thesis_Daniela Noethen_print final - Jacobs University

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Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective where N is the group size and Γ is the Gamma function. A table of q values for different group sizes can be found in Cureton (1968). Team tenure was measured with one item, specifying years and months of affiliation with the present team. The difference in team tenure for a dyad was then calculated in equivalence to the age difference, subtracting the recipient’s team tenure from the source’s team tenure. Knowledge transfer. In accordance with previous research, we measured knowledge transfer as a frequency (Cummings, 2004; Kankanhalli et al., 2005). Following a round robin design (Gleason & Halperin, 1975; Warner, Kenny, & Stoto, 1979) or sociometric technique with a fixed roster (Reagans & McEvily, 2003; Wasserman & Faust, 1994), we supplied participants with a coded list of the names of their team colleagues (information obtained from the respective coordinating unit within the administration) and asked them how often they had a) shared knowledge with and b) sought knowledge from each of their colleagues within the last three months, rating the frequency on a five-point Likert scale ranging from “very seldom” to “very often”. Answers in the survey were coded accordingly, so as to allow for assignment of colleague ratings to the right source and recipient without including actual names in the questionnaire. With this procedure, we obtained, for each dyad, information about four processes; the frequency of the source sharing knowledge with the recipient, of the recipient seeking knowledge from the source, the frequency of the recipient sharing knowledge with the source, and of the source seeking knowledge from the recipient. To receive an estimation of the frequency of transfer from a source to a recipient within a dyad, two items, namely the source’s rating concerning the frequency of sharing knowledge with the recipient, and the recipient’s rating concerning the frequency of seeking knowledge from the source, were averaged. By averaging the subjective information from two sources, we thus obtained a more objective measure of the dependent variable. The correlation between the source’s and recipient’s rating reached 0.47. Similar to the calculation of an 104
Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective ICC(1), we analyzed the distribution of variance for knowledge transfer across the different levels (see section 3.4.3. for the respective formula); 77% of the variance was due to differences between dyads “within a person”, 11% of the variance was due to individual differences within teams, and 12% of the variance was due to mean differences between teams. According to Bliese (2000), values for ICC(1), in our case equivalent to the amount of variance located at the two higher levels, normally lie between 0.05 and 0.30, so the values obtained here are within a normal range. This shows that knowledge transfer was influenced to a certain degree by characteristics of the source and the team, suggesting nonindependence, and calling for a multilevel analysis. Control variables. To rule out alternative influences on the dependent variable, we controlled for several factors in our analyses. First, as posited by social exchange theory (Blau, 1964; Molm, 1997) and shown by several empirical studies (Bock et al., 2005; Kankanhalli et al., 2005; Wasko & Faraj, 2000), knowledge transfer within a dyad should be largely influenced by reciprocity. Accordingly, the frequency with which the source receives knowledge from the recipient (knowledge reception) should influence the frequency of knowledge transfer from source to recipient. Knowledge reception was measured in equivalence to knowledge transfer by averaging the source’s rating concerning the frequency of seeking knowledge from the recipient, and the recipient’s rating concerning the frequency of sharing knowledge with the source. Thus, we again averaged two subjective ratings to receive a more objective measure. The correlation was, as before, 0.47. The variance of the resulting variable was distributed across the three levels in the following manner: 83% of the variance was located at the dyadic level, 5% at the individual level, and 12% at the team level. According to the sociometric approach described to measure knowledge transfer and knowledge reception, the data of each dyad A-B appear 105
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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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Acknowledgements Last but not least
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General Introduction and of older w
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General Introduction in the smooth
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Outline Outline The core of the pre
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Outline on knowledge sharing, as we
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Knowledge, Knowledge Management, an
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Multilevel Investigation of Anteced
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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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