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Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.

Introduction Is stickiness the Holy Grail for e-tailing? In general, stickiness refers to the amount of time a person spends on a Web site during a visiting session (such as, session stickiness ) or over a specified time period (such as, site stickiness ). Zauberman equates stickiness and "within-site lock-in" and uses it to approximate visitors' loyalty to a Web site. The conventional wisdom suggests that stickiness is crucial and can contribute to e-tailers' bottom lines considerably. However, the direct economic impacts of stickiness have not been duly examined empirically, particularly from the perspective of consumers' within-session visiting behaviors. E-tailing offers an exciting global virtual channel for marketing and exchanges. According to the U.S. Department of Commerce, the e-retailing industry has grown at a 29% compounded annual rate between 2000 and 2004, amounting to $81 billion in sales in 2005 and projected to reach $144 billion by 2010. Accompanied by this impressive growth is the increasingly fierce market competition that results from greatly reduced search costs, diminished product/service differentiation, and rapidly eroding customer loyalty. To survive and excel in this highly competitive market, e-tailers must be effective in converting their Web site visitors into paying customers. Such conversions are challenging, as manifested by the dismal conversion rate (for example, estimated at 2.3% by e-tailing.com) that is likely to continue to decline. Stickiness serves as a common indicator of customer loyalty to e-tailers. Accordingly, firms have focused on effective Web site design and business strategies to "lock in" visitors by making their Web sites increasingly sticky. Despite the salient beliefs about the business value of stickiness in e-tailing, empirical evidence of its direct economic impacts is surprisingly limited. Moe and Fader discuss the importance of stickiness for business profits and advocate the use of visiting behaviors to investigate the relationship. Straub et al. also highlight the importance of analyzing prominent visitors' behaviors and particularly suggest the use of click-stream data to examine the effects of essential visiting behaviors on business outcomes, such as online purchases. However, few (if any) studies have examined these effects empirically. In this study, we use within-session visiting behaviors, recorded by a designated client-side monitoring program, to examine the relationship between stickiness and conversion, an outcome metric directly affecting e-tailers' bottom lines. We respond to the call by Moe and Fader by examining session stickiness through analyses of visitors' within-session behaviors. We measure session stickiness using both visiting session duration and total number of pages accessed during a visiting session. We empirically test whether stickiness significantly affects conversion, and further investigate whether product category moderates the focal stickiness--conversion relationship.