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David Grosse Kathoefer and Jens Leker Never<strong>the</strong>less, our study tries to improve <strong>the</strong> measurement model for academic NIH making it more robust. Therefore, we rely on existing measures from literature and adapt <strong>the</strong> wording to <strong>the</strong> university context. We propose a reflective four-factor model: NIH 1: Knowledge <strong>of</strong> external partners NIH 2: Performance <strong>of</strong> external knowledge NIH 3: Trust in own competence NIH 4: Sourcing <strong>of</strong> knowledge The first construct was taken from <strong>the</strong> study <strong>of</strong> Bock, Zmud, Kim and Lee (2005). Their factor “Sence <strong>of</strong> Self-Worth” evaluated <strong>the</strong> helpfulness <strong>of</strong> <strong>the</strong> participants’ knowledge sharing for o<strong>the</strong>rs. This helpfulness was replaced by <strong>the</strong> knowledge <strong>of</strong> external partners and <strong>the</strong> point <strong>of</strong> view was changed to <strong>the</strong> participant. Additionally, <strong>the</strong> wording was adapted to <strong>the</strong> university setting. The second factor was developed by Mehrwald (1999). The item asking for financial expenses for <strong>the</strong> projects was eliminated in advance as it did not fitted <strong>the</strong> respective context. The third construct was also taken from Mehrwald’s study. In both cases, <strong>the</strong> wording <strong>of</strong> all items was changed from technology orientation to <strong>the</strong> broader scope <strong>of</strong> knowledge. The last factor was initiated by an item <strong>of</strong> McDonough and Barczak (1991) asking for <strong>the</strong> extent to which technological developments for a product have come from internal or external sources, respectively. In <strong>the</strong> course <strong>of</strong> <strong>the</strong> pre-tests, this item was enlarged to a factor, differentiating between several stages <strong>of</strong> a typical academic experimental research process. These four constructs are fur<strong>the</strong>r merged to <strong>the</strong> reflective second-order factor coined NIH. To secure unidimensionality, all single factors were assessed in principal component analyses. All items <strong>of</strong> a single construct should show higher factor loadings than 0.5 and in combination form one single factor (Hair, 2006). The respective items should fur<strong>the</strong>r explain more than 50% <strong>of</strong> <strong>the</strong> total variance (Hair, 2006). As a reliability measure, Cronbach’s alpha was used (Cronbach, 1951). In this context, early stage studies and mature scales can be distinguished. For <strong>the</strong> former, a cut<strong>of</strong>f value <strong>of</strong> 0.6 can be regarded as sufficient, whereas for <strong>the</strong> latter a value <strong>of</strong> 0.7 is <strong>of</strong>ten required (Hair, 2006). Although <strong>the</strong> scales <strong>of</strong> this study are taken from existing literature, <strong>the</strong> transfer to <strong>the</strong> new context shows an exploratory nature. Thus, we accept a threshold value <strong>of</strong> 0.6 regarding Cronbach’s alpha. In total, one item <strong>of</strong> each <strong>of</strong> <strong>the</strong> factors NIH 3 and NIH 4 had to be eliminated, ending in α = 0.677 (for fur<strong>the</strong>r details concerning factor loadings and quality criteria see Appendix). In a second step, a confirmatory analysis was applied to validate our measurement model, including predictor and control variables. Although multivariate normal distribution was not given, we used maximum likelihood as <strong>the</strong> procedure <strong>of</strong> choice. West, Finch and Curran (1995) demonstrate that, as long as skewness and kurtosis do not exceed values <strong>of</strong> two and seven, respectively, maximum likelihood can be regarded as a robust technique providing reasonable results. During <strong>the</strong> analysis, modification indices gave hints that five error variances should correlate. Finally, <strong>the</strong> global criteria are very well fulfilled (Comparative Fit Index (CFI) = 0.953; Root Mean Square Error <strong>of</strong> Approximation (RMSEA) = 0.039; Standardized Root Mean Residual (SRMR) = 0.046) (Hu and Bentler, 1999). To assess <strong>the</strong> single construct measurements, different quality criteria, like factor and item reliability and average variance extracted, were employed in this step (Herzog, 2011). Fur<strong>the</strong>r, we used <strong>the</strong> Fornell- Larcker criterion to test for discriminant validity (Fornell and Larcker, 1981). Overall, we could show that <strong>the</strong> measurement model <strong>of</strong> NIH fulfills <strong>the</strong> statistical requirements to use it in <strong>the</strong> fur<strong>the</strong>r analysis. 3.2.2 Independent variables The operationalizations <strong>of</strong> <strong>the</strong> independent constructs were tested in <strong>the</strong> same way as <strong>the</strong> dependent one. The first predictor variable, “Opinion <strong>of</strong> colleagues”, is based on <strong>the</strong> construct “Attitude towards <strong>the</strong> act” by Ajzen and Fishbein (1980). Using a semantic differential, <strong>the</strong> construct was confirmed in several studies (e.g. Chang, 1998, O'Hara, Netemeyer and Burton, 1991). The introducing sentence was adapted to <strong>the</strong> respective context (α = 0.969). Fur<strong>the</strong>r, “Internal communication” was taken from Holtorf (2011). Here, it was used to measure communication inside teams. Therefore, we transferred it to <strong>the</strong> organizational level <strong>of</strong> <strong>the</strong> university (α = 0.933). “Work routine” was based on “routine discretion” in <strong>the</strong> study <strong>of</strong> Kelley, Longfellow and Malehorn (1996). In <strong>the</strong> run <strong>of</strong> <strong>the</strong> analysis, two items had to be deleted due to missing cut-<strong>of</strong>f criteria. Hence, <strong>the</strong> construct consisted only <strong>of</strong> three items (α = 0.747). Finally, “Group pride” was adapted from Tyler, Degoey and Smith (1996). Here, we chose <strong>the</strong> construct analyzed in <strong>the</strong> university setting as it best fits our context as well. During 364
