This article introduces three distinct theories emerging from contemporary Chinese literary circles: the search for “global elements” proposed by Chen Sihe, Cao Shunqing’s variation theory, and Wang Ning’s concept of “global poetics.” Each of these theories creatively reimagines the relationship between Chinese literature and world literature, challenging the dominant image of a literary system that has been mostly shaped by Western aesthetics and concepts. In this context, world literature is conceptualized in the article as a kind of ideoscape. Although each of these three contributions pursues slightly different aims, they all hold the potential to expand our understanding of the global circulation of literary ideas.
As global migration continues to grow, its impact increasingly extends to Central and Eastern Europe, including Poland, a country that remained culturally homogeneous for decades after World War II. With this growing cultural pluralism comes the need for an adequate and reliable instrument to measure endorsement of multiculturalism. The present study addresses this gap by adapting and validating the Revised Multicultural Ideology Scale (MCI-r) for use in Poland and examining its psychometric properties. The study was conducted in 2023 in two waves, approximately three weeks apart, on a nationally representative sample of Polish adults (N = 2,132) stratified by age, gender, and municipality size. Results were most consistent with a six factor structure (with one item removed), consistent with the theoretical specification of the construct but differing from the four-factor solutions typically found in Western European and Canadian samples. Confirmatory factor analyses supported configural, metric, and scalar invariance across the two waves of the study and across gender, and the scale demonstrated high test–retest stability. Moreover, the MCI-r showed clear predictive validity: stronger endorsement of multiculturalism was associated with more favorable attitudes toward war refugees from Ukraine and with lower social and, to a much lesser extent, economic conservatism. Overall, the MCI-r emerged as a positive and, in fact, the strongest predictor of attitudes toward Ukrainian refugees, providing a robust tool for monitoring diversity ideologies in contemporary Polish society.
Digital transformation (DT) is increasingly shaping business strategies, a shift driven by the rapid development of information and communication technologies (ICT). This dynamic has intensified ICT job demands, such as ICT Workload, leading to increased digital transformation stress (DTS). Many studies also attribute digital stress to an aging workforce and their presumed low ICT skills. Therefore, to explore this issue, we conducted two studies based on the Job Demands-Resource model. A cross-sectional analysis (N = 165) first identified five specific ICT job demands as predictors of DTS. Remarkably, this study found that even employees with high ICT skills experience DTS, suggesting that DTS is a direct response to ICT demands rather than ICT skill levels. Building on these findings, a longitudinal study (NT1 = 1115, NT2 = 799, and NT3 = 512 participants) with employees aged 45 and above confirmed which ICT demands have the strongest and most sustained impact on DTS over time. In summary, our findings consistently reveal that ICT Workload, Hassles, and Poor Communication are the most significant and persistent predictors of DTS. This research provides a more nuanced understanding of DTS, highlighting the role of rising ICT demands of the workplace.
Pozostałe osiągnięcia naukoweArtykuły (zamknięty dostęp)Journal article
The partnership principle represents a cornerstone of European Cohesion Policy, requiring meaningful involvement of diverse stakeholders in program design, implementation, and monitoring. This case study demonstrates how generative AI transformed traditional document analysis in evaluation practice, moving from labor-intensive manual coding to sophisticated automated extraction and categorization. Using Google’s Gemini API, we systematically analyzed monitoring committee protocols and membership compositions across several European operational programs in Poland. The AI-powered workflow automated stakeholder extraction, attendance tracking, and analysis of substantive contributions during committee deliberations, processing approximately 200 protocols that would have required months of manual analysis.
Purpose
Although human-centric governance emphasizes subjective well-being, a structural gap persists between retrospective Quality of Life (QoL) measurement and anticipatory decision-making. This article addresses this gap by asking: how can validated QoL indicators and Large Language Model (LLM)-based simulations be systematically integrated within a coherent governance framework?
Design/methodology/approach
The study adopts a structured dual-track research design. Study 1 employs a quasi-experimental mixed-methods evaluation of a Norwegian-funded social innovation project using the “Impactometer,” grounded in OECD-aligned subjective well-being scales. Study 2 develops a proof-of-concept LLM-based simulation (Bielik model) applying the Ryff Psychological Well-Being Scale to synthetic professional personas in an organizational policy scenario. The two studies are analytically distinct but conceptually integrated within a governance cycle linking empirical grounding and anticipatory modeling.
Findings
Study 1 demonstrates an observed increase in life satisfaction (+0.8 points), accompanied by qualitative evidence of enhanced meaning, autonomy, and social connectedness. Study 2 shows that LLM-based simulation can generate differentiated and internally consistent well-being distributions across professional roles. However, simulation results establish plausibility rather than predictive validity and require empirical validation.
Originality/value
The article advances governance theory by proposing and empirically illustrating a hybrid model that bridges validated well-being measurement with structured AI-based simulation. This integration addresses the temporal mismatch in evidence-based policy and clarifies the methodological and ethical conditions under which AI can complement, rather than replace, human-centered governance.
