Strategic Resource Management Research Articles

A Comparative Analysis of Human Resource for Health at three Teaching Hospitals in Sri Lanka: Availability and Workload

Background: This study investigates the availability and workload of Human Resources for Health (HRH) at three major teaching hospitals in Sri Lanka: National Hospital Sri Lanka (NHSL), Colombo South Teaching Hospital (CSTH), and Colombo North Teaching Hospital (CNTH). Understanding these hospitals' operational capacities and efficiency is crucial for enhancing healthcare delivery.Methods: A descriptive cross-sectional study was conducted using secondary data sources. Data extraction sheets were distributed to relevant hospital administrative personnel. The study analyzed bed strength, staff numbers, patient admissions, outpatient visits, and surgical throughput. Comparisons were made to evaluate efficiency and resource utilization across the three hospitals.Results: NHSL has the highest bed strength (3,321 beds), followed by CNTH (1,836 beds) and CSTH (1,242 beds). Bed occupancy rate was highest at NHSL at 75.76%, whereas CSTH and CNTH recorded 75.51% and 61.68%, respectively. Bed turnover was highest at CSTH (10.83/month), while the average length of stay was found to be lowest at CSTH (2.09 days). NHSL employs 1,135 doctors and 3,554 nurses, CSTH employs 537 doctors and 529 nurses, and CNTH employs 418 doctors and 607 nurses. NHSL handled the highest number of admissions and clinic patients, while CNTH had the highest number of clinic patients per doctor, indicating a higher operational efficiency but more significant strain on healthcare providers.Conclusions: NHSL, CSTH, and CNTH exhibit significant differences in operational capacities and efficiencies. To balance workload and improve healthcare outcomes, strategic resource management, enhanced staffing, and improved operational systems are recommended. Future studies should explore patient satisfaction and outcomes to improve policy decisions further.

Resource Management Practices and Completion of Road Construction Projects in Rwanda: Base-Butaro-Kidaho Road Project

The study assessed the effect of resource management practices on completion of road construction projects in Rwanda, with a specific focus on the Base-Butaro-Kidaho road construction project. The research aimed to evaluate the effect of material resource management, financial resource management, and human resource management on the project's successful and timely completion. A mixed-methods approach was employed, combining quantitative and qualitative data collection techniques. The study population consisted of 393 stakeholders involved in the planning, execution, and oversight of the Base-Butaro-Kidaho road project. A sample of 198 respondents was selected using stratified and simple random sampling techniques, complemented by purposive sampling to identify key respondents Descriptive statistics, correlation coefficients, and multiple linear regression were employed to analyze the data. Key findings indicate that material resource management had an overall average mean score of 4.53, showing that it was highly effective in ensuring the project's completion. Financial resource management showed a strong positive correlation with project completion (r = .504, p < .01), while human resource management had the strongest positive impact (r = .570, p < .01). The overall resource management practices demonstrated a significant correlation with project success (r = .643, p < .01). The regression analysis further revealed that human resource management had the most substantial positive impact on project completion, followed by financial and material resource management. The ANOVA results confirmed the statistical significance of all three management practices in predicting project success. The study concludes that comprehensive and strategic resource management practices are essential for the successful completion of road construction projects, with human resource management being the most influential factor. Based on these findings, the study recommends enhancing human resource management, strengthening financial resource management, and improving material resource management to ensure the continued success of future projects. Suggestions for further research include expanding the scope to other infrastructure projects, investigating additional factors influencing project success, and conducting cross-regional comparisons to understand contextual influences on resource management practices.

Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland II. A hierarchical dynamic linear model

The sustainability of the salmon farming industry is being challenged by increased mortality rates. Scotland’s open-source salmon production data provides the possibility of developing an industry-wide mortality monitoring model, valuable for identifying and addressing unexpected increases in mortality without needing data sharing agreements across different companies. This study aimed to utilize these data to develop a hierarchical dynamic linear model (DLM) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. We evaluated whether considering the hierarchical structure present in the data (country, region, and site) would improve mortality predictions when compared to the production cycle level DLMs developed in a previous study. Our findings demonstrated that the hierarchical DLM outperformed the production cycle level DLMs, confirming the value of this more complex modelling approach. Nevertheless, the hierarchical model, like the production cycle level DLMs, exhibited some uncertainty in the mortality predictions. When mortality is higher than expected, site level warnings are generated, which can encourage producers and inspectors to further investigate the cause. Between 2015 and 2020, approximately 25% of the production cycles and 50% of the sites encountered at least one warning, with most warnings happening in the summer and autumn months. Additionally, the hierarchical model enabled monitoring mortality at multiple levels. This information is useful for various stakeholders as part of a monitoring system, offering insights into mortality trends at national, regional, and sites levels that may benefit from strategic resource management. Recommendations for model improvements include utilizing shorter data aggregation periods, such as weekly, which are not currently available as open-source data.

