Modeling the Challenges of Establishing a Data-Driven Organizational Culture in Iranian Government Organizations (Case Study: Organizations under the Ministry of Defense)

Document Type : Case study

Authors

1 Associate Professor, Department of Industrial Management and Faculty Member of Malek Ashtar University of Technology, Tehran, Iran

2 PhD, Researcher of Malek Ashtar University of Technology, Tehran, Iran

3 MA., Malek Ashtar University of Technology, Tehran, Iran

Abstract

In the era where paying attention to information and data in organizations leads to the growth of performance and a positive attitude towards the organization, in large government organizations we see a lack of attention to data and data-driven, which will lead to the benefit of the organization-based organization. The present study was conducted to identify and model the challenges of establishing a data-driven culture in Iranian government organizations in order to remove barriers and establish a data-driven culture in these organizations. Government organizations in the present study (scope of research) means organizations subordinated to the Ministry of Defense (VDJA). This applied-descriptive research has been done in the paradigm of interpretivism and with a mixed approach (qualitative-quantitative). Data were collected using existing knowledge resource study methods, semi-structured interviews and interpretive structural questionnaires and analyzed using oriented qualitative content analysis and interpretive structural modeling. The statistical population of the present study in both quantitative and qualitative parts of this study included experts in organizational culture and data science in defense organizations, which was conducted by purposive sampling method and until the theoretical saturation of 16 interviews. The content of the content analysis was provided to the experts in order to check the validity of the extracted dimensions and components, and the necessary adjustment was performed. The reliability of the content analysis output was measured by the Holstie method with a coefficient of 0.8. The result led to the design of a model of challenges to establishing a data-driven culture in defense organizations. Findings showed that the eight dimensions of organizational, decision making, data context, environmental culture, management, strategic, logistics, individual - personality are important and key challenges in establishing a data-driven culture that these dimensions form the integrated model. They interact with each other. The results also showed that the most effective and influential dimension is environmental culture and the dimensions of logistics, data texture and individual-personality components were the most effective dimensions. The structural-interpretive model itself has the required reliability and validity and there is no reason to examine them.

Keywords

References

Reference:
Agarwal, A., Shankar, R., & Tiwari, M. K. (2007). Modeling agility of supply chain. Industrial marketing management, 36(4), 443-457.
Azar, A., & Bayat, K. (2008). Designing trade based on- process model by interpretive structural modeling, Journal of Information Technology Management.
Azar, A. (2010). Modeling agility of supply chain, using interpretive structural modeling approach-Structural, Journal of Humanities TeacherManagment Research in Iran, 14(4) ,138, 13.(in Persian).
Anderson, C. (2015). Creating a data-driven organization: Practical advice from the trenches. O'Reilly Media, Inc.
Balabonienė, I., & Večerskienė, G. (2015). The aspects of performance measurement in public sector organization. Procedia-social and behavioral sciences, 213, 314-320.
Bean, R., & Davenport, T. H. (2019). Companies are failing in their efforts to become data-driven. Harvard Business Review, 5-8.
Berndtsson, M., Forsberg, D., Stein, D., & Svahn, T. (2018). Becoming a data-driven organisation. In 26th European Conference on Information Systems (ECIS2018), Portsmouth, United Kingdom, 23-28.
Berndtsson, M., Forsberg, D., Stein, D., and Svahn, T. (2018). Becoming a Data-Driven Organisation. ECIS 2018 Proceedings, pp, 1-9.
Boyce, A. S., Nieminen, L. R. G., Gillespie, M. A., Ryan, A. M., & Denison, D. R. (2015). Which comes first, organizational culture or performance? A longitudinal study of causal priority with automobile dealerships. Journal of Organizational Behavior, 36(3), 339–359.
Carillo, K. D. A., Galy, N., Guthrie, C., & Vanhems, A. (2019). How to turn managers into data-driven decision makers: Measuring attitudes towards business analytics. Business Process Management Journal.
Davenport, T. H., & Bean, R. (2018). Big companies are embracing analytics, but most still don’t have a data-driven culture. Harvard Business Review, 6, 1-4.
de Medeiros, M. M., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: Benefits, challenges and opportunities. The Bottom Line.
Halper, F., Stodder, D.: What it takes to be data-driven. TDWI, vol. Q4 (2017).
Janićijević, N. (2011). Methodological Approaches in the research of Organizational Culture.Economic Annals, LVI (189), pp.69–100.
Lane,J.E. Viesasis sektorious: savokos, modeliai ir poziuriai. Vilnus: Margi rastai, 2005. ISBN 9986-09-228-0.
Mandel, M. (2017). The Economic Impact of Data: Why Data Is Not Like Oil. Progressive Policy InstituteRetrieved from https://www.progressivepolicy.org/ wp-content/uploads/ 2017/07/PowerofData Report_ 2017.pdf
Nair, S. (2021). Is your Bussiness Masquerading as data-driven?. Harvard Business Review.
Parkins, D. (2017). The world’s most valuable resource is no longer oil, but data. https://www.progressivepolicy.org/ wp-content/uploads/ 2017/09/PowerofData Report_ 2017.pdf.
Storm, M., & Borgman, H. (2020). Understanding challenges and success factors in creating a data-driven culture. In Proceedings of the 53rd Hawaii International Conference on System Sciences.
Smalskys v. (2010). Viesasis valdymas: vadovelis. Vilnus.Mykolo Riomerio universitetas.
Schein, E. (2004). Organizational Culture and Leadership. Thousand Oaks, CA: Sage Publications.
Redman,T. (2021). Become more data-driven by Breaking These Bad Habits. Harvard Business  Review.
Svensson, R. B., & Taghavianfar, M. (2020). Toward Becoming a Data-Driven Organization: Challenges and Benefits. In International Conference on Research Challenges in Information Science (pp. 3-19), Cham.
Thomas, H., & Davenport, T.H. (2020). How CEO can Lead Data-driven culture. Harvard Business Review.
Waller,D. (2020). 10 steps to creating a data-driven culture. Harvard Business  Review.
Wang, S., & Krisch, U. (2019). A foundation for building a data-driven culture. Applied Marketing Analytics, 4(3), 238-252.
Whiteman,H. (2020). 7 ways HR can Build a Farier, Data-informed culture.  Harvard Business Review.
Windt, B., Borgman, H., & Amrit, C. (2019). Understanding leadership challenges and responses in data-driven transformations.
Scientific Journal of Strategic Management of Organizational Knowledge
Volume 4, Issue 4 - Serial Number 15
Serial No.15- Winter Quarterly
March 2022
Pages 111-149

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History

  • Receive Date: 23 February 2022
  • Revise Date: 14 March 2022
  • Accept Date: 11 March 2022

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