This study aims to characterize the digital skills and pedagogical practices using digital technology among primary teachers (first CEB), as well as to map their understanding and practices of promoting gender equality using digital technology. To this end, an online questionnaire survey was conducted among teachers, which yielded 3871 valid responses, representing 17.5% of the population. The sample structure is identical to that of the population, in terms of sociodemographic and territorial characteristics, as well as to the years of schooling covered. It was found that teachers perceive themselves as digitally competent to carry out the essential part of their teaching tasks. Most have already tried to carry out a diverse set of pedagogical practices and activities using digital technology. However, as the overwhelming majority did not find differences between the uses that boys and girls make of digital technology, they also do not act to promote digital skills differently among girls. Some contextual variables were also found that explain these differences in perception and practices regarding digital in education.

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop.

Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platforms, traveling blogs, forums, etc. Retrieving and analyzing the aforementioned content is not a straightforward task and in order to address this challenge, we have developed the StreetLines platform, a novel information system that is able to collect, analyze and produce insights from the available tourism-related data. Its highly modular architecture allows for the continuous monitoring of varying pools of heterogeneous data sources whose contents are subsequently stored, after preprocessing, in a data repository. Following that, the aforementioned data feed a number of independent and parallel processing modules that extract useful information for all individuals involved in the tourism domain, like place recommendation for visitors and sentiment analysis and keyword extraction reports for professionals in the tourism industry. The presented platform is an outcome of the StreetLines project and apart from the contributions of its individual components, its novelty lies in the holistic approach to knowledge extraction and tourism data mining.

ChatGPT has significantly increased in popularity in recent months because of its capacity to generate novel content and provide genuine responses to questions. Nevertheless, like all technologies, it is crucial to assess its limitations and features prior to implementing it into an educational setting. A major obstacle associated with ChatGPT is its tendency to produce consistent yet occasionally unreliable and inaccurate responses. Our study provides students with training in this area, and its objective was to analyse the opinion of those same university students studying education-related degrees regarding the efficacy of the usefulness of ChatGPT for their learning. We used a mixed methodology and two instruments for data collection: questionnaires and discussion groups. The sample comprised 150 university students pursuing degrees in teaching and social education. The results show that the majority of students are familiar with the technology but have not had any formal training in a university. They use this tool to complete academic assignments outside the classroom, and they emphasise the need for training in it. Furthermore, following the training, the students highlight an increase in motivation and a positive impact on the development of generic skills, such as information analysis, synthesis and management, problem solving, and learning how to learn. Ultimately, this study provides an opportunity to consider the implementation of educational training of this tool at the university level in order to ensure its appropriate use.

Data envelopment analysis (DEA), which is frequently used in efficiency analysis, has also been applied to the measurement of entrepreneurial efficiency for the attainment of desired values of macroeconomic indicators (such as the objectives of sustainable economic growth). For this application, DEA takes into account the economic, environmental, and social impact of entrepreneurship as the three dimensions of sustainability. This paper aimed to investigate the potential for a scalable (in diversity, equity, and inclusion dimensions) DEA application in sustainable entrepreneurship performance (SEP) assessment through three channels (assessing SEP without ESG activities; ESG→SEP; ESG (DEI)→SEP) and present an empirical study related to economic growth assessment and its environmental, social, and governance (ESG), and diversity, equity and inclusion (DEI) determinants across selected Western Balkans (WB) and European Union (EU) companies, based on the use of the proposed scalable DEA. It highlights how crucial a scalable nonparametric approach to macroeconomic efficiency analysis is and provides a more comprehensive perspective to the researchers on this issue. This study used a non-oriented DEA model with variable return-to-scale in a group of 60 WB and 60 EU companies, all of which adopted ICT/Blockchain (BC) technologies (the 11 ESG metrics). The annual corporate data was collected for seven years from 2017 until 2023. We projected the selected data to three country particularities (mass acceptance, adoption, and implementation of ICT/BC; mass labor force return from overseas; and ethnic, cultural, and religious particularities) and performed statistical analysis. Our findings estimate the influence of these three particularities on economic growth potential. In all countries’ cases, we found a statistically sound (significant, positive) correlation between ESG and SEP’s economic growth quality performance. Particularly, when corporate social and DEI initiatives mediate (channel III), SEP’s economic growth gains the best performance (+18%) in countries with ethnic, cultural, and religious particularities (BiH, NM), a +17% in countries enjoying massive labor force return from overseas (AL) and performs well in quality (particularly in the innovation and integrity) SEP performance success dimensions (all WB and EU countries). The proposed scalable DEA shows clearly, by performing an empirical analysis, which modern business (adopting ICT/BC) is the most effective in achieving sustainability projected to country particularities, helping corporate management to improve economic growth efficiency.