Decision-making Process Research Articles

AN ASSESSMENT OF CITIZENS PARTICIPATION IN COMMUNITY DEVELOPMENT PROJECT IN AUCHI, EDO STATE, NIGERIA

Community development projects are essential for improving the quality of life and fostering sustainable growth in a community. However, effective implementation and success of these projects heavily depend on active citizen participation. This research explores an assessment of citizens participation in community development project in Auchi. The objectives of the study are; identify some existing projects carried out through citizens participation, examine the strategies adopted in carryout the projects, Assess the impacts of the projects in the area, and identify some of the challenges faced by citizens in participating in community development projects. Research methodology, both primary and secondary sources of data were adopted .sample size .one hundred (100) respondents were examined with a structured questionnaire, but a total o f ninety seven (97) questionnaires was retrieved and find suitable for analysis. The study identifies key barriers such as lack of awareness, limited access to resources, socio-economic constraints, and inadequate support from local authorities, as the major hindrance to effective citizenships participation in community development .Conclusively, the researcher concludes that provision of incentive, interest in development, felt-need motive, participatory in decision making and so on are the motivating factors of citizens participation in community development. The study therefore recommends that citizens should always involve in decision-making process, and they should be educated about their roles. By addressing these barriers, the study seeks to contribute to more effective and sustainable community development practices.

Development Strategies for the Mediterranean Coastal Landscape: Adaptive Decision-Making Processes for Implementing the Circular Economy in the Redevelopment of the Reggio Calabria Waterfront

This document explores a circular approach to the redevelopment of the city–port system of Reggio Calabria, an area characterized by complex challenges involving economic, social, and environmental needs. By developing a multidimensional decision-making process, three development scenarios were identified to support a sustainable transition. The methodology integrates both quantitative and qualitative assessments, actively involving the local community and stakeholders. The proposed methodology operationalizes the principles of the circular economy by aligning sustainable regeneration scenarios with local needs and environmental constraints. The integrated assessment ensures the applicability of circular models for the resilient redevelopment of the waterfront. The results demonstrate how this model can be applied to other Mediterranean port cities to promote sustainable and resilient regeneration.

EXPRESS: Eye on Context: Individual Differences Reveal the Mechanisms of Statistical Learning.

If a searched-for target object is consistently encountered within repeating spatial distractor arrangements, target detection becomes more efficient relative to non-repeated, i.e., random arrangements (contextual-cueing effect). On the other hand, target location changes within otherwise unchanged distractor arrays substantially weaken the cueing effect. Previous studies reported substantial variations in individual participants' abilities to learn and relearn invariant contexts. Therefore, the current study examined how individual differences in attentional control and focus, as indexed by the well-established Stroop and Navon tasks, respectively, relate to contextual cueing (CC) in a learning phase/relocation phase design. During the visual search, we recorded behavioral reaction times (RTs) and fixation locations, the latter permitting us to decompose search RTs into search- and motor-related substages. We could thus evaluate the processes responsible for CC and the lack thereof after target relocation while also testing whether search and motor components of CC are different for individuals depending on their Stroop/ Navon scores. Repeated contexts yielded faster RTs (and reduced fixation numbers), though there was a substantial decrease in cueing from learning to adaptation, consistent with previous studies. Critically, contextual learning, but not relearning, varied across individuals: participants with high-Stroop interference displayed overall larger CC during early target search, while a more local Navon task bias was associated with increased CC during later processes of target response decisions. Our results demonstrate that analyzing individual differences can help validate the processes responsible for CC in search tasks, particularly distinguishing between early search and later response-related mechanisms.

The U.S. Withdrawal from UNESCO under the Reagan Administration: A Multilevel Analysis

