<abstract xmlns="http://www.w3.org/1999/xhtml">This study aims to explore the role of social networks in the internationalisation of startups. For this purpose, the social network LinkedIn is used, and two case studies of Portuguese technological startups are employed. The findings indicate that social networks can contribute to the acceleration of the internationalisation process and decrease their costs. Their relevance is greater in the initial phase of the internationalisation process. However, its relevance is limited in more advanced phases of this process. LinkedIn can be used by startups to obtain several benefits such as brand awareness, identification of new opportunities, customer feedback, among others. The results of this study are essentially useful in a practical dimension for companies that plan to start or improve their internationalisation process sustained on social networks.</abstract>

The Role of Social Networks in the Internationalisation of Startups: LinkedIn in Portuguese Context

<abstract xmlns="http://www.w3.org/1999/xhtml">The article explores the impact of some specific barriers to the integration of non-EU immigrants on the European Union labour market, measuring the influence of age, level of education and investments on the employment rate of non-EU immigrants. The study is based on a mixed approach, combining a statistically descriptive analysis of the 2008-2018 European labour market trends (in terms of the non-EU immigrants’ employment rate) with an econometric evaluation, aiming to measure the influence of investments (in terms of % of Gross fixed capital formation in Gross Domestic Product), age of asylum seekers (in terms of % of total asylum seekers) and level of education of non – EU immigrants (in terms of % of total non – EU immigrants). The analysis highlights the fact that the highest impact of non-EU immigrants on the employment rate is found in the case of non-EU immigrants with age between 18-34 and 35-64 years and with a tertiary level of education. For the other categories of non-EU immigrants, with ages outside the aforementioned range and with a lower level of education, the challenges are even much greater, which indicates the importance of specific educational integration policies, focusing mainly on continuous education and training.</abstract>

Integrating the non-EU immigrants into the EU labour market. An econometric analysis of some of the specific factors

<abstract xmlns="http://www.w3.org/1999/xhtml">The purpose of this paper is to analyze the impact of the Information and communication technology (ICT) on the competitiveness of the republics of former Yugoslavia (Serbia, North Macedonia, Montenegro, Bosnia and Herzegovina, Croatia and Slovenia) as tourism destinations. This paper relies on the correlation analysis and panel regression method. Regression analysis has examined the impact of the ICT on the competitiveness of the republics of Former Yugoslavia (Serbia, North Macedonia, Montenegro, Bosnia and Herzegovina, Croatia and Slovenia) as tourism destinations. The destination competitiveness is measured by international tourist arrivals and international tourism receipts, while the ICT is measured by the ICT Development Index (IDI). The results indicate that the IDI use has a significant impact on the number of international tourist arrivals and an indirect positive impact on the international tourism receipts. The originality of the research lies in the fact there are no previous studies about the impact of ICT on the competitiveness of the republics of former Yugoslavia as tourism destinations. This study contributes to a better understanding of the impact of ICT on the competitiveness of a tourism destination by linking the IDI with tourist arrivals and tourism revenues.</abstract>

Faculty of Hotel Management and Tourism in Vrnjačka Banja

ICT as a factor of destination competitiveness: The case of the republics of former Yugoslavia

<abstract xmlns="http://www.w3.org/1999/xhtml">Recent increase in peer-to-peer lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, Support Vector Machines, neural networks and decision tree-based models. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing F1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing F1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, debt-to-income ratio and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).</abstract>

Will they repay their debt? Identification of borrowers likely to be charged off

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