Structure Decisions Research Articles

Sistem Jakarta Pendukung Keputusan Pemilihan Siswa-Siswi Terbaik Dengan Metode Saw (Simple Additive Weigthing) Pada Sekolah SMP Negeri 16

The selection of the best students in schools is essential to recognize both academic and non-academic achievements and to motivate other students to improve their skills. SMP Negeri 16 Jakarta requires an objective decision support system to select the best students based on predefined criteria. This study aims to design and implement a decision support system using the Simple Additive Weighting (SAW) method to facilitate the selection of the best students at SMPN 16 Jakarta. The SAW method is chosen due to its capability to accommodate multiple criteria through weighting and ranking for each alternative. The research process involves observation, interviews, and documentation, which are then used to determine primary criteria such as academic achievement, social skills, and participation in school activities. The findings show that the application of the SAW method enables a more accurate and objective selection of the best students by ranking based on the final score of each criterion. This system is expected to assist SMPN 16 Jakarta in making more structured decisions and supporting educational quality improvement.

Placing environmental democracy at the heart of co-governance: rethinking the governance of complex waterways

ABSTRACT The ways in which complex systems are governed often generate democratic malaise due to a gap between people’s expectations and the ability of authorities to meet these expectations. Additionally, misalignment between ecological and social systems makes it difficult for decision-makers to adapt to dynamic environments. Co-governance, which involves collaboration and engagement between governments and a range of stakeholders, is a common mechanism to address these challenges. However, co-governance has been criticized for being exclusionary, ad hoc, and counter to democratic principles. We argue that principles of environmental democracy offer a way to strengthen the effectiveness and legitimacy of co-governance. We base this argument on findings from research on the governance of national historic waterways in Ontario, Canada, specifically the Rideau Canal and Trent-Severn Waterway. These waterways reach across multiple watersheds, territories, and jurisdictions, involving a range of authorities and different stakeholder groups, many of whom have expressed dissatisfaction with current decision-making structures. By examining the difficulties involved in governing these waterways, we propose three reforms to co-governance that would enhance environmental democracy: (1) tiered-mechanisms that facilitate collaborative governance, (2) targeted collaborative exercises to create and strengthen within-ties across stakeholder groups, and (3) semi-regular forums to support communication among stakeholder groups.

A data-driven large-scale group decision-making framework for managing ratings and text reviews

Even though the integration of sentiment analysis and decision-making techniques has become popular in recent years, most of the related studies only consider the obtained sentiment score, thus neglecting the numerical ratings that are usually attached to text reviews. This paper introduces STandR (Sentiment from Text and Ratings)-BUI (Basic Uncertain Information), a novel preference-modeling structure for data-driven decision-making using social media microblogging information. STandR-BUI combines both the numerical rating and the sentiment score of a product into a BUI value, which provides a more precise representation of users’ opinions. In addition, we propose a consensus framework to make decisions based on the STandR-BUI values which can manage thousands of user reviews. Finally, an illustrative example is provided to demonstrate its effectiveness.

Adaptive Prediction Structure for Learned Video Compression

Learned video compression has developed rapidly and shown competitive rate-distortion performance compared with the latest traditional video coding standard H.266 (VVC). However, existing works were restricted to fixed prediction direction and GoP size. The inflexibility on prediction structure hinders learned video compression towards optimal compression efficiency in diverse motion scenarios. In this paper, we propose to advance learned video compression with adaptive prediction structure decision. Specifically, we propose a unified compression framework that supports both forward prediction and bi-directional prediction. The framework can flexibly switch to different prediction direction to achieve better prediction performance. Meanwhile, we propose a low-complexity prediction structure decision algorithm, where prediction direction and GoP size are adaptively determined based on motion complexity to achieve optimal compression efficiency. Experimental results demonstrate that the proposed unified framework with adaptive decision algorithm improves compression efficiency of pure forward prediction-based or bi-directional prediction-based framework with neglectable ( \(0.9\%\) ) encoding time increment. Meanwhile, it achieves comparable compression performance with VVC and recent learned video coding methods.

