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Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

The semiconductor industry is a resource-intensive sector that heavily relies on energy, water, chemicals, and raw materials. Within the semiconductor manufacturing process, the diffusion furnace, ion implantation machine, and plasma etching machine exhibit high energy demands or operate at extremely high temperatures, resulting in significant electricity consumption, which is usually carbon-intensive. To address energy conservation concerns, the industry adopts batch production technology, which allows for the simultaneous processing of multiple products. The energy-efficient parallel batch scheduling problem arises from the need to optimise product grouping and sequencing. In contrast to existing heuristics, meta-heuristics, and exact algorithms, this paper introduces the Deep Q-Network (DQN) algorithm as a novel approach to address the proposed problem. The DQN algorithm is built upon the agent's systematic learning of scheduling rules, thereby enabling it to offer guidance for online decision-making regarding the grouping and sequencing of products. The efficacy of the algorithm is substantiated through extensive computational experiments. [ABSTRACT FROM AUTHOR]

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes. [ABSTRACT FROM AUTHOR]

In today's increasingly complex stock era, resilient community planning for uncertainty risks is important for building resilient cities and communities and for realizing the refinement, process, and systematization of planning. Therefore, this paper proposes the concept of community planning resilience based on resilience theory and explores a framework system for evaluating the planning resilience of renewal and transformation community by taking renewal and transformation future community planning of Zhejiang as the research object. At the same time, the technical path of realizing public participation in community planning is explored through the method, mechanism, and technical platform of fuzzy group decision-making. Finally, the feasibility of the aforementioned framework and approach is experimentally explored and verified based on a typical case of renewal and renovation of future community planning. In order to make community regeneration planning more planning resilient, this paper develops a community planning group decision-making study from the perspective of community planning resilience, which provides a new research perspective for the field of community resilience and provides a reference for community planning evaluation and the construction of resilient communities. Practical Applications: In China's advancing urbanization, major cities are grappling with diverse risks, necessitating urban planning and governance with heightened resilience to face unforeseen black swan events and foreseeable gray rhinoceros challenges. This urgency is particularly pronounced in community renewal, prompting a vital shift from rigid to resilient planning methodologies. To address this shift, this paper advocates for resilient community construction to be a focal point in both policy and practical implementation of community regeneration. A novel approach utilizing the fuzzy decision-making method is proposed to establish a comprehensive resilient community renewal decision-making technology support system. This system employs a fuzzy comprehensive evaluation method, integrating an empowerment approach to simplify the complexities of a multicriteria index system. By mitigating the risks inherent in resilience planning, our approach enhances the scientific foundation and feasibility of decision-making in community renewal. A case study on the Future Community in Zhejiang Province validates the effectiveness of our approach, providing practical insights for constructing resilient community renewal models and system technology platforms. This research contributes to the ongoing exploration of strategies and technologies capable of addressing diverse challenges in urban development, providing references in enhancing urban resilience. [ABSTRACT FROM AUTHOR]

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses. [ABSTRACT FROM AUTHOR]

It is widely known that symmetry does exist in management systems, such as economics, management, and even daily life. In addition, effective and qualified decision-making methods can enhance the performance and symmetry of management systems. Hence, this paper focuses on a decision-making method. Linguistic interval-valued q-rung orthopair fuzzy sets (LIVq-ROFSs) have recently been proposed as being effective in describing decision-makers' evaluation values in complex situations. This paper proposes a novel multi-attribute group decision-making (MAGDM) method with LIVq-ROFSs to handle realistic decision-making problems. The main contributions of this study are three-fold. First, a new method for determining the weight information of attributes based on decision makers' evaluation values is proposed. Second, the classical TODIM is extended into LIVq-ROFSs and a new decision-making method is proposed. Third, our proposed MAGDM method is applied to a real decision-making problem to reveal its effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution. Design/methodology/approach: Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function. Findings: Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved. Originality/value: This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Why do human beings exhibit enduring personality differences, and to what extent are these differences shaped by biological and cultural evolution? Despite ongoing efforts, a consensus framework remains elusive. This paper introduces the Diversity Advantage Theory, partially shifting the focus from how personality impacts individuals to its influence within groups. Through an evolutionary lens, connections among personality, collective cognition, and group dynamics are explored, revealing that diversity in personality traits would tend to enhance collective intelligence and foster deliberation, mitigating group biases. The framework proposes that biologically evolved psychological mechanisms underlying personality, including individual niche-finding, adapt and conform to culturally evolving heuristic personality traits. This co-evolution stabilized the Big Few personality traits in western, educated, industrialized, rich, and democratic (WEIRD) societies, addressing recurring group challenges related to engagement, closure, task, risk, and change. The Diversity Advantage Theory offers a novel explanatory framework for stable personality traits, aligning with biology and testable in contemporary group contexts. At least three novel perspectives are introduced: (1) personality trait dimensions have emerged, in part, to establish normative behavior for functional group processes; (2) normative diversity along trait dimensions enhances collective intelligence; and (3) such diversity should also promote deliberation, mitigate group biases, and improve group decision-making. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM). Design/methodology/approach: This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed. Findings: The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness. Originality/value: MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises. [ABSTRACT FROM AUTHOR]

