An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm |
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| Affiliation: | 1. School of Computing Science and Engineering, VIT University, Vellore 632014 Tamil Nadu, India;2. Department of Electrical Engineering and Automation, Aalto University, Aalto, Finland;3. Department of Mathematics, Alagappa University, Karaikudi, Tamil Nadu, India;1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy;1. School of Industrial Engineering and Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran, Iran;2. School of Business, UNSW, Canberra, BC 2610, Australia;3. Department of Industrial Engineering, University of Tafresh, Iran;4. Decision Support and e-Service Intelligence Lab, Quantum Computation and Intelligent Systems, School of Software, University of Technology, Sydney, Australia;5. School of Mathematics and Statistics, Sejong University, Gwangjin-gu, Seoul, South Korea;1. Middle Black Sea Development Agency, Ilkadim, Samsun, Turkey;2. Department of Industrial Engineering, Yıldız Technical University, 34349 Beşiktas, İstanbul, Turkey |
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| Abstract: | 
The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods. |
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| Keywords: | Service climate Global software development Adaptive neuro-fuzzy inference system Taguchi-genetic learning algorithm |
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