Search Results (358)

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this work. By performing CCA (canonical correspondence analysis) and cluster and correlation coefficient (Pearson) analyses, the temporal variation characteristics of benthic abundance, dominant species, community structure and biodiversity were analyzed. A total of 79 species of macro-benthic animals were found in four months, including 32 species of polychaetes, cnidarians, 1 species of Nemertean, 19 species of crustaceans, and 24 species of molluscs. The use of conventional grab-type mud collectors revealed that the Musculus senhousei dominated the survey (Y > 0.02). While only a small number of Ruditapes philippinarum were collected from bottom-dwelling species, a certain number of bottom-dwelling species (Ruditapes philippinarum and Scapharca subcrenata) were also collected during the trawl survey. Additionally, a significant population of Rapana venosa was found in the area. It is speculated that the dual effects of predation and competition are likely the primary reasons for the relatively low abundance of bottom-dwelling species. The density and biomass of macro-benthos were consistent over time, which was the highest in May, the second highest in January, and the lowest in September and November. The main environmental factors affecting the large benthic communities in the surveyed sea areas were pH, DO, NO₂⁻-N, T, SAL and PO₄³⁻-P. Combined with historical data, it was found that although the environmental condition in the Caofeidian sea area has improved, the Musculus senhousei has been dominant. In addition, the abundance of other species is much less than that of the Musculus senhousei, and the diversity of the benthic community is still reduced. Our work provides valuable data support for the management and improvement of bottom Marine pasture and promotes the transformation of Marine resources from resource plunder to a sustainable resource. Full article

As climate change consequences intensify, higher education institutions (HEIs) have an opportunity and responsibility to model sustainable operations. This study examines how embracing shared knowledge and inclusion in sustainability decision making facilitates green human resource management (GHRM) efforts to invigorate organizational environmental performance. The study examines the effects of shared and inclusive governance on campus sustainability via a regression model and the mediating role of employee participation via a structural equation modeling approach. The results show that shared governance and inclusive governance positively predict the commitment of HEIs to reducing greenhouse gas emissions, and campus engagement mediates these relationships, underscoring the importance of participation. These findings align with stakeholder theory in demonstrating that diverse voices in decision making can enhance commitment to organizational goals like sustainability. The findings also highlight the importance of shared and inclusive governance arrangements at college campuses not only for ethical reasons but also for achieving desired outcomes like carbon neutrality. For campus leaders striving to “green” their institutions, evaluating cross-departmental representation in governance structures and promoting inclusive cultures that make all students and staff feel welcome appear as important complements to GHRM practices. Full article

Achieving high-level green technological innovation in heavily polluting enterprises is critical for advancing sustainable development, particularly in the context of both organizational and regional digitalization. This study adopts a configurational perspective grounded in the Technology–Organization–Environment (TOE) framework and integrates theoretical insights from resource orchestration, resource dependence, and IT capability theories. It investigates how different types of skilled talent, such as production, technical, sales, and managerial employees, contribute to green innovation under varying digital conditions. By applying fuzzy-set qualitative comparative analysis (fsQCA) to a sample of 96 publicly listed firms from China’s heavily polluting industries, this study identifies four distinct talent-based configurations that can lead to high levels of green innovation: production-centric, management-led, technical talent driven, and regionally enabled models. Each configuration reflects a specific system state in which a core group of skilled employees plays a leading role, supported by complementary functions, and shaped by the interaction between internal digital transformation and the external digital environment. This study contributes to the systems literature by elucidating the combinational roles of digital resources and talent deployment within the systemic TOE framework, and offers practical guidance for enterprises aiming to strategically utilize human capital to enhance green innovation performance amid ongoing digital transformations. Full article

Amid structural changes driven by the greening and digital transformation of the maritime industry, the demand for career transitions of seafarers with onboard experience to shore-based positions—particularly ship superintendents—is steadily increasing. However, the current lack of a systematic education and career development framework to support such transitions poses a critical challenge for shipping companies seeking to secure sustainable human resources. The aim of this study was to develop a competency-based training program that facilitates the effective transition of seafarers to shore-based ship superintendent roles. We integrated a developing a curriculum (DACUM) analysis with competency-based job analysis to achieve this aim. The core competencies required for ship superintendent duties were identified through three expert consultations. In addition, social network analysis (SNA) was used to quantitatively assess the structure and priority of the training content. The analysis revealed that convergent competencies, such as digital technology literacy, responsiveness to environmental regulations, multicultural organizational management, and interpretation of global maritime regulations, are essential for a successful career shift. Based on these findings, a modular training curriculum comprising both common foundational courses and specialized advanced modules tailored to job categories was designed. The proposed curriculum integrated theoretical instruction, practical training, and reflective learning to enhance both applied understanding and onsite implementation capabilities. Furthermore, the concept of a Seafarer Success Support Platform was proposed to support a lifecycle-based career development pathway that enables rotational mobility between sea and shore positions. This digital learning platform was designed to offer personalized success pathways aligned with the career stages and competency needs of maritime personnel. Its cyclical structure, comprising career transition, competency development, field application, and performance evaluation, enables seamless career integration between shipboard- and shore-based roles. Therefore, the platform has the potential to evolve into a practical educational model that integrates training, career development, and policies. This study contributes to maritime human resource development by integrating the DACUM method with a competency-based framework and applying social network analysis (SNA) to quantitatively prioritize training content. It further proposes the Seafarer Success Support Platform as an innovative model to support structured career transitions from shipboard roles to shore-based supervisory positions. Full article

