Economic Policy Stability, Digital Governance Capability, and Artificial Intelligence Innovation Performance

Ahmad Rizani; Tri Darsono; Gehad Mohammed Sultan Saif

doi:10.69725/jebi.v3i1.342

Authors

Ahmad Rizani Department of Economic Development, Faculty of Economics and Business, Universitas Palangka Raya, Palangka Raya, Indonesia
Tri Darsono Department of Economics, Faculty of Economics and Business, Universitas Sebelas Maret, Central Java, Indonesia
Gehad Mohammed Sultan Saif Department of Accounting, University of Aden, Aden, Yemen

DOI:

https://doi.org/10.69725/jebi.v3i1.342

Keywords:

Economic Policy Stability, AI Innovation, Digital Governance, Digital Infrastructure, R&D Capability

Abstract

Purpose—This study examines how Economic Policy Stability, Digital Infrastructure Readiness, Research and Development Capability, and Artificial Intelligence Talent Capability influence Artificial Intelligence Innovation Performance. It assesses the mediating role of Digital Governance Capability in Indonesian manufacturing firms.

Design/methodology/approach—This study uses a quantitative, explanatory approach grounded in Real Options Theory, Dynamic Capabilities Theory, the National Innovation System Theory, and the Resource-Based View. Data were gathered from 250 respondents in Indonesian manufacturing firms and analyzed with Partial Least Squares Structural Equation Modeling via SmartPLS 4.

Findings—The results indicate that Economic Policy Stability, Digital Infrastructure Readiness, Research and Development Capability, and Artificial Intelligence Talent Capability each have a positive and significant impact on Artificial Intelligence Innovation Performance. Additionally, these factors also significantly enhance Digital Governance Capability. Moreover, Digital Governance Capability positively influences Artificial Intelligence Innovation Performance and partially mediates all the relationships proposed.

Originality/value—This study advances AI innovation research by integrating policy stability, digital resources, R&D capacity, AI talent, and digital governance into a comprehensive model. It underscores Digital Governance Capability as a key strategic mechanism that converts institutional and organizational strengths into AI-driven innovation results.

Implications—The findings indicate that manufacturing companies need to bolster AI innovation not just by investing in technology, but also by ensuring consistent policy support, improving digital infrastructure, advancing R&D, developing AI expertise, and implementing responsible digital governance.

Downloads

Download data is not yet available.

Author Biographies

Ahmad Rizani, Department of Economic Development, Faculty of Economics and Business, Universitas Palangka Raya, Palangka Raya, Indonesia

Ahmad Rizani is a researcher in economic development and innovation policy at Universitas Palangka Raya, Indonesia. His research focuses on economic policy stability, digital transformation, artificial intelligence innovation, and digital governance capability in emerging economies. He is particularly interested in how institutional stability, technological readiness, and organizational capabilities shape innovation performance in developing-country contexts. His current academic work explores the interaction between public policy, industrial transformation, and AI-driven innovation in manufacturing and digital business ecosystems.
Tri Darsono, Department of Economics, Faculty of Economics and Business, Universitas Sebelas Maret, Central Java, Indonesia

Tri Darsono is a researcher in economics and business at Universitas Sebelas Maret, Indonesia. His academic interests include economic development, innovation systems, policy analysis, institutional capability, and business transformation. He has a strong interest in examining how policy frameworks, organizational resources, and economic institutions influence firm competitiveness and innovation performance. His research also addresses the role of governance, strategic capability, and development policy in supporting sustainable economic growth and strengthening innovation ecosystems in emerging economies.
Gehad Mohammed Sultan Saif, Department of Accounting, University of Aden, Aden, Yemen

Gehad Saif is a researcher in accounting and finance at the University of Aden, Yemen. His research interests include financial management, digital governance, innovation performance, organizational capability, and business sustainability. He is particularly interested in how governance mechanisms, financial decision-making, and digital transformation influence organizational performance in developing economies. His academic work also explores the relationship between institutional practices, technological adoption, and strategic management in enhancing firm resilience, accountability, and long-term value creation.

