2,659 research outputs found

    Special Libraries, December 1974

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    Volume 65, Issue 12https://scholarworks.sjsu.edu/sla_sl_1974/1008/thumbnail.jp

    Toward a three-dimensional framework for omni-channel

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    The omni-channel, as an emerging trend in retail, aims to coordinate processes and technologies across supply and sales channels. The evolution of this concept is still nascent. This paper develops a conceptual framework for omni-channel systems, configured by three dimensions of channel stage, channel type and channel agent. Integration and visibility are also explored and discussed as the main enablers, which support the implementation of omni-channel framework. This research is built upon the empirical and secondary data. Multiple case studies and expert interview methods are employed for data collection to validate the recommended framework and to explore its applicability. The framework proposed, along with the key integration and visibility enablers identified for the omni-channel, can be applied to a wide range of retail supply chains. It helps managers to develop, run and monitor omni-channel systems; it may also serve as a stepping-stone for development of the literature on omni-channel systems

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    Early Detection of Online Auction Opportunistic Sellers Through the Use of Negative-Positive Feedback

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    Apparently fraud is a growth industry. The monetary losses from Internet fraud have increased every year since first officially reported by the Internet Crime Complaint Center (IC3) in 2000. Prior research studies and third-party reports of fraud show rates substantially higher than eBay’s reported negative feedback rate of less than 1%. The conclusion is most buyers are withholding reports of negative feedback. Researchers Nikitov and Stone in a forensic case study of a single opportunistic eBay seller found buyers sometimes embedded negative comments in positive feedback as a means of avoiding retaliation from sellers and damage to their reputation. This category of positive feedback was described as “negative-positive” feedback. An example of negative-positive type feedback is “Good product, but slow shipping.” This research study investigated the concept of using negative-positive type feedback as a signature to identify potential opportunistic sellers in an online auction population. As experienced by prior researchers using data extracted from the eBay web site, the magnitude of data to be analyzed in the proposed study was massive. The nature of the analysis required - judgment of seller behavior and contextual analysis of buyer feedback comments – could not be automated. The traditional method of using multiple dedicated human raters would have taken months of labor with a correspondingly high labor cost. Instead, crowdsourcing in the form of Amazon Mechanical Turk was used to reduce the analysis time to a few days and at a fraction of the traditional labor cost. The research’s results found that the presence of subtle buyer behavior in the form of negative-positive type feedback comments are an inter-buyer signal indicating that a seller was behaving fraudulently. Sellers with negative-positive type feedback were 1.82 times more likely to be fraudulent. A correlation exists between an increasing number of negative-positive type feedback comments and an increasing probability that a seller was acting fraudulently. For every one unit increase in the number of negative-positive type feedback comments a seller was 4% more likely to be fraudulent

    Special Libraries, October 1947

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    Volume 38, Issue 8https://scholarworks.sjsu.edu/sla_sl_1947/1007/thumbnail.jp

    Cargo/Logistics Airlift System Study (CLASS), Volume 2

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    Air containerization is discussed in terms of lower freight rates, size and pallet limitations, refrigeration, backhaul of empties, and ownership. It is concluded that there is a need for an advance air cargo system as indicated by the industry/transportation case studies, and a stimulation of the air cargo would result in freight rate reductions

    How to Rank Answers in Text Mining

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    In this thesis, we mainly focus on case studies about answers. We present the methodology CEW-DTW and assess its performance about ranking quality. Based on the CEW-DTW, we improve this methodology by combining Kullback-Leibler divergence with CEW-DTW, since Kullback-Leibler divergence can check the difference of probability distributions in two sequences. However, CEW-DTW and KL-CEW-DTW do not care about the effect of noise and keywords from the viewpoint of probability distribution. Therefore, we develop a new methodology, the General Entropy, to see how probabilities of noise and keywords affect answer qualities. We firstly analyze some properties of the General Entropy, such as the value range of the General Entropy. Especially, we try to find an objective goal, which can be regarded as a standard to assess answers. Therefore, we introduce the maximum general entropy. We try to use the general entropy methodology to find an imaginary answer with the maximum entropy from the mathematical viewpoint (though this answer may not exist). This answer can also be regarded as an “ideal” answer. By comparing maximum entropy probabilities and global probabilities of noise and keywords respectively, the maximum entropy probability of noise is smaller than the global probability of noise, maximum entropy probabilities of chosen keywords are larger than global probabilities of keywords in some conditions. This allows us to determinably select the max number of keywords. We also use Amazon dataset and a small group of survey to assess the general entropy. Though these developed methodologies can analyze answer qualities, they do not incorporate the inner connections among keywords and noise. Based on the Markov transition matrix, we develop the Jump Probability Entropy. We still adapt Amazon dataset to compare maximum jump entropy probabilities and global jump probabilities of noise and keywords respectively. Finally, we give steps about how to get answers from Amazon dataset, including obtaining original answers from Amazon dataset, removing stopping words and collinearity. We compare our developed methodologies to see if these methodologies are consistent. Also, we introduce Wald–Wolfowitz runs test and compare it with developed methodologies to verify their relationships. Depending on results of comparison, we get conclusions about consistence of these methodologies and illustrate future plans

    Special Libraries, September 1970

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    Volume 61, Issue 7https://scholarworks.sjsu.edu/sla_sl_1970/1006/thumbnail.jp
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