Search Results (133)

In everyday life, individuals frequently encounter situations in which they may join new groups; however, previous research has primarily focused on issues that arise after group formation, leaving insufficient attention to the processes preceding spontaneous group affiliation. This study investigates how different levels of group competence influence individuals’ willingness to join, using Optimal Distinctiveness Theory as its theoretical framework. Through two experiments, it systematically examines participants’ willingness to join groups of varying competence levels and how this willingness is moderated by participants’ own competence. The results indicate that high-competence groups demonstrate stronger member attractiveness and effectively promote individuals’ willingness to join. Crucially, the group’s attraction to individuals is moderated by individuals’ own competence. When group competence is lower than one’s own competence level, willingness to join increases as group competence rises toward one’s own level. Strikingly, when group competence exceeds one’s own, willingness to join remains uniformly high and stable, rather than decreasing as Optimal Distinctiveness Theory would predict. These findings suggest that individuals engage in a psychological trade-off based on competence alignment when autonomously choosing whether to join social or professional groups. We interpret this pattern as evidence for a “downward aversion, upward assimilation” heuristic in group affiliation decisions. The present research also has implications for understanding how groups can strategically manage their reputation to attract prospective members, as well as how individuals make group-joining decisions at different stages of career development. Full article

This paper revisits assimilation theory—developed to explain immigrant incorporation into U.S. society—and advances a reformulation centered on reciprocal assimilation. Classical models describe a linear convergence toward dominant Anglo-American norms, while segmented assimilation highlights multiple pathways shaped by context, race, and class. Both, however, tend to frame incorporation as a directional process in which minority groups adapt to dominant institutions. Drawing on contemporary autism scholarship, this paper brings assimilation theory into dialogue with neurodiversity to examine how its core assumptions extend beyond immigrant contexts. Using autism as a critical case, we show that social adaptation often occurs through camouflaging (masking, compensation, and behavioral adjustment), producing outward conformity without changing underlying neurological differences and often carrying psychological costs. These dynamics suggest that inclusion is frequently conditional on sustained performance of normative behavior rather than true structural incorporation. We identify an underlying assumption of universal assimilability within assimilation research and show how engaging with disability calls for a broader conception of incorporation. In response, we propose reciprocal assimilation as a framework in which adaptation emerges through dynamic interaction among individuals, institutions, and social structures. Integrating life-course concepts—turning points, cumulative (dis)advantage, agency, and social bonds—we illustrate how participation trajectories are shaped by accessibility, accommodations, stigma, and support over time. We conclude that a reciprocal model shifts emphasis from cultural convergence to meaningful participation, offering a more flexible framework for understanding incorporation across diverse populations, with implications for research, measurement, and policy. Full article

Small and Medium-sized Enterprises (SMEs) are central to the industrial fabric of Sub-Saharan Africa (SSA). Yet, they confront increasing demands to implement sustainability practices originating from institutional contexts markedly different from their own. Existing research has tended to neglect the cultural and institutional negotiations inherent in this process, often framing sustainability adoption as a technical or compliance-oriented exercise rather than as a multifaceted cultural adaptation. This study proposes and empirically examines the concept of sustainability acculturation—the process by which firms align global sustainability norms with local business cultures. Drawing on Institutional Theory, the Resource-Based View, and Berry’s Acculturation Model, we present a context-specific framework, tested using a sequential explanatory mixed-methods approach: survey data from 284 manufacturing SMEs across six SSA countries, followed by 24 semi-structured interviews. Structural equation modeling reveals that international market pressure and owner–manager values are direct drivers, whereas local regulatory pressure exhibits only a weak association with deep cultural integration. Managerial commitment and organizational learning mediate these relationships, while Ubuntu values enhance social sustainability integration, and institutional voids diminish regulatory effectiveness. The model accounts for 57% of the variance in sustainability acculturation. Findings show that SSA SMEs employ distinct acculturation strategies—Integration, Assimilation, Resilient Adaptation, and Decoupling—shaped by the interplay of external pressures, internal capabilities, and contextual conditions. The study underscores the importance of culturally attuned, context-specific interventions for sustainable industrial development in SSA. Full article

