Purpose – the main goal of the current research was to provide a deep understanding of the concept of digital transformation, specifying its key elements/components/categories. Research methodology – an analysis of the textual information was performed, applying various techniques in the framework of the content analysis. Information database included 30 definitions of the term “digital transformation” and the related terms proposed by academicians and organizations. Findings – the analysis of the text segments of the investigated concept yielded three categories of digital transformation in business; 1) technologies, 2) processes and management, 3) people. Based on the research results, the conceptual model of digital transformation was created. Research limitations – the research was limited by the number of the analyzed definitions, as well as by the variety of methods applied. Practical implications – the research instrument for a survey among business sector representatives can be developed using the findings of the given study, in particular, for structuring the questionnaire’s blocks. Originality/Value – the current research provides a platform for further empirical investigation of the level of a company’s digital transformation

The paper aims to improve the methodology of measuring efficiency of Latvian banks. Efficiency scores were calculated with application of non-parametric frontier technique Data Envelopment Analysis (DEA). Input-oriented DEA model under Variable Returns to Scale (VRS) assumption was used. Potential model variables were selected based on the intermediation and profitability approach. Fourteen alternative models with different inputs-outputs combinations were developed for the research purposes. To substantiate the variables selection for DEA model the received data was processed, using such methods, as correlation analysis, linear regression analysis, analysis of mean values, and two-samples Kolmogorov-Smirnov test. The research results assisted the authors in providing general recommendations about the variables selection for DEA application in the Latvian banking sector. The present research contributes to the existing analytical data on bank performance in Latvia. The empirical findings provide a background for further studies, in particular, the efficiency of Latvian banks could be analysed in the extended time period.

The issues regarding measuring and managing bank performance are always on the agenda due to the critically important role of banks in the national economies of new member states of the European Union. The goal of the given study is to explore drivers of bank profitability in Latvia and Lithuania. Research period covers 2008–2014. Performance of the banking sector is proxied by profitability ratios. The set of explanatory factors involves financial and non-financial measures. The core research method is a multiple regression analysis. Data processing is performed in SPSS environment. The paper contributes to the scope of knowledge regarding bank performance drivers and the research results provide the basis for the future studies in the related field

The purpose of the study is to investigate the employees’ attitudes to CSR practices and reveal the differences in staff perceptions within different groups of responadents. The tasks to be accomplished are: (1) to investigate the differences of CSR perception between males and females; (2) to investigate the differences of CSR perception among representatives of different generations; (3) to investigate the differences of CSR perception among representatives of different sectors of the economy. The online survey was conducted using the authors’ developed research instrument (questionnaire). In total, 129 respondents—employed Latvian citizens—participated in the survey. To assess the influence of age, gender, and other control characteristics in the sample, the authors implemented regression-based moderation analysis in the SPSS environment. The analysis indicated the moderating role of gender in the perception of corporate social responsibility, which appeared to be strong, especially in terms of leveraging business honesty, as a predictor. The sector, too, appeared to be a moderator, though it was much less statistically significant than gender. On the other hand, generation appeared to be an independent and significant predictor of corporate social responsibility perception itself, while its moderating effect was insignificant. The findings in the paper support existing literature in terms of moderating the role of gender, as outlined previously; yet, this research estimated that males value corporate social responsibility higher than females, while our results estimate the opposite. Our research revealed that age (generation) is a significant factor, which defines a personal attitude towards corporate social responsibility, i.e., the older generation values CSR higher than the youngsters. We have not found any correlation between the economic sector and corporate social responsibility perception.

Artificial Intelligence (AI) tools have been used across various sectors of the global economy. The use of AI has been associated with both benefits and drawbacks, which is why the goal of this research was to identify the attitudes of academic staff of higher education institutions (HEIs) towards using AI for both academic and research purposes. To attain the goal, there was designed a questionnaire, which was distributed to members of academic staff of different biological genders and ages from 10 European countries. Irrespective of biological gender, age, or country, responses were similar. First, academic staff emphasized the importance of having AI-related regulations at HEIs. Second, academic staff were positive about using AI for information searchers and preparation of teaching materials. Third, academic staff were concerned about AI-related plagiarism issues, which is why they were reluctant to approve the AI use for research and thesis writing. Fourth, slightly more than 40% of the respondents indicated the use of AI. This points to the lack of AI skills among academic staff, which was further supported by a set of basic purposes for which AI was claimed to have been used. One implication of this research relates to the organization of the study process. Managers of HEIs should introduce institutionalized training in AI for academic and research purposes for academic staff to promote digital equality. Another implication of the study relates to the areas of AI training for academic staff. It should cover the topics of AI for the design of teaching materials, formative and summative assessment, and plagiarism check.

The present paper aims to make cross country analysis in banking sector. The relative efficiency of Latvian and Lithuanian banks is estimated, using non-parametric frontier technique Data Envelopment Analysis (DEA). Input-oriented DEA model under the assumption of variable returns to scale (VRS) is applied. The choice of variables is based on the intermediation and production approaches. Pre- and post-crisis periods of time are used to test the hypothesis about the relationship between bank size and efficiency scores. Besides, the yielded results are compared with traditional performance evaluation ratios, calculated for the whole banking sector of both countries for different periods. The research contributes to the existing analytical data on bank performance in new member states of the European Union