Sustainability Implications of Commodity Price Shocks and Commodity Dependence in Selected Sub-Saharan Countries

Sub-Saharan economies often rely heavily on a narrow range of commodities, making them particularly vulnerable to price fluctuations in global markets. This volatility predisposes these countries to economic instability, threatening short-term growth and long-term development goals. As a result, this study examines the sustainability implications of commodity price volatility and commodity dependence for 31 Sub-Saharan African countries from 2000 to 2023. Eleven agricultural commodity-dependent countries, six energy commodity-dependent countries, and fourteen mineral and metal ore-dependent countries were chosen. This study uses balanced annual panel data from World Development Indicators, World Bank Commodity Price Data, and Federal Reserve Bank Data. The data were analyzed using the VECM, and this study’s findings were threefold and unanimous for all three categories of commodities (agricultural, energy and mineral, and metal ore). First, commodity dependence is positively related to economic growth, suggesting that higher commodity prices benefit the economy in the long run. Second, commodity price volatility is negatively related to economic growth, indicating adverse impacts on economic stability in the long run. Third, commodity dependence is positively related to commodity price volatility in the long run. By analyzing the interconnectedness of these factors, this study underscores the need for diversified economic policies and sustainable practices to reduce vulnerability and promote sustainable development in the region. The findings highlight the critical role of strategic resource management and policy interventions in achieving economic stability and ensuring the well-being of future generations.

Managing resources for digital transformation in supply chain integration: The role of hybrid governance structures

Digital transformation (DT) plays a crucial role in shaping operations and supply chain management. Through real-time data sharing and automated decision-making systems, DT fosters supply chain integration (SCI), facilitated by key digital technologies like Internet of Things (IoT), cloud computing, big data, and artificial intelligence. Effective DT necessitates managing organizational digital resources encompassing technical domains such as software, hardware, and data science, alongside financial resources and managerial capabilities. Navigating the complexities of DT requires strategic decisions in supply chain governance regarding the management of these digital resources. While in-house development offers control and differentiation, it entails organizational and financial risks. Conversely, outsourcing digital activities reduces risks but increases technological dependence. Hybrid governance (concurrently sourcing in-house and outsourcing activities) presents a reconciliatory approach that can be valuable for DT in SCI. This study investigates the contribution of hybrid governance for DT, focusing on its effects on SCI and delivery performance. Through quantitative analysis of 379 manufacturing companies, our findings reveal that hybrid governance positively influences the digitalization of SCI and contributes to delivery performance through a digitalized SCI. Our research contributes to the theoretical understanding of hybrid governance in DT, offering insights into strategic digital resource management for SCI. It underscores the importance of balancing internal and external resources to optimize supply chain performance in the digital era.

Political and legal analysis of the mechanisms of providing Ukraine with resources of strategic importance in the context of increasing the national defence potential

This study addresses the critical issue of enhancing Ukraine’s defence capabilities through the efficient management of strategic resources amidst ongoing Russian aggression. The research aims to develop substantiated recommendations for optimizing the legal and political mechanisms that govern the provision of strategic resources to Ukraine. The study employs a formal legal method to analyze key national and international legal frameworks, including the Law of Ukraine “On the National Material Reserve” and the Code of Ukraine “On Subsoil,” focusing on their efficacy in wartime conditions. Also, political analysis, comparative analysis, legislative review and document analysis were used. The research highlights the gaps in existing legislation, particularly in adapting to the needs of martial law and the complexities of interagency coordination. The findings reveal that while Ukraine has established an extensive legal framework for managing strategic resources, significant issues persist in areas such as transparency, interagency cooperation, and bureaucratization. The study advocates for legislative amendments to better suit wartime needs, the implementation of a unified monitoring system, and enhanced international cooperation, particularly with bodies like the UN and World Bank. The conclusions emphasize the importance of Ukraine’s participation in international transparency initiatives and the need to align national legislation with EU standards. Overall, the thesis of this work is the development of substantiated recommendations for optimizing Ukraine’s strategic resource management in the context of war, while the research niche focuses on the analysis of political and legal mechanisms that influence the state’s defence capabilities.