David Grosse Kathoefer and Jens Leker analysis, again one item had to be eliminated (α = 0.814). All o<strong>the</strong>r exogenous variables were measured as single-item scales due to <strong>the</strong>ir unambiguous nature. Additionally, we employed <strong>the</strong> age <strong>of</strong> <strong>the</strong> scientist, <strong>the</strong> type <strong>of</strong> research (basic or applied) and his research discipline as controls in <strong>the</strong> structural equation model. All three may distort <strong>the</strong> influencing factors on NIH significantly. Table 1 contains <strong>the</strong> correlation matrix <strong>of</strong> all employed variables and constructs. 3.3 Methodology To test our hypo<strong>the</strong>ses, we employ structural equation modelling (SEM) disclosing causal relationships with multiple independent and dependent variables (Bentler, 1988). A SEM consists <strong>of</strong> a measurement model and a structural model (Hair, 2006). As we already assessed <strong>the</strong> quality <strong>of</strong> <strong>the</strong> measurement model in a confirmatory factor analysis (CFA), <strong>the</strong> structural model has to be tested in a second-step (Hair, 2006). One <strong>of</strong> <strong>the</strong> most wide-spread estimation methods in SEM is <strong>the</strong> maximumlikelihood (ML) procedure. Similar to <strong>the</strong> CFA, we also employ this estimation technique for <strong>the</strong> SEM. To evaluate <strong>the</strong> overall fit <strong>of</strong> <strong>the</strong> model, same quality criteria as in <strong>the</strong> CFA are used. Table 1: Correlation matrix Variables Scale Mean St. dev. NIH a 1-7 3.95 0.78 1.00 Opinion <strong>of</strong> colleagues a Internal communication a 1-7 2.17 1.09 .54*** 1.00 1-7 4.21 1.33 -.08 -.11* 1.00 Work routine a 1-7 3.87 1.37 -.04 -.02 -.09 1.00 Group pride a 1-7 4.74 1.40 -.11* -.10* .41*** .05 1.00 Industry collaboration a University collaboration a Years at university a Quality <strong>of</strong> past projects a 1 2 3 4 5 6 7 8 9 10 11 12 13 Open 0.77 0.78 .10* .05 -.06 .03 .11* 1.00 Open 1.96 0.67 -.14* -.12* .14** -.06 .15** .22*** 1.00 Open 9.68 8.18 .04 .04 -.12** -.04 -.07 .08 -.02 1.00 1-7 5.05 1.16 -.42*** -.27*** .11* -.10* .02 .07 .10* .15** 1.00 Age a Open 49.52 8.39 -.02 -.00 -.14** -.01 -.09 .10* -.04 .81*** .09 1.00 Type <strong>of</strong> research a 1-20 5.51 4.52 .01 -.01 -.15** .01 .01 .56*** .03 .11** .03 .13** 1.00 Dummy chemistry b 0-1 0.30 0.46 .13** .11** -.04 -.01 .06 .37*** .09* -.01 .01 -.06 .20*** 1.00 Dummy physics b 0-1 0.34 0.47 -.06 -.08 .07 -.16*** .02 -.15** -.03 .01 .08* -.01 -.07 -.47*** 1.00 Notes: N = 477; factor scores were calculated using summated scales; Industry Collaboration and University Collaboration are transformed values using <strong>the</strong> square root function, a correlations are based on Pearson’s R; b correlations are based on Kendall’s Tau-b; * p < 0.05; ** p < 0.01; *** p < 0.001. 4. Results Taking <strong>the</strong> measurement model from our CFA and implementing it into our hypo<strong>the</strong>ses framework, we construct our structural model (Byrne, 2001). The structural model represents <strong>the</strong> suggested regression paths and completes <strong>the</strong> SEM. According to <strong>the</strong> proposed relationships, we set up a model representing all hypo<strong>the</strong>ses as regression paths between <strong>the</strong> respective variables and latent constructs. Testing this model shows a ra<strong>the</strong>r bad fit as CFI only amounts to 0.839, RMSEA to 0.068 and SRMR to 0.095. Therefore, we respecify our model to achieve a better fit to <strong>the</strong> covariance matrix (Hair, 2006). The final model is presented in figure 2 and demonstrates an adequate fit, keeping in mind that a structural model can never achieve a better fit than its underlying measurement model (Hair, 2006). CFI is at 0.935, RMSEA at 0.043 and SRMR is at 0.070. Taking <strong>the</strong>se values as acceptable, we use <strong>the</strong> final model for evaluating <strong>the</strong> hypo<strong>the</strong>ses. 365
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Abdallah Al-Shawabkeh, Alexander K<
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Table 1: Inn-KM approach contents 2
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Knowledge Dynamics and Organisation
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Eckhard Ammann We distinguish three
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Eckhard Ammann quality dimension ar
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Eckhard Ammann The second cycle, Cy
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Figure 7: Supervised learning-by-do
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Identifying and Ranking the
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Manouchehr Ansari et al users <stro
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2.5 leadership and strategy Manouch
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Manouchehr Ansari et al conducted s
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Serious Games in the</stron
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Albena Antonova and Anandasivakumar
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Albena Antonova and Aniko Csepregi
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Seyed Esmaeil Asgharpour and Gholam
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Attitude of Senior
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4.5 Comparative Advantage Seyed Esm
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Contextual Adaptive Visualization E
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2.4 Rights transfer Simona-Clara B
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A Framework for the</strong
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Leila Beig et al Additionally, Wei
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Leila Beig et al The proposed KM au
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Leila Beig et al source context Ele
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Leila Beig et al Figure 1: Learning
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Leila Beig et al Figure 3 visually
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Leila Beig et al based on collectiv
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Experiential Knowledge Creation Pro
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Didiosky Benítez et al Figure 1: E