In this paper, we show that it is possible to obtain an optimal configuration variant of Modular Strongrooms (MSR) that satisfies the individual requirements of customers and is most economically advantageous for manufacturers. A model of the automatic configuration system for configuring MSR type MODULPRIM was developed, integrating the procedures for generating product variants, choosing the optimal configuration, and designing detailed products and technological processes. The developed model's importance lies in its ability to automatically select the optimal configuration from a set of possible configurations on a multidisciplinary basis. The problem of choosing was solved by integrating the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods from the group of Multi-Attribute Decision Making (MADM). Validation of the proposed model was performed on eight examples of Modular Strongrooms type MODULPRIM 5 and showed great opportunities to improve efficiency and effectiveness in the process of innovative product development, as well as to obtain a product configuration with significantly improved quality. The proposed model has a high degree of flexibility and universality; thus, it can be further upgraded and integrated into a company's business system. [ABSTRACT FROM AUTHOR]

Online and offline integration is an increasingly popular method of performing modern medical services. To provide suggestions for physician selection by patients with full use of previous online and offline patient evaluations, this paper investigates online and offline social network group decision making (OAOSNGDM) in depth. With the aim of inferring the indirect trust relationships of online and offline patients, we first construct a q-rung orthopair fuzzy dual trust propagation operator based on the q-rung orthopair fuzzy trust function, which can effectively deal with inconsistency in trust functions among patients. Considering patient inconsistency in online and offline scenarios, which can increase the uncertainty of the trust relationship in cross-network propagation, we propose a q-rung orthopair fuzzy dual trust cross-network propagation operator by introducing cross-network propagation efficiency. Considering the signal-to-noise ratio, we calculate the trust propagation efficiency and introduce it into the trust propagation operators. To aggregate the trust information of multiple trust paths among patients, we introduce the Dempster rule from evidence theory which can handle the uncertainty of trust functions. In addition, to accurately determine the patient weights according to online and offline social networks, we integrate the ranking results of patients in terms of degree centrality, neighbor importance and betweenness centrality by developing an improved linear assignment method. We then propose a novel decision-making method for OAOSNGDM and design a complete decision-making process for the evaluation of physicians. Finally, we verify the effectiveness of our proposed method for the evaluation of physicians in an online and offline scenario. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a mathematical programming model that calculates the relative efficiency of homogenous Decision Making Units (DMUs). The conventional DEA models used to calculate the efficiency require the exact amount of inputs and outputs; in real business situations, however, it is often impossible to determine the exact numeral value of some inputs and outputs. At the same time the Common Set of Weights (CSW) overcomes the weakness of DEA models for assessment under same conditions. On the other hands, it is important to considering the balance in evaluation and calculation of indicators. This study develops a new model to calculate the CSW in fuzzy environments, considering the balanced environment using the Balanced Scorecard (BSC). Our proposed model is linear for fairly and equitably evaluating the DMUs on the same scale, also enables us to deal with fuzzy environment and greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in fuzzy DEA models. From a managerial point of view, this paper aims to provide an integrated framework to form a better strategic decision-making process about organization performance, which ultimately leads to the competitive advantages and success of the organization in the long run. Finally, in the field of performance management, the proposed model was applied to evaluate the performances of ten manufacturing enterprises in to confirm the validity and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization construction, the contradiction between supply and demand of urban public transportation resources is becoming increasingly prominent, resulting in increasingly serious problems such as traffic congestion and environmental pollution, which has a great impact on the sustainable development of cities. The experience of mature markets both domestically and internationally indicates that priority must be given to the development of public transportation networks with rail transit as the backbone. Due to the fact that the operation of the rail transit system should not only focus on market benefits but also meet social benefits as much as possible, evaluating efficiency has become a prerequisite for managing rail transit and an important topic in the performance evaluation of urban public transportation. The urban public transport operations evaluation is a multiple-attribute group decision-making (MAGDM). Then, the TODIM and EDAS method has been constructed to deal with MAGDM issues. The interval neutrosophic sets (INSs) are constructed as an effective tool for representing uncertain information during the urban public transport operations evaluation. In this manuscript, the interval neutrosophic number TODIM-EDAS (INN-TODIM-EDAS) method is constructed to solve the MAGDM under INSs. Finally, a numerical example study for urban public transport operations evaluation is constructed to validate the INN-TODIM-EDAS method. The main research contribution of this paper is constructed: (1) the INN-TODIM-EDAS method is put up for MAGDM with INSs; (2) the INN-TODIM-EDAS method is put up for urban public transport operations evaluation and were compared with existing methods; (3) Through the detailed comparison, it is evident that INN-TODIM-EDAS method for urban public transport operations evaluation proposed in this paper are effective. [ABSTRACT FROM AUTHOR]

