Taking into consideration the complex interaction between new emerging technologies and social transformations, the importance of consumer attitudes toward fashion innovations should not be ignored. There are thousands of new patents related to nanotechnology being announced each year being undoubtedly perceived as one of the fundamental technologies of the present century. When it comes to the fashion sector, nanotechnology offers an innovative mean of processing fabrics that could change the clothing industry. Previous studies conducted in the domain of technology revealed that consumer attitude toward nanotechnology is determined by the perceived risks and benefits of applying nanotechnology and consumer’s scepticism when encountering new challenges. This research aims to analyse the determinants that affect the consumer’s knowledge toward nanotechnologies used in the fashion industry in Romania. In this respect, I applied a questionnaire in the Romanian public universities to identify individuals’ attitude toward technology and their knowledge regarding the usage of nanotechnology in this industry. The consumer’s level of knowledge regarding the nanotechnology implementation in fashion production is expected to be influenced by their attitude toward technology. Also, the consumer’s requirements regarding the labelling of nanotextile are expected to be influenced by their level of nanotechnology knowledge.
Based on the empirical results, this study is intended to provide suggestions that could contribute to the expansion of the acceptance of the innovations applied in the clothing industry.
The main aim of the paper is to investigate European Union people’s perceptions on the gender pay gap, concentrating on the differences between countries and social groups. The focus of our analysis is on the following research questions: a) Are people in countries with fewer low-wage earners more aware of the gender pay gap than the others? Are high educated people and older workers more affected by the gender pay inequalities? Descriptive statistics and logistic regression applied on 28093 observations extracted from EUROBAROMETER 87.4 (2017) confirmed the two hypotheses and brought in addition other interesting and somehow conflicting results. Namely, in some of the countries, people’ perceptions are not supported by statistical data. In Luxembourg and Belgium, for example, even though statistical data demonstrate that the gender pay gap is between the lowest among European Union countries the percentage of those who perceives the inequalities is very high, exceeding 70% in both countries. More than that, in certain countries like Romania, a large number of people considers that gender pay gap is an acceptable phenomenon. Our contribution to the literature is two-fold. First, we have analysed the perceptions on gender pay gap of a large number of respondents and correlated with the reality demonstrated by statistical data. Second, we drew the attention to the fact that closing the ‘gender pay gap’ should be a target of public and private policies especially in high-wage countries, whereas in low-wage countries, the policy makers should focus rather on closing the ‘countries pay gap’, i.e. ensuring that wages are brought closer to the European average, with many economic and social benefits (raising living standards of the citizens, reducing poverty and migration and so on).
Universities all around the world operate by following several institutional missions, with a central purpose on teaching and research activities. The importance of each aspect alongside the connection between them provide a disputed topic in the literature, many authors confirming or rejecting the intuitive inverse relationship by using various means, more or less quantitative. This paper aims to examine the teaching and research dimensions of the research-active European universities from a data mining perspective. For each dimension previously considered we employ the K-means Clustering in order to identify the groups of similar higher education institutions and we analyze the insights produced by the results. In addition, we build some target variables considering the teaching and research outputs and we investigate their drivers by employing the Logistic Regression. Furthermore, we explore the controverted relationship between the two institutional missions we considered through the use of Correspondence Analysis. Preliminary results illustrate that the dataset contains two types of universities: a category of very large and prestigious institutions and a second group of small and medium sized institutions, quite different from each other. Interest insights are given by the third part of the study, in which the Correspondence Analysis confirms an inverse relationship between teaching and research activities. Unfortunately, this is very likely a consequence of the time constraint – both activities require the same limited resources and therefore increasing the teaching burden for academics may diminish the time and energy dedicated to research.
The main purpose of this article is to determine the impact that Big 4 companies have had after the adoption of IFRS became mandatory and if the level of the fees related to the audit services registered a significant increase. Thus, after a thorough research of the specialized studies, we analyzed the impact of financial reporting according to the International Financial Reporting Standards, determining how the audit fees were influenced and which was the impact of the largest companies targeted in this study. In order to determine the number of companies audited by Big 4, we analyzed all the companies listed on Bucharest Stock Exchange, during the period 2010-2018, which trade premium shares. It is obvious that the financial reporting according to the International Financial Reporting Standards increases the number of companies audited by Big 4, although there has audit fees with higher values. In the following, we will analyze the changes that have occurred in the audit market of Romania.
