Search Results (2,916)

Researchers are increasingly engaged in studies to determine and correct negative welfare consequences of animal husbandry and behaviour modification procedures, not least in response to industries’ growing need to maintain their social licence through demonstrable welfare standards that address public expectations. To ensure that welfare recommendations are scientifically credible, the studies must be rigorously designed and conducted, and the data produced must be interpreted with full regard to conceptual, methodological, and experimental design limitations. This commentary provides guidance on these matters. In addition to, and complementary with, the ARRIVE guidelines that deal with animal studies in general, there is a need for additional specific advice on the design of studies directed at procedures that alter behaviour, whether through training, handling, or restraint. The COMPASS Guidelines offer clear direction for conducting welfare-focused behaviour modification research. They stand for the following: Controls and Calibration, emphasising rigorous design, baseline measures, equipment calibration, and replicability; Objectivity and Open data, ensuring transparency, validated tools, and data accessibility; Motivation and Methods, with a focus on learning theory, behavioural science, and evidence-based application of positive reinforcers and aversive stimuli; Precautions and Protocols, embedding the precautionary principle, minimising welfare harms, listing stop criteria, and using real-time monitoring; Animal-centred Assessment, with multimodal welfare evaluation, using physiological, behavioural, functional, and objective indicators; Study ethics and Standards, noting the 3Rs (replacement, reduction, and refinement), welfare endpoints, long-term effects, industry independence, and risk–benefit analysis; and Species-relevance and Scientific rigour, facilitating cross-species applicability with real-world relevance and robust methodology. To describe these guidelines, the current article is organised into seven major sections that outline detailed, point-by-point considerations for ethical and scientifically rigorous design. It concludes with a call for continuous improvement and collaboration. A major purpose is to assist animal ethics committees when considering the design of experiments. It is also anticipated that these Guidelines will assist reviewers and editorial teams in triaging manuscripts that report studies in this context. Full article

As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in this domain. We delineate ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations (e.g., transformer-based models for semantic analysis), empirical linkages between AI-extracted signals and market/governance outcomes, and normative discussions on AI’s auditing potential, we demonstrate AI’s efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. Nonetheless, causal evidence linking managerial AI adoption to stakeholder-perceived enhancements remains limited, compounded by biases in multilingual applications and interpretability deficits. We propose a forward-looking agenda, prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards, to harness AI for substantive, equitable improvements in ESG reporting and accountability. Full article

Environmental health practice is facing an identity crisis. Not because our work lacks impact, but because our work lacks a coherent narrative. Environmental health practice needs a brand that speaks our truth and secures our place in the future of health protection. The brand of environmental health practice must be more than a label. It needs to be an asset that our industry can leverage to build identity, recognisability, credibility, relevance and a unique value proposition. A strong brand will allow us to express the unique impact of our role and thus, it needs to capture the most central component of our work that unmistakably differentiates it from anything else. A rapid review of the literature was performed using systematic methods to discern a coherent narrative capturing the essence of environmental health practice. Through application of a critical lens shaped through professional experience, a novel narrative was synthesized of the key defining component of our work. This narrative establishes the work of environmental health practitioners as a unique value proposition and forms the basis for a brand that we can fearlessly project and reverberate. It represents the makings for an unmistakable identity. The future of environmental health practice depends on environmental health practitioners establishing a brand by building awareness of the unique value of our work. Full article

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability. Full article

Internal accounting control personnel constitute the operational foundation through which firms ensure the accuracy and reliability of financial reporting, yet their relevance to capital market outcomes remains insufficiently documented. This study evaluates whether investment in internal accounting control personnel is incorporated into corporate bond pricing by considering both the quantitative dimension of staffing levels and the qualitative dimension of personnel expertise. Corporate bond issuance data are merged with mandatory disclosures on internal accounting control personnel for manufacturing firms listed on the Korea Exchange between 2011 and 2021. The analysis shows a significantly negative association between internal accounting control personnel and corporate bond yield spreads, with personnel expertise further reinforcing this relationship. These patterns are consistent with the view that enhanced monitoring capacity and stronger reporting credibility reduce information asymmetry and perceived default risk among bond investors. The evidence positions internal accounting control personnel as an operational and signaling indicator of internal control effectiveness reflected in debt market pricing and suggests that investment in internal control staff extends beyond compliance to produce measurable financial benefits through lower borrowing costs. Full article