Cubic q -rung orthogonal fuzzy sets (C q -ROFSs) are a sophisticated mathematical tool used to handle complex evaluation information in multi-attribute decision-making problems. In specific decision-making problems, the power Bonferroni mean (PBM) operator can reflect the correlation between different attributes and mitigate the impact of extreme evaluation information, thereby providing more practical value. This paper focuses on expanding the PBM operator into the C q -ROFS environment and deriving new PBM operators: the cubic q -rung orthogonal power Bonferroni averaging operator and weight cubic q -rung orthogonal PBM operator. The proposed operator shows strong flexibility and stability in the cubic q -rung orthogonal fuzzy environment. In the absence of weight information, there is a dearth of literature addressing the acceptable advantage and decision stability in the C q -ROFS environment; considering the regret behavior of decision information, a VIKOR method based on regret theory is proposed. The proposed method aggregates information using the proposed operator, determines the scheme and weights at two levels of attributes, and constructs a relative proximity decision matrix. Then, the VIKOR method calculates the group utility value and individual regret value based on the regret perception value to rank the alternatives. Finally, the method is applied to evaluate the cotton foreign fiber content, and its stability and effectiveness are verified through sensitivity analysis and comparison with existing methods. [ABSTRACT FROM AUTHOR]

To successfully solve complex real-world problems, decision makers with broad knowledge and experience should be involved in the decision-making process. In this paper, we address the question of how to determine weights of decision makers. The aim of our study is to propose a new approach that derives objective weights for decision makers based on projection method, which emphasizes their agreement by assigning higher weights to similar opinions. The new projection method DProj is specifically adapted for Decision Making Trial and Evaluation Laboratory (DEMATEL), which allows to determine the relationship between cause and effect factors of the system, while we show that some projection methods from the literature are not suitable for DEMATEL. To validate DProj-DEMATEL, we performed two analyses based on Manhattan and Euclidean distances on three examples from the literature. The validation results show that DProj-DEMATEL is a reliable method that can contribute to satisfactory group decision making. [ABSTRACT FROM AUTHOR]

The complexity of the decision-making problems being analysed has led to the development of multiple multi-criteria decision-making (MCDM) methods. One of the more recent methods belonging to this group is the evaluation based on distance from average solution (EDAS) method. To date, it has found extensive use in solving real-world decision-making problems and has seen many extensions to input data types other than real numbers. One of these is the EDAS method for group decision-making in a fuzzy environment. This method aggregates individual evaluations of decision-makers into a group evaluation using the arithmetic mean. This may result in equal group ratings despite the variety of individual ratings, making it difficult or even impossible to rank alternatives because the EDAS algorithm will be blocked. The paper proposes a new fuzzy extension of EDAS called the PFEDAS method for group decision-making. The main difference between the proposed method and the original one is that at the initial stage the individual decision matrices are not aggregated into a group matrix but are transformed into matrices of alternatives. As a result, the new PFEDAS method is based on the initial data instead of their averaged values which allows a more accurate comparison of alternatives. Using a numerical example, the PFEDAS method is compared with other similar methods known from the literature. [ABSTRACT FROM AUTHOR]

