A Model to Evaluate the Organizational Readiness for Big Data Adoption

Mahdi Nasrollahi, Javaneh Ramezani

Abstract


Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.

Keywords


organizational readiness, big data adoption, industry 4.0, fuzzy best-worst method, principal component analysis

Full Text:

PDF

References


Almoqren N.; Altayar, M. (2016). The motivations for big data mining technologies adoption in saudi banks, 2016 4th Saudi Int. Conf. Inf. Technol. Big Data Anal., KACSTIT, 2016.
https://doi.org/10.1109/KACSTIT.2016.7756075

Baig, M.I.; Shuib L.; Yadegaridehkordi, E. (2019). Big data adoption: State of the art and research challenges, Inf. Process. Manag., 56(6), 102095, 2019.
https://doi.org/10.1016/j.ipm.2019.102095

Camarinha-Matos, L.M.; Fornasiero, R.; Ramezani, J.; Ferrada, F. (2019). Collaborative Networks: A Pillar of Digital Transformation, Appl. Sci., 9(24), 5431, 2019.
https://doi.org/10.3390/app9245431

Erl, T.; Khattak, W.; Buhler, P. (2016). Big Data Fundamentals Concepts, Drivers & Techniques 1st edn.,Prentice Hall, 2016.

Filip, F.G.; Zamfirescu, C.B.; Ciurea, C. (2017). Computer Supported Collaborative Decision Making, Springer, 2017.
https://doi.org/10.1007/978-3-319-47221-8

Gantz, B.J.; Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east, IDC iView: IDC Anal, Future, 2007, 1-16, 2012.

Guo, S.; Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications, Knowledge-Based Systems, 121, 23-31, 2017.
https://doi.org/10.1016/j.knosys.2017.01.010

Izhar T. A.T.; Shoid, M.S.M. (2016). A Research Framework on Big Data awareness and Success Factors toward the Implication of Knowledge Management: Critical Review and Theoretical Extension, Int. J. Acad. Res. Bus. Soc. Sci., 6(4), 325-338, 2016.
https://doi.org/10.6007/IJARBSS/v6-i4/2111

Konishi, S. (2014). Introduction to multivariate analysis: Linear and nonlinear modelings, CRC Press Taylor & Francis Group, New York, 2014.
https://doi.org/10.1201/b17077

Lai, Y.; Sun, H.; Ren, J. (2017). Understanding the determinants of big data analytics, Int. J. Logist. Manag., 2017.

Low, C.; Chen, Y.; Wu, M. (2011). Understanding the determinants of cloud computing adoption, Ind. Manag. Data Syst., 111(7), 1006-1023, 2011.
https://doi.org/10.1108/02635571111161262

Mikalef, P.; Pappas, I.O.; Krogstie, J.; Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda, Inf. Syst. E-bus., 16(3), 547-578, 2018.
https://doi.org/10.1007/s10257-017-0362-y

Mneney J.; Van Belle, J.P. (2016). Big Data capabilities and readiness of South African retail organisations, Cloud Syst. Big Data Eng. Conflu., 279-286, 2016.
https://doi.org/10.1109/CONFLUENCE.2016.7508129

Nam, D.W.; Kang, D.; Kim, S.H. (2015). Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process, Proc. Annu. Hawaii Int. Conf. Syst. Sci., 2015(March), 4792--4801, 2015.

Nguyen T.; Petersen, T.E. (2017). Technology Adoption in Norway: Organizational Assimilation of Big Dat, a. Technol. Adopt. Norw. Organ. Assim. Big Data, 24, 2017.

Ochieng, G. F. O. (2015). The Adoption of Big Data Analytics by Supermarkets in Kisumu County, University of Nairobi, 2015.

Olszak, C. M.; Mach-Król, M. (2018). A conceptual framework for assessing an organization's readiness to adopt big data, Sustain., 10(10), 1-27, 2018.
https://doi.org/10.3390/su10103734

Pappas, I.O; Mikalef, P.; Dwivedi, Y.K.;, Jaccheri, L.; Krogstie, J.; Mäntymäki, M.(2019). Digital Transformation for a Sustainable Society in the 21st Century, Lect. Notes Comput. Sci., 1(August), 451-463, 2019.
https://doi.org/10.1007/978-3-030-29374-1

Ramezani, J.; Camarinha-Matos, L.M. (2019). A collaborative approach to resilient and antifragile business ecosystems,In: 7th International Conference on Information Technology and Quantitative Management (ITQM): Information technology and quantitative management based on Artificial Intelligence, Procedia Computer Science, 162, 604-613, 2019.
https://doi.org/10.1016/j.procs.2019.12.029

Ramezani, J.; Sadraei, M.; Nasrollahi, M. (2019). Identification and Ranking of Effective Criteria in Evaluating Resilient IT Project Contractors, In: Proceedings of YEF-ECE 2019, 3rd Young Engineers Forum, IEEE Xplore, 2019.
https://doi.org/10.1109/YEF-ECE.2019.8740829

Rezaei, J. (2015). Best-worst multi-criteria decision-making method, Omega, 53, 49-57, 2015.
https://doi.org/10.1016/j.omega.2014.11.009

Salleh K. A., Janczewski, L. (2016). Adoption of Big Data Solutions: A study on its security determinants using Sec-TOE Framework, International Conference on Information Resources Proceedings, 66, 2016.

Shah, N.; Irani, Z.; Sharif, A. M. (2017). Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors, J. Bus. Res., 70, 366-378, 2017.
https://doi.org/10.1016/j.jbusres.2016.08.010

Sun, S.; Cegielski, C.; Jia, L.; J Hal, D. (2018). Understanding the Factors Affecting the Organizational Adoption of Big Data, J. Comput. Inf. Syst.,58(3), 193-203, 2018.
https://doi.org/10.1080/08874417.2016.1222891

Verma, S.; Bhattacharyya, S.S. (2017). Perceived strategic value-based adoption of Big Data Analytics in emerging economy: A qualitative approach for Indian firms, J. Enterp. Inf. Manag., 30(3), 354-382, 2017.
https://doi.org/10.1108/JEIM-10-2015-0099

[Online]. Available: https://www.forbes.com/sites/louiscolumbus/2018/12/23/big-data-analyticadoption- soared-in-the-enterprise-in-2018, forbes, 2018.

[Online]. Available: https://www.slideshare.net/denisreimer/big-data-industry-insights-2015, Gartner, 2015.




DOI: https://doi.org/10.15837/ijccc.2020.3.3874



Copyright (c) 2020 Javaneh Ramezani

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

IJCCC is an Open Access Journal : CC-BY-NC.

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]


INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.

 

 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2);

SCImago Journal & Country Rank

Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.