The management of any company depends on performance evaluation to make informed decisions based on the company’s progress and history. Performance management helps a company measure the impacts of choices on companies’ operations. Companies face risks of failure if they flop to conduct performance management. Therefore, performance strategies should ensure that performance appraisal does not have any distortions, intentional or unintentional. Expert Engineering incorporated has a long and unique history. The founders of the company believe in feedback and source evaluation from different sources to promote a culture based on merit and does not allow favoritism. However, the firm initiated hiring a dozen engineers, of which nine of them were that Demitri, a recently promoted principal, attended, and he played a part in the hiring. The hiring of the engineers caused discontent among other principals in the company who were afraid that favoritism and biases were used in the hiring process of the engineers. This paper addresses the various possible distortions that come into play in a case like Demitri’s. It also discusses the different effective methods in preventing such distortions with recommendations.
Performance ratings are distorted by biases that may be intentional or unintentional. A rater can be compelled to distort ratings or provide accurate ones. A rate may provide inflated ratings to avoid confronting employees, remove undesired employees by promoting them to another unit, and impress the manager (Kuhto and Pankov, 2017). Ratings can also be deflated to teach a rebellious employee a lesson. Additionally, performance raters are also susceptible to making unintentional errors. However, these errors in performance evaluation evaluators’ skills in evaluation. In the case of Expert engineering incorporated, several intentional distortions of ratings may come in. First, every evaluator is required to have a personal value system that they use as a standard to rate appraisals (Waldman and Zax, 2016). Some evaluators mark low, while others mark high relative to individual exhibits and performance. This phenomenon is called the leniency error. If an appraisal marks low, it is a negative leniency error, and if it keeps high, it is a positive leniency error (Pleger and Sager, 2018). In the case of Expert Engineering, the evaluator’s values could have ranked his former institute of education highly and, in turn, labeled its graduates as competent employees.
Another factor that may have come into place is the similarity error. Similarity error is when an evaluator looks for traits in a candidate similar to theirs. It is done because they might believe that possession of those attributes is needed to fill a specific job vacancy. One of the attributes that an evaluator could be looking for is aggressiveness (Maggian, Montinari, Nicolò, 2018). An evaluator may deem aggressiveness part of the ideal candidate, locking out candidates that the evaluator does not see as aggressive. These biases in hiring based on the perceptions of the evaluator could be costly to the company in the long run. In the case of Expert engineering, Dimitri might have deemed scholars from his former colleague as people who possess the same traits as him. This distortion, because of his perception that graduates from the same university are good employees, made them benefit in getting jobs while others may be more suited people for the jobs missed out.
First, a company can standardize interviews. Standardizing is done by allowing the interviewer to focus on the issues that relate directly to the company’s performance. Structured interviews, instead of open-ended questions that let the candidate’s expertise unfold through conversation, are used. To minimize bias to create a third data point. For cases like the one of Demitri, as an interviewer, you should ask yourself if it matters if the candidate graduated from the same school, which increases likeability (Jeong et al., 2017). If it does matter, you should consider rating them with other skills during the hiring process.
Secondly, a company can have diversity goals. Diversity should be the issue and center of all recruitment practices. Diversity in a company has many benefits in increasing efficiency and innovations in the workplace. Companies should have a set of diversity goals to avoid distortions in the hiring process which might be intentional or unintentional (Sharma and Mann, 2018). After all hiring processes, these diversity goals should be assessed to track and determine how well they are advancing. By practicing this method, the people involved in hiring are encouraged to be more involved in equality and diversity in their company.
Thirdly, a company can offer sample tests of the work they will be assigned. They are mimicking the jobs that candidates will be posted if they got the job is the best. Sample tests aid the interviewers in clarifying their judgment about how a candidate compares to another. They give essential insights in a skills test of a candidate rather than judging them based on their personality, age, gender, appearance, or other relations that might give an unfair advantage to the candidate (Riggs, 2017). Work samples provide the employee with a taste of what they will be assigned if hired while giving employers insights on if the employee will be effective in the company. They remove all types of distortion because hiring is from personal competence in productivity.
I would recommend that companies use tools like software that could conduct the hiring practice without distortion. Artificial intelligence is the future in removing both conscious and unconscious distortion in performance ratings (Cappelli, Tambe, Yakubovich, 2019). The interviewer mustn’t include any non-professional opinions in the hiring process to meet the objectives of the process (Laua, Keongb, and Luenc, 2018). Softwares eliminate all distortion possibilities because they have no relations with humans and cannot have personal preferences. With the use of software, companies can enjoy diversity in the workplace while also recruiting the best candidates for the job eliminating distortion.
A recruiter can be biased in their hiring if left unchecked. They can hire people based on looks, social circles, or even if they went to the same college as in the case of Expert engineering Inc. Hiring bias is unhealthy for any company because it increases the risk of employing people who are not the perfect fit for your job descriptions. Companies must reduce hiring bias because it could lead to many defects like legal troubles based on discrimination.
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