From policy to practice: How NATO joined forces with NGOs for the protection of civilians

North Atlantic Treaty Organization’s (NATO’s) active cooperation with non-governmental organisations (NGOs) on the protection of civilians (PoC) challenges the academic assumption that NATO’s decision-making structures are generally closed to civil society participation. What explains the decision of NATO officials to work with NGOs when developing a Policy for the PoC, and how did this cooperation take place? Combining insights from resource dependency and organisational learning theories, we explore whether and how concerns about public legitimacy and operational effectiveness can explain that NATO engaged with NGOs during its learning process on PoC. Methodologically, we combine data from 26 interviews with NATO officials and NGO representatives with a document analysis of 648 NATO publications. Our analysis shows that NATO’s collaboration with NGOs in the adoption of a PoC policy was very much a bottom-up process. Military commanders began engaging with NGOs on the ground during NATO’s International Security Assistance Force operation in Afghanistan (2001–2014). NGOs then gradually gained access to the formulation and institutionalisation of the PoC policy. While legitimacy concerns triggered NATO’s initial cooperation with NGOs, effectiveness concerns related to policy implementation facilitated stronger cooperation once the process was underway.

Carbon emission reducing and financing strategies of capital-constrained supply chains using equity financing under different power structures

AbstractThis study considers a low-carbon supply chain model comprised of a capital-constrained manufacturer and a retailer under a cap-and-trade system. These two parties can choose from two financing modes: bank financing or mixed financing, in which the latter combines bank financing with equity financing. Two decision-making power structures are formulated, namely the Nash game model and the retailer-led Stackelberg game model. Subsequently, a comparative analysis of equilibrium outcomes for both parties within these power structures is performed, yielding the following insights: (1) In the bank financing mode, retailers find greater profitability in adopting dominance. When emission reduction efficiency is high, manufacturers obtain more profit within a Nash game context. However, when emission reduction efficiency is low, manufacturers accrue more profit in a Stackelberg game context. In the mixed financing mode without cap-and-trade regulations, supply chain participants do not express a preference for any specific game structure. (2) Without cap-and-trade regulation, in both the Nash and Stackelberg game models, the supply chain participants find more profitability in mixed financing when carbon emission reduction efficiency is high. Meanwhile, bank financing proves more profitable when the carbon emission reduction is low. Moreover, this study investigates the impact of wholesale and retail prices of low-carbon products on the financing strategies of the supply chain participants under different power structures and financing modes. Additionally, extended models are considered, including the manufacturer-led Stackelberg game structure and the capital-constrained manufacturer with a certain amount of initial capital.

Analysing the Impact of Capital Structure on Firm Value: A Study in Corporate Finance

Abstract: This study investigates the relationship between capital structure and firm value across various industries. Using a cross-sectional quantitative analysis, the research examines how different levels of debt, equity, and financial leverage affect firm value, measured by market capitalization and Tobin's Q. The findings suggest that firms with moderate debt levels experience enhanced firm value, benefiting from the tax shields provided by debt without incurring significant financial distress. However, excessive financial leverage negatively impacts firm value, as firms face increased risk of financial instability. The study supports key theories such as the trade-off theory and pecking order theory, demonstrating that capital structure decisions are influenced by factors such as firm size, profitability, and industry characteristics. The implications for corporate managers and investors include optimizing debt-equity balance to maximize firm value while managing financial risk. Future research should focus on industry-specific dynamics and longitudinal analyses to further explore these relationships.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

The influence of peer effects, commodity prices and its hedging on corporate capital structure: Evidence from the oil and gas industry

This paper investigates the influence of peer financial choices on the capital structure decisions of European and North American listed companies in the oil and gas sector. It also examines how commodity prices, particularly oil and natural gas prices, and their corporate hedging affect capital structure policies. The findings underscore the existence of peer effects in the oil and gas industry, indicating that companies consider their peers' financial decisions when determining their capital structure. Further analysis reveals that there is significant cross-country heterogeneity in capital structure peer effects conditional on financial and institutional development, and disclosure quality. Additionally, the research highlights that oil and natural gas prices, along with the hedging against these prices exposure, impact the capital structure of oil and gas companies, providing invaluable insights for industry practitioners and policymakers.