The United States positions itself as a global leader and upholder of the world order whose pivotal role in the system of international relations is legitimized through its active participation in multilateral institutions. At the same time, it was the United States which resorted to unilateral actions more often than any other state, exerted significant diplomatic and financial leverage on international organizations to achieve its national interests, undermining the very principle of multilateral cooperation. This inconsistency has been most clearly manifested in the U.S. conflict behavior towards UNESCO — an integral part of the UN system, which focuses on addressing the global issues of the present. In terms of providing a better understanding of the logic behind the formation of the U.S. policy towards this organization and to the UN in general, the case of the first U.S. withdrawal from UNESCO in 1984 under the Reagan administration is of particular interest. On the basis of K. Waltz’s methodology of multilevel analysis the article reconstructs the chronology of the decision-making process, identifies its internal and external determinants, and outlines its key actors. The first section focuses on the systemic constraints that forced the Reagan administration to reconsider its attitude towards the UN system in general and UNESCO in particular. The second section examines the domestic political determinants of the withdrawal decision, centered around the results of complex bargaining between the key players in the American political system, government agencies and interest groups. The third section outlines the motivation and politicalpsychological portraits of individual politicians who were personally interested in pulling USA out of UNESCO. In this regard, the emphasis is given to the figure of the Assistant Secretary of State for International Organization Affairs G. Newell. In conclusion, the author argues that the withdrawal decision was deliberately made by ideologically motivated politicians in the Reagan administration, despite the alternative voices from Congress, a number of government agencies and civil society. It became a sort of response of the conservative-minded American establishment to changing international status quo, including the realignment of power in the UN specialized agencies.

How to assess sudden cardiac death risk in hypertrophic cardiomyopathy? Current challenges and future directions.

Over the past decade, knowledge about the risk of sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM) has advanced significantly. A standard well-recognized approach to risk stratification is based on the fundamental risk factors and SCD risk models that should be incorporated into the shared decision-making process. More detailed analysis including additional indicators, such as reduced left ventricular systolic function, the presence of late gadolinium enhancement, or in some cases genetic variants, may provide valuable insights for intermediate-risk patients, enabling more personalized diagnosis and treatment. Risk stratification remains challenging in specific groups, such as patients who have undergone septal reduction therapy, those taking mavacamten, or those with phenocopies of HCM. The advancement of modern methodologies, including multifactorial approaches supported by artificial intelligence algorithms, offers hope for more precise and individualized SCD risk assessment in HCM patients.

Bodily feelings as information: the embodied cognition perspective on robotic services

Purpose The study aims to investigate the effects of bodily feelings on preference for robotic service by examining direct and indirect sensations from physical and metaphorically projected bodily feelings. Design/methodology/approach Through four empirical experiments involving video and recall tasks to metaphorically manipulate participants’ bodily warmth and directly manipulate ambient temperature, the authors explored the mediating role of the need for warmth and the moderating role of robotic features (warmth vs competence) on consumer willingness to engage with and pay for robotic services. Findings Warmth perception exhibits a positive correlation with robotic services. This relationship is mediated by the need for warmth. Moreover, when customers experience a sensation of physical warmth, they show a greater willingness to pay for a robotic service exhibiting competence versus warmth. Research limitations/implications This research contributes to the literature by integrating the feelings-as-information theory and the mind perception view to understand the judgment of robotic services. It extends the application of the embodied cognition theory, highlighting the significance of bodily feelings as a source of information in customer decision-making processes. Furthermore, this research explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations. Practical implications Managers should consider using different robots based on seasonal settings to meet customers’ need for warmth. Understanding customers’ bodily feelings and the metaphoric influence of service features contributes to the design of more effective and customer-centric robotic services. Originality/value This inquiry explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations.

Improving Reusability of Electronic Components

Abstract: The project “Improving Reusability of Electronic Components” deals with the ever-increasing concern for electronic waste, which calls for a need for sustainability in terms of electronic components' disposal and repurposing. Electronic devices have seen massive growth due to technological advancement and are increasingly used in every corner of the globe, thereby turning into an immense environmental issue called e-waste. Based on the focus on research and development, this project establishes a robust system that shall estimate, classify, and strengthen the reusable potential of e-components, majorly focusing on laptops, using techniques of artificial intelligence and machine learning. The prediction model classified the condition of the various e-components into either fully working or non-functioning units which fall within the disposable category. A predicted category aided by the condition of a particular component guides the decision-making process to reuse, repair, recycle, or dispose of it. It actually presents a two-condition approach by giving actionable output on whether or not the device is fully functioning or non-functional. For functioning devices, it recommends possible fixes or upgrades and thereby extends the device's useful life. The system identifies components to be recycled for non-functional devices or states whether they can be disposed of as e-waste. Using automated classification and condition specific guidelines, the project aims to reduce e-waste, reduce environmental impact and prolong the lifespan of e-components. As a whole, the system seeks to promote sustainable usage, definitively achieving the reuse and recycling of valuable e-waste materials, making the electronics industry a greener one

AI-Driven Strategic Insights: Enhancing Decision-Making Processes in Business Development

