thesis_Daniela Noethen_print final - Jacobs University

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Multilevel Investigation of Antecedents of Knowledge Sharing and Seeking in Teams When individual-level predictors were added , (1) and when team-level predictors were added . (2) In order to test our general propositions that influences on sharing and seeking differ as well as influences at the individual and team level, we defined contrasts for the respective coefficients in HLM (Enders & Tofighi, 2007; Hox, 2002). A contrast is a composite hypothesis on a set of parameters that postulates, for example, that parameters are equal. Contrasts are tested, automatically within HLM 6.06, with an asymptotic chi-square test (Hox, 2002; Raudenbush & Bryk, 2002; Tabachnick & Fidell, 2001). To ascertain if individual age has the same association with both sharing and seeking, we tested the null hypothesis represented by the contrast 1* coefficient for individual-level age (sharing) + (-1)* coefficient for individual-level age (seeking) = 0. Likewise, to determine if age has the same association with sharing at the individual and team level, the null hypothesis represented by the contrast 1* coefficient for individual-level age (sharing) + (-1)* coefficient for team-level age (sharing) = 0. 2.5. Results 2.5.1. Descriptive statistics Table 1 depicts the descriptive statistics, ICC(1)s, reliability coefficients, and zero-order correlations. However, estimations in this correlation matrix should be interpreted with caution, since they only depict individual level zero-order correlations and do not take the non-independence of the data into account. 60
Multilevel Investigation of Antecedents of Knowledge Sharing and Seeking in Teams Table 1 Descriptive Statistics and Individual Level Zero-Order Correlations M SD ICC(1) 1 2 3 4 5 6 7 8 9 10 11 1. Age 39.87 8.21 0.15 - 2. Intrinsic Motivation 3.46 0.49 0.08 -.16*** .66 3. Extrinsic Motivation 2.54 0.62 0.03 -.14** .29*** .67 4. Job Autonomy 2.69 0.61 0.22 -.09* .19*** .17*** .69 5. Experience 9.15 7.89 0.29 .53*** -.18*** -.15*** -.15*** - 6. White collar a 1.85 0.35 - -.18*** .20*** .15*** .25*** -.39*** - 7. Full-time b 1.91 0.28 - -.05 -.07 † .08 † .08 † -.01 -.11** - 8. Company dummy 0.39 0.49 - -.23*** .20*** -.03 .11** -.14** .34*** -.30*** - 9. Climate Innovativeness 3.32 0.83 0.21 -.13** .23*** .21*** .38*** -.15** .14** .03 .17*** .78 10. Knowledge Sharing 4.39 0.95 0.04 .05 .23*** .13** .21*** -.01 .09* .12** .01 .25*** .86 11. Knowledge Seeking 3.89 1.06 0.10 -.33*** .23*** .29*** .13** -.37*** .24*** .07 † .05 .24*** .27*** .86 Note. N=571. Cronbach’s alphas in the diagonal. a White collar coded as 1 = blue collar worker, 2 = white collar worker. b Full-time coded as 1 = part-time employment, 2 = full-time employment. † p < .10. * p < .05. ** p < .01. ***p < .001. 61
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Knowledge Transfer in Teams and Its
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Table of Contents Contents Acknowle
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Acknowledgements Acknowledgements T
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Acknowledgements Last but not least
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Intergenerational Knowledge Transfe
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Preventing Knowledge Loss When Empl
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the demographic change. Accordingly
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analysis was dyadic knowledge trans
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questions. The limitations that wil
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would be very different than for a
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“there are situations where knowl
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employing the distinctions recommen
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valuable addition to the existing l
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covers a very specific area of know
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the importance of including lower l
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Foss, N. J., Husted, K., & Michailo
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Watson, S., & Hewett, K. (2006). A
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