The analysis of road traffic accidents often reveals asymmetric patterns, providing insights that support the development of preventive measures, reduce fatalities, and improve road safety interventions. The Rayleigh distribution, a continuous distribution with inherent asymmetry, is well suited for modeling right-skewed data and is widely used in scientific and engineering fields. It also shares structural characteristics with other skewed distributions, such as the Weibull and exponential distributions, and is particularly effective for analyzing right-skewed accident data. This study considers several approaches for constructing confidence intervals, including the percentile bootstrap, bootstrap with standard error, generalized confidence interval, method of variance estimates recovery, normal approximation, Bayesian Markov Chain Monte Carlo, and Bayesian highest posterior density methods. Their performance was evaluated through Monte Carlo simulation based on coverage probabilities and expected lengths. The results show that the HPD method achieved coverage probabilities at or above the nominal confidence level while providing the shortest expected lengths. Finally, all proposed confidence intervals were applied to fatalities recorded during the seven hazardous days of Thailand’s Songkran festival in 2024 and 2025. Full article

In this paper, Flare Bright presents flight test results gathered using a ~2m fixed wingspan drone to demonstrate the capability that has been achieved using an Inertial Navigation System (INS) augmented by Machine Learning tuned software. INSs, using Inertial Measurement Units (IMUs), are invaluable for position estimation in GNSS-compromised environments as no external information is required. However, with no absolute measurement of a vehicle’s position or attitude, INSs suffer from significant drift over time. The results from a robust flight test programme, over multiple vehicles, terrains and flight paths, show how Flare Bright combined a low cost and low SWaP (space, weight and power) IMU, with their patent-pending software-only techniques, to boost INS performance to the degree of besting a ‘tactical grade’ IMU in ~20 min. These results credibly demonstrate the value of Flare Bright’s solution as an effective, low-cost and low-weight INS for extended flight operations of small uncrewed aerial systems in GNSS-compromised environments, with performance comparable to heavier, more expensive high-end IMUs. Full article

The growth of e-commerce in emerging economies presents a critical opportunity to steer digital consumption toward sustainability, yet the mechanisms that drive online green purchases in these contexts remain underexplored. This study examines how green digital marketing signals, specifically eco-labeling and sustainability claims, influence the online purchase intention for sustainable coffee in urban Ethiopia, introducing digital literacy as a key moderating factor. Grounded in an integrated framework of Signaling Theory and the Theory of Planned Behavior, the research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data from 451 urban e-commerce consumers. The results confirm that both eco-labeling and sustainability claims significantly enhance purchase intention, with green trust serving as a significant partial mediator. A pivotal finding is that digital literacy positively and significantly moderates the relationship between green trust and purchase intention, indicating that consumers with higher digital competence are more effective at translating trust into purchase decisions. These findings underscore that the success of online green marketing is contingent not only on signal credibility but also on user capability, offering crucial insights for platform design and policy in emerging digital markets. Full article