AI-Driven Strategic Insights: Enhancing Decision-Making Processes in Business Development

The impact of ESG factors on performance of real estate equities

ABSTRACT This paper examines the influence of ESG practices on the market performance of Public Real Estate Companies (PRECs), as reflected by Tobin’s Q, and their financial performance, assessed through profitability and risk profiles, using a global sample of firms from 38 countries between 2015 and 2021. Employing an instrument-based methodology (2SLS) and a Heckman correction to address concerns about endogeneity and selection bias, our global analysis reveals that ESG-aligned firms demonstrate higher market performance and improved operational efficiency through reduced capital costs and improved risk management, despite initial setbacks such as negative Net Operating Income (NOI) and higher G&A expenses. In-depth analysis shows that the Environmental (E) and Social (S) components follow a similar trend, though the (E) component has no significant effect on systematic risk. While the (G) component has not demonstrated significant effects on firm market performance, its influence on improved risk management remains relevant. Regional characteristics and distinctions between developed and emerging markets introduced subtle nuances in our analysis, emphasising the crucial role of local contexts in influencing these relationships. These findings serve as a catalyst for investors, stakeholders, and real estate companies to incorporate these strategies into their investment decision-making processes.

Selection of Low-Carbon Contractors in a Fuzzy Multi-Objective Environment

The selection of contractors entails multiple uncertainties, including the lack of clear delineation of contractor qualifications, ambiguity surrounding project manager capabilities, and the uncertain nature of carbon emissions. These uncertainties compound the complexity involved in genuinely evaluating and choosing contractors. This paper establishes a fuzzy multi-objective contractor selection model that accounts for cost reduction, optimization of contractor qualifications, organizational management enhancement, carbon emissions reduction, and reputation optimization. Leveraging the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Compromise Programming (F-CP) methods, we have devised a fuzzy multi-objective contractor selection model and outlined the procedural steps for its implementation. A case study was conducted to validate the effectiveness of the model we proposed.By applying this model to the case, we successfully transformed the complex multi-objective programming into a straightforward single-objective linear programming. Furthermore, based on the model, the weights assigned to cost, qualification indicators, management level, carbon emissions, and contractor reputation are 0.54, 0.09, 0.05, 0.19, and 0.13, respectively. By solving the model, we were able to identify the most suitable contractor.This paper aids the decision-making process in selecting an appropriate low-carbon contractor.

A Q-learning-based algorithm for the block relocation problem

The Block Relocation Problem (BRP), also known as the Container Relocation Problem, is a challenging combinatorial optimization problem in block stacking systems and has many applications in real-world scenarios such as logistics and manufacturing industry. The BRP is about finding the optimal way to retrieve blocks from a storage area with the objective of minimizing the number of relocations. The BRPs have been studied for a long time, and have been solved primarily using conventional optimization techniques, including mathematical programming models, as well as both exact and heuristic algorithms. For the first time, this paper tackles the problem using a reinforcement learning method. We focus on one of the major variants of the BRP—the restricted BRP with duplicate priorities (RBRP-dup). We first model the RBRP-dup as a Markov decision process and then propose a Q-learning-based algorithm to solve the problem. The Q-learning-based algorithm contains two phases. In the learning phase, two innovative mechanisms: an optimal rule-integrated behaviour policy and a heuristic-based dynamic initialization method, are incorporated into the Q-learning model to reduce the size of the state-action space and accelerate convergence. In the optimization phase, the insights obtained in the learning phase are combined with a heuristic algorithm to improve decision-making. The performance of our proposed method is evaluated against the state-of-the-art exact algorithm and a commonly used heuristic algorithm based on benchmark instances from the literature. The computational experiments demonstrate the superiority of our proposed method regarding solution quality in large and complex instances.

A multi-faceted approach for leveraging AI and intellectual capital for enhanced supply chain decision-making

PurposeThis study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.Design/methodology/approachThe study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.FindingsThe results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.Originality/valueThis study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.