Unraveling the Collapse: A Study of Failed Startups in India

The Indian startup ecosystem has experienced remarkable growth in a relatively short period, driven by a combination of factors such as government initiatives, improved access to capital, demographic strengths, and rapid technological advancements. Despite ongoing challenges, the resilience and creativity of Indian entrepreneurs continue to foster innovation and inspire optimism for the future. Startup failures in India highlight the importance of adopting a comprehensive approach that includes sound business planning, strategic resource management, and a resilient entrepreneurial mindset. With the right mix of policies, infrastructure, and investment, India is well-positioned to become a global hub of innovation, generating employment, boosting economic growth, and addressing some of the world’s most pressing issues through entrepreneurship. This paper examines the key factors contributing to the recent struggles of startups in India, focusing on 12 companies.

Optimization of microgrid operations using renewable energy sources

Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Operations, Renewable Energy, Energy Sources.

Kepemimpinan dan Komunikasi serta Keterampilan Manajerial Pendidikan

This research explores the relationship between leadership, communication, and managerial skills in an educational context. The main focus is how effective leadership and good communication can improve managerial skills in an educational environment. The research results show that participative leadership and open communication contribute significantly to improving managerial skills, including strategic planning, decision making, and resource management. These findings emphasize the importance of leadership and communication training in managerial development in the education sector, which can ultimately improve the performance and effectiveness of educational institutions.

Landscape planning and polycentricity: potentialities for regional integration in transboundary natural resources

This article proposes a discussion on the potential application of polycentricity strategies in the management of strategic transboundary natural resources, such as shared water bodies and river basins with regional potential for water supply and energy production.As a method, it presents a review of the literature on polycentricity, focusing on the challenges and opportunities of governance of shared territories among polycentric cities. It identifies key aspects of polycentricity, focusing on topics such as the integration of cities in polycentric urban regions, regional policies and governance in polycentric regions, and polycentricity and natural resource management.Complementarily, potential landscape planning strategies that can be applied to strengthen regional identity between centralities, as well as to strengthen the functional, institutional and cultural identity and integration of a polycentric region, are also presented.

Economic Feasibility of LNG Business: An Integrated Model and Case Study Analysis

Liquefied natural gas (LNG), recognized as the fossil fuel with the lowest carbon emission intensity, is a crucial transitional energy source in the global shift towards low-carbon energy. As the natural gas industry undergoes rapid expansion, the complexity of investment business models has increased significantly. Optimizing the combination of various segments within the value chain has become standard practice, making it essential to control risks and enhance economic benefits in these multifaceted scenarios. This paper introduces an integrated economic model encompassing upstream, liquefaction, shipping, regasification, and consumption, suitable for both upstream and downstream integration. The model offers a comprehensive analysis of the primary business models and key factors across each segment of the value chain. By constructing a robust economic evaluation framework, the study aims to provide a holistic approach to understanding the economic feasibility of LNG projects. Two detailed case studies are conducted to demonstrate the application and effectiveness of the proposed model, highlighting its capability to guide investment decisions, support risk management, and optimize asset portfolios. The integrated economic model developed in this study serves as a valuable tool for stakeholders in the LNG industry. It not only facilitates informed investment decision-making but also enhances the strategic management of risks and resources. By leveraging this model, investors and managers can better navigate the complexities of the LNG business, ensuring sustainable and economically viable operations.

Integrating Total Quality Management with Strategic, Operational, and Human Resource Management: A Qualitative Exploration of Synergies for Enhanced Organizational Performance

Integrating Total Quality Management with Strategic, Operational, and Human Resource Management: A Qualitative Exploration of Synergies for Enhanced Organizational Performance

Anyalytic Hierarchy Process to Optimization of Strategic Resource Management in the Context of Innovative Development

Anyalytic Hierarchy Process to Optimization of Strategic Resource Management in the Context of Innovative Development

Comprehending hydrochemical fingerprint, spatial patterns, and driving forces of groundwater in a topical coastal plain of Northern China based on hydrochemical and isotopic evaluations

To comprehensively comprehend the hydrochemical features, spatial patterns, and controlling mechanisms of groundwater in the Longkou coastal plain (LCP), a prime coastal plain in northern China, a detailed study utilizing statistical analyses, Piper diagram, Gibbs model diagram, ion ratio methods, and reverse hydrogeochemical simulation was performed on 76 groundwater samples collected in 2021. The findings of this study reveal that the groundwater in the LCP has an average pH value of 7.6, indicating weak alkalinity. The groundwater types are predominantly mixed, followed by Cl–Ca. The NO3− concentration in groundwater is relatively high, with a mean value of 326 mg/L, originating mainly from agricultural production activities. In addition to NO3−, the spatial distribution analysis of the principal chemical constituents in the groundwater demonstrated a subtle north-south gradient. The chemical signatures of groundwater are primarily influenced by the process of rock weathering, resulting from the dissolution of silicates and carbonates minerals as well as gypsum. Indeed, cation exchange represents a pivotal hydrogeochemical reaction within the groundwater system of the region. The intrusion of seawater into groundwater primarily exhibits a mild nature, mostly in the form of mixing intrusion in the initial stage, affecting the western and northern regions of the LCP to a greater extent. The primary source of groundwater recharge is attributed to atmospheric precipitation; however, it is partially affected by evaporation. In addition, LCP groundwater is also being recharged by seawater intrusion, mainly in the western and northern parts of the study area. Overall, the findings of this research hold significant implications for the strategic management of groundwater resources and the implementation of measures to prevent and mitigate seawater intrusion not only in the LCP but also in analogous coastal regions across the globe.

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

Strategic water resource management: pump stations in Fayoum City, Egypt

Water scarcity presents significant challenges to sustainable development, particularly in arid regions like Fayoum City, Egypt, which faces particular water challenges due to its unique topography. This study explores the pivotal role of pump stations and wastewater reuse in mitigating water scarcity and promoting sustainable water management practices. Utilizing a comprehensive mixed-method approach, including desk research, field surveys, stakeholder interviews, and integrating the Analytic Hierarchy Process (AHP) into a decision-making framework. The study categorizes pump stations into priority ranking groups based on the evaluation of the following criteria: efficiency, operating hours, working unit ratio, and discharge capacity. The investigation reveals that the 72 pump stations in Fayoum City play a vital role in regulating water levels, optimizing distribution, and facilitating the reuse of irrigation wastewater. Despite operational challenges, such as manpower shortages and maintenance issues, these stations are crucial for sustaining agricultural productivity and addressing water scarcity concerns, including the prevention of recurring inundation events like the one in 2012. Moreover, the study underscores the potential of wastewater reuse as a sustainable solution to water scarcity, particularly in meeting agricultural water demands and mitigating water balance issues, such as inundation. Based on the findings, the study proposes actionable recommendations, including upgrading high-priority pump stations, evaluating lower-priority ones, enhancing canal infrastructure, and promoting water-efficient irrigation methods. In conclusion, this study provides valuable insights into the pivotal role of pump stations and wastewater reuse in addressing water scarcity challenges in arid regions. By implementing the proposed recommendations, Fayoum City can optimize its water management practices, ensure water security, and support the long-term development of the region.

Turkey Paper and Paper Products Sector Import Forecasting with Artificial Neural Networks

The paper and paper products sector is a crucial component of the Turkish economy, characterized by significant interactions with various other sectors. Türkiye imports substantial amounts of paper, playing a vital role in the growth and sustainability of this sector. Accurate import forecasting is essential for strategic planning and resource management. This study aims to forecast the imports of the Turkish paper sector for the period from April 2023 to March 2024 using two artificial neural network (ANN) models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset, obtained from the Turkish Statistical Institute (TurkStat), covers 219 months of data from 2005 to 2023. The dependent variable is Türkiye’s monthly import value of paper and paper products, while the independent variables include the monthly average US Dollar exchange rate, monthly imports of Türkiye, the Manufacturing Industry Production Index, the Paper Production Index, and the monthly exports of paper and paper products from Türkiye. The MLP model forecasts that the monthly imports of paper and paper products will range between 270 to 300 million USD, while the RBF model predicts values between 268 and 321 million USD. These findings underscore the efficacy of ANNs in providing accurate and reliable forecasts. This study addresses a gap in the literature by applying ANN methods to forecast imports in the paper and paper products sector, presenting a novel approach that can assist companies in making better-informed decisions regarding inventory management, production planning, and marketing strategies. By leveraging the advanced computational power and pattern recognition capabilities of ANNs, the study aims to enhance the strategic planning processes in the paper and paper products industry. The traditional methods often used in trade data analysis and forecasting are limited in capturing the complex and non-linear relationships present in economic data. This study's application of ANNs offers a significant advancement by utilizing models that can better handle such complexities. The accuracy of the MLP and RBF models highlights their potential as valuable tools for economic forecasting, providing insights that are crucial for optimizing supply chain operations and improving market responsiveness. The results indicate that companies can achieve better operational performance and increased customer satisfaction by effectively forecasting future import requirements. The originality of this study lies in its methodological approach, utilizing ANN models to forecast import values in a sector where traditional methods have been predominant. This innovative approach not only contributes to the existing body of knowledge but also offers practical applications for businesses within the sector. The detailed analysis of the data, combined with the robust modeling techniques employed, provides a comprehensive framework for understanding the dynamics of paper imports and making strategic decisions based on accurate predictions. In conclusion, the study demonstrates the significant success of artificial neural networks in predicting import values for the Turkish paper and paper products sector. The findings provide valuable information that can aid companies in strategic planning, enhancing their ability to manage inventory, plan production, and develop effective market strategies. The research contributes to the literature by filling a gap with its innovative approach, offering new perspectives and practical applications for improving decision-making processes in the industry.

Trends of Sediment Resuspension and Budget in Southern Lake Michigan Under Changing Wave Climate and Hydrodynamic Environment

AbstractSediment suspension and transport driven by waves and currents play a significant role in both the ecological and physical environments of large lakes. Lake Michigan has faced a rapidly increasing water level associated with intensified wind waves in the past decade. To investigate the spatiotemporal characteristics of suspended sediment concentration (SSC) and associated coastal sediment budgets in southern Lake Michigan, a 30‐year (1991–2020) hindcast was performed using a coupled wave‐current‐sediment model (SWAN‐FVCOM‐CSTMS). We found that in southern Lake Michigan, the basin‐wide mean SSC increased, and the coastal sediment loss accelerated dramatically, corresponding with intensified waves, currents and lake water level rises over the past decade. The basin‐wide mean SSC, coastal sediment loss, wave height, wind speed, current speed, and water level in southern Lake Michigan are highly correlated. Spatially, the results reveal decreases in coastal SSC and sediment loss in the western portion of the southern basin, while the eastern sectors show an increase in both metrics. This reflects a clear shift in the wave climate and hydrodynamic environment. The alterations in long‐term coastal sediment budgets imply that considerable shoreline transformations are being influenced by modifications in the wave climate. Understanding the spatiotemporal characteristics of SSC and coastal sediment budgets is crucial for strategic water resource management and coastal infrastructure planning.

Unraveling Ghana’s Resource Curse Hypothesis: Analyzing Natural Resources and Economic Growth with a Focus on Oil Exploration

This study examines the intricate relationship between natural resource abundance, with a specific focus on oil production, and its impact on economic growth in Ghana. Through the application of the robust Fully Modified OLS methodology and using data spanned from 1960–2021 the research underscores the essential inclusion of oil as a significant variable in comprehending economic growth dynamics. Contrary to traditional resource curse theories, the study unveils a positive nexus between oil production and economic growth, particularly within a comprehensive variable framework. This finding challenges simplistic resource curse notions and underscores the need for a holistic economic perspective. Overall, the results show that the impact of oil production on economic growth is sensitive to the inclusion or exclusion of other variables in the model. In Model 1, where all variables are included, oil production has a significant positive (0.0112**) impact on growth. Ghana’s success in avoiding the resource curse is attributed to a multifaceted strategy encompassing diversified economic approaches, transparent governance, and responsible oil revenue management. Importantly, the inclusion of oil as a pivotal variable is well-justified by its tangible contributions to economic growth. The observed positive impacts emphasize the benefits of harnessing oil resources while maintaining a holistic view of the broader economic context. Looking ahead, the insights inform policymakers in resource-rich nations, illustrating how strategic resource management—illustrated by oil—can drive resilient and comprehensive economic growth. Ghana’s experience serves as a compelling template for informed policy decisions, offering valuable lessons for achieving sustainable prosperity.

Patterns of Resource Allocation and Divisional Budgets: The CEO's Strategic Lens Across Various Industries

This paper explores how Chief Executive Officers (CEOs) across various industries adapt their resource allocation and divisional budgeting strategies in response to contemporary business challenges. It aims to shed light on the evolving role of CEOs in steering organizational success through strategic initiatives, highlighting the shift from traditional, efficiency-focused models to flexible, value-driven approaches. By conducting a qualitative analysis of recent literature, the study examines patterns in strategic decision-making, focusing on the balance between operational needs and strategic goals amidst changing market conditions. The research emphasizes the impact of industry-specific factors, such as market volatility and organizational size, on these strategic adaptations. Findings reveal a significant trend towards more adaptable and strategic resource management practices among CEOs, moving beyond short-term operational efficiency to support long-term goals. This shift is evident across different sectors, including healthcare, manufacturing, and e-commerce, though the extent and nature of its implementation vary according to each industry's unique challenges and opportunities. The study underscores the importance of strategic adaptability for organizational resilience and success in a rapidly evolving business landscape.