thedata. Based on the similarities between the subprocesses loading onto a factor, we can label the factors as‘passive search’ (PRESENT and DISCOVER), ‘goal attainment (WRITTEN, PHYSICAL, SEARCH,APPLICAT, and COURSE,), ‘cooperation’ OUTSOURC, COOPERAT, PEOPLE) and ‘integration’(STORAGE, INTERNAL, EXPLOIT, and DIFFUSIO). We will refer to these four factors as the ‘four-

Table 2Correlation table for customer/market knowledge and technological knowledge

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Mean 2.47 4.02 2.55 3.32 3.36 2.07 2.74 2.77 2.29 3.92 2.73 3.40 3.18 3.25

S.D. .82 .85 .92 .98 1.02 1.11 1.08 1.16 1.08 .82 .94 1.17 1.29 1.11

1. DISCOVER 2.83 .90 .531⁎⁎ − .170⁎⁎ .191⁎⁎ − .010 − .114 − .010 .029 − .089 .054 − .093 .063 .066 .005 .0082. SEARCH 3.90 .90 − .189⁎⁎ .625⁎⁎ .108 .179⁎⁎ .242⁎⁎ .115 .197⁎⁎ .109 .045 .297⁎⁎ .027 .152⁎ .210⁎⁎ .143⁎

3. PRESENT 2.53 .94 .148⁎ .082 .487⁎⁎ .053 .025 .167⁎⁎ .116 − .004 .125⁎ − .033 .122 .112 .183⁎⁎ − .0484. WRITTEN 3.11 .98 − .084 .317⁎⁎ .204⁎⁎ .443⁎⁎ .380⁎⁎ .103 .181⁎⁎ .130⁎ .090 .192⁎⁎ .162⁎⁎ .100 .161⁎⁎ .0765. PHYSICAL 3.13 1.11 − .085 .334⁎⁎ − .016 .353⁎⁎ .637⁎⁎ .147⁎ .267⁎⁎ .224⁎⁎ .140⁎ .218⁎⁎ .128⁎ .182⁎⁎ .190⁎⁎ .322⁎⁎

6. PEOPLE 1.95 1.06 − .014 .211⁎⁎ .221⁎⁎ .134⁎ .229⁎⁎ .667⁎⁎ .250⁎⁎ .323⁎⁎ .420⁎⁎ .130⁎ .001 .108 .233⁎⁎ .0757. COURSE 2.33 1.09 − .111 .194⁎⁎ .152⁎ .182⁎⁎ .238⁎⁎ .236⁎⁎ .615⁎⁎ .142⁎ .240⁎⁎ .118 .070 .041 .215⁎⁎ .123⁎

8. COOPERAT 2.58 1.14 − .003 .137⁎ .121 .185⁎⁎ .196⁎⁎ .264⁎⁎ .140⁎ .647⁎⁎ .437⁎⁎ .216⁎⁎ .147⁎ .167⁎⁎ .181⁎⁎ .196⁎⁎

9. OUTSOURC 2.11 .99 − .034 .208⁎⁎ .094 .089 .189⁎⁎ .352⁎⁎ .209⁎⁎ .335⁎⁎ .540⁎⁎ .161⁎⁎ .061 .058 .266⁎⁎ .07410. APPLICAT 3.75 .87 − .042 .300⁎⁎ .002 .163⁎ .202⁎⁎ .132 .033 .237⁎⁎ .147⁎ .686⁎⁎ .218⁎⁎ .138⁎ .231⁎⁎ .224⁎

