ISSN: 2222-6990
Open access
Organizations expect to achieve excellence and more productiveness in their business. To sustain the current business landscape, organizations continuously look for sufficient ways to utilize their valuable resources, regardless of the existing Big Data. Big Data Adoption (BDA) is described as placing an advanced method to the existing and incoming data in the organizations for operational activities. However, in the development of BDA, there is an absence of an in-depth review of certain topics, issues, and classification of the existing studies in this area. This study aims to increase the understanding of BDA at present by underlining the studies conducted in this area, theoretical models, the relevant factors, and tools and techniques used. The review methodology of this Systematic Literature Review (SLR) follows the guidelines from Meta-Analyses (PRISMA) that can be accessed through the website www.prisma-statement.org. This type of SLR involves four phases; identification, screening, eligibility, and inclusion. The set of questionnaires was set up to be answered resulting in thirty studies identified in the domain of BDA after reviewing and extracting relevant information. 1) As a result, Technology–Organization–Environment and Diffusion of Innovations are the most popular theoretical models used for BDA in various domains. 2)This study exposed thirty-five factors in technology, organization, and environment that are relevant to the BDA and, 3) The BDA research in various field. This study exposes the factors that can be considered by researchers and management in BDA for improvisation.
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 3.
Acharjya, D. P., & Ahmed, K. (2016). A survey on big data analytics: challenges, open research issues and tools. International Journal of Advanced Computer Science and Applications, 7(2), 511-518.
Agrawal, K. P. (2017). Investigating Organizational Adoption of Big Data Analytics(BDA) Technology. Full Research Paper. Chandragupt Institute of Management Patna. 1 – 9.
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459-471.
Ajab, M. (2017). Data Analytics Case Studies for Healthcare. Master Degree Thesis. Long Island University.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics, 182, 113-131.
Baraka, Z. (2014). Opportunities to manage big data efficiently and effectively (Doctoral dissertation, Dublin Business School).
Bamiah, S. N., BROHI, S. N., & RAD, B. B. (2018). Big data technology in education: Advantages, implementations, and challenges. Journal of Engineering Science and Technology, 13, 229-241.
Behrisch, M., Streeb, D., Stoffel, F., Seebacher, D., Matejek, B., Weber, S. H., ... & Keim, D. (2018). Commercial visual analytics systems–advances in the big data analytics field. IEEE Transactions on Visualization and Computer Graphics, 25(10), 3011-3031.
Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99-113.
Bremser, C. (2018). Starting points for big data adoption.
Bremser, C., Piller, G., & Rothlauf, F. (2017). Strategies and Influencing Factors for Big Data Exploration
Brünink, L. A. (2016). Cross-functional Big Data integration: Applying the UTAUT model (Master's thesis, University of Twente).
Carnelley, P., & Schwenk, H. (2016). Big Data: Turning Promise into Reality. IDC White paper.
Collymore, A., Rosado-Munoz, F. J., & Ojeda-Castro, A. (2017). Big Data Analytics, Competitive Advantage and Firm Performance. International Journal of Information Research and Review. 4(2), 3600 – 3603. Retrieved from
Cuquet, M., Vega-Gorgojo, G., Lammerant, H., & Finn, R. (2017). Societal impacts of big data: challenges and opportunities in Europe. arXiv preprint arXiv:1704.03361.
Curry, E. (2016). Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. Insight Centre for Data Analytics. Chapter 3. National University of Ireland Galway, Ireland. pp. 29 -37.
De Mauro, A., Greco, M., & Grimaldi. (2015). What is Big Data? A Consensual Definition and a Review of Key Research Topics, Proceedings of the 4th International Conference on Integrated Information (IC-ININFO), Vol. 1644, 97–104.
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1-4), 631-645.
French, C. M. (2019). The Integration of Mobile and Cloud Technology with Big Data Platforms in the Oil and Gas Industry (Doctoral dissertation, Capella University).
El-Haddadeh, R., Weerakkody, V., Osmani, M., Thakker, D., & Kapoor, K. K. (2019). Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement. Government Information Quarterly, 36(2), 310-320.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417-428.
Gupta, S., Qian, X., Bhushan, B., & Luo, Z. (2019). Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective. Management Decision.