To address complex issues, facilitated modelling aims to represent and accommodate plural worldviews from many stakeholders and experts. In these contexts, group homogeneity can become problematic when participants' plurality of perspectives and information is missing and people attending facilitated sessions have similar problem perceptions and interests. This is a challenge because it can lead to narrow discussion, groupthink and undermine output quality. Despite not being uncommon, effective approaches to deal with homogeneity are hardly reported. This paper presents a new role—the New Devil's Advocate—in which some facilitators leave their neutrality-oriented stance and act as the missing stakeholders. The paper illustrates a first application to a group model building process aimed at supporting the development of energy efficiency policies in the UK. To evaluate the results, workshop transcripts were coded, participants' and facilitators' feedback collected, and the modelling output assessed with respect to the New Devil's Advocate interventions during the workshop. Although the role performance appears to increase facilitators' workload and be influenced by role performers' personality and background, the analysis shows positive results as a promising practice to address homogeneity. Additionally, it offers a practical experience of how facilitation teams may temporarily abandon neutrality and intervene on content. [ABSTRACT FROM AUTHOR]

This paper considers the impact of the composition of the top management team on the credit default risk of the firm. Finance theory suggests that shareholders prefer higher levels of risk than the risk-averse executives managing the firm. Increasing the influence of female executives may reduce credit default risk, as female executives have been shown to be associated with lower firm risk. Alternatively, as diversity has been shown to improve the quality of group decision-making, a higher but optimal credit default risk may result. This paper uses a matched sample of 6,652 firm-year observations of publicly traded American firms over the period 2010–2020 to investigate the relationship between gender power within the top management team and credit default risk as measured by the Altman Z-score. This paper finds a convex relationship between the Altman Z-score and the influence of female executives. In other words, top management teams where power is shared between female and male executives accept higher levels of credit default risk than teams dominated by just female (or just male) executives. However, this paper also finds that an excessively high credit risk is negatively associated with the influence of female executives. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus. [ABSTRACT FROM AUTHOR]

Background: Globally, a growing number of calls to formalize and strengthen evidence-support systems have been released, all of which emphasize the importance of evidence-informed decision making. To achieve this, it is critical that evidence producers and decision-makers interact, and that decision-makers' evidence needs can be efficiently translated into questions to which evidence producers can respond. This paper aims to create a taxonomy of demand-driven questions for use by evidence producers, intermediaries (i.e., people working in between researchers and decision-makers) and decision-makers. Methods: We conducted a global cross-sectional survey of units providing some type of evidence support at the explicit request of decision-makers. Unit representatives were invited to answer an online questionnaire where they were asked to provide a list of the questions that they have addressed through their evidence-support mechanism. Descriptive analyses were used to analyze the survey responses, while the questions collected from each unit were iteratively analyzed to create a mutually exclusive and collectively exhaustive list of types of questions that can be answered with some form of evidence. Results: Twenty-nine individuals completed the questionnaire, and more than 250 submitted questions were analysed to create a taxonomy of 41 different types of demand-driven questions. These 41 questions were organized by the goal to be achieved, and the goals were grouped in the four decision-making stages (i) clarifying a societal problem, its causes and potential impacts; (ii) finding and selecting options to address a problem; (iii) implementing or scaling-up an option; and (iv) monitoring implementation and evaluating impacts. Conclusion: The mutually exclusive and collectively exhaustive list of demand-driven questions will help decision-makers (to ask and prioritize questions), evidence producers (to organize and present their work), and evidence-intermediaries (to connect evidence needs with evidence supply). [ABSTRACT FROM AUTHOR]

