Digital competencies in selected European countries among university and high-school students: Programming is lagging behind

Draganac, Dragana; Jović, Danica; Novak, Ana Article Digital competencies in selected European countries among university and high-school students: Programming is lagging behind Business Systems Research (BSR) Provided in Cooperation with: IRENET - Society for Advancing Innovation and Research in Economy, Zagreb Suggested Citation: Draganac, Dragana; Jović, Danica; Novak, Ana (2022) : Digital competencies in selected European countries among university and high-school students: Programming is lagging behind, Business Systems Research (BSR), ISSN 1847-9375, Sciendo, Warsaw, Vol. 13, Iss. 2, pp. 135-154, https://doi.org/10.2478/bsrj-2022-0019 This Version is available at: https://hdl.handle.net/10419/318797 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 135 Business Systems Research | Vol. 13 No. 2 |2022 Digital Competencies in Selected European Countries among University and High-School Students: Programming is lagging behind Dragana Draganac, Danica Jović The University of Belgrade, Faculty of Economics and Business, Serbia Ana Novak The University of Zagreb, Faculty of Economics and Business, Croatia Abstract Background: Constant integration of digital technologies in economic and social life is rapidly and significantly shaping and changing our environment and ourselves. To function in such a world, even in daily routines, it is necessary to possess certain digital competencies. Objectives: This paper aims to examine how university and high-school students of economic orientations from selected European countries self-assess their digital competencies, and to analyse the identified differences. This will enable further understanding of university and high-school students’ digital competencies that can serve as guidance for improving teaching practices and curricula. Methods/Approach: A survey was conducted to collect data that were analysed using non-parametric statistic tests (Mann-Whitney U test and Kruskal-Wallis H test) and Spearman Rank-Order Correlation coefficient. Results: University and high-school students consider to have below intermediate level of digital competencies. Highschool students self-assessed digital competencies at a higher level than university students. University students of higher years of study self-assessed digital competencies at a higher level. There is no universal pattern among high-school students of different years of study. University students in the Accounting module and high-school students in the Tourism module assessed their digital competencies at the lowest level in several areas. There is a consistency in self-assessment of digital knowledge and digital skills. Conclusions: The identified below intermediate level of digital competencies and discovered discrepancies indicated the need for educational process improvements to provide university and high-school students with a higher degree of digital competencies. Programming is the most lagging behind in all the observed groups. Keywords: economic education; digitalisation; digital knowledge; digital skills; selfperception; programming JEL classification: A21, A22, C12, I20 Paper type: Research article Received: 07 Mar 2022 Accepted: 14 Aug 2022 Citation: Draganac, D., Jović, D., Novak, A. (2022), “Digital Competencies in Selected European Countries among University and High-School Students: Programming is lagging behind”, Business Systems Research, Vol. 13 No. 2, pp. 135-154. DOI: https://doi.org/10.2478/bsrj-2022-0019 136 Business Systems Research | Vol. 13 No. 2 |2022 Introduction The contemporary world is a digital world where it is crucial to demonstrate an appropriate level of digital knowledge and skills to increase the chances for professional and personal development. This statement is based on two indisputable facts. Firstly, information technologies are being broadly implemented in economic and social life. Secondly, information technologies are available to many of the world’s population. Due to the wide application of information technology for private and business purposes and in education, there is a growing emphasis on the importance of possessing digital competencies. Digital competencies are of the utmost importance for the progress of society in general, quality of education, employability, successful integration in the labour market and progress in the career. The intense globalisation and the emergence of the COVID-19 pandemic highlighted the necessity for further development of digital knowledge and skills. The relationship between digitalisation and education is two-way. On the one hand, education has to provide university and high-school students with an adequate level of digital competencies. On the other hand, the digitalisation fostered by the COVID-19 pandemic faced professors and teachers with the challenge of continuing high-quality teaching in new circumstances. The key motivation for our research was to investigate the relationship between digitalisation and education with the objectives to identify the current level of digital competencies among university and high-school students of economic orientation, find out the space for its improvement through innovations in the curricula and continuous advancement in teaching methods, and give a recommendation to education policymakers. We strive to answer the following research questions: o RQ1. What is the level of university and high-school students’ digital competencies by their perception, and are there significant differences across these two groups of respondents? o