A web-based consumer-oriented intelligent decision support system for personalized e-services

Abstract

Due to the rapid advancement of electronic commerce and web technologies in recent years, the concepts and applications of decision support systems have been significantly extended. One quickly emerging research topic is the consumer-oriented decision support system that provides functional supports to consumers for efficiently and effectively making personalized decisions. In this paper we present an integrated framework for developing webbased consumer-oriented intelligent decision support systems to facilitate all phases of consumer decision-making process in business-to-consumer e-services applications. Major application functional modules comprised in the system framework include consumer and personalized management, navigation and search, evaluation and selection, planning and design, community and collaboration management, auction and negotiation, transactions and payments, quality and feedback control, as well as communications and information distributions. System design and implementation methods will be illustrated using an example. Also explored are various potential e-services application domains including e-tourism and e-investment.

Perceived interactivity leading to e-loyalty: Development of a model for cognitive–affective user responses

“…It ranges from Ahamad et al (1991) who developed an efficient algorithm for finding a given quorum, to Jones et al (2004) who tested audience voting at the Athens Olympic Games. More relevant to ecommerce contexts, Yu (2004) conducted a usability study of a web page for evaluating package tours, but the interface did not feature any interactive information visualization. In fact, guidance for designing web-polls is limited.…”

Intelligent Techniques for Decision Support System in Human Resource Management

“…Personalized E-services (Yu, 2004) UbiDSS -Proactive services (Kwon et al, 2005) areas and problem domains that can be explored by the intelligent system researchers or system developers. This can help to increase the IDSS products in market place as alternative tools to support and improve decision making processes for the specific problem domains.…”

“…These abilities are used to support the decision making processes. There are various types of intelligent techniques that are applied in IDSS applications such as knowledge base system (Quintero et al, 2005), (Adla & Zarate, 2006), (Waiman et al, 2005), (Malhotra et al, 2003), (Palma-dos-Reis & Zahedi, 1999), (Matsatsinis & Siskos, 1999), (Linger & Burstein, 1998) and (Seder et al, 2000), data warehouse (Yu, 2004), fuzzy set theory (Liqiang et al, 2001), ANN (Sajjad & Slobodan, 2006), rough set classifier (Gorzalczany & Piasta, 1999), multi agent (Kwon et al, 2005) and etc.…”

Making intelligent business decisions by mining the implicit relation from bloggers’ posts

“…Many research works proved that intelligent business decisionmaking is helpful in integrating critical business processes and gathering intelligent and consistent information (Chandra and Smirnov 2003;Yu 2004;Azvine et al 2003;Ergazakis et al 2008;Muntean and Mircea 2008;Wu et al 2007;Simić and Simić 2007). Chandra and Smirnov (2003) proposed a complex business system-integrated intelligent information support, group decision-making and agreement modeling.…”

“…The most important character of this system is combining two distributed decision-making methods, ''ontology-driven knowledge management'' and ''multi and intelligent agents technologies''. Yu (2004) proposed an integrated framework for developing Web-based, consumer-oriented, intelligent decision support systems. This framework is composed of many e-business modules, such as consumer and personalized management, planning and design, community and collaboration management, auction and negotiation, transactions and payments, etc.…”