Search Results (827)

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience. Full article

The growing importance of Artificial Intelligence (AI) in enterprises has increased interest in the organizational benefits and barriers associated with its implementation in the context of digital transformation. The study aimed to assess enterprises’ perceptions of the benefits and barriers related to AI implementation and to identify organizational factors influencing its use. The research was conducted in 2025 among 311 enterprises implementing AI-based solutions. Descriptive statistics and Shannon’s information entropy were used to analyze the degree of diversity in perceptions of implementation benefits and barriers. The results indicate that AI is primarily perceived as a tool supporting operational efficiency, particularly through time savings and task automation. The most significant implementation barrier was identified as a lack of trust in AI technology. The entropy analysis showed greater diversity in perceptions of benefits than of barriers. The study contributes to the literature by combining survey research with information entropy analysis to examine the multidimensional perception of AI implementation in enterprises. Full article

Blockchain offers significant potential to enhance transparency, traceability, and trust in e-commerce supply chains, yet adoption among small- and medium-sized enterprises (SMEs) remains uneven due to its simultaneous advantages and implementation complexity. This study conceptualizes blockchain adoption as the outcome of an organizational evaluative system shaped by digital readiness and dual cognitive assessments. Using survey data from 548 Chinese e-commerce SMEs, we examine how AI familiarity, representing digital preparedness, shapes perceived benefits and perceived costs, thereby influencing adoption intention. Structural equation modeling shows that AI familiarity increases perceived benefits, reduces perceived costs, and strengthens adoption intention both directly and indirectly, suggesting that prior technological exposure recalibrates internal benefit–cost evaluations. Perceived benefits promote adoption intention, whereas perceived costs inhibit it, confirming the central role of evaluative integration. Response surface analysis reveals that adoption intention depends on the configuration of benefits and costs: intention rises when benefits exceed costs, and benefits exert a stronger influence, indicating asymmetric weighting. Multi-group SEM suggests that the structural relationships remain broadly stable across domestic- and internationally oriented firms. By modeling blockchain adoption as a structured evaluative process conditioned by digital readiness, this study contributes to a more integrated understanding of organizational technology adoption under digital complexity. Full article

The volume of daily email traffic continues to grow rapidly, creating challenges in efficiently distinguishing important from irrelevant messages. Beyond spam detection, modern email systems classify messages into categories such as promotions, social, updates, and forums, many of which are ignored or deleted without review. To address this issue, researchers have explored intelligent classification systems to predict the importance of emails, enhance user productivity, and improve organizational communication efficiency. This study proposes an email classification model that adapts to different users’ work functions and communication patterns within an organizational context. Using three-month historical real corporate anonymized email data from 9788 individuals across 12 work functions, the proposed Adaptive Semi-Personalized Email Classification Model (ASPEC) automatically retrieves each employee’s occupational profile—including job category and years of work experience—from the organization’s Human Resources (HR) system, enabling seamless personalization without manual configuration. ASPEC significantly improves email classification accuracy over the best-performing baseline of 73.50%, with incremental learning further enabling continuous adaptation to evolving data streams and achieving accuracy up to 92.57% in stable user segments. Unlike most existing email classification frameworks, which rely on static batch-learning models and lack memory-based or incremental update mechanisms, ASPEC addresses this gap by continuously adapting to evolving communication patterns without requiring full model retraining. The adoption of this incremental learning framework offers tangible benefits for organizations, including reduced manual email filtering workload, improved communication efficiency, and decreased operational burden on IT departments in managing email-related tasks and issues. Full article

The Erlang B equation is directly applicable to smaller hospital departments such as maternity and paediatrics departments. The bed occupancy margin is directly linked to size and not ‘efficiency’. A figure of 0.1% turn-away has been recommended as a planning target, i.e., only 1 in a thousand admissions suffer a delay before a bed can be found. Two bed calculators are provided which can be used for paediatric, obstetric, maternity, midwife-led, birthing wards and neonatal/paediatric critical care capacity. The negative effects of turn-away are likely to be context specific, hence, critical care > theatres > birthing unit > maternity unit. The uncertainty regarding future births is discussed along with the variable nature of seasonality in births. For paediatrics, much of bed demand is also influenced by the trend in births. Weighted population density (WPD) is associated with the size distribution of hospitals/units within countries and regions. This influences the average cost per birth/admission. The USA has a low WPD and a significant problem with small hospitals/departments. Only 10% of countries have WPD higher than England. Some countries choose to operate with even more hospitals than needed and this acts to elevate costs. Suggestions are made for a pragmatic approach to bed planning, especially where a dispersed population dictates a need for small hospitals, and hence, issues regarding size and costs. For maternity/paediatrics admissions (and other relatively short-stay admissions) the majority of overhead/indirect costs and most staffing costs should be apportioned based on admissions, and not LOS. Apportionment based on LOS creates the spurious illusion that LOS is the major cost driver and that reducing LOS will immediately save costs. Below 20 beds, Poisson statistical variation plus environment-induced randomness in daily arrivals imply that staff costs may become increasingly fixed irrespective of LOS. Around >30 beds, it looks possible to save costs by reducing LOS. Allocating total organizational costs to individual units and then to patients is less precise than realized and can be done in different ways, which all heavily rely on the steady-state assumption. When bed availability is the bottleneck, then reducing LOS may increase throughput per bed and increase income; however, is this for the benefit of the patient or for the benefit of the organization, and does it lead to higher unanticipated total costs including patient harm? The older economy-of-scale literature has been demonstrated to be flawed, with a recent focus on economy of scale at the department level being entirely consistent with the application of the Erlang B equation. A list of nine catastrophic pitfalls is given for doctors to identify dubious capacity advice from managers and external experts. Full article