RQ2. Is there a relationship between the self-assessment of digital knowledge and digital skills among university students as well as among high-school students? o RQ3. Are there significant differences in the self-assessment of digital competencies between university students of different years of study and between high-school students of different years of study? o RQ4. Are there significant differences in the self-assessment of digital competencies between university students of different major areas and between high-school students of different major areas? In the following part of this introductory section, we present several concepts related to digitalisation and their meaning and specify which particular definitions we are using in the paper. The term digitisation can be assigned a wide range of meanings depending on the context in which it is used. In its initial meaning, the term denotes the process of converting information stored in the form of text, sound, or image into binary code, in which information is presented in a string of only two digits (zero or one). The process of digitalisation began in the period of production of the first computers, in the sixth decade of the last century. From then to nowadays, the term has been used in more and more different fields in which its meaning was constantly broadening. A range of new terms related to the digitalisation concept has emerged: digitalisation of business, digitalisation of governance, digitalisation of communications, digitalisation of education, digital knowledge, digital skills, digital literacy, and so on. 137 Business Systems Research | Vol. 13 No. 2 |2022 The terms competence, knowledge, skills, digital competence, digital knowledge and digital skills are defined in the existing literature in many different ways. According to the Council of the European Union (2017, p. 20) “competence means the proven ability to use knowledge, skills and personal, social and/or methodological abilities in work or study situations and professional and personal development”, while the term skill means “the ability to apply knowledge and use know-how to complete tasks and solve problems”. “In the context of the European Qualifications Framework for lifelong learning (EQF), skills are described as cognitive (involving the use of logical, intuitive and creative thinking) or practical (involving manual dexterity and the use of methods, materials, tools and instruments)” (Council of the European Union, 2017, p. 20). The European Parliament and the Council of the European Union (2006, p. 13) define competence as “a combination of knowledge, skills and attitudes appropriate to the context”. They recognise “eight key competencies that all individuals need for personal development, active citizenship, social inclusion and employment: 1. communication in the mother tongue, 2. communication in foreign languages, 3. mathematical competence and basic competencies in science and technology, 4. digital competence, 5. learning to learn, 6. social and civic competencies, 7. sense of initiative and entrepreneurship, and 8. cultural awareness and expression” (European Parliament and the Council of the European Union, 2006, p. 13). European Commission (2016, p. 2) considers that the term skills “refers broadly to what a person knows, understands and can do”. A basic definition of the concept of digital competence is that it is the ability to use information and communication technologies (ICTs). However, like the definition of the term competence in general, the meaning and scope of the concept of digital competence vary between authors (such as Ilomäki et al., 2011; Krumsvik, 2011, 2012; Käck et al., 2012). Digital competencies are “the confident, critical and creative use of ICTs to achieve goals related to work, employability, learning, leisure, inclusion and/or participation in society” (Ferrari, 2013, p. 2). Ferrari (2013, p. 2) states that “digital competence is a transversal key competence which, as such, enables us to acquire other key competencies (e.g. language, mathematics, learning to learn, cultural awareness)”. According to the European Parliament and the Council of the European Union (2006, p. 15), “digital competence involves the confident and critical use of information society technology for work, leisure, and communication, including basic skills in ICTs: the use of computers to retrieve, assess, store, produce, present and exchange information, and to communicate and participate in collaborative networks via the Internet”. In the European Digital Competence Framework for Citizens (DigComp), digital competence is grouped into five areas: “information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving” (Carretero et al., 2017, p. 11). Spante et al. (2018, p. 1), in a systematic review of higher education research for the period 1997 - 2017, found out there are a lot of definitions for the concept of digital competence depending on whether the concept is defined by policy, by researchers, or both, and whether it is focused on social practices or technical skills. Spante et al. (2018, p. 15) conclude that the perspective of the digital competence concept has been transformed from solely operational and technical-oriented to knowledge and cognitive-oriented. Krumsvik (2011, p. 40) argues that “it is not clear whether the underlying epistemology of digital competencies within education is steered by policymakers or by academics”. In the existing literature, digital competence is mainly used as a comprehensive term that includes both digital knowledge and digital skills. This paper is based on the data obtained in