Decision Procedure Research Articles

A generative design-based optimization model for multi-objective construction site layout planning

PurposeGiven the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.Design/methodology/approachA novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.FindingsThe results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.Practical implicationsThe findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.Originality/valueA new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

Lie-algebraic Kähler sigma models with U(1) isotropy

We discuss various questions that emerge in connection with the Lie-algebraic deformation of the CP1 sigma model in two dimensions. First, we supersymmetrize the original model endowing it with the minimal N=(0,2) and extended N=(2,2) supersymmetries. Then we derive the general hypercurrent anomaly in both cases. In the latter case this anomaly is one-loop but is somewhat different from the standard expressions one can find in the literature because the target manifold is nonsymmetric. We also show how to introduce the twisted masses and the θ term, and study the Bogomol’nyi–Prasad–Sommerfield equation for instantons, in particular the value of the topological charge. Then we demonstrate that the second loop in the β function of the Lie-algebraic sigma model is due to an infrared effect. To this end we use a regularization. We also conjecture that the above statement is valid for higher loops too, similar to the parallel phenomenon in four-dimensional N=1 super-Yang-Mills. In the second part of the paper we develop a special dimensional reduction—namely, starting from the two-dimensional Lie-algebraic model we arrive at a quasi-exactly solvable quantum-mechanical problem of the Lamé type. Published by the American Physical Society 2024

Practices and challenges of safety management in outsourced facilities management

Introduction: Outsourcing is a commonly occurring organizational activity, but one associated with negative occupational safety outcomes. Improving the management of safety in workplaces where contractors are employed is vital, but under-researched in the service sectors. The aims of this paper were to investigate both the practices and challenges of safety management in outsourced facility management (FM), an important global service sector. Method: Twenty-three semi-structured interviews were conducted with clients and contractors in three different FM outsourcing arrangements between large corporations in the UK. Data were thematically coded against frameworks derived from existing literature to identify deployed safety management practices and reveal challenges associated with safety management in these outsourced relationships. Results: Safety management practices in outsourced FM conformed to known practices clustering into four previously identified categories (planning, selecting, on-site working, and checking). A fifth category (reviewing) was not observed. Operating across national boundaries, applying national contracts locally, working with mandated KPIs, and contract specifications all created new challenges for safety management not previously reported. Other known challenges associated with economic pressure and disorganization were observed. Conclusion: Safety management practices observed in safety critical industries also apply in FM. However, the challenges of safety management in these three cases included regulatory failures that have not been routinely identified in other empirical studies of safety in outsourcing arrangements. Practical application: Adopting widely accepted safety management practices support safer working in outsourced FM and encourage cross-sector learning. New challenges for safety management noted here encourage consideration of unintended consequences of contract terms and conditions, require corporate agreement on how to ensure safety compliance when working transnationally, and a review of decision-making and processes and procedures to enable effective and safe working locally.

Brain Tumors Detection Using Deep Learning (SGO and SE Algorithm)

Brain abnormality is the harsh illness among humans and is usually diagnosed with medical imaging procedures. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and Diffusion-Weighted (DW) modalities. The combination of the Social- Group-Optimization (SGO) and Shannon’s Entropy (SE) supported Multi-threshold is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES2015 and BRATS (2013 and 2015), and also clinical MRI mages obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with related segmentation trials employed. The performance of the ANFIS is validated with other classifiers, such as Decision-Tree, Random-Forest and KNN. The ANFIS classifier obtained an accuracy of 97.04% on the used DW modality and accuracy of 96.67% on the Flair MR images.

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.

Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection's robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model.

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

Fundamental Logic is Decidable

It is shown that Holliday’s propositional Fundamental Logic is decidable in polynomial time and that first-order Fundamental Logic is decidable in double-exponential time. The proof also yields a double-exponential–time decision procedure for first-order orthologic.

