Identifying the Factors and Outcomes of Establishing Big Data Governance in Governmental Organizations Using a Mixed Methodology

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran.

2 Associate Prof., Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran.

3 Prof., Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran.

Abstract

Objective: In the present study, an attempt has been made to examine and explain the factors and outcomes of establishing big data governance in governmental organizations.
Methods: A mixed-methods study was chosen as the design for this research and the two main types of analysis typically used were quantitative (deductive) and qualitative (inductive). In the qualitative stage, the method of thematic analysis, based on analysis, description, and composition of texts was discussed, and then in-depth interviews were conducted to extract and categorize the topics. The software used in the quality phase was Nvivo 12. Kappa-Cohen analysis and CVR coefficient were also applied to evaluate the validity and reliability of the qualitative part of the study. In the quantitative part, the fuzzy Delphi method was used to examine the verifiability of the factors and the outcomes of establishing big data governance. The statistical population of the present study in the qualitative stage included 12 organizational and academic experts while the quantitative stage included 21 affiliated experts.
Results: After analysing the texts and conducting the interview, 48 basic themes were identified. Then, by applying the opinions and modifications expressed by the experts, 36 final themes (24 factors and 12 outcomes) were grouped. To check the verifiability of the identified themes, the fuzzy Delphi method was employed in two stages. Examining the values of the fuzzy-deactivated mean of the first and second stages, the aforementioned preconditions and consequences were confirmed.
Conclusion: The performance of public organizations in making informed decisions, planning and strategic analysis, expanding good governance, and administrative transparency highly relies on the large amount of data they collect, process, and secure from within and outside the organizational environment. The issue has not received proper attention in Iranian public organizations and institutions. Accordingly, it is vital to establish big data governance in these organizations, which depends on recognizing the capacities and capabilities of the organization, drivers, and governance mechanisms and highlights the significance of the obtained results of establishing big data governance.

Keywords


 
Abbady, M. A. S., Akkaya, M., & Sari, A. (2019). Big data governance, dynamic capability and decision-making effectiveness: Fuzzy sets approach. Decision Science Letters, 8(4), 429–440. https://doi.org/10.5267/j.dsl.2019.5.003
Akoka, J., & Comyn-Wattiau, I. (2019). Evaluation of Big Data Governance - Combining a Multi-Criteria Approach and Systems Theory. IEEE World Congress on Services, 398–399. https://doi.org/10.1109/SERVICES.2019.00122
Al-Badi, A., Tarhini, A., & Khan, A. I. (2018). Exploring Big Data Governance Frameworks. Procedia Computer Science, 141(6), 271–277. https://doi.org/10.1016/j.procs.2018.10.181
Analytics, M. (2016). The age of analytics: competing in a data-driven world. McKinsey Global Institute Research.
Basukie, J., Wang, Y., & Li, S. (2020). Big data governance and algorithmic management in sharing economy platforms: A case of ridesharing in emerging markets. Technological Forecasting and Social Change, 161(1), 120310. https://doi.org/10.1016/j.techfore.2020.120310
Bean, R. (2016). Just using big data isn’t enough anymore. Harvard Business Review, 2, 2016.
Belhadi, A., Kamble, S. S., Zkik, K., Cherrafi, A., & Touriki, F. E. (2020). The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa. Journal of Cleaner Production, 252(6), 119903. https://doi.org/10.1016/j.jclepro.2019.119903
Chang, P.-L., Hsu, C.-W., & Chang, P.-C. (2011). Fuzzy Delphi method for evaluating hydrogen production technologies. International Journal of Hydrogen Energy, 36(21), 14172–14179. https://doi.org/10.1016/j.ijhydene.2011.05.045
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: A data management perspective. Frontiers of Computer Science, 7(2), 157–164. https://doi.org/10.1007/s11704-013-3903-7
de Medeiros, M. M., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: Benefits, challenges and opportunities. The Bottom Line.
Elahi, S., Marashi pour, O., & HassanZadeh KarimAbad, A. (2017). Developing a Framework of Big Data Governance in Central Bank of Iran. Jurnal of Monetary and Banking Research. 10(32), 319-352. (in Persian)
Fadler, M., & Legner, C. (2021, January). Toward big data and analytics governance: redefining structural governance mechanisms. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 5696).
Fan, J., Han, F., & Liu, H. (2014). Challenges of Big Data Analysis. National Science Review, 1(2), 293–314. https://doi.org/10.1093/nsr/nwt032
Feki, M., & Boughzala, I. (2016, May). Big data governance: a literature review and research agenda. In CIG 2016: 15ième Conférence Internationale de Gouvernance de l'AAIG (Association Académique Internationale de Gouvernance).
Garmaki, M., Boughzala, I., & Wamba, S. F. (2016, June). The effect of Big Data Analytics Capability on Firm Performance. In PACIS (p. 301).
Ghavami, P. (2016). Big Data Governance: Modern Data Management Principles for Hadoop, NoSQL & Big Data Analytics.
Ghavami, P. (2020). Big Data Management: Data Governance Principles for Big Data Analytics. Walter de Gruyter GmbH & Co KG.
Grover, V., Chiang, R. H.L., Liang, T.-P., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951
Hasani, M. H. (2010). An Introduction to Mixed Methods in Interdisciplinary Social Sciences. Interdisciplinary Studies in the Humanities. 2(4). 137-153. (in Persian)
Jiang, Shao, Liou, & Shi (2019). Improving the Sustainability of Open Government Data. Sustainability, 11(8), 2388. https://doi.org/10.3390/su11082388
Li, Q., Lan, L., Zeng, N., You, L., Yin, J., Zhou, X., & Meng, Q. (2019). A Framework for Big Data Governance to Advance RHINs: A Case Study of China. IEEE Access, 7, 50330–50338. https://doi.org/10.1109/ACCESS.2019.2910838
Liu, X., Sun, X., & Huang, G. (2020). An emerging decentralized services computing paradigm for big data governance: A position paper. IEEE Trans. Services Comput.13(2), 343-355.
Löfgren, K., & Webster, C. W. R. (2020). The value of Big Data in government: The case of ‘smart cities’. Big Data & Society, 7(1). https://doi.org/10.1177/2053951720912775
Malik, P. (2013). Governing Big Data: Principles and practices. IBM Journal of Research and Development, 57(3/4), 1:1-1:13. https://doi.org/10.1147/JRD.2013.2241359
Maniam, J. N., & Singh, D. (2020). Towards Data Privacy and Security Framework in Big Data Governance. International Journal of Software Engineering and Computer Systems, 6(1), 41–51. https://doi.org/10.15282/ijsecs.6.1.2020.5.0068
McMahon, A., Buyx, A., & Prainsack, B. (2020). Big Data Governance Needs More Collective Responsibility: The Role of Harm Mitigation in the Governance of Data Use in Medicine and Beyond. Medical Law Review, 28(1), 155–182. https://doi.org/10.1093/medlaw/fwz016
Mikaelnejad, S., & Azizi, S. (2020). Use big data analysis to improve the performance of decision support systems in oil refineries. The First National Conference on New Management Approaches in Interdisciplinary Studies. (in Persian)
Mikalef, P., & Krogstie, J. (2018, June). Big Data Governance and Dynamic Capabilities: The Moderating effect of Environmental Uncertainty. In PACIS (p. 206).
Morabito, V. (2015). Big Data and Analytics: Strategic and Organizational Impacts. Big Data Governance (1st ed. 2015). Cham: Springer International Publishing; Imprint: Springer.
Mousavi, S. N., Saedi, A., & Momenimofrad, M. (2020). Identifying and Explaining the Antecedents and Consequences of Human Resources Mum Effect Using Delphi Fuzzy Approach. Organizational Behavior Studies Quarterly. 9(1), 57-82. (in Persian)
Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2020). Big data management and environmental performance: Role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management, ahead-of-print(ahead-of-print), 429. https://doi.org/10.1108/JEIM-04-2020-0137
Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015). Business Analytics in the Context of Big Data: A Roadmap for Research. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1CAIS.03723
Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135. https://doi.org/10.1016/j.im.2018.12.003
Sheng, J., Amankwah-Amoah, J., & Wang, X. (2019). Technology in the 21st century: New challenges and opportunities. Technological Forecasting and Social Change, 143(6), 321–335. https://doi.org/10.1016/j.techfore.2018.06.009
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70(1), 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
Soares, S. (2015). Data Governance Tools: Evaluation Criteria, Big Data Governance, and Alignment with Enterprise Data Management. Chicago: MC Press.
Soleymani Khoeini, M., Daneshfard, K., & Najafbeygi, R. (2019). Contingency Model for Identifying Public Issues in Iran's Policy-Making Process with an Emphasis on Triggering. Journal of Public Administration. 11(4), 530-556. (in Persian)
Suoniemi, S., Meyer-Waarden, L., Munzel, A., Zablah, A. R., & Straub, D. (2020). Big data and firm performance: The roles of market-directed capabilities and business strategy. Information & Management, 57(7), 103365. https://doi.org/10.1016/j.im.2020.103365
Terzi, D. S., Terzi, R., & Sagiroglu, S. (2015, December). A survey on security and privacy issues in big data. In 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST) (pp. 202-207). IEEE.
Trom, L., & Cronje, J. (2019, March). Analysis of data governance implications on big data. In Future of Information and Communication Conference (pp. 645-654). Springer, Cham.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(4), 3–13. https://doi.org/10.1016/j.techfore.2015.12.019
Wetering, R., Mikalef, P., & Pateli, A. (2017). A strategic alignment model for IT flexibility and dynamic capabilities: Toward an assessment tool.
Wilkin, C., Ferreira, A., Rotaru, K., & Gaerlan, L. R. (2020). Big data prioritization in SCM decision-making: Its role and performance implications. International Journal of Accounting Information Systems, 38(1), 100470. https://doi.org/10.1016/j.accinf.2020.100470
Wulfovich, S., & Meyers, A. D. (Eds.). (2020). Digital health entrepreneurship. Springer.
Xhafa, F., & Barolli, L. (2014). Semantics, intelligent processing and services for big data. Future Generation Computer Systems, 37(6), 201–202. https://doi.org/10.1016/j.future.2014.02.004
Yallop, A., & Seraphin, H. (2020). Big data and analytics in tourism and hospitality: Opportunities and risks. Journal of Tourism Futures, 6(3), 257–262. https://doi.org/10.1108/JTF-10-2019-0108
Yang, L., Li, J., Elisa, N., Prickett, T., & Chao, F. (2019). Towards Big data Governance in Cybersecurity. Data-Enabled Discovery and Applications, 3(1), 21. https://doi.org/10.1007/s41688-019-0034-9
Zeng, J., & Khan, Z. (2019). Value creation through big data in emerging economies. Management Decision, 57(8), 1818–1838. https://doi.org/10.1108/MD-05-2018-0572
Zhang, X., Ming, X., & Yin, D. (2020). Application of industrial big data for smart manufacturing in product service system based on system engineering using fuzzy DEMATEL. Journal of Cleaner Production, 265(1), 121863. https://doi.org/10.1016/j.jclepro.2020.121863
Zotoo, I. K., Lu, Z., & Liu, G. (2021). Big data management capabilities and librarians' innovative performance: The role of value perception using the theory of knowledge-based dynamic capability. The Journal of Academic Librarianship, 47(2), 102272. https://doi.org/10.1016/j.acalib.2020.102272
Zwitter, A. (2020). International humanitarian and development aid and Big Data governance. In The Routledge Handbook to Rethinking Ethics in International Relations. Routledge.