Advertisement

Big Data Research—How to Structure the Changes of the Past Decade?

  • Mathias EggertEmail author
Chapter

Abstract

In the past decade, many IS researchers focused on researching the phenomenon of Big Data. At the same time, the relevance of data protection gets more attention than ever before. In particular, since the enactment of the European General Data Protection Regulation in May 2018 Information Systems research should provide answers for protecting personal data. The article at hand presents a structuring framework for Big Data research outcome and the consideration of data protection. IS Researchers might use the framework in order to structure Big Data literature and to identify research gaps that should be addressed in the future.

References

  1. Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), i–xxxii.CrossRefGoogle Scholar
  2. Akhbar, F., Chang, V., Yao, Y., & Méndez Muñoz, V. (2016). Outlook on moving of computing services towards the data sources. International Journal of Information Management, 36(4), 645–652.CrossRefGoogle Scholar
  3. Akter, S., & Wamba, S. F. (2016). Big data analytics in e-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173–194.CrossRefGoogle Scholar
  4. Arnott, D., & Pervan, G. (2008). Eight key issues for the decision support systems discipline. Decision Support Systems, 44(3), 657–672.CrossRefGoogle Scholar
  5. Carstensen, K.-U., Ebert, C., Ebert, C., Jekat, S. J., Klabunde, R., & Langer, H. (Eds.). (2010). Computerlinguistik und Sprachtechnologie. Eine Einführung (3., überar). Heidelberg: Spektrum Akad. Verl.Google Scholar
  6. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88.CrossRefGoogle Scholar
  7. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.CrossRefGoogle Scholar
  8. Davenport, T. H. (2013). Analytics 3.0: In the new era, big data will power consumer products and services. Harward Business Review, 91, 64–72.Google Scholar
  9. Dreger, C., Kosfeld, R., & Eckey, H.-F. (2014). Ökonometrie: Grundlagen—Methoden—Beispiele (5., überar). Wiesbaden: Springer Gabler.CrossRefGoogle Scholar
  10. Frey, R., Xu, R., Ammendola, C., Moling, O., Giglio, G., & Ilic, A. (2017). Mobile recommendations based on interest prediction from consumer’s installed apps–insights from a large-scale field study. Information Systems, 71, 152–163.CrossRefGoogle Scholar
  11. Goes, P. B. (2014). Big data and IS research: Editor’s comments. MIS Quarterly, 38(3), iii–viii.Google Scholar
  12. Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191–209.CrossRefGoogle Scholar
  13. Han, S. P., Park, S., & Oh, W. (2016). Mobile app analytics: A multiple discrete-continuous choice framework. MIS Quarterly, 40(4), 983–1008.CrossRefGoogle Scholar
  14. Hennig-Thurau, T., & Sattler, H. (2015). VHB-JOURQUAL 3: Teilranking Wirtschaftsinformatik. Retrieved from http://vhbonline.org/vhb4you/jourqual/vhb-jourqual-3/teilrating-wi/.
  15. Kowalczyk, M., Buxmann, P., & Besier, J. (2013). Investigating business intelligence and analytics from a decision process perspective: A structured literature review. In Association for Information Systems (Ed.), Proceedings of the 21st European Conference on Information Systems. Completed Research. Utrecht (NL).Google Scholar
  16. Krumeich, J., Werth, D., & Loos, P. (2016). Prescriptive control of business processes: New potentials through predictive analystics of big data in the proccess manufacturing industry. Business & Information Systems Engineering, 58(4), 261–280.CrossRefGoogle Scholar
  17. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242.CrossRefGoogle Scholar
  18. Laudon, K. C., Laudon, J. P., & Schoder, D. (2016). Wirtschaftsinformatik. (E. Martin, H. Knebel-Heil, & P. Alm, Eds.), Always learning (3., vollst). Hallbergmoos: Pearson.Google Scholar
  19. Lee, E. A. (2008). Cyber physical systems: Design challenges. (Institute of Electrical and Electronics Engineers, Ed.), 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC). Orlando, FL (USA): IEEE.Google Scholar
  20. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–66.Google Scholar
  21. Nunamaker, J. F., Dennis, A. R., Valacich, J. S., & Vogel, D. R. (1991). Information technology for negotiating groups: Generating options for mutual gain. Management Science, 37(10), 1325–1346.CrossRefGoogle Scholar
  22. Oates, B. J. (2006). Researching information systems and computing. London (UK)/Thousand Oaks, CA (USA)/New Delhi (IN): Sage Publications.Google Scholar
  23. Papageorgiou, M., Leibold, M., & Buss, M. (2015). Optimierung: Statische, dynamische, stochastische Verfahren für die Anwendung (4., korrig). Heidelberg: Springer.CrossRefGoogle Scholar
  24. Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015). Business analytics in the context of big data: A roadmap for research. Communications of the Association for Information Systems, 34(8), 448–472.Google Scholar
  25. Santos, M. Y., Oliveira e Sá, J., Andrade, C., Vale Lima, F., Costa, E., Costa, C., … Galvão, J. (2017). A big data system supporting bosch braga industry 4.0 strategy. International Journal of Information Management, 37(6), 750–760.CrossRefGoogle Scholar
  26. Shi, C., & Yu, P. S. (2017). Heterogeneous information network analysis and applications. Data Analytics. Data analytics. Cham: Springer International Publishing.CrossRefGoogle Scholar
  27. Shollo, A., & Kautz, K. (2010). Towards an understanding of business intelligence. (Association for Information Systems, Ed.).Google Scholar
  28. The Economist. (2011). Beyond the PC, special report on personal technology. Retrieved from https://www.economist.com/special-report/2011/10/08/beyond-the-pc.
  29. Trieu, V.-H. (2017). Getting value from business intelligence systems: A review and research agenda. Decision Support Systems, 93(1), 111–124.CrossRefGoogle Scholar
  30. Turban, E. (2008). Business intelligence: A managerial approach. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  31. Vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. In Proceedings of the 17th European Conference on Information Systems (ECIS 2009) (pp. 2206–2217). Verona (IT).Google Scholar
  32. Web Analytics Association. (2008). Web Analytics Definitions.Google Scholar
  33. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii–xxiii.Google Scholar
  34. Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17.CrossRefGoogle Scholar
  35. Wixom, B., & Watson, H. (2010). The BI-based organization. International Journal of Business Intelligence Research, 1(1), 13–28.CrossRefGoogle Scholar
  36. Yacioob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., et al. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231–1247.CrossRefGoogle Scholar
  37. Zikopoulos, P. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. New York: McGraw-Hill.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.FH Aachen—University of Applied SciencesAachenGermany

Personalised recommendations