Towards a kernel theory of external knowledge integration ... - CiteSeerX

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

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 EKIstages, 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

⁎ 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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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 in which these systems are used because these processes strongly affect the user's requirements [1]. With an adequate model of a process, designers can assess which IS design is most likely to be successful for that particular process. Conversely, without such a process model, designers run the risk of designing isolated and underused ISs. Thus, since it enables an assessment of IS designs, an adequate process model serves as a kernel theory for the design of IS [2,3]. For many organizations, a crucial process is that of external knowledge integration (EKI), which is defined here as the identification, acquisition, and utilization of external knowledge. Since their general lack of internal resources, in particular small and medium sized enterprises (SMEs) depend heavily on this process. This paper pays attention not only to the issues central to a small firm but particularly those information issues that are endemic to high-technology based small firms. Schumpeter [4] argued that the interface between technology and strategy is the driving force behind capitalism, that new production methods were centric to nations and firms search for competitive advantage, and that entrepreneurial firms were central to this effort. Hence, high-tech firms play a crucial role in the developments towards current society. High-tech efforts have been separated by the type of transformation process [5], by the ability to change the strategic value statement in an industry or disruptive 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 most extreme need for useful external information since the basis of their company is based on a technology that must constantly be refreshed. Moreover, their degree of novelty versus competitors is a cornerstone of their competitive advantage and their struggle to find a market that can readily adapt their value statements 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 are internal to organizations, there remains an expressed need to better understand the organizational processes through which external knowledge is integrated [10,11]. This paper reflects an attempt to address this lacuna in the literature by empirically analyzing a three-stage model of the EKI process. The model distinguishes 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 empirically valid in high-tech SMEs, and if so, what are the consequences for the design of ISs that are to support this process. Our main assumption is that subprocesses that go together in practice should be supported by the same system or by a coherent set of systems because if they support only part of a process, this results in less than optimal performance [12]. Thus, we propose that the EKI process is subdivided in stages of knowledge 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 is highly relevant: new product development (NPD) in high-tech SMEs. Particularly here we expect finding the stage model confirmed because SMEs can do only few things at a time because of their limited span of attention and resources. Consequently, they are likely to concentrate either on identification, acquisition, or utilization of external knowledge. It seems likely, for example, that start-up firms concentrate on identification because they have to search for partners. Established firms, however, supposedly have a network of partners and concentrate on the utilization of knowledge gained from this network. The paper reports the findings of a survey on the proposed EKI model among high-tech SMEs. Contrary to our expectations, a confirmatory factor analysis of a data set consisting of 317 observations suggests a rejection 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 and Rohrbaugh [14]. The paper is organized as follows: The Next section discusses the research framework, which is followed by an explanation of the research method. Thereafter we present the results of our study and discuss 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 of identification, acquisition, and utilization of knowledge from external sources for the NPD-process within an 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 this process, we have identified subprocesses within each stage and have measured how frequently NPD managers in SMEs executed them. The remaining part of Fig. 1 has extensively been studied in several disciplines. It has repeatedly been shown that SMEs use mainly knowledge of their customers and suppliers [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 is needed during NPD, including market, technological, and organizational knowledge [19] of which