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Group decision-making in capital investment involves a collaborative process where multiple stakeholders contribute their perspectives, insights, and expertise to evaluate investment opportunities and make informed decisions. This process's practical and methodological aims can vary depending on the organization's goals, industry, and market conditions. Researching capital investment group decision-making is motivated by several factors, such as optimizing investment decisions, enhancing performance, managing risk, adapting to changing conditions, and building knowledge and expertise. The investment decision relates to the distribution of financial resources. Investors select the most appropriate investment opportunities based on risk profiles, investment aims, and expected returns. Fuzzy multicriteria group decision-making in capital investment extends the traditional decision-making process to accommodate multiple criteria that are often uncertain, vague, or subjective in nature. This paper aims to suggest a technique known as the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for group decision-making about investment in the respective vehicle. The fuzzy TOPSIS technique represents a decision for experts, which is multicriteria and includes an aggregated decision-making process. This paper shows the application of this method when choosing the best alternative, considering the choice of vehicle. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the healthcare sector of the United Arab Emirates (UAE) to explore the significance of servant leadership and collaborative culture in fostering social sustainability. The primary objective of this paper is to investigate how servant leadership and a collaborative culture contribute to social sustainability in health care in the UAE. With a focus on promoting well-being within healthcare organizations, the paper aims to uncover the synergies between servant leadership, collaborative culture, and social sustainability. Design/methodology/approach: This paper conducted a multilayer literature review of existing literature on servant leadership, collaborative culture and social sustainability in health care, both globally and specifically in the UAE context, and a conceptual model was proposed. Findings: Servant leadership proves to be a culturally pertinent and effective leadership model within the UAE due to its alignment with cultural values, emphasis on community support, and the robust health-care system that contributes to individual well-being. This combination establishes a solid foundation for fostering a healthy and sustainable society. Research limitations/implications: Limitations and implications are discussed. The current research has not identified the boundary conditions under which servant leadership and collaborative culture may be more or less effective. This could involve exploring industry-specific influences or contextual factors. Theoretical and practical implications are discussed. Originality/value: The research seeks to unravel the interconnections between servant leadership, collaborative culture and social sustainability. To the best of the author's knowledge, none of the studies have explored the interrelationships of these constructs, particularly in the UAE context. [ABSTRACT FROM AUTHOR]

Floating cities have emerged as an efficient long-term solution over unsustainable practiced solutions to combat the rising seas problem; nevertheless, the world lacks an international, official, and comprehensive framework regarding floating cities. Although previous research approached modular floating city design; however, resulted in configurations with various critical design restrictions mainly regarding interlocking capabilities and space utilization. The purpose of this paper is to offer a new systematic strategy for configuring modular and expandable floating cities without such restrictions. This paper explores Euclidean tilings as a strategy to offer numerous configurations based on regular, semi-regular, and demi-regular tilings. Selecting the ideal configuration is complicated; therefore, both quantitative and qualitative data methods were implemented to attain the objectives. Via an extensive literature review, this research derives key factors for configuring floating cities, then sets a brainstorming session with experts for group decision making before providing findings upon calculations via analytic hierarchy process, one of the most used quantitative data methods of multiple-criteria decision analysis. Through comprehensive literature review: seakeeping, modularity, zoning and circulation, and feasibility have been identified as the most significant criteria in floating city research. It explores the qualities and limitations of triangular, squared, hexagonal, octagonal, and dodecagonal platforms. Regarding criteria, seakeeping was the most significant criterion for platform selection by 53.6%. Regarding platforms, the hexagonal platform scored the highest with 25.31%. Relying on this method and the design considerations presented, numerous dynamic configurations can be offered and assessed through specific contexts without any of the past restrictions. [ABSTRACT FROM AUTHOR]

Political goals are often sought by groups, whether they be political parties, trade associations, interest groups, or of any other sort. The formalities of modern political systems rely on individual voting, but group identities shape both individual behavior and the workings of government. To build a metaphor from the natural sciences, fluids (groups) are composed of molecules (individuals), but the fluid has properties of its own that affect its motion (actions) and interactions with other fluids (groups). This paper develops a model based on fluid dynamics relationships to explain group interactions based on observable group characteristics. It complements many strands of literature by explaining the circumstances in which existing models are valid, and in which they are incomplete. In so doing, it does not refute any existing models, but links them together and addresses their shortcomings. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers. [ABSTRACT FROM AUTHOR]

