Research background: There is currently a need for empirical research regarding the validity of specific work environment model elements supported by strong statistical evidence. The amount of research conducted in this field has been particularly limited in Central-Eastern Europe. The desire to fill in these gaps was at least in part responsible for the uniqueness of the research approach and its differences from previous similar studies. Purpose of the article: The purpose of this study was to examine factors affecting employee engagement and to examine their relationship with each other using Visegrad countries as an example. Methods: The initial data is taken from the fourth European Company Survey (2019) for management respondents. After data cleaning, the sample sizes for Czechia, Hungary Poland, and Slovakia are N(CZ)=904, N(HU)=682, N(PL)=511, and N(SK)=361, respectively. As a result of the exploratory factor analysis (EFA), the following five dimensions were identified for this research: (1) inclusiveness, (2) empowerment, (3) work autonomy, (4) organizational learning environment, and (5) work engagement. An analysis of structural equation modeling (SEM) was conducted to determine the links that exist among these dimensions of a constructive work environment. Findings & value added: The structural model indicates that inclusiveness and empowerment have a significant positive impact on work engagement in all the countries examined. Inclusiveness was the strongest predictor of work engagement, followed by empowerment. Both of the two other predictors in the model (workplace autonomy and organizational learning environment) generally had less or no effect on employee engagement. The present study extends recent literature on work engagement by empirically validating the influence of workplace environment-related factors, as well as providing useful organizational policy recommendations for managers.

This study identified and examined significant factors affecting the risk tolerance of Hungarian travellers towards abroad trips. Data for the empirical research was derived from an online survey. The final questionnaire included 45 statements describing aspects of travel safety and travel preferences. There was a total of 496 responses, which enabled them to be analyzed. A structural equation modeling (SEM) approach was used to develop the model of risk tolerance assessment. As a result of the study, physical security, price sensitivity, mood-driven factor, and destination's image in the media may be considered as substitutes for determining travellers' risk tolerance. This research contributes to the body of knowledge by supporting the proposition that inner personality traits and image factors may influence tourism preferences, suggesting future studies should use a multivariate approach to explain and predict tourism choices. Tourism managers and private companies operating in this sector can find the results of this study very helpful in planning and promoting tourism to risky destinations.

Generative AI has made the production of fluent drafts, summaries, and recommendations inexpensive and rapid, shifting the bottleneck of performance from producing outputs to judging whether outputs are good enough to use. This shift challenges prevailing accounts of AI literacy in human resource development (HRD) that emphasise tool proficiency and broad critical awareness while leaving the evaluative core under-specified. Drawing on an integrative literature review, this article reframes AI literacy as culturally mediated quality judgement: the capacity to interpret, appraise, justify, and responsibly act on AI-assisted outputs as warranted claims relative to domain standards and situational demands. The article makes three contributions: it (1) specifies four domains of quality judgement – relevance, warrant, applicability, and responsibility; (2) identifies the sociocultural conditions through which judgement is enacted and developed at work; and (3) outlines HRD design principles and testable propositions for cultivating calibrated reliance on AI assistance.