Purpose This article examines how governance quality relates to digital competitiveness in the G20 by governance dimension and competitiveness sub-pillar. Design/methodology/approach The panel covers 18 G20 countries over 2018 to 2022 (N = 90). Governance is measured by the first principal component of the six Worldwide Governance Indicators. Outcomes are the IMD World Digital Competitiveness Ranking and its Knowledge, Technology and Future Readiness sub-pillars. The main specification is two-way fixed effects with country-clustered standard errors, supplemented by a Mundlak decomposition, a wild cluster bootstrap, leave-one-country-out estimation, a nine-advanced and nine-emerging split, and dynamic panel estimation. Findings A one standard-deviation rise in governance is associated with a 16.8-position improvement in overall rank (p < 0.01), strongest for Technology (−26.0) and intermediate for Future Readiness (−16.8). For Knowledge, it is significant between countries but not within them, consistent with human capital adjusting over longer horizons. A realistic within-country movement corresponds to 1.2 positions. Research limitations/implications The five-year window and small sample limit identification. Dynamic panel generalized method of moments (GMM) is uninformative at this length, so findings are reported as conditional associations rather than causal effects. Practical implications Regulatory quality and government effectiveness are the dimensions most consistently associated with digital competitiveness, and the rule of law with Technology. The association is stronger within emerging than advanced members, but with nine countries per group that difference is not statistically significant. Originality/value This is the first dimension-decomposed analysis of the governance and digital competitiveness link in the G20, and the first to show the Knowledge association is detectable between countries but not within them.
Micro, small, and medium-sized enterprises (MSMEs) in Cikondang Village, Cianjur Regency, face marketing challenges, including dependence on intermediaries, limited digital literacy, and the lack of integration between product information and online market access. This article describes the marketing digitalization assistance process, identifies supporting and inhibiting factors, and outlines requirements for program sustainability. The community engagement program was conducted from 15 July to 23 August 2026 using a participatory qualitative descriptive approach. Data were collected through observation, semi-structured interviews, and activity documentation involving MSME owners and village officials. Activities included coordination and data collection, socialization, and assistance with Business Identification Numbers (NIB), QRIS, Google Maps/Google Business Profile, and a village product-catalog website. The findings indicate that a combination of direct assistance, village institutional support, and youth involvement helped establish a more structured foundation for digital presence. However, limited digital skills and internet connectivity remained obstacles. As the evaluation was conducted during the program period, increases in revenue, profit margins, and competitiveness cannot be concluded as measurable impacts. Program sustainability requires the appointment of a website manager, regular information updates, and local technical assistance. This article argues that marketing digitalization should be understood as a process of empowerment and ecosystem strengthening rather than merely the provision of a platform.
Purpose This study investigates how digital infrastructure, socioeconomic structure and institutional quality are associated with digital financial inclusion (DFI) across Europe. Design/methodology/approach Using World Bank Global Findex and World Development Indicators data for 24 European countries across the 2014, 2017 and 2021 survey waves, the study estimates fixed-effects panel regressions with country-clustered standard errors separately for developed and emerging and frontier economies. Random-effects comparisons are also conducted for the latter group. Robustness analyses examine the influence of Kazakhstan, population sub-grouping, economic classification and survey-wave effects, while wild-cluster-bootstrap tests assess sensitivity to the small number of country clusters. Findings Digital infrastructure, particularly Internet connectivity, exhibits the most recurring positive associations with DFI, especially for payment activity. These relationships are more consistent in developed economies, whereas the emerging and frontier results are broader but more sensitive to estimator choice, survey-wave controls and small-cluster inference. Population sub-grouping and economic classification reveal patterns concealed by aggregate and pooled estimates. Excluding Kazakhstan preserves the principal Internet-related payment findings and selected Gross Domestic Product (GDP) per capita associations but weakens the automated teller machines and institutional evidence. Originality/value The study extends the DFI literature beyond composite country-level indices by examining payment and receipt behaviour separately across gender, age and income indicators. Its transaction- and population-specific lens identifies variations across population groups and stages of economic development that aggregate measures may conceal.