A hybrid decision support framework for planning a risk-based audit engagement

Internal audit engagement planning is a fundamental component of internal audit activities, and a poor plan can impair the audit quality. However, studies on internal audit engagement planning have limited exposure in the literature. To the best of our knowledge, a comprehensive and robust model has not been established to address the decision-making problems associated with the audit engagement planning process. To fill these gaps, an integrated multi-stage framework is presented for developing a risk-based audit engagement plan. The designed framework includes three stages to carry out the decision procedure. First, a combined method of best worst method and weighted probability-impact score is used to evaluate the risk exposure. Second, risk assessment results are integrated into the weighted multi-choice goal programming (WMCGP) model for selecting the audit scope and allocating budgeted audit hours. Third, WMCGP model assigns audit staff to the selected audit scope to satisfy auditor preference and fitness. Two real-life case studies illustrate that the proposed framework is a useful tool for selecting the critical audit scope and deploying appropriate resources. The research results outperform the manual procedures, enabling the audit team to use constrained resources more effectively and efficiently.

Conducting volcanic ash cloud exercises: practising forecast evaluation procedures and the pull-through of scientific advice to the London VAAC

The London Volcanic Ash Advisory Centre (VAAC) provides forecasts on the expected presence of volcanic ash in the atmosphere to mitigate the risk to aviation. It is fundamentally important that operational capability is regularly tested through exercises, to guarantee an effective response to an event. We have developed exercises which practise the pull-through of scientific advice into the London VAAC, the forecast evaluation process, and the decision-making procedures and discussions needed for generating the best possible forecasts under real-time conditions. London VAAC dispersion model forecasts are evaluated against observations. To test this capability in an exercise, we must create observation data for a hypothetical event. We have developed new methodologies for generating and using simulated satellite and lidar retrievals. These simulated observations enable us to practise our ability to interpret, compare, and evaluate model output and observation data under real-time conditions. Forecast evaluation can benefit from an understanding of how different choices of model setup (input parameters), model physics, and driving meteorological data impact the predicted extent and concentration of ash. Through our exercises, we have practised comparing output from model simulations generated using different models, model setups, and meteorological data, supplied by different institutions. Our exercises also practise the communication and interaction between Met Office (UK) scientists supporting the London VAAC and external experts, enabling knowledge exchange and discussions on the interpretation of model output and observations, as we strive to deliver the best response capability for the aviation industry and stakeholders. In this paper, we outline our exercise methodology, including the use of simulated satellite and lidar observations, and the development of the strategy to compare output generated from different modelling systems. We outline the lessons learnt, including the benefits and challenges of conducting exercises which practise our ability to provide scientific advice for an operational response at the London VAAC.

Post-Modern Planning Theory: the New era of Indonesia Energy Planning

Objective: The objective of this study is to explore how decision-making procedures based on postmodern planning theories can facilitate the integration of social and environmental aspects into energy planning. This is critical in the context of global agendas such as the Sustainable Development Goals (SDGs) and the New Urban Agenda (NUA), which emphasize the provision of clean energy and equitable access to energy. Theoretical Framework: This study is grounded in postmodern planning theories, which prioritize inclusivity, communication, and the negotiation of values among various stakeholders. These theories contrast with traditional, market-oriented energy planning approaches that often marginalize social and environmental considerations. Method: The research employs a qualitative approach, analyzing case studies where postmodern planning principles have been applied to energy planning. This includes a review of policy documents, interviews with key stakeholders, and an examination of the interactions between government sectors and between the government and society. Result and Discussion: The findings indicate that postmodern planning approaches can effectively bridge the gap between social/environmental goals and economic interests in energy planning. Through enhanced communication and negotiation among stakeholders, it is possible to achieve more inclusive and sustainable energy policies. The study highlights specific instances where these approaches have led to more balanced energy planning outcomes, especially in regions focused on rapid economic growth. Research Implication: The implications of this research suggest that adopting postmodern planning principles can significantly improve the inclusivity and sustainability of energy planning processes. Policymakers are encouraged to foster greater communication and value exchange among stakeholders to balance economic, social, and environmental objectives. Originality/Value: This study contributes to the literature by demonstrating the practical application of postmodern planning theories to energy planning. It provides a novel perspective on how to reconcile the often competing interests of economic growth and sustainable development, particularly in the energy sector.