most knowledge is primarily tacit [20]. It has also been shown that the need for knowledge is different in various 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 with knowledge management (KM) we call knowledge utilization. Since external knowledge needs to be acquired before it can be utilized, an essential preceding stage is knowledge acquisition. Similarly, before acquiring external knowledge it must first be identified. Acquisition is therefore preceded by a knowledge identification stage. Based on existing research, a number of subprocesses are identified within each stage and outlined below. The identification stage consists of subprocesses involved in locating relevant knowledge outside the organization. Following literature on information seeking and environmental scanning, this stage is in a continuous 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], and Choo [23] identify the level of intrusiveness of the seeker as a distinguishing aspect of information seeking behavior. In his distinction between solicited and unsolicited information, Aguilar also deemed this aspect distinguishing for the information source. When the levels of intrusiveness of both source and seeker are seen as dichotomies, four identification subprocesses can be distinguished. The first subprocess – high intrusive 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 example on the Internet, fairs, or in his personal network. The second subprocesses (low-high) is unsolicited presentation of knowledge by the source [26]. An example is the dissemination of information on new technologies by a source to potential partners. The third subprocess (low–low) is accidental discovery and occurs, 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 fourth theoretically possible subprocess (high–high) is believed to be not relevant within this study, because dependent on who is most intrusive, it will be similar to intentional search or unasked presentation from the perspective of the seeker. For example, when the seeker is most intrusive (i.e. she finds the source), we expect 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 can take several forms, ranging from a document transfer (for explicit knowledge, cf. Nonaka, [28]) to interactive cooperation (for tacit knowledge) [29]. We base a more fine-grained distinction of acquisition subprocesses on several possible carriers of knowledge. Firstly, knowledge that is codifyable can be represented in written form and transferred in documents or files. Secondly, physical objects can be transferred from the source to the recipient. An example in NPD is reverse engineering of a competitor's product [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 knowledge without necessarily being employed, for example in the form of courses [32]. Fifthly, when knowledge is embedded in work processes, transfer of knowledge is possible by cooperation between the source and the recipient, for example by cooperative development. Finally, when knowledge is embedded in the source organization's structure or culture (cf. [33]), it can be acquired by outsourcing a problem to the source and staying in contact. Another option is acquiring the source organization. However, since we expect 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, is applied, and is integrated in the organization. Each of these three subprocesses can take place as a onetime-only static process, or as an ongoing dynamic process, which suggests six subprocesses within this stage. Providing access on a one-time-only basis, is done by storing knowledge somewhere in the organization, for example in archives or individual people. The corresponding dynamic subprocess is that of diffusion. Using the image of a jigsaw puzzle, Galunic and Rodan [34] distinguish two forms of diffusion: distribution and dispersion. “A picture on a jigsaw puzzle is distributed when each person receives a photocopy of the picture. The same image would only be dispersed when each of the pieces is given to a different person” (1998: 1198). One-time application of knowledge is the process of putting the obtained knowledge to use in the situation it was needed for. Ongoing application can be referred to as knowledge reuse [35] or exploitation [36]. The integration of knowledge on a one-time-only basis is what Grant [10] has called direction: codifying tacit knowledge into explicit rules and instructions so that it can be communicated at low cost throughout the organization ([10]: 379). The second form integration that Grant gives, is routinization. An organizational routine is “(…) a set of activities (…) routinized to the extent that choice has been simplified by the development of a fixed response to defined stimuli” [37]. The three 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 used a commonly made distinction between technological knowledge (e.g. about materials or production processes), customer/market knowledge (e.g. about demanded quality or functionality), and


