Resource Management Research Research Articles

To Form From The Administration of Environmental Resources For the Care of The Habitat

Objective: Based on the provided text, a central concern emerges: the need to improve natural resource management and habitat conservation. The importance of environmental management as a tool to achieve sustainability and guarantee the well-being of future generations is highlighted. Theoretical Framework: Research in environmental resource management draws on a wide range of concepts and theories from various disciplines, such as ecology, economics, sociology, and administration. Method: The research employed both quantitative and qualitative methods. It was a descriptive study, and data was collected through surveys, which helped to gather information related to the proposed objective. Results and Discussion: The research has highlighted the crucial importance of environmental education and natural resource management to ensure a sustainable future. It has been evidenced that environmental education is fundamental to raising awareness and promoting sustainable practices, while resource management is essential for optimizing their use and achieving sustainable development goals. Implications: The implications of this research transcend academic spheres and extend to the political, business, and social realms, demanding a profound transformation towards development models that prioritize human well-being and planetary protection. In this sense, the research urges greater collaboration among governments, businesses, civil society organizations, and citizens to build a more sustainable future. Originality/Value: The originality of this research lies in its interdisciplinary approach, integrating ecological, economic, social, and administrative perspectives to offer a comprehensive view of natural resource management. By connecting theory with practice and emphasizing citizen participation, this research provides a novel and relevant perspective. Its value lies in its capacity to generate scientific knowledge, inform policymaking, and promote changes in management practices.

Improving daily precipitation estimation using a double triple collocation-based (DTC) merging framework

The availability of accurate precipitation data is crucial for water resources management, disaster prevention, and related research. While gridded products offer precipitation information at high spatial resolution, they still exhibit significant errors in precipitation estimation. The merging of multi-source gridded products has become a mainstream approach for improving precipitation estimation. However, many existing frameworks rely on gauge observations to estimate the merging weights, which limits their applicability in data-scarce regions. Moreover, these frameworks predominantly focus on enhancing precipitation estimation rather than on precipitation events. This study proposes a novel Double Triple Collocation-based (DTC) merging framework, which combines time–space TC (TC_2D)-based precipitation rate merging with categorical triple collocation (CTC)-based rain/no-rain merging. The objective is to minimize errors in precipitation estimation and enhance the detection capability for precipitation events without relying on rain gauge observations. Given that the TC_2D-based method is initially applied to precipitation merging, its effectiveness must be verified by comparing it with classic TC-based merging approaches (TC_Space and TC_Time). Taking the Jiulong River Basin (JRB) as a case study, the performance of the DTC and its comparative objects was evaluated with three triplets composed of independent precipitation products. The results indicated that all merged precipitation products outperform their parent products. Furthermore, the merged precipitation datasets, after being corrected using rain/no-rain time series generated by CTC-based merging, showed enhanced capability in detecting precipitation events. The performance of merged precipitation products was found to be highly dependent on the quality of the satellite precipitation products (SPPs) within the triplets. This study provides a promising approach for generating high-quality precipitation datasets, particularly in regions with limited observation data availability.

Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province

The acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We combined six commonly land cover datasets and forest age, canopy height, above-ground biomass, and tree species distribution datasets to reconstruct 30 m spatially accurate forest refinement dataset (FRD) for Guangdong Province. In addition, the distribution characteristics of forest structure and function were evaluated using forest morphological spatial pattern analysis. The results show that the overall accuracy of FRD of the Guangdong Province in 2020 reached 86.07 %. Forest types in the Guangdong Province were mainly dominated by evergreen needle-leaf forests. Tsuga chinensis, Red cedar, and Pinus sylvestris were more commonly planted. Older and taller trees were found in northern and eastern Guangdong. In addition, forest above-ground biomass (AGB) was larger in the coastal areas of northern and western Guangdong. The core and perforation had the oldest age and the highest tree height, and the islet had the lowest for all forest structure and function indicators. Based on multi-source datasets, this study contributes to a better understanding of the attributes characterizing the structure and function of forests. The refined dataset and research framework will effectively enhance forest management efficiency and policy making, as well as provide case references for research on climate change response, forest conservation and biodiversity assessment.