The role of board gender diversity in financial performance: role of sustainability, climate risk and internal control systems

ABSTRACT The aim of the study is to explore the role of board gender diversity, sustainability, climate risk and internal control systems in financial performance. To achieve the study’s objectives, we employed data of 490 listed firms from 2000 to 2023 belonging to MENAT countries. The data is extracted from the data stream database. The ordinary least square (OLS) is used as a method of estimation for analysis, and the Generalized method of movement (GMM) is used for the robustness analysis. Our findings show that board gender diversity has a significant positive impact on corporate financial performance, indicating that the women in board composition make a more diverse board, which leads to help in the decision-making process. Moreover, our results show that the sustainability score has a significant positive impact on financial performance, indicating that the sustainability of the organization creates goodwill for the organization, which leads to a rise in financial performance. Moreover, climate risk decreases financial performance by increasing operational costs, disrupting supply chains, and impacting asset values. Similarly, the strong internal control in Turkish firms leads to a rise in financial performance through compliance with the code of the corporate governance mechanism and other laws enforced by the country’s legislation. Our results also show that the large board size has a positive relationship with financial performance. Our results also show that the experienced firm maintain their stability due to experience. Our study advocated the significant implications for government, investors, stakeholders and legislators.

It takes two flints to start a fire: A focus group study into PhD supervision for responsible research

ABSTRACT Background Supervision is one important means of promoting responsible research. However, what a responsible supervisor should do and how to foster a responsible supervisory climate is unclear. Methods Between January 2023 and February 2024, I conducted 17 focus groups in The Netherlands and Denmark with 85 PhD candidates and PhD supervisors to understand what practices supervisors engage in to promote responsible conduct of research and what strategies could promote a responsible supervisory relationship. Results Supervisors were found to promote responsible research by rigorously evaluating the relevance of studies, ensuring transparency, and taking the initiative in establishing clear authorship guidelines. They critically discussed the alignment between research questions, design, and analyses. Furthermore, supervisors implemented clear data management policies and normalized conversations about ethics. They led by example through clear and coherent writing. To foster a responsible supervisory relationship, supervisors challenged the PhD candidates’ ideas and supported their decision-making processes. They were mindful of the PhD candidates’ needs, tailoring the research trajectory. Supervisors also practiced self-awareness and cultivated a culture of care where every contribution was appreciated. Conclusions The North-Western context is a major limitation. I connect these findings to established leadership theories and consider their implications for fostering responsible supervision.

Evaluating the Buy vs Build Dilemma: A Case Study Approach to Corporate Accounting Information Systems

Purpose: This study examines the buy-versus-build dilemma in corporate accounting information systems (AIS), with a particular focus on the decision-making process and its implications for the development of these systems. Design: This research uses a qualitative method to study The Trio Borneo Medika, a trade company in Indonesia. It includes focus group talks with various departments and analyzes the data based on themes that emerge. Findings: The study shows that custom-built systems fit specific business needs, but they can lead to longer delivery times and operating problems. Using ready-made software speeds up system setup and enhances business processes by following the best practices in the industry. A hybrid method has been developed as a good solution by combining customized parts with standard software. Originality/Value: This study adds to existing knowledge by offering practical advice on whether to buy or build in AIS. It shows how combining both options can help achieve a good balance between customization and efficiency.

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.

Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network's decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.

A quantitative method for syntactic gradience: words, phrases, and the constructions in between

Abstract Syntactic gradience has been studied mainly from a qualitative perspective. In this paper, I propose a new method, based on the process of multi-criteria decision-making, that can quantify the word-phrase gradience against the standardized morphosyntactic continuum. This method may be applied to both synchronic and diachronic research, with the discrepancies between compounds and idioms and the lexicalization of compound pronouns in English examined as illustrations.