Haddad, A., Ameen, A., & Mukred, M. (2018). The impact of intention of use on the success of big data adoption via organization readiness factor. International Journal of Management and Human Science (IJMHS), 2(1), 43-51.
Hassani, A., & Gahnouchi, S. A. (2017). A framework for Business Process Data Management based on Big Data Approach. Procedia computer science, 121, 740-747.
Halaweh, M., & Massry, A. E. (2015). Conceptual model for successful implementation of big data in organizations. Journal of International Technology and Information Management, 24(2), 2.
Hwang, B. N., Huang, C. Y., & Wu, C. H. (2016). A TOE approach to establish a green supply chain adoption decision model in the semiconductor industry. Sustainability, 8(2), 168.
Ismail, S. A., Bandi, S., & Maaz, Z. N. (2018). An appraisal into the potential application of big data in the construction industry. International Journal of Built Environment and Sustainability, 5(2).
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience, 46(1), 79-105.
Malik, M. (2018). System, Architectural and Application Level Analysis for Big Data Applications for Performance and Energy-efficiency. PH Dissertation. George Mason university.
Mazumder, S. (2016). Big data tools and platforms. In Big data concepts, theories, and applications (pp. 29-128). Springer, Cham.
Metcalf, J., Keller, E. F., & Boyd, D. (2016). Perspectives on big data, ethics, and society. The Council for Big Data, Ethics and Society.
Mgudlwa, S. & Iyamu, T. (2017). Integration of Social Media with Healthcare Big Data for Improved Delivery Service. South African Journal of Information Management. 20(1). a894.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.
Olszak, C. & Mach-krol, M. (2018). Conceptual Framework of Assesing Organization’s Readiness to Big Data Adoption. Dept. of Business Informatics, University of Economics in Katowice. Retrieved at: doi:10.20944/preprints
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431-448.
Queiroz, M. M., & Pereira, S. C. F. (2019). Intention to adopt big data in supply chain management: A Brazilian perspective. Revista de Administração de Empresas, 59(6), 389-401.
Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716.
Raguseo, E. (2018). Big Data Technologies: An Empirical Investigation on their Adoption, Benefits and Risks for Companies. Retrieved at:
https://www.researchgate.net/publication/320101739
Rogers, E. M. (1962). Diffusion of innovations. Glencoe. Free Press (1976)," New Product Adoption and Diffusion," Journal of Consumer Research, 2, 290-304.
Salleh, K. & Janczweski, L. (2016). Adoption of Big Data Solutions: A Study on its Security Determinants using Sec-TOE Framework. International Conference of Information Resources Management (CONS-IRM), CONS-IRM 2016.
Sam, K. M., & Chatwin, C. R. (2018, December). Understanding adoption of big data analytics in China: from organizational users’ perspective. In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 507-510). IEEE
Shan, S., Luo, Y., Zhou, Y., & Wei, Y. (2019). Big data analysis adaptation and enterprises’ competitive advantages: the perspective of dynamic capability and resource-based theories. Technology Analysis & Strategic Management, 31(4), 406-420.
Silva, J., Hernández-Fernández, L., Cuadrado, E. T., Mercado-Caruso, N., Espinosa, C. R., Ortega, F. A., ... & Delgado, G. J. (2019, July). Factors Affecting the Big Data Adoption as a Marketing Tool in SMEs. In International Conference on Data Mining and Big Data (pp. 34-43). Springer, Singapore.
Su, X. (2017). Introduction to big data. IFUD1123.
Sun, S., Hall, D. J., & Cegielski, C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109-121.
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovations. Lexington, MA: Lexington Books.
Varma, A. (2018). Big Data Usage Intention of Management Accountants: Blending the Utility Theory with the Theory of Planned Behavior in an Emerging Market Context. Theoretical Economics Letters, 8(13), 2803.
Woodside, J. M., Amiri, S., & Boldrin, B. (2015). The impact of ICT and big data on e-Government. In International Conference on Advances in Big Data Analytics (ABDA’15) (pp. 27-30).
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231-1247.
Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., ... & Yang, L. (2015). Big data for social transportation. IEEE Transactions on Intelligent Transportation System.
Hashim, H. (2024). Assessing the Big Data Adoption (BDA) Factors: A Systematic Literature Review. International Journal of Academic Research in Business and Social Sciences, 14(10), 3000–3014.
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