a questionnaire conducted for the project “Challenges and 138 Business Systems Research | Vol. 13 No. 2 |2022 practices of teaching economic disciplines in the era of digitalisation“ - DIGI4Teach. Particularly, the section “Self-assessment of digital competencies” was the focus of our analysis. All questions are divided into two categories: one refers to digital knowledge (proficiency) and the other to digital skills. Digital knowledge means that participants have some theoretical knowledge, while digital skill means they know how to apply their theoretical knowledge in practice. The most common verb used in the questions about digital knowledge is ‘know’, while the verbs ‘apply’, ‘perform’, and ‘conduct’ are most frequently used in part about digital skills. In the paper, we use these definitions of digital competence, digital knowledge and digital skills. The remainder of the paper is organised as follows. A literature review follows the introduction. The next section describes the methodology used in the data collection process and analysis of the results. After that, the results are presented and discussed. Concluding remarks are given in the last section. Literature review Numerous international studies indicate that a lot of people lack digital competencies despite the fact they need to be digitally competent for education, employment, and lifelong learning (Ferrari, 2013, p. 4). Almost half of the European Union population lacks basic digital skills (European Commission, 2016, p. 7). The COVID-19 pandemic highlighted the lack of digital skills in the labour market (European Commission, 2020, p. 3). Eurostat’s (2020) publication states a significant difference between the two age groups in having basic or above basic digital skills. Namely, in 2019 in the group aged between 16 and 74, 56% of individuals had basic or above basic digital skills, while for the group aged 16 to 24, this number was 80%. Araiza-Vazquez and Pedraza-Sanchez (2019) study revealed that university students perceive having high ICT competencies. The respondents were university students of business administration, accounting and international business, and the accounting students self-reported to have the highest ICT competencies. Martzoukou et al. (2020, p. 1413) conducted a study where university students from Scotland, Ireland and Greece with library and information science as major areas self-assessed their digital competencies. The authors concluded that students’ digital competencies were low in several areas: “development of information literacy, digital creation, digital research and digital identity management” (Martzoukou et al., 2020, p. 1413). Crawford-Visbal et al. (2020) adopted the European Commission’s Digital Competence Framework 2.0 (2017) to analyse the digital competencies of university students of communication in Argentina, Colombia, Peru, and Venezuela. Questionnaires, focus groups and semi-structured interviews were used to gather the data. The results showed that students have a high internet connectivity level but a low level of information literacy. Also, the study found that students overestimate their digital competencies, although they often do not meet minimum job market standards. The recommendation for education policymakers was to take action to improve students’ digital competencies and ICT skills. Colas-Bravo et al. (2017) concluded that non-university students in Spain selfperceive to have an average level of digital skills. The sample consisted of 50.3% of primary school and 49.7% of high-school students. Studies comparing employers’ expectations and potential employees’ selfperception of digital competence are very important. The study of Torres-Coronas (2015) identified the gap in the perception of digital competencies between university students and employers, which represents a discrepancy between education and labour market needs. The participants in a study by Sicilia et al. (2018) were university students, employers, and representatives of civic institutions from Spain, Poland, the 139 Business Systems Research | Vol. 13 No. 2 |2022 UK, Ireland and Belgium. The DigComp 2.1. framework was used as a reference for digital competencies to assess the relative importance of digital competencies and the possibilities and best approaches to acquire them. The study pointed out differences in self-perception of digital competencies across examined groups and a gap between the requirements of the labour market and the actual students’ competencies. Methodology As noted above, the research conducted in this paper is based on the data obtained through a questionnaire prepared by the project members from the University of Zagreb, Faculty of Economics & Business, as the project coordinator. The “Selfassessment of digital competencies” section of the questionnaire is based on the following sources: CARNet (2016), Ferrari (2013) and Ferrari et al. (2014). Following these authors, a three-point Likert scale was used, meaning that respondents could report having a basic, intermediate or advanced level of digital knowledge and skills. The project members who prepared the questionnaire defined respondents as students from universities of economics and high-school students of economics in the fields of Accounting, Finance, Trade and Tourism, coming from Croatia, Germany, Poland and Serbia. The survey participants filled out the questionnaire in November and December 2021 and January 2022. The sample consists of 2482 respondents, where 1679 are university students and 795 are high-school students. All years of studies were represented in the sample. The distribution of the