The European Commission (EC) has monitored Member States' digital progress through the Digital Economy and Society Index (DESI) since 2014. The DESI index currently ranks the EU Member States and monitors their progress based on four core and 33 individual indicators. We sought to determine whether convergence between the Member States could be detected using the DESI’s annual databases. By examining the variation in the indices, we propose the existence of a so-called “Matthew effect”, i.e., the “rich get richer” syndrome among the 27 EU Member States. We also hypothesised that the COVID-19 pandemic would influence the change in the DESI. Issues investigated were those using bibliometric, statistical-mathematical methods. The σ-convergence analysis was used to estimate the reduction over time of the differences between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. A PCA analysis was performed to verify the Mathew effect with additional λ-variances considering real GDP per capita change. The σ-convergence was confirmed over the period 2016–2021. The β-convergence was significantly confirmed, and the research also revealed that the half-life of catching up is approximately 20 years. The suggestion of a Matthew effect in the 2016–2021 period, although not significantly confirmed, tends to suggest its existence. The COVID-19 pandemic’s impact on the value of the DESI index is likely to be affected, but future studies are needed to find support for this hypothesis. The study concludes that convergence between the EU-27 Member States can be detected based on the DESI, but this does not imply convergence for all four core DESI indicators.

Nowadays, rural depopulation is one of the biggest problems in developed and developing countries, especially due to the COVID-19 pandemic. This pandemic impacts various sectors, especially the economic sector. However, the development of the 4.0 Industrial Revolution forced all sectors, including agriculture, to adapt and take advantage of internet-based digital technology by developing smart agriculture. Therefore, the objective of this study is to analyze the development of smart agriculture in rural areas. This study uses a systematic literature review with a TAA approach with secondary data collected from Scopus databases. The downloaded documents will be further analyzed using VOSviewer software to find the clustering. After the coding process by first-order categories, second layer theme, and aggregate dimension, this research found several theoretical concepts about the “Smart Village”. The definition of the ‘smart village’ concept is a combination of smart agriculture and industry 4.0 technology utilized by rural MSMEs to use the information and communication technology integrated into public services and economic activities. There are still many obstacles encountered in the application of ‘smart villages,’ such as the lack of youth participation and the low quality of human resources in the agricultural sector, limited internet network coverage, and the lack of optimal capital support. This research will contribute to the systematic literature review method by purposing filtering criteria of TAA to shade the light of the lack of data accuracy in several leading databases of scholarly impacts. This research also gives a clear direction for further research regarding industry 4.0 and Indonesia's smart agriculture concept application.

Since the 2000s, the European Commission has been paying particular attention to the digital development of the European economy and society. Since 2014, the Digital Economy and Society Index (DESI) has been one of the key tools for monitoring and measuring. In 2021, the DESI index's core indicators were aligned with the objectives of the 2030 Digital Agenda, which has four dimensions: human capital, connectivity, integration of digital technology, and digital public services. We sought to determine whether convergence between the Member States could be detected using the third dimension of DESI's annual database. The σ-convergence analysis was used to estimate the reduction over time of the differences in digital technology integration between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. The σ- and β-convergence were not confirmed. We examined the individual indicators (11) of the integration of digital technology to identify critical areas that need to be addressed in the future to ensure that digital inclusion is as widespread as possible. Our analysis focused on the Member States that joined the EU during the Eastern European enlargements. The performance of all individual indicators is typically below the EU average, and, as the β-convergence analysis shows, the average catching-up rate is not encouraging.

The incorporation of financial technology (FinTech) into contemporary business development has emerged as a critical factor for small medium enterprises (SMEs), which contribute to their viability. In light of the significant collaboration between Hungary and Indonesia, this research endeavors to delve into unexplored dimensions pertaining to the potential of fintech in bolstering the operational resilience of SMEs within the contexts of these developed and developing economies, using the technology-organization-environment (TOE) framework. Specifically, this study aims to investigate how the adoption of digital financial services can facilitate inclusive economic growth and foster entrepreneurial activities in both Hungary and Indonesia. The research adopts a quantitative methodology, employing statistical hypothesis testing and regression analysis to achieve its objectives. A sample of 349 participants, with 164 representing Hungary and 185 representing Indonesia, was purposively selected by scientific criteria to examine the patterns of FinTech adoption within the SME industry. The results show that when partial least squares–structural equation modeling (PLS-SEM) is used to examine the direct effect of TOE on fintech adoption, technological factors and environmental factors have a significant effect on fintech adoption, whereas organizational factors have no significant effect on fintech adoption. Further results from the PLS-MGA method used to investigate group differences show that Hungary–Indonesia significantly differ in terms of the impact of technological factors on fintech adoption; in the organizational context of fintech adoption, Indonesia has a stronger relationship than Hungary does, and in terms of environmental factors, Indonesia has a stronger relationship because, compared with Hungary, Indonesia has a higher level of trust in the government. The findings of this research are highly important, serving as a noteworthy reference point for assessing the collaborative efforts between the two countries in enhancing SMEs through the adoption of fintech.

A well-functioning public transport system is one of the foundations of the Smart city concept and sustainable cities. The COVID period offers an excellent opportunity to examine the impact of different factors, because throughout this period society was faced with both health and governmental impacts that had different effects. Public transport use in the EU member states from the turn of the millennium until the beginning of the pandemic ranged from 17–18% to 27% of total journeys. The COVID-19 virus caused a 5–7% decrease in public transport use. The present study analyses the relationship between these effects and the number of paying passengers on public transport in Debrecen. Four hypotheses were put forward, i.e., that (1) the evolution of travel tickets, (2) general passes, (3) discounted passes and (4) replacement tickets were influenced by the evolution of the active COVID-19 case rate. The data were collected from the Debrecen Transport Company (DKV) for the period 1 January 2020–12 December 2021, and the Worldometers.info database. Statistical analyses were performed using an autoregressive and moving average (ARMA) model. We found that COVID’s active case numbers did not affect sales, but some of the government measures did have an effect. The results of this research can be used to inform future decisions to maintain and improve urban transport.