Managing Talent: How Google Searches for Performance Measures Jacqueline Jones
MT203: Human Resource Management – Section 01
Professor Carrie Stringham
April 6, 2015
According to the cae study, “If there’s one thing Google knows, it’s how to use software to wade through massive amounts of data and find what is most relevant. Like most businesses, Google had files of data about managers— results of performance reviews, surveys measuring employee attitudes, and nominations for management awards. Unlike most businesses, Google figured out how to analyze all that data to come up with a profile of the kind of manager whose team is most successful. This paper discusses how Google’s approach to performance management meets the five criteria for effectiveness of a performance management system as well as the possible errors in this approach and ways to resolve them.
1. Describe the five criteria for effectiveness of a performance management system and summarize how Google’s approach to performance management meets these criteria.
There are five criteria for effectiveness of a performance management system. The first is fit with strategy which states that a performance management system should aim at achieving employee behavior and attitudes that support the organization’s strategy, goals, and culture. The second is validity which refers to whether the appraisal measures all the relevant aspects of performance and omits irrelevant aspects of performance. The third is reliability which describes the consistency of the results that the performance measure will deliver. Acceptability, the fourth criteria, indicates that whether or not a measure is valid and reliable, it must meet the practical standard of being acceptable to the people who use it. The fifth is specific feedback which states that a performance measure should specifically tell employees what is expected of them and how they can meet those expectations. Being specific helps performance management meet the goals of supporting strategy and developing employees. (Gerhart, Hollenbeck, Noe, & Wright, 2014)
Google’s approach to performance management meets these criteria in that they are making sure that in order for their employees to succeed their managers must fit bill in supporting the company’s strategy, goals, and culture. The case study states, “if there’s one thing Google knows, it’s how to use software to wade through massive amounts of data and find what is most relevant”, hence they are ensuring that the measurements are valid. The list of behaviors they came up with after their findings showed that these were the most consistent and reliable behaviors that make a good manager. The actuality that they continue to use this approach to performance management indicates that these were not only accepted but they received positive feedback from managers and employees on what is expected of them in order to achieve success in the organization.
2. Identify errors that could arise in the way Google collects performance data on managers. Describe how it could minimize these errors.
According to an article on WhatMakesAGoodLeader.com there are 6 key rating errors or biases commonly associated with completing a job performance appraisal that raters should be made aware of. The halo effect is the tendency of the leader to judge all aspects of an individual using a general impression that was formed on only one or a few of the individual’s characteristics. The contrast error occurs when a leader compares subordinates with one another instead of against performance standards. The recency bias occurs where a leader assigns ratings based only on the employee’s most recent performance rather than the employee’s performance over the entire period being rated. The leniency bias occurs where a leader is too soft or too generous when rating the employee’s performance. This is often due to manager discomfort...
References: Gerhart, B., Hollenbeck, J., Noe, R., & Wright, P. (2014). Fundamentals of human resource management. (3rd ed.). New York, NY: McGraw-Hill.
Whatmakesagoodleader.com (2009). Job performance appraisal: discovering common performace rating errors. Article. Retrieved from
Please join StudyMode to read the full document