Online Shopping Continuance Intention: A Case Study of Online Shopping in Thailand

This study determines individual’s online shopping continuance intention in Bangkok, Thailand , how  to retain existing consumers and attract new consumers,  to find the most influencing key to Thai consumer’s shopping continuance intention, to understand Thai consumers’ online shopping continuance intention . The study included 400 respondents  living in  Bangkok and have purchased IT products online in the past six months. This study used non-probability purposive sampling technique and questionnaire for data collection. The results of study showed that time-oriented lifestyle, price-oriented lifestyle, and net-oriented lifestyle have  significant impact on satisfaction with online shopping,  while satisfaction with online shopping has a significant impact on continuance intention

Measuring Customers Satisfaction of E-Commerce Sites Using Clustering Techniques: Case Study of Nyazco Website

Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected organizations to provide platform on the Internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data mining technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on independent attributes such as gender, product classification, recency, frequency and monetary. For this purpose, the data related to Nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. The authors of this paper will define four clusters by using k-means algorithm and RFM model by IBM SPSS Modeler 14.2 software. Customers in the third cluster and fourth cluster will be identified as the most important customers. Therefore, providing the demands of these customers should be prioritized