Strengthening urban deer management with structured decision making

Urban deer management (UDM) decision‐makers face social, ecological, regulatory, and economic pressures when creating an agreeable deer management plan for stakeholders. Historically, decision making techniques (e.g. consensus‐based analyses) have not effectively balanced UDM elements leading to short‐lived management progress. Structured decision making (SDM) is a formal, values‐based approach to identifying an optimal management solution. Although SDM has been applied to other wildlife management decisions, it has not been applied in UDM. We provide the first case‐study of SDM‐based UDM and streamline the process for wildlife managers. We focused on one suburban and one semi‐rural community near Atlanta, Georgia, USA. We established a problem statement to capture what decision‐makers must address in UDM programs and reviewed the primary literature and UDM plans from five states to establish four fundamental objectives. We then utilized a support–effectiveness analysis to identify acceptable UDM alternatives and gathered expert insights to calculate the consequences of alternatives on objectives. Finally, we asked each community's decision‐makers to weigh objectives against each other. Using this framework, education was the most optimal UDM technique to implement in the suburban community, and sharpshooting was the most optimal UDM technique to implement in the semi‐rural community. This paper positions SDM as a transparent, defensible, inclusive, and adaptive approach to UDM. Furthermore, our SDM framework provides managers with the means and justification to create an optimal plan of action for communities in need of UDM.

Habitat management decisions for conservation: a conceptual framework

The focus of this selection of papers is the linkage of habitat and population dynamics for the purpose of conservation. We thus provide a general framework for making conservation decisions, emphasizing how knowledge of habitat–population linkages fits into this framework. We begin by describing structured decision-making (SDM) as a general approach to making conservation decisions. SDM requires the development of the following elements: objectives, actions, model(s), monitoring, and decision algorithm. We then describe adaptive resource management (ARM), a specific type of SDM developed for recurrent decisions characterized by potentially resolvable uncertainty. Many different classes of actions can be used to influence animal population dynamics, and modification of habitat is one class of action that is frequently used. Habitat management requires models for predicting responses of the managed system to management actions, and these models are based on our knowledge of habitat–population linkages. Frequently, these models are decomposed into two submodels: one used to predict habitat changes expected to result from management actions and another used to predict population responses to habitat changes. This latter modeling focuses generally on the influence of habitat change on vital rates governing the dynamics of population state variables (variables such as population size or density that describe the status or health of a population). Specific recommendations depend on 1) the vital rates and state variable(s) being considered, 2) the relative spatial scales of animal movement and habitat measurement, and 3) the relative temporal scales of habitat change and vital rate estimation. Finally, we present an example of an ARM program for habitat management, highlighting the role of habitat-linked population modeling in this effort.

Stakeholder-based management of ecosystem services in agricultural areas: Integrating the leader-follower game and a conflict resolution model

Given the substantial effects of agricultural practices on the environment, this paper introduces a novel stakeholder-based framework for assessing the ecosystem services (ESs) provided by agricultural areas. Ecosystem services include essential functions such as water supply, food production, carbon storage, soil erosion control, and habitat support. In addition to ESs, water footprint is also taken into account to evaluate the impacts of agricultural activities on water resources. Some of the mentioned ESs are assessed using the Soil and Water Assessment Tool (SWAT). Then, by extending and combining the Conflict Resolution Model with the Composition of Probabilistic Preferences (CRMCPP) method and the leader-follower game (LFG), while considering the hierarchical structure of decision-makers, the best scenario for enhancing the ESs is selected.The Zarrinehroud River Basin (ZRB) in Iran has been chosen as a case study to evaluate the performance of the proposed framework, as this basin is vital for supplying water to Lake Urmia, the largest hypersaline lake in the Middle East. In this paper, 16 Water and Environmental Resources Management (WERM) scenarios have been defined according to the Urmia Lake Restoration National Committee (ULRNC) projects. Then, the mentioned ESs have been evaluated under different WERM scenarios. Ultimately, by utilizing the CRMCPP-LFG method and taking into account the hierarchical structure of decision-makers, we can identify the optimal WERM scenario. The criteria for making this decision include various factors, such as ecosystem services and the costs involved in implementing the WERM scenarios. In the selected scenario, the average water inflow into Lake Urmia is projected to rise to 1329 million cubic meters per year, which is 6.3% more than the average inflow in the current condition. Key initiatives in this scenario include reducing cultivated areas, altering irrigation methods, changing crop patterns, and incorporating water-efficient plant species.

