DOI 10.12700/THTO.1.01.2017.1.1 COMPARING RISK DEFINITIONS GIVEN BY HUNGARIAN AND BELGIAN BACHELOR STUDENTS THINKING TOGETHER 1 COMPARING RISK DEFINITIONS GIVEN BY HUNGARIAN AND BELGIAN BACHELOR STUDENTS Anita Kolnhofer-Derecskei and Viktor Nagy Abstract: Students can detect the changes of newdays and easily adapt to new challenges. The aim of this paper is to observe and test the Domain-Specific Risk Taking Scale on Hungarian and Belgian Bachelor Students. This survey contains different risk attitudes depending on making decision involving Ethical, Financial, Health or Safety, Recreational, and Social risks. According to the DOSPERT Scale we are trying to find differences between ‘Risk-Taking’, ‘Risk-Perceptions’, and ‘Expected Benefits’. At the same time, we are trying to measure how university students define risk. Therefore, three definitions were explored with content analysis technique, which helped to highlight and organise the most important attitudes. Furthermore, our results indicate how we can use this validated psychometric scale for our population in the future. Keywords: Risk, DOSPERT Scale, Survey Introduction Reviewing the literature for collecting different approaches on risk, Vasvári (2015) is found to be one of the authors who summarized the different meanings of risk in the most satisfying way from our point of view, i.e. using psychological, economic, sociological and technical approaches. In the field of economics, risk management focuses on risks (not surprisingly) where probabilities play special roles. The terms risk and uncertainty are usually used as synonyms in everyday life. For those who do not deal with decision theory this is understandable. It is not only scientific research where the meanings must be clearly differentiated but also among students in the field of management or business. The complete decision theory system first consisted of three kinds of decisions regarding knowledge outcomes (Luce and Raiffa, 1957):  decision under certainty;  decision under risk;  decision under uncertainty. We talk about certainty when we are fully informed, have accurate data and knowledge of the outcome for each option. For each alternative to be chosen there is only one possible outcome and there is a sure cause-and-effect relationship. In that case there definitely is an optimal decision but it is supposed that we are able to compute with perfect accuracy in a fully rational way. Here methods of operational research such as linear programming and dynamic programming are to be applied. We talk about uncertainty when several outcomes for each option can be identified but there is no knowledge at all of the probability to be assigned to each. In that case some criteria are available to help to choose an alternative.