Users Of Web-based Systems Research Articles

Quality-Driven Information Filtering Using the WIQA Policy Framework

Web-based information systems, such as search engines, news portals, and community sites, provide access to information originating from numerous information providers. The quality of provided information varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore confronted with the increasingly difficult task of selecting high-quality information from the vast amount of web-accessible information. How can information systems support users to distinguish high-quality from low-quality information? Which filtering mechanisms can be used to suppress low-quality information? How can filtering decisions be explained to the user? This article identifies information quality problems that arise in the context of web-based systems, and gives an overview of quality indicators as well as information quality assessment metrics for web-based systems. Afterwards, we introduce the WIQA — Information Quality Assessment Framework. The framework enables information consumers to apply a wide range of policies to filter information. The framework employs the Named Graphs data model for the representation of information together with quality related meta-information. The framework uses the WIQA-PL policy language for expressing information filtering policies against this data model. WIQA-PL policies are expressed in the form of graph patterns and filter conditions. This allows the compact representation of policies that rely on complex meta-information such as provenance chains or combinations of provenance information and background information about information providers. In order to facilitate the information consumers’ understanding of filtering decisions, the framework generates explanations of why information satisfies a specific policy.

The antecedents of customer satisfaction and its link to complaint intentions in online shopping: An integration of justice, technology, and trust

Complaint behaviors are critical to maintaining customer loyalty in an online market. They provide insight into the customer's experience of service failure and help to redress the failures. Previous studies have shown the importance of customer satisfaction as a mediator for complaint intentions. It is important to examine the antecedents of customer satisfaction and its link to complaint intentions. Online shoppers are both buyers of products/services and users of web-based systems. Trust also plays a critical role in forming a psychological state with positive or negative feelings toward e-vendors. In this context, there are three major concerns: justice, technology and trust. This study proposes a research model to combine these issues, in order to investigate complaint intentions. Data were collected from an online survey wherein subjects were encouraged to reflect on recent service failure experiences. The results from testing a structural equation model indicate that distributive and interactional justice contribute significantly to customer satisfaction and, in turn, to complaint intentions, but procedural justice does not. Technology-based features and trust are also important in determining the two target variables. The implications for managers and scholars are also discussed.

Understanding privacy policies

Their is growing recognition that users of web-based systems want to understand, if not control, what customer's data is stored by whom, for what purpose, for what duration, and with whom it is shared. We inform current language-based privacy efforts with an empirical study of P3P--the W3C domain-specific language for privacy policies. We use methods of software language engineering to study usage profiles, correctness of policies, metrics, cloning, and language extensions. The study supports the conclusion that P3P's approach to policy validation is too weak to ensure correct use of the language. The study also discovers common, dominating policies, which may suggest a simpler approach to web privacy. Further, the study investigates a range of metrics for policies in an attempt to discover particularly interesting or complex policies. Finally, the study also attempts to discover symptoms of the need for extending the P3P language, but the found results are not conclusive here.

Quality-driven information filtering using the WIQA policy framework

Web-based information systems, such as search engines, news portals, and community sites, provide access to information originating from numerous information providers. The quality of provided information varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore confronted with the increasingly difficult task of selecting high-quality information from the vast amount of web-accessible information. How can information systems support users to distinguish high-quality from low-quality information? Which filtering mechanisms can be used to suppress low-quality information? How can filtering decisions be explained to the user? This article identifies information quality problems that arise in the context of web-based systems, and gives an overview of quality indicators as well as information quality assessment metrics for web-based systems. Afterwards, we introduce the WIQA—Information Quality Assessment Framework. The framework enables information consumers to apply a wide range of policies to filter information. The framework employs the Named Graphs data model for the representation of information together with quality-related meta-information. The framework uses the WIQA-PL policy language for expressing information filtering policies against this data model. WIQA-PL policies are expressed in the form of graph patterns and filter conditions. This allows the compact representation of policies that rely on complex meta-information such as provenance chains or combinations of provenance information and background information about information providers. In order to facilitate the information consumers’ understanding of filtering decisions, the framework generates explanations of why information satisfies a specific policy.