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Peter Buxmann - One of the best experts on this subject based on the ideXlab platform.

  • Big Data and Information Processing in Organizational Decision Processes
    Business & Information Systems Engineering, 2014
    Co-Authors: Martin Kowalczyk, Peter Buxmann
    Abstract:

    Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing Mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing Mechanisms and their complementary relationship to data-centric Mechanisms. Third, it demonstrates how information processing theory can be applied to assess the Dynamics of Mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.

  • Big Data und Informationsverarbeitung in organisatorischen Entscheidungsprozessen
    WIRTSCHAFTSINFORMATIK, 2014
    Co-Authors: Martin Kowalczyk, Peter Buxmann
    Abstract:

    Datenzentrische Ansätze wie Big Data und verwandte Ansätze im Bereich Business Intelligence und Analytics (BI&A) haben in jüngster Zeit viel Aufmerksamkeit auf sich gezogen, da sie versprechen – basierend auf neuen geschäftsrelevanten Erkenntnissen und besseren Entscheidungen – die Leistungsfähigkeit von Unternehmen stark zu verbessern. Die Einbeziehung datenzentrischer Ansätze in die Entscheidungsprozesse von Unternehmen ist herausfordernd und es ist nicht selbstverständlich, dass die erwarteten Nutzen auch realisiert werden. Frühere Studien haben einen mangelnden Forschungsfokus auf den Kontext von Entscheidungsprozessen in datenzentrischen Ansätzen festgestellt. Mittels eines multiplen Fallstudienansatzes werden im vorliegenden Aufsatz unterschiedliche Typen von BI&A-gestützten Entscheidungsprozessen untersucht und die drei folgenden Beiträge gemacht. Erstens wird gezeigt, wie die Facetten von Big Data und verschiede Kompositionen von Mechanismen der Informationsverarbeitung in unterschiedlichen Typen von BI&A-gestützten Entscheidungsprozessen genutzt werden. Zweitens werden neue Erkenntnisse über organisatorische Mechanismen der Informationsverarbeitung und ihre komplementäre Beziehung zu datenzentrischen Mechanismen zur Theorie der Informationsverarbeitung beigetragen. Drittens wird demonstriert, wie die Theorie der Informationsverarbeitung angewendet werden kann, um die Dynamik der Komposition von Mechanismen der Informationsverarbeitung über verschiedene Entscheidungstypen hinweg zu bewerten. Abschließend werden die theoretischen und praktischen Implikationen der Studie diskutiert. Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing Mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing Mechanisms and their complementary relationship to data-centric Mechanisms. Third, it demonstrates how information processing theory can be applied to assess the Dynamics of Mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.

Martin Kowalczyk - One of the best experts on this subject based on the ideXlab platform.

  • Big Data and Information Processing in Organizational Decision Processes
    Business & Information Systems Engineering, 2014
    Co-Authors: Martin Kowalczyk, Peter Buxmann
    Abstract:

    Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing Mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing Mechanisms and their complementary relationship to data-centric Mechanisms. Third, it demonstrates how information processing theory can be applied to assess the Dynamics of Mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.

  • Big Data und Informationsverarbeitung in organisatorischen Entscheidungsprozessen
    WIRTSCHAFTSINFORMATIK, 2014
    Co-Authors: Martin Kowalczyk, Peter Buxmann
    Abstract:

    Datenzentrische Ansätze wie Big Data und verwandte Ansätze im Bereich Business Intelligence und Analytics (BI&A) haben in jüngster Zeit viel Aufmerksamkeit auf sich gezogen, da sie versprechen – basierend auf neuen geschäftsrelevanten Erkenntnissen und besseren Entscheidungen – die Leistungsfähigkeit von Unternehmen stark zu verbessern. Die Einbeziehung datenzentrischer Ansätze in die Entscheidungsprozesse von Unternehmen ist herausfordernd und es ist nicht selbstverständlich, dass die erwarteten Nutzen auch realisiert werden. Frühere Studien haben einen mangelnden Forschungsfokus auf den Kontext von Entscheidungsprozessen in datenzentrischen Ansätzen festgestellt. Mittels eines multiplen Fallstudienansatzes werden im vorliegenden Aufsatz unterschiedliche Typen von BI&A-gestützten Entscheidungsprozessen untersucht und die drei folgenden Beiträge gemacht. Erstens wird gezeigt, wie die Facetten von Big Data und verschiede Kompositionen von Mechanismen der Informationsverarbeitung in unterschiedlichen Typen von BI&A-gestützten Entscheidungsprozessen genutzt werden. Zweitens werden neue Erkenntnisse über organisatorische Mechanismen der Informationsverarbeitung und ihre komplementäre Beziehung zu datenzentrischen Mechanismen zur Theorie der Informationsverarbeitung beigetragen. Drittens wird demonstriert, wie die Theorie der Informationsverarbeitung angewendet werden kann, um die Dynamik der Komposition von Mechanismen der Informationsverarbeitung über verschiedene Entscheidungstypen hinweg zu bewerten. Abschließend werden die theoretischen und praktischen Implikationen der Studie diskutiert. Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing Mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing Mechanisms and their complementary relationship to data-centric Mechanisms. Third, it demonstrates how information processing theory can be applied to assess the Dynamics of Mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.

Yong Chen - One of the best experts on this subject based on the ideXlab platform.

  • The Edge of Chaos in Kinematics and Dynamics of Mechanism
    Mechanisms Transmissions and Applications, 2017
    Co-Authors: Zhaohui Liu, Jin Xie, Yong Chen
    Abstract:

    Edge of chaos is a new concept deriving from complexity. Its significance for Mechanism science is described in this paper. It is demonstrated that one can obtain all solutions of nonlinear equations by Newton’s method utilizing the edge of chaos. And in dynamic analysis and controlling of Mechanism, where the models usually are nonlinear dynamic system, one is able to understand something about the arising of chaos, and find efficient measures to control and anti-control chaos through studying of edge of chaos. Following these, some methods to detect the edge of chaos are introduced briefly.

Zhaohui Liu - One of the best experts on this subject based on the ideXlab platform.

  • The Edge of Chaos in Kinematics and Dynamics of Mechanism
    Mechanisms Transmissions and Applications, 2017
    Co-Authors: Zhaohui Liu, Jin Xie, Yong Chen
    Abstract:

    Edge of chaos is a new concept deriving from complexity. Its significance for Mechanism science is described in this paper. It is demonstrated that one can obtain all solutions of nonlinear equations by Newton’s method utilizing the edge of chaos. And in dynamic analysis and controlling of Mechanism, where the models usually are nonlinear dynamic system, one is able to understand something about the arising of chaos, and find efficient measures to control and anti-control chaos through studying of edge of chaos. Following these, some methods to detect the edge of chaos are introduced briefly.

Jin Xie - One of the best experts on this subject based on the ideXlab platform.

  • The Edge of Chaos in Kinematics and Dynamics of Mechanism
    Mechanisms Transmissions and Applications, 2017
    Co-Authors: Zhaohui Liu, Jin Xie, Yong Chen
    Abstract:

    Edge of chaos is a new concept deriving from complexity. Its significance for Mechanism science is described in this paper. It is demonstrated that one can obtain all solutions of nonlinear equations by Newton’s method utilizing the edge of chaos. And in dynamic analysis and controlling of Mechanism, where the models usually are nonlinear dynamic system, one is able to understand something about the arising of chaos, and find efficient measures to control and anti-control chaos through studying of edge of chaos. Following these, some methods to detect the edge of chaos are introduced briefly.