Performance management is a hot topic with organizations rethinking how they evaluate the work of their employees to improve performance and meet business goals. In this cover story for the June 2016 edition of Workspan magazine, we share three Mercer case studies illustrating why organizations should carefully consider their options before abandoning performance ratings.
Brian Levine, Innovation Leader, Workforce Strategy & Analytics
Linda Chen, Consultant, Workforce Strategy and Analytics
Several high-profile organizations have recently moved to eliminate performance ratings — a trend that reflects the evolution of organizational thinking about how employees should be assessed and motivated. Indeed, there are good reasons to consider large-scale changes in performance management, but caution is needed.
For many organizations, review processes generate dissatisfaction from leaders and employees alike. Significant time and expense related to these processes add to the general frustration. When it is unclear what ratings measure and they are not strongly associated with the organization’s ability to attract, retain, develop and motivate talent, the value proposition should be questioned.
However, whether the move to abandon performance ratings is right for a given organization depends on the context. It’s therefore helpful to share some insight about three organizations, derived from predictive analytics linking performance processes to desired outcomes. Such
an examination should be part of the due diligence undertaken before making a large-scale change such as eliminating performance ratings. In two of the three cases, performance ratings are shown to drive significant value, though there is potential for improvement in processes. In the third, major changes should be considered.
A financial-services company was concerned about the potential for systemic differences in performance ratings — across gender and racial lines — given the strong association between ratings and both compensation and future career opportunities. Statistical models that accounted for both employee experience (proxied in the data by age tenure and time in job) and the economic vitality of the geography served by the employee could not explain these differences.
Still, the objectivity of performance rating processes seemed to limit bias. Consistently, those in the support functions where there was more subjectivity in evaluation criteria were more prone to see differences in ratings by race than those in the businesses where clear, objective measures of performance were available.
Simply put, those in units with more structured review processes showed less bias in performance ratings across groups. Furthermore, there was evidence that even where there were objective criteria, discretion in the process (i.e., how those criteria were used to drive the final rating) was seen to be associated with bias (e.g., rounding differences in the determination of final ratings across racial groups). For other organizations, we have noted a corollary: Where ratings related to objectives and related to values are separately gathered, the ratings related to more subjective criteria (e.g., values) show greater bias.