Innovation is crucial for economic development and international competitiveness, manifesting in a nation's National Innovation Capability (NIC). The National Innovation System (NIS), encompassing economic, political, and social factors, significantly influences NIC. Previous research primarily focuses on the static impact of NIS elements on NIC within a single year, neglecting the dynamic interactions and temporal stability of these relationships. This study addresses this gap by employing fsQCA to analyze cross-economy and cross-year data from 2011 to 2022, exploring how five NIS elements (institutions, human capital and research, infrastructure, market sophistication, and business sophistication) jointly influence NIC in high-income and upper-middle-income economies. The research questions are: 1) How do individual and combined NIS elements affect NIC in these economies, and which configurations yield high NIC? 2) Do these configurations exhibit temporal or cross-sectional effects, and are they stable? 3) Are there configurations that drive high NIC in both types of economies? Analyzing high-income and upper-middle-income economies provides valuable insights into successful innovation strategies and offers lessons for other economies striving for sustainable development. The study contributes theoretically by enhancing the transparency of innovation paths, incorporating temporality into fsQCA, and revealing the stable allocation of NIS elements, ultimately informing innovation policy adjustments.

The literature review examines the existing research on NIS and NIC. It establishes the NIS as a theoretical framework where government and institutional structures coordinate innovation activities. Studies highlight the interaction of key organizational structures and systems within the NIS for knowledge creation and technology transfer, leading to NIC upgrades. Intangible national investments in education, infrastructure, and R&D are identified as key drivers. The role of government policy in coordinating various institutional structures is also emphasized. The review notes that research on NIC often lacks integration, utilizing cross-sectional data which limits the understanding of temporal differences and configuration continuity. The study then introduces the five elements of the NIS from the GII report: institutions, human capital and research, infrastructure, market sophistication, and business sophistication, and their individual importance to NIC, underpinning the propositions tested in the study.

This study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) using panel data from the Global Innovation Index (GII) Reports (2011-2022). fsQCA is chosen for its ability to identify multiple pathways leading to a specific outcome, analyzing the combined effects of antecedent conditions on NIC. The use of panel data allows for the exploration of temporal dynamics, addressing the limitations of previous cross-sectional studies. The sample includes 44 high-income and 31 upper-middle-income economies. Data on the five NIS elements and NIC are obtained from the GII Reports. Calibration is performed using three thresholds (0.95, 0.5, and 0.05) to determine fuzzy set membership levels. The analysis proceeds in two stages: necessity analysis to identify necessary conditions for high NIC and sufficiency analysis to uncover configurations of NIS elements sufficient for achieving high NIC. Consistency and coverage thresholds are set based on established guidelines. The time effect is analyzed by incorporating panel data techniques, examining pooled consistency (POCONS), between consistency distance (BECONS distance), and within consistency distance (WICONS distance). The analysis determines whether the relationships identified are stable across time and economies.

The necessity analysis reveals that none of the five NIS elements individually constitute a necessary condition for high NIC. However, the sufficiency analysis identifies four configurations leading to high NIC in high-income economies (H1-H4) and two in upper-middle-income economies (H5-H6). Configuration H1 involves institutions, human capital and research, and market sophistication; H2 involves institutions, market sophistication, and business sophistication; H3 involves human capital and research, market sophistication, and business sophistication; H4 involves institutions, human capital and research, infrastructure, and business sophistication; H5 involves institutions, infrastructure, and business sophistication; and H6 involves human capital and research, market sophistication, and business sophistication. Notably, configurations H3 and H6 are identical, suggesting a common path to high NIC for both high-income and upper-middle-income economies, irrespective of time. Panel data analysis reveals a time effect for infrastructure's necessity, showing its increasing importance for high NIC over time. The configurations identified are largely stable across time and economies, except for some minor fluctuations. The HCR*MS*BS combination (H3 and H6) exhibits high consistency and coverage, signifying its strong explanatory power and generalizability across both types of economies and over the entire period.

The findings demonstrate that achieving high NIC requires a nuanced understanding of the combined effects of NIS elements, rather than relying on individual factors. The lack of necessary conditions highlights the complexity of innovation and the importance of synergistic interactions among NIS elements. The increasing time effect of infrastructure underscores its growing significance in facilitating knowledge transfer and diffusion, particularly in the context of digital technologies. The common path identified (HCR*MS*BS) highlights the consistent importance of investing in human capital and research, fostering sophisticated markets, and developing business capabilities for both high-income and upper-middle-income economies. The study's findings support previous research on the complexity of innovation while enriching the literature by explicitly incorporating temporal dynamics.

This study contributes to a deeper understanding of the dynamic relationship between NIS elements and NIC. It reveals diverse, yet stable, pathways to high NIC in different economies and highlights the growing importance of infrastructure. The common path identified, combining human capital and research, market sophistication, and business sophistication, offers valuable insights for policymakers in various economies. Future research could investigate configurations leading to low NIC, analyze the sequential effects of NIS elements, incorporate additional factors into the analysis, and further explore the role of infrastructure in the context of emerging digital technologies.

The study focuses on configurations leading to high NIC, overlooking those associated with low NIC. The analysis does not explicitly address the order of influence among NIS elements. The reliance on GII data, while comprehensive, might not fully capture the nuances of innovation processes in all economies. Future research could address these limitations to provide a more comprehensive understanding of the complex dynamics driving NIC.