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The concept of ecosystem is becoming increasingly popular in economic, scientific and policy systems alike, and its use is embedded more and more in everyday practice. It provides a theoretical framework for policy planning documents, business plans and scientific research. This is facilitated by a holistic approach that understands ecosystems not only as resource allocation systems, but also as a complex structure of governance that includes cognitive elements and interactions. In parallel with its rise in popularity, indices and rankings have been developed for measuring and comparing the performance of individual ecosystems, influencing not only the associated discourse but also the decisions of ecosystem actors. Compared to the innovation and the entrepreneurship ecosystem frameworks, the concept of a startup ecosystem is a relatively new category. Related startup ecosystem rankings, emerging in recent years, are meant to provide performance comparability of these ecosystems. These rankings and their underlying variables have not been in the center of academic interest, but the holistic nature of the ecosystem conceptual framework requires a deeper understanding and analysis of the field. As a first step, a structured synthesis of appropriate research questions and future research directions must be ensured. The present paper attempts to do this by describing the specificities of the holistic ecosystem approach and a possible research agenda on startup ecosystem rankings. [ABSTRACT FROM AUTHOR]

The aim of this study is to benchmark the digital development of Hungary vis-à-vis other countries of the European Union, using the five principal dimensions of the digital economy and society index (DESI), but replacing the European Commission's subjective scoring model (which is built on an arbitrary weighting system) with more objective ranking methods that are based on the statistical properties of the dataset. The countries are ranked with DEA-type composite indicators and TOPSIS (technique for order of preference by similarity to ideal solution) methods of decision theory, and the two rankings are compared, with a particular emphasis on the place of Hungary amongst the countries of the European Union. The main advantage of these ranking methods over the traditional data envelopment analysis is that they do not require data transformation, even if data only contain output criteria (to be maximised). According to the authors' results, Hungary is ranked among the second tertile of the countries in the European Union, and can be considered a medium performing country in terms of its digital development. [ABSTRACT FROM AUTHOR]

The unexpected economic situation in the spring of 2020 can be considered the consequence of the restrictive measures necessitated by the COVID-19 pandemic. So far, it has never occurred in the history of modern statistics that several areas of the economy had to be shut down almost overnight. This resulted in a decrease in economic performance that was unpredictable with traditional estimation methods. Generally used econometric models extrapolate trends from preceding periods during forecasting, however, in such an extreme situation, previous correlations could not be used for estimation. Another problem is that the indicators of short-term, so-called flash estimates are mainly based on data of organizations with large economic weight, however, dynamics of the production of micro, small and medium-sized enterprises may differ significantly from these in the event of a large downturn. The situation in the spring of 2020 encouraged the examination of alternative economic indicators, of which the diffusion indexes are presented in this study. These indexes are used to identify crises affecting the whole economy and are based on the aggregation of various indicators of economic statistics, taking into account the direction of their changes. In an organizational approach, the diffusion indexes can be calculated on administrative or Big Data. The authors choose the former way and use VAT data. (A Big Data approach would be the use of e-invoices but in Hungary no such data are currently available in homogeneous time series.) In this study, first the history of diffusion indexes is introduced, then a VAT-based version of them is presented on Hungarian microdata, which are available at the end of the month following each quarter. Next, the methodology for developing the VAT-based index is described and time series are analysed from 2008 Q1 to 2020 Q4. The authors examine the usability of their index in light of the downturn unfolded in 2020. Based on the results, their diffusion index proves to be an effective tool to show the monthly dynamics of economic changes. [ABSTRACT FROM AUTHOR]