11. EXPLOIT 2.60 .85 .113 .096 .205⁎⁎ .151⁎ .201⁎⁎ .098 .043 .234⁎⁎ .033 .227⁎⁎ .634⁎⁎ .292⁎⁎ .242⁎⁎ .230⁎⁎

12. STORAGE 3.26 1.09 − .009 .200⁎⁎ .087 .221⁎⁎ .295⁎⁎ .097 .153⁎ .147⁎ .087 .272⁎⁎ .408⁎⁎ .667⁎⁎ .235⁎⁎ .418⁎⁎

13. DIFFUSIO 3.00 1.23 .008 .294⁎⁎ .244⁎⁎ .159⁎ .175⁎ .192⁎⁎ .083 .117 .237⁎⁎ .204⁎⁎ .214⁎⁎ .176⁎ .819⁎⁎ .333⁎⁎

14. INTERNAL 3.06 1.08 − .052 .266⁎⁎ − .002 .100 .298⁎⁎ .092 .114 .146⁎ .034 .232⁎⁎ .273⁎⁎ .436⁎⁎ .315⁎⁎ .789⁎⁎

Normal font: results for customer/market knowledge; italics for technological knowledge.⁎ Significant at .05 level (2-tailed); ⁎⁎ Significant at .01 level (2-tailed).

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Table 4Confirmatory factor analysis for variations of the three-functions model

Model Technological knowledge (N≈270) Customer/market knowledge (N≈220)

GFI AGFI χ2 df p-value GFI AGFI χ2 df p-value

A .888 .838 204.11 54 .00000 .902 .859 143.17 54 .00000B .945 .916 93.50 51 .00026 .948 .921 72.01 51 .02739C .944 .915 95.21 51 .00017 .948 .921 72.31 51 .02645D .948 .920 87.82 50 .00076 .955 .930 62.25 50 .11460E .947 .918 90.12 50 .00044 .955 .929 62.60 50 .10888

Table 3Rotated factor matrix of KI subprocesses (principal component)

Item Technological knowledge Customer/market knowledge

Com. 1 Com. 2 Com. 3 Com. 4 Com. 1 Com. 2 Com. 3 Com. 4

3. PRESENT .724 .7971. DISCOVER .667 (− .398) .6164. WRITTEN .687 .6245. PHYSICAL .655 (.400) (.454)2. SEARCH .629 .59810. APPLICAT (.463) (.302) .547 (.346)7. COURSE (.311) (.462) .6609. OUTSOURC .807 .7768. COOPERAT .725 .7426. PEOPLE .714 .62512. STORAGE .734 .72714. INTERNAL .716 .70911. EXPLOIT .634 .672 (.335)13. DIFFUSIO (.462) (.369) (.314)

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functions model’. For customer/market knowledge the KMO was .741. The emerged factors explained52.84% of total variance and were similar to technological knowledge, except for APPLICAT, which loadedonto ‘integration’ instead of ‘goal attainment’ with factor loading .547.

The model emerging from the exploratory factor analysis was tested in a confirmatory factor analysis.Again, testing the complete model did not result in a fit. Statistically this is explained by the positivecorrelations of DISCOVER and PRESENT (together ‘passive search’) and the negative correlations ofDISCOVER and other subprocesses. Semantically, it can be explained by the passivity of the twosubprocesses that contrasts with the more active character of other subprocesses. When ‘passive search’was omitted, the fit of the model improved. Based on LISREL's suggested modifications we tested fivemodels, of which Table 4 presents the results.

— Model A: 12 separate subprocesses, no stages, DISCOVER and PRESENT excluded.— Model B: 3 functions, ‘passive search’ excluded, COURSE under ‘goal attainment’.— Model C: similar to Model B, but COURSE under ‘cooperation’.— Model D: similar to Model B, but error covariance between DIFFUSIO and STORAGE.— Model E: similar to Model C, but error covariance between DIFFUSIO and STORAGE.

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For each model APPLICAT is included under ‘goal attainment’ for technological knowledge and under‘integration’ for customer/market knowledge because of the results of the factor analysis.

The best fitting models (Model D for both types of knowledge) are depicted in Figs. 3 and 4. As indicated,their main difference lies in the position of APPLICAT, which we think is not surprising. Most high-techSMEs are technology-oriented firms that develop and produce products using a specific technology. Forthem, customer/market knowledge is important for knowing what products to develop but less for knowinghow they can be developed. In other words, whereas technological knowledge is at the center of their goalattainment, customer/market knowledge is more supportive as part of the integrative function.

4.2. Reliability, convergent and discriminant validity

Because SPSS does not allow modeling error covariance, Model B was used instead of Model D toanalyze reliability, convergent validity and discriminant validity. Cronbach's alphas, average inter-itemcorrelations, and lowest and highest item-to-total correlations are presented in Table 5.