Work‐Related Intimate Partner Violence (WIPV): A Systematic Review and Feminist Conceptual Analysis

ABSTRACTIntimate partner violence (IPV) is a long‐standing and egregious social issue that inherently impacts and is impacted by work and workplaces. Work‐related intimate partner violence (WIPV) is a pattern of IPV that both directly and indirectly undermines victim‐survivors' employment, work, and careers, yet scant attention has been paid in management studies to workplace responses and responsibilities regarding this form of violence. Hence, we conduct a systematic review and conceptual analysis of 111 WIPV‐focused papers utilizing three well‐established feminist lenses to ascertain and conceptualize what we know and do not know about the WIPV domain and, more importantly, to outline an agenda for what we should know from a gendered perspective about WIPV. We develop a gender‐sensitive, multilevel framework with which we investigate and challenge the WIPV research agenda from individual, interactionist, and structural viewpoints, including the interaction between these perspectives. A key purpose of this paper is to extend debate about WIPV beyond the current focus of human resource management (HRM) research and practice on micro‐level employment issues to include meso‐level organizational and macro‐level societal issues. A second and connected purpose is to draw attention to the need to address big societal challenges related to workplaces and to highlight the pivotal role of HRM in fostering ethical and socially sustainable workplaces. Thus, we advance the vital debate about WIPV, provide a template for how gender can be conceptualized in HRM and workplace research, and challenge the boundaries for organizational involvement in areas traditionally deemed as private or societal.

Controversial insights into irrigation water quality in arid and semi-arid regions using AI driven predictions: Case of southern Gabès

Effective groundwater management is critical in arid and semi-arid regions, where water resources are essential for agriculture. This study assesses the Irrigation Water Quality Index (IWQI) of the Southern Gabès aquifer in Tunisia using a combination of traditional hydrochemical analysis and machine learning models—specifically, Classification and Regression Tree (CART) and Support Vector Machine (SVM). A total of 83 groundwater samples were analyzed based on five key parameters: Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Chloride (Cl-), Sodium (Na+), and Bicarbonate (HCO3-). The results show that the CART model demonstrated superior performance with an R2 value of 0.99 and a Root Mean Square Error (RMSE) of 0.43, while the SVM model achieved an R2 of 0.87. These findings underscore CART's robustness in predicting IWQI, offering high precision even with limited datasets.The groundwater quality was categorized, revealing that 62% of samples were classified as "satisfactory" for irrigation, while 31% were deemed "unsuitable" without treatment, highlighting areas of concern for agricultural use. The study also emphasizes the importance of continuous monitoring and adaptive management strategies to ensure sustainable water use in the region.Overall, this research demonstrates the effectiveness of machine learning models, particularly CART, in accurately assessing groundwater quality. These insights provide valuable tools for resource managers to make informed decisions, ensuring the sustainable exploitation of groundwater in arid and semi-arid regions. The findings pave the way for future research and policy development in water resource management.

Looking back to look forward: Disruption, innovation and future trends in international human resource management

Recent global challenges, particularly the COVID-19 pandemic, have underscored the critical role of human resources in international business, pushing disruptions and innovations to the forefront of international human resource management (IHRM) research and practice. This special issue editorial provides a timely and in-depth examination of the evolving IHRM landscape, reflecting on the profound changes brought by the pandemic and investigating the transformative potential of AI-driven innovations in shaping an uncertain future. Through our exploration, we challenge several dangerous assumptions that global leaders must navigate and propose a forward-looking research agenda for the IHRM field. Our analysis highlights five key themes: (1) demographic shifts and diversity, (2) evolving patterns in globalization and global mobility, (3) the complexities of managing global workforces, (4) emerging dynamics in international careers, and (5) the role of multinational enterprises (MNEs) and international small- and medium-sized enterprises (SMEs). Together, these themes offer a comprehensive framework for advancing IHRM research and practice in an increasingly complex and dynamic global environment.

ExtendinG SUb-DAily River Discharge data over INdia (GUARDIAN).

River discharge information is crucial for various applications, but the measurement process often remains impeded by factors that hinder near real-time (NRT) data availability in India. However, leveraging telemetry-based water surface elevation (WSE) data across the country provides an opportunity to convert it into river discharge. This conversion is made possible by utilizing rating curves (RCs) derived from historical collocated measurements of WSE and discharge. In this study, NRT WSE from the Central Water Commission (CWC) flood portal is obtained via the web-scraping tool. Through the application of RCs, discharge data is extended across 210 gauging stations in India from the year 2020 to the present, encompassing sub-daily discharge during the non-monsoon and hourly discharge series during monsoon (June-September) across the Indian rivers. Annually, the study generated over 800,000 discharge data points for Indian rivers, accounting for more than 4000 discharge measurements per station annually. These comprehensive datasets provide valuable insights for water resource and flood management research, offering NRT access to WSE, and discharge, along with the local RCs.

