Resource Allocation Practices Research Articles

Developing Ethical Frameworks for Sustainable Food Pricing Through Supply Chain Transparency

Developing ethical frameworks for sustainable food pricing is crucial in addressing the interconnected challenges of profitability, ethical food access, and supply chain transparency. This study presents a decision analysis model designed to evaluate the sustainability and ethics of food pricing strategies, with a strong emphasis on future stakeholder analysis. The model incorporates a systematic approach to balancing profitability with ethical considerations, focusing on transparent supply chain practices as a foundation for sustainable pricing decisions. By evaluating factors such as production costs, environmental impact, and fair labor practices, the model aims to create a comprehensive pricing strategy that promotes both ethical food access and corporate responsibility. To account for diverse stakeholder interests, this decision analysis model integrates key metrics from suppliers, producers, distributors, consumers, and regulatory bodies, facilitating a balanced approach that aligns with sustainable development goals (SDGs). Stakeholder analysis is central to this framework, enabling companies to predict potential reactions and adapt pricing strategies that support long-term economic, social, and environmental objectives. By analyzing data from multiple stages of the supply chain, the model fosters transparency and allows stakeholders to track how pricing decisions impact resource allocation, waste reduction, and fair trade practices. This approach addresses rising consumer demand for ethical consumption choices while offering companies insights into profitability impacts associated with sustainable practices. Furthermore, it provides an actionable pathway for food industry leaders to reconcile competing priorities, such as affordable pricing for consumers and adequate returns for producers. Implementing this framework can help foster a culture of accountability and sustainability across the food industry, enhancing brand reputation and encouraging ethical consumer behavior. In conclusion, this study’s decision analysis model for sustainable food pricing offers a viable tool to evaluate ethical and transparent pricing practices, highlighting how strategic stakeholder analysis can align profitability with responsible corporate behavior. This framework supports sustainable food pricing as a core element of ethical business practices in the global food industry.

A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs

This study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employed to accurately detect and classify various NPS sources, such as livestock barns, compost heaps, greenhouses, and mulching films. The spatial information, including the area and volume of detected objects, was analyzed to track temporal changes and evaluate management strategies. The framework integrates remote sensing, deep learning, and geographic information system (GIS) analysis to enhance decision-making processes, providing detailed insight into NPS pollution dynamics over time. This approach not only improves the efficiency of NPS monitoring but also facilitates proactive management by offering precise location and environmental impact data. The results indicate that this framework can significantly improve resource allocation and environmental management practices, particularly in agriculture-dominated regions susceptible to NPS pollution, thereby contributing to the sustainable development of these areas.

A Personalist Approach to the Just Allocation of Resources in the Midst of a Pandemic

This paper examines the ethical implications of healthcare resource allocation during the first wave of COVID-19 in Italy, from 21 February to 31 May 2020, with a focus on the utilitarian principles that prioritized age-based resource allocation. By comparing this approach to an ontological personalist bioethics framework, the study aims to offer a more equitable strategy for healthcare allocation applicable to any pandemic. Data from governmental reports, healthcare policies, and ethical guidelines were analyzed, revealing that Italy’s utilitarian method led to significant ethical concerns, particularly regarding age discrimination. In contrast, the ontological personalist approach emphasizes the dignity and intrinsic worth of each individual, irrespective of age or health condition. The findings demonstrate that this personalist framework fosters a more balanced and fair allocation of resources, respecting human dignity and offering a universally applicable alternative to age-based prioritization. Ultimately, the study suggests that integrating ontological personalist principles into healthcare policies could enhance ethical decision-making in future pandemics, ensuring that resource allocation practices align more closely with human rights and individual dignity.

Study on the impact of the DRG model on human resource allocation at public hospitals

Diagnosis-related groups (DRGs) are gaining prominence in healthcare systems worldwide to standardize potential payments to hospitals. This study, conducted across public hospitals, investigates the impact of DRG implementation on human resource allocation and management practices. The research findings reveal significant changes in job roles and skill requirements based on a mixed-methods approach involving 70 healthcare professionals across various roles. 50% of respondents reported changes in daily responsibilities, and 42% noted the creation of new roles in their organizations. Significant challenges include inadequate training (46%), and coding complexity (38%). Factor analysis revealed a complex relationship between DRG familiarity, job satisfaction, and staff morale. The study also found a moderate negative correlation between the impact on morale and years of service in the current hospital, suggesting that longer-tenured staff may require additional support in adapting to DRG systems. This study addresses a knowledge gap in the human resource aspects of DRG implementation. It provides healthcare administrators and policymakers with evidence to inform strategies for effective DRG adoption and workforce management in public hospitals.