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Fig. 2. Subprocesses of external knowledge integration.

organizational knowledge (e.g. about planning or logistics). We assumed and tested that our respondents could 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 indepth semi-structured interviews and a large-scale self-administered questionnaire. Both the interviews and the questionnaire were conducted in Germany, Israel, Netherlands, and Spain as part of a larger European study on EKI. The authors were responsible for overall development, coordination, and analysis as 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 an expert panel of academics and practitioners. In the four countries, a total of 33 interviews were done with NPD managers. Interviews lasted between one and two-and-half hours. Sampling was based on convenience, 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 we are not aware of any database that covers the four countries, we had to select four different databases that allowed selection on similar criteria. Because of their high-quality reputation and similarity, the following databases were selected: Hoppenstedt (Germany), D and A HiTech Information Ltd. (Israel), National Chamber of Commerce (Netherlands), and AXESOR (Spain). From these databases, we selected a stratified 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), and industry (industries 24 and 29–35 from the International Standard Industrial Classification). These companies were contacted by phone, were asked to identify a key informant, received a questionnaire, and were reminded twice if they did not respond. Although the validity of single-informants research has been debated, we agree with Kumar, Stern, and Anderson [38] who state that informants are not selected to be representative of the members of a studied organization, but because they are supposedly knowledgeable and 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 person to respond. Hereafter we label these respondents ‘NPD managers’. A total of 317 NPD managers responded, leading to a response rate of 24.3%, which is considerably high for 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 the Netherlands was surprising. One possible explanation is that Dutch governments pay relatively much attention in their policies to the acquisition and use of external knowledge by SMEs. During the interviews, the interest of NPD managers in our study also seemed higher in the Netherlands than in the other countries. The profile of the responding companies and individuals is given in Table 1. A comparison (T-test and Mann-Whitney test) of respondents with non-respondents showed no significant differences on industry. However, regarding company size, companies with 10–49 employees were relatively underrepresented in the response set, while companies with over 100 employees were relatively overrepresented. Moreover, younger companies were relatively underrepresented, while older companies were overrepresented. A comparison of early and late respondents on all variables in this study showed however no significant differences ( p b 0.05). 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 is concerned “(…) with a wide range of factors of economy, convenience, and interpretability” [42]. In particular in SMEs, practicality is important, because managers are usually overloaded with their daily survival and have little time to fill out questionnaires (cf. [43]). Illustrative is a remark of one participant of our study: he at times receives up to ten questionnaires a week, of which some are obligatory. For the development of the questionnaire we preferred using existing scales because of their proven validity and Table 1 Profile of respondents and their companies Industry

