thesis_Daniela Noethen_print final - Jacobs University

In lab studies, knowledge contents can be created and manipulated, and the transfer of
knowledge can be measured, e.g., via observation. In field studies concerned with knowledge
transfer via databases, emails, or other electronic pathways, the knowledge transfer activities
are usually stored electronically and can then be analyzed. In both of these cases, you have a
rather objective measure of knowledge transfer. In survey studies, on the other hand, such an
objective measurement is not possible. Instead, researchers have to rely on subjective
statements of participants. In the extant literature, the measurement approach in such survey
studies has usually been to let participants either rate their agreement with statements such as
“In my work team, I have learned new things from my colleagues that only they knew.”
(Zárraga & Bonache, 2005, p.669; similar items in: Bock, Zmud, Kim, & Lee, 2005;
Kankanhalli et al., 2005; Cabrera, Collins, & Salgado, 2006), or rate the frequency of
knowledge transfer behaviors, e.g., “On average, how often did you share each type of
knowledge during the project with group members?” (Cummings, 2004, p. 357; similar items
in: Kankanhalli et al., 2005; Watson & Hewett, 2006). The latter approach was followed in
the present work.
Unfortunately, survey studies are not only inferior to lab studies and the analysis of stored
electronic data with respect to measurement objectivity, but also with respect to
characteristics or types of knowledge transfer that can be measured (e.g., explicit vs. tacit).
When, for example, knowledge transfer via electronic pathways is considered, researchers can
perform a content analysis of the knowledge transferred and differentiate between different
types or qualities of knowledge transfer. In survey studies, such categories have to be defined
beforehand, and with each additional category, you multiply the number of items used for
measurement. Furthermore, especially when you are dealing with participants with very
different occupations, as in the present dissertation, it is hard to define such different types of
knowledge and phrase items accordingly; examples of tacit knowledge for an IT administrator
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