How Does the Use of Artificial Intelligence Reflect on Business Administration and Management? A Perspective on Knowledge Production at the Postgraduate Level

Objectives: The extensive adoption of AI applications in many industries raises concerns over their potential to significantly transform employment in various aspects, including job creation, automation, and decision-making procedures. The impact that AI has on employment dynamics presents an array of obstacles and opportunities for the fields of business administration and management. Given all these advancements, the present study conducts a thorough examination of postgraduate theses, recognizing them as a systematic representation of the expanding influence of AI in the fields of business administration and management. Postgraduate research, which entails a thorough academic investigation, provides a crucial perspective for understanding the present condition and future course of AI use in business environments. Through an in-depth examination of postgraduate theses, this research attempts to reveal trends in the application of AI technologies in various organizational contexts. Design/methodology/approach: The 73 master's and doctoral theses used in this study were obtained from the repository of the Council of Higher Education National Thesis Center. The sample spans a range of institutions, demonstrating the diversity and depth of AI research across the academic landscape of management studies. To comprehensively examine and understand the information categories encompassed in these theses, the research utilizes two techniques: document analysis and descriptive content analysis. Document analysis systematically examines the theses as data sources, enabling a thorough investigation of the material that focuses on the identification, assessment, and synthesis of information pertaining to AI in business administration and management. Descriptive content analysis classifies and facilitates a methodical examination of the data. The distribution of theses subjected to document analysis was based on thesis type, universities and sub-disciplines, publication year and language, sample characteristics, methodology, theories, AI application areas, and keywords. Results: The findings show that studies on AI have gained momentum in the last four years, with a high percentage of theses focusing on management, organization, and marketing. The quantitative research method has been the most preferred for postgraduate theses. Additionally, human resource management, machine learning, and artificial neural networks constitute most of the research focus. The data indicates that most master's theses concentrate on human resource management and marketing. The finance and information technology sectors are predominant in terms of industry focus. Practical implications: The findings have the potential to significantly assist practitioners in understanding the ongoing research on AI, enabling them to align their strategic planning with the latest discoveries and approaches in higher education. The prevalence of machine learning and artificial neural networks signifies an inclination towards increasingly complex AI implementations within organizations. Organizations may leverage this understanding to increase innovation, enhance decision-making procedures, and sustain a competitive advantage through the implementation of cutting-edge AI. Furthermore, this study can assist researchers, such as master's and doctoral students, with the topic, research question, data source, data collection tool, and analysis type selection in future studies. Originality/value: The study is expected to provide insights into research focus, scope, and methodology for future research by revealing the current state of AI research in business administration and management domains in Turkey. Through the examination of postgraduate theses, this study not only highlights the increasing academic attention towards AI but also establishes a foundation upon which subsequent research can be built. This study represents an initial effort to gain deeper insights into AI research in Turkey, specifically focusing on knowledge production within universities.

Organizational Design and Work Culture

The relationship between work culture and organizational design has drawn a lot of attention in the modern corporate environment as a success factor for organizations. This abstract explores the mutually beneficial link between these two components and explains how their alignment promotes creativity, output, and worker engagement. The framework that governs the division of labour, roles, and duties inside an organization is known as organizational design. It establishes decision-making procedures, communication routes, and hierarchies. In addition to being effective, a well-designed organizational structure may also be adjusted to the changing needs of the market. At the same time, an organization's work culture encompasses the rules, beliefs, and behaviours that are common within it. It represents the general way of thinking, feeling, and doing things in work. A dynamic workplace culture fosters a feeling of community, promotes teamwork, and increases worker happiness. When it comes to organizational design The alignment of work culture and organizational design becomes a critical success factor in the pursuit of organizational excellence. This abstract delves deeper into this relationship and clarifies practical ways that firms can successfully align their structural frameworks with their cultural ethos. The understanding that organizational design and work culture are dynamic phenomena shaped by interconnected influences rather than static entities is at the core of this alignment. Because of this, promoting alignment calls for a comprehensive strategy that recognizes the complex interactions between structure and culture. An essential tactic is to perform a thorough analysis of organizational structure and workplace culture. This means assessing both cultural elements like values, norms, and communication as well as structural elements like hierarchies, workflows, and decision-making processes.