Purpose This study examines how corruption and institutional quality interact to influence long-run economic growth in nine South American economies over the period 1980–2019. It explores whether the economic effects of corruption are uniform or conditional on institutional strength, with particular emphasis on judicial and legislative integrity as moderating factors. Design/methodology/approach The study develops an extended Solow-type framework where corruption affects total factor productivity and institutions moderate this effect. Using V-Dem corruption indicators, the analysis applies Common Correlated Effects Mean Group (CCE-MG), Dynamic CCE, Dynamic Ordinary Least Squares and panel threshold estimators to account for heterogeneity and cross-sectional dependence. Findings Corruption has a persistent negative effect on economic growth, while stronger judicial and legislative integrity improves productivity. Threshold results show that corruption is more damaging under weak institutional conditions and less harmful once institutional quality passes a critical level. Research limitations/implications The study focuses on South America and uses governance indicators that may not fully capture informal corruption. Future research could extend the framework to other regions and include additional institutional or financial channels. Practical implications The findings suggest that anti-corruption policies are more effective when combined with stronger judicial independence and legislative accountability. Institutional reforms are crucial for reducing the economic costs of corruption. Social implications Improved institutional quality can enhance public trust, strengthen accountability and improve the efficiency of public resource allocation, contributing to more inclusive and sustainable development. Originality/value The article contributes by combining disaggregated corruption measures with a panel threshold framework, showing that corruption's growth effects are non-linear and conditional on institutional quality. It provides new evidence for South America and extends recent debates on governance and development.
Purpose This article examines spillovers between US financial conditions and artificial intelligence (AI)-related equity performance, amid debate over AI-driven financial fragility reminiscent of the 2000 dot-com bubble. Design/methodology/approach The analysis combines the three subcomponents of the Chicago Fed National Financial Conditions Index (credit, leverage and risk) with the S&P Kensho Artificial Intelligence Enablers & Adopters Index, used as a proxy for US AI market performance, over the period from June 1, 2018 to December 12, 2025. It applies the quantile-on-quantile connectedness framework of Gabauer and Stenfors (2024), with a time-varying extension and bootstrap inference. Findings The results show that connectedness between the AI market and financial conditions is state-dependent and becomes intense when the receiving side of the system is in an extreme regime. Credit and leverage conditions affect the AI market asymmetrically and dominate transmission when AI valuations themselves move into tail states. The risk component emerges as the most persistent transmitter across quantile combinations, indicating that changes in volatility, uncertainty and risk appetite are the main drivers of the AI-finance nexus. Feedback spillovers from the AI market appear episodic and arise mainly in cross-tail configurations, when financial conditions are sharply tightening or easing. Research limitations/implications The AI market should be viewed neither as an autonomous speculative sphere detached from macro-financial conditions nor as a passive recipient of broader financial shocks. Rather, it evolves within an asymmetric and regime-dependent system in which financial conditions tend to dominate when AI market returns themselves enter extreme states. By contrast, AI-related valuations feed back into the broader financial environment only under more selective configurations. Practical implications US regulators should monitor periods when high AI valuations coincide with low risk premia, easy leverage conditions and weaker credit quality, using regime-sensitive scenario analysis and stress tests. The objective is to detect not only direct valuation losses, but also indirect effects through lower collateral values, refinancing difficulties, margin pressures and broader balance sheet adjustment. US macro-prudential authorities should also monitor the channels through which AI-related valuations may become systemically important, including market concentration in a small number of AI-exposed firms, the role of high valuations in supporting collateral and borrowing capacity, and the degree of refinancing dependence. Originality/value This article is one of the first attempts to study US AI market evolution and financial conditions from a macro-financial outlook. It contributes to the emerging AI-finance literature by introducing a financial-conditions perspective into the analysis of AI-related valuations. It also extends the literature on AI-related risk transmission by showing that the relationship between the AI market and macro-financial conditions is nonlinear, asymmetric and regime-dependent. Methodologically, it is among the first applications of the quantile-on-quantile framework, extending Gabauer and Stenfors (2024) by introducing a time-varying dimension and bootstrap inference.