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations, and the MG-PLPRs are obtained by statistical calculation to represent sub-group's preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established for achieving consensus-reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens. [ABSTRACT FROM AUTHOR]

Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals’ cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals’ metacognition and recording and utilizing users’ confidence in their answers? [ABSTRACT FROM AUTHOR]

In multi-criteria group decision-making (MCGDM) problems with incomplete fuzzy preference relations (FPRs), the consistency based on bounded rationality is preferred to depend on the historical preferences of decision makers to generate complete FPR matrices. There are two factors in influencing the effect of historical preferences on complete FPR matrices and further decision makers' satisfaction of group solutions. One is the effectiveness of historical preferences and the other is their relative weights. On this basis, this paper proposes a new MCGDM method with FPRs based on the filtration-and-weighting-based triangular bounded consistency (FWBTBC), where historical FPRs between three adjacent pairs of alternatives are characterized by triangles and these two factors are considered comprehensively. A triangle filtration process is designed based on the Grubbs criterion to filter the abnormal triangles in the collected ones. A weight determination process is developed, where a distance measure between remaining triangles is constructed to calculate their weights. Based on the combination of the two processes, the FWBTBC of FPRs is created and then the proposed method is presented. The validity of the proposed method is demonstrated by its application in analyzing a drainage tube supplier selection problem for a tertiary hospital located in Hefei, Anhui, China. [ABSTRACT FROM AUTHOR]

The large-scale multi-attribute group decision-making (LSMAGDM) problem has become a hot research topic in the field of decision science. An R-numbers large-scale multi-attribute group decision-making (R-LSMAGDM) model is proposed to be constructed in this paper based on the advantages of R-numbers in capturing risks. First, the most commonly used clustering method, k-means, is introduced to determine the sub-groups. Then, a new sub-group weighting determination model is constructed by considering sub-group size and sub-group entropy. Next, we also build an optimized consensus-reaching model by improving the calculation method of the mean value. Then, the R-numbers weighted Hamy mean (RNWHM) operator is proposed to aggregate the sub-group information. In addition, the logarithmic percentage change-driven objective weighting (LOPCOW) method and the compromise ranking of alternatives from distance to ideal solution (CRADIS) method are used for attribute weighting calculation and alternative ranking, respectively. Finally, the effectiveness of the model is verified by an application example of hydrogen fuel cell logistics path selection. [ABSTRACT FROM AUTHOR]

This paper proposes a multidimensional social open model to evaluate the teaching strategies adopted during the COVID-19 pandemic by assessing the decisions made by teachers by a group of teachers acting as evaluators. Based on the analysis of previous studies on teaching, this study aims to propose a formal model for the evaluation of teaching strategies in four dimensions: sustainability, usability, accessibility, and creativity. The use of information technologies to measure teaching strategies can bring decisive advantages. This work has been inspired by social rating systems of social networks to propose a measurement system in which a potential large number of evaluators with different levels assess the strategies. In addition, the proposal also includes the evaluation of the evaluators' own work, assigning confidence levels that are based on their experience but also on their evaluations. In this way, we have a social measurement system, in the sense that participation is open to a large number of evaluators. A large community of teacher evaluators will increase the objectivity of the measurement. The outcome of the system will be a characterization of the teaching strategies that will allow to decide in the future which ones should be adopted according to the needs of each one. [ABSTRACT FROM AUTHOR]

As a process for ensuring the agreeable degree of individual opinions, consensus analysis is crucial for GDM. This paper focuses on the adaptive consensus mechanism. That's, different adjustment strategies are employed for various consensus levels. Unlike the feedback iteration method, this paper introduces an optimization model-based consensus-reaching procedure. To do this, optimal models are built to determine the minimum consensus adjustment at different levels. Then, the individual minimum consensus adjustment is analyzed, and the inconsistency between individual and group minimum consensus adjustments is concluded. After that, consensus adjustment cooperative games at three levels are proposed to allocate the total minimum consensus adjustment in view of the comprehensive evaluation. We can obtain the coalitional stability allocation scheme using the core of constructed cooperative games. Additionally, core-Nash bargaining games at three levels are proposed to ensure the fairness and coalitional stability of allocation results. Finally, a numerical example is offered to indicate the application of the new theoretical developments. [ABSTRACT FROM AUTHOR]

