Relationship between requirements analyzability, interpersonal trust, and software project performance: Moderating role of customization

Based on the information processing perspective, this study empirically investigated the effects of requirements analyzability, an indicator of technological complexity and interpersonal trust as the soft factor of buyer-supplier relationships on software project development performance. Beyond that, the possible moderator role of the degree of software customization was examined on the analyzability-interpersonal trust and analyzability-performance links. Data were collected from 138 companies through a questionnaire-based survey of IT purchasing managers in Turkey. Partial Least Squares Structural Equation Modeling was used to test the research hypotheses. This study indicated that requirements analyzability and interpersonal trust have a significant effect on software performance. Overall, the study provides evidence that interpersonal trust mediates the relationship between requirements analyzability and software project performance. The research findings support the moderation role of customization in the relationship between requirements analyzability and software project performance. However, partial support is provided for the moderating effect of customization on the relationship between requirements analyzability and interpersonal trust.

Fusion of Fuzzy Multi-Criteria Decision Making Approaches for Discriminating Risk with Relate to Software Project Performance

The prediction and estimation software risks ahead have been key predictor for evaluating project performance. Discriminating risk is vital in software project management phase, where risk and performance has been closely inter-related to each other. This chapter aims at hybridization of fuzzy multi-criteria decision making approaches for building an assessment framework that can be used to evaluate risk in the context of software project performance in following dimensions: 1) user, 2) requirements, 3) project complexity, 4) planning and control, 5) team, and 6) organizational environment. For measuring the risk for effectiveness of project performance, we have integrated Fuzzy Multi-Criteria Decision Making (FMCDM) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches. Moreover the fusion of FMCDM and TOPSIS has not been adequately investigated in the exiting studies.

Optimization of software project evaluation system

Starting from the problems of imperfect evaluation index system, non-standard evaluation method and single factor of performance evaluation model, this paper applies the unified grey clustering analysis algorithm to establish the index system of software organization status and software project characteristics, and defines the connotation of software project performance. A new network topology design method is proposed, and a software project performance evaluation model based on fuzzy neural network is established. An improved particle swarm optimization algorithm is introduced to solve the problem of determining the connection weight coefficient of the evaluation model accurately and efficiently. This paper introduces the basic concept of project performance evaluation, and explains the necessity and importance of performance evaluation in the project.

Fuzzy evaluation model of software project performance based on grey clustering analysis

This paper introduces the general principles that should be followed in project performance evaluation: through the analysis of the main factors affecting software project performance evaluation, it provides a guiding principle for the construction of project performance evaluation index system including software project characteristics and project characteristics. The empirical study shows that the model can effectively evaluate software project performance and knowledge. It provides decision support information for software organizations to formulate risk aversion strategies, improve project performance level and other risk factors.