Social Change and Development

We used national survey data from 2,526 US households to validate a four-item water provider trust scale. We then explored correlates of this scale using regression models to assess (1) whether the relationship between household water insecurity and water provider trust held after adjusting for personal demographics, health perceptions, and politics; (2) the mediating effects of antiestablishment orientations and perceived stress; and (3) whether these mediating effects vary by race and ethnicity. The water provider trust scale demonstrated a good fit and satisfied standard tests of dimensionality, reliability, and validity. Higher water insecurity was associated with lower water provider trust (β = −0.111, SE = 0.013, P < 0.001) adjusting for covariates. Both antiestablishment orientations and perceived stress, when specified as mediators, yielded negative indirect effects on water provider trust and negative direct effects of water insecurity on water provider trust. These effects from antiestablishment orientations were only significant among White respondents, while the effects from perceived stress were significant across racial and ethnic groups. The water provider trust scale can help utilities assess public perceptions of their services and improve public engagement in water system resilience-building, particularly in underserved communities.

Abstract The aim of the book chapter is to examine how the metaverse has reshaped marketing practices and consumer trends. It brings to light how immersive technologies are rewriting consumer experiences, interactions, and purchasing habits. The chapter’s approach is to examine case studies and real examples of data-driven personalization, interactive consumer interaction, and virtual brand presence in the metaverse. It evaluates how emerging technology, virtual worlds, and augmented reality (AR) influence marketing strategies. Whereas the chapter’s findings reveal that by creating experiences that enhance engagement with brands, the metaverse facilitates a shift in consumer practice. New tactics appropriate for virtual environments are called for as mainstream marketing methods fade. In order to remain relevant, companies need to leverage virtual engagement, tailoring, and interactivity. This chapter also presents the TAM to advise on how the real-world metaverse is navigating the evolving digital landscape for professionals, companies, and researchers. It gives brands a roadmap for adaptation to the metaverse-led market, ensuring sustained customer engagement and competitiveness.

In contemporary engineering collaboration environments, teams face challenges such as inefficient communication, fragmented workflows, and limited integration of essential tools. Existing platforms often lack real-time semantic understanding, reducing efficiency and knowledge sharing. This paper proposes an intelligent collaborative system architecture that integrates a DistilBERT-based natural language processing framework within the Melon platform to enhance communication, task coordination, and information retrieval. The methodology involves comprehensive preprocessing of communication logs, task descriptions, and meeting transcripts, incorporating noise reduction and data balancing to improve model fairness. The DistilBERT model demonstrates superior performance, achieving a mean absolute error of 0.087 and a root mean square error of 0.112, outperforming the Autoencoder model with 0.124 and 0.154, and the Neural Collaborative Filtering model with 0.139 and 0.171, respectively. Classification evaluation further confirms its effectiveness, attaining 96.345% accuracy, 95.872% precision, 96.11% recall, and a 95.99% F1-score, significantly exceeding the performance of other models. These results demonstrate the model's robustness and reliability for dynamic engineering collaboration contexts. The proposed system enhances innovation, supports capacity building, and promotes inclusive digital collaboration through intelligent task detection, semantic document retrieval, and automated summarization. Overall, this integration contributes to the engineering design of advanced collaborative infrastructures, improving workflow efficiency, decision-making, and user experience in modern technical environments.

The COVID-19 pandemic caused unprecedented economic crises and supply chain disruptions, adversely affecting access to food. The existing studies in India are based on micro-surveys, and show affluent groups improving dietary intake, while poorer sections face food scarcity and reduced nutrient consumption. Using difference-in-differences, this study evaluates the impact of the COVID-19 pandemic on food consumption of women for seven types of foods—milk and curd, pulses and beans, dark green leafy vegetables, fruits, eggs, fish, and chicken and meat—from the nationally representative National Family Health Survey data (N = 1,213,973). Without granular data on pre-pandemic trends, the common trends assumption could not be tested. Instead, matching is used to ensure comparability between treatment and control groups. The results suggest that the pandemic caused an increase in the proportion of women eating nutrient-dense animal products such as eggs, fish, and chicken and meat infrequently by 2.4, 3.2, and 3.8 percentage points respectively (p < 0.001). This is more pronounced in the urban areas—where the prevalence of COVID-19 cases and the disruption of the food supply chain due to the pandemic were greater—than in the rural, and more among poor women. Probably as a compensatory act, consumption of relatively cheaper plant-based foods increased to an extent, as the proportion of women eating pulses and beans, and dark green leafy vegetables infrequently has reduced by 2.6 and 1.3 percentage points respectively (p < 0.001). Increase in consumption of plant-based foods is more pronounced in rural areas where they are more easily available from own agricultural produce or collection from kitchen gardens and common places. The reduction in consumption of animal products also caused a statistically significant reduction in dietary diversity, particularly among the urban poor women (2.6 percentage points; p < 0.01). Given the already low level of consumption of animal foods in India, which is vital for nutrient adequacy, the effect of the pandemic is worrisome. Strengthening of Government food support programmes through the expansion of India’s Public Distribution System and proper implementation of supplementary feeding schemes, such as the Mid Day Meal programme for school going children and Integrated Child Development Scheme for pregnant and lactating mothers and children, is recommended.

The proliferation of small hydropower plants has profoundly altered the hydrological environment, leading to significant habitat fragmentation. However, the specific impacts of varying dam heights on the hydrological habitats of the Scaly-sided Mergan

The rapid expansion of e-commerce platforms has resulted in a vast repository of mobile phone reviews, creating both opportunities and challenges for personalized recommendation systems. Current approaches often face difficulties in handling high-dimensional textual data, class imbalance, and the semantic ambiguity of customer reviews, leading to suboptimal product recommendations. Traditional collaborative filtering and content-based models struggle to effectively capture nuanced sentiment expressions and contextual dependencies within user feedback. To address these issues, this work presents a RoBERTa-Based mobile phone recommendation system that leverages fine-tuned deep contextual embeddings for robust sentiment classification and preference modeling. The framework employs balanced training data, cross-entropy loss optimization, and cosine similarity to compute user-product relevance, thereby overcoming challenges related to data sparsity and biased sentiment representation. Key features of this approach include the integration of advanced transformer-based language modeling for semantic understanding, a dual-layer filtering mechanism combining sentiment and product attributes, and a scalable architecture designed for high classification accuracy and efficient recommendation generation. Experimental evaluation using benchmark datasets demonstrates superior performance in terms of accuracy, precision, recall, F1-score, and RMSE compared to traditional methods. The proposed methodology ensures more reliable, context-aware recommendations that align closely with individual user preferences, effectively bridging the gap between large-scale review analysis and personalized mobile phone suggestions.