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Relocations in terms of outsourcing to a non-affiliated company and offshoring, the cross-border relo- cation within the company, are widely used in recent years and in many cases cause collective em- …

Relocations in terms of outsourcing to a non-affiliated company and offshoring, the cross-border relo- cation within the company, are widely used in recent years and in many cases cause collective em- ployee layoffs. Even if one of the main intentions is the reduction of costs, relocations may not produce the highly anticipated financial benefits that most companies pursue. One reason is that organizations often have overlooked and underestimated social or 'hidden' consequences of reloca- tions. The goal of the project was to investigate the research question whether there is a kind of hier- archical 'shadow'. Do former hierarchical structures still exist among victims and survivors of the re- location? How is this structure affected by hierarchy even years after the event and how is the shadow affecting the hierarchy of the firm itself? To answer these questions and to test whether former em- ployees are still connected among one other, a pilot study was carried out among a German manufac- turer of electrical equipment which relocated its entire workforce in 2006. The pilot study also tested the feasibility of Respondent-Driven-Sampling (RDS) as an effective and efficient form to sample rare and hidden populations.

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  • Could gain retribution for the organization’s actions through lawsuits. media campaigns. or sabotage. In general. justice concerns discussed previously appear to be important factors for those who lose their jobs during downsizing. although the form of these justice effects is different than among employees who remain following downsizing. Interviews with displaced workers from a number of industries demonstrated that lack of advanced notification and insufficient severance pay were common complaints (Leana & Feldman. 1992). or equivalently. downsized workers report adequate severance was a major reason they viewed their former employers positively (Goldenberg & Kline. 1997). Auf jede 3-4 Verlagerung folgt eine Rückverlagerung. Häufig wird dabei auf ehemalige Mitarbeiter zurückgegriffen. Bei Verlagerungen fallen die Jobs im Gegensatz zum Downsizing nicht weg Gibt es Unterschiede bezüglich der Zielregion der Verlagerung sowie dem Grund der Verlagerung? In Downsizing literature individuals may accept the explanations and justifications for lay-offs as legitimate . and thereby reason that the organization is reacting in a fair and appropriate manner given the surrounding conditions and circumstances that may be beyond the control of the organization (e.g. poor economic conditions).
  • Institutional Sampling (Gewerkschaften. Outplacementberatung. ...) Time/ Space Sampling (Firma zum Zeitpunkt der Verlagerung) Targeted Sampling (Arbeitsämter) Chain-Referral Sampling (Convenience Sample – keine Rückschlüsse auf Grundgesamtheit)
  • Methode unterstellt. dass Mitglieder der zu untersuchenden Gruppe in sozialen Netzwerken verbunden sind. Implication: Making chain-referral sampling a form of probability sampling requires a statistical theory of the sampling process. – Markov-Chains This is part of a new class of sampling methods termed adaptive/link-tracing designs (Thompson and Frank 2000) Anwendbar ist diese Methode allerdings nur. wenn überprüfbar ist. ob die potentiellen Interviewpartner tatsächlich der Untersuchungsgruppe angehören. also nicht nur. um eine Geldprämie zu erhalten. dies vorgeben. Institutional Sampling (Gewerkschaften. Outplacementberatung. ...) Time/ Space Sampling (Firma zum Zeitpunkt der Verlagerung) Targeted Sampling (Arbeitsämter. Transfergesellschaft) Chain-Referral Sampling (Convenience Sample – keine Rückschlüsse auf Grundgesamtheit)
  • 06/07/11 RDS reduces the possibility of violating participant confidentiality during recruitment. Unlike the traditional chain-referral sampling . RDS does not ask participants to provide personal information about their peers; instead . it asks participants to recruit their peers. Importantly . it also helps reduce the “ masking effect . ” which means that respondents tend to protect their peers by not referring them in chain-referral sampling. Together with the financial reward for the referral effort . the rationing helps prevent the emergence of semi-professional recruiters and to reduce the effects of volunteerism . It is not necessary to have seeds randomly selected as long the seeds are members of the target population. No matter how the seeds are selected . the final RDS sample compositions will reach equilibrium (i.e. . stabilize) . independent of the characteristics of the seeds. Information about recruitment patterns in RDS can be used to estimate asymptotically unbiased population compositions using a mathematical model . called the reciprocity model . The estimated population compositions and sample compositions can be used create a sample weight . which is used to weight the sample. As such . unbiased sample statistics can be estimated from the weighted sample. This weight is defined as the inverse of the ratio of the group sample proportion to the estimate of corresponding population proportion. For example . suppose the gender compositions in an RDS sample were 60% male and 40% female; and the estimated corresponding gender compositions in the target population were 55% and 45% . respectively. This indicates that males were over-sampled and females were under-sampled. The weights used to account for male over-sampling and female under-sampling would be 1/(0.60/0.55) and 1/(0.40/0.45)=1.125 . respectively. The estimates of weights are available in the output of RDSAT. RDS sample analysis provides information for analysis of social structures. in which members of the target population are embedded.