Purpose: Digital lean implementation can solve the dual problem of stagnating quality and rising costs in healthcare. Although technology adoption in healthcare has increased in the post-COVID world, value unlocking using technology needs a well-thought-out approach to achieve success. This paper provides a prescriptive framework for successfully implementing digital lean in healthcare. Design/methodology/approach: This study uses a mixed-method approach to achieve three research objectives. Whilst it uses a narrative review to identify the enablers, it uses qualitative thematic analysis techniques to categorise them into factors. The study utilises the delphi method for the thematic grouping of the enablers in the broader groups. The study used an advanced ordinal priority approach (OPA) to prioritise these factors. Finally, the study uses concordance analysis to assess the reliability of group decision-making. Findings: The study found that 20 identified enablers are rooted in practice factors, followed by human resource management (HRM) factors, customer factors, leadership factors and technology factors. These results further counter the myth that technology holds the utmost significance in implementing digital lean in healthcare and found the equal importance of factors related to people, customers, leadership and best practices such as benchmarking, continuous improvement and change management. Originality/value: The study is the first of its kind, providing the prescriptive framework for implementing digital lean in healthcare. The findings are useful for healthcare professionals and health policymakers. [ABSTRACT FROM AUTHOR]

Crude oil, the backbone of modern industry, holds unparalleled significance as a global energy cornerstone. Unlocking its potential hinges on effective pretreatment techniques, ensuring purity, and maximizing efficiency. This study extends the established Spherical Fuzzy Set paradigm to explore the domain of Disc Spherical Fuzzy Sets (D-SFSs) in critical decision-making for crude oil preparation. Investigating D-SFSs within the Aczel Alsina norm, the research employs comparison rules, conversion rules, and distance metrics. Primary operations of the Aczel Alsina norm in D-SFSs are examined, laying the groundwork for introducing unique aggregation operations within this framework. The paper's primary aim is to propose a hybrid method, termed MEREC-SWARA-MARCOS-D-SFSs Multiple Attribute Group Decision Making, which integrates the aforementioned aggregation procedures. A case study on crude oil pretreatment validates the effectiveness of the proposed method. Furthermore, a comprehensive comparison with CoCoSo underscores the reliability of the method. This study represents a significant stride in enhancing decision-making by providing a robust framework to tackle complex situations, particularly in the critical domain of crude oil pretreatment. [ABSTRACT FROM AUTHOR]

Most practical decision-making problems commonly contain several alternatives evaluated by more than one decision maker (DM) according to the built criteria system. It is known as multi-criteria group decision making problems. The decision-making results should be based on highly similar individual opinions to derive the most suitable choice. Nevertheless, the divergence of individual judgments is usually significant due to various subjective and objective reasons. In this case, some DMs need to modify their judgments to derive final results, the consensus adjustment in group decision making. Like supply chain management, the centralized consensus process can minimize individual adjustments under the set consensus condition. Based on the hypothesis of rational humans that individuals always pursue maximum benefits, this paper proposes an iterative consensus process in a centralized environment. According to non-consensus adjustments, the iterative procedure contains non-transferable and transferable two parts. When the non-transferable consensus adjustment procedure is completed, the transferable consensus adjustment procedure is activated using an asymmetrical Nash bargaining game allocation mechanism. Additionally, a case study is provided to demonstrate the efficaciousness and practicality of the new method. Meanwhile, sensitivity and comparison analyses are done. [ABSTRACT FROM AUTHOR]

In the context of the development of the new era, computer network technology has become an indispensable and important technological means in people's daily work and life. Through network technology, information resources can be collected, integrated, processed, and applied, thereby improving information analysis and mining capabilities, constructing big data environments for various industries, providing convenient and fast intelligent information services, and promoting social transformation and development. However, in practical development, network security issues seriously affect information security and social stability, and computer viruses and hackers pose a huge threat to computer systems. The computer network security evaluation is the MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) and TOPSIS technique has been utilized to cope with MAGDM issues. The interval neutrosophic sets (INSs) are utilized as a technique for characterizing uncertain information during the computer network security evaluation. In this paper, the interval neutrosophic number Logarithmic TODIM-TOPSIS (INN-LogTODIM-TOPSIS) technique is conducted to solve the MAGDM under INSs. Finally, a numerical case study for computer network security evaluation is utilized to validate the proposed technique. The prime contributions of this paper are put forward: (1) The entropy technique based on score values and accuracy value are conducted to obtain weight information under INSs; (2) an integrated INN-LogTODIM-TOPSIS technique is conducted to put forward the MAGDM issue; (3) An illustrative example for computer network security evaluation has been accomplished to put forward the INN-LogTODIM-TOPSIS technique. [ABSTRACT FROM AUTHOR]