%

Year of foundation

%

24 Chemicals and chemical products 29 Machinery and equipment n.e.c. 30 Office machinery and computers 31 Electrical machinery and apparatus n.e.c. 32 Radio, TV and communication equipment 33 Medical, precision and optical instruments 34 Motor vehicles, trailers and semi-trailers 35 Other transport equipment Missing

10.7 28.4 11.7 4.1 19.9 12.6 5.0 3.2 4.4

Before 1965 1966–1980 1981–1990 1991–1995 1996–1998 1999–2001 Missing (after 2001 excluded)

13.1 13.1 18.0 14.6 15.5 16.2 9.5

# of employees

Total

On R&D

Position of respondent

%

2–9 10–49 50–99 N =100 Missing Mean

14.3% 28.7% 16.5% 35.1% 5.5% 89.5

58.5% 23.2% 5.2% 3.4% 9.8% 14.8

Director/general manager Manager/head R&D Manager/head marketing Other Missing

29.9 37.8 14.3 12.8 5.2


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reliability. However, a search in 500+ relevant journal articles and the ISWorld MIS Survey Instruments database yielded no scales that concern EKI processes. What we found, were, for example, scales on capabilities and outcomes [44,45], on IT support [46], on learning [47], or on institutionalisation of knowledge 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 new questionnaire in close interaction with respondents. Based on the interviews, a draft English questionnaire was developed and discussed in an expert panel of fifteen academics and practitioners. Consequently, the clarity and validity of this draft questionnaire was tested with three to four potential respondents in each country. After improving the questionnaire it was tested again in a similar way before it was double blindly translated in the four national languages. The translated versions were also tested and transformed into an online questionnaire, which was finally tested again. The pretests of the questionnaire showed the need for simplifications. With respect to the types of knowledge 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 the same questionnaire for three categories of knowledge. Consequently, the category ‘organizational knowledge’ was omitted. With respect to the identification stage, we initially had distinguished different types of sources (supplier or customer) that provided information. The pretests showed however that this distinction 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. The difference between direction and routinization and between direction and diffusion in the utilization stage was not clear to the respondents. Also after an explanation of the difference, they indicated that this difference was too subtle. Moreover, it turned out that explanations and instruction within the questionnaire were simply not read. Consequently, direction and routinization were combined in one subprocess: internalization. A final modification was the replacement of the term ‘diffusion’ by ‘dissemination’ because respondents were more familiar with this second term. The final English questionnaire is included in the Appendix. The question numbers correspond to the subprocess numbers in Fig. 2. For each of the subprocesses respondents were asked about the frequency of executing that subprocess for technological as well as for customer/market knowledge. A single balanced 5-point Likert-type scale was used, with only the two extremes given to the respondent. We used the extremes ‘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. A more fine-grained scale than the 5-point scale was indicated as being too subtle. In addition to the questions on the subprocesses the questionnaire 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 have given us confidence that the content validity of the questionnaire is satisfying. This can be judged in the Appendix. To test construct validity of the several models, confirmatory factor analysis was used, using LISREL 8.30. Goodness of Fit (GFI), Adjusted Goodness of Fit (AGFI) and p were used as measures of fit. Moreover, we used SPSS 10.0 for exploratory factor analysis, using principal components analysis as the extraction technique and varimax as the method of rotation. Convergent validity was evaluated 1) by identifying the smallest ‘within stage’ correlation and test it whether it is significant; and 2) by testing whether 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 more highly 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 measurements of 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 an efficient 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 give means and standard deviations for technological knowledge, the second and third column for customer/ market knowledge. Rather than giving variations, the diagonal provides correlations between identical subprocesses 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 not result in a fit, even after 1000 iterations. For customer/market knowledge there was reached a fit, but only poor (GFI = .907, AGFI = .868, χ2 = 157.51 at 74 df, p = .00000). The suggested modifications did also not substantially reduce χ2, which lead to a rejection of the full stage model. As shown in Table 2, most correlations for DISCOVER are negative (though mostly insignificant), while correlations for virtually all the other subprocesses is positive. Because of this observation, the three-stage model was also tested with DISCOVER 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 with all subprocesses separate (tested including and excluding DISCOVER) did also only result in a very poor fitting model. The best fitting model (customer/market knowledge, DISCOVER excluded) scored GFI = .914, AGFI = .873, χ2 = 177.08 at 65 df, and p = .00000. Consequently, we can conclude that the threestage model does not fit the data and should therefore be rejected. Therefore, no further analyses were done on this model. Alternatively, we continued our analysis to find alternative factors that explain the significant correlations 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 (values N .6 are regarded acceptable). Without specifying the number of factors, four factors with eigenvalues N 1 emerged from the data. Together, these factors explain 50.38% of total variance. Table 3 shows the factor loadings greater than .3 (loadings b .5 in parentheses). A factor analysis with unweighted least squares and maximum likelihood instead of principal components 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 for technological knowledge, and most factor loadings are greater than .6, there seem to be four factors in the data. 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-

Mean

1. DISCOVER 2. SEARCH 3. PRESENT 4. WRITTEN 5. PHYSICAL 6. PEOPLE 7. COURSE 8. COOPERAT 9. OUTSOURC 10. APPLICAT 11. EXPLOIT 12. STORAGE 13. DIFFUSIO 14. INTERNAL