  • 06/07/11 The target population size needs to be large enough . theoretically . infinitely large . to apply RDS. That is . a reduction in population size due to earlier recruitment efforts will not influence the probabilities for recruiting the remaining population members. If the size of the target population is not large enough . RDS is not suitable. The reciprocity model used for population composition estimation in RDS sample analysis assumes reciprocal relationships between recruiters and recruitees. Heckathorn and colleagues (2002 . pp. 64) state that “In the absence of volunteerism and masking . recruits would be drawn randomly from respondents’ personal network.” As a matter of fact . social and geographic proximity among group members are likely to influence the way participants recruit from their personal networks. Information on personal network sizes . which is crucial for population composition estimation . is self-reported. To the best of our knowledge . reliability studies on the size and compositions of self-reported personal networks have never been conducted. Reliability studies on the size and compositions of self-reported personal networks have never been conducted. A good feature of RDS is that weights can be generated from the model-estimated unbiased population compositions together with sample compositions; unbiased statistics can . therefore . be estimated from the weighted sample. The RDS recruitment process is expected to grow geometrically (Heckathorn . 1997). Practically . however . this may not be the case.
  • Survey was carried out from the end of December 2009 to March 2010 Three qualitative interviews . 96 former employees of that company could be identified. This number is similar to press releases about the relocation at that time . where 95 to 100 employees affected from the relocation were reported. 90 of those identified were eligible for the study purpose. From 37 former colleagues . none or wrong contact information could be found in the three preliminary interviews. This means only 53 questionnaires could be distributed among the former company members . which were filled out by 28 interviewees giving a response rate of 53 percent.
  • Number of people identifiable through the network approach exceeds the number of eligible respondents identified through the three preliminary qualitative interviews Also show that there are bridges between people of various sub-groups in the network (i.e. between blue and white collar workers) Also shows is that there are still 15 isolated nodes in the network from which no path to another node in the component exists. While all 38 former white collar workers are part of the giant component (see also Table 1). 15 former blue collar workers are still missing. 14 of them are female with a high proportion of members from ethnic minorities (i.e.. 6 of them are Turkish. 4 are Yugoslav. 1 is Polish and only 3 are German).
  • While the degree distribution in a random graph follows a bell curve. social networks often show a power law distribution. Most social networks are scale-free. Besides a high amount of people that have only one or two connections there are also a few nodes with up to 20 links to former colleagues. Those highly connected nodes are known as ‘hubs’ in network analysis. Hubs play a key role in keeping the network together (Ravasz & Barabási. 2003). which is especially important for the robustness of a shrinking network like those of former colleagues Additionally. hubs are also important for bridging different sub-populations in the network and will have important implications for the spread of information or rumors between those two sub-groups. Réka et al. (2000) demonstrated a high degree of robustness for scale-free networks. They show that even when as many as 5% of the nodes in a scale-free network are eliminated. “the communication between the remaining nodes in the network is unaffected” (Réka et al.. 2000. p. 380). High vulnerability to the removal of highly connected hubs in the network. for instance through death of a person with a high amount of links to other network members.
  • z.B. würde die 73 die 38, 62 und 85 einladen
  • Hinweis auf evtl. Couponverschwendung aber auch Peer-Pressure (Social Norm) z.B. Node 12 bekommt die Einladung sowohl von der 14 als auch von der 31 zu unterschiedlichen Zeitpunkten (Mehrfachteilnahme durch Speicherung der IP und diverser anderer Merkmale ausgeschlossen)
  • Transcript

    • 1. Danny Pająk Research Factory SoSe 2011 THE SHADOW OF HIERARCHY How to Sample a Hidden Population of Former Employees? 7. Juni 2011 Cartoon Crowd, Complex System © higyou #15575655 Kommunikation, Business und Logistik © ag visuell #16575699
    • 2. Outline
      • Research topic overview, shadow of hierarchy and G.R.D.
