The glycosidically bound volatiles were isolated from dried plant material by percolation with ethyl acetate and by extraction with water during hydrodistillation of essential oil. Fifteen volatile aglycones were identified by gas chromatography‐mass spectrometry (GC‐MS) on two columns with different polarity of the stationary phases. The main aglycones were: thymoquinone, benzyl alcohol, thymol, 2‐phenylethanol, carvacrol and 1‐octen‐3‐ol. The content of aglycones was 19 and 21 mg kg−1 with respect to the method of isolation. The chemical composition of aglycones was compared with the chemical composition of essential oil, which consisted mainly of thymol and carvacrol. After the hydrolysis of glycosides, D‐(+)‐glucose and unknown disaccharide were identified. The hydrolysis of disaccharide, D‐(+)‐glucose and D‐(+)‐galactose were detected by TLC and GC‐MS as trimethylsilylethers. Copyright © 2000 John Wiley & Sons, Ltd.

Selecting human resources represents complex tasks and ultimately determines an organisation's future stability and development. High-quality selection procedures serve as the basis for proper employee utilisation and individual career development, which generate organisational success. To be most effective, in addition to applicant competency in the field, decision makers use various selection techniques and tools. These are applied to reveal and distinguish applicants' characteristics, qualifications and competencies, as well as to predict their success and organisational contribution specified for each profession and position. The main aim of this paper is to develop a fuzzy multicriteria model based on the technique for order preference by similarity to ideal solution (TOPSIS) as support for decision making when teaching and research staff are selected for employment within higher education. The model is based on hierarchically structured selection criteria fortified by the different qualitative and quantitative nature of each criterion, as well as the influence of required experts' competencies. We apply this model in the higher education sector for selecting teaching and research staff in Croatia.

Purpose Crypto-asset can be traded on many different exchanges worldwide with servers located in countries with different financial characteristics and institutional surroundings. Trading volume on these servers varies considerably regarding the server’s location, even though the prices do not differ greatly. Crypto-asset markets are poorly regulated and, as such, may leave a place for potential fraudulent activities and be linked to corruption. This paper aims to examine the role of country’s institutions in attracting Bitcoin traders. Design/methodology/approach Assuming heterogeneity between countries where crypto-asset exchange servers are located, the Pool Mean Group Estimator is used. Findings Results indicate that, from institutional variables, corruption in the country attracts while internal and external conflicts repel investors. Additionally, the growth of global uncertainty and the decline in the local stock markets motivate investors to trade Bitcoin. Originality/value Previous research has empirically proved the importance of institutions’ quality for financial market development. This paper goes one step further and tries to empirically confirm the theoretical assumptions and investigate in detail the role of institutions in choosing servers in a particular country for Bitcoin trading.

This paper aims to examine the behavioural determinants of Bitcoin trading volume within a cross-country framework of 14 world economies plus the Eurozone. We introduce a basic taxonomy of behavioural indicators, distinguishing between consumer confidence, economic policy uncertainty (EPU), and indicators of financial volatility. Our estimations reveal that the Bitcoin trading volume can be predicted more accurately by EPU than by any other class of indicators. Finally, we identify the COVID-19 shock as a catalyst for a psychologically-driven Bitcoin market and find evidence that Bitcoin was a macro hedging instrument in the pandemic. To obtain our results, we conducted a panel Granger causality test, employing the Least Squares Dummy Variables (LSDV) estimator. Contrary to previous research, we found that market fundamentals (industrial production and equity market volume) became significant drivers of Bitcoin trading during the pandemic. This conclusion was preserved when we used the LSDV corrected estimator, which is more suitable for panels with a smaller time dimension. Apart from the practical implications for traders, this paper provides researchers with detailed steps for applying Granger causality testing in panel data settings.

Appropriate securities selection is an important step in formation of an investment portfolio. The expected utility-entropy (EU-E) decision-making model is one of the models that can be applied to investment portfolio stock selection. The decision-maker subjective preference is reflected by the expected utility, and the objective uncertainty is measured using Shannon entropy. In this model, the measure of risky action is the weighted linear average of expected utility and entropy using a risk tradeoff factor. This chapter tests whether tradeoff coefficient depends on capital market development. With this aim, EU-E model was applied on European Union (EU) capital markets with different development according to FTSE equity country classification. It tests whether the EU-E model applied to the three different capital markets gives the best stock selection results for the same tradeoff coefficient values, or whether tradeoff coefficient depends on capital market development.