References

Akram, R., Li, Q., Srivastava, M., Zheng, Y., & Irfan, M. (2024). Nexus between green technology innovation and climate policy uncertainty: Unleashing the role of artificial intelligence in an emerging economy. Technological Forecasting and Social Change, 209. https://doi.org/10.1016/j.techfore.2024.123820

Åström, J., Reim, W., & Parida, V. (2022). Value creation and value capture for AI business model innovation: a three-phase process framework. Review of Managerial Science, 16(7). https://doi.org/10.1007/s11846-022-00521-z

Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188. https://doi.org/10.1016/j.techfore.2022.122264

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4). https://doi.org/10.1093/qje/qjw024

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1). https://doi.org/10.1177/014920639101700108

Bernanke, B. S. (1983). Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics, 98(1). https://doi.org/10.2307/1885568

Bhattacharya, U., Hsu, P. H., Tian, X., & Xu, Y. (2017). What Affects Innovation More: Policy or Policy Uncertainty? Journal of Financial and Quantitative Analysis, 52(5). https://doi.org/10.1017/S0022109017000540

Cockburn, I. M., Henderson, R., & Stern, S. (2019). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In The Economics of Artificial Intelligence: An Agenda (Number May).

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1). https://doi.org/10.2307/2393553

Cui, X., Wang, C., Sensoy, A., Liao, J., & Xie, X. (2023). Economic policy uncertainty and green innovation: Evidence from China. Economic Modelling, 118. https://doi.org/10.1016/j.econmod.2022.106104

Dixit, A., & Pindyck, R. (1994). Investment Under Uncertainty. Princeton U. press.

Dwivedi, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Williams, M. D., Y. K., & Hughes. (2022). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57.

European Commission. (2025). Commission Notice – The 2025 annual Union work programme for European standardisation. Official Journal of the European Union, 1818(November 2022).

Fagerberg, J., & Srholec, M. (2018). National innovation systems, capabilities and economic development. In Innovation, Economic Development and Policy: Selected Essays. https://doi.org/10.1016/j.respol.2008.06.003

Gonzales, J. T. (2023). Implications of AI innovation on economic growth: a panel data study. Journal of Economic Structures, 12(1). https://doi.org/10.1186/s40008-023-00307-w

Gulen, H., & Ion, M. (2016). Policy uncertainty and corporate investment. In Review of Financial Studies (Vol. 29, Number 3). https://doi.org/10.1093/rfs/hhv050

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4). https://doi.org/10.1177/0008125619864925

HAI. (2025). Artificial Intelligence Index Report 2025 - Stanford University Human-Centered Artificial Intelligence. ArXiv Preprint ArXiv ….

Hair, et al. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). European Journal of Tourism Research, 6(2).

Hair, J. F., Tomas, H. G., Ringle, C. M., & Marko, S. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2).

Hsu, P. H., Tian, X., & Xu, Y. (2014). Financial development and innovation: Cross-country evidence. Journal of Financial Economics, 112(1). https://doi.org/10.1016/j.jfineco.2013.12.002

Huang, T., & Arnold, Z. (2020). Immigration policy and the global competition for AI talent. Center for Security and Emerging Technology, June 2020.

IDC Worldwide AI and Generative AI Spending Guide - 2025 Jul. (n.d.).

Igna, I., & Venturini, F. (2023). The determinants of AI innovation across European firms. Research Policy, 52(2). https://doi.org/10.1016/j.respol.2022.104661

Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change, 179. https://doi.org/10.1016/j.techfore.2022.121636

Julio, B., & Yook, Y. (2012). Political uncertainty and corporate investment cycles. Journal of Finance, 67(1). https://doi.org/10.1111/j.1540-6261.2011.01707.x

Kafka, K. I., & Kostis, P. C. (2024). Unravelling the innovation puzzle: The interplay between uncertainty, economic institutions, and innovation performance in advanced and developing economies. Technological Forecasting and Social Change, 198. https://doi.org/10.1016/j.techfore.2023.123027

Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1). https://doi.org/10.1016/j.bushor.2019.09.003

Kostis, A., & Ritala, P. (2020). Digital Artifacts in Industrial Co-creation: How to Use VR Technology to Bridge the Provider-Customer Boundary. California Management Review, 62(4). https://doi.org/10.1177/0008125620931859

Lim, S., Kwon, O., & Lee, D. H. (2018). Technology convergence in the Internet of Things (IoT) startup ecosystem: A network analysis. Telematics and Informatics, 35(7). https://doi.org/10.1016/j.tele.2018.06.002

Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122. https://doi.org/10.1016/j.technovation.2022.102623

McKinsey. (2025). The state of AI: How organizations are rewiring to capture value. McKinsey Global Institute, (March).

Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics, 26(4). https://doi.org/10.1007/s11948-019-00165-5

Neves, P. C., Afonso, O., Silva, D., & Sochirca, E. (2021). The link between intellectual property rights, innovation, and growth: A meta-analysis. Economic Modelling, 97. https://doi.org/10.1016/j.econmod.2021.01.019

Ng, T. H., Lye, C. T., Lim, Y. S., & Lim, Y. Z. (2026). Economic policy uncertainty and artificial intelligence (AI) innovation: A cross-country analysis. Journal of Open Innovation: Technology, Market, and Complexity, 12(1). https://doi.org/10.1016/j.joitmc.2025.100698

OECD. (2024). Using AI in the workplace: Opportunities, risks and policy responses. In OECD Artificial Intelligence Papers (Vol. 11).

Park, J. (2024). Analyzing the direct role of governmental organizations in artificial intelligence innovation. Journal of Technology Transfer, 49(2). https://doi.org/10.1007/s10961-023-10048-4

Qureshi, I., Park, D., Crespi, G. A., & Benavente, J. M. (2021). Trends and determinants of innovation in Asia and the Pacific vs. Latin America and the Caribbean. Journal of Policy Modeling, 43(6). https://doi.org/10.1016/j.jpolmod.2020.06.008

Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent class analysis in PLS-SEM: A review and recommendations for future applications. Journal of Business Research, 138. https://doi.org/10.1016/j.jbusres.2021.08.051

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling. In: Homburg C., Klarmann M., Vomberg A. (eds) Handbook of Market Research. In Springer International Publishing.

Sharma, A., Sousa, C., & Woodward, R. (2022). Determinants of innovation outcomes: The role of institutional quality. Technovation, 118. https://doi.org/10.1016/j.technovation.2022.102562

Tajaddini, R., & Gholipour, H. F. (2021). Economic policy uncertainty, R&D expenditures and innovation outputs. Journal of Economic Studies, 48(2). https://doi.org/10.1108/JES-12-2019-0573

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7). https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

Tian, J., Li, H., & You, P. (2022). Economic policy uncertainty, bank loan, and corporate innovation. Pacific Basin Finance Journal, 76. https://doi.org/10.1016/j.pacfin.2022.101873

Tursunbayeva, A., Chalutz-Ben Gal, H., Santos Fonseca, S. S., Benito, P. A., Piña Ramírez, C., Rubeis, G., Filz, M.-A., Bosse, J. P., Herrmann, C., Cavalieri, A., Reis, J., Amorim, M., Santarsiero, F., Carlucci, D., Schiuma, G., Sukums, F., Wamala-Larsson, C. W., Kisenge, R., Ravi, S. J., … Kazancoglu, Y. (2024). A Spotlight on Environmental Sustainability in View of the European Green Deal. Sustainability (Switzerland), 16(1).

Wang, Y., Chen, C. R., & Huang, Y. S. (2014). Economic policy uncertainty and corporate investment: Evidence from China. Pacific Basin Finance Journal, 26. https://doi.org/10.1016/j.pacfin.2013.12.008

William, M., & Fengrong, W. (2022). Economic policy uncertainty and industry innovation: Cross country evidence. Quarterly Review of Economics and Finance, 84. https://doi.org/10.1016/j.qref.2022.01.012

Zhang, S., Wu, Z., Dou, W., & Hao, Y. (2025). How does economic policy uncertainty affect corporate green innovation? Evidence from China. Journal of Environmental Planning and Management, 68(6). https://doi.org/10.1080/09640568.2023.2286926

Zwetsloot, R., Zhang, B., Dreksler, N., Kahn, L., Anderljung, M., Dafoe, A., & Horowitz, M. C. (2021). Skilled and Mobile: Survey Evidence of AI Researchers’ Immigration Preferences. AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462617