This article examines the early formation of Confucian–Islamic synthesis during the Yuan dynasty, arguing that institutional and intellectual adaptations in this period laid the groundwork for the later systematic synthesis known as “Yi-Ru Huitong” (伊儒會通). Moving beyond narratives of assimilation or resistance, it analyzes how Muslim communities navigated China’s pluralistic sociopolitical landscape through a process of creative adaptation. Employing a multidisciplinary approach that integrates textual analysis, historical comparison, and transcultural theory, the study investigates three key dimensions: the development of hybrid religious institutions, legal-political negotiations, and mechanisms of social integration. Drawing on multilingual sources—including Persian Islamic manuals, Yuan administrative archives, and epigraphic evidence—it demonstrates how Yuan-era Muslims established patterns of selective adaptation that preserved Islamic identity while enabling meaningful engagement with Chinese cultural norms. These developments not only ensured the survival of Islam in China but also generated a range of transcultural achievements in astronomy, medicine, architecture, and the literary arts, thereby creating the necessary conditions for the profound philosophical syntheses of the Ming-Qing era. By positioning the Yuan period as a crucial incubator of Sino-Islamic civilization, this study offers insights for comparative philosophy and the global history of civilizational dialog, inviting reflection on the early Chinese Islamic experience as a significant case of sustainable cross-civilizational engagement. Full article

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context. Full article

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies. Full article

Advancements in earth observation technologies are ushering in the big data era, yet this potential is compromised by intrinsic challenges: inherent uncertainty, spatiotemporal heterogeneity, multi-scale character, and pervasive data gaps. Traditional methods often fail to address these issues within a single, coherent system. The main contributions of this review are to systematically establish the Remote Sensing Geostatistical Paradigm (RSGP) as a comprehensive, unified framework. Powered by its core theory, Bayesian Maximum Entropy (BME), RSGP is a broadly designed epistemic framework that transcends a mere conceptual reorganization of established methods. It addresses the above challenges by highlighting two pivotal concepts within a spatiotemporal random field: (1) uncertainty quantification via probabilistic soft data, which redefines observations as probability density functions, representing a fundamental epistemological shift from deterministic scalars to probabilistic entities, and provides a universal interface for rigorous assimilation of heterogeneous remote sensing or in situ observations and synergy with other computational models, such as machine learning; and (2) spatiotemporal structure exploitation, which integrates the underlying structure embedded in remote sensing data of natural attributes, moving beyond mere optical properties to incorporate a broader range of available spatiotemporal information, for robust estimation and mapping purposes. Furthermore, the evolution of key technologies is illustrated by using real-world application cases, guiding how to implement RSGP in terms of different scenarios. Finally, the paradigm’s features and limitations are discussed. This synthesis provides the remote sensing community with a robust foundation for uncertainty-aware analysis and multi-source integration, bridging geostatistical logic with next-generation AI-driven Earth observation. Full article

The spatial distribution of immigrants and associated patterns of residential segregation and integration can manifest not only at the metropolitan scale but also at finer micro-spatial resolutions, reflecting the interaction between path dependence and structural reconfiguration. This article examines the micro-spatial residential patterns of Chinese immigrants in Seoul under institutional and market constraints. Using a Spatial Durbin Model and Multiscale Geographically Weighted Regression, it shows that from 2011 to 2025, immigrant settlements shifted from a monocentric pattern to a polycentric, functionally differentiated, and networked structure. While overall spatial embeddedness is high and segregation remains low, traditional cores such as Guro–Daerim persist. Selective clustering is shaped by path-dependent migrant networks, urban redevelopment policies, and intra-group differentiation, while infrastructure homogenization renders transportation accessibility a background condition. The findings support segmented assimilation theory in high-density East Asian cities and underscore the importance of incorporating immigrant needs into urban policy to promote inclusive integration. Full article