Opening Doors: (in)Equity Audits in Accounting Education

ABSTRACT An equity audit is a data-driven approach to identify and mitigate inequitable student achievement outcomes. Equity audits are a tool K–12 teachers and administrators use to incorporate data into decision-making, identify achievement gaps, and prompt remedial action. The working hypothesis behind equity audits is the existence of a subset of students who could meet high academic standards and produce exceptional work but are prevented from doing so by current resource allocation and andragogic (i.e., adult education) practice. This learning strategy presents a simple, four-step process to address inequitable student outcomes: find a goal, gather data, uncover root causes, and enact change. Data Availability: Contact the authors. JEL Classifications: I23; O42.

Dollars and Disparities: Unraveling Executive Compensation Patterns in American Non-Profit Healthcare.

This study examines trends in executive compensation at non-profit hospitals across the United States from 2018 to 2022. The research aims to address gaps in the current literature by providing an up-to-date analysis of compensation practices and their relationship to hospital financial metrics. A descriptive longitudinal study was conducted using data from Form 990 filings of 20 randomly selected non-profit hospitals across four U.S. regions. Executive compensation data, along with hospital financial metrics, were extracted and analyzed. Statistical analyses included descriptive statistics, non-parametric tests for trend analysis, and correlation studies using Kendall's Tau B. The median increase in executive compensation over the five-year period was 23.4%, with a median annual increase of 6.2%. Significant positive correlations were found between executive compensation and hospital revenue (τ = 0.676, p < 0.001), net assets (τ = 0.600, p < 0.001), and net income (τ = 0.273, p < 0.001). However, percentage changes in these variables over time were not significantly correlated. The study reveals significant growth in executive compensation at non-profit hospitals, with notable regional variations. While compensation correlates strongly with hospital financial metrics, the lack of correlation in their respective changes over time suggests a complex relationship. These findings raise important questions about resource allocation and compensation practices in non-profit healthcare institutions, highlighting the need for further research and policy discussions.

Financial Management and Resource Allocation Practices in the Schools Division of Surigao Del Norte, Philippines

The purpose of this study is to investigate the financial management and resource allocation practices of schools in the Schools Division of Surigao del Norte. Specifically, the researcher aims to identify the obstacles encountered by these school heads in allocating the Maintenance and Other Operating Expenses (MOOE) budget provided by the government, as well as the strategies they employ to address these challenges. Specifically, it described the profile of the school such as school type, school category, school classification in MOOE and school head’s appointment status. It made use of a descriptive research design employing survey technique to find the extent of financial management and resource allocation practices of school heads. The participants were the selected 56 school heads and 711 teachers of the entire schools’ division. A researcher made questionnaire was used to gather the data needed in this study. The statistical tools used included: frequency count and percentage computation, mean and standard deviation, T-test and Chi-square test. This study found out that schools Slightly practiced accounting on managing their finances. Least prioritize by the school heads is the student-support activities. Majority of the school heads overlooked the student-support activities and tend to allocate so much on infrastructure development. There is a significant difference of responses of school heads and teachers when it comes to assessing the financial management and resource allocation of their school. School administrators lack the necessary financial management abilities and as a result they fail to effectively and efficiently oversee spending through the budget. Teachers were not involved in the budgetary process and as a result, they tend to assess not so good scores on the financial management and resource allocation practices of their respective school.

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system

ObjectiveThe worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data. LimitationA significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach. MethodInitially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR). ResultA simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture. ConclusionThe study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.

Resource Allocation Change Management Practice and Performance of Kenya Revenue Authority in Nairobi City County, Kenya

Purpose: Kenya Revenue Authority is mandated to gather revenue in place of Kenyan government. The revenue to be collected is normally set as a target by the National Treasury. Over the time, the authority has continuously missed hitting the revenue collection target. As a result, the authority has endeavored to implement changes with the aim of improving and enhancing revenue collection. The current study aimed at establishing how resource allocation change management practice influenced performance of Kenya Revenue Authority in Nairobi City County, Kenya. The study was guided by Resource-Based View Theory. Methodology: The study employed a descriptive research design and targeted 470 Kenya Revenue Authority employees working at Customs and Border Control Department in Nairobi City County. A stratified random sampling approach was applied in selecting a sample of 141 respondents derived from top level, middle level, and lower level of management. The study utilized a 5-Point Likert scale questionnaire containing close ended question to gather primary data. Inferential as well as descriptive statistics were utilized to analyze the gathered data. Prior data collection, the study conducted a pilot study on 13 randomly selected respondents to assess the level of validity and reliability of the questionnaire. Findings: The descriptive statistics results revealed that all respondents on average were in agreement with statements on resource allocation change management practice. The correlation analysis results established that resource allocation change management practice positively and significantly correlates with performance of Kenya Revenue Authority. The study further established that resource allocation change management practice positively and significantly influence performance of KRA in Nairobi City County. This is shown by beta value of 0.538 and significant values of 0.000. The results bears the implications that increasing the resource allocation change management practice with one unit results to increase in the performance levels of KRA with 0.538 units. The study findings led to conclusions that resource allocation change management practice positively and significantly influences the performance of Kenya Revenue Authority in Nairobi City County. Unique contribution to theory, policy and practice: The study recommended the management Kenya Revenue Authority to enhance the resource allocation practices since the practices bears a positive and significant influence on performance.

Evaluation of Project Portfolio Management using the WSM Method

Project Portfolio Management (PPM) is a strategic approach that organizations employ to effectively manage their projects and align them with their overall business objectives. It involves the selection, prioritization, and control of projects to maximize their value and ensure successful project delivery. By implementing PPM, organizations can optimize resource allocation, mitigate risks, and improve decision-making processes. This abstract provides an introductory overview of PPM, highlighting its importance and key components. It emphasizes the role of PPM in driving organizational success by creating a balanced and diversified project portfolio that aligns with strategic goals. The abstract also mentions the benefits of PPM, such as improved resource utilization, enhanced project visibility, and better decision-making based on objective criteria. Overall, PPM serves as a vital tool for organizations to effectively manage their projects, achieve their strategic objectives, and maximize business value. Research on Project Portfolio Management (PPM) holds significant value as it contributes to the development of best practices and informed decision-making in project management. By investigating strategic alignment, resource optimization, risk management, decision-making processes, performance measurement, organizational agility, and stakeholder engagement, researchers can help organizations improve project selection, resource allocation, and overall portfolio management practices. This research significance is crucial for practitioners and organizations aiming to enhance project outcomes, achieve strategic objectives, and maximize business value. In this research we will be using weighted-sum method. Project 1, Project 2, Project 3, Project 4, Project 5 and Time, Cost, Quality, Risk, work health and safety out of all the 5 projects, Project 1 gets first rank in Project Portfolio Management. With the weighted sum method, we are able to find the best project in Project Portfolio Management has been evaluated with various parameters and methodology.

Enhancing large-span structure design and maintenance through the synergy of CAD, BIM, and IoT-AI technologies

The integration of Computer-Aided Design (CAD), Building Information Modeling (BIM), and Internet of Things (IoT) with Artificial Intelligence (AI) represents a transformative shift in the architectural and engineering practices for large-span structures. This paper delves into the quantitative and qualitative enhancements brought about by these technologies, focusing on their pivotal roles in design precision, structural analysis, project management, and maintenance strategies. CAD's evolution from simple drafting to complex 3D modeling has significantly reduced design time and errors, while its integration with structural analysis software has improved load distribution accuracy and material cost efficiency. BIM's contribution to design, construction, and sustainability emphasizes optimized resource allocation and energy-efficient practices. Furthermore, IoT and AI's role in real-time monitoring and predictive maintenance underpins a proactive approach to structural health, ensuring longevity and safety. Through statistical analysis, mathematical modeling, and case studies, this paper highlights the synergistic impact of these technologies on reducing project timelines, enhancing collaboration, and fostering innovation in large-span structure engineering. The findings advocate for a multidisciplinary approach, combining engineering expertise with advanced computational tools to achieve superior outcomes in the construction and maintenance of large-span structures.

Gendered Vulnerabilities in Disaster Responses: A Case Study of Majuli Island, Assam

This study employs a qualitative research methodology to study the gender disparities in disaster responses, focusing on the case study of Majuli Island in Assam, India, to provide nuanced insights into the distinct vulnerabilities experienced by women, particularly in the context of floods. Through an in-depth exploration of social norms, behavioural restrictions, and resource allocation practices, the research elucidates how women’s roles as caregivers and providers constrain their mobility and exacerbate their susceptibility to harm. The gendered ramifications of disasters on health, encompassing reproductive health issues and malnutrition, are underscored, alongside the psychological and physiological impacts that precipitate enduring mental anguish and heightened risks of violence and abuse. The findings underscore the imperative for gender-sensitive approaches to disaster preparedness, response, and recovery, advocating for comprehensive strategies that foreground women’s safety, well-being, and empowerment. By adopting a qualitative research methodology grounded in descriptive investigation, the study delves deeply into the lived experiences of communities grappling with disaster risks, offering invaluable insights for policymakers, practitioners, and communities striving to foster inclusive and resilient societies.

Resource assignment optimization in design firms

Resource allocation is a critical aspect of business operations that can significantly affect efficiency, quality, and profitability. In the context of design firms, the importance of resource allocation cannot be overstated, as it directly impacts the core operations and reputation of the business. However, despite its importance, resource allocation in design firms has received relatively scant attention in the literature, the paper aims to address this gap in the literature by proposing a resource allocation model specifically designed for architecture design firms. The developed model utilizes a Greedy algorithm approach to optimally allocate resources for specific design tasks, taking into consideration the qualifications and expertise of team members. By selecting the most suitable team members for each task, the model aims to maximize profitability and efficiency while minimizing costs. The study emphasizes the crucial role of resource allocation in architecture design firms and introduces a practical and adaptable optimization model, employing a Greedy algorithm approach to strategically assigning tasks and significantly improving profitability compared to random allocation methods. The findings contribute to addressing a notable gap in the literature on resource allocation within design firms and offering valuable insights for firms seeking to enhance operations and establish sustainable practices. The study is specific to architecture design firms, and future research could explore the model's impact on employees’ job satisfaction and the role of architectural management in influencing resource allocation practices.

Scheduling and Management of Sand Excavation Quarry

Abstract: With an emphasis on scheduling tactics and operational effectiveness, this study explores the crucial area of sand excavation quarry management. This study provides useful insights for the optimization of sand quarry operations by a thorough investigation of scheduling strategies, resource allocation practices, and management approaches specifically designed for the complex dynamics of quarry excavation

Bibliometric Mapping of Green Technology Innovation in the Manufacturing Industry

This bibliometric mapping study provides a comprehensive analysis of green technology innovation within the manufacturing industry, employing quantitative methods to uncover thematic clusters, research trends, author collaborations, and research gaps. Three distinct thematic clusters—Sustainable Manufacture, Enterprise, and Challenge—emerge, each offering insights into prevalent terms and themes. The analysis of research trends reveals the evolution of key terms over time, highlighting emerging topics. Author collaboration mapping identifies influential researchers and collaborative networks, facilitating interdisciplinary partnerships. The study's implications extend to resource allocation, policy formulation, and industry best practices, emphasizing academia's pivotal role in sustainable manufacturing. Overall, this research serves as a roadmap for future endeavors, fostering collaboration, informing policy, and driving innovation in green technology within the manufacturing sector.

Implementation, Challenges, Best Practices, and Innovation of Distance Education and Online Training in Vocational Education

This quantitative descriptive study investigated the landscape of online vocational education and training, focusing on applications, challenges, best practices, and strategic planning. Conducted in Yiwu City, Zhejiang Province, China with a specific emphasis on the Zhejiang Institute of Mechanical and Electrical Technicians. Using stratified random sampling to ensure a representative sample of 98 participants. In their research, the authors provide an in-depth look at the online vocational education and training platform, highlighting its strengths and areas for improvement. While the platform is praised for its user-friendliness, navigation, and alignment with learning objectives, it also faces challenges in overall usability and learner engagement. Users appreciate the platform's intuitive interface and visually appealing design, but they note a need for better language diversity support in accessibility features. Content organization is generally logical, and search functionalities receive positive feedback, although there's room for improvement in terms of clarifying explanations and content progression. Turning to vocational education institutions, the research reveals key insights. These institutions are recognized for their effective technological support and the importance of continuous investment in keeping up with evolving technologies. Quality assurance, accreditation, curriculum updates, and hiring qualified instructors are seen as essential priorities. Improving assessment methods is also crucial for enhancing the quality of online vocational education. While efforts to promote access and equity are commendable, proactive measures are needed to address potential barriers. The institutions' commitment to resource allocation and financial sustainability practices, guided by data and cost-effectiveness, garners strong support. The research findings emphasize the critical components of online vocational education and training courses. Users highly value interactive and engaging content, suggesting opportunities for enhancements in multimedia integration and gamification. A user-friendly interface is deemed crucial, with potential for improvements in intuitiveness and interactivity. Effective communication channels are highlighted as vital for fostering a sense of community among learners. Aligned with the research, the authors identify crucial focus areas and strategies for enhancing online vocational education and training. These encompass improving interface usability, strengthening communication channels, enriching multimedia engagement, ensuring accessibility, and introducing innovative collaboration features. These strategies serve as concrete commitments aimed at augmenting educational quality, accessibility, and inclusivity for all learners. In conclusion, the research offers a comprehensive view of the online vocational education and training landscape, recognizing its strengths while outlining opportunities for improvement. It calls for collaborative efforts to enhance user engagement, interface usability, and multimedia integration. Furthermore, the study's recommendations provide actionable strategies to shape a more inclusive, engaging, and accessible future for vocational education and training.

LOCAL GOVERNMENT ADAPTATION STRATEGIES IN FACING CLIMATE CHANGE: AN INNOVATIVE FRAMEWORK

In the face of escalating climate change challenges, this study presents an innovative framework for local government adaptation strategies, emphasizing the integration of scientific research and policy-making. Through a comprehensive analysis of existing adaptation models and the incorporation of case studies from diverse geographical regions, the paper identifies key factors influencing effective local government responses to climate change. The proposed framework highlights the importance of community engagement, resource allocation, and sustainable development practices in enhancing resilience. By evaluating the effectiveness of various adaptation strategies, the study offers insights into best practices and potential pitfalls, aiming to guide local governments in developing tailored approaches to climate change challenges. The findings underscore the necessity for a dynamic, multi-stakeholder approach, bridging the gap between theoretical understanding and practical implementation of climate adaptation measures.

Strategic Leadership Practices and Devolved Administrative Structures in Selected Semi-Arid Counties in Kenya

Purpose: The presence of devolved administrative structures plays a critical role in safeguarding against the abuse of power and promoting political stability. Consequently, ensuring the effective operation of these structures in Kenya, especially given the introduction of devolution, is of paramount importance. The primary reason for the existing inadequacies in service delivery has been the suboptimal implementation of these devolved administrative entities, often stemming from ineffective managerial practices. To bridge this gap, this study had four specific objectives aimed at delineating the scope of strategic leadership practices within these devolved administrative structures: assessing the impact of resource allocation practices, evaluating the influence of public accountability practices, scrutinizing the effect of stakeholder involvement, and examining the moderating impact of legal factors on the relationship between strategic leadership practices and these administrative structures. The study incorporated three theoretical frameworks: the resource-based view theory, the transformational leadership theory and the institutional theory.&#x0D; Methodology: The study's target population encompassed 500 participants from Taveta and Makueni counties. Employing purposive and stratified random sampling techniques, a sample size of 223 was derived from the target population. The research design adopted was descriptive research, intended to provide insights into questions regarding what, where, and when. Quantitative and qualitative analytical methods were applied, with SPSS for data coding and STATA version 12 for analysis. Multiple linear regression analysis was conducted to examine the collective impacts of resource allocation practices, public accountability practices, and stakeholder involvement practices on devolved administrative structures.&#x0D; Findings: The study ascertained that strategic leadership practices have the potential to enhance the efficiency of decentralized administrative organizations and streamline the dissemination of information.&#x0D; Recommendations: The research recommends that county leaders articulate plans in a manner that encourages the development of devolved administrative structures, leading to sustainable transformation.

Government resource allocation practices toward carbon neutrality in China: A hybrid system approach

Government resource allocation practices for achieving carbon neutrality need to be guided by dynamic system theory to identify potential dynamics in China. In particular, dynamic system theory addresses the complex interrelationships and identifies the changes in dynamics to support the decision-making process in pursuing carbon neutrality among international trade, transportation, industrial demand and dynamic control. However, carbon neutrality involves various activities to better understand the complex activity interrelationships. The data are collected from four major databases including 30 Chinese provinces/cities covering a six-year period with the accumulated frequency in the Baidu Index. This study proposes a hybrid system approach to dynamic resource analysis. The results showed that carbon intensity control dynamics impose a greater influence on the other dynamics toward carbon neutrality. Many provinces can achieve carbon neutrality by regulating the electronics and telecommunications equipment industry.