respondents between countries, years of study and major areas are provided in Table 1. Table 1 Distribution of University and High-school students between Countries, Years of Study and Major Areas University students High-school students # % # % Country Croatia 656 39.07 642 80.75 Germany 29 1.73 16 2.01 Poland 699 41.63 0 0.00 Serbia 295 17.57 137 17.23 Total 1679 100.00 795 100.00 Year of Study 1st 411 24.48 124 15.60 2nd 455 27.10 182 22.89 3rd 449 26.74 298 37.48 4th 239 14.23 186 23.40 5th 125 7.44 5 0.63 Major Area Accounting 355 21.14 134 16.86 Finance 438 26.09 189 23.77 Trade 404 24.06 117 14.72 Tourism 162 9.65 220 27.67 Other 320 19.06 135 16.98 Total 1679 100.00 795 100.00 Source: Authors’ calculation 140 Business Systems Research | Vol. 13 No. 2 |2022 To investigate whether there are differences in self-assessment of digital competencies between the university and high-school students, we applied the Mann-Whitney U test for two independent samples. To answer whether there are differences in self-assessment of digital competencies between university/high-school students of different years of study and whether there are differences in selfassessment of digital competencies between university/high-school students of different major areas, we used the Kruskal-Wallis H test for five independent samples. We also applied post hoc analysis to identify where the differences came from. We calculated the Spearman Rank-Order Correlation coefficient to investigate if there is a correlation between the self-assessment of digital knowledge and the selfassessment of digital skills. Results Comparison between university and high-school students The average levels of self-assessed digital knowledge and skills for university and highschool students are presented in Table 2. Table 2 Average values of the self-assessed digital knowledge and skills for university and highschool students Digital Competence University students High-school students Knowledge Skills Knowledge Skills Browsing, searching and filtering data, information, and digital content 2.01 1.71 2.04 1.77 Data, information, and digital content management 1.85 1.93 1.88 1.76 Data, information, and content sharing via digital technologies 1.92 1.79 1.91 1.79 Interacting (collaboration) through digital technologies 1.86 1.86 1.84 1.81 Developing digital content 1.81 1.86 1.88 1.85 Programming 1.40 1.37 1.51 1.51 Protecting devices 1.70 1.58 1.76 1.58 Protecting personal data and privacy 1.78 1.72 1.86 1.77 Solving technical problems 1.58 1.59 1.61 1.65 Creative problem-solving by using digital technologies 1.57 1.53 1.66 1.64 Note: The level of digital knowledge was estimated with grades: 1-foundation level, 2- intermediate level, 3-advanced level Source: Authors’ work The average value of self-assessed digital knowledge for university students is 1.75, the minimum value is 1.40, and the maximum value is 2.01. Regarding digital skills for university students, the average value is 1.69, the minimum value is 1.37, and the maximum value is 1.93. In the sample of high-school students, the average value of self-assessed digital knowledge is 1.80, the minimum value is 1.51, and the maximum value is 2.04. High-school students self-assessed their digital skills at an average value of 1.71, with a minimum value of 1.51 and a maximum of 1.85. It can be concluded that, on average, both university and high-school students self-perceive to have below intermediate-level digital competencies. High-school students reported higher levels of digital competencies than university students, while both university and highschool students reported having a lower level of digital skills than digital knowledge. 141 Business Systems Research | Vol. 13 No. 2 |2022 Such results open space for the analysis of the digital adequacy of teaching methods. Professors, teachers and educational policymakers need to constantly modernise curricula and apply contemporary digital tools in teaching. In Table 3, standardised Mann-Whitney U test statistics and p-values are reported to examine differences in the self-assessment of digital competencies between students and high-school students. Table 3 Differences in the self-assessment of digital competencies between university and high-school students Question Knowledge Skills Z p-value Z p-value Browsing, searching and filtering data, information, and digital content -0.999 0.318 -1.983 0.047* Data, information, and digital content management 0.892 0.372 -5.102 <0.001* Data, information, and content sharing via digital technologies -0.273 0.785 -0.096 0.924 Interacting (collaboration) through digital technologies -0.523 0.601 -1.711 0.087 Developing digital content -2.627 0.009* -0.210 0.834 Programming -4.290 <0.001* -5.848 <0.001* Protecting devices -1.700 0.089 -0.261 0.794 Protecting personal data and privacy -2.035 0.042* -1.740 0.082 Solving technical problems -1.252 0.211 -1.955 0.051 Creative problem-solving by using digital technologies -3.575 <0.001* -4.367 <0.001* Note: The asterisk * indicates a 5% significance level. Z shows standardised Mann-Whitney U test statistics. Source: Authors’ work Based on the results shown in Table 3, we can conclude: university students selfreported lower digital knowledge than high-school students in Developing digital content, Programming, Protecting personal data and privacy, and Creative problemsolving by using digital technologies. Regarding digital skills, university students selfassessed them at a higher level than high-school students in the area of Data, information, and digital content management, while high-school students selfreported higher digital skills