Considering that the reliability of reserves valuation directly influences the financial strength of an insurance company, the main aim of this paper is to present a claims reserving estimation for a Romanian non-life insurer based on the most popular chain methods which are typically used in practice for the estimation of outstanding claims reserves in general insurance industry: Standard Chain Ladder and Munich Chain Ladder both on the claims incurred data and claims paid data. The tail development factors have been estimated based on the curve-fitting methods. The obvious advantage of these methods is represented by its simplicity of the practicality application. The results of the research under two chain claims reserving models reveal significant differences between the Standard Chain Ladder and Munich Chain Ladder with respect to the claims reserves level. Probably the Standard Chain Ladder based on paid method underestimates the outstanding loss liabilities and Standard Chain Ladder based on Incurred method overestimates the claims reserves. The claims reserves predictions under the Paid Munich Chain Ladder and Incurred Munich Chain Ladder are between the two Standard Chain Ladder outstanding loss liabilities estimates. The results of the tail extrapolation shown that the incorporation of the tail factors can have a significant impact on claims predictions.
The main purpose of this paper is to provide an objective analysis of the economic development level of countries. This is done by measuring it through a new index and by classifying the countries in an optimal number of clusters, each group characterizing different levels of economic development. The proposed methodology is based on three steps: creating a composite index (by applying the principal component analysis), establishing the optimal number of development groups (based on the number of principal components and on the hierarchical clustering) and clustering countries into them (with the help of k-means analysis). Therefore, this approach solves the difficulty of classifying the countries, complication that is mentioned in the specialized literature. Also, the paper creates a better understanding on the economic development level of countries, as, usually, the papers examine the economic growth level of countries. The analysis is conducted at the level of 60 countries for year 2015, using 12 indicators from categories that influence economic development (income, inequality, health, education and living conditions). The empirical results revealed that the countries can be grouped in two groups: economical developed countries (approximatively 2/3) and economic developing countries (approximatively 1/3). The countries that are most developed from an economic point of view are: Singapore, Luxemburg and Finland.
In this paper we evaluate comparatively the performance of non-banking financial institutions in Romania by the means of unsupervised neural networks in terms of Kohonen’ Self-Organizing Maps algorithm. We create a benchmarking model in the form of a two-dimensional map (a self-organizing map) that can be used to assess visually the performance of non-banking financial institutions based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. We use the following indicators: Equity ratio (Leverage) for the capital adequacy dimension, Loans granted to clients (net value) / total assets (net value) for the assets’ quality dimension and Return on assets (ROA) for the profitability dimension. We have excluded from our analysis the other three dimensions used in evaluating the performance of banks, due to lack of data (for the two qualitative dimensions: quality of ownership and management) and irrelevance with the NFIs’ sector (liquidity). The proposed model is based on the Self-Organising Map algorithm which creates a two-dimensional map (e.g. 6x4 = 24 neurons) from p-dimensional input data. The data were collected for eleven non-banking financial institutions for four years 2007-2010, in total 44 observations. Using the visualization capabilities of the Self-Organising Map model and the trajectories we show the movements of the three non-banking financial institutions with the worst performance: the largest underperformer denoted with X, the second largest underperformer denoted with Y and the third largest underperformer denoted with Z between 2007 and 2010.
Economic growth is one of the most studied topics in the literature in the field due to its significant role in the development of each country. Studies divide economic determinants into two categories based on their influence on economic growth: endogenous and exogenous. The study aims to estimate economic growth against two types of determinants for Romania and Central and Eastern European countries using data for 1995-2017 in order to compare the two cases. For Romania, we used time series specific methods (e.g. stationarity checking using Augmented Dickey-Fuller test, OLS model). In case of Central and Eastern European countries, we employed methods specific for panel data (e.g. estimation of the OLS general model, fixed effects model, random effects model, and feasible generalized least squares model). The results showed that in Romania, in the studied period, only the exogenous determinants (e.g. high technology exports) have a significant influence on economic growth, while Central and Eastern European countries were influenced by both types of determinants (e.g. life expectancy, foreign direct investments). In case of Romania, foreign direct investment did not represent a significant determinant for economic growth during 1995-2017 due to slower transition from communist regime to market economy.
The phenomenon of corruption is known all over the world, and its intensity varies according to economic, behavioral and educational factors. Transparency International is a global civil society that carries out regular opinion surveys and publishes the perceptions of corruption in countries around the world. This index identifies the level of corruption perceived in the world and contributes achieving a ranking of countries in this regard. The corruption perception index should be correlated with economic situation of a country. The economic situation of a country can be reflected by GDP and unemployment rate. The purpose of this study is to determine whether the index of corruption is influenced by the economic situation of a country, so the study analyses the corruption perception index, GDP and unemployment rate, establishing whether there is a link between them.