Healthcare Waste Management through Multi-Stage Decision-Making for Sustainability Enhancement

The possible threats that healthcare waste management (HWM) poses to the environment and public health are making it more and more crucial for medical facility administrators to be worried about it. This is in line with the global trend towards firms giving sustainability more of a priority. Many organizations, including the World Health Organization (WHO) and other organizations, as well as national and state laws, have mandated the proper disposal of infectious and hazardous healthcare waste. To effectively address the complex problem of selecting the best treatment option for HWM, a multi-criteria decision-making (MCDM) procedure must be used. The alternative ranking order method accounting for two-step normalization (AROMAN) methodology is provided in the context of q-rung orthopair fuzzy environment. This method comprises two steps of normalization and is based on the criteria importance through intercriteria correlation (CRITIC) paradigm. Whereas the AROMAN methodology uses vector and linear normalization techniques to improve the accuracy of the data for further computations, the CRITIC method assesses the intercriteria correlations and scores the significance of each criterion. The ranking from the proposed method is Al5>Al4>Al3>Al1>Al2. The study’s conclusions indicate that recycling (Al5) is the best option since it lessens trash production, aids in resource recovery, and protects the environment. Using this method helps decision makers deal with subjectivity and ambiguity more skillfully, promotes consistency and transparency in decision making, and streamlines the process of choosing the best waste management system. Sustainable waste management practices have been implemented in the biomedical industry with some success. The proposed technique is a helpful tool for legislators and practitioners seeking to improve waste management systems.

Examining the problem-posing performances of seventh-grade students in relation to response order

In this study, 176 Turkish seventh-grade students were asked to pose three problems for each of the swimming and purchasing tasks to examine how the solvability and complexity of the problems changed depending on the order of responses. The purchasing task required to pose a problem to derive a specific answer before posing new problems, whereas the swimming task required to pose problems based on existing data. The findings indicated that students performed poorly in terms of posing solvable problems in both tasks. The majority of solvable responses in the purchasing task were at the high complexity level, but most in the swimming task were at the low complexity level. No evidence of a relationship between the order of the responses and any of the analysis units (solvability and complexity) was found. Furthermore, when comparing the groups that posed and did not pose solvable problems for the specific answer in the purchasing task, the former group had a stronger tendency to maintain the complexity points in their other responses compared to their first response.

Automated ethical decision, value-ladenness, and the moral prior problem

AbstractPart of the literature on machine ethics and ethical artificial intelligence focuses on the idea of defining autonomous ethical agents able to make ethical choices and solve dilemmas. While ethical dilemmas often arise in situations characterized by uncertainty, the standard approach in artificial intelligence is to use rational choice theory and maximization of expected utility to model how algorithm should choose given uncertain outcomes. Motivated by the moral proxy problem, which proposes that the appraisal of ethical decisions varies depending on whether algorithms are considered to act as proxies for higher- or for lower-level agents, this paper introduces the moral prior problem, a limitation that, we believe, has been genuinely overlooked in the literature. In a nutshell, the moral prior problem amounts to the idea that, beyond the thesis of the value-ladenness of technologies and algorithms, automated ethical decisions are predetermined by moral priors during both conception and usage. As a result, automated decision procedures are insufficient to produce ethical choices or solve dilemmas, implying that we need to carefully evaluate what autonomous ethical agents are and can do, and what they aren’t and can’t.

Iterative Construction of Conserved Quantities in Dissipative Nearly Integrable Systems.

Integrable systems offer rare examples of solvable many-body problems in the quantum world. Because of the fine-tuned structure, their realization in nature and experiment is never completely accurate, and therefore effects of integrability are observed only transiently. One way to overcome this limitation is to weakly couple nearly integrable systems to baths and driving: this will stabilize integrable effects up to arbitrary time and encode them in the stationary state approximated by a generalized Gibbs ensemble. However, the description of such driven dissipative nearly integrable models is challenging and no exact analytical methods have been proposed so far. Here, we develop an iterative scheme in which integrability breaking perturbations (baths) determine the conserved quantities that play the leading role in a highly efficient truncated generalized Gibbs ensemble description. Our scheme paves the way for easier calculations in thermodynamically large systems and can be used to construct unknown conserved quantities.

