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Minimizing Performance Evaluation Biases at Expert Engineering, Inc.
Introduction:
For the progress and success of a company, performance management takes a significant role. To make an informed decision, performance evaluation is the most useful technique considered by the management. If there are any errors or biases occurred then the risks of failure could be high. So, the management of a company must ensure that the performance appraisal is free from any type of distortion. This paper will outline the techniques and strategies adopted by the company (Expert Engineering Inc.) to minimize the biases in performance evaluation.
There are mainly two types of errors in case of performance evaluation. One is intentional errors and another is unintentional errors (Chen et al., 2017). According to our case study, some examples of distortions made by raters can be as under:
Intentional Errors:
Unintentional Errors:
Before making any strategies to minimize the performance biases, it is necessary to understand the importance of the performance information sources. Reliable and informative sources automatically reduce errors. Some sources can be the supervisors, subordinates, vendors, consumers, and peer group. Remember, the worst type of distortion is biases. When a manager/rater fails to use examined and accurate performance management principles then it may occur. In case of unfair promotion decisions and unchecked favoritisms, resolving discrepancies among the sources of information is a pre-requisite factor (Chen et al., 2017). The following techniques can help:
To minimize the distortion rating performance, the raters should take a valuable part in this by justifying their ratings in a face-to-face meeting. Finally, if we have to take a brief look at the useful training programs that could help to lower the rating biases in performance evaluation, Rater Training Program is probably the best of them (Mehu et al., 2015). The primary goal of the training is to provide effective/efficient raters with tools for implementing a performance management system (Chen et al., 2017).
Provide a detailed discussion of the intentional and unintentional rating distortion factors that may come into play in this situation.
Problem 2