Expert Engineering Inc: Performance Management and Bias Reduction

This case study examines performance management at Expert Engineering Inc., focusing on minimizing intentional and unintentional biases in employee evaluations. Demetri, the leader, considers factors like leniency error, low appraiser motivation, negativity bias, and confirmatory/similarity bias. The analysis suggests implementing technical, interpersonal skills, and literacy training programs for newly recruited employees to improve skills, relationships, and problem-solving abilities. The recommendation emphasizes technical training to enhance proficiency and performance standards, ultimately boosting employee confidence and multitasking capabilities. This document is a student contribution available on Desklib, a platform offering AI-based study tools and a wealth of academic resources for students.

Performance Management
Minimizing Biases in Performing Evaluation at
Expert Engineering, Inc.
Name of the Student:
Name of the University:
Author’s Note

The performance management in Expert Engineering Inc. can be defined as
the constant process that Demetri applies between the communication
process and the behavior of the employee. The aim of the presentation is to
highlight intentional and unintentional rating of the distortion factors that
fits the situation mentioned in the case study of Minimizing Biases in
Performing Evaluation at Expert Engineering, Inc (Ahmed et al., 2016).
The different training program will help the newly recruited employee to
excel in the field or post they would work. the last phase of the
presentation caters to the evaluation of different kinds of training program
which Demetri can formulate in reducing the distortion factors.
Introduction

Demetri have considered the distorting factors which caters to
the fundamental values from the original gesture. Some of the
factors are:
 Leniency error: It is relative to the true performance which
an individual can exhibits or evaluators can rank on the basis
of high or low (Razzaq et al., 2016). The evaluators in the
engineering sector have their own value of system which acts
against the appraisals.
 Low appraiser motivation: the employee may be reactive if
the evaluator does not mark on an appropriate basis.
Intentional and unintentional rating distortion factors

 Negativity bias: feedback tends to be an effective way in
improving the performance of the employee. When the
employees are used to receive feedback they tend to know and
examine the efficiency although some of the employees learn
the skills.
 Confirmatory and similarity bias: The halo effect occur when
the manger of the organisation have a positive view on
particular employee.

The different training sessions that can be formulated for the
newly recruited employee in Expert Engineering Inc are:
 Technical training: the candidates are trained in improving the skills in
hardware and software.
 Interpersonal skills training: the employees are trained in maintaining a
positive relationship or bond with the employee and employer (Aguirre &
Pantoya, 2016).
 Literacy training: the employee is trained in writing, reading and improving
the power of solving problems or conflict that occur within the employees.
Kinds of training program

Hence it can be recommended that to work in Expert
Engineering Inc under the leadership of Demetri the best
training program would be in technical areas as it will assist
the candidates to be proficient in technology. The training will
assist the employee to perform at higher standard, cater to
more of self confidence and also multi task in one time.
Recommendation


 Aguirre‐Muñoz, Z., & Pantoya, M. L. (2016). Engineering literacy and
engagement in kindergarten classrooms. Journal of Engineering
Education, 105(4), 630-654.
 Ahmed, T. M., Bezemer, C. P., Chen, T. H., Hassan, A. E., & Shang, W.
(2016, May). Studying the effectiveness of application performance
management (APM) tools for detecting performance regressions for web
applications: an experience report. In 2016 IEEE/ACM 13th Working
Conference on Mining Software Repositories (MSR) (pp. 1-12). IEEE.
 Razzaq, S., Iqbal, M. Z., Ikramullah, M., & van Prooijen, J. W. (2016).
Occurrence of rating distortions and ratees’ fairness perceptions per
raters’ mood and affect. Career Development International.
Reference

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