Background an Research Objectives

The topic of my doctoral thesis is the ‘hidden costs’ of company relocations due to breaches of the implicit psychological work contract. Relocations, in terms of outsourcing to a nonaffiliated company and off-shoring, the cross-border relocation within the company are widely used in recent years and causes in many cases collective employee layoffs. Although the primary objective is to reduce costs, relocations may not produce those highly anticipated financial benefits that most companies are pursuing. One reason is that organization often overlooked and underestimated social or ‘hidden’ consequences of relocations. While there is considerable research in general downsizing, “the act of eliminating employees by permanent layoffs, cutbacks, attrition, early retirement and termination” (Gandolfi, 2006: 73), relatively little empirical research has been focused upon those people affected from relocations.

Methodology

In common with the downsizing literature, the work will distinguish between three categories of people directly involved and affected by relocations – executioners, victims and survivors. Following this, structural equation models are developed to test the influences of fairness and trust on the implicit psychological work contract during a relocation process.

Standard sampling and estimation procedures require sampling from a know frame. For the population of people affected by relocation a known sampling frame simply does not exist. Even if a frame could be constructed, a broad random sample of the overall population would not be cost-effective given the low base rate of affected people in the overall population. The target population of the directly affected ‘victims’ for instance, with ca. 0,5% in the years from 2001 to 2006, is rarely distributed among the German labour force.

To overcome these difficulties respondent-driven-sampling (RDS) a snowball-type method, developed by Heckathorn  (Heckathorn, 1997, 2002) will be used in the research project. RDS is from the family of sampling methods termed link-tracing/ adaptive sampling designs that are designed to be used in a setting where the traditional probability sampling methods are infeasible. Usually chain-referral sampling are non-probabilistic methods in which members of a hidden, rare, or hard-to-reach population are asked to provide referrals to other members of their group. RDS is designed to overcome the limitations associated with snowball-types of sampling, while maintaining the advantages of chain-referral sampling’s broad coverage and ease of implementation. RDS software, capable of estimating parameters and statistics, is freely available at http://www.respondentdrivensampling.org. (Coryn, Gugiu, Davidson & Schröter, 2007)

Similar to other snowball-type methods, RDS starts with a modest number of initial respondents (i.e. the RDS seeds), who provide the researchers with information on their network connections. These connections then form the pool from which the second wave of respondents is drawn from and so on. RDS research design includes the means for encouraging subjects by rewarding successful recruiters (e.g. incentives) and making recruitment rights scarce through quotas  (Wejnert & Heckathorn, 2008). While the original RDS studies used face-to-face interviews this research project will transfer this method to an online survey environment. The field time of the survey is planned for the beginning of the year 2010.

References

Coryn, C. L., Gugiu, P. C., Davidson, E. J., & Schröter, D. C. (2007). Needs assessment in hidden populations using respondent-driven sampling. Evaluation Journal of Australasia, 7(2), 3-11.

Gandolfi, F. (2006). Corporate downsizing demystified: A scholarly analysis of a business phenomenon. ICFAI Books.

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. (2002). Respondent-Driven sampling II: Deriving valid population estimates from chain-referral samples of hidden populations. Social Problems, 49(1), 11-34.

Wejnert, C., & Heckathorn, D. D. (2008). Web-Based network sampling: Efficiency and efficacy of respondent-driven sampling for online research. Sociological Methods & Research, 37(1), 105-134.