277,025 research outputs found

    Data and Predictive Analytics Use for Logistics and Supply Chain Management

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    Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

    USA Triathlon: A Race Toward Sustainability

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    abstract: The staging of sport events occurs over a fixed duration of time, requiring an influx of resources and human involvement. This situation can result in environmental issues such as excess greenhouse gas emissions and waste generation. Furthermore, economic outcomes are not always equally shared amongst local host communities, and unequal access to participation can manifest in unforeseen ways from the event organizer's perspective. Sports organizations are recognizing the potential for operation related negative impacts, yet most efforts to mitigate these adverse outcomes lack theoretical grounding and holistic approaches aligned with principles of sustainability.  USA Triathlon (USAT) is not exempt from the challenges faced in sustainable event management. With 400,000 plus members, USAT has the largest membership of any sport's governing body in the country. Through managing five owned events and sanctioning over 4,300 on an annual basis, the combined potential for a negative footprint is significant. To temper the potential impacts of USAT events, this project focused on an overarching sustainable event strategy to equip management, operations, and race directors with a suite of resources to manage and mitigate the overall sustainability footprint of events toward desired outcomes that adhere to principles of sustainability

    Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

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    The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

    Perception of Nuclear Energy and Coal in France and the Netherlands

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    This study focuses on the perception of large scale application of nuclear energy and coal in the Netherlands and France. The application of these energy-sources and the risks and benefits are judged differently by various group in society. In Europe, France has the highest density of nuclear power plants and the Netherlands has one of the lowest. In both countries scientists and social scientists completed a questionnaire assessing the perception of the large scale application of both energy sources. Furthermore, a number of variables relating to the socio cultural and political circumstances were measured. The results indicate that the French had a higher risk perception and a more negative attitude toward nuclear power than the Dutch. But they also assess the benefits of the use of nuclear power to be higher. Explanations for these differences are discussed

    THE IMPACT OF CAPITAL ADEQUACY RATIO (CAR), NET INTEREST MARGIN (NIM), LOAN TO DEPOSIT RATIO (LDR), AND COST TO INCOME RATIO (CIR) TOWARD BANKS PROFITABILITY (Comparison Study of Domestic Bank and Foreign Bank in Indonesia from 2011 to 2015)

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    The purpose of this research is to analyze Capital Adequacy Ratio (CAR), Net Interest Margin (NIM), Loan to Deposit Ratio (LDR), Cost to Income Ratio (CIR) toward Return On Asset (ROA) of Domestic Banks and Foreign Banks in Indonesia from 2011 to 2015. The population of this research is all domestic banks and foreign banks in Indonesia that operate from 2011 to 2015. This research uses purposive sampling method as sampling technique, therefore total samples of this research is 92 domestic banks and 10 foreign banks. The analysis technique that is used in this research is multiple linear regression analysis. This research also uses Chow test to compare the influence of CAR, NIM, LDR, CIR toward ROA between domestic banks and foreign banks. The result of analysis shows that CAR, NIM, LDR, and CIR have significant influence toward ROA in domestic banks. Meanwhile, only CAR and CIR in foreign banks show significant influence toward ROA. Chow test result shows that there is different influence of CAR, NIM, LDR, CIR toward ROA between domestic banks and foreign banks
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