Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Szalavetz
Title: Artificial Intelligence-Based Development Strategy in Dependent Market Economies - Any Room Amidst Big Power Rivalry?
Abstract: This paper investigates whether the activities of start-ups specialising in artificial intelligence (AI)-powered solutions could contribute to upgrading in dependent market economies. Mapping the ecosystem of Hungarian AI-solution providers, collecting, and analysing data of their solutions, activities, and performance, we identify the main mechanisms of AI-driven upgrading. We argue that AI-solution providers induce productivity and resource efficiency improvement at technology adopters by enabling process upgrading. By selling their services to the local subsidiaries of global companies, they intensify the local backward linkages of these companies. Increased local embeddedness of subsidiaries is an important manifestation of economic upgrading. Additionally, AI-solution providers diversify the drivers of growth. In dependent market economies, where export-oriented manufacturing activities controlled by efficiency-seeking foreign investors used to be the main (unique) growth engine, the activities of domestic-owned AI solution providers represent a new driver of growth: technology-oriented entrepreneurship. We found, however, that the economic impact of Hungarian AI-oriented ventures is limited, no matter how innovative their solutions are. Managerial implications include the indispensability of devising an adequate business development strategy and a value capture strategy. Without adequate entrepreneurial skills, and without being visible on the global stage of ‘AI-start-ups to watch’, the development prospects of even the most innovative ventures are limited. A key policy implication for supporting the scaling up of AI start-ups by promoting the adoption of AI-powered solutions and stimulating venture capital financing promises good return on public investments.
Keywords: upgrading, start-ups, Hungary, artificial intelligence
Classification-JEL: M13, O33
Pages: 40-54
Volume: 2019
Issue: 4
Year: 2019
File-URL: http://www.vse.cz/cebr/download.php?jnl=cebr&pdf=219.pdf
File-URL: http://www.vse.cz/cebr/219
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Template-Type: ReDIF-Article 1.0
Author-Name: Marta Necadova
Title: Changes in Economic Sentiment Indicators Before and After Economic Crisis (Position of Visegrad Group and Germany in EU)
Abstract: Confidence factors play an important role not only in the assessment of business cycles but also in the evaluation of national competitiveness (e.g. the Global Competitiveness report published by the World Economic Forum) due to both using soft data obtained from opinion surveys. The subjective nature of confidence leads to questions about the soundness of such findings. Since the answers from opinion surveys are subjective, the empirical relationship between sentiment indicators and economic variables is not unambiguously established. In these conditions, the results of business surveys and the findings published by competitiveness rankings should not be accepted unconditionally. This paper starts with the comparison of soft data (respondents' sentiment) assessing the quality of institutions among world regions in the Global Competitiveness Report 2017-18. The aim of this paper is to offer a more precise view of the development of economic sentiment in the EU countries, especially in Germany and the Visegrad group countries (the V4). As tools for this description, a business sentiment indicator (ESI) and confidence indicators were applied. For analysis of changes in business sentiment and respondents' confidence, a graphical examination of variables, correlation analysis, changes in standard deviation, changes in countries' ranking, and the comparison of average sentiment (confidence) in the pre-crisis and the post-crisis period were used. Our analysis indicates the equivalent of the so-called halo effect in the pre-crisis period for the V4 (the positive expectation connected with the EU accession) and the deterioration in sentiment and confidence indicators in the post-crisis period.
Keywords: World Economic Forum, hallo effect, Global Competitiveness report, Executive Opinion Survey, economic sentiment indicator, confidence indicators, business sentiment, animal spirits
Classification-JEL: E21, E32, M20
Pages: 55-85
Volume: 2019
Issue: 4
Year: 2019
File-URL: http://www.vse.cz/cebr/download.php?jnl=cebr&pdf=220.pdf
File-URL: http://www.vse.cz/cebr/220
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Template-Type: ReDIF-Article 1.0
Author-Name: Miroslav Navratil
Author-Name: Andrea Kolkova
Title: Decomposition and Forecasting Time Series in Business Economy Using Prophet Forecasting Model
Abstract: There are many methods of forecasting, often based on the specific conditions of the given time series which are frequently the result of research in scientific centres and universities. Nevertheless, there are also models that were created by scientists in a particular company, examples may be Google or Facebook. The latter one has developed one of the latest Prophet forecasting models published in 2017 by Taylor & Letham. This model is completely new and so it is appropriate to subject it to further research, which is the topic of this article. To accomplish this research objective, the aim of this work is to identify seasonal trends in revenue development in a selected e-commerce segment based on the assessment of the applicability of the Facebook Prophet forecasting tool. To accomplish this goal, the Python Prophet is decomposed with a subsequent two-year forecast. Accuracy of this model is measured by RMSA and coverage. The e-commerce subject selected is active primarily in the field of sales of professional outdoor supplies and organizing outdoor educational courses, seminars and competitions. It is clear from the prediction that the e-commerce entity shows a strong sales period with the beginning of the spring season and then, due to the summer, decline, until the pre-Christmas period. The subject has little growth potential and a new impetus is needed to increase sales and thus restore the growth trend. It has been confirmed that Prophet is a suitable tool for debugging seasonal tendencies.
Keywords: RMSE, prophet, forecasting, decomposition, coverage
Classification-JEL: C53, M21
Pages: 26-39
Volume: 2019
Issue: 4
Year: 2019
File-URL: http://www.vse.cz/cebr/download.php?jnl=cebr&pdf=221.pdf
File-URL: http://www.vse.cz/cebr/221
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Handle: RePEc:prg:jnlcbr:v:2019:y:2019:i:4:id:221:p:26-39
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Template-Type: ReDIF-Article 1.0
Author-Name: Paweł Lula
Author-Name: Anna Kovaleva
Author-Name: Renata Oczkowska
Author-Name: Małgorzata Tyrańska
Author-Name: Sylwia Wiśniewska
Title: Bipartite Competency Schemas on Polish Labour Market
Abstract: The complexity and variability of the contemporary labour markets creates the need for continuous improvement of methods used for their description, analysis and forecasting. Looking for a tool that allows for the simultaneous analysis of various aspects of contemporary labour markets, the authors focused their attention on k-partite graph models (with particular emphasis on bipartite graphs). The assessment of the usefulness of models based on bipartite graphs for analysis of regularities occurring on the Polish labour market is the main aim of the paper. The authors studied the regional distribution of the demand for employee competencies and evaluated the specificity of localities and competencies. The concept of bipartite competency schemas is also introduced in the paper. These schemas can be used as models representing strongly related competencies and localities. The usefulness of bipartite competency schemas was confirmed by empirical research presented in the paper. The content of job offers published online formed the main source of data examined. All analyses were performed with the use of the R programming language.
Keywords: labour market, competency schema, bipartite
Classification-JEL: C55, J40, M54
Pages: 1-25
Volume: 2019
Issue: 4
Year: 2019
File-URL: http://www.vse.cz/cebr/download.php?jnl=cebr&pdf=222.pdf
File-URL: http://www.vse.cz/cebr/222
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Handle: RePEc:prg:jnlcbr:v:2019:y:2019:i:4:id:222:p:1-25
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