Capital structure decisions in the energy transition: Insights from Spain

The renewable energy transition depends heavily on capital structure decisions of energy firms. Most previous research has focused on the decisions of oil companies. This study investigates the financing of new investment in the Spanish renewable energy sector, including decisions about: i) equity versus debt, ii) short versus long-term debt, and iii) equity issuances versus retained earnings for new investment. Our analysis is based on data from over 22,000 energy firms from 2008 to 2021 and shows that these firms prefer debt over equity (more than 90% of changes in assets are financed through increasing debt) and have extended the term of the debt over the period studied (reaching a maximum of almost 73% long-term debt in 2021). The deployment of retained earnings is less usual than raising new equity capital. Our analysis points to a generalized use of project finance to support new investments. Policymakers should focus on increasing financing through equity and thus diversifying the financial risk of the energy transition. In addition, a supportive fiscal policy and a stable regulatory environment are desirable for achieving this goal.

Capital Structure and Enterprise Market Value: An Empirical Analysis Based on S&P 500 Companies

Abstract: This study explores the impact of capital structure on enterprise market value, specifically examining the influence of the debt ratio on the market valuation of S&P 500 companies. Capital structure decisions are crucial for corporate managers and investors, as they significantly affect firm performance, risk, and market perception. The research is motivated by the need to understand how financial leverage influences market valuation, particularly in large, publicly traded companies. Utilizing two-way fixed effect panel data models spanning from 2020 to 2023, we find significant evidence that higher leverage is associated with an increase in market valuation, offering a comprehensive view of the factors that influence corporate market values. Additionally, the study reveals that the impact of leverage varies across company sizes and examines sector-specific effects and the role of long-term debt. The findings provide key insights for investors and corporate managers regarding the impact of financial structure on company valuation, emphasizing the importance of optimizing capital structure to enhance firm value and strategic decision-making.

Capital structure of special-purpose governments

PurposeThe paper examines whether special purpose governments follow the pecking order model when raising capital, replacing firm equity in the original model with local intergovernmental revenues. Special purpose governments are a relatively underexamined component of state and local governments, and their capital structure choices have important implications for America’s aggregate fiscal health. This paper seeks to illuminate special purpose governments’ choices among available forms of capital.Design/methodology/approachTo address our research inquiry regarding whether special-purpose governments adhere to the pecking order theory, we narrow our focus to a specific group of transit entities. Our investigation utilizes data sourced from two distinct repositories: the United States Census of Governments and the National Transit Database. To facilitate integration of these disparate datasets, we establish a correspondence between them using the Federal Information Processing Standard and the transit identification number. Initially, our analysis employs maximum likelihood estimation, comparing these estimates to those generated by a naïve model. Subsequently, we derive parameter estimates through the application of a bivariate probit model.FindingsThe paper supports the pecking order hypothesis where internal funds are consumed first, debt is consumed next and IGR is consumed last. The results are robust and are not influenced by simultaneity bias.Research limitations/implicationsThe paper uses transit special purpose governments as the special purpose government of interest. These transit special purpose governments have high fixed costs that may inform their capital structure decisions relative to non-transit special purpose governments. Additionally, transit special purpose governments often have a profit-maximizing objective that may not uniformly apply to other special purpose governments, such as school districts, who lack such an incentive.Practical implicationsOur research should grab the attention of state and local politicians, voters, policy experts and scholars. Special-purpose governments, a key part of our governmental system, are on the rise. Understanding them better can guide decision-making on how to allocate resources, set policies and plan strategically. This knowledge boosts financial reporting quality, transparency, accountability and public confidence. Journalists can leverage our findings to ensure accurate and thorough reporting, fostering accountability and trust. Additionally, debt markets and analysts can factor this information into their risk assessments.Originality/valueThe paper enhances our understanding of how special purpose governments interact with existing models of corporate finance when making capital structure decisions.

