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dc.contributor.authorDabrowski, Maciejen
dc.contributor.authorActon, Thomasen
dc.contributor.authorGolden, Willieen
dc.description.abstractConsumers often face a task to select a best option from a large set of alternatives, such as choosing a car to buy1, an apartment to rent2, or an unforgettable trip to book3. E-commerce sites frequently provide the possibility to search for structured items, usually by asking a user to fill in a form asking about the requirements that a desired product has to satisfy (preferences). This process is used, for example, when searching for a used car, or a flight on popular websites, and is also referred to as preference- based search or parametric search. Although such choice-based approaches are prevalent, both users and retailers can find them unsatisfying. One of the major reasons is that users are often not able to correctly transform their preferences into requirements using online forms, and thus they are rarely provided with the information they need. On the other hand, consumers making purchase decisions in online shops are often unable to evaluate all available alternatives in great depth, and so seek to reduce the amount of information processing. Interactive decision aids that provide support to consumers are particularly valuable in helping to determine which alternatives are worth further, detailed consideration. Customers are being provided with a number of different decision aids that, ideally, should enable them to search, browse and compare vast numbers of available products. Retailers offer various recommender systems that attempt to provide customers with manageable and relevant set of products based on user profiles, history of interactions and product descriptions. Moreover, various techniques for preference elicitation (e.g. dialogs in conversational recommender systems) are used to enable better understanding of customers¿ needs. However, there are many factors (e.g. the number of available products but also by the precision of information preference elicited) that can impact the performance of decision aids in online shops. We discuss the most popular decision aids in the context of online shopping and decision-making. We also introduce the concept of a soft-boundary pre-filtering decision aid. The decision aid modifies the pre-filtration criteria provided by the decision maker and thus, allows him to reconsider selected alternatives she initially eliminated. We propose a model of such a decision aid, give an overview of its different configurations, and provide the illustrative example of the scenario of use in the apartment selection decision problem. We also hypothesise about the impact of the proposed decision aid on decision quality and consideration set size and quality. We conclude the paper with an overview of potential directions for future research and a discussion of benefits of application of the soft-boundary pre-filtering decision aid.en
dc.subjectPurchase decisionsen
dc.subjectEnterprise Agilityen
dc.titleChallenges for aided online shopping and product selection - a decision making perspective.en
dc.typeConference Paperen

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