2.83 3.90 2.53 3.11 3.13 1.95 2.33 2.58 2.11 3.75 2.60 3.26 3.00 3.06

1

2

3

4

5

6

7

8

9

10

11

12

13

14

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

.90 .90 .94 .98 1.11 1.06 1.09 1.14 .99 .87 .85 1.09 1.23 1.08

.531⁎⁎ − .170⁎⁎ .191⁎⁎ −.010 −.114 −.010 .029 −.089 .054 −.093 .063 .066 .005 .008 −.189⁎⁎ .625⁎⁎ .108 .179⁎⁎ .242⁎⁎ .115 .197⁎⁎ .109 .045 .297⁎⁎ .027 .152⁎ .210⁎⁎ .143⁎ .148⁎ .082 .487⁎⁎ .053 .025 .167⁎⁎ .116 −.004 .125⁎ −.033 .122 .112 .183⁎⁎ − .048 ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎ −.084 .317 .204 .443 .380 .103 .181⁎⁎ .130⁎ .090 .192⁎⁎ .162⁎⁎ .100 .161⁎⁎ .076 −.085 .334⁎⁎ − .016 .353⁎⁎ .637⁎⁎ .147⁎ .267⁎⁎ .224⁎⁎ .140⁎ .218⁎⁎ .128⁎ .182⁎⁎ .190⁎⁎ .322⁎⁎ −.014 .211⁎⁎ .221⁎⁎ .134⁎ .229⁎⁎ .667⁎⁎ .250⁎⁎ .323⁎⁎ .420⁎⁎ .130⁎ .001 .108 .233⁎⁎ .075 −.111 .194⁎⁎ .152⁎ .182⁎⁎ .238⁎⁎ .236⁎⁎ .615⁎⁎ .142⁎ .240⁎⁎ .118 .070 .041 .215⁎⁎ .123⁎ −.003 .137⁎ .121 .185⁎⁎ .196⁎⁎ .264⁎⁎ .140⁎ .647⁎⁎ .437⁎⁎ .216⁎⁎ .147⁎ .167⁎⁎ .181⁎⁎ .196⁎⁎ −.034 .208⁎⁎ .094 .089 .189⁎⁎ .352⁎⁎ .209⁎⁎ .335⁎⁎ .540⁎⁎ .161⁎⁎ .061 .058 .266⁎⁎ .074 −.042 .300⁎⁎ .002 .163⁎ .202⁎⁎ .132 .033 .237⁎⁎ .147⁎ .686⁎⁎ .218⁎⁎ .138⁎ .231⁎⁎ .224⁎ ⁎⁎ ⁎ ⁎⁎ ⁎⁎ .113 .096 .205 .151 .201 .098 .043 .234 .033 .227⁎⁎ .634⁎⁎ .292⁎⁎ .242⁎⁎ .230⁎⁎ −.009 .200⁎⁎ .087 .221⁎⁎ .295⁎⁎ .097 .153⁎ .147⁎ .087 .272⁎⁎ .408⁎⁎ .667⁎⁎ .235⁎⁎ .418⁎⁎ .008 .294⁎⁎ .244⁎⁎ .159⁎ .175⁎ .192⁎⁎ .083 .117 .237⁎⁎ .204⁎⁎ .214⁎⁎ .176⁎ .819⁎⁎ .333⁎⁎ −.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).


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

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

Technological knowledge Com. 1

3. PRESENT 1. DISCOVER 4. WRITTEN 5. PHYSICAL 2. SEARCH 10. APPLICAT 7. COURSE 9. OUTSOURC 8. COOPERAT 6. PEOPLE 12. STORAGE 14. INTERNAL 11. EXPLOIT 13. DIFFUSIO

Com. 2

Customer/market knowledge Com. 3

Com. 4

Com. 1

.724 .667

(.311) .807 .725 .714

.687 .655 .629 (.463) (.462)

Com. 3

.547

Com. 4 .797 .616

(−.398) .624 (.454) .598

(.400) (.302)

Com. 2

(.346) .660 .776 .742 .625

.734 .716 .634 (.462)

.727 .709 .672 (.369)

(.335) (.314)

functions model’. For customer/market knowledge the KMO was .741. The emerged factors explained 52.84% of total variance and were similar to technological knowledge, except for APPLICAT, which loaded onto ‘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 positive correlations of DISCOVER and PRESENT (together ‘passive search’) and the negative correlations of DISCOVER and other subprocesses. Semantically, it can be explained by the passivity of the two subprocesses 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 five models, 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.

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

A B C D E

Technological knowledge (N ≈ 270)

Customer/market knowledge (N ≈ 220)

GFI

AGFI

χ

df

p-value

GFI

AGFI

χ2

df

p-value

.888 .945 .944 .948 .947

.838 .916 .915 .920 .918

204.11 93.50 95.21 87.82 90.12

54 51 51 50 50

.00000 .00026 .00017 .00076 .00044

.902 .948 .948 .955 .955

.859 .921 .921 .930 .929

143.17 72.01 72.31 62.25 62.60

54 51 51 50 50

.00000 .02739 .02645 .11460 .10888

2


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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-tech SMEs are technology-oriented firms that develop and produce products using a specific technology. For them, customer/market knowledge is important for knowing what products to develop but less for knowing how they can be developed. In other words, whereas technological knowledge is at the center of their goal attainment, 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 to analyze reliability, convergent validity and discriminant validity. Cronbach's alphas, average inter-item correlations, and lowest and highest item-to-total correlations are presented in Table 5. Cronbach's alphas did not reach the recommended α N 0.7. However, considering the small number of items, 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.

