Global discourse on the humanities and social sciences (HSS) crisis has framed the problem primarily as aggregate resource deprivation. This article argues that this framing is insufficient: the HSS crisis is a structurally differentiated set of position-specific experiences generated by a common governance architecture. Drawing on seven focus group interviews with 29 participants across five structural positions in research-active sectors of Korean higher education—research institute directors, precarious instructors, faculty members, graduate students, and undergraduate students—the study used a multi-position qualitative design to trace how the same governance structures produce qualitatively distinct effects at each position. The analysis develops three concepts and two mechanisms, named and formally defined in the discussion on the basis of the empirical findings. The three concepts identify: the process by which a shared governance architecture produces incommensurable problem worlds at different structural locations; the configuration in which strong central steering operates through mutually incoherent instruments administered by independent agencies; and the resulting reform paradox in which broadly endorsed reforms cannot advance because no single governance pathway traverses the full chain of policy tools. The two mechanisms specify how the current crisis is sustained through the structural silencing of the actors most knowledgeable about the repercussions and anticipatory behavioral cascades that maintain dysfunctional architecture even when direct compliance pressures relax. The findings offer comparative higher education research a transferable framework for diagnosing why well-understood problems persist unresolved in systems where the knowledge required for reform is structurally separated from the authority to act on it.
Research on active learning environments increasingly examines how technology-rich learning spaces shape educational outcomes. However, evidence regarding Future Classroom Lab (FCL) settings, particularly their effects on pre-service teachers’ (PSTs) affective domains and mathematics achievement, remains limited. This study compared two FCL-based STEAM interventions that differed in technology integration and spatial and subject breadth to test effects on PSTs’ emotions, self-efficacy, attitudes, and achievement. Using a four-year quasi-experimental design, Group 1 (G1, n=105) employed fewer digital tools distributed across multiple FCL zones, whereas Group 2 (G2, n=97) used more technologies concentrated within fewer zones during mathematics sessions. Affective measures were administered before and after the intervention, and academic achievement was assessed through final examination scores and participation in non-compulsory assessments (NCA). Results showed that G1 demonstrated greater gains across all affective variables than G2 and obtained higher final examination scores. Participation in NCA predicted higher achievement regardless of group. These findings suggest that combining moderate technology and subject integration with broader use of FCL learning spaces supports PSTs’ affective development and academic performance than more technology-intensive, spatially restricted approaches. Design implications suggest using FCL learning spaces to support integrated STEAM activities that connect mathematics with other disciplines and real-world contexts, helping PSTs experience mathematics as creative, collaborative, and dynamic rather than procedural. Aligning FCL spatial design with purposeful, measured technology use appears more effective than intensive, zone-restricted configurations for promoting positive affect and consistent outcomes in mathematics teacher education, with implications for course design, policy, and scalable implementation.
This study examines brand-level EV sentiment in relation to country-of-origin positioning and expressed national-identity cues in YouTube comments. A BERT-based Turkish language model (BERTurk) was used to classify sentiment in 5,274 user-generated YouTube comments (2021–2025), transformed into a continuous confidence-weighted polarity score (−1 to +1). Expressed ethnocentric cues were identified using a validated domain-specific dictionary (Cohen’s κ = 0.88, inter-coder agreement). ANOVA, Welch’s t-tests, and OLS regression with interaction terms tested brand-level differences and moderation. Results indicate significant sentiment differences across brands: TOGG and Tesla receive moderately positive evaluations, whereas BYD is evaluated negatively. Hypothesis H2 (domestic advantage) received only partial support: TOGG differed significantly from BYD but not from Tesla, so brand-level differentiation is best described as driven primarily by BYD. The presence of expressed ethnocentric or national-identity cues is associated with a negative Origin × Cue interaction whose statistical magnitude is sensitive to the inference assumption (cluster-robust SE vs. more conservative video-level permutation test). Topic modeling further reveals identity-based themes for TOGG and technological-competence themes for foreign brands. Observed discourse patterns are consistent with country-of-origin positioning and expressed national-identity interpretations. Findings are framed as patterns in expressed brand-level sentiment rather than evidence of EV adoption or purchase behavior.
This study aimed to determine the factors influencing the acceptance of AI robo-advisory services for financial decision-making among bank fintech app users in an emerging economy such as Bangladesh. The study also explored the moderating effect of digital financial literacy. This study employed a modified version of the Use of Technology and User Acceptance Theory 2 (UTAUT2) model. A survey was conducted using Google Forms. A total of 309 fintech app users in Bangladesh from selected districts participated in the survey. The study utilized partial least square structural equation (PLS-SEM) modeling to examine the correlations between the variables using SMARTPLS software. Moreover, to complement the sufficiency logic assessed with PLS-SEM, Necessary Condition Analysis was applied to identify antecedents that function as non-compensatory prerequisites for actual use (AU) of robo-advisors. Combining NCA with PLS-SEM reveals that behavioral intention is both necessary and sufficient for AI robo-advisor adoption, while digital financial literacy, facilitating conditions, and hedonic motivation serve as critical thresholds for achieving higher-intensity usage, especially in emerging markets. Digital financial literacy also moderates the relationship between behavioral intention and the actual usage of AI robo-advisor for financial decision-making. The outcome offers a new viewpoint on how users perceive utilizing robo-advisors, which will benefit the technical, customer care, and financial service delivery channels. Additionally, by linking the use of robo-advisors in the banking industry, the study adds to the body of knowledge on the UTAUT2 paradigm.
Using data of 45,602 urban and rural households across 29 provinces from the 2017, 2019, and 2021 China Household Finance Survey (CHFS) and provincial-level lottery sales, this paper examines how lottery sales affect household investment in risky financial assets and explores the underlying mechanisms. Results from the Probit model show that increased regional lottery sales significantly raise both the likelihood of investing in risky financial assets and the proportion of such investments within household portfolios. Further analysis indicates that the household head’s risk preference mediates the relationship between lottery sales and risky financial investment. Additionally, subjective financial literacy enhances the positive impact of lottery sales on risky investments, whereas objective financial literacy has a dampening effect. The heterogeneity analysis shows that the positive effect of lottery sales is more pronounced among urban households, high-income households, and households in eastern China, with stronger effects on risky asset preference among highly educated households and on risky asset allocation among less-educated households. These findings illustrate how socio-cultural factors influence household financial behavior through psychological mechanisms and financial knowledge, providing new theoretical insights and empirical evidence regarding residents’ engagement in high-risk financial investments.
