Search Results (727)

This study evaluates the effectiveness of the B Impact Assessment (BIA) as a catalyst for integrating sustainability into industrial firms through a qualitative case study of LC Paper, the first B Corp-certified tissue manufacturer globally and a pioneer in applying BIA in the pulp and paper sector. Based on semi-structured interviews, organizational documents, and direct observation, this study examines how BIA influences corporate governance, environmental practices, and stakeholder engagement. The findings show that BIA fosters structured goal setting and the implementation of measurable actions aligned with environmental stewardship, social responsibility, and economic resilience. Tangible outcomes include improved stakeholder trust, internal transparency, and employee development, while implementation challenges such as resource allocation and procedural complexity are also reported. Although the single-case design limits generalizability, this study identifies mechanisms transferable to other firms, particularly those in environmentally intensive sectors. The case studied also illustrates how leadership commitment, participatory governance, and data-driven tools facilitate the operationalization of sustainability. By integrating stakeholder and institutional theory, this study contributes conceptually to understanding certification frameworks as tools for embedding sustainability. This research offers both theoretical and practical insights into how firms can align strategy and impact, expanding the application of BIA beyond early adopters and into traditional industrial contexts. Full article

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. Full article

Background: Environmental enrichment is a promising strategy to improve the welfare of dairy goats and sheep. However, studies in this field remain scattered, and its effects on productivity are unclear. Objectives: To evaluate the effects of environmental enrichment on behavioral, physiological, and productive parameters in dairy goats and sheep. Data sources: Scopus and Web of Science were searched for studies published from 2010 to 2025. Study eligibility criteria: Experimental or observational peer-reviewed studies comparing enriched vs. non-enriched housing in dairy goats or sheep, reporting on welfare or productivity outcomes. Methods: This review followed PRISMA 2020 guidelines and the PICO framework. Two independent reviewers screened and extracted data. Risk of bias was assessed with the SYRCLE tool. Results: Thirteen studies were included, mostly with goats. Physical, sensory, and social enrichments showed benefits for behavior (e.g., activity, fewer stereotypies) and stress physiology. However, results varied by social rank, enrichment type, and physiological stage. Only three studies assessed productive parameters (weight gain in kids/lambs); none evaluated milk yield or quality. Limitations: Most studies had small samples and short durations. No meta-analysis was conducted due to heterogeneity. Conclusions: Environmental enrichment can benefit the welfare of dairy goats and sheep. However, evidence on productivity is scarce. Long-term studies are needed to evaluate its cost-effectiveness and potential impacts on milk yield and reproductive performance. Full article

Systematic reviews are a cornerstone of evidence-based research, providing comprehensive summaries of existing studies to answer specific research questions. This article offers a detailed guide to conducting high-quality systematic reviews in biology, health and social sciences. It outlines key steps, including developing and registering a protocol, designing comprehensive search strategies, and selecting studies through a screening process. The article emphasizes the importance of accurate data extraction and the use of validated tools to assess the risk of bias across different study designs. Both meta-analysis (quantitative approach) and narrative synthesis (qualitative approach) are discussed in detail. The guide also highlights the use of frameworks, such as GRADE, to assess the certainty of evidence and provides recommendations for clear and transparent reporting in line with the PRISMA 2020 guidelines. This paper aims to adapt and translate evidence-based review principles, commonly applied in clinical research, into the context of biological sciences. By highlighting domain-specific methodologies, challenges, and resources, we provide tailored guidance for researchers in ecology, molecular biology, evolutionary biology, and related fields in order to conduct transparent and reproducible evidence syntheses. Full article

This study explores the challenges and theoretical transformations that the widespread application of AI technology in social governance brings to the protection of citizens’ fundamental rights. By examining typical cases in judicial assistance, technology-enabled law enforcement, and welfare supervision, it explains how AI characteristics such as algorithmic opacity, data bias, and automated decision-making affect fundamental rights including due process, equal protection, and privacy. The article traces the historical evolution of privacy theory from physical space protection to informational self-determination and further to modern data rights, pointing out the inadequacy of traditional rights-protection paradigms in addressing the characteristics of AI technology. Through analyzing AI-governance models in the European Union, the United States, Northeast Asia, and international organizations, it demonstrates diverse governance approaches ranging from systematic risk regulation to decentralized industry regulation. With a special focus on China, the article analyzes the special challenges faced in AI governance and proposes specific recommendations for improving AI-governance paths. The article argues that only within the track of the rule of law, through continuous theoretical innovation, institutional construction, and international cooperation, can AI technology development be ensured to serve human dignity, freedom, and fair justice. Full article

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation. Full article

This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and 5-fold cross-validation for robust model training. A comprehensive fairness analysis is conducted, considering sensitive attributes such as gender, school type, and socioeconomic factors, including parental education (Medu and Fedu), cohabitation status (Pstatus), and family size (famsize). Using the AIF360 library, we compute the demographic parity difference (DP) and Equalized Odds Difference (EO) to assess model biases across diverse subgroups. Our results demonstrate that XGBoost achieves high predictive performance (accuracy: 0.789; F1 score: 0.803) while maintaining low bias for socioeconomic attributes, offering a balanced approach to fairness and performance. A sensitivity analysis of bias mitigation strategies further enhances the study, advancing equitable artificial intelligence in education by incorporating socially relevant factors. Full article

