Description
Using big data analytics is generally considered to improve organizational performance. However, we argue here that the role of fit between different organizational resources associated with big data use needs to be better understood in order to explore how organizations can create value, increase agility, and ultimately improve overall performance from the use of big data analytics. This research-in-progress study draws on the theory of resource-based view (RBV) and the person-environment (P-E) fit perspective to develop a theoretical model explaining the impacts of fit between various elements including (i.e., tools, data, tasks, employees) on organizational performance. A survey-based methodology is outlined to empirically validate the proposed research model using structural equation modeling techniques. Potential contributions from this research to theory and practice are also outlined.
Recommended Citation
Ghasemaghaei, Maryam; Hassanein, Khaled; and Turel, Ofir, "Impacts of Big Data Analytics on Organizations: A Resource Fit Perspective" (2015). AMCIS 2015 Proceedings. 19.
https://aisel.aisnet.org/amcis2015/BizAnalytics/GeneralPresentations/19
Impacts of Big Data Analytics on Organizations: A Resource Fit Perspective
Using big data analytics is generally considered to improve organizational performance. However, we argue here that the role of fit between different organizational resources associated with big data use needs to be better understood in order to explore how organizations can create value, increase agility, and ultimately improve overall performance from the use of big data analytics. This research-in-progress study draws on the theory of resource-based view (RBV) and the person-environment (P-E) fit perspective to develop a theoretical model explaining the impacts of fit between various elements including (i.e., tools, data, tasks, employees) on organizational performance. A survey-based methodology is outlined to empirically validate the proposed research model using structural equation modeling techniques. Potential contributions from this research to theory and practice are also outlined.