The rising demand for sustainable aquaculture necessitates innovative solutions to environmental and operational challenges. Biofloc technology (BFT) has emerged as an effective method, leveraging microbial communities to enhance water quality, reduce feed costs, and improve fish health. However, traditional BFT systems are susceptible to water quality fluctuations, demanding precise monitoring and control. This review explores the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart BFT systems, highlighting their capacity to automate processes, optimize resource utilization, and boost system performance. IoT devices facilitate real-time monitoring, while AI-driven analytics provide actionable insights for predictive management. We present a comparative analysis of AI models, such as LSTM, Random Forest, and SVM, for various aquaculture prediction tasks, emphasizing the importance of performance metrics like RMSE and MAE. Furthermore, we discuss the environmental and economic impacts, including quantitative case studies on cost reduction and productivity increases. This paper also addresses critical aspects of AI model reliability, interpretability (SHAP/LIME), uncertainty quantification, and failure mode analysis, advocating for robust testing protocols and human-in-the-loop systems. By addressing these challenges and exploring future opportunities, this article underscores the transformative potential of AI and IoT in advancing BFT for sustainable aquaculture practices, offering a pathway to more resilient and efficient food production. Full article

This study addresses the critical gap in understanding how internal green capabilities and practices drive environmental sustainability in small and medium-sized enterprises (SMEs), particularly within the context of a developing economy. While prior studies have explored green intellectual capital (GIC), green human resource management (GHRM), and green innovation separately, their combined effects and mechanisms have been underexplored. Grounded in the Resource-Based View (RBV), this research examines how GIC and GHRM influence environmental performance, considering green innovation as a mediating factor and green transformational leadership as a moderating factor. Data were collected through a structured questionnaire from 329 manufacturing SMEs in major industrial regions of Pakistan and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings demonstrate that GIC and GHRM significantly enhance environmental performance both directly and indirectly via green innovation, while green transformational leadership strengthens the effect of GHRM on innovation. These results provide practical insights for managers and policymakers in developing countries to implement integrated green strategies and leadership development to achieve sustainability targets. Given its focus on Pakistani SMEs, the generalizability of the findings may be limited, suggesting directions for future research in other cultural and economic settings. Full article

This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management. Full article

The transformation of organizational platformization provides a technological path and collaborative framework for sustainable development. In this context, platform-based human resource management (HRM) has attracted a lot of attention in academia and the industry, but there is a lack of in-depth research on what dimensions are included in the practice of platform-based HRM and how to measure it. Firstly, this study adopts a theory-based approach to decompose platform-based HRM practices into six functional dimensions, namely “adaptive employee recruitment”, “autonomous job design”, “empowering employee development”, “self-managed compensation management”, “team-based performance management” and “facilitating development planning”. Secondly, based on the scale development procedure, a measurement scale for platform-based HRM practices containing 22 items was developed and passed the reliability test. Finally, the paper conducted a predictive test of the scale with passion for harmonious work as the distal predictor variable and sense of self-determination as the proximal predictor variable, which confirmed the scale’s good predictability. This paper provides a quantifiable tool for related research on HRM in platform-based organizations and offers theoretical guidance and a reference model for building HRM empowerment systems in platform-based enterprises. At the same time, it also provides ideas and references for enterprises to practice platform-based human resources and achieve sustainable development. Full article