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration. Full article

Background/Objectives: Traditional dietary screeners face significant limitations: they rely on subjective self-reporting, average intake estimates, and are influenced by a participant’s awareness of being observed—each of which can distort results. These factors reduce both accuracy and reproducibility. The Guide Against Age-Related Disease (GARD) addresses these issues by applying Assembly Theory to objectively quantify food and food behavior (FFB) complexity. This study aims to validate the GARD as a structured, bias-resistant tool for dietary assessment in clinical and research settings. Methods: The GARD survey was administered in an internal medicine clinic within a suburban hospital system in the southeastern U.S. The tool assessed six daily eating windows, scoring high-complexity FFBs (e.g., fresh plants, social eating, fasting) as +1 and low-complexity FFBs (e.g., ultra-processed foods, refined ingredients, distracted eating) as –1. To minimize bias, patients were unaware of scoring criteria and reported only what they ate the previous day, avoiding broad averages. A computer algorithm then scored responses based on complexity, independent of dietary guidelines. Internal (face, convergent, and discriminant) validity was assessed using Spearman rho correlations. Results: Face validation showed high inter-rater agreement using predefined Assembly Index (A_i) and Copy Number (N_i) thresholds. Positive correlations were found between high-complexity diets and behaviors (rho = 0.533–0.565, p < 0.001), while opposing constructs showed moderate negative correlations (rho = –0.363 to −0.425, p < 0.05). GARD scores aligned with established diet patterns: Mediterranean diets averaged +22; Standard American Diet averaged −10. Full article

Background/Objectives: Sunk cost bias—continuing failing endeavours due to prior investments—is among the most studied decision-making biases. Despite decades of vignette-based research, these measures lack systematic psychometric validation. We examined whether widely-used sunk cost scenarios reliably measure the same psychological construct. Methods: Across two experiments (N = 395), we tested established sunk cost vignettes, including classic scenarios from Arkes and Blumer (1985). English-speaking participants from Prolific Academic completed vignettes alongside cognitive reflection and social desirability measures. We assessed internal consistency and intercorrelations between scenarios. Results: Internal consistency was consistently poor (ω = 0.14–0.57) with weak intercorrelations between scenarios. Even highly similar vignettes correlated only moderately. External validity was problematic, showing inconsistent relationships with cognitive reflection and social desirability across vignettes. Conclusions: These measurement failures have critical implications for neuroimaging research, where unreliable behavioural measures may be mistaken for genuine neural differences. The field needs systematic categorization of scenarios to identify which vignettes engage specific psychological processes and neural circuits, enabling more targeted theoretical development. Full article

The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen’s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO’s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities. Full article

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals. Full article

Background/Objectives: Speech intelligibility (SI) is a key indicator of spoken language development, especially for children with hearing loss, as it directly impacts communication and social engagement. However, due to logistical and methodological challenges, SI assessment is often underutilised in clinical practice. This study aimed to evaluate the accuracy and consistency of an artificial intelligence (AI)-based transcription model in assessing SI in young children with cochlear implants (CI), hearing aids (HA), or normal hearing (NH), in comparison to naïve human listeners. Methods: A total of 580 speech samples from 58 five-year-old children were transcribed by three naïve listeners and the AI model. Word-level transcription accuracy was evaluated using Bland–Altman plots, intraclass correlation coefficients (ICCs), and word error rate (WER) metrics. Performance was compared across the CI, HA, and NH groups. Results: The AI model demonstrated high consistency with naïve listeners across all groups. Bland–Altman analyses revealed minimal bias, with fewer than 6% of sentences falling outside the 95% limits of agreement. ICC values exceeded 0.9 in all groups, with particularly strong agreement in the NH and CI groups (ICCs > 0.95). WER results further confirmed this alignment and indicated that children with CIs showed better SI performance than those using HAs. Conclusions: The AI-based method offers a reliable and objective solution for SI assessment in young children. Its agreement with human performance supports its integration into clinical and home environments for early intervention and ongoing monitoring of speech development in children with hearing loss. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

Prejudices, particularly those related to social biases, are shaped by various cognitive and sensory mechanisms. This study investigates the interaction between olfactory perception and propensity and implicit or explicit prejudices through three experimental protocols in a metaverse condition. Experiment 1 examines the impact of five different odors on proxemic behavior when interacting with individuals from stigmatized social groups. Experiment 2 integrates electroencephalography (EEG) to analyze the neural correlates of prejudice-related responses to olfactory stimuli. Experiment 3 explores implicit biases through the Implicit Association Test (IAT) in relation to different fragrances, without employing virtual reality. The proposed protocol is expected to demonstrate a significant relationship between olfactory cues, linked to social relationships, and implicit or explicit prejudices, with variations based on individual differences. These insights will contribute to psychological, neuroscientific, and social interventions, offering new perspectives on the unconscious mechanisms of bias formation. Additionally, this study highlights the potential of virtual reality and olfactory stimuli as innovative tools for studying and addressing social biases in controlled environments. Full article