thesis_Daniela Noethen_print final - Jacobs University

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Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective at different levels (e.g., individual level and team level). The integration of these two approaches, the social network approach and multilevel analysis, has been first proposed by Snijders, Spreen, and Zwaagstra (1995) and then been further developed by van Duijn and colleagues (1999). According to the latter group of authors, the application of multilevel analysis to social networks is rather simple: when the relations between actors constitute the dependent variable, then these relations between actors are located at the first, dyadic level, and the individual actors within which the relations are nested are located at the second, individual level. In the case of the present research model, the teams that the individual employees are nested in constitute a third level, i.e., the team level. Thus, by combining the social network approach with a multilevel analysis, we have an ideal framework to test our model, we furthermore enrich the present literature by taking a third, i.e., the team level into account, and moreover answer the call for multilevel approaches, which include the micro level of analysis, in knowledge transfer research (Foss et al., 2010). 3.4. Method 3.4.1. Sample and data collection The study was conducted at three branches of a German public administration. This setting was chosen as it presents a stable environment for employees in which experience and knowledge can accumulate with age, other than in some rapidly changing contexts. N=349 online-questionnaires were filled out by employees and supervisors stemming from 72 teams. Two teams which formed subunits of another, larger team in the data sample had to be excluded to avoid the double inclusion of the same dyads. Eleven teams had to be eliminated from the data set as only one team member each had filled out the questionnaire, and no dyadic information could be computed. Thus, the sample resulted in N=1940 dyads, stemming from 331 participants (283 employees, 48 supervisors) belonging to 59 teams. 102
Intergenerational Knowledge Transfer in Work Teams: A Multilevel Social Network Perspective Participants had a mean age of 48.3 years (SD=9.4) and a mean team tenure of 7.0 years (SD=6.5), with 22% of them working part-time. After an open information session, prospective participants received an email-invitation to take part in the study, and had three weeks to fill in the online questionnaire. The overall response rate was 58.4%. This sample was especially appropriate to test our research question as the age structure within the sample already mirrored very well the demographic change that many organizations will be facing; 4.8% of the sample were younger than 30 years, 30.8% between 30 and 45 years, and 64.4% over 45 years old. Thus, the perceived pressure to prevent knowledge loss was large. 3.4.2. Measures As the study was conducted in German, scales stemming from the English-speaking literature were translated into German, then independently back-translated into English and compared to the original version. Where there were inconsistencies, they were resolved by the translators (a procedure recommended by Brislin, 1980). Independent variables. Age was registered via birth date. To compute the age difference for a dyad, the age of the recipient was subtracted from the age of the source. For age diversity within the team, the standard deviation (corrected for a bias due to group size) SD N was calculated according to Biemann and Kearney (2010) in the following manner: ∑ , (1) with , (2) and , (3) 103
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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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