Create a model for evaluating and assessment IT professionals and define the key performance indicators for the IT team members.
THESIS TOPIC: Model for Performance Appraisal for IT Professionals.
MOTIVATION: Improve an IT professionals team skills to reach a flat level of experience among the team and create a motive for better productivity.
RESEARCH GAP: Lack of a scientific assessment model that focuses on IT technicians’ operations.
RESEARCH OBJECTIVE: Create a model for evaluating and assessment IT professionals and define the key performance indicators for the IT team members.
The sub objectives are:
Defining evaluation criteria for an IT team member.
Define team member engagement and criticality through a model designed based on the evaluation criteria.
Identifying sources of uncertainties.
RESEARCH PROPOSED METHODOLOGY/APPROACH: The proposed (mathematical) methodology(ies) is (are):
Expert judgment
Cohort analysis
Decision tree
Sensitivity analysis
ANTICIPATED FINDINGS: A model that allows monitoring the workflow and the quality of the job done by an IT team.
RESEARCH/PRACTICAL IMPLICATIONS: Accurate assessment based on a mathematical model.
RESEARCH LIMITATIONS: Network engineering is on a continuous progress, that cannot be limited to a defined methodology or concept. Future revolutions or evolutions in the information technology field will definitely impact the work methods applied nowadays.
ORIGINALITY/VALUE: It will never be accurate to answer the originality, however based on my literature review there’s no similar research topics. Moreover, this paper should result a new unique IT technician’s assessment model based on the job criteria.
KEYWORDS: Assessment, key performance, productivity, efficiency.
MULTI-DISCIPLINES:
DISCIPLINE 1: Networking Engineering
DISCIPLINE 2: Human Resources Management
MILESTONE PLAN:
Literature review.
Define the methods to be used.
Define the activities and key performances for IT technicians.
Create a model that fit with the criteria of the job.
Apply the model on network teams, update it and improve it.
Run a sensitivity analysis on the efficiency of that model.
