Nivet Chirawichitchai

Work place: School of Information Technology, Sripatum University, Bangkok 10900, Thailand



Research Interests: NLP, Big Data Analytics, Data Mining, Machine Learning


Nivet Chirawichitchai received his B.B.A. in Industrial Management and B.A. in Mass Communication from Ramkhamhaeng University, M.S. in Computer Technology and the Ph.D degree in Information Technology from King Mongkut's Institute of Technology North Bangkok, Thailand. He currently has the rank of director, Master of Science program in Computer Information Systems, School of Information Technology, Sripatum University, Thailand. His main research interests are in the field of machine learning, data mining and knowledge representation and reasoning. Dr. Nivet Chirawichitchai has several published articles in these fields. He is also a lecturer for undergraduate and graduate level in Information Technology Department, Sripatum University.

Author Articles
A Roadmap for Establishing Trust Management Strategy in E-Commerce Services Using Quality Based Assessment

By Rath Jairak Prasong Praneetpolgrang Nivet Chirawichitchai

DOI:, Pub. Date: 8 Oct. 2014

Trust has been reported as a key role in e-business, especially for a monetary based system like e-commerce. Therefore, many previous studies have been conducted to investigate the antecedents and consequences of consumer trust. But there has been little work done on establishing sensible solutions for leveraging consumer trust. Furthermore, previous studies in managerial trust have not demonstrated trust management that can illustrate a method to link their solutions with the consumers' point of view. In this paper, we therefore propose a practical roadmap for establishing trust management strategy that is consistent with consumer perceptions. Within this roadmap, firstly, a component extraction is performed on survey data in order to identify the quality criteria that actually impact buying decision process. Based on these criteria, the sensible factors for establishing trust and satisfaction are discovered from the regression analysis. Then, the prediction equations for trust and satisfaction are generated. After that the results of prediction equations are applied with a fuzzy linguistic approach in order to convert these results in linguistic terms. Finally, trust management strategy is established. By using our proposed method, website managers can identify which are the quality criteria that are consistent with their customer perceptions and they can use these criteria as a basis for improving trust and satisfaction in their websites.

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