This paper investigates how Toronto’s Portuguese-Azorean community has shaped the city’s multicultural and psychological landscape, focusing particularly on intergenerational experiences of trauma among immigrant youth. Framed within North America’s broader migration dynamics, the study explores the creation and transformation of the ethnic enclave “Little Portugal” as both a space of cultural resilience and chronic urban stress. It introduces the concept of chronic urban trauma to describe the persistent psychosocial impact of displacement, assimilation pressures, and gentrification on young Portuguese-Azorean Canadians. While first-generation immigrants constructed cohesive ethnic infrastructures grounded in work, faith, and language, younger generations face cultural dissonance, linguistic loss, and identity fragmentation that manifest as emotional distress and social alienation. These experiences illustrate how structural urban change can perpetuate transgenerational trauma within immigrant families. By integrating perspectives from urban geography, trauma studies, and migration theory, this theoretical work underscores the need for trauma-informed educational and social policies that promote inclusion, belonging, and mental well-being among immigrant youth. Ultimately, the study positions “Little Portugal” as a microcosm of how multicultural cities negotiate the intersections of ethnicity, urban transformation, and psychological resilience. Full article

Liu Zhi’s (1664–1734) seminal work The Interpretation of the Five Pillars systematically employs Confucian doctrine to explicate the Five Pillars of Islam. As part of the Ming-Qing cultural movement of “interpreting Islamic scriptures through Confucianism,” Liu assimilated Neo-Confucian philosophical concepts to develop a Sinicized Islamic religious philosophy. Building upon this foundation, he analyzed the Five Pillars through three conceptual lenses: realm theory (境界论), cultivation theory (修养论), and praxis methodology (工夫论). By synthesizing the Confucian cultivation path of “exhausting the mind and knowing human nature” (尽心知性 jin xin zhi xing) with Islamic daily rituals, Liu Zhi developed a distinctive theory of mind-cultivation (心性论 xinxing lun) through ritual practice. This philosophical framework guided Chinese Muslims to transcend external ritual observance towards internal spiritual refinement, as encapsulated in the triad of “self-cultivation, mental purification, and fulfillment of human nature” (修身、清心、尽性). His synthesis of Islamic ritual with Confucian culture maintained fidelity to Islamic teachings while incorporating China’s profound Confucian heritage. Liu Zhi’s efforts in Islamic inculturation provide both a paradigmatic model for the cultural adaptation of religious rituals across traditions and a valuable reference for contemporary construction of Sinicized religious thought. His achievement, an exemplary exercise in interreligious theology, demonstrates how doctrinal fidelity and cultural localization can be harmoniously reconciled through philosophical innovation. Full article

The 1974 Supreme Court decision Lau v. Nichols established a legal and moral foundation for linguistic equity in American public education. However, the legacy of Lau is still up for debate fifty years later. Through the entwined histories of bilingual education, federal enforcement, and ideological shift, this paper re-examines the ruling. It charts the evolution of dual-language immersion models from transitional bilingual programs, showing how local politics and federal policy have alternately increased and limited linguistic rights. The paper makes the case that Lau’s original vision has been altered by cycles of progress and backlash, reflecting larger conflicts between assimilation and pluralism, rights and resources, equity and gentrification. It does this by drawing on theories of language ideology and raciolinguistics. This analysis shows that language justice in the US depends on institutional and civic commitment as well as legal precedent by placing Lau within the political economy of education reform. Full article

This study re-examines the ongoing debate over corporate disclosure frequency amid renewed calls to replace quarterly with semiannual reporting in U.S. markets. While traditional theories hold that frequent disclosure enhances informational efficiency by reducing asymmetry, emerging evidence highlights trade-offs involving managerial myopia, earnings management, and heightened short-term volatility. Using data from 2007 to 2024, the study compares Dow Jones Industrial Average firms, which report quarterly, with STOXX 50 firms, which report semiannually, to assess how disclosure cadence affects market reactions to earnings news The methodology involves identifying volatility regimes using Self-Exciting Threshold Autoregressive (SETAR) models, estimating dynamic betas with the GARCH(1,1) model, and analyzing shock transmission through vector autoregressions with cumulative impulse response functions (CIRFs). The results show that quarterly reporters exhibit larger immediate price reactions but faster normalization, implying that more frequent reporting accelerates information assimilation while amplifying contemporaneous volatility. Sectoral heterogeneity is pronounced: cyclical industries display higher beta volatility and steeper, but shorter-lived responses, whereas defensive stocks exhibit smoother convergence. These findings suggest that disclosure frequency influences both the intensity and duration of information shocks, providing insights for regulators who aim to balance transparency, market efficiency, and reporting costs across varying volatility and sectoral environments. Full article