Background: Breastfeeding is considered the optimal source of nutrition for infants admitted to Neonatal Intensive Care Units (NICUs). Despite these well-documented benefits, establishing and sustaining breastfeeding in NICU settings remains challenging due to inadequate uptake of breastfeeding support measures. This scoping review aimed to examine the evidence on factors influencing the uptake of breastfeeding support practices in NICUs. Methods: The search was undertaken across four electronic databases: PubMed, MEDLINE, CINAHL, and the Cochrane Library. Primary studies published in English between 1994 and 2025 were included. Eligible studies focused on factors influencing breastfeeding support, implementation, or uptake of breastfeeding-related interventions in NICU settings. Exclusion criteria included studies not involving NICU populations, studies not addressing breastfeeding outcomes or support, secondary literature, and non-English publications. A total of 30 peer-reviewed studies met the inclusion criteria. Data were charted and synthesized using thematic analysis. Results: A total of 30 studies met the inclusion criteria. Four major themes influencing breastfeeding support uptake in NICUs were identified: (1) institutional commitment to the Neonatal Baby-Friendly Hospital Initiative (Neo-BFHI), (2) NICU breastfeeding protocols and care practices, (3) breastfeeding training for NICU staff and mothers, and (4) parental breastfeeding motivation and family support. Across studies, breastfeeding support was strengthened by organizational breastfeeding policies, staff education, lactation support services, family-centred care practices, and parental involvement. However, variations in NICU resources, institutional practices, and staff support contributed to inconsistencies in breastfeeding implementation and continuation. Conclusions: Breastfeeding support in NICUs is influenced by interconnected organizational, clinical, educational, and psychosocial factors. The findings highlight the importance of integrated breastfeeding-supportive approaches that combine institutional commitment, standardized NICU practices, healthcare provider education, and family-centred care to improve breastfeeding support for vulnerable infants in NICU settings. Full article

Extended Foster Care (EFC) provides services and benefits on a voluntary basis to young adults leaving the foster care system without having attained legal permanency. In the US, more than 19,000 young adults transitioned out of foster care without achieving legal permanency in 2021. As states seek to improve supports to young adults eligible for EFC, it is important to identify institutional barriers and needed practice reforms. This study reports on analyses of qualitative focus group data gathered from workers (N = 24) and interviews with community practitioners (N = 14) as part of Washington state’s collaborative systems assessment of EFC. Data from transcripts were analyzed using qualitative thematic analysis of coded content. Rapid qualitative analysis allowed for efficient analysis and sorting of data to gather findings prior to the legislative session. Key themes identified related to (1) service and benefit gaps and needs, (2) organizational practice reforms, and (3) a need for culturally responsive services and a representative workforce. Specifically, workforce staff and community partners emphasized the need for EFC-specific units, developmentally tailored training, working from a clearly articulated practice model relevant to youth and young adults, the importance of reducing caseloads, recruitment and retention of a representative workforce, and more evidence-based practice options for EFC. Full article

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

In today’s dynamic business environment, organizations are increasingly investing in strategies to protect themselves against information system violations. While these technologies offer remarkable benefits—boosting efficiency, productivity, and overall performance—they also bring significant risks, particularly regarding information security breaches. This study delves into the critical connections between technostress, techno-strain, and the violation of information security policies. Our research aims to shed light on how technostress, which is commonly experienced by engineers working in technology-intensive environments within the IT sector, drives information security violations. Importantly, we will also explore how techno-strain mediates this relationship. By focusing on engineers who are consistently engaged with advanced technology, we seek to answer essential questions about their experiences. It is worth noting that the requirements for information security technology can widely vary based on factors such as industry type, organizational structure, departmental roles, and cultural norms. Therefore, this study examines how technostress increases the likelihood of information security policy violations and how techno-strain mediates this relationship. Looking ahead, future research should consider both the broader institutional contexts and the individual characteristics that may shape the relationship between information security violations and technostress. Furthermore, understanding the repercussions of information security violations—stemming from technostress—on a company’s financial health is vital for organizations aiming to safeguard their assets and maintain a competitive edge. Emphasizing these insights can lead to more effective strategies for managing both technology and talent in the workplace. Full article