than university students in the areas of Browsing, searching and filtering data, information, and digital content, Programming, and Creative problem solving by using digital technologies. There are no statistically significant differences in digital knowledge between university and high-school students in the following areas: Browsing, searching and filtering data, information, and digital content, Data, information, and digital content management, Data, information, and content sharing via digital technologies, Interacting (collaboration) through digital technologies, Protecting devices, and Solving technical problems. There are no statistically significant differences in digital skills between university and high-school students in the following areas: Data, information, and content sharing via digital technologies, Interacting (collaboration) through digital technologies, Developing digital content, Protecting devices, Protecting personal data and privacy, and Solving technical problems. Accordingly, there are differences in self-assessment of digital knowledge between university and high-school students in four out of 10 areas, always in favour of highschool students. Concerning digital skills, there are differences in four out of 10 areas: 142 Business Systems Research | Vol. 13 No. 2 |2022 high-school students self-reported higher levels in three areas, while university students only in one. Such results may seem a little bit counterintuitive since it is expected that a higher level of education means a higher level of knowledge and skills. However, we analysed a special kind of knowledge and skills – digital ones. Possible factors of observed differences between analysed groups of respondents can be age differences: younger respondents started to be exposed to the digital world and content at an earlier stage of their life; they adapt to the digital world faster; highschool students have more free time than university students. The additional possible explanatory factor that has to be further examined may be higher self-confidence in high-school students compared to university students. Table 4 contains correlation coefficients and p-values calculated to investigate the association between digital knowledge and skills: the left panel contains results for university students, while the right one is for high-school students. Table 4 Correlation between digital knowledge and skills among university and high-school students Digital Competence University students High-school students r p-value r p-value Browsing, searching and filtering data, information, and digital content 0.456 < 0.001* 0.405 < 0.001* Data, information, and digital content management 0.462 < 0.001* 0.426 < 0.001* Data, information, and content sharing via digital technologies 0.539 < 0.001* 0.489 < 0.001* Interacting (collaboration) through digital technologies 0.563 < 0.001* 0.537 < 0.001* Developing digital content 0.562 < 0.001* 0.533 < 0.001* Programming 0.576 < 0.001* 0.542 < 0.001* Protecting devices 0.512 < 0.001* 0.532 < 0.001* Protecting personal data and privacy 0.521 < 0.001* 0.519 < 0.001* Solving technical problems 0.605 < 0.001* 0.485 < 0.001* Creative problem-solving by using digital technologies 0.621 < 0.001* 0.553 < 0.001* Note: The asterisk * indicates a 5% significance level. Spearman rank correlation coefficient (r) is reported. Source: Authors’ work There is a significant positive correlation in the self-assessment of digital competencies (digital knowledge and digital skills) for both groups of respondents (university and high-school students). Further, this means that there is consistency in the self-assessment of digital knowledge, on the one hand, and digital skills, on the other hand, in the same areas. Comparison according to the year of study of university students Tables 5 and 6 show the average values of the self-assessed digital knowledge and digital skills, respectively, for university students of different years of study. 149 Business Systems Research | Vol. 13 No. 2 |2022 Table 15 Average values of the digital skills of high-school students of different majors of study The average level of digital skills Accounting Finance Trade Tourism Other Browsing, searching and filtering data, information, and digital content 1.81 1.75 1.87 1.71 1.79 Data, information, and digital content management 1.76 1.78 1.85 1.65 1.80 Data, information, and content sharing via digital technologies 1.81 1.80 1.90 1.71 1.81 Interacting (collaboration) through digital technologies 1.81 1.78 1.89 1.72 1.93 Developing digital content 1.90 1.79 1.92 1.73 2.04 Programming 1.56 1.52 1.58 1.44 1.50 Protecting devices 1.60 1.62 1.73 1.46 1.59 Protecting personal data and privacy 1.81 1.80 1.84 1.63 1.88 Solving technical problems 1.62 1.66 1.74 1.59 1.71 Creative problem-solving by using digital technologies 1.65 1.65 1.67 1.54 1.74 Note: Level was estimated with grades: 1-foundation level, 2-intermediate, 3-advanced level Source: Authors’ work In Table 16, Kruskal-Wallis H test statistics and p-values for testing if there are differences in the self-assessment of digital competencies between high-school students of different major areas are reported. Table 16 Differences in the self-assessment of digital competencies between high-school students of different majors of study Digital competence Knowledge Skills KW p-value KW p-value Browsing, searching and filtering data, information, and digital content 3.400 0.493 5.395 0.249 Data, information, and digital content management 4.484 0.344 6.742 0.150 Data, information, and content sharing via digital technologies 15.888 0.003* 4.963 0.291 Interacting (collaboration) through digital technologies 8.709 0.069 9.324 0.054 Developing digital content 20.940 < 0.001* 16.696 0.002* Programming 17.400 0.002* 6.319 0.177 Protecting devices 5.747 0.219 14.033 0.007* Protecting personal data and privacy 13.514 0.009* 14.040 0.007* Solving technical problems 16.325 0.003* 5.857 0.210 Creative problem-solving by using digital technologies 12.371 0.015* 8.420 0.077 Note: The asterisk * indicates a 5% significance level. KW shows Kruskal-Wallis H test statistics. Source: Authors’ work High-school students of different majors differ in digital knowledge in the following areas: Data, information, and content sharing via digital technologies (between modules Tourism and Others), Developing digital content (between Tourism and Trade and between Tourism and Others), Programming (between Tourism and Trade), Protecting personal data and privacy (between Tourism and Trade, and between Tourism and Others), Solving technical problems (between Tourism and Trade), and 150 Business Systems Research | Vol. 13 No. 2 |2022 Creative problem solving by using digital technologies (between Tourism and Trade). In all cases, high-school students of Tourism as their major area self-reported a lower level of digital knowledge. Statistically significant differences in digital skills between high-school students of different main areas of interest exist in Developing digital content (between Tourism and Others, and between Finance and Others), Protecting devices (between Tourism and Trade), and Protecting personal data and privacy (between Tourism and Others). High-school students in the Tourism module have a lower level of digital skills than highschool students in all other modules. In contrast, in the mentioned pair Finance and Others, high-school students of modules grouped as Others have a higher level of digital skills. Conclusion In this paper, we investigated how university and high-school students in economics self-assess their digital competencies. We aimed to identify university and high-school students’ current levels of digital knowledge and skills and to propose ways to improve their digital competencies with the ultimate goal of facilitating the learning process and providing a smooth transition and inclusion of university and high-school students in the labour market. Additionally, our goal was to propose ways to improve teaching methods to ensure a high-quality teaching process despite the challenges caused by the COVID-19 pandemic. To this end, we analysed data on self-perception of digital competencies obtained through conducting a questionnaire. Our main findings can be summarised as follows: (1) university and high-school students self-assess their digital competencies at the below intermediate level; (2) high-school students’ digital knowledge self-assessment is higher than university students’ ones in four out of 10 analysed areas; (3) high-school students’ digital skills self-assessment is higher than university students’ ones in three out of 10 analysed areas, while the opposite is the case in one out of 10 analysed areas; (4) there is the accordance in the selfassessment of digital knowledge and skills for the same areas, but self-assessment of digital skills is lower than self-assessment of digital knowledge; (5) university students of higher years of study self-assessed digital knowledge at a higher level in six out of 10 areas, while in one out of 10 the direction is the opposite; (6) university students of higher years of study self-assessed digital skills at a higher level in eight out of 10 areas; (7) there is no universal pattern in self-assessment of digital competencies between high-school students of different years of study; and (8) university students of Accounting module and high-school students of Tourism module reported lowest levels of digital competencies . Like in Eurostat’s (2020) study, we discovered differences between the two age groups regarding digital competencies. However, our respondents are much younger (university and high-school students), and there is no large difference in age as in Eurostat’s (2020) research. Contrary to Araiza-Vazquez and Pedraza-Sanchez (2019), our results show that all university students self-report to have below intermediate level of digital competencies, while university students in the Accounting module perceive to have the lowest level of digital competencies. Like Martzoukou et al. (2020), we identified that university students self-reported to have a below-average level of digital competencies in most investigated areas. Contrary to the results of Colas-Bravo et al. (2017), we identified that high-school students self-perceive to have below intermediate level of digital competencies. The below intermediate level of digital competencies of both university and highschool students suggests that education policymakers must innovate teaching 151 Business Systems Research | Vol. 13 No. 2 |2022 methods and curricula by including new courses that will allow university and highschool students to improve their digital competencies. Education policymakers should pay special attention to the Accounting module at universities and the Tourism module at high schools of economics due to identified lowest levels of digital competencies for these groups of respondents. The monitoring of the digital aspects of the quality of teaching methods and the quality of teaching outcomes by educational policymakers must be continuous due to the fast-paced digital world. The reasons why high-school students self-assess their digital competencies at a higher level than university students could be