Background: Paragangliomas of the head and neck are rare, benign and indolent to slow-growing tumors. Not all tumors require immediate active intervention, and surveillance is a viable management strategy in a large proportion of cases. Treatment decisions are based on several tumor- and patient-related factors, with the tumor progression rate being a predominant determinant. Accurate prediction of tumor progression has the potential to significantly improve treatment decisions by helping to identify patients who are likely to require active treatment in the future. It furthermore enables better-informed timing for follow-up, allowing early intervention for those who will ultimately need it, and optimization of the use of resources (such as MRI scans). Crucial to this is having reliable estimates of the uncertainty associated with a future growth forecast, so that this can be taken into account in the decision-making process. Methods: For various tumor growth prediction models, two methods for uncertainty estimation were compared: a historical-based one and a Bayesian one. We also investigated how incorporating either tumor-specific or general estimates of auto-segmentation uncertainty impacts the results of growth prediction. The performance of the uncertainty estimates was examined both from a technical and a practical perspective. Study design: Method comparison study. Results: Data of 208 patients were used, comprising 311 paragangliomas and 1501 volume measurements, resulting in 2547 tumor growth predictions (a median of 10 predictions per tumor). As expected, the uncertainty increased with the length of the prediction horizon and decreased with the inclusion of more tumor measurement data in the prediction model. The historical method resulted in estimated confidence intervals where the actual value fell within the estimated 95% confidence interval 94% of the time. However, this method resulted in confidence intervals that were too wide to be clinically useful (often over 200% of the predicted volume), and showed poor ability to differentiate growing and stable tumors. The estimated confidence intervals of the Bayesian method were much narrower. However, the 95% credible intervals were too narrow, with the true tumor volume falling within them only 78% of the time, indicating underestimation of uncertainty and insufficient calibration. Despite this, the Bayesian method showed markedly better ability to distinguishing between growing and stable tumors, which has arguably the most practical value. When combining all growth models, the Bayesian method using tumor-specific auto-segmentation uncertainties resulted in an 86% correct classification of growing and non-growing tumors. Conclusions: Of the methods evaluated for predicting paraganglioma progression, the Bayesian method is the most useful in the considered context, because it shows the best ability to discriminate between growing and non-growing tumors. To determine how these methods could be used and what their value is for patients, they should be further evaluated in a clinical setting. Full article

The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO₂) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO₂ emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO₂ emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO₂ emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. Full article

This study explores how digital and traditional marketing tools influence higher education students’ career decision-making, satisfaction, and career commitment during students’ educational trajectories in Iraq’s rapidly expanding university sector. Using an explanatory sequential mixed-methods design, a survey of 622 students was analysed with partial least squares structural equation modelling (PLS-SEM), followed by 24 semi-structured interviews with marketing and recruitment professionals. The quantitative findings show that students’ first-choice preferences, demographic factors, and engagement with LinkedIn, WeChat, blogs, and university webpages significantly shaped their career choices and satisfaction levels. Qualitative insights reveal that authenticity, transparent communication, and alignment between institutional messaging and lived experiences were key to sustaining trust. Traditional channels such as brochures and fairs remained important for credibility, supporting a hybrid marketing approach. The study contributes to management theory and practice in universities by linking digital communication strategies to student engagement and institutional performance. It also highlights the need for inclusive, transparent, and culturally adaptive marketing that reflects local and global contexts. These findings provide actionable guidance for higher education administrators seeking to build sustainable student trust, enhance recruitment effectiveness, and strengthen institutional reputation in competitive and resource-constrained systems. Full article

The paper presents a statistical model based on the two-weighted exponential distribution (TWED) to examine censored Human Immunodeficiency Virus (HIV) survival information. Identifying HIV as a disability, the study endorses an inclusive and sustainable health policy framework through some predictive findings. The TWED provides an accurate representation of the inherent hazard patterns and also improves the modelling of survival data. The parameter estimation is achieved in both a classical maximum likelihood estimation (MLE) and a Bayesian approach. Bayesian inference can be carried out under general entropy loss conditions and the symmetric squared error loss function through the Markov Chain Monte Carlo (MCMC) method. Based on the symmetric properties of the inverse of the Fisher information matrix, the asymptotic confidence intervals (ACLs) for the MLEs are constructed. Moreover, two-sided symmetric credible intervals (CRIs) of Bayesian estimates are also constructed based on the MCMC results that are based on symmetric normal proposals. The simulation studies are very important for indicating the correctness and probability of a statistical estimator. Implementing the model on actual HIV data illustrates its usefulness. Altogether, the paper supports the idea that statistics play an essential role in promoting disability-friendly and sustainable research in the field of public health in general. Full article