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) has emerged as a promising strategy for refractory cardiac arrest, enabling the restoration of systemic perfusion when conventional resuscitation fails. However, uncertainties remain regarding patient selection, timing and implementation. Methods: A narrative review of experimental data, clinical studies, randomized trials and international recommendations was performed. Particular emphasis was placed on the interplay between physiological mechanisms, real-world organizational models and decision-making processes. Results: ECPR can restore effective circulation, preserve end organ perfusion and serve as a bridge to definitive etiologic treatment, with the potential to improve survival and neurological outcomes in highly selected patients. However, its effectiveness is strongly dependent on rapid deployment, structured systems of care and multidisciplinary coordination. Significant challenges remain, including in relation to the heterogeneity of protocols, high resource utilization, complications with extracorporeal support and the complexity of post-resuscitation management. Furthermore, ECPR fundamentally alters traditional resuscitation paradigms, introducing ethical dilemmas related to patient selection, prognostication and the allocation of limited resources. Conclusions: ECPR represents a transition from procedure-based resuscitation to system-based extracorporeal support. Its clinical benefit is contingent upon timely implementation within optimized organizational frameworks and integration with definitive treatment pathways. Future research should focus on refining selection criteria, standardizing care pathways and addressing ethical sustainability challenges to ensure appropriate and effective use of this evolving technology. Full article

Construction sectors in developing countries continue to experience disproportionately high fatality rates, largely due to reactive safety practices and the limited adoption of digital safety technologies. While Building Information Modeling (BIM) and the Internet of Things (IoT) offer significant potential for proactive safety management, their integrated application remains underexplored in resource-constrained contexts. This study examines stakeholder readiness to adopt IoT-enhanced BIM-based safety monitoring systems in large-scale infrastructure projects in Pakistan, including China–Pakistan Economic Corridor (CPEC) initiatives and the Barakahu Bypass project. An integrated Technology Acceptance Model–Technology–Organization–Environment (TAM–TOE) readiness framework is employed, wherein TAM-derived cognitive-motivational factors (Technology Awareness and Perceived Benefits) and TOE-derived contextual factors (Organizational Readiness and Perceived Barriers) are examined as joint predictors of Behavioral Intention (BI). Data were collected from 107 purposively sampled construction professionals using a structured questionnaire. The results indicate high attitudinal readiness (BI mean = 4.7; perceived benefits mean = 4.6) alongside moderate organizational readiness (mean = 3.4). Regression analysis reveals that perceived benefits (β = 0.42, p < 0.001) and technology awareness (β = 0.29, p = 0.003) are the strongest positive predictors of adoption intention. In contrast, perceived barriers exert a significant negative effect (β = −0.22, p = 0.022). The model explains 61.2% of the variance in behavioral intention. This study advances the literature by providing empirical evidence on stakeholder readiness for BIM–IoT safety adoption within construction management processes, estimated through a multiple regression model. It offers practical implications for policymakers and industry stakeholders seeking to accelerate data-driven decision-making and digital safety transformation in developing economies. Full article

In response to inherent occupational and operational stress in public safety personnel (PSP), multiple policies and interventions have been implemented, often with sparse or low-quality research. The National Standard of Canada for Psychological Health and Safety in the Workplace (the Standard) is a comprehensive framework aimed at promoting mental health and preventing psychological harm in Canadian workplaces. This longitudinal multiple-cohort implementation science project describes mental health strategies implemented and associated organizational outcomes across five PSP organizations implementing change within the standard framework. Data were collected at two levels over a three-year span from the five public safety organizations that identified priority areas for improvement within the Standard based on local data and consultations. The organization selected and implemented a range of proactive mental health interventions, including resiliency training. Individual pre-post surveys assessed a variety of mental health disorders and work-related items. Annual organizational data included sick leave hours and extended health benefits for psychological services. Survey responses were aggregated at the organizational level. Rank-based correlation analyses (Kendall’s tau) described associations among occupational stress, work engagement, stigma, and organizational indicators. Organizations demonstrated multiple indicators of progress in meeting the Standard. Post-mental health symptom scores were positively correlated with extended health costs. Higher organizational stress scores were associated with higher extended health costs (psychological) (τ = 1.0 at pre-intervention; 0.67 post-intervention). Positive changes in organizational stress scores and higher engagement scores over the implementation process were both associated with lower average extended health costs (τ = 1.0/−1.0 respectively). Resilience scores were inversely related to health costs (τ = −0.67), consistent with the protective role of resilience. The Standard can serve as a framework for improving workplace health and safety when integrated with multi-modal action plans and structured resilience programs. Full article