Ownership structure and capital structure dynamics in South African firms

Background: The ownership structure can impact capital structure decisions as owners are an influential group that govern the financial decisions of firms. Hence, the ownership structure and the categories of its owners have important implications for the capital structure of firms.Aim: This study investigates the linear and non-linear effects of different ownership types (managerial, foreign, institutional, government, and family) on the debt ratios of firms.Setting: This study includes 267 non-financial firms listed on the Johannesburg Stock Exchange (JSE) from 2004 to 2021, accounting for fluctuation in listings during this period.Method: The fixed effects model is used to estimate the impact of ownership structure on capital structure, while the Sasabuchi-Lind-Mehlum test evaluates non-linearity. Additionally, the Durbin-Wu-Hausman test is employed to detect whether endogeneity is present in this study.Results: The findings indicate that government ownership has a positive linear relation with the total debt and long-term debt ratio, while a non-linear inverse U-shaped relationship is found between institutional ownership and long-term debt, with an optimal level of 34.3%.Conclusion: The study concludes that the ownership structure is an important factor driving capital structure decisions, with government and institutional ownership directly impacting the debt ratios of firms.Contribution: This article expands the limited knowledge of the impact of ownership structure on capital structure and is the first to explore non-linear effects in the South African context. The findings offer valuable insights for boards and management that may aid them in strategically configuring ownership structures that optimise their capital structures.

Unpacking Capital Structure Decisions: Analyzing Short-Term Debt to Equity Dynamics in Shariah Compliant Financially Distressed Firms

Unpacking Capital Structure Decisions: Analyzing Short-Term Debt to Equity Dynamics in Shariah Compliant Financially Distressed Firms

Unpacking the Impact of Asset Structure and Sales Growth on Capital Structure: The Moderating Influence of Profitability

This study explores the impact of asset structure and sales growth on capital structure, emphasizing the moderating influence of profitability. As firms navigate the complexities of financing decisions, understanding how these variables interact is crucial for optimizing capital structure. The findings reveal that asset structure and sales growth significantly affect capital structure, with profitability playing a critical role in moderating these relationships. Firms with substantial tangible assets are better positioned to leverage debt financing, while those demonstrating strong sales growth are viewed favorably by investors and creditors. However, the extent to which sales growth influences capital structure is contingent upon profitability; high profitability enables firms to capitalize on growth opportunities, whereas low profitability may inhibit their capacity to leverage growth potential. Empirical research supports these conclusions, indicating that asset structure, sales growth, and profitability significantly shape capital structure decisions across various industries. Ultimately, this study provides valuable insights for financial managers, highlighting the importance of balancing growth aspirations with profitability to achieve effective capital structure management. This, in turn, can lead to sustained competitive advantage, a state where a firm outperforms its competitors over a prolonged period in a dynamic economic environment.

Optimizing Education: How Dual Teacher Technology, Communication, and Decision-Making Drive Performance?

In the complex environment of academic institutions, optimizing organizational effectiveness is a paramount concern. Driven by a growing awareness of the need for tailored organizational strategies in academic settings, this study seeks to uncover the dynamics shaping the relationships among these key variables. The primary purpose of this research is to provide a comprehensive understanding of how efficiency orientation, information exchange, decision-making structures, and talent management collectively influence continuous performance improvement within academic institutions. Employing a mixed-methods approach, this study integrates quantitative surveys and qualitative interviews to capture the multifaceted nature of the studied variables. The research targets educators from Sichuan Film and Television University and Sichuan University of Media and Communications. A sample size of 435 respondents participated in the survey, and eight interviewees were selected for in-depth qualitative insights. Statistical analyses reveal intricate correlations among efficiency orientation, information exchange, localization of decision-making, talent management, and continuous performance improvement within academic institutions. Streamlined processes positively influence talent management, emphasizing the importance of organizational efficiency. Transparent communication channels play a pivotal role in effective talent management, underscoring the significance of information exchange. Decision-making structures impact talent management strategies, necessitating a balanced approach. Talent management emerges as a proactive strategy, influencing and being influenced by continuous performance improvement efforts. This research contributes to organizational theory by offering context-specific insights into the relationships among key variables within academic institutions. The study's originality lies in addressing research gaps, providing a nuanced understanding of decision-making dynamics, and uncovering the reciprocal relationship between talent management and continuous performance improvement.

Comprehensive Preview Decision Method for Direction and Speed of Intelligent Vehicle Based on Rules and Learning

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers’ trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios.