Development and Validation of a Big Data Policy-Making Model Affecting Digital Economy Growth: A Case Study of Startups
Keywords:
Policy-Making, Big Data, Digital Economy, Startups, Data Governance, Data-Driven EconomyAbstract
This study aimed to develop and validate a comprehensive big data policy-making model influencing digital economy growth, with a particular focus on the role and requirements of startups within Iran’s digital ecosystem. A mixed-methods design was employed. In the qualitative phase, a systematic grounded theory approach based on Strauss and Corbin’s methodology was applied through semi-structured interviews with 21 experts in public policy, digital economy, information technology, data analytics, and startup management. The extracted dimensions and components were subsequently validated through a three-round Delphi process involving 17 experts. In the quantitative phase, 305 startup managers, digital entrepreneurs, data analysts, information technology specialists, and digital economy policymakers in Tehran were selected through cluster sampling. Data were analyzed using Structural Equation Modeling (SEM) with Smart PLS software. The qualitative analysis identified 121 indicators, 27 subcategories, and 11 core categories organized within a paradigm model comprising causal conditions, contextual conditions, intervening conditions, strategies, and outcomes. Delphi findings demonstrated strong expert consensus and validity across all identified components, with Kendall’s coefficients exceeding 0.85 in all dimensions. Structural equation modeling confirmed the adequacy and empirical validity of the proposed model. Macro-enablers, data value creation mechanisms, economic-market conditions, data governance, executive enablement factors, and data-driven strategies significantly contributed to explaining digital economy growth. Furthermore, economic-competitive and socio-governance dimensions were validated as the principal outcomes of effective big data policy implementation. The findings indicate that sustainable digital economy growth among startups requires an integrated big data policy framework encompassing robust data infrastructure, effective data governance, skilled human capital, cross-sector collaboration, and efficient implementation mechanisms. The proposed model provides a localized and evidence-based framework that can assist policymakers, digital economy stakeholders, and innovation ecosystem actors in leveraging big data capabilities to accelerate digital transformation and economic development.
Downloads
References
Akter, S., Hossain, M. A., Lu, Q., & Shams, S. M. R. (2021). Big Data-Driven Strategic Orientation in International Marketing. International Marketing Review, 38(5), 927-947. https://doi.org/10.1108/IMR-11-2020-0256
Baghdadi, M., Mohammadi, M., Elyasi, M., & Radfar, R. (2021). Identifying Factors Affecting the Development of Startup Business Models in Line with Startup Maturity Stages. Technology Development Management Quarterly, 9(4), 12-43.
Bahrami, F., Kanani, F., Turkina, E., Moein, M. S., & Shahbazi, M. (2021). Key Challenges of Big Data Startups: An Exploratory Study in Iran. Management Studies Journal, 14(2), 273-289.
Chen, B., Nie, G., Jiang, S., & Hu, N. (2022). Research on the Big Data-Based Product Quality Data Package Construction and Application. 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC),
Cheng, H., & Qiu, L. (2023). Government-Supported E-Commerce Infrastructure and Entrepreneurship in Underdeveloped Regions. https://ssrn.com/abstract=4392492
Creswell, J. W. (2023). Qualitative Inquiry and Research Design: Choosing among Five Approaches (H. Danaei Fard & H. Kazemi, Trans.). Eshraghi and Saffar Publications.
Hossin, M. A., Du, J., Mu, L., & Asante, I. O. (2023). Big Data-Driven Public Policy Decisions: Transformation Toward Smart Governance. Sage Open, 1-19. https://doi.org/10.1177/21582440231215123
Ma, L., & Sun, B. (2020). Machine Learning and AI in Marketing: Connecting Computing Power to Human Insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005
Mamatzhonovich, O. D., Khamidovich, O. M., & Esonali o'g'li, M. Y. (2022). Digital Economy: Essence, Features and Stages of Development. Academicia Globe: Inderscience Research, 3(04), 355-359.
Merhi, M. I., & Bregu, K. (2020). Effective and Efficient Usage of Big Data Analytics in Public Sector. Transforming Government: People, Process and Policy, 14(4), 605-622. https://doi.org/10.1108/TG-08-2019-0083
Mirzaei, A., AlKhazali, O., Aguir, I., & Aguir, W. (2026). The impact of FinTech credit on growth in capital expenditure: Evidence from Chinese manufacturing firms. International Review of Economics & Finance, 106, 104911. https://doi.org/10.1016/j.iref.2026.104911
Plikas, J. H., & Kenourgios, D. (2026). Renewable Energy and Bank Credit Risk: The Mediating Role of Economic Growth. Economic Modelling, 107576. https://doi.org/10.1016/j.econmod.2026.107576
PourEzzat, A. A., Esmaili Givi, M., & Mahmoudi, M. (2021). Functions of Big Data in Smartening the Public Policy Cycle: An Analysis of Capacities and Challenges. Fourth Conference on Governance and Public Policy,
Rahmani, A., Vaziri Nejad, R., Ahmadinia, H., & Rezaeian, M. (2020). Methodological Foundations and Applications of the Delphi Method: A Narrative Review. Journal of Rafsanjan University of Medical Sciences, 19(5), 515-538.
Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management, 56, 102231. https://doi.org/10.1016/j.ijinfomgt.2020.102231
Sayehmiri, A., & Shayesteh, M. (2023). Investigating the Effects of Digitalization and Energy Intensity on Economic Growth in Selected MENA Countries. Econometric Modeling Quarterly of Semnan University, 8(4), 44-65.
Sedighian, N., Haji Ali Akbari, F., Doroudi, H., & Lotfizadeh, F. (2023). Providing a Model of the Consequences of Visual Metaphors in Interactive Advertising on Consumer Behavior Using the Delphi Technique. Consumer Behavior Studies, 10(1), 185-213.
Shah, S. I. H., Peristeras, V., & Magnisalis, I. (2021). Government Big Data Ecosystem: Definitions, Types of Data, Actors, and Roles and the Impact in Public Administrations. Journal of Data and Information Quality, 13(2), 1-25. https://doi.org/10.1145/3425709
Supriyanto, E. E., Warsono, H., & Herawati, A. R. (2021). Literature Study on the Use of Big Data and Artificial Intelligence in Policy Making in Indonesia. Administratio: Jurnal Ilmiah Administrasi Publik dan Pembangunan.
Wahyudi, M., Meilinda, V., & Khoirunisa, A. (2022). The Digital Economy's Use of Big Data Technologies and Data Science. International Transactions on Artificial Intelligence, 1(1), 62-70. https://doi.org/10.33050/italic.v1i1.167
Wan, L. J. (2020). Big Data Strategies for Government, Society and Policy-Making. Journal of Asian Finance, Economics and Business, 7(7), 475-487. https://doi.org/10.13106/jafeb.2020.vol7.no7.475
Wang, D., Zhou, T., & Wang, M. (2021). Information and Communication Technology (ICT), Digital Divide and Urbanization: Evidence from Chinese Cities. Technology in Society, 64, 101516. https://doi.org/10.1016/j.techsoc.2020.101516
Yaşa, A. A., & Şentürk, N. K. (2025). Inflation, Economic Growth and External Debt Dynamics: Evidence from Emerging Market Economies. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(3), 1060-1082. https://doi.org/10.30798/makuiibf.1633414