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to distinguish by producing vivid news or images. Additionally, modern social networks are becoming flatter and more diverse, making conventional trust management methods unfeasible. There is an urgent need to design a suitable blockchain consensus for decentralized trust management. In this paper, we propose a novel three-layer autonomous trust management model for online social users based on blockchain technology and group decision making. Instead of solving a hash puzzle, which consumes too much computational resources for social network users, this paper proposes a lightweight and verifiable blockchain consensus proof of trust for decentralized trust management. Furthermore, performance analysis and simulation results show that our model is valid and flexible for both synthetic networks and real social networks. [ABSTRACT FROM AUTHOR]

The aim of this paper is to construct the lattice structure for Z-soft covering based rough set. First, we define an equivalence relation R' on a universal set to obtain the equivalence classes induced by Z-soft covering-based rough set. Also, we define a relation RS on the family of Z-soft covering-based rough set (TS) to show that the relation RS is a poset on TS. Second, we define two operations join ∨ and meet ∧ on TS. Using these two operations, we prove that every pair of elements of RS has a least upper bound and a greatest lower bound and as a result, TS is a lattice. Finally, we develop a novel Multiple Attribute Group Decision Making (MAGDM) model using Z-soft covering based rough set in medical diagnosis to determine the patients at high risk of chronic kidney disease using the collected data from the UCI Machine Learning Repository. [ABSTRACT FROM AUTHOR]

In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results. [ABSTRACT FROM AUTHOR]

As a new form of tourism, forest health has gradually attracted the attention of scholars and the public, becoming a new focus of research. There have been many qualitative studies on the product forms and development models of forest health tourism, but there is still a gap in the evaluation indicators and development systems for the development potential of forest health tourism. Using the research results of forest health tourism at home and abroad for reference, we will build an evaluation system for the development potential of forest health tourism, providing new research perspectives and operational practical support for the development of forest health tourism. The development potential evaluation of forest health tourism is a classical MAGDM problems. Recently, the TODIM and VIKOR technique has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are employed as a tool for characterizing uncertain information during the development potential evaluation of forest health tourism. In this paper, the single-valued neutrosophic number TODIM–VIKOR (SVNN–TODIM–VIKOR) technique is built to solve the MAGDM under SVNSs. In the end, a numerical case study for development potential evaluation of forest health tourism is given to validate the proposed technique. The prime contribution of this paper is managed: (1) the information entropy based on score values are constructed to obtain weight values under SVNSs; (2) an integrated SVNN–TODIM–VIKOR technique is constructed to address the MAGDM issue; and (3) an illustrative example for development potential evaluation of forest health tourism has accomplished to verify the SVNN–TODIM–VIKOR technique. [ABSTRACT FROM AUTHOR]

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers' fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper undertakes an analysis and discussion of the methodological challenges and insights derived from three longitudinal qualitative studies, all conducted in Chile during the COVID-19 pandemic and subject to comprehensive theoretical-methodological reflection processes centred on their respective designs. This analysis makes a significant contribution to interdisciplinary discussions within social research, with a particular emphasis on longitudinal trajectories. First, we present a comparative analysis of three studies in social work, utilising Saldaña's questions addressing changes and learning in longitudinal studies. The first study explores the labour trajectories of researchers, the second focuses on the educational trajectories of students, and the last examines therapeutic alliance trajectories between social workers and families within the child protection system. Following this, we delve into the methodological decisions made by the research group during the execution of these longitudinal studies. This encompasses an examination of participant involvement, temporal definitions of the adopted designs, and the most suitable methodological tools for analysing change processes over time. The outcomes of this comparative analysis reveