Global economic integration and sustainability challenges underscore the need to examine trade liberalization, green supply chains, and energy transitions. Using a mixed-methods framework, this study addresses the limitations associated with single-method approaches, combining thematic qualitative analysis with quantitative correlation analysis. Data were gathered from 427 participants in Chengdu, encompassing surveys and 30 semi-structured interviews. The mixed-method approach offers a more reliable assessment of policy effectiveness. Notably, this method combines quantitative analysis with real-world market insights. The study results highlight that both trade liberalization and governmental policy measures result in higher adoption of eco-friendly supply chains and energy transition efforts. Hence, the study findings put forward practical guidance to policymakers and industrial stakeholders to develop successful sustainability programs that will help advance sustainable economic growth. Based on this, policymakers must enhance regulatory frameworks and incentives for green technologies, while industries should proactively adopt flexible and sustainable supply chain strategies, in order to support long-term competitiveness and environmental performance.
This conceptual paper develops the notion of liminal ambidexterity to explain how organizations remain effective when their established routines continue to support performance but cease to orient them toward future advantage. Using concepts such as ambidexterity, liminality, dynamic capabilities, resilience, and management control, the paper argues that transitional environments require firms to maintain executional simplicity in the operational core while creating sufficient adaptive complexity at the edge. The framework recognizes three indexed domains: the Simplicity Discipline Index (SDI), the Complexity Capability Index (CCI), and the Complexity Load Index (CLI). In addition, translational governance is introduced as a mechanism that converts ambiguous signals into a limited set of executable commitments. The paper contributes a quadrant model, a capability-load fit logic, a refined definition of translational governance, illustrative empirical anchors, and a measurement architecture for future empirical testing.
This study investigates the factors and policy measures that condition the high-quality progress of China's sports economy, underpinned by the New Structural Economics approach. While there have been advancements in the sector, there are still issues such as regional imbalance, insufficient skills in the workforce, and inadequate integration with other sectors, notably tourism and technology. Six hypotheses encompassing policy support, infrastructure, human capital, technological innovation, market stimulation, and industrial structure were empirically tested using data were collected from 200 athletes, coaches, and policymakers. Descriptive statistics show that policy support (mean=4.161) and policy-market synergy (mean=4.083) received the highest mean ratings, while industrial integration received the lowest (mean=3.649). Correlation analysis confirmed that infrastructure investment (r = 0.463) showed the strongest association with high-quality development, followed by technological innovation (r = 0.445) and human capital development (r = 0.412) Path analysis had likewise shown that policy measures ( beta = 0.421 . p < 0.001 ) and technical innovation ( beta = 0.367 , p = 0.001 ) had the highest direct impact, while market demand ( beta = 0.238 , p = 0.012 ) had a moderate impact. In conclusion, as recommended by the findings, integrated policy tracks, technology, and infrastructure are critical points to improve the overall sports economy towards sustainability and inclusion. The findings provide important policy and managerial implications by emphasizing the critical role of policy support, technological innovation, and infrastructure development in driving high-quality growth. It is recommended that policymakers prioritize coordinated policy-market mechanisms and targeted investments in innovation and human capital, while managers focus on improving resource allocation and operational efficiency to achieve sustainable and inclusive development in the sports industry.
This study examines scientific research on paywalls and clickbait as contrasting revenue models in online media and explores how artificial intelligence (Al) is disrupting these models and journalism more broadly. It evaluates the scope of academic interest in these topics and identifies gaps in current scholarship. Using bibliometric analysis of Web of Science (WoS) Core Collection data and science-mapping techniques in VOSviewer, the study analyses publication trends, influential authors, and thematic clusters from the early 2010s to 2024. Findings show that although clickbait receives more scholarly attention, paywall research achieves greater impact, reflecting its focus on revenue strategies, consumer behaviour, and willingness to pay. The prominence of clickbait studies relates to concerns about engagement, credibility, and misinformation. Another key finding is that, while Al is transforming journalistic production, personalisation, and automation, its integration into media business models remains limited. The study underscores the need for interdisciplinary approaches to support sustainable digital media ecosystems.
Housing affordability has become an increasingly important socio-economic issue across the European Union, as housing prices have grown faster than household incomes in many countries. This study investigates the macroeconomic determinants of housing affordability using the house price-to-income ratio as the main indicator. The empirical analysis is based on panel data for EU member states covering the period 2000-2024 and combines macroeconomic indicators from Eurostat, including GDP per capita, purchasing power parity indicators, and GDP price level indices. Panel econometric models with country-and time-fixed effects are used to estimate the relationship between macroeconomic variables and housing affordability dynamics. In addition, cluster analysis is applied to identify groups of countries with similar housing affordability trajectories. The results indicate that macroeconomic factors play a significant role in shaping housing affordability in EU countries. Higher price levels are associated with higher house price-to-income ratios, while income growth partially mitigates affordability pressures. Cluster analysis reveals substantial heterogeneity between European housing markets, highlighting different housing market regimes across EU member states.