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

.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 validity we counted the number of times the correlation of items between functions was higher than within a function. For technological knowledge this yielded 11 out of 66 violations and 8 out of 66 violations for customer/market knowledge. Given that this is considerably less than the suggested upper bound limit of 50%, we conclude discriminant validity also to be reasonable.

Table 5 Reliability and convergent validity Model B

Technological knowledge

Customer/market knowledge

Valid Items Cronbach's Avg. interN α item correlation GOAL_ATT 254 COOPERAT 262 INTEGRAT 258

5 3 4

.58 .66 .62

.22 .39 .29

Item-total correlation

Valid Items Cronbach's Avg. interN α item correlation

Item-total correlation

.27–.42 .43–.53 .35–.48

215 215 210

.28–.43 .37–.45 .32–.48

4 3 5

.59 .59 .64

.27 .32 .26


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In sum we can conclude that the fit of the model that emerged from the exploratory factor analysis is by no means perfect. However, considering that our intention was to test the three-stage model of EKI, the fit of 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 results suggest that we were wrong. This indicates that NPD managers in SMEs do not concentrate on one EKIstage, but spread their attention more equally over the stages. We can, however, only conclude this at the aggregated level of KI processes used in this study. Whether this also applies to KI processes in single NPD 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 new questionnaire. Moreover, we had to limit the length of the questionnaire because of the time that SME's NPD managers were willing to spend on it. Although we have tried to address this limitation to the best possible extent with our pretest procedures, the reliability and validity tests are not fully convincing yet and 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 we will provide suggestions for complementary research in order to test the four-functions model. Also the data collection was not without limitations, in particular because the selection of a sample from four different databases introduces potential sources of bias. We have investigated the impact of this limitation by comparing the results of the four countries. Whereas the means for each of the items of the questionnaire differ significantly between the countries, the four-functions model remains rather stable when the data from single countries are excluded from analysis. Thus, we have confidence that this limitation has had no substantial impact on this study. Finally, the results of the study are also not without limitations. The support of the four-functions model is not conclusive in terms of factor loadings, reliability, convergent validity, discriminant validity. This is not surprising since we did not intend to measure that model. However, considering that the model was confirmed for both technological and customer/market knowledge with only small differences gives us confidence that the four-function model is an interesting model for further investigation. Moreover, the differences that did appear were easily explicable. The rejection of the three-stage model has changed the nature of this study from theory testing to exploration. The exploratory factor analysis suggested an alternative ‘four-function’ model. Although by no means conclusive, we find the fit of the model interesting, particularly because it was not anticipated in the design of the study. The four-functions model shows an interesting resemblance to the four organizational 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) by distinguishing five mnemonic functions (knowledge acquisition, retention, search, retrieval, and maintenance) 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 internal knowledge, the stages are not identical to the MFs. However, their similarities remain obvious. More interestingly, 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 “…the ability of the organization to set goals and evaluate the degree of their fulfillment” (1995: 96). Although goal setting and evaluation were not explicitly included in the current study, searching, acquiring by documents and files, analyzing products, following courses, and using knowledge for the goal it was acquired for seem clearly elements of a goal attainment process. Another similarity is between the integrative functions of both models, which are defined as “(…) the organizational coordination and management of information across the organization” (1995: 