thesis_Daniela Noethen_print final - Jacobs University

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Multilevel Investigation of Antecedents of Knowledge Sharing and Seeking in Teams done to account for possible differences between contexts in different business units and the two organizations. The climate supporting innovativeness was measured with three items (e.g., “My team encourages suggesting ideas for new opportunities.”) on a five-point Likert scale ranging from “strongly disagree” to “strongly agree” (Bock et al., 2005). Individual ratings of the climate supporting innovativeness were aggregated to the team level, with a mean r WG(J) of 0.75 (Cohen, Doveh, & Eick, 2001; James, Demaree, & Wolf, 1984), an ICC(1) of 0.21 and ICC(2) of 0.69 (Shrout & Fleiss, 1979; McGraw & Wong, 1996), showing that there is sufficient agreement between team member ratings to justify aggregation. 2.4.3. Analytic strategy and statistical approach We first conducted a missing variable analysis and found that missing data did not amount to more than 5% in any of the variables. Monte Carlo studies have shown that with such a small percentage of missing data, employing the expectation-maximization (EM) algorithm to replace them produces less bias than listwise (Graham & Donaldson, 1993; Malhotra, 1987; Muthen, Kaplan, & Hollis, 1987) or pairwise deletion (Muthen et al., 1987). Therefore, missing data were filled using EM statistics (Dempster, Laird, & Rubin, 1977). The EM algorithm is an iterative process with two steps for each iteration; the first step computes expected values based on observed data and estimates from the last iteration, the second step provides maximum-likelihood estimates of the parameters in question based on values from the first step. We then computed simple descriptives and zero-order correlations, conducted ANOVAs to compute ICC(1)s, and calculated the r WG(J) . To test our hypotheses, we had to take into account that subjects who originate from different teams within an organization, as in the present sample, are nested within teams and data are therefore non-independent (Bliese, 1998; Hoffman, 1997; Hox, 2002; Raudenbush & Bryk, 2002). If non-independence is not accounted for, standard errors are too small and 58
Multilevel Investigation of Antecedents of Knowledge Sharing and Seeking in Teams results from regression-based statistics are biased. Consequently, we used multilevel modeling to test our hypotheses, employing the HLM 6.06 statistical package. Since we wanted to demonstrate that influences of predictors differ with regard to sharing and seeking, we needed to directly compare these influences. Thus, we used multivariate multilevel modeling techniques. These techniques allow for more than one dependent variable by adding another level to the model (the outcome level in addition to the person and team level). This additional level only contains the two dependent variables. Thereby, we could create a model with both, sharing and seeking, as dependent variables instead of analyzing two separate models. This allowed us to directly compare the coefficients for influences on the two transfer behaviors (Hox, 2002). For the hypotheses tests, we built two such multivariate multilevel models in the following manner: In a first step, we entered the control variables at the individual level, centered around their group mean, and at the team level, centered around their grand mean (Model 1). These and other centering decisions were based on recommendations by Enders and Tofighi (2007). In a second step, we entered age, intrinsic and extrinsic motivation, and job autonomy at the individual level, again centered around their group mean, and team mean intrinsic motivation, team mean age and team mean job autonomy at the team level, centered around the grand mean (Model 2). In order to check if the two multilevel models calculated could account for variance in the dependent variables, goodness of fit equivalent to the R 2 in regression statistics was calculated following a procedure suggested by Bryk and Raudenbush (2002). First, a one-way ANOVA model was computed in HLM with knowledge sharing and knowledge seeking as dependent variables. The resulting values for σ 2 (within-group variance) and τ 00 (between-group variance) from this baseline model were then compared with σ 2 and τ 00 values from subsequent models in the following manner: 59
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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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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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