IN today's data-driven business world, measuring employee performance accurately and fairly is more crucial than ever.
Organisations often grapple with the choice between subjective measures, such as manager ratings, and objective measures, like sales figures or output metrics. But which approach yields the most accurate picture of an employee's true performance?
A closer look at the research reveals that both methods have their merits and drawbacks, and the most effective approach often involves a thoughtful combination of the two.
Objective performance measures
Objective performance measures, often regarded as the gold standard, focus on quantifiable outcomes and behaviours.
These measures typically involve metrics such as sales figures, production units, or customer satisfaction ratings. The allure of objective measures lies in their perceived impartiality and precision. After all, numbers do not lie, right?
However, research challenges this assumption. A meta-analysis by Bommer et al. (1995) revealed that objective measures may be influenced by various contextual factors beyond an employee's control.
For instance, a salesperson's performance can be affected by economic downturns or changes in consumer preferences. Bommer et al. (1995) found that the overall correlation between objective and subjective performance measures was only moderate (r = .39).
- Performance measures: Striking the right balance
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This indicates that these two types of measures are not interchangeable and are likely capturing different aspects of performance. Similarly, a study by Rich et al. (1999) focusing on salespeople found a correlation of .45 between objective and subjective measures of sales performance.
These findings suggest that relying solely on objective metrics may provide an incomplete picture of an employee's overall performance. A meta-analysis of longitudinal studies by Sturman et al. (2005) found that objective measures, particularly for complex jobs, tend to have lower test-retest reliability.
Many organisations are drawn to objective performance measures, believing that quantifiable "hard" metrics provide the most reliable and unbiased assessment of employee performance. However, a substantial body of research suggests that objective measures are not as infallible as we might assume.
Objective measures can be susceptible to factors outside an employee's control. For instance, consider an orthopedic surgeon whose performance is measured by the number of patients treated or surgical procedures performed.
At first glance, these seem like solid objective metrics. However, as pointed out in the research, this surgeon may also be responsible for teaching and supervising junior doctors, which could reduce their patient numbers compared to other surgeons.
Additionally, they might take on more complex cases with a higher risk of complications, affecting metrics like re-admission rates (Roth et al., 2012).
In knowledge-based professions, the challenge of finding meaningful objective metrics is even more pronounced. As Ramirez and Steudel (2008) note, knowledge workers seldom have a single, standard outcome, and their outputs are often difficult to quantify and heavily influenced by contextual factors beyond their control.
Subjective performance measures
Subjective performance measures, on the other hand, rely on human judgment to assess employee performance. These measures often involve supervisor ratings, peer evaluations, or self-assessments.
While subjective measures offer valuable insights into qualitative aspects of performance, they are susceptible to biases. A meta-analysis by Podsakoff et al. (2013) revealed that supervisor ratings can be influenced by factors such as ethnicity, gender, or the quality of the leader-member relationship.
The primary advantage of subjective measures is their ability to capture nuanced aspects of performance that may not be reflected in raw numbers. Managers and colleagues can account for an employee's adaptability, teamwork, and other crucial "soft" skills that are difficult to quantify objectively.
For example, the Job Adaptability Inventory (JAI) developed by Pulakos et al. (2000) measures eight dimensions of adaptive performance, including handling emergencies, dealing with uncertain work situations, and demonstrating interpersonal adaptability.
These aspects of performance, while critical in many roles, are not easily captured by objective metrics.
However, subjective measures are not without their flaws. Research has consistently shown that various biases can influence subjective ratings. Studies have demonstrated that factors such as an employee's ethnicity, gender, age, or sexual orientation can negatively bias subjective performance ratings (Bowen et al., 2000; Kraiger and Ford, 1985). The quality of the relationship between the rater and the employee can also significantly impact ratings (Elicker et al., 2006; Sutton et al., 2013).
Another factor to consider is the purpose of the performance measurement. A meta-analysis by Jawahar and Williams (1997) found that supervisor and peer ratings obtained for administrative purposes (e.g., decisions on promotion and compensation) tend to be higher than those obtained for employee development purposes.
Striking the balance
Given the limitations of both objective and subjective measures, how can organisations effectively assess employee performance? The research points to several key principles:
Use a combination of objective and subjective measures: Multiple studies have found that combining different types of measures leads to a more accurate assessment of true performance. As Heneman (1986) and Sturman et al. (2005) suggest, supervisors and co-workers are often aware of contextual factors that might affect objective performance metrics and can take these into account in their subjective assessments.
Be aware of potential biases in subjective ratings: Organisations should implement strategies to mitigate biases in subjective ratings. This might include rater training, using multiple raters, and implementing structured rating systems.
Ensure objective metrics are measuring what truly matters: Avoid an overreliance on easily quantifiable metrics that may not capture true performance.
Consider the purpose of the performance measurement: Be aware that ratings may differ based on whether they are being used for administrative or developmental purposes.
Use multiple raters when possible: This can increase the reliability of subjective measures.
Recognise that performance tends to be relatively stable over time: A meta-analysis by Sturman et al. (2005) found that individual performance tends to remain stable over time. Major changes in ratings should, therefore, be scrutinised.
Tailor performance measures to the specific role: What constitutes good performance can vary significantly across different jobs.
Real-world applications
To illustrate these principles in action, consider a software development team. Objective measures might include metrics like lines of code written, number of bugs fixed, or on-time project delivery.
However, these metrics alone do not capture important aspects of a developer's performance, such as code quality, collaboration with team members, or ability to solve complex problems.
A more comprehensive approach might combine these objective metrics with subjective assessments from managers and peers. This would allow for the evaluation of not just task performance but also contextual performance (like helping colleagues) and adaptive performance (such as coming up with innovative solutions to problems).
For a sales team, while objective metrics like sales numbers are crucial, they could be complemented with subjective assessments. This would provide insight into important aspects of performance, like customer service quality, that might not be reflected in sales figures alone.
Conclusion
Effectively measuring employee performance is a complex challenge but one that's crucial for organisational success. By thoughtfully combining objective and subjective measures, organisations can develop more robust and fair performance measurement systems. This balanced approach allows for data-driven decision-making while still accounting for the peculiarities and complexities of human performance in the workplace.
As the nature of work continues to evolve, particularly with the rise of knowledge work and remote working, the need for sophisticated, multi-faceted approaches to performance measurement will only grow.
- Nguwi is an occupational psychologist, data scientist, speaker and managing consultant at Industrial Psychology Consultants (Pvt) Ltd, a management and HR consulting firm. https://www.linkedin.com/in/memorynguwi/ Phone +263 24 248 1 946-48/ 2290 0276, cell number +263 772 356 361 or e-mail: [email protected] or visit ipcconsultants.com.
