Predictive Analytics in Information Systems Research

By Galit Shmueli, Otto R. Koppius. MIS Q., 2011

1,467 citations recorded by the source corpus.

Subfields: Management Information Systems, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bb02f492ee4cec6e7d52fb2832b2374c826536a4

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In Management Information Systems

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Impact of Industry 4.0 on supply chain performance (2020, methodology): “This is done through BDA developing behavioural patterns out of data analysis (Li et al. 2016; Shmueli and Koppius 2011), which results in the creation of detailed frameworks that allow for the identification of correlations between operational variables.”
Improving Decision Analytics with Deep Learning: the Case of Financial Disclosures (2015, background): “In fact, predictive analytics can be instrumented for “generating new theory, developing new measures, comparing competing theories, improving existing theories, assessing the relevance of theories, and assessing the predictability of empirical phenomena” [13].”
An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline (2015, background): “‘‘Predictive analytics are useful for generating new theory, developing new measures, comparing competing theories, improving existing theories, assessing the relevance of theories, and assessing the predictability of empirical phenomena’’ (Shmueli & Koppius, 2011, p. 554).”
Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations (2018, background): “Predictive capability is the ability to build and assess a model aimed at generating accurate predictions of new observations, where new can be interpreted temporally and or cross-sectionally (Shmueli and Koppius, 2011).”
How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service (2018, neutral): “analytics) characteristics of BDA [44].”
Research Dilemmas with Behavioral Big Data (2017, neutral): “Predictive modeling and assessment are necessary and beneficial for scientific development.(33,52) However, their use for theory building, evaluation, and develop-”
Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem (2013, background): “If a researcher would like to validate the predictive power of her causal model, it is easier to do so with a large sample (Shmueli 2010, Shmueli and Koppius 2011).”
Impact of Organizational Culture Values on Organizational Agility (2017, background): “The predictive power of a model is a model’s ability to generate accurate predictions of new interpretable observations, temporal or cross-sectional [84].”
Consumer behavior in social commerce: A literature review (2016, background): “Shmueli and Koppius [89] posited that predictive analytics is more data-driven and can further help to generate new theory, compare existing theories, and assess the predictability of a phenomenon.”
Business intelligence & analytics in management accounting research: Status and future focus (2018, background): “Management accountants in this respect can become champions in promoting the integration of BI&A into decision-making.”
Explaining Data-Driven Document Classifications (2014, background): “In statistical predictive modeling, the “rule trace” often entails simplythe application of a mathematical function to the case data, with the result being a score representing the likelihood of the case belonging to the class of interest, with no justification of why.”