122 research outputs found

    Information Outlook, August 2006

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    Volume 10, Issue 8https://scholarworks.sjsu.edu/sla_io_2006/1007/thumbnail.jp

    Information Outlook, December 2006

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    Volume 10, Issue 12https://scholarworks.sjsu.edu/sla_io_2006/1011/thumbnail.jp

    Managing Information in Online Product Review Communities: Two Approaches

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    Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability

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    It is important to understand factors affecting the perceived online review helpfulness as it helps solve the problem of information overload in online shopping. Moreover, it is also crucial to explore the factors’ relative importance in predicting review helpfulness in order to effectively detect potential helpful reviews before they exert influences. Applying Elaboration Likelihood Model (ELM), this study first investigates the effects of central cues (review subjectivity and elaborateness) and peripheral cues (reviewer rank) on review helpfulness with readability as a moderator. Second, it also explores their relative predicting power using the machine learning technique. ELM is tested in online context and the results are compared between experience and search goods. Our results provide evidence that for both types of products review subjectivity can play a more significant role when the content readability is high. Furthermore, this study reveals that the dominant predictor is varied for different product types

    Cheap Speech and What It Will Do

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    FAKE REVIEWS AND MANIPULATION: DO CUSTOMER REVIEWS MATTER?

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    With the prevalence of fake reviews across web and e-commerce platforms it has become difficult for the customers to make an informed purchase decision. Considering this we examine the influence of review manipulation on customer’s purchase decision. A qualitative approach employing interviews with frequent online shoppers was employed to explore the phenomenon. The results of the study suggest that customers accord recommendations from their social network more weightage than the reviews available on an e-commerce platform. Further, we found that customers apply either or both interactive and extractive strategies to deal with review manipulation. Keywords: information processing, review manipulation, fake reviews, grounded theory

    Information Outlook, August 2007

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    Volume 11, Issue 8https://scholarworks.sjsu.edu/sla_io_2007/1007/thumbnail.jp

    Internet shopping - A taxonomy of consumer online actions.

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    This thesis applied the theory of activity and goal-directed action to the study of online shopping actions. It first studied qualitatively the structures of online shopping actions using the self-confrontation interview method. The qualitative findings established the structural, cognitive and dispositional dimensions of online shopping actions including knowledge and value structures, attention processes and flow. The typical behavioural traits of online shoppers were also identified. Findings also emerged about the tensions between consumers' online and offline actions and the consequences of the technological mediation of shopping. From these qualitative findings, a survey instrument was developed to query online shoppers on various dimensions of their online shopping actions. Cluster analysis of the survey results produced a taxonomy of consumer online actions from which a typology of online shoppers was generated. The qualitative findings on the typical behavioural traits of online shoppers were then used as criteria for the qualitative usability analysis of retail websites. Retail websites of four product and service categories were analysed for their usability, i.e. ability to accommodate the typical behavioural traits of online shoppers such as propensity to experience information overload and to multi-task, potential for experiencing affect and flow etc. This thesis made several theoretical, methodological and practical contributions. It extended goal-directed action theory beyond its traditional scope of work actions and group activity to the realm of consumer behaviour. It also introduced a different theoretical framework to consumer psychology by applying the theory of activity and goal directed action to consumer behaviour. It made a methodological contribution by applying the self-confrontation interview method to the study of online behaviour. This thesis' findings also have practical implications for the understanding of online behaviour, the diffusion of e-commerce and the design of Internet interfaces

    Personalizing online reviews for better customer decision making

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    Online consumer reviews have become an important source of information for understanding markets and customer preferences. When making purchase decisions, customers increasingly rely on user-generated online reviews; some even consider the information in online reviews more credible and trustworthy than information provided by vendors. Many studies have revealed that online reviews influence demand and sales. Others have shown the possibility of identifying customer interest in product attributes. However, little work has been done to address customer and review diversity in the process of examining reviews. This research intends to answer the research question: how can we solve the problem of customer and review diversity in the context of online reviews to recommend useful reviews based on customer preferences and improve product recommendation? Our approach to the question is through personalization. Similar to other personalization research, we use an attribute-based model to represent products and customer preferences. Unlike existing personalization research that uses a set of pre-defined product attributes, we explore the possibility of a data-driven approach for identifying more comprehensive product attributes from online reviews to model products and customer preferences. Specifically, we introduce a new topic model for product attribute identification and sentiment analysis. By differentiating word co-occurrences at the sentence level from at the document level, the model better identifies interpretable topics. The use of an inference network with shared structure enables the model to predict product attribute ratings accurately. Based on this topic model, we develop attribute-based representations of products, reviews and customer preferences and use them to construct the personalization of online reviews. We examine personalization from the lens of consumer search theory and human information processing theory and test the hypotheses with an experiment. The personalization of online reviews can 1) recommend products matching customer's preferences; 2) improve custom's intention towards recommended products; 3) best distinguish recommended products from products that do not match customer's preferences; and 4) reduce decision effort
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