Abstract

The article presents the results of research conducted in an international enterprise responsible for IT project implementation. The carried out analysis of the case study with the use of surveys and data synthesis allowed the major factors causing problems connected with project management to be identified. The identified factors were aggregated and then, by using four key variables, a rhomboidal model adaptation was proposed to facilitate the choice of the best method of project management. The proposed solution may aid Project Managers choosing the most appropriate method of project management as well as measuring and monitoring risk indicators.

Highlights

  • The development of methods and tools of project management is a challenge for people responsible for project management [1, 5]

  • The objective of this step was to verify the correctness of the assumed diagram model construction, the usage of which would allow the correct selection of the IT project management method on the basis of the identified variables

  • The proposed solution allows the analysis of key project parameters and eases the selection of the optimal approach for project management

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Summary

INTRODUCTION

The development of methods and tools of project management is a challenge for people responsible for project management [1, 5]. High dynamics of changes taking place in IT projects regarding client expectations and awareness means that it becomes more and more challenging to achieve the final success of the project [4, 8]. It is necessary to continue to explore solutions that, based on the experience of completed projects, facilitate taking the right decision on the choice of methods of IT project management because incorrect decisions made in the initial phase of the life cycle are much more risky for the project. The high costs associated with removing the consequences of wrong decisions transfer into a project and are often not accepted by customers [7]

THE DEVELOPMENT OF IT PROJECT MANAGEMENT
RESEARCH METHODOLOGY
VARIABLES IDENTIFICATION
MODEL VALIDATION
Findings
SUMMARY
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