Assessing the readiness of the company for big data adoption
Keywords:
big data, big data adoption, TOEAbstract
Nowadays, big data analytics can give companies a competitive advantage by better understanding customer needs and improving their business processes. On the other hand, the adoption of big data analytics is not easy and requires many significant changes in business processes, organizational changes, the deployment of new technologies, and employees' qualification. For these reasons, managers must decide on the appropriate time to adapt the company to adopt big data. However, this is not possible unless managers can assess the company's readiness to adopt big data. For their qualified decision, it is necessary to consider various criteria, requiring the cooperation of several highly qualified experts and sufficient resources. As a result, big data first began to be implemented in large companies in which, in addition to suitable conditions, there is sufficient capital to invest in innovation. The situation is different in SMEs. A model for assessing the readiness of the company for the adoption of big data was proposed. This model was then implemented into the diagnostic tool, which facilitates the initial decision on adopting big data, which helps managers judge the company's readiness for big data adoption.References
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Shahzad, K. (2018). Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique. Sustainability, 10. doi:10.3390/su10113956
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A chronology of big data adoption: Review of literature. I J A B E R, 14(1), 521-544.
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193-203.
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation: Lexington books.
Warmerdam, M., & Bredveld, P. (2003). A holistic approach to delivering the value of IT: business service management. IDC White Paper.
Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Conceptualization and measurement of organizational readiness for change: a review of the literature in health services research and other fields. Medical care research and review, 65(4), 379-436.
Athamena, B., & Houhamdi, Z. (2018). Model for decision-making process with big data. Journal of Theoretical and Applied Information Technology, 96, 5951-5961.
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2019). Big data adoption: State of the art and research challenges. Information Processing & Management, 56(6). doi:10.1016/j.ipm.2019.102095
Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-2018.
Bremser, C. (2018). Starting points for big data adoption. Paper presented at the Twenty-Sixth European Conference on Information Systems (ECIS2018), Portsmouth, UK.
Bremser, C., Piller, G., & Rothlauf, F. (2017). Strategies and Influencing Factors for Big Data Exploration. Paper presented at the AMCIS, 23rd American Conference on Information Systems, Boston, MA, USA.
Columbus, L. (2017). IBM predicts demand for data scientists will soar 28% by 2020. IBM White Paper.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Diniz, E., Luvizan, S., Cassitas Hino, M., & Ferreira, P. (2018). Unveiling the Big Data Adoption in Banks: Strategizing the Implementation of a New Technology. In Digital Technology and Organizational Change (pp. 149-162). Cham: Springer-Verlag
Gangwar, H. (2018). Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors. Information Resources Management Journal 31( 4).
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.
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.
Han, Q., Liang, S., & Zhang, H. (2015). Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Network, 29(2), 40-45.
Holt, D. T., Helfrich, C. D., Hall, C. G., & Weiner, B. J. (2010). Are you ready? How health professionals can comprehensively conceptualize readiness for change. Journal of general internal medicine, 25(1), 50-55.
Jeble, S., Kumari, S., & Patil, Y. (2018). Role of Big Data in Decision Making. Operations and Supply Chain Management: An International Journal, 11, 36. doi:10.31387/oscm0300198
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of information technology, 21(1), 1-23.
Kamarulzaman, M. S., Hassan, N. H., Drus, S. M., & Ismail, S. A. (2019). A Review on Factors for Big Data Adoption towards Industry 4.0. Open International Journal of Informatics (OIJI), 7(2), 200-207.
Kart, L. (2015). Big Data Industry Insights. Stamford: Gartner.
Kościelniak, H., & Puto, A. (2015). BIG DATA in Decision Making Processes of Enterprises. Procedia Computer Science, 65, 1052-1058. doi:10.1016/j.procs.2015.09.053
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.
Nasrollahi, M., Ramezani, J., & Sadraei, M. (2020). The Impact of Big Data Adoption on SMEs Performance. doi:10.21203/rs.3.rs-66047/v1
Rakovská, E., & Hudec, M. (2019). A Three–Level Aggregation Model for Evaluating Software Usability by Fuzzy Logic. International Journal of Applied Mathematics and Computer Science, 29(3), 489-501.
Ramezani, J., & Nasrollahi, M. (2020). A Model to Evaluate the Organizational Readiness for Big Data Adoption. International Journal of Computers Communications & Control, 15. doi:10.15837/ijccc.2020.3.3874
Rogers, E. M. (1995). Lessons for guidelines from the diffusion of innovations. The Joint Commission journal on quality improvement, 21(7), 324-328.
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. doi:https://doi.org/10.1016/j.ipm.2018.01.010
Shahzad, K. (2018). Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique. Sustainability, 10. doi:10.3390/su10113956
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A chronology of big data adoption: Review of literature. I J A B E R, 14(1), 521-544.
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193-203.
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation: Lexington books.
Warmerdam, M., & Bredveld, P. (2003). A holistic approach to delivering the value of IT: business service management. IDC White Paper.
Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Conceptualization and measurement of organizational readiness for change: a review of the literature in health services research and other fields. Medical care research and review, 65(4), 379-436.
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2021-06-02
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