Cronbach's alphas did not reach the recommended αN0.7. However, considering the small number ofitems, we think alphas of about 0.6 are sufficiently high to be at least interesting at this exploratory stage.Table 2 shows that the smallest ‘within-function’ correlations are all significant at .05 level and most at

Fig. 3. Model D: technological knowledge.

Fig. 4. Model D: customer/market knowledge.

Table 5Reliability and convergent validity

Model B Technological knowledge Customer/market knowledge

ValidN

Items Cronbach'sα

Avg. inter-itemcorrelation

Item-totalcorrelation

ValidN

Items Cronbach'sα

Avg. inter-itemcorrelation

Item-totalcorrelation

GOAL_ATT 254 5 .58 .22 .27–.42 215 4 .59 .27 .28–.43COOPERAT 262 3 .66 .39 .43–.53 215 3 .59 .32 .37–.45INTEGRAT 258 4 .62 .29 .35–.48 210 5 .64 .26 .32–.48

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.005 level. Moreover, as shown in Table 5, all item-to-total correlations are positive and significant.Together, these results suggest a moderately reasonable convergent validity. To test discriminant validitywe counted the number of times the correlation of items between functions was higher than within afunction. For technological knowledge this yielded 11 out of 66 violations and 8 out of 66 violations forcustomer/market knowledge. Given that this is considerably less than the suggested upper bound limit of50%, we conclude discriminant validity also to be reasonable.

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In sum we can conclude that the fit of the model that emerged from the exploratory factor analysis is byno means perfect. However, considering that our intention was to test the three-stage model of EKI, the fitof the four-functions model is at least sufficiently interesting to investigate further.

5. Discussion

Although we assumed the three-stage-model would explain a great share of variation, the resultssuggest that we were wrong. This indicates that NPD managers in SMEs do not concentrate on one EKI-stage, but spread their attention more equally over the stages. We can, however, only conclude this at theaggregated level of KI processes used in this study. Whether this also applies to KI processes in singleNPD projects is a question for future research.

This study has a number of limitations. The first relates to the development of the questionnaire.Since we found no existing instruments that measured EKI processes, we had to develop a newquestionnaire. Moreover, we had to limit the length of the questionnaire because of the time that SME'sNPD managers were willing to spend on it. Although we have tried to address this limitation to the bestpossible extent with our pretest procedures, the reliability and validity tests are not fully convincing yetand need further improvements. Another limitation relates to the shift from theory testing to exploration.The questionnaire was developed to test the three-stage model and not the four-functions model.Consequently, the current operationalizations do not completely fit the four-functions model. Below wewill provide suggestions for complementary research in order to test the four-functions model. Also thedata collection was not without limitations, in particular because the selection of a sample from fourdifferent databases introduces potential sources of bias. We have investigated the impact of thislimitation by comparing the results of the four countries. Whereas the means for each of the items of thequestionnaire differ significantly between the countries, the four-functions model remains rather stablewhen the data from single countries are excluded from analysis. Thus, we have confidence that thislimitation has had no substantial impact on this study. Finally, the results of the study are also notwithout limitations. The support of the four-functions model is not conclusive in terms of factorloadings, reliability, convergent validity, discriminant validity. This is not surprising since we did notintend to measure that model. However, considering that the model was confirmed for bothtechnological and customer/market knowledge with only small differences gives us confidence that thefour-function model is an interesting model for further investigation. Moreover, the differences that didappear were easily explicable.