96). Storing, diffusing, and internalizing knowledge to reuse it typically contribute to this function. The similarity between the pattern maintenance function and our cooperation function is less apparent, though still present since both are concerned with human relations. Stein and Zwass [13] have defined this function as “(…) the ability of the organization to maintain the cohesion and the morale of the workforce” (1995: 96). Although this definition concentrates on 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, and outsourcing) to the other subprocesses, they distinguish themselves by their focus on a strong relationship between source and seeker. Because the focus of the current study is on external knowledge, it is not surprising 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 maintenance function and the cooperation function are not identical, they have considerable common features. This leaves us with one potential pair of functions: the adaptive function of Stein and Zwass [13] and the passive 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 and Zwass and Quinn and Rohrbaugh emphasize the openness and receptivity of an organization as important features of this function. Although the two passive search subprocesses in the current study (accidental discovery 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 have considered it already in the initial stages of this study. Rather than finding the results surprising, should we blame ourselves for not having done an appropriate literature review? We think not, since the OEF model has, 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 OEFs to the process under study. However, with hindsight we think the fit of the OEF model is not completely surprising. Stein and Zwass [13] have suggested that each of the four OEFs rests on the foundation of all MFs. Given this suggestion it is not completely surprising that the three-stage model was not confirmed within 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 provide sufficient 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 EKI process. Since we did not intend to test it, our operationalization of EKI was poor with respect to the OEF model. Future research should operationalize EKI starting from the OEF model. A confrontation of the work of Stein and Zwass [13] and Quinn and Rohrbaugh [14] with the results of the current study provides directions for such research. Following their theory, goal setting and evaluation should be included as items for goal-attainment. We therefore suggest adding a goal setting and a goal evaluation item. For the integrative 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-maintenance function we suggest to pay more attention to cohesion and morale, for example by introducing items for interorganizational 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 active form — for example in terms of environmental scanning, correlations will be positive. As a result, we also expect that an inclusion of this function in the model will yield acceptable results for a confirmatory factor analysis. In addition to further quantitative research we also suggest doing additional qualitative research to 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. Stein and Zwass [13] argue that the OEFs should be supported by different subsystems. Extending this argument to the EKI process would suggest that four EKI subsystems should be distinguished because users' expectations and system interactions are different for each function. This extension is not as obvious as it seems, because Stein and Zwass' [13] analysis is on organizations, whereas ours is on a process, which in itself contributes to (or is part of) one or more of the four OEFs. Despite of this reservation and because Stein and Zwass' [13] work is rooted in social systems theory [51] – which is supposed to be applicable at different levels – we propose however that also on this level four subsystems should be designed. Similar to Stein and Zwass, Table 6 suggests meta requirements in terms of subprocesses to be supported, and meta design features in terms of types of software components that can support that subsystem. The subprocesses to be supported follow direct from our study, since; after all, it was the grouping of subprocesses in the exploratory factor analysis that made us consider the OEF framework. 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 IS classifications in two important ways. Firstly, whereas many other authors classify IS according to the MF they support [52–54], Table 6 is ordered to OEFs. Secondly, Table 6 is more than a classification per se.

