The main challenge for questionnaire designers is to create survey measurement instruments that capture the true responses from the population, minimizing the measurement errors. While there is expert knowledge and standard procedures, there is still not enough empirical evidence for the influence of all the different possibilities questionnaire designers have when creating a survey item. In this study we use the data from seven rounds of Multitrait-Multimethod (MTMM) experiments ran in the European Social Survey to shed light on the effect of response scale characteristics (number of response categories; presentation on visual aid, horizontal or vertical presentation; labels, fixed reference point; order of response categories) and characteristics of the request for an answer (formulation, type and use of gradation) on both the True Score reliability and True Score validity of survey questions. Our data set includes more than 3000 survey items, their question characteristics as well as their True Score reliability and True Score validity estimates from 22 MTMM experiments that have been fielded in 28 languages. Our analysis accounts for the hierarchical structure of the data where survey items are nested in experiments and languages. Applying regression methods to the experimental data set allows us to estimate causal effects of each question characteristic while controlling for the others.
This study develops an integrative methodological framework that combines Qualitative Network Analysis (QNA) and semiotic analysis into a semantic network approach for investigating the narrative practices of death doulas. Grounded in relational sociology and cognitive semiotics, this combined method is applied to in‑depth interviews with Russian death doulas to examine how dynamic relational processes and symbolic meaning‑making are embedded in end‑of‑life care discourse under conditions of institutional uncertainty. The analysis reconstructs how diverse individual representations of death can become relatively shared professional knowledge and normative practices, highlighting the agency of symbolic forms—metaphors, narratives, and cognitive invariants—as active components of social construction in this emerging field. By mapping semantic networks, the study illustrates the interplay between relational positions and discursive structures that contribute to the production and legitimation of images of “good dying.” Methodologically, the integration of QNA and semiotic analysis demonstrates how qualitative inquiry can bridge relational and semiotic analytics to address complex social phenomena where material, symbolic, and normative dimensions intersect, while also revealing important limitations related to interpretive selectivity and contextual specificity. Substantively, the findings contribute to research on professionalization, end‑of‑life care, and the sociology of death by showing how, in the case of Russian death doulas, discursive processes of narrative standardization participate in the gradual formation of professional repertoires rather than constituting a universally generalizable model.
This study empirically examines whether artificial intelligence, economic complexity, and clean energy productivity reduce ESG uncertainty and enhance progress toward Sustainable Development Goal (SDG) 7. The analysis focuses on the five largest economies—China, the United States, Japan, Germany, and India—over the period 2002–2023. The Bias-Corrected Method of Moments (BCMM) estimator is used for empirical estimation. The findings indicate that artificial intelligence, economic complexity, and clean energy productivity have positive and significant effects on SDG 7 progress, facilitating renewable energy optimization and efficiency gains. For ESG uncertainty, artificial intelligence and economic complexity tend to increase uncertainty through algorithmic opacity, ethical risks, and resource-intensive structures, while clean energy productivity significantly reduces it by promoting transparent and efficient sustainability practices. These results highlight the necessity of governance-aligned technological deployment and productivity-focused policies to harness renewable energy potential, stabilize sustainability frameworks, and support equitable energy transitions in leading economies.
Public comprehension of blockchain technology, especially concerning its societal implications, is a relatively underexplored area. This study seeks to fill this void by investigating how such technological advancements might influence consumer purchasing decisions. Specifically, it examines whether individuals are inclined to buy “social” craft beers (produced by social farms) that offer transparent details regarding their origin, social aspects (including the involvement of women who have experienced domestic violence in production), and processing techniques. To achieve these findings, a survey based on a modified Technology Acceptance Model within the Italian context was carried out. The results demonstrate that information on the origin of craft beer, its social impacts and processing methods should provide reciprocal advantages among Italian consumers and social farms. In this sense, blockchain would be a useful tool to generate knowledge both to support the economic inclusion of female victims of domestic violence, and to improve social farms contexts. In other words, blockchain technology should offer transparent information regarding the origin of beer, its social aspects (supporting women economic inclusion) and processing techniques, thereby providing the end consumer with a high-quality product.
This study examines the roles of FinTech development and green finance in inclusive green growth and whether government effectiveness mediates these relationships. The study uses fixed effects with Driscoll–Kraay standard errors, cross-sectionally augmented autoregressive distributed lag, augmented mean group, system GMM, quantile regression, and panel threshold models to address cross-sectional dependence, heterogeneity, persistence, endogeneity, and nonlinear effects. The measurements of inclusive green growth include the Inclusive Green Growth Index, which quantifies digital financial innovation, sustainable capital placement, and institutional capability. The results show that both FinTech and green finance have positive and significant effects on inclusive green growth in both static and dynamic specifications. Government effectiveness increases the direct effects of inclusive green growth and enhances the impact of both FinTech and green finance. The quantile and threshold results also indicate that these effects are stronger in countries with higher performance and governance above the thresholds. This study endorses a conditional leapfrogging process propelled by digital and green finance and competent institutions in developing countries.