Neurosymbolic AI: Bridging neural networks and symbolic reasoning

Artificial Intelligence (AI) has made tremendous strides in recent decades, powered by advancements in neural networks and symbolic reasoning systems. Neural networks excel at learning patterns from data, enabling breakthroughs in tasks like image recognition, natural language processing, and autonomous driving. On the other hand, symbolic reasoning systems provide structured, rule-based frameworks for logical inference and knowledge representation, making them well-suited for domains requiring explainability, generalization, and interpretability. However, these paradigms often operate in isolation, resulting in limitations when faced with tasks that demand both robust learning capabilities and logical reasoning. This paper explores the emerging field of Neurosymbolic AI, which seeks to integrate neural networks and symbolic reasoning into unified frameworks, overcoming their respective shortcomings and unlocking new possibilities in AI development. The primary objective of this research is to investigate the theoretical and practical aspects of Neurosymbolic AI, emphasizing the interplay between data-driven learning and structured reasoning. We present a novel hybrid framework that seamlessly combines the pattern recognition prowess of neural networks with the structured inference capabilities of symbolic reasoning. The proposed framework employs a dual-layer architecture: a neural layer designed for feature extraction and representation learning and a symbolic layer for encoding domain knowledge and performing logical reasoning. A dynamic integration mechanism ensures bidirectional communication between the layers, enabling effective collaboration in decision-making and problem-solving processes. The effectiveness of the framework is demonstrated through experimental evaluations on multiple tasks, including visual question answering, natural language understanding, and robotics navigation. Results indicate significant improvements in performance, particularly in scenarios requiring explainability and reasoning under uncertainty. Compared to state-of-the-art models, the proposed framework exhibits superior accuracy, generalization across unseen tasks, and robustness against adversarial perturbations. This paper also delves into the broader implications of Neurosymbolic AI for critical domains such as healthcare, finance, and education. For instance, in medical diagnosis, the framework’s ability to integrate patient data with domain-specific medical rules enables more accurate and interpretable predictions. In education, neurosymbolic models personalize learning experiences by combining student behavior analysis with predefined pedagogical strategies. Additionally, we discuss how Neurosymbolic AI addresses ethical challenges, such as algorithmic bias and lack of transparency, which are prevalent in purely neural approaches. Despite its promise, Neurosymbolic AI faces challenges related to scalability, computational complexity, and seamless integration of heterogeneous systems. This research identifies these challenges and outlines potential avenues for addressing them, including the use of advanced optimization techniques and modular architectures. The paper concludes by emphasizing the transformative potential of Neurosymbolic AI in bridging the gap between human cognition and artificial intelligence, paving the way for next-generation AI systems that are not only powerful but also interpretable, reliable, and aligned with human values. In summary, this study contributes to the growing body of work in Neurosymbolic AI by proposing a hybrid framework, showcasing its application potential, and identifying key research challenges. By leveraging the complementary strengths of neural and symbolic paradigms, Neurosymbolic AI holds the promise of enabling machines to learn, reason, and interact with the world in ways that closely mirror human intelligence.

Integrating machine learning in business analytics consulting for proactive decision-making and innovation

Integrating machine learning (ML) into business analytics consulting represents a paradigm shift in enabling organizations to adopt proactive decision-making and foster innovation. As businesses face increasing complexity and competition, the demand for data-driven strategies has grown exponentially. Machine learning, with its capacity to analyse vast datasets, uncover hidden patterns, and predict future trends, has become a cornerstone of modern business analytics. This integration empowers consultants to deliver actionable insights and predictive solutions, enhancing operational efficiency and competitive advantage. Applications of ML in business analytics include customer segmentation, churn prediction, demand forecasting, and anomaly detection, all of which contribute to optimizing resource allocation and improving decision-making processes. For example, predictive models can help businesses anticipate market shifts and customer behaviours, while recommendation systems drive personalized marketing strategies. Incorporating ML also facilitates innovation by identifying untapped opportunities, automating repetitive tasks, and enabling real-time analytics. However, successful implementation requires addressing challenges such as data silos, algorithm biases, and the need for skilled professionals. Establishing robust data governance, fostering a culture of analytics adoption, and leveraging scalable cloud-based ML platforms are crucial for overcoming these barriers. This paper explores the theoretical foundations and practical applications of machine learning in business analytics consulting. It provides a framework for integrating ML into consulting practices, highlighting best practices and potential pitfalls. By adopting ML-driven approaches, consultants can help organizations navigate uncertainty, enhance strategic agility, and accelerate innovation.

Predicting the Price of Molecules Using Their Predicted Synthetic Pathways.

Currently, numerous metrics allow chemists and computational chemists to refine and filter libraries of virtual molecules in order to prioritize their synthesis. Some of the most commonly used metrics and models are QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores to name only a few. To our knowledge, among the known metrics, a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has not been considered before in literature. Being able to make such a prediction could improve and accelerate the decision-making process related to the cost-of-goods. Taking advantage of recent advances in the field of Computer Aided Synthetic Planning (CASP), we decided to investigate if the predicted retrosynthetic pathways of a given molecule and the prices of its associated starting materials could be good features to predict the price of that compound. In this work, we present a deep learning model, RetroPriceNet, that predicts the price of molecules using their predicted synthetic pathways. On a holdout test set, the model achieves better performance than the state-of-the-art model. The developed approach takes into account the synthetic feasibility of molecules and the availability and prices of the starting materials.