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作者：Yahav, Inbal; Shmueli, Galit; Mani, Deepa
作者单位：Bar Ilan University; National Tsing Hua University; Indian School of Business (ISB)
摘要：In this paper, we introduce a tree-based approach adjusting for observable self-selection bias in intervention studies in management research. In contrast to traditional propensity score (PS) matching methods, including those using classification trees as a subcomponent, our tree-based approach provides a standalone, automated, data-driven methodology that allows for (1) the examination of nascent interventions whose selection is difficult and costly to theoretically specify a priori, (2) dete...
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作者：Breuker, Dominic; Matzner, Martin; Delfmann, Patrick; Becker, Joerg
作者单位：University of Munster; University of Koblenz & Landau
摘要：Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techni...
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作者：Shi, Zhan (Michael); Lee, Gene Moo; Whinston, Andrew B.
作者单位：Arizona State University; Arizona State University-Tempe; University of Texas System; University of Texas Arlington; University of Texas System; University of Texas Austin
摘要：In this article, we propose a new data-analytic approach to measure firms' dyadic business proximity. Specifically, our method analyzes the unstructured texts that describe firms' businesses using the statistical learning technique of topic modeling, and constructs a novel business proximity measure based on the output. When compared with existent methods, our approach is scalable for large datasets and provides finer granularity on quantifying firms' positions in the spaces of product, market...
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作者：Baesens, Bart; Bapna, Ravi; Marsden, James R.; Vanthienen, Jan; Zhao, J. Leon
作者单位：KU Leuven; University of Minnesota System; University of Minnesota Twin Cities; University of Connecticut; KU Leuven; City University of Hong Kong
摘要：The era of big data and analytics is upon us and is changing the world dramatically. The field of Information Systems should be at the forefront of understanding and interpreting the impact of both technologies and management so as to lead the efforts of business research in the big data era. We need to prepare ourselves and our students for this changing world of business. In this discussion, we focus on exploring the technical and managerial issues of business transformation resulting from t...
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作者：Rai, Arun
作者单位：University System of Georgia; University System of Georgia; Georgia State University
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作者：Menon, Syam; Sarkar, Sumit
作者单位：University of Texas System; University of Texas Dallas
摘要：Scalability and privacy form two critical dimensions that will eventually determine the extent of the success of big data analytics. We present scalable approaches to address privacy concerns when sharing transactional databases. Although the benefits of sharing are well documented and the number of firms sharing transactional data has increased over the years, the rate at which this number has grown is not quite what it could have been. Concerns about revealing proprietary information have pr...
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作者：Brynjolfsson, Erik; Geva, Tomer; Reichman, Shachar
作者单位：Massachusetts Institute of Technology (MIT); Tel Aviv University
摘要：Big data generated by crowds provides a myriad of opportunities for monitoring and modeling people's intentions, preferences, and opinions. A crucial step in analyzing such big data is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method crowd-squared, as it l...
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作者：Martens, David; Provost, Foster; Clark, Jessica; de Fortuny, Enric Junque
作者单位：University of Antwerp; New York University; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
摘要：Organizations increasingly have access to massive, fine-grained data on consumer behavior. Despite the hype over big data, and the success of predictive analytics, only a few organizations have incorporated such fine-grained data in a non-aggregated manner into their predictive analytics. This paper examines the use of massive, fine-grained data on consumer behavior-specifically payments to a very large set of particular merchants-to improve predictive models for targeted marketing. The paper ...
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作者：Ghose, Anindya; Todri-Adamopoulos, Vilma
作者单位：New York University; Korea University
摘要：The increasing availability of individual-level data has raised the standards for measurability and accountability in digital advertising. Using a massive individual-level data set, our paper captures the effectiveness of display advertising across a wide range of consumer behaviors. Two unique features of our data set that distinguish this paper from prior work are (1) the information on the actual viewability of impressions as on average 55% of the display ads are not rendered viewable and (...
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作者：Saboo, Alok R.; Kumar, V.; Park, Insu
作者单位：University System of Georgia; Georgia State University
摘要：Marketing resource allocation has been a topic of intense scrutiny, yet the literature on the topic has not paid adequate attention to the fact that the effectiveness of marketing-mix elements varies over time. Despite the fact that firms collect volumes of data on their customers, existing estimation approaches do not readily lend themselves to modeling the temporal variations for big data and provide little guidance to managers in terms of their resource allocation decisions. We address this...
