thesis_Daniela Noethen_print final - Jacobs University

Multilevel Investigation of Antecedents of Knowledge Sharing and Seeking in Teams
theoretical approaches and looking at very different situations, a large variety of knowledge
transfer antecedents has been proposed and studied in the last decade (Argote et al., 2000;
Foss et al., 2010; van Wijk, Jansen, & Lyles, 2008)
Nevertheless, results are somewhat inconclusive and inconsistent, there are still
considerable gaps, and it is difficult to see clear patterns regarding the most important factors
leading to employees’ knowledge transfer (Argote, 1999; Foss et al., 2010; Ko, Kirsch, &
King, 2005; van Wijk et al., 2008). We argue that the picture becomes clearer when effects
are disentangled with respect to two factors; that is, if knowledge sharing and knowledge
seeking are analyzed as two different behaviors, and if the level of analysis is taken into
account. For example, Gupta and Govindarajan (2000) observed that not all behaviors that
constitute knowledge transfer have the same antecedents. In their study, they found that, at the
subsidiary level, motivation was decisive for knowledge sharing, but not for knowledge
receiving. Furthermore, it has been found in many research areas that a predictor can work
differently at different levels (Bliese, 1998; Chan, 1998; Firebaugh, 1978). In the public
health or educational literature, this problem is well known. For example, individual poverty
may have a negative impact on health outcomes (due to bad nutrition, less money for medical
expenses etc.), but the impact of aggregated neighborhood poverty on health might even be
stronger, as the lack of a medical infrastructure or sports facilities adds to the aforementioned
effects (Schwartz, 1994). Surprisingly, although this problem of differing effects is well
known in other disciplines and the method of analyzing multiple levels simultaneously is
gaining increasing importance (Enders & Tofighi, 2007), the distinction between effects at
different levels has not yet received much attention in the knowledge transfer literature (Foss
et al., 2010; Quigley et al., 2007).
We suggest that to distil the most important influences on knowledge transfer, it is
necessary to take a closer look and specify for a certain predictor which transfer behavior it
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