O Overall Satisfaction Prediction

Strides in information technology and improvements in networking technology have set the pace for rapid growth in new applications of electronic commerce in a variety of settings. Business to business (B2B), business to customer (B2C), customer to business (C2B), and customer to customer (C2C) have become prevalent business channels and have reshaped the ways that business transactions are conducted in the marketplace. According to Internet Data Corporation (IDC), the number of Internet users worldwide will exeed 1 billion Internet users by 2007 (IDC, 2004). Given recent trends and forecasting, it is clear that no business enterprise can afford to ignore the tremendous potential of these emerging technologies in terms of the rate of creating, processing, and distributing the volume of business. The proliferation of the Web potential for business, together with its profuse customer information, have offered an alternative sales channel for a growing number of firms and have prompted extensive research on the effect of negative critical incidents on customer satisfaction with Internet shopping. The increase in business-tocustomer (B2C) channels has made several firms look for new strategies to understand online shopping behavior in order to attract, retain and satisfy customers’ needs (Ranganathan & Ganapathy, 2002). In fact, many researchers have considered that customer satisfaction leading to higher levels of customer retention would depend on the success of critical factors, such as quality design (Huizingh, 2000; Liu & Arnett, 2000; Stefani & Xenos, 2001), security concerns (Belanger, Hiller, & Smith, 2002; La & Kandampully, 2002), and other factors for electronic commerce (Loiacono, Watson, & Goodhue, 2002; Yang, Cai, Zhou, & Zhou, 2005). However, Waterhouse and Morgan (1994) reported an interesting finding that just one factor of dissatisfaction and defection would be enough to cause customers to become disenchanted with Internet shopping. Thus, the call for the managers to find and discriminate the dissatisfaction or defection in the velocity and dynamic nature of the Internet environment becomes loud. According to Fang, Shih, and Liu (2004), the slow response affected overall satisfaction indirectly by quality attributes satisfaction (QASAT) seems to be more important to customers who have less purchase frequency or purchase amount than high one. Furthermore, online bookstores with incomplete content and have untrustworthy transaction would affect overall satisfaction indirectly to customers with high loyalty by QASAT. The main purpose of this study was threefold. First, it designed a set of quality attributes satisfaction, in term of the negative critical incidents concept, to measure individual satisfaction, an online shopping bookstore served as empirical cases. Second, from predictive model standpoint, a method, call multiple discriminant analysis (MDS), was used to analyze customers’ satisfaction based on QASAT and estimated data. Finally, it adopted holdout samples to confirm the ability of generalization with a predictive model.