The effects of technological innovation and ICT development on economic growth in Indonesia: evidence from the ARDL approach

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Abdul Rahim Ridzuan, Debrina Puspita Andriani, Januar Kustiandi, Ahmad Al Izham Izadin, Smarnika Ghosh, Naila Erum

2026 Quality and Quantity Article Cited by 0

Abstract

As one of the largest emerging economies in Southeast Asia, Indonesia faces the dual challenge of sustaining economic growth while undergoing rapid technological and digital transformation. The purpose of this study is to analyze the effect of technological innovations and development of information and communication technology (ICT) toward Indonesia’s economic growth on period 1990–2024. Using annual time-series data, the Autoregressive Distributed Lag (ARDL) approach is employed to estimate both short-run and long-run dynamics, with Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators used for robustness. The findings reveal asymmetric effects. Technological innovation, human capital, capital formation, and foreign direct investment positively and significantly contribute to economic growth in the long run, consistent with endogenous growth theory. In contrast, ICT development has a significant adverse long-run impact, which may indicate that the growth of digitalization entails structural costs in terms of adjustment in absorptive capacity, institutional preparedness, and productive digital use in the economy. The results point to technological innovation as a continuing primary engine of growth, and the impact of ICT on growth as conditional rather than automatic. The study contributes to the endogenous growth literature by distinguishing between aggregate technological innovation and ICT development within a unified empirical framework and by providing long-run evidence from an emerging economy context. The findings offer policy insights for aligning innovation, green technological upgrading, human capital development, and digital transformation to achieve sustainable long-run growth. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.

Affiliations

Universiti Teknologi MARA (UiTM), Selangor, Shah Alam, 40450, Malaysia; Universitas Brawijaya, Malang, Indonesia; Universitas Negeri Malang, Malang, Indonesia; Institute of Research in Finance and Analytics (IRFAN), Selangor, Malaysia; Noakhali Science and Technology University, Noakhali, Bangladesh; Universiti Teknologi MARA, Seremban, Malaysia