Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm.
A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling.
SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data.
The study showcases cross-sectional data from India. It would be interesting for this study to see if the country-level differences would influence these relationships between cloud ERP and financial performance, BDPA and financial performance and cloud ERP and BDPA.
This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory.
This work is partially supported by Natural Science Foundation of China 6171101169, National Key R&D Plan – Key Special Plan on Public Security Risk Mitigation/Response 2017YFC0804003, Technologies and Equipment Guangdong Education Bureau Fund 2017KTSCX166, the Science and Technology Innovation Committee Foundation of Shenzhen JCYJ20170817112037041.
Gupta, S., Qian, X., Bhushan, B. and Luo, Z. (2019), "Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective", Management Decision, Vol. 57 No. 8, pp. 1857-1882. https://doi.org/10.1108/MD-06-2018-0633
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