Ridderprint

Paradoxes in global talent pipelines 131 6.6.1 Turnover in Context Considerable differences existed between the organizations’ turnover rates. Both Consumer Goods and Shell extensively promoted their traineeships at university campuses in order to recruit fresh graduates. Voluntary turnover among recruited trainees seems less of an issue at Shell, where turnover rates ranged between 2% and 4% annually. In contrast, Consumer Goods experienced more leakage in their talent pipeline, with rates ranging between 4% and 12% annually during our observational period. Yet, Consumer Goods appears relatively capable of retaining its employees in comparison to the wider consumer goods sector, where annual turnover rates range anywhere between 6%, 13% (Mercer, 2015), and 18% annually (Bureau of Labor Statistics, 2016). Potentially, these fast-paced consumer markets (Mercer, 2015) attract employees with a different mindset (e.g., more opportunistic) and thus requires the shorter-term-oriented HRM policies (e.g., talent status until promotion). Shell’s turnover rate remains low even when viewed in light of the broader oil and gas industry (Mercer, 2015; Bureau of Labor Statistics, 2016). Looking at the development of the turnover rates, both organizations experience low turnover at the start of traineeships (Figure 6.1). Afterwards, the turnover rate increases linearly at Consumer Goods, but curvilinear at Shell, the hazard function demonstrated. While such differences cannot be explained based on our current data, they do have implications for the viability of management strategies. For instance, from a resource-based perspective (Barney, 2001), oil and gas majors such as Shell reap the long- term benefits of its investments in graduate personnel whereas, in the consumer goods industry, organizations seem to develop talent in part for the competition. 6.6.2 Performance High performing trainees were less likely to turnover voluntarily in both organizations, in line with Hypothesis 1. Similar effects have been found in other employee cohorts (e.g., Griffeth et al., 2000; Nyberg, 2010). The financial and career benefits linked to performance systems along with the appraisal and visibility may incentivize high performers to stay. Nevertheless, the consequent sorting effect may not necessarily be good news. First, the enforced normal distribution of performance ratings may not resemble the underlying true distribution of employee performance (see Beck, Beatty, & Sackett, 2014; O’Boyle Jr. & Aguinis, 2012). This could cause employees to leave because of relatively low ratings whereas they actually demonstrate high performance, or vice versa. Second, the definition of high performance within relative rating systems is not stable over time. Leaving low-performers raise the (real) average of peer cohorts to a point where previous high performers will become average and thus incentivized to leave. This “ up-or-out ” principle or “ tournament model ” (Rosenbaum, 1984) may not be viable in departments withmany non-strategic positions (Huselid & Becker, 2011) or in contexts with stable peer groups, such as network organizations.