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Suche nach „[O.] [Hinz]“ hat 5 Publikationen gefunden
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    DigitalTC Grafenau

    Zeitschriftenartikel

    Michael Scholz, C. Brenner, O. Hinz

    AKEGIS: automatic keyword generation for sponsored search advertising in online retailing

    Decision Support Systems, vol. 119, no. April, pp. 96-106

    2019

    DOI: 10.1016/j.dss.2019.02.001

    Abstract anzeigen

    Sponsored search advertisers face several complex decisions when planning and implementing a new sponsored search advertising campaign. These decisions include the selection of keywords, the definition of landing pages, and the formulation of bidding strategies. Relatively low attention has been paid on supporting the selection of keywords in recent research and most studies on sponsored search advertising focus on the formulation of bidding strategies and strategies for budget planning. We present a novel approach for automatically generating sponsored search keywords that relies on the theory of consumer search behavior. Our approach uses an online store's internal search log to extract keywords used by consumers within their search process, because recent research has shown that especially consumers with a high conversion probability that exhibit goal-directed instead of exploratory search patterns use an online store's internal search engine. We empirically test our approach based on a store's internal search engine and identify the effects of this approach by comparing it to a state-of-the-art approach. Our analysis reveals that our approach substantially increased the number of profitable keywords, improved the store's conversion rate by approximately 41%, and decreased the average cost per click by more than 70%.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Michael Scholz, M. Franz, O. Hinz

    Effects of decision space information on MAUT-based systems that support purchase decision processes

    Decision Support Systems, vol. 97, no. May, pp. 43-57

    2017

    DOI: 10.1016/j.dss.2017.03.004

    Abstract anzeigen

    This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Michael Scholz, M. Franz, O. Hinz

    The Ambiguous Identifier Clustering Technique

    Electron Markets, vol. 26, no. 2, pp. 143-156

    2016

    DOI: 10.1007/s12525-016-0217-2

    Abstract anzeigen

    Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Michael Scholz, V. Dorner, M. Franz, O. Hinz

    Measuring consumers' willingness to pay with utility-based recommendation systems

    Decision Support Systems, vol. 72, no. April

    2015

    DOI: 10.1016/j.dss.2015.02.006

    Abstract anzeigen

    Our paper addresses two gaps in research on recommendation systems: first, leveraging them to predict consumers' willingness to pay; second, estimating non-linear utility functions – which are generally held to provide better approximations of consumers' preference structures than linear functions – at a reasonable level of cognitive consumer effort. We develop an approach to simultaneously estimate exponential utility functions and willingness to pay at a low level of cognitive consumer effort. The empirical evaluation of our new recommendation system's utility and willingness to pay estimates with the estimates of a system based on linear utility functions indicates that exponential utility functions are better suited for predicting optimal recommendation ranks for products. Linear utility functions perform better in estimating consumers' willingness to pay. Based on our experimental data set, we show how retailers can use these willingness to pay estimates for profit-maximizing pricing decisions.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    M. Franz, Michael Scholz, O. Hinz

    2D versus 3D Visualizations in Decision Support - The Impact of Decision Makers' Perceptions

    Proceedings of the 36th International Conference on Information Systems (ICIS 2015)

    2015