A Conceptual Framework for Big Data Analytics Adoption towards Organization Performance in Malaysia
DOI:
https://doi.org/10.24191/jikm.v12i1.5899Keywords:
Big data adoption, toe framework, resource-based view, data quality managementAbstract
The rise of Big Data has inspired business organizations to venture into Big Data analytics, however academic research and empirical evidence about the business value remains scarce. This paper attempts to evaluate the readiness of Malaysia companies in taking advantage of Big Data adoption. The research finds a great interest about Big Data Analytics (BDA) solutions that fuel with sound decision-making and influence organizations into growth mindset. Big Data provides various advantages to organization that would seriously consider all its perspectives alongside its lifecycle in the pre-adoption or implementation phase. The research attempts to outline the different aspects of Big Data as a management practice to leverage the values of Big Data adoption in future organizations. As for the underpinning theory, the technology-organization-environment (TOE) framework is chosen to describe the organizational adoption towards innovation decisions and Resource Based View to manage the upskill of the workforce. This is of great interest to researchers, professionals, and policy makers.
References
Abdullah, M. F., Ibrahim, M., & Zulkifli, H. (2017). Resolving the misconceptions on big data analytics implementation through government research institute in Malaysia. IoTBDS 2017 - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security. https://doi.org/10.5220/0006293902610266
Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14, 2. https://doi.org/10.5334/dsj-2015-002
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. https://doi.org/10.1145/2463676.2463712
Davenport, T. H., Barth, P., & Bean, R. (2012). How ‘ Big Data ’ is Different. MIT Sloan Management Review, 54(1), 22–24.
Gupta, S., & Chaudhari, M. S. (2015). Big Data Issues and Challenges. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 62–67. https://doi.org/10.1109/HICSS.2013.645
Halevi, G. (2012). Welcome to the 30th issue of Research Trends. Research Trends Special Issue on Big Data, 30. http://www.researchtrends.com/wpcontent/uploads/2012/09/Research_Trends_Issue30.pdf
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94. https://doi.org/10.1145/2611567
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big Data: Issues and Challenges Moving Forward. 2013 46th Hawaii International Conference on System Sciences, 995–1004. https://doi.org/10.1109/HICSS.2013.645
McAfee, A., & Brynjolfsson, E. (2012). Big Data. The management revolution. Harvard Buiness Review, 90(10), 61–68. https://doi.org/10.1007/s12599-013-0249-5
McGuire, T., Manyika, J., & Chui, M. (2012). WHY BIG DATA IS THE NEW COMPETITIVE ADVANTAGE. Ivey Business Journal, 76(4), 1–4. http://search.ebscohost.com/login.aspx?direct=true&db=plh&AN=78946511&site=edslive
Saha, B., & Srivastava, D. (2014). Data quality: The other face of Big Data. Proceedings - International Conference on Data Engineering, 1294–1297. https://doi.org/10.1109/ICDE.2014.6816764
Sridhar, P., & Dharmaji, N. (2013). A Comparative Study on How Big Data is Scaling Business Intelligence and Analytics. International Journal of Enhanced Research in Science Technology & Engineering, 2(8), 87–96. http://www.erpublications.com/uploaded_files/download/download_03_09_2013_17_59_21.pdf
Stonebraker, M., & Hong, J. (2012). Researchers’ big data crisis; understanding design and functionality. Communications of the ACM, 55(2), 10.
https://doi.org/10.1145/2076450.2076453
Tornatzky, L, & Fletscher, M. (1990). The Deployment of Technology. In The Processes of Technological Innovation (pp. 118–147).
Tornatzky, Lg, & Klein, K. (1982). Innovation characteristics and innovation adoption implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–43. https://doi.org/10.1109/TEM.1982.6447463
Vesset, D., Morris, H. D., Eastwood, M., Woo, B., Villars, R. L., Bozman, J. S., & Olofson, C. W. (2012). Worldwide Big Data Technology and Services 2012--2015 Forecast. Big Data: Global Overview: Market Analysis, Volume: 1, 1–30. ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=6242
Zikopoulos, P. C., DeRoos, D., Parasuraman, K., Deutsch, T., Corrigan, D., & Giles, J. (2012). Harness the Power of Big Data. In Osborne/McGraw-Hil. https://doi.org/10.1007/s13398-014-0173-7.2
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright of articles that appear in the journal belongs exclusively to Faculty of Information Science, Universiti Teknologi MARA (Publisher). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