Virtual Reality (VR), particularly when integrated with Building Information Modeling (BIM), is transforming architectural practice in developed economies. However, its adoption in developing countries remains limited due to infrastructural, economic, and organizational challenges. This study addresses this gap by empirically evaluating VR readiness among emerging architectural professionals in Oman through a novel integrated framework. This framework combines the Unified Theory of Acceptance and Use of Technology (UTAUT), which focuses on functional drivers like usefulness, with Presence Theory, which captures experiential drivers like immersion. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the survey data and assess VR readiness. The analysis revealed that prior VR exposure significantly predicts adoption intention, a relationship that is partially mediated by perceived usefulness. Organizational support emerged as a key moderator, effectively mitigating the impact of technical barriers on adoption decisions. The model explained the variance in adoption intention, highlighting that experiential familiarity, functional evaluation, and institutional support were critical for advancing digital transformation. The findings provide actionable insights for educational institutions, policymakers, and industry stakeholders aiming to prepare the next generation of architects in Oman and similar economies for VR adoption. By validating a dual-pathway adoption framework, this research contributes both theoretically and practically to understanding immersive technology assimilation in resource-constrained professional contexts. Full article

The assimilation–accommodation theory provides a crucial theoretical framework for understanding the neural mechanisms of second language (L2) processing. Chinese characters, as logographic scripts, contain diverse strokes and components with high visual complexity, and their grapheme–phoneme conversion differs fundamentally from alphabetic writing systems. Existing studies have identified unique neural patterns in Chinese language processing, yet a systematic synthesis of L2 Chinese processing remains limited. This review focuses on the brain mechanisms underlying Chinese language processing among L2 learners with diverse native language backgrounds. On the one hand, Chinese language processing relies on neural networks of the native language (assimilation); on the other hand, it recruits additional right-hemisphere regions to adapt to Chinese characters’ visuospatial complexity and grapheme–phoneme conversion strategies (accommodation). Accordingly, this review first synthesizes current brain imaging studies on L2 Chinese processing within this theoretical framework, noting that prevailing paradigms—limited to lexical and sentence-level processing—fail to capture the complexity, hierarchy, and dynamics of natural language. Next, this review examines the application and implications of naturalistic stimuli paradigms in neuroimaging research of L2 Chinese processing. Finally, future directions for this field are proposed. Collectively, these findings reveal neuroplasticity in processing complex ideographic scripts. Full article

Understanding the growth dynamics and ecological constraints of Porphyra spp. is essential for optimizing sustainable seaweed aquaculture. However, most existing models lack physiological detail and exhibit limited performance under variable environmental conditions. This study developed a mechanistic Dynamic Energy Budget (DEB) model to simulate structural biomass accumulation, carbon and nitrogen reserve dynamics, and blade area expansion of Porphyra under natural environmental conditions in Korean coastal waters. The model incorporates temperature, irradiance, and nutrient availability (NO₃⁻ and CO₂) as environmental drivers and was implemented using a forward difference numerical scheme. Field data from Beein Bay were used for model calibration and validation. Simulations showed good agreement with the observed biomass, reserve content, and blade area, with root-mean-square error (RMSE) typically within ±10%. Sensitivity analysis identified temperature-adjusted carbon assimilation and nitrogen uptake as the primary drivers of growth. The model was further used to estimate dynamic carrying capacity, revealing seasonal thresholds for sustainable biomass under current farming practices. Although limitations remain—such as the exclusion of reproductive allocation and tissue loss—the results demonstrate that DEB theory provides a robust framework for modeling Porphyra aquaculture. This approach supports scenario testing, spatial planning, and production forecasting, and it is adaptable for ecosystem-based management including integrated multi-trophic aquaculture (IMTA) and climate adaptation strategies. Full article