The rejection of the three-stage model has changed the nature of this study from theory testing toexploration. The exploratory factor analysis suggested an alternative ‘four-function’ model. Although byno means conclusive, we find the fit of the model interesting, particularly because it was not anticipated inthe design of the study. The four-functions model shows an interesting resemblance to the fourorganizational effectiveness functions (OEFs) of Stein and Zwass [13] and the underlying model [14].Stein and Zwass provide meta-requirements for organizational memory information systems (OMIS) bydistinguishing five mnemonic functions (knowledge acquisition, retention, search, retrieval, andmaintenance) and four organizational effectiveness functions (integrative, adaptive, goal attainment,and pattern maintenance function). The three-stage model of EKI corresponds with what Stein and Zwass[13] call mnemonic functions (MFs). Since EKI concerns external knowledge and OM more internalknowledge, the stages are not identical to the MFs. However, their similarities remain obvious. Moreinterestingly, we also find a resemblance between the four emerged functions in this study and the four

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OEFs. This is most apparent for the goal-attainment functions in both models, which are defined as “…theability of the organization to set goals and evaluate the degree of their fulfillment” (1995: 96). Althoughgoal setting and evaluation were not explicitly included in the current study, searching, acquiring bydocuments and files, analyzing products, following courses, and using knowledge for the goal it wasacquired for seem clearly elements of a goal attainment process. Another similarity is between theintegrative functions of both models, which are defined as “(…) the organizational coordination andmanagement of information across the organization” (1995: 96). Storing, diffusing, and internalizingknowledge to reuse it typically contribute to this function. The similarity between the pattern maintenancefunction and our cooperation function is less apparent, though still present since both are concerned withhuman relations. Stein and Zwass [13] have defined this function as “(…) the ability of the organization tomaintain the cohesion and the morale of the workforce” (1995: 96). Although this definition concentrateson human resources, the underpinning model of Quinn and Rohrbaugh [14]) is a human relations model.When comparing the three subprocesses grouped under this function (hiring people, cooperation, andoutsourcing) to the other subprocesses, they distinguish themselves by their focus on a strong relationshipbetween source and seeker. Because the focus of the current study is on external knowledge, it is notsurprising that these three subprocesses regard external human relations, whereas Stein and Zwass [13]and Quinn and Rohrbaugh [14] regard internal human relations. Thus, though the pattern maintenancefunction and the cooperation function are not identical, they have considerable common features. Thisleaves us with one potential pair of functions: the adaptive function of Stein and Zwass [13] and thepassive search function of the current study. Stein and Zwass' [13] definition of the adaptive function is“(…) the ability of the organization to adapt to changes in its environment” (1995: 96). Both Stein andZwass and Quinn and Rohrbaugh emphasize the openness and receptivity of an organization as importantfeatures of this function. Although the two passive search subprocesses in the current study (accidentaldiscovery and unsolicited presentation of knowledge) are more passive than Stein and Zwass' [13]definition of the adaptive function, the aspect of receptivity is recognizable.

The tentative fit of the OEF model and our data raises the question whether we shouldn't haveconsidered it already in the initial stages of this study. Rather than finding the results surprising, should weblame ourselves for not having done an appropriate literature review? We think not, since the OEF modelhas, to our knowledge, not been applied before to the EKI process but only to organizational systems.Thus, next to this study, there are no studies that confirm or even suggest the importance of the four OEFsto the process under study. However, with hindsight we think the fit of the OEF model is not completelysurprising. Stein and Zwass [13] have suggested that each of the four OEFs rests on the foundation of allMFs. Given this suggestion it is not completely surprising that the three-stage model was not confirmedwithin this study: the EKI stages together are a foundation for each of the OEFs.

Although our arguments and empirical backing are not conclusive, we believe the results providesufficient reason to further analyze EKI and its consequences for IS design from the perspective of OEFs.We suggest two important directions for further research.

The first direction is towards further empirical research on the relationship between OEFs and the EKIprocess. Since we did not intend to test it, our operationalization of EKI was poor with respect to the OEFmodel. Future research should operationalize EKI starting from the OEF model. A confrontation of thework of Stein and Zwass [13] and Quinn and Rohrbaugh [14] with the results of the current study providesdirections for such research. Following their theory, goal setting and evaluation should be included asitems for goal-attainment. We therefore suggest adding a goal setting and a goal evaluation item. For theintegrative function we suggest refining the items for storage and diffusion, because their negative error

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covariance suggests that they are alternatives of the same process. Within the pattern-maintenancefunction we suggest to pay more attention to cohesion and morale, for example by introducing items forinterorganizational team building. With respect to the negative correlations between the adaptive function(passive search) and the other functions, we expect that if we operationalize this function to a more activeform— for example in terms of environmental scanning, correlations will be positive. As a result, we alsoexpect that an inclusion of this function in the model will yield acceptable results for a confirmatory factoranalysis. In addition to further quantitative research we also suggest doing additional qualitative researchto better understand the nature and interaction of the four OEFs in the EKI process.

Secondly, our findings have implications for the design of IS that are to support the EKI process. Steinand Zwass [13] argue that the OEFs should be supported by different subsystems. Extending thisargument to the EKI process would suggest that four EKI subsystems should be distinguished becauseusers' expectations and system interactions are different for each function. This extension is not asobvious as it seems, because Stein and Zwass' [13] analysis is on organizations, whereas ours is on aprocess, which in itself contributes to (or is part of) one or more of the four OEFs. Despite of thisreservation and because Stein and Zwass' [13] work is rooted in social systems theory [51] – which issupposed to be applicable at different levels – we propose however that also on this level four subsystemsshould be designed. Similar to Stein and Zwass, Table 6 suggests meta requirements in terms ofsubprocesses to be supported, and meta design features in terms of types of software components that cansupport that subsystem. The subprocesses to be supported follow direct from our study, since; after all, itwas the grouping of subprocesses in the exploratory factor analysis that made us consider the OEFframework. The above-mentioned suggested modifications are also included in Table 6.

Although at first sight Table 6 seems ‘just another IS classification’ it is different from existing ISclassifications in two important ways. Firstly, whereas many other authors classify IS according to the MFthey support [52–54], Table 6 is ordered to OEFs. Secondly, Table 6 is more than a classification per se.

Table 6Proposed consequences for EKI IS design

EKI subsystem Meta-requirements: system should support… Meta design: examples of relevant software components

Goal attainment Intentional search (ID) Search engines, cataloguesWritten form (AC) EDI, transaction systems, downloadsPhysical objects (AC) CAD/CAM systems, measurement systemsCourses (AC) Online training systems, e-learning systemsApplication (UT) CAD/CAM systems, databases at the workplaceGoal setting (not in this study) Forecasting systems, planning systems, DSS, MISGoal evaluation (not in this study) Planning and evaluation systems

Integrative Storage and Diffusion (UT) Shared databases, intranets, e-mail, document managemenInternalization (UT) Workflow systems, project planning software,Exploitation (UT) Lessons learned, best practices

Pattern maintenance People transfer (AC) HRM systems, online job centerCooperation (AC) Groupware, e-mail, video conferencing, chat softwareOutsourcing (AC) Online partner finding systemTeambuilding (not in this study) Management games, team composition software

Adaptive Accidental discovery (ID) Communities of practice, professional portalsUnsolicited presentation (ID) Filtering systemsEnvironmental scanning Web crawlers, e-mail alerts, pattern recognition systems

t

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Markus and Keil [12] remarked that if a system supports only part of a process, this results in less thanoptimal performance. Consequently, we propose that the software components for each OEF should beconsidered as part of a coherent subsystem rather than as systems in themselves. We expect that users willinteract simultaneously with systems grouped under one function more often than with systems groupedunder different functions. The proposition that software components should be designed as part of acoherent whole is in itself not new. Table 6, however, provides some tentative guidelines on whichcomponents should be considered together in design and which could be designed more independent ofeach other. The main difference with existing work is that this study suggests that the coherence is notcaused by MFs, but by OEFs. Although we did not provide detailed implications, we believe to haveprovided significant empirical input for further research on IS design, in particular by analyzing theconsequences of an organizational process for IS design. Obviously, Table 6 needs much additional work.Therefore, future research needs to specify our guidelines further and to analyze what are their specificconsequences for design and tuning of various ISs.

The results of this study also have broader implications for the conceptualization of organizationsand the development of their functions. The corroboration of the OEF model in a study on a processrather than on an organizational system extends its reach and relevance substantially. We expectthat research from the OEF perspective on other organizational processes than the EKI processwill provide interesting new insights in their dynamics and contribution towards organizationaleffectiveness.