Provisions for nullification of conservation and management measures in RFMO objection procedures

In order to successfully exercise their mandate to adopt binding conservation and management measures (CMMs) for straddling and highly migratory fish stocks, regional fisheries management organizations (RFMOs) and arrangements (RFMAs) need effective decision-making procedures. The constitutive instruments of a considerable number of such bodies contain objection procedures which allow individual members to unilaterally opt-out of an adopted CMM. Most scholars and RFMO performance review panels consider that unconstrained unilateral objection procedures undermine the decision-making procedures of both RFMOs and RFMAs. This article examines the phenomenon of CMM nullification provisions, which are present in five RFMOs. Such provisions deprive adopted CMMs of their binding force for all members if a certain number of members of the RFMO object to the CMM. This article argues that there is a case for a phase-out of CMM nullification provisions together with the unilateral objection procedures to which they are closely tied. Compared to unilateral objections more generally, CMM nullification provisions raise additional legitimacy questions given that they result in the nullification of CMMs that have been democratically adopted in conformity with the legitimate decision-making procedures of the respective RFMO. This article first provides a brief overview of the rationale for objection procedures by placing the concept of objections in the broader context of RFMO decision-making. Thereafter, this article briefly examines the requirements and legal effects of individual objections of RFMO members to CMMs. The final substantive section analyzes and compares existing CMM nullification provisions, focusing on the requirements for their activation, the nature of their legal effects and the legitimacy of such procedures considering the broader decision-making procedures of individual RFMOs.

Hybrid Machine Learning Algorithms to Evaluate Prostate Cancer

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure.

The Influence of Online Learning Components on Academic Motivation in China Vocational Training Programs: The Mediating Role of Student Engagement

The correlation between management leadership and faculty job satisfaction plays a vital role in determining an organization's performance and the well-being of teachers in the ever-changing field of higher education. This study examines the distinct aspects of management leadership, including vision and strategic orientation, decision-making and problem-solving abilities, coaching and development, and ethical leadership, and their influence on faculty job satisfaction in public universities located in Sichuan province, China. The study utilized a quantitative approach to analyze survey data from faculty members in order to investigate the extent of managerial leadership practices and their impact on faculty job satisfaction. The results demonstrated a significant and positive correlation between management leadership practices and faculty members' satisfaction in their jobs. This emphasizes the crucial role of effective leadership in establishing a work environment that is conducive to productivity and contentment. Important aspects of management leadership, such as having a clear vision and strategic direction, transparent decision-making procedures, helpful coaching and development programs, and ethical behavior, are found to impact faculty's impression of job satisfaction significantly. Administrators prioritizing these qualities are more likely to cultivate a favorable organizational culture, increase faculty involvement, and advance overall institutional success. The study's conclusions pertain to strategic interventions and leadership development programs specifically designed to improve management leadership practices. Public university administrators can cultivate a climate that supports faculty well-being, professional development, and academic performance by investing in leadership training, encouraging open communication, and establishing collaborative decision-making methods. This study adds to the current discussion on effective leadership in higher education and provides practical insights for public university administrators aiming to improve leadership practices and increase faculty job satisfaction in Sichuan province and different parts of the world.

The Impact of Managerial Leadership on Faculty Job Satisfaction in Chinese Public Universities

The correlation between management leadership and faculty job satisfaction plays a vital role in determining an organization's performance and the well-being of teachers in the ever-changing field of higher education. This study examines the distinct aspects of management leadership, including vision and strategic orientation, decision-making and problem-solving abilities, coaching and development, and ethical leadership, and their influence on faculty job satisfaction in public universities located in Sichuan province, China. The study utilized a quantitative approach to analyze survey data from faculty members in order to investigate the extent of managerial leadership practices and their impact on faculty job satisfaction. The results demonstrated a significant and positive correlation between management leadership practices and faculty members' satisfaction in their jobs. This emphasizes the crucial role of effective leadership in establishing a work environment that is conducive to productivity and contentment. Important aspects of management leadership, such as having a clear vision and strategic direction, transparent decision-making procedures, helpful coaching and development programs, and ethical behavior, are found to impact faculty's impression of job satisfaction significantly. Administrators prioritizing these qualities are more likely to cultivate a favorable organizational culture, increase faculty involvement, and advance overall institutional success. The study's conclusions pertain to strategic interventions and leadership development programs specifically designed to improve management leadership practices. Public university administrators can cultivate a climate that supports faculty well-being, professional development, and academic performance by investing in leadership training, encouraging open communication, and establishing collaborative decision-making methods. This study adds to the current discussion on effective leadership in higher education and provides practical insights for public university administrators aiming to improve leadership practices and increase faculty job satisfaction in Sichuan province and different parts of the world.