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https://doi.org/10.21256/zhaw-19915| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review |
| Autor/-in: | Guirguis, Katharina |
| et. al: | No |
| DOI: | 10.5334/ssas.140 10.21256/zhaw-19915 |
| Erschienen in: | Jahrbuch der Schweizerischen Verwaltungswissenschaften |
| Band(Heft): | 11 |
| Heft: | 1 |
| Seite(n): | 55 |
| Seiten bis: | 65 |
| Erscheinungsdatum: | 2020 |
| Verlag / Hrsg. Institution: | Schweizerische Gesellschaft für Verwaltungswissenschaften |
| ISSN: | 2296-8717 |
| Sprache: | Englisch |
| Schlagwörter: | Big data; Organizational performance; Dynamic capability; Systematic literature review |
| Fachgebiet (DDC): | 350: Öffentliche Verwaltung 658.403: Entscheidungsfindung, Informationsmanagement |
| Zusammenfassung: | This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19915 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Management and Law |
| Organisationseinheit: | Institut für Verwaltungs-Management (IVM) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Guirguis_Potential-of-Big-Data.pdf | 640.84 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Guirguis, K. (2020). From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review. Jahrbuch Der Schweizerischen Verwaltungswissenschaften, 11(1), 55–65. https://doi.org/10.5334/ssas.140
Guirguis, K. (2020) ‘From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review’, Jahrbuch der Schweizerischen Verwaltungswissenschaften, 11(1), pp. 55–65. Available at: https://doi.org/10.5334/ssas.140.
K. Guirguis, “From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review,” Jahrbuch der Schweizerischen Verwaltungswissenschaften, vol. 11, no. 1, pp. 55–65, 2020, doi: 10.5334/ssas.140.
GUIRGUIS, Katharina, 2020. From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review. Jahrbuch der Schweizerischen Verwaltungswissenschaften. 2020. Bd. 11, Nr. 1, S. 55–65. DOI 10.5334/ssas.140
Guirguis, Katharina. 2020. “From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance : A Systematic Literature Review.” Jahrbuch Der Schweizerischen Verwaltungswissenschaften 11 (1): 55–65. https://doi.org/10.5334/ssas.140.
Guirguis, Katharina. “From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance : A Systematic Literature Review.” Jahrbuch Der Schweizerischen Verwaltungswissenschaften, vol. 11, no. 1, 2020, pp. 55–65, https://doi.org/10.5334/ssas.140.
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