thesis_Daniela Noethen_print final - Jacobs University

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
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