The increasing emphasis on integrating sustainability into corporate operations has prompted supply chain managers to incorporate not only economic objectives but also environmental and social considerations into their network designs. This study presents a structured six-stage methodology to develop a fuzzy multi-objective optimization model for the sustainable design of a multi-level, multi-product forward supply chain network. The model incorporates two conflicting objectives: minimizing total network costs and reducing environmental impact. To quantify environmental performance, a comprehensive life cycle assessment is conducted in accordance with the ISO 14040 standard and the ReCiPe 2016 method, focusing on three impact categories: human health, resources, and ecosystems. To address uncertainty in demand and production costs, fuzzy mixed-integer linear programming is employed. The model is validated and applied to a real-world case study of a dairy small-to-medium enterprise in Ecuador. Using the epsilon-constraint method, a Pareto frontier is generated to illustrate the trade-offs between the economic and environmental objectives. This research provides a robust decision-making tool for uncertain environments and advances knowledge on the integration of life cycle assessment with supply chain optimization and network design methodologies for sustainable development. Full article

This study examines the relationship between organizational commitment and administrative management within a public service institution operating in an emerging governance context. Grounded in the three-component model of organizational commitment (affective, continuance, and normative) and classical administrative theory (planning, organizing, directing, and controlling), the research investigates how internal psychological bonds among frontline personnel influence institutional performance. A quantitative, cross-sectional, non-experimental design was applied, surveying 30 operational police officers using validated Likert-scale instruments. The results reveal a strong and statistically significant positive correlation between organizational commitment and administrative management (Spearman’s ρ = 0.775, p < 0.01), with normative commitment displaying the highest effect size (ρ = 0.812). These findings underscore the critical role of ethical obligation, loyalty, and affective alignment in enhancing managerial coherence and institutional responsiveness. The study contributes to ongoing debates on public sector reform and strategic human capital management by emphasizing the need for emotionally engaged and ethically anchored personnel. It aligns with Sustainable Development Goals (SDGs) 16 (Peace, Justice, and Strong Institutions) and 8 (Decent Work and Economic Growth), promoting inclusive, accountable governance and resilient administrative practices in resource-constrained environments. Full article

Maowusu sandy land is characterized by a fragile ecological environment and extreme sensitivity to external disturbances such as climate change and human activities. Identifying and zoning ecological spaces in this region are crucial for maintaining eco-environmental safety and promoting sustainable regional development. With Maowusu sandy land as the study object, the temporal and spatial characteristics of land use and the driving forces were explored via spatial analysis technology—the geographic information system. Then, a 2D relation judgment matrix was constructed by evaluating the importance of ecosystem service functions and ecological sensitivity. Next, restoration zoning of natural ecological space was performed, and relevant restoration suggestions were put forward accordingly. Results show that the land use in Maowusu sandy land has significantly changed in the past 30 years, with construction land and forest continuously expanding, cropland and grassland being squeezed, and some areas of unutilized land being transformed into other land use types. Ecosystem service functions tend to weaken from southwest to northeast, whereas the ecologically sensitive zones are mainly distributed in the middle of Maowusu sandy land. The high-importance and high-sensitivity zones of natural ecological space account for 3.60% of the total area of natural ecological space, mainly distributed near Ejin Horo Banner. A comprehensive restoration project of soil and water conservation should be conducted in this zone to alleviate soil erosion and maintain the management and restoration of ecological protection red lines. Moderately important sensitive zones account for the largest proportion (80.42%) of the total area of natural ecological space, being widely distributed. In such zones, water resources should be taken as constraints, with emphasis on ecological protection and improvement measures. Low-importance and low-sensitivity zones account for the smallest proportion, in which ecosystem protection, near-natural restoration, and moderate development and utilization should be carried out. This study aims to provide a scientific basis for reasonably protecting natural ecological resources and promoting the healthy and ordered development of natural ecosystems. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article

The uncontrolled urbanization of Brazilian cities accentuates the imbalance between population demands and urban space planning. The integrated management of human and technological resources constitutes a fundamental governance strategy for the proposition of sustainable and effective responses to the challenges faced by cities. To generate references to public management, the objective of this research was to develop a management tool to verify the maturity level of Brazilian cities for smart city actions. A performance measurement system (PMS) organized smart city actions into 11 thematic areas, quantitatively measuring smart city actions in a down–top structure since the indicators and metrics are described in a standardized scale to obtain the general maturity index (GMI). The PMS was implemented in the city of Santa Maria/Brazil, where its GMI equal to 43.72% indicated a currently intermediate maturity level of smart city actions, mainly related to the low performance in the thematic areas of mobility, coexistence and reciprocity, and security and protection. To improve the current performance, five incremental actions were proposed, contemplating the key performance indicators “Public roads”, “Multipurpose lanes”, “Public accessibility”, “Accessibility signage”, and “Monitoring”, projecting a GMI equal to 49.75% and 55.78%, respectively, for an intermediate and an advanced maturity level scenario. Full article