In this study we aim to reveal consumer confidence linkages among EU countries using hierarchical cluster analysis method. Identifying clusters in terms of consumer confidence structure, we strive to reveal countries with similar consumers' socio-economic behaviour patterns. Results show that consumer confidence structure similarities define clusters of EU countries, located in the same sub-region of Europe in cases of all Northern Europe countries and almost all Western Europe countries (excluding Netherlands and Luxembourg). We find that Southern and Central-Eastern EU countries don't have a single socio-economic behaviour pattern, but rather tend to distribute to various clusters in terms of consumer confidence structure. Keywords: consumer expectations, consumer confidence, determinants of consumer confidence, cluster analysis, economic behaviour. Straipsniu siekiama atskleisti ES valstybių tarpusavio ryšius vartotojų ekonominių lūkesčių srityje, taikant klasterinės analizės metodą. Nustatant šalių klasterius vartotojų ekonominių lūkesčių atžvilgiu siekiama atskleisti šalių grupes, kurių vartotojams būdinga panaši socioekonominė elgsena. Tyrimo rezultatai parodė, kad Šiaurės Europos ES šalių atveju ir beveik visų Vakarų Europos ES šalių atveju (išskyrus Nyderlandus ir Liuksemburgą), vartotojų ekonominių lūkesčių struktūros panašumai ES šalyse skatina klasterių formavimąsi pagal atitinkamą Europos geografinę šalių klasifikaciją. Rezultatai taip pat parodė, kad Pietų bei Vidurio ir Rytų ES valstybių ekonominių lūkesčių struktūros skirtumai lemia jų pasiskirstymą į skirtingus klasterius ekonominių lūkesčių struktūros atžvilgiu. Reikšminiai žodžiai: vartotojų ekonominiai lūkesčiai, vartotojų ekonominių lūkesčių veiksniai, klasterinė analizė, ekonominė elgsena. JEL Classifications: D84/D12/C38/D10.
When country's home market is quite small, companies develop exports in order to achieve greater sales volume and profit. Export is one of the ways to survive and develop business for companies of small open-economy countries. Such a tendency especially became evident during economic decline, when consumption in home market shrank. Recent Lithuania's economic growth is based on increasing exports, as well. On the other hand, estimation of trade credit risk factors is getting more and more important for exporting companies as the most popular settlement mode in the world is trade on an open account. Exporters, aiming to be the first in rivalry struggle and make a contract with a customer, have to propose the most beneficial conditions to the customer they can, i.e. to provide the customer with a trade credit. Exporter, when providing a trade credit for a foreign customer, takes a risk to lose financial resources. Risk of provided trade credit is evaluated on purpose to avoid the risk, i.e. the factors determining credit non-repayment and factors envisaging the risk are identified. One of the main factors determining trade credit risk is customer's insolvency, therefore designing a model to evaluate trade credits risk factors it is essential to analyse what predicts customer's ability to repay the trade credit given. Two approaches may be found in literature: evaluation is based on the analysis of financial indicators or on the analysis of both financial and non-financial indicators. Usually only financial indicators are used. In a traditional credit analysis mainly a company's accounting data is used aiming to assess if the company is able to generate cash enough to meet its liabilities. The research done shows, however, financial assessment is insufficient and does not give complete view about a company's business. In literature researches are found to be aimed at choosing such non-financial indicators that would be able to predict a company's insolvency, though a systematic approach to the use of certain indicators is scarce, especially which of them should be used to evaluate foreign customer's reliability in the case of export. Providing a foreign customer with the credit, an exporter incurs impact of the foreign country environment forces, as well. This is true because, when an exporter provides a trade credit, crediting relations bind subjects from two different countries and those subjects are both affected by different countries' forces. Country risk evidences for the exporter as the customer is another country; here country risk is understood as the manifestation of forces that affect customer only in the home business environment (customer's home country). These forces may circumvent the exporter from getting repaid or affect customer in such a way that the latter will become insolvent.
This article presents a method to evaluate determinants of consumer confidence and a research, based on suggested method, where determinants of consumer confidence in developed and developing European Union countries are examined. To set the main determinants of consumer confidence a time series ADL model is used. This research aims to contribute to research field of determinants of consumer confidence in EU developing countries: it reveals essential differences between determinants of consumer confidence in EU developed and developing countries, since there was no research on this issue so far.
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