that they belong to the younger cohort of Generation Z that began to be influenced by the fast-changing digital world in early childhood and that they have less intense school assignments and therefore more free time to explore digital contents. The explanation for higher levels of selfreported digital competencies among university students of higher years of study may be that higher-level courses are more specialised and applicative. More digital tools are used at those courses compared to theoretical courses in lower years of study. Our study contributes to the existing literature in several ways. First, the research conducted during the COVID-19 pandemic emphasised the necessity for digitalisation of the teaching process and the advantage of possessing digital competencies. Second, the study is international, with respondents from four countries. Third, the perceptions of university and high-school students of all years of study and different economic disciplines as majors are analysed. Additionally, digital knowledge and skills as components of digital competencies are analysed separately. There are more studies about the digital competencies of professors and teachers than about the digital competencies of university and high-school students. In this regard, our study is an important addition to the existing literature. The limitation of the research is that educational systems among analysed countries are not the same. The countries are at different levels of economic development with different abilities to buy access to digital content and tools. The results of our study opened space for further research directions. Additional groups of respondents need to be included: employers, university and high-school students from all fields of social, natural and technical sciences, and primary school students. The motivation for including employers is the importance of digital competencies for employability and success in the labour market. University and highschool students from all fields of social, natural and technical sciences have to be included since all people need to be digitally competent. Primary school students have to be examined since it is crucial to start acquiring digital competencies correctly in the early stages of education. Additionally, the factors that affected the identified differences in self-assessment of digital competencies, such as the overconfidence of younger respondents, need to be further investigated. Acknowledgements: This paper is a result of the project “Challenges and practices of teaching economic disciplines in era of digitalisation” – DIGI4Teach (2020-1-HR01- KA202-077771) co-funded by the European Union’s Erasmus+ program. 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She obtained her PhD in 2019 with the doctoral thesis “Rational Price Bubbles in Traditional and Behavioral Finance: An Experimental Study“. In the 2018-2019 academic year, Dragana was a visiting researcher at the Massachusetts Institute of Technology and Harvard University under the Fulbright Non-Degree Research Program for Doctoral Students. The main research fields are Corporate Finance, Behavioral Economics and Behavioral Finance, and Experimental Economics and Experimental Finance. At the undergraduate academic studies level, she teaches the following courses: Corporate Finance, Principles of Corporate Finance and Principles of Behavioral Finance. At the undergraduate international programme in cooperation with the University of London, LSE, she is engaged in a course called Principles of Corporate Finance. At the master’s academic studies level, she teaches courses in Corporate Financial Management and Behavioral Finance. In contrast, at international master programmes, she is involved in teaching Corporate Finance and Behavioral Economics and Finance. She is a member of the Society for Experimental Finance (SEF), Economic Science Association (ESA) and Fulbright & Friends Association. The author can be contacted at [email protected]. Danica Jović, PhD, graduated and obtained a magister’s and PhD degrees from the University of Belgrade, Faculty of Economics and Business. She is employed at the University of Belgrade, Faculty of Economics and Business, at the Department of Accounting and Corporate Finance. The main fields of interest and research are Accounting and Financial Reporting of Entities in the Public and Private Sectors, Consolidation of Financial Statements, and Financial Analysis. Courses taught: Financial Accounting, Public Sector Accounting (undergraduate studies); Consolidated Financial Statements: Advanced Issues (master academic studies); and Principles of Accounting and Core Management Concepts (LSE Business and Management program). She is a coordinator of the CEEPUS program for the University of Belgrade, Faculty of Economics and Business, certified accountant and member of the Association of Accountants and Auditors of Serbia, the Scientific Society of Economists of Serbia, and the Society of Economists of Belgrade. The author can be contacted at [email protected]. Ana Novak, PhD, is an Associate Professor of Accounting at the Faculty of Economics and Business, University of Zagreb, Croatia. She received PhD in Accounting from the Faculty of Economics and Business, University of Zagreb. She is the (co)author of several scientific and professional papers. She is a member of the Association of Accountants and Financial Experts and the Internal Audit Section (national). Her main research interests are accounting, accounting information systems, financial reporting, and accounting education. The author can be contacted at [email protected].