PurposeThe purpose of this research is to understand the effect of innovation and networks in mitigating the effect of COVID-19 on small and medium-sized enterprises' (SMEs’) performance. Moreover, we aim to explore how different regions withstand the effects of the COVID-19 recession. To do this, we carried out panel data analysis on firm-level data and generated several interesting results. We explore how networking and innovation can help mitigate the effects of extreme shocks on SMEs’ performance. Design/methodology/approachWe use a rich and detailed longitudinal dataset and apply panel data econometric methods over the period 2015–2021. We also conduct several robustness tests to address endogeneity and examine the regional disparities between core and peripheral regions. FindingsOur study uniquely focuses on the effects of COVID-19, innovation and external advice (as a proxy for networks) on the performance of small and medium-sized enterprises (SMEs). Importantly, we also explore the interaction effects of innovation and external advice with COVID-19. First, we find that both external advice and financial obstacles are associated with firm performance. Second, we find the interaction effect between innovation and the COVID-19 recession dummy to be positive and statistically significant. This suggests that innovation can be an important resilience strategy for SME performance during periods of economic downturn. Third, we find significant regional differences between the SMEs that operate in peripheral regions and those operating in core regions. Our findings are generally robust to potential endogeneity concerns. Originality/valueOverall, the paper contributes to the theoretical and empirical literature on pandemic-driven and/or financial crises and the resilience of SMEs.
This paper examines the bidirectional relationship between competition and systemic risk in dual financial systems where Islamic and conventional financial institutions operate side by side. Using a sample of publicly listed financial institutions from the Asia-Pacific, Gulf Cooperation Council (GCC), and Middle East and North Africa (MENA) regions from 2000 to 2019, we estimate systemic risk through Delta CoVaR and competition using the Lerner index. Employing a panel vector autoregressive framework, we analyse how this relationship evolves across different economic phases, with particular focus on the Global Financial Crisis (GFC). We find that lower competition is consistently associated with reduced systemic risk, with this effect being stronger-by approximately 25%-in conventional financial institutions. Notably, the competition-risk relationship is asymmetric and time-varying. Put simply, competition enhances stability pre-crisis; it amplifies systemic risk during the GFC, especially in the conventional sector. Post-crisis, this fragility effect persists in conventional institutions but dissipates in Islamic ones. Our findings contribute to the literature on competition, systemic risk, and comparative banking by highlighting how alternative financial models and economic conditions jointly shape financial stability.
This longitudinal study monitored Theory-of-Mind development in monolingually raised but bilingually educated Spanish children (age 5-6) with varied L2-English curricula (13%-83%) to assess whether higher L2-exposure resulted in advantages on seven ToM concepts (emotion, desires, belief, reference, moral-reasoning, lies, sarcasm). Attention (selective, switching, inhibition) and a full suite of individual-difference effects were also monitored. GLMMs linked greater L2-exposure to higher ToM accuracy, and although all three attention measures contributed to ToM scores, the effect of selective attention was the strongest. L1-vocabulary and NVR routinely predicted ToM scores, and girls surpassed boys on sarcasm. We conclude that bilingualism spurs ToM development quickly and is not linked to L2-vocabulary at this stage. In addition, the fact that L2-exposure and individual differences impacted cognitive, affective, and conative ToM differentially supports an approach that analyses these components separately.
Small and medium-sized enterprises (SMEs) encounter cyber security risks, yet the factors underlying variation in these risks remain unclear. This study examined whether 1) cyber security controls, 2) awareness, knowledge, attitude, culture and 3) support routes vary according to SME characteristics (size, type, maturity and sector). It also explored the factors that shape SMEs' decisions to access resources that help reduce cyber security risk. A mixed-methods design combined a survey of 374 participants and interviews with 12 SMEs. ANOVAs analyzed differences across organizational categories, and thematic analysis was applied to qualitative data. The study shows that many SMEs in the sample have inadequate security controls and limited awareness, knowledge and capability in cyber security. Qualitative insights show that SMEs may underestimate risk, face competing priorities and are unsure where to find support. These findings highlight the need for practical, accessible support to help SMEs implement effective cyber security.
This study demonstrates how deep learning technologies can enhance the efficiency and accuracy of urban spatial layout optimization, cultural heritage preservation, and transportation network planning. The study explores the application of deep learning in the digital transformation of urban planning, specifically focusing on Beijing's central axis, an area rich in historical and cultural significance. By utilizing an improved DeepLabv3+ model, pixel-level classification of urban functional zones was achieved, with accuracy exceeding 90%, significantly surpassing traditional models. Furthermore, the study applied an enhanced graph attention network to analyze the spatial relationships between different regions, plots, roads, and intersections, revealing the complex interactions between urban functions and connectivity. These findings validate deep learning's capability to accurately capture intricate spatial features, optimize urban layouts, and support data-driven decision-making. The study also underscores the importance of balancing technological innovation with cultural heritage preservation and community involvement. A strategic framework for integrating artificial intelligence and big data into smart city development is proposed, offering valuable guidance for policymakers, data scientists, and engineers. This framework contributes to the advancement of sustainable and intelligent urban environments.
