<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The experience of many organizations so far has shown that the risk of failure of the software implementation project is high, and in order to reduce this risk, significant efforts have been made so far to implement the project management methodology. However, research to date shows that, despite improvements in project management, a relatively significant number of projects still fail by some of the performance criteria. A methodology is proposed that includes: (i) analysis of previous research; (ii) design a research instrument to measure project performance and define key determinants of project performance; (iii) development of a multi-criteria model for measuring the performance of software projects; (iv) development of software project management performance models for Croatian organizations using a data mining approach. These activities can be carried out using the following methods: bibliographic analysis of previous research, survey research method, descriptive and inferential statistics methods, structural equations method and data mining methods (supervised and unsupervised learning, text mining). The results of this research framework can be used as a starting point for researching the success of business software implementation projects.</p>
</abstract>

The experience of many organizations so far has shown that the risk of failure of the software implementation project is high, and in order to reduce this risk, significant efforts have been made so far to implement the project management methodology. However, research to date shows that, despite improvements in project management, a relatively significant number of projects still fail by some of the performance criteria. A methodology is proposed that includes: (i) analysis of previous research; (ii) design a research instrument to measure project performance and define key determinants of project performance; (iii) development of a multi-criteria model for measuring the performance of software projects; (iv) development of software project management performance models for Croatian organizations using a data mining approach. These activities can be carried out using the following methods: bibliographic analysis of previous research, survey research method, descriptive and inferential statistics methods, structural equations method and data mining methods (supervised and unsupervised learning, text mining). The results of this research framework can be used as a starting point for researching the success of business software implementation projects.

Success determinants of projects of business software implementation: Research framework

Josip Juraj Strossmayer University of Osijeku

Croatian Regional Development Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The experience of many organizations so far has shown that the risk of failure of the software implementation project is high, and in order to reduce this...

Success determinants of projects of business software implementation: Research framework

Data publikacji: 02 lut 2022

Zakres stron: 1 - 16

Otrzymano: 03 wrz 2021

Przyjęty: 07 wrz 2021

DOI: https://doi.org/10.2478/crdj-2021-0010

Słowa kluczowe
project management, software project management, research framework, neural networks, multicriteria decision making, leadership

© 2021 Mirjana Pejić Bach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Success determinants of projects of business software implementation: Research framework

Data publikacji: 02 lut 2022

Zakres stron: 1 - 16

Otrzymano: 03 wrz 2021

Przyjęty: 07 wrz 2021

DOI: https://doi.org/10.2478/crdj-2021-0010

Słowa kluczoweproject management, software project management, research framework, neural networks, multicriteria decision making, leadership

© 2021 Mirjana Pejić Bach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
project management, software project management, research framework, neural networks, multicriteria decision making, leadership