Pathway Selection for Enhancing the Efficiency of Psychological Crisis Intervention Resource Integration during Major Emergencies: Evidence from China

In major emergencies, psychological crisis intervention plays a critical role in safeguarding public mental health and supporting post-disaster recovery. However, uneven resource allocation underscores the urgent need for more efficient resource integration pathways. This study, based on data from Chinese governmental and health departmental reports in 2022, employs a Data Envelopment Analysis (DEA) and a fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate the efficiency of psychological crisis intervention resource integration across various regions. It investigates the combined effects of internal and external factors on improving integration efficiency through a configurational approach. The findings reveal that while the overall efficiency of resource integration is relatively high, there are notable differences between institutions. The analysis identifies five key pathways: policy support-driven (H1), professional capability-driven (H2), comprehensive synergy-driven (H3), resource optimization (NH1), and community empowerment (NH2). Despite regional disparities, effectively integrating key resources can enhance overall efficiency. Coordinating internal and external factors and optimizing essential resources are crucial for improving the effectiveness of psychological crisis interventions. This research offers actionable insights into integration strategies to strengthen psychological crisis intervention during emergencies. The findings also provide practical solutions to improve disaster preparedness and intervention efficiency, addressing a key gap in crisis resource management research.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

The relative effects of diversity on collective learning in local collaborative networks in Belgium

Collaborative networks are horizontal settings of public governance that enhance interactions between a diversity of actors (for example, civil servants, companies or citizens). They can help cross-cutting public policies (for example, climate policies) to gain coherence and become more innovative. To do so, collective learning, defined as the broadened and mutual understanding of public issues arising out of repeated social interactions, is critical but not spontaneous. In particular, the diversity of participants creates learning opportunities that do not necessarily transform into concrete learning. So, how does diversity lead to collective learning in collaborative networks? To address this research question, this article researched two collaborative networks within the city administration of Schaerbeek (Belgium). Based on semistructured interviews, mental models were used to assess collective learning, and social network analysis was performed to understand the structure of interactions between diverse members. The findings show that the influence of diversity on collective learning was contingent on the collaborative network, but fostered by social interactions, with noticeable links between formal and informal interactions. From these findings, the article makes three scholarly contributions. First, it deepens our understanding of collective learning, with a focus on the development of shared understandings as a condition of consensus formation. Second, it builds on psychology and resource management research to assess collective learning through mental models, and provides a new approach to the measurement of policy learning. Third, it contributes to the debate on the implications of different inclusion levels and conditions for the results of collaborative governance and their transformation in policy innovations.

Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

This study employs an improved Carnegie–Ames–Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.

대학 행정직원의 심리적 자본이 직무만족에 미치는 영향 분석:직무순환의 매개효과를 중심으로

Most universities in Korea have regularly introduced and operated a job rotation system. However, not all administrative employees positively evaluate the job rotation system. Some employees use the job rotation system to their advantage, but some believe that the job rotation system lowers the efficiency of their work. This study aims to deal with the process of accepting job rotation by university administrative staff who are introducing the job rotation system more systematically. In this study, the purpose of the following study was proposed. First, the psychological capital of university administrative staff, which is an organization that carries out a regular job rotation system, is identified. Second, the relationship between the psychological capital of university administrative staff and their perception of job rotation is investigated. Third, the relationship between job satisfaction and job satisfaction in the job rotation system is confirmed. Fourth, theoretical and management implications for human resource management and organizational behavior research are presented based on the relationship between employees' psychological capital and the effect of job rotation.

A temporal evolution of human resource management and technology research: a retrospective bibliometric analysis

A temporal evolution of human resource management and technology research: a retrospective bibliometric analysis

Creating Job Satisfaction through Improving the Quality of Transformational Leadership and Organizational Culture in Blister City Government, Indonesia

Civil Servants (PNS). As elements of the state apparatus, state servants bear a hefty burden in carrying out their duties and functions, considering the various phenomena of dynamic changes in the strategic environment in the current era of globalization. Based on these problems, this research aims To determine the description of transformational leadership style, organizational culture, and job satisfaction in the Blitar City Government. To analyze the influence of transformational leadership, organizational culture and motivation on job satisfaction in the Blitar City Government Environment. The population in this study consisted of civil servants within the Blitar City government. Based on data from Regional Personnel Agency Blitar City, in 2022, the number of employees will be 886. Sampling from the research population was done using a proportional random sampling technique. The number of samples in this study used the Slovin formula with an error rate of 10% of 274. This research concludes that Transformational leadership style and organizational culture affect employee job satisfaction at the Blitar City Government. The results of this research are expected to significantly contribute to the development of knowledge relevant to research problems. They are expected to strengthen the body of theories and for further research in human resource management.

A review on human resource management practices as driver for employee engagement

There is a growing emphasis on employee engagement in organizations and academia. It is reflected through an increasing number of academic publications that explores the link between human resource management practices and employee engagement. The present study investigates this relationship using bibliometric analysis. It is crucial to understand how human resource management practices influence employee engagement for creating a more productive and engaged workforce. The publications that focused on “human resource management” and “employee engagement” between 1996 and 2023 were analysed using the Biblioshiny package in R from the Web of Science (WoS) database. The analysis examined the existing research trends and also included comparative analysis across different geographic regions. It identified the emerging trends in human resource management research and the interconnectedness of various sub-disciplines within human resource management. This study offers a comprehensive analysis of the relationship between human resource management practices and employee engagement that revealed new avenues for future research and collaboration within the human resource management field. In other words, it will certainly provide valuable insights for future research agendas.