Table 6 Proposed consequences for EKI IS design EKI subsystem Goal attainment

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

Intentional search (ID) Written form (AC) Physical objects (AC) Courses (AC) Application (UT) Goal setting (not in this study) Goal evaluation (not in this study) Integrative Storage and Diffusion (UT) Internalization (UT) Exploitation (UT) Pattern maintenance People transfer (AC) Cooperation (AC) Outsourcing (AC) Teambuilding (not in this study) Adaptive Accidental discovery (ID) Unsolicited presentation (ID) Environmental scanning

Search engines, catalogues EDI, transaction systems, downloads CAD/CAM systems, measurement systems Online training systems, e-learning systems CAD/CAM systems, databases at the workplace Forecasting systems, planning systems, DSS, MIS Planning and evaluation systems Shared databases, intranets, e-mail, document management Workflow systems, project planning software, Lessons learned, best practices HRM systems, online job center Groupware, e-mail, video conferencing, chat software Online partner finding system Management games, team composition software Communities of practice, professional portals Filtering systems Web crawlers, e-mail alerts, pattern recognition systems

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Markus and Keil [12] remarked that if a system supports only part of a process, this results in less than optimal performance. Consequently, we propose that the software components for each OEF should be considered as part of a coherent subsystem rather than as systems in themselves. We expect that users will interact simultaneously with systems grouped under one function more often than with systems grouped under different functions. The proposition that software components should be designed as part of a coherent whole is in itself not new. Table 6, however, provides some tentative guidelines on which components should be considered together in design and which could be designed more independent of each other. The main difference with existing work is that this study suggests that the coherence is not caused by MFs, but by OEFs. Although we did not provide detailed implications, we believe to have provided significant empirical input for further research on IS design, in particular by analyzing the consequences 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 specific consequences for design and tuning of various ISs. The results of this study also have broader implications for the conceptualization of organizations and the development of their functions. The corroboration of the OEF model in a study on a process rather than on an organizational system extends its reach and relevance substantially. We expect that research from the OEF perspective on other organizational processes than the EKI process will provide interesting new insights in their dynamics and contribution towards organizational effectiveness. 6. Conclusion We started our analysis with the question whether the three-stage model of EKI is empirically valid in high-tech SMEs, and if so, what are consequences for the design of information systems that are to support this process. Given the results of our analysis we cannot conclude differently on the first part of this question than that the three-stage model should be rejected. However, an alternative four-functions model emerged that has remarkable similarities with the four OEF model of Stein and Zwass [13] and Quinn and Rohrbaugh [14]. The emergence of this model from the data seems a direct corroboration of the general 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 a specific process in high-tech SMEs. Despite the limitations of our study we find the fit of the OEF model with our data remarkable. Regarding the second part of our question, we have suggested that designers of ISs that are to support the EKI processes should derive meta-requirements and meta-designs from the four OEFs rather than from the three stages. Rather than designing isolated systems, we proposed that IS should be designed according to the OEF they are to support because users have different expectations and interact differently with IS in each function. As such, the four-function model rather than the three-stage model of EKI should serve as a tentative kernel theory for IS development. Acknowledgements The authors thank the editor and the anonymous reviewers for their constructive comments and suggestions. This research was partly funded by the European Community in the project ‘Knowledge Integration 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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[46] R. Ruggles, The state of the notion: knowledge management in practice, California Management Review 40 (3) (1998) 80–89. [47] M.A. Lyles, J.E. Salk, Knowledge acquisition from foreign parents in international joint ventures: an empirical examination in the Hungarian context, Journal of International Business Studies 27 (5) (1996) 877–903. [48] M.D. Santoro, S. Gopalakrishnan, The institutionalization of knowledge transfer activities within industry–university collaborative ventures, Journal of Engineering and Technology Management 17 (2000) 299–319. [49] W.J. Doll, G. Torkzadeh, The measurement of end-user computing satisfaction, MIS Quarterly 12 (2) (1988) 259–274. [50] D.T. Campbell, D.W. Fiske, Convergent and discriminant validation by the multitrait–multimethod matrix, Psychological Bulletin 56 (1) (1959) 81–105. [51] T. Parsons, General theory in sociology, in: R. Merton, L. Broom, L.S. Cotrell Jr. (Eds.), Sociology Today: Problems and Prospects, Basic Books, New York, 1959, pp. 3–37. [52] M. Alavi, D.E. Leidner, Review: knowledge management and knowledge management systems: conceptual foundations and research issues, MIS Quarterly 25 (1) (2001) 107–136. [53] D. Binney, The knowledge management spectrum — understanding the KM landscape, Journal of Knowledge Management 5 (1) (2001) 33–42. [54] M. Nissen, M. Kamel, K. Sengupta, Integrated analysis of design of knowledge systems and processes, Information Resources Management Journal 13 (1) (2000) 24–43. Jeroen Kraaijenbrink is assistant professor at NIKOS, the Dutch Institute for Knowledge Intensive Entrepreneurship at the University of Twente. He holds a MSc and a PhD in Industrial Engineering and Management and a MSc in Public Administration from the University of Twente. His research interests include knowledge intensive entrepreneurship, knowledge management in networks, organization theory, and social systems theory. Fons Wijnhoven is associate professor of Knowledge Management and Information Systems at the University of Twente. He researches the development and exploitation of information services and organizational memories in the University's Center of Telematics and IT. In the last decade over 50 of his articles appeared in academic journals and peer reviewed conference proceedings. He published books on organizational learning, 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 for Knowledge 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 received his 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-chaired the High Tech Small Firms conference series held in Enschede in 2004 and 2006. He is a member of the Dutch Flemish academy of entrepreneurship, European summer school on entrepreneurship, and delivered key notes to conferences in The Netherlands, South Africa and Russia.