Abstract

AbstractExpatriation is undoubtedly an expensive undertaking. Companies spend billions of dollars annually to send their employees on international assignments, and yet, based on recent reports (e.g. Brookfield Global Relocation Services, Global relocation trends survey report, Woodridge, IL, 2012) the return on investment (ROI) from expatriates has been altogether ‘unsatisfactory’. And so, year-on-year, organizations still struggle to define what international assignment success really means, and have made little to no progress on expatriate ROI in practice. More alarmingly, few have a clear strategy for how to measure expatriate ROI in a meaningful way (Cartus & Primacy, Global mobility policy and practices survey, Wilmington, NC, 2010). In addition, these same companies often have a short-term profit-driven focus, ignoring such forces as international careers and the “global war for talent.” Many also fail to run their mobility programs like they often do other areas of their business: with rational strategic practices, and a clear strategy and focus to ensure an acceptable level of “success” (McNulty and Inkson, Managing expatriates: A return on investment approach, Business Expert Press, New York, 2013). The question then is: if expatriates are among an organization’s most expensive employees, surely we ought to be able to justify the money spent and manage them more effectively?The purpose of this chapter is to delve deeper into the “added value of expatriation”. Specifically, what purpose does expatriation serve and which types of international assignments and international assignees add real value? How has expatriate return on investment (‘ROI’) been historically defined and measured? And how should the return on investment of international assignments be evaluated in the future?KeywordsMobility ManagerInformal MeasureGlobal MobilityAction Research ApproachInternational AssignmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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