      • Hidden populations and Respondent-Driven Sampling (RDS)
        • Decision framework
        • Advantages and limitations
      • Feasibility study
        • Degree distribution
        • Masking or memory gaps
        • Exemplary RDS chains
        • Homophily and Equilibrium
        • Using XING to find the initial seeds
      • Discussion and perhaps representation of ShareSurvey
      Folie /29
    • 3. Narrowing down the research topic
      • No stakeholder group has been examined more comprehensively than have employees who remain after a downsizing has occurred: Survivor Literature
      • Kammeyer-Mueller . Liao & Arvey , 2001: 278
      • There has (…) been insufficient consideration of differences across organizations , with most downsizing research conducted in single organizations.
      • Kammeyer-Mueller . Liao & Arvey , 2001: 281
      • What about the victims? - Mitchell et al. (1997) specifically noted that although laid off employees might seem to be peripheral stakeholders , they can exercise power in a number of ways after downsizing takes place.
      • A recent study by Kinkel et al. (2007) reports that among every third to fourth German relocation is at least one company that is relocating its production back. Since those back-shoring companies often reemploy former employees firms should care about the victims of layoffs.
      Folie /29
    • 4. The shadow of hierarchy Victims node link or tie
      • Victims could gain retribution for the organization’s actions through law-suits , media campaigns , or sabotage.
      • How is this ‘shadow’ affected by hierarchy even years after the event and how does it affect hierarchy?
      • Former social networks still exist even years after the initial event
      • Key research questions
      • Willingness of re-employment
      • Organizational endorsement
      Folie /29 Survivors White-collar male Blue-collar female
    • 5. Institute for Management & Marketing (M&M) Research Factory SoSe 2011 7. Juni 2011 - Danny Pająk Folie /29
    • 6. Folie /29 Institute for Management & Marketing (M&M) Research Factory SoSe 2011 7. Juni 2011 - Danny Pająk
    • 7. Folie /29
    • 8. Folie /29
    • 9. Studying hidden populations
      • Institutional sampling (e.g., unions or outplacement agencies):
      • May provide easy access to numerous individuals. Limited to institutional members.
      • Time/Space sampling (e.g., former employee get-together):
      • Sampling frame combines places and times where the population gathers. Biased towards participants of those meetings.
      • Targeted sampling (e.g., employment agency):
      • Overweight of subgroups that are more readily accessible.
      • Chain-referral sampling:
      • Recruitment through networks reaches respondents who avoid public venues and institutions. Until recently, these have been considered convenience samples.
      Source: Kendall, 2006, p. 5 Folie /29
    • 10. Respondent-driven sampling (RDS)
      • The target population of directly affected victims of relocations , is with 0 . 44% in the years 2001 to 2006 rarely distributed among the German labor force Hidden or Rare Population
      • RDS combines "snowball sampling" with a mathematical model that weights the sample to compensate for the fact that the sample was collected in a non-random way Heckathorn 1997 , 2002
      • Data requirements for RDS analysis are minimal , there are three fields which are essential for analysis:
        • Personal Network Size (Degree) - Number of people the respondent knows within the target population.
        • Respondent's Serial Number - Serial number of the coupon the respondent was recruited with.
        • Respondent's Recruiting Serial Numbers - Serial numbers from the coupons the respondent is given to recruit others.
      Folie /29
    • 11. Snowball sampling biases and RDS “solutions” Respondents may refer to an unlimited number of peers Social network properties are ignored Respondents refer , surveyors must find referred Convenience sample – analysis limited to proportions of sample , not generalisable
      • Differential recruitment: Those with larger network sizes can recruit more peers , who are likely to have similar traits
      • Clustering: leads to lower effective sample size
      • Clustering by network traits cannot be measured
      • Size of social networks affect probability of selection
      Only members accessible to “outsider” participate Limiting recruitment coupons to individuals limits clusters , thereby reducing recruitment bias and high homophily
      • Coded coupons permit linking respondent with recruiter and recruits
      • Analysis weighted to account for measurable network properties
      Peers recruit peers , which includes ability to exert social influence where surveyors likely have none; surveyors remain in office Probability of selection is unknown Collect size of peer network to calculate probability of selection within network , i.e. sampling weight , use known network properties to account for clustering effects Bias Issue Respondent Driven Sampling “Solution” Snowball Sampling Source: Johnston & Sabin, 2010, p. 39 Folie /29
    • 12. Traditional sampling and estimation and respondent-driven sampling Source: Salganik & Heckathron , 2004, p. 200
      • Schematic of traditional
      • sampling and estimation
      • Schematic of respondent-driven
      • sampling
      Folie /29 Population Sample Collection Estimation Population Sample Social Network Estimation Estimation Collection
    • 13. How to decide whether RDS is a suitable sampling method? Folie /29 Based on: Johnston & Sabin, 2010, p. 45
    • 14. Advantages of RDS
      • Peer recruitment in RDS would minimize the issues about violation of subject confidentiality during recruitment.