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Didiosky Benítez et al meanings ac
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Pavel Bogolyubov Figure 1: number <
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4. Results and discussion Pavel Bog
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Pavel Bogolyubov Knowledge Manageme
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Pavel Bogolyubov Table 3: Countries
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Pavel Bogolyubov McNamee, R. C., N.
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Ettore Bolisani, Francesca Gambarot
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Table 2: Synthesis
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Ettore Bolisani, Marco Paiola and E
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Strategies for Increasing Knowledge
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Constantin Bratianu, Adriana Agapie
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Klaus Bredl, Amrei Groß and Jane F
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Camelia Burja and Vasile Burja Appr
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∀ , i ≥ 0 v u i where: ui repre
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Camelia Burja and Vasile Burja Tabl
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Camelia Burja and Vasile Burja info
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Camelia Burja and Vasile Burja Jack
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Adriana Schiopoiu Burlea With respe
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Adriana Schiopoiu Burlea 3. ESF rol
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Adriana Schiopoiu Burlea In <strong
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3.2 Discussions Adriana Schiopoiu B
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Adriana Schiopoiu Burlea Nonaka, I.
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Simon Cadez and Vlado Dimovski It i
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Why Should I Share my new ideas? Cu
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Francesco Calza, Rossella Canestrin
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National Knowledge Management Strat
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Behiye Çavuşoğlu and Mustafa Sag
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Implementing a work-life balance cu
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Juan-Gabriel Cegarra-Navarro, Mª E
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The Influence of R
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Huei-Fang Chen and Yi-Wen Lin outco
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3.2.4 Knowledge-sharing Huei-Fang C
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Huei-Fang Chen and Yi-Wen Lin that
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Huei-Fang Chen and Yi-Wen Lin Hair,
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2.1.2 Social capital Marguerite Cro
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Marguerite Cronk 2.3 Social capital
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Marguerite Cronk Figure 4: The rela
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An Exploratory Study of</st
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Françoise de Viron, Thomas Lederer
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4.1.2 The researchers Françoise de
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The Application of
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Nasser Easa and Robin Fincham 3. Th
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Nasser Easa and Robin Fincham knowl
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Network Management as a way to Mana
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Eva Eckenhofer An
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Initiation Configuration Interessem
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Eva Eckenhofer Eve
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The Essence of Kno
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Emmanuel Innocents Edoun and Valden
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4. Case study and analysis Emmanuel
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2. The PUS-project Anandasivakumar
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Jamal El-Den and Xin Zhou This rese
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Jamal El-Den and Xin Zhou Boyce, C.
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Tiit Elenurm development visions. C
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References Tiit Elenurm Accorsi, F.
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Knowledge Workers: A Typology Frame
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Jiří Franek and Eva Grublova crea
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Jiří Franek and Eva Grublova Know
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Middle Managers’ Maturity <strong
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Zoltán Gaál et al investigated m
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Zoltán Gaál et al characterised b
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Developing a Knowledge Strategy Usi
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3.1 TKI Regression results Harold H
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Understanding the
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3.1 Acquire Ciara Heavin and Freder
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4.2.1 Discussion - Case 2 Tore Hoel
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Using Web 2.0 Technologies to Suppo
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Expert Knowledge: Does it Help or H
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Selvi Kannan are people who have ex
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Integration of Kno
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Knowledge Management Across <strong
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5. Conclusions Aino Kianto, Tatiana
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Facilitating Knowledge Sharing in V
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Andrea Kő, Péter Fehér and Krisz
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Modelling Knowledge Sharing Into a
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From Knowledge Acquisition to Knowl
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