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Big Data and Information Processing in Organizational Decision Processes

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  • 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. Copyright Springer Fachmedien Wiesbaden 2014

Suggested Citation

  • Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    • Handle: RePEc:spr:binfse:v:6:y:2014:i:5:p:267-278
    DOI: 10.1007/s12599-014-0341-5
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    References listed on IDEAS

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    1. Ágnes Szukits & Péter Móricz, 2024. "Towards data-driven decision making: the role of analytical culture and centralization efforts," Review of Managerial Science, Springer, vol. 18(10), pages 2849-2887, October.
    2. Swapnajit Chakraborti & Shubhamoy Dey, 2019. "Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 345-355, June.
    3. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    4. Ossi Ylijoki & Jari Porras, 2016. "Conceptualizing Big Data: Analysis of Case Studies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 295-310, October.
    5. Božič, Katerina & Dimovski, Vlado, 2019. "Business intelligence and analytics for value creation: The role of absorptive capacity," International Journal of Information Management, Elsevier, vol. 46(C), pages 93-103.
    6. Ninja Soeffker & Marlin W. Ulmer & Dirk C. Mattfeld, 2019. "Adaptive State Space Partitioning for Dynamic Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 261-275, June.
    7. Ionut HRUBARU & Marin FOTACHE, 2017. "On the Performance of Three In-Memory Data Systems for On Line Analytical Processing," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 21(1), pages 5-15.
    8. Emmanuel P. Paulino, 2022. "Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 18(2), pages 69-104.
    9. Julian Krumeich & Dirk Werth & Peter Loos, 2016. "Prescriptive Control of Business Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(4), pages 261-280, August.
    10. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    11. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    12. Marin FOTACHE & IonuÈ› HRUBARU, 2017. "Performance Analysis Of Two Big Data Technologies On A Cloud Distributed Architecture. Results For Non-Aggregate Queries On Medium-Sized Data," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 63(3), pages 21-50, January.
    13. Fotache Marin & Hrubaru Ionuț, 2016. "Performance Analysis of Two Big Data Technologies on a Cloud Distributed Architecture. Results for Non-Aggregate Queries on Medium-Sized Data," Scientific Annals of Economics and Business, Sciendo, vol. 63(s1), pages 21-50, December.
    14. Awan, Usama & Shamim, Saqib & Khan, Zaheer & Zia, Najam Ul & Shariq, Syed Muhammad & Khan, Muhammad Naveed, 2021. "Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

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