      • RDS helps reduce the effects of “volunteerism.”
      • The sample compositions will converge to reach equilibrium within a limited number of recruitment waves , independent of the characteristics of the initial sample (seeds).
      • RDS allows to estimate asymptotically unbiased population compositions.
      • RDS sample analysis enables to estimate unbiased sample statistics.
      • RDS sample analysis provides information for analysis of social structures.
      • RDS is cheaper , quicker , and easier to implement , compared with convenience sampling methods such as targeted sampling.
      Folie /29 Source: Wang, 2004; Wang et al., 2005
    • 15. Limitations of RDS
      • The target population size needs to be large enough , theoretically , infinitely large , to apply RDS.
      • There must exists a contact pattern among members of the target population.
      • Assume that participants recruit randomly from their personal networks.
      • Measurement errors are not taken into account.
      • Low ratios of referrals.
      • A successful RDS sample may not necessarily be representative of the target population.
      Folie /29 Source: Wang, 2004; Wang et al., 2005
    • 16. Feasibility study Folie /29 Manufacturer of electrical equipment which relocated its entire workforce from Berlin to a rural area in the federal state of Lower Saxony in 2006
    • 17. Giant component Folie /29
      • There have to be at least five links on average from one node to another to ensure long referral chains.
      • Most social networks posses a so-called giant component , which means that every person is connected with every other person in the network.
      • Average out-degree = 8.6
      • Although only the answers of 25 respondents (28% of the initial sample size of n=90) were usable for the network analysis , the giant component already contains 75 (83.3%) former colleagues.
    • 18. Degree distribution 1/2 Folie /29
      • Social networks often show a power law distribution , that means power-law or scale-free networks are characterized by few highly connected nodes and many nodes with only a few connections.
      • A standard approach to outline the degree distribution of a social network is to plot the histogram on a logarithmic scale and see whether this looks linear
    • 19. Degree distribution 2/2
      • Réka et al. (2000) demonstrated that even when as many as 5% of the nodes in a scale-free network are eliminated , “the communica-tion between the remaining nodes in the network is unaffected” . (Réka et al. . 2000 . p. 380)
      • High vulnerability to the removal of highly connected hubs in the network , for instance through death of a person with a high amount of links to other network members.
      • The network's diameter would increase and the structure of the network could break into many isolated clusters.