Introduction: The growing integration of artificial intelligence (AI) into recruitment and selection is reshaping how organizations identify, evaluate, and choose talent. Although prior research emphasizes improvements in efficiency and automated decision-making, concerns related to fairness, transparency, trust, and applicant experience remain insufficiently resolved. Despite increasing scholarly attention, the field continues to evolve in a fragmented manner. This scoping review addresses this gap by systematically mapping and synthesizing the literature on the advantages and challenges of AI-based recruitment and selection, considering both organizational outcomes and human implications. Materials and Methods: A scoping review was conducted following established methodological frameworks. A structured search and screening process across major academic databases resulted in a final corpus of 33 peer-reviewed studies. The analysis combined descriptive mapping with a hybrid thematic synthesis organized around five dimensions: efficiency and decision support, bias and fairness, transparency and trust, applicant experience, and governance and ethics. Results: The evidence indicates that AI-based recruitment enhances efficiency, scalability, and consistency in decision processes. At the same time, these benefits are accompanied by challenges related to algorithmic bias, limited interpretability, reduced trust, and concerns about procedural fairness. The findings highlight a persistent interdependence between performance outcomes and legitimacy-related responses. Conclusions: This review proposes a socio-technical framework that explains AI-based recruitment as a system shaped by the interaction between technological design, human judgment, and governance structures. The results underscore the importance of integrating oversight, transparency, and ethical accountability to support responsible and sustainable implementation. Full article

Organic farming has gained increasing relevance worldwide due to its environmental benefits and its prominent role in sustainable food systems; however, the persistence of organic farms remains uneven across regions, particularly within the European Union. While the number of organic farms has grown overall in the EU, significant exits from organic production highlight the need to better understand the factors shaping farm survival, especially in newer Member States, where organic conversion and maintenance support schemes are often implemented through area-based CAP payments. This study aims to identify the structural and contextual determinants of short-term organic farm persistence in Hungary within a broader European context. Using farm-level data for the period 2020–2023, including Standard Output (SO) indicators, we applied a combined modelling framework based on Logistic Regression, Decision Trees, and Random Forest algorithms to assess the relative importance of economic, structural, and regional variables. The results show that organic farm persistence is primarily driven by structural characteristics such as farm size, economic scale, degree of conversion to organic farming and regional embeddedness, while production specialization and organizational features play a secondary, conditional role. The convergence of results across modelling approaches indicates that survival is shaped by hierarchical structural constraints rather than isolated management decisions. Our findings suggest that policy measures aiming to stabilize and expand the organic sector should move beyond uniform incentives, such as area-based payments, and should place greater emphasis on the structural conditions of farms and region-specific support mechanisms. Full article

Digitalization and artificial intelligence (AI) are seen as promising pathways for small and medium-sized enterprises (SMEs) to enhance performance while preserving environmental and social resources. This paper identifies organizational determinants of AI maturity that can enable SMEs to use AI in a more sustainable, responsible, and capacity-enhancing manner. AI adoption becomes relevant to sustainability not only because a company adopts advanced technology but because this technology is embedded in leadership practices, employee competencies, interdisciplinary collaboration, and organizational learning. From this perspective, perceived benefits and management commitment are not outcomes of sustainability but mechanisms that help explain how SMEs transition from technological awareness to building organizational capacity. Such capacity building can be a necessary prerequisite for subsequent sustainability-oriented outcomes, such as efficient resource utilization, employee upskilling, responsible AI management, and long-term resilience. We conducted a cross-sectional survey among 402 managers from Polish SMEs (62 micro, 193 small, 147 medium) across manufacturing, services and trade industries. Respondents (mean age ≈ 42.5 years) assessed perceived benefits of AI, engagement of top leadership, AI maturity and competitive pressure. Partial least-squares structural equation modeling revealed that perceived benefits strongly predicted leadership engagement (β = 0.647), explaining 62.8% of its variance. Perceived benefits (β = 0.384) and leadership engagement (β = 0.362) in turn were the key drivers of AI maturity, with the model accounting for 65.5% of variance in AI maturity. Competitive pressure positively but weakly moderated the relationship between perceived benefits and leadership engagement (β = 0.011), while its moderating effect on the relationship between perceived benefits and AI maturity was not significant (β = −0.008). These findings suggest that articulating clear benefits of AI and securing active leadership engagement are more decisive for advancing AI maturity than external competitive pressure. The contribution of the study is to integrate the perceived benefits of AI, top management commitment and AI maturity into a model, empirically validated and interpreted from a socio-technical perspective of sustainable digital transformation in SMEs, while quantifying the moderating role of competitive pressure in the under-researched context of Central and Eastern Europe. For practitioners, investing in awareness of AI’s benefits and developing committed leadership may yield more sustainable digital transformation than reacting solely to external pressures. Full article