Greenwashing in environmental, social, and governance reporting poses a significant threat to corporate accountability and stakeholder trust. This article provides a comprehensive synthesis of existing research to evaluate the role and effectiveness of sustainability assurance as a primary mechanism to combat greenwashing and proposes a framework for it. Based on a systematic literature review, this paper consolidates empirical findings indicating that sustainability assurance has a significant inhibitory effect on corporate greenwashing and is positively valued by capital markets, as evidenced by lower equity capital costs. However, the analysis also reveals that the effectiveness of assurance is not uniform; it is moderated by contextual factors such as the strength of the national legal environment and, in particular, regulatory environments, which can be exploited to legitimize overstated disclosures. This paper proposes a conceptual framework for anti-greenwashing assurance that integrates five interconnected pillars (regulatory, stakeholder engagement, third-party verification, corporate culture and internal controls, and technologies), forming a synergistic ecosystem of deterrents which collectively shape the integrity and credibility of sustainability reporting practices. To enhance the effectiveness of greenwashing mitigation, the proposed framework must be further strengthened by integrating the core principles of transparency, materiality, and verifiability across all its pillars. Full article

As corporate accountability, credibility, transparency, and stakeholders’ trust continue to attract global attention, this study examines how corporate reputation influences audit fees and whether audit fees, in turn, shape reputation. Hence, this study examines the bidirectional relationship between audit fees and corporate reputation in South African listed firms. This study reviewed three theories, such as capital reputation, stakeholder, and agency theories. Exploring panel data from sixteen listed firms over a period of ten years (2015–2024), this study employs panel regression analysis and two-step system generalised method of moments (System GMM) estimates in accounting for endogeneity, heterogeneity, and dynamic relationships. Data was sourced from the annual reports and accounts of selected firms. The results from the fixed effects model indicate that corporate reputation exerts a statistically significant and positive influence on audit fees. Conversely, findings from the random effects model reveal that audit fees positively influence corporate reputation. The two-step GMM result confirms a bidirectional causal relationship as lagged corporate reputation significantly influences subsequent audit fees, while lagged audit fees also significantly influence future corporate reputation. This study recommends that the board of directors should view audit not as an expense but as a strategic investment in sustaining stakeholder trust and reputation. Among other things, policymakers and regulators should also strengthen audit market transparency in ensuring that audit pricing reflects genuine reputational consideration and audit quality. Full article

The textile sector provides essential goods, yet it remains environmentally and socially intensive, driven by high water use, pesticide dependent monocropping, chemical pollution during processing, and growing waste streams. This review examines credible pathways to sustainability by integrating emerging plant-based fibres from hemp, abaca, stinging nettle, and pineapple leaf fibre. These underutilised crops combine favourable agronomic profiles with competitive mechanical performance and are gaining momentum as the demand for demonstrably sustainable textiles increases. However, conventional fibre identification methods, including microscopy and spectroscopy, often lose reliability after wet processing and in blended fabrics, creating opportunities for mislabelling, greenwashing, and weak certification. We synthesise how advanced molecular approaches, including DNA fingerprinting, species-specific assays, and metagenomic tools, can support the authentication of fibre identity and provenance and enable linkage to Digital Product Passports. We also critically assess environmental Life Cycle Assessment (LCA) and social assessment frameworks, including S-LCA and SO-LCA, as complementary methodologies to quantify climate burden, water use, labour conditions, and supply chain risks. We argue that aligning fibre innovation with molecular traceability and harmonised life cycle evidence is essential to replace generic sustainability claims with verifiable metrics, strengthen policy and certification, and accelerate transparent, circular, and socially responsible textile value chains. Key research priorities include validated marker panels and reference libraries for non-cotton fibres, expanded region-specific LCA inventories and end-of-life scenarios, scalable fibre-to-fibre recycling routes, and practical operationalisation of SO-LCA across diverse enterprises. Full article