1. How well does Google’s approach to performance management meet the five criteria for effectiveness of a performance management system? How well does it fit with the company’s mission to organize information and make it universally accessible and useful?
2. What errors could arise in the way Google collects performance data on managers? How could it minimize these errors?
3. Suppose you are responsible for delivering performance feedback to managers at Google. How would you present the information so as to promote the managers’ success at the company?
The management of the company focused on building performance measures in the specified key areas and this helped the company to evaluate the performance of the managers and recognized those who required improving in the specified areas. Therefore, it can be stated that the approach adopted by Google towards performance administration convene the principles for helpfulness of the performance management scheme. It can be stated that it is required for the managers to be a good coach and secondly, it is necessary to express interest in the success of the team members along with the personal well-being (Green et al., 2012). The main aim of the company at this moment is to develop better managers and in order to do that, they have been looking deeply into data. At that point of time, the HR Vice President of the company condensed its results into a catalog of specified behaviors. Therefore, it can be stated that Google’s approach to the performance management fits properly with the mission of the company to organize information and make this universally accessible as well as useful (Rabl et al., 2012).
In this part, it is required to find out the mistakes that could happen in the procedure Google gathers performance statistics on the managers in difference mistakes. These blunders might take place when an individual is compared against other workers as a substitute of the objective standard. Apart from that, it can be stated that one more mistake is the distributional issue and this takes place when they only use one part of the rating scale. The halo error that could take place is when the bias is in a favorable direction. Rolstadas (2012) has stated that the halo error can mistakenly inform the employees they do not tell the employees that they do not need to enhance in any areas, while horns errors can cause the employees to feel frustrated and defensive. Apart from that, it can be stated that the central tendency errors takes place when appraisers rate all the workers as the average or in the center of the scale. However, it can be stated that Leniency errors takes place when the assessors rate all the workers as performing specifically well.
In this part, it is required to give an overall progress that is an analysis of accomplishments and the shortcomings. It is necessary to discuss any issue that might have encountered in meeting the job requirements and set up some long and the short term goals in order to make sure that they are fixing the issues. The presentation is required to be in a motivating way, so that the managers feel interested enough to use this feedback for enhancing their present performance in the organization. The report is required to deliver to the managers in confidential manner, as the managers might not like to disclose the reports to other employees in the organization. The entire process is required to be in a systematic manner and the managers as well as the employees are required to involve in a particular manner (Van Dooren et al., 2015).
After completion of the entire task, it can be stated that this particular research work has tried to focus on approach of Google towards performance management in order to meet the five effective criteria for the effectiveness of the performance system. Moreover, focus has been shed on the errors that might arise in the way Google collects data on performance management on the managers.
Green Jr, K. W., Zelbst, P. J., Meacham, J., & Bhadauria, V. S. (2012). Green supply chain management practices: impact on performance. Supply Chain Management: An International Journal, 17(3), 290-305.
Rabl, T., Gómez-Villamor, S., Sadoghi, M., Muntés-Mulero, V., Jacobsen, H. A., & Mankovskii, S. (2012). Solving big data challenges for enterprise application performance management. Proceedings of the VLDB Endowment, 5(12), 1724-1735.
Rolstadas, A. (Ed.). (2012). Performance management: A business process benchmarking approach. Springer Science & Business Media.
Van Dooren, W., Bouckaert, G., & Halligan, J. (2015). Performance management in the public sector. Routledge.
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