The Role of Time in Strategic Human Resource Management Research: A Review and Research Agenda

Although time is an essential component of the relationships between human resource (HR) systems and their antecedents and consequences, strategic human resource management (SHRM) research has been long criticized for not paying enough attention to the role of time in theory development and research design. To evaluate how the time issue has been addressed in this research field, we reviewed 237 empirical studies on HR systems that incorporated time, using temporal features. We found that while the number of studies incorporating time has increased substantially over time, there is a lack of progress regarding testing and theorizing temporal effects, thus we lack understanding of change or relationships over time. Based on our findings, we offer specific guidance on hypothesizing and theorizing the role of time in SHRM, and we offer suggestions for research design and statistical analyses in temporal research of SHRM. By integrating temporal models, temporal features, and methodologies for testing temporal relationships with SHRM research, this review aims to advance SHRM research to adopt more truly dynamic views to examine HR systems and their antecedents and effects.

Review of Human Resource Management in the Field of Public Management and Public Administration

The goal of this research is to elevate the importance of Human Resources Management within public management and administration. Many empirical studies published in academic journals between 2000 and 2019 corroborate this aim. The study's outcomes underscore variations among different disciplines and pinpoint shortcomings in particular nations. Furthermore, the findings provide directions for future research endeavors, proposing alternative theories, frameworks, research methodologies, and approaches to enhance the pertinence of Human Resources Management research in public sector contexts.

Pedagogical implications of pragmatic HRM research.

This chapter examines the interplay between human resource management theory and human resource management practice. It advocates that effective human resource management practice and theory are intrinsically intertwined, and this indispensable link ought to be central to the pedagogy of management research methods. Through greater embeddedness of the institutional and societal context in research method teaching, students can develop as scholars who understand their roles as facilitators of dialogue between researchers and a significant part of their audience (practitioners). The chapter conceptualizes this perspective as a collaborative model in human resource management research, which then must hold centrality in the teaching of research methods in our university and college classrooms.

Green Human Resource Management Practices and Environment Sustainability: From Empirical Evidence

Green human resource management, if well designed and implemented, is undeniably one of the avenues that is envisaged to contribute towards the actualization of the Sustainable Development Goals (SDGs). This paper set out to interrogate the extant literature on Green Human Resource Management (GHRM) Practices and their nexus to environmental sustainability. One of the emerging issues within business communities is the campaign of going green (GG). GHRM is a drive which helps to create a green workforce that can understand and appreciate green culture in businesses and institutions. It is paramount to note that human resource and their systems are the basic foundation of any business. It is a fact that the human resource function in an organization is responsible for planning and executing those eco-friendly policies to create a green environment. The study was mainly a desktop, where a review and synthesis of the existing empirical literature was undertaken. The main sources of the data and information for purposes of this paper were largely relevant reports, journals and books. Past writings indicate that there is a growing need for the integration of environmental management into Human Resource Management (HRM) research practice. This article pursues an integrated view of the literature on Green HRM. It examines and interrogates the current empirical literature in the area of green human resource management with a view to pointing out and synthesizing the gray areas and suggesting a way forward towards enriching knowledge and practice in the area of green human resource management. Finally, the paper suggests some key HR initiatives towards creating and nurturing GHRM practices and behaviour for environmental sustainability. This article draws together the extant literature in this area in suggesting managerial implications and research direction in GHRM. Hence, the papers demystifies the debate and discussion on GHRM and suggests new fronts that requires research focus.

Green Human Resources Management: Review from Theory to Empirical Research

This study aims to provide a comprehensive understanding of green human resource management by examining, analyzing, and synthesizing findings from 109 empirical studies conducted worldwide. These studies were pub lished between 2017 and July 2023. The findings of this study revealed that the examination of green human resource management predominantly revolves around two perspectives: business assessment and employee assessment. When viewed from a business perspective, research in green human resource management encompasses the following categories of factors: (i) Environmental impact; (ii) Business operations impact; and (iii) Corporate efficiency impact. On the other hand, when approached from the employee perspective, it focuses on factors such as: (i) Employee environmental behavior; (ii) Employee performance; (iii) Miscellaneous factors. Quantitative research methods were commonly employed in these studies, with a primary focus on surveying employees. The findings from this research play acrucial role in providing future researchers with a comprehensive overviewof the green human resource management field. Furthermore, we suggest potential avenues for future research in this area.