6. Conclusion

We started our analysis with the question whether the three-stage model of EKI is empirically valid inhigh-tech SMEs, and if so, what are consequences for the design of information systems that are tosupport this process. Given the results of our analysis we cannot conclude differently on the first part ofthis question than that the three-stage model should be rejected. However, an alternative four-functionsmodel emerged that has remarkable similarities with the four OEF model of Stein and Zwass [13] andQuinn and Rohrbaugh [14]. The emergence of this model from the data seems a direct corroboration of thegeneral applicability of the OEF model. Whereas Stein and Zwass [13] and Quinn and Rohrbaugh [14]followed a theoretical approach on mainly larger organizations, we started from an empirical study on aspecific process in high-tech SMEs. Despite the limitations of our study we find the fit of the OEF modelwith our data remarkable.

Regarding the second part of our question, we have suggested that designers of ISs that are to supportthe EKI processes should derive meta-requirements and meta-designs from the four OEFs rather thanfrom the three stages. Rather than designing isolated systems, we proposed that IS should be designedaccording to the OEF they are to support because users have different expectations and interact differentlywith IS in each function. As such, the four-function model rather than the three-stage model of EKI shouldserve as a tentative kernel theory for IS development.

Acknowledgements

The authors thank the editor and the anonymous reviewers for their constructive comments andsuggestions. This research was partly funded by the European Community in the project ‘KnowledgeIntegration and Network eXpertise' (KINX), No. G1RD-CT-2002-00700.

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Appendix A

No.

Item Question (5-point Likert-type, ranging from never (1) to always (5))

There are several ways to find external knowledge. How often do the following ways occur in your company?

1 DISCOVER We come across knowledge without really looking for it 2 SEARCH We intentionally search for knowledge 3 PRESENT Another organization presents knowledge unasked There are many ways to obtain knowledge if its source its known. How often do the following ways occur in your company? 4 WRITTEN We receive documents or files from a source 5 PHYSICAL We analyze products from a source 6 PEOPLE We hire or employ persons from a source 7 COURSE We attend a course given by a source 8 COOPERAT We develop a product together with a source 9 OUTSOURC We outsource a problem to a source Obtained knowledge can be used in several ways. How often do the following ways occur in your company? 10 APPLICAT We use it for the goal we acquired it for 11 EXPLOIT We use it for other goals than we acquired it for 12 STORAGE We store it for potential later use 13 DIFFUSIO We disseminate it to everybody concerned 14 INTERNAL We make sure that we have similar knowledge internally available next time

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Jeroen Kraaijenbrink is assistant professor at NIKOS, the Dutch Institute for Knowledge Intensive Entrepreneurship at the University ofTwente. He holds a MSc and a PhD in Industrial Engineering and Management and a MSc in Public Administration from the University ofTwente. His research interests include knowledge intensive entrepreneurship, knowledge management in networks, organization theory, andsocial systems theory.

Fons Wijnhoven is associate professor of Knowledge Management and Information Systems at the University of Twente. He researches thedevelopment and exploitation of information services and organizational memories in the University's Center of Telematics and IT. In the lastdecade over 50 of his articles appeared in academic journals and peer reviewed conference proceedings. He published books on organizationallearning, IT impact assessment, organizational memories, and knowledge integration.

Aard Groen is associate professor marketing and entrepreneurship, research fellow of IGS, scientific director of NIKOS, the Dutch Institute forKnowledge Intensive Entrepreneurship at the University of Twente, the Netherlands, and head of department of Entrepreneurship, Marketing,Strategy and International Management. Groen's research interest is focusing on knowledge intensive entrepreneurship in networks. He receivedhis PhD in business administration at the University of Groningen, and studied public administration (MSc) at the University of Twente.Groen is member of the steering group of EISB the EFMD-chapter on entrepreneurship, several Dutch policy councils. Dr. Groen has co-chairedthe High Tech Small Firms conference series held in Enschede in 2004 and 2006. He is a member of the Dutch Flemish academy ofentrepreneurship, European summer school on entrepreneurship, and delivered key notes to conferences in The Netherlands, South Africa andRussia.