      Folie /29
    • 20. Memory gaps or masking Folie /29
    • 21. Outdegree layout Folie /29
    • 22. Exemplary RDS chains Folie /29 in-degree/out-degree 15/15 10/10 6/4 4/12 6/3 11/11 4/.. 2/.. 1/.. 2/.. 2/.. 3/.. 4/.. 1/.. 10/7 2/.. 1/.. 1/1 1/.. 6/6 4/.. 16/15 11/10 8/8 7/6 2/.. 5/5 2/.. 7/.. 20/19 seeds wave 1 wave 2 wave 3 wave 4 wave 5 wave 6 oversampling undersampling wave n female male blue- collar withe- collar victims sur- vivors German Turkish others 0 4 50.0% 50.0% 50.0% 50.0% 100.0% 0.0% 100.0% 0.0% 0.0% 1 10 50.0% 50.0% 30.0% 70.0% 80.0% 20.0% 100.0% 0.0% 0.0% 2 18 61.1% 38.9% 44.4% 55.6% 83.3% 16.7% 94.4% 5.6% 0.0% 3 21 57.1% 42.9% 38.1% 61.9% 81.0% 19.0% 90.5% 4.8% 4.8% 4 23 52.2% 47.8% 34.8% 65.2% 82.6% 17.4% 91.3% 4.3% 4.3% 5 24 50.0% 50.0% 37.5% 62.5% 83.3% 16.7% 91.7% 4.2% 4.2% 6 26 46.2% 53.8% 34.6% 65.4% 84.6% 15.4% 92.3% 3.8% 3.8% True Population 90 58.9% +12.7% 41.1% -12.7% 57.8% +23.2% 42.2% -23.2% 76.7% -7.9% 23.3% +7.9% 64.4% -27.9% 25.6% +21.7% 10.0% +6.2% Giant Component 75 52.0% +5.8% 48.0% -5.8% 50.7% +16.1% 49.3% -16.1% 72.0% -12.6% 28.0% +12.6% 72.0% -20.3% 22.7% +18.9% 5.3% +1.5%
    • 23. Homophily
      • Tendency for in-group recruitment, where respondents tend to recruit participant with similar sociometric or demographic characteristics
      Folie /29 Male (41.1%) Female (58.9%) 37.5% 40.0% 62.5% 60.0% Blue-collar workers (57.8) White-collar workers (42.2%) 76.9% 53.8% 23.1% 46.2%
    • 24. Exemplary recruiting behavior by citizenship German (64.4%) Turkish (25.6%) 8% 20% Yugoslav (5.6%) Others (4.4%) 5% 5% 4% 2% 60% 2% 50% 1% 10% 10% Folie /29 88% 35% 75% 25%
    • 25. Recruiting pattern during different recruitment waves Starting point: All German subjects True German Population 64.4% True Turkish Population 25.6% Folie /29
    • 26. Theoretical growth of recruitment Folie /29 Source: Wang et al., 2005, p. 152
    • 27. Using XING to find the initial seeds for RDS
      • Limitations
      • Maximum of 300 search results
      • Messages to non-contacts are limited to 20 per day
      • Users can block to receive message from non-contacts or can hide their profile
      Folie /24 Source: Xing ( - Modified and anonymized profile screenshot, retrieved May 9, 2011.
    • 28. References
      • Heckathorn, D. D. (1997). Respondent-Driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174-199.
      • Heckathorn, D. D., Semaan, S., Broadhead, R. S., & Hughes, J. J. (2002). Extensions of respondent-driven sampling: A new approach to the study of injection drug users aged 18-25. AIDS and Behavior, 6(1), 55-67.
      • Johnston, L. G. & Sabin, K. (2010). Sampling hard-to-reach populations with respondent driven sampling. Methodological Innovations Online, 5(2), 38-48.
      • Kammeyer-Mueller, J., Liao, H., & Arvey, R. D. (2001). Downsizing and organizational performance: A review of the literature from a stakeholder perspective. In G. Ferris (Ed.), Research in personnel and human resources management (G. Ferris, Ed.). (pp. 269 - 329). New York, NY: JAI Press.
      • Kendal, C. (2006, July 29). Responent-Driven sampling. New Orleans, LA, USA: Tulane University. Retrieved June 5, 2011, from
      • Kinkel, S., Dachs, B., & Ebersberger, B. (2007). Production relocations and backshorings from a european comparative perspective. [Produktionsverlagerungen und Rückverlagerungen im europäischen Vergleich] Industrie-Management, 23(1), 47-51.
      • Mitchell, R. K., Agle, B. R., & Wood, D. J. (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. The Academy of Management Review, 22(4), 853-886.
      • Réka, A., Jeong, H., & Barabási, A. -L. (2000). Error and attack tolerance of complex networks. Nature, 406(27), 378-382.
      • Salganik, M. J. & Heckathorn, D. D. (2004). Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology, 34(1), 193-240.
      • Wang, J. (2004). Respondent-Driven sampling: A new form of chain-referral sampling. Dayton, OH, USA : Center for Interventions, Treatment & Addictions Research, Wright State University School of Medicine. Retrieved June 5, 2011, from
      • Wang, J., Carlson, R. G., Falck, R. S., Siegal, H. A., Rahman, A., & Li, L. (2005). Respondent-Driven sampling to recruit MDMA users: A methodological assessment. Drug and Alcohol Dependence, 78(2), 147-157.
      Folie /29
    • 29. Danny Pająk - Lehrstuhl für Internationales Management Tel.: +49-(0)335-5534-2813 Europa-Universität Viadrina Große Scharrnstraße 59 15230 Frankfurt (Oder) Folie /29 Thank you