Critical success factors for software projects: A comparative study

Nasir, Mohd Hairul Nizam Md; Sahibuddin, Shamsul

doi:10.5897/sre10.1171

Cited by 122 publications

(26 citation statements)

References 70 publications

(83 reference statements)

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“…Although a large number of studies categorize CSFs into key areas, suggesting alternative frameworks (e.g. [10], [12], [48], [66]), there is no general categorization of CFS for software projects [67]. To illustrate, many authors have categorized CSFs for either agile or traditional software projects.…”

Section: Literature Reviewmentioning

confidence: 99%

Systems Thinking in Software Projects-an Artificial Neural Network Approach

et al. 2020

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The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create projectoutcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success. INDEX TERMS Artificial neural networks, critical success factors, project success, prediction models, systems thinking.

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Section: Literature Reviewmentioning

confidence: 99%

Systems Thinking in Software Projects-an Artificial Neural Network Approach

et al. 2020

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“…Some authors (Gupta et al, 2014;Ziemba and Oblak, 2013;Nasir and Sahibuddin, 2011;Huang, 2010) classified CSFs into 3 main groups: human (people-related), organisational (process-and company-related), and technical (software technology-and IT system-related) factor groups.…”

Section: Literature Reviewmentioning

confidence: 99%

A Primary Human Critical Success Factors Model for the ERP System Implementation

Jenko¹,

Roblek

2016

Organizacija

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Background and Purpose: Many researchers have investigated various Critical success factors (CSFs) and the different causes of ERP implementation project failures. Despite a detailed literature preview, we were unable to find an appropriate research with a comprehensive overview of the true causes behind CSFs, observed from a human factors perspective. The objective of this research was therefore to develop and evaluate the Primary human factors (PHFs) model and to confirm the significant impact of PHFs on traditional CSFs and on the project success. Design/Methodology/Approach: The comprehensive PHFs research model was developed and examined in empirical quantitative research with the use of available literature and the application of the Root cause analysis. A survey was conducted in various Slovenian organisations in different branches that had previously implemented the ERP system SAP. The model was verified on a sample of 21 experts from 18 organisations. Results: The results show that the PHFs have a significant positive impact on the ERP implementation project success, but only the Competence and Team composition factors are significant linear predictors in an adapted regression model and contribute significantly in predicting project success. These results therefore confirm both proposed hypotheses and the adapted regression model. Conclusion: This study improves the understanding of PHFs and confirms that they have a significant impact on traditional CSFs and the ERP implementation project success. The proposed PHFs model offers project managers and other stakeholders an effective risk assessment of CSFs and is leading the way to human oriented model of ERP implementations.

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“…Hoegl and Gemuenden (2001) and Lindsjørn et al (2016) found a strong correlation between teamwork quality (which includes communication) and the success of software development teams. Nasir and Sahibuddin (2011) conducted a comparative study and found that communication is one of the critical success factors for software projects. The results of Destefanis et al (2016) showed that the level of politeness in the communication process among developers does have an effect on the time required to fix issues in software projects.…”

Section: Introductionmentioning

confidence: 99%

Descriptions of issues and comments for predicting issue success in software projects

Ramírez‐Mora¹,

Oktaba²,

Gómez-Adorno³

2020

Journal of Systems and Software

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Software development tasks must be performed successfully to achieve software quality and customer satisfaction. Knowing whether software tasks are likely to fail is essential to ensure the success of software projects. Issue Tracking Systems store information of software tasks (issues) and comments, which can be useful to predict issue success; however; almost no research on this topic exists. This work studies the usefulness of textual descriptions of issues and comments for predicting whether issues will be resolved successfully or not. Issues and comments of 588 software projects were extracted from four popular Issue Tracking Systems. Seven machine learning classifiers were trained on 30k issues and more than 120k comments, and more than 6000 experiments were performed to predict the success of three types of issues: bugs, improvements and new features. The results provided evidence that descriptions of issues and comments are useful for predicting issue success with more than 85% of accuracy and precision, and that the predictions of issue success vary over time. Words related to software development were particularly relevant for predicting issue success. Other communication aspects and their relationship to the success of software projects must be researched in detail using data from software tools.

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Critical success factors for software projects: A comparative study

Cited by 122 publications

References 70 publications

Systems Thinking in Software Projects-an Artificial Neural Network Approach

Systems Thinking in Software Projects-an Artificial Neural Network Approach

A Primary Human Critical Success Factors Model for the ERP System Implementation

Descriptions of issues and comments for predicting issue success in software projects

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