thesis_Daniela Noethen_print final - Jacobs University

Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective
Each employee can act as a source and transfer knowledge to every other team colleague
(e.g., knowledge transfer from A to B, KT A to B , with A as source) as well as act as a recipient
and receive knowledge transfer from each of the team colleagues (KT B to A , with A as
recipient).
Following such a design, each knowledge transfer from one employee to another (i.e., each
dyadic transfer) constitutes an observation. Dyadic knowledge transfer is also the unit of
analysis in the research model presented in Figure 4. As mentioned by several authors (Levin
& Cross, 2004; Reagans & McEvily, 2003; van Duijn, van Busschbach, & Snijders, 1999),
this dyadic level of analysis bears the problem of non-independence of observations or data:
Considering Figure 4a, KT A to B will be more similar to KT A to C than to KT C to D , simply
because, in both relationships, the source is the same (A) and influences both transfers in a
specific manner. Using the terminology of multilevel analysis (Bliese, 1998; Hofmann, 1997;
Hox, 2002; Raudenbush & Bryk, 2002), dyads are nested within sources (i.e., employees).
Furthermore, as we do not only look at different dyads and different employees, but also at
different teams and their characteristics, these considerations have to be taken one step further
(see Figure 4b). Following the same line of argument, KT A to B should be more similar to KT C
to D (because both A and C belong to the same team) than to KT E to F . Thus, for our research
model, dyadic knowledge transfer relations are not only nested within employees (sources),
but employees (sources) are also nested within teams.
One statistical solution for the problem of non-independence is multilevel analysis, also
known as hierarchical linear modeling. This method is often used when students are nested
within schools, employees within teams, or several observations across time within one
individual (Bliese, 1998; Hofmann, 1997; Hox, 2002; Raudenbush & Bryk, 2002). The
advantage of multilevel modeling is that it not only allows the analysis of data with a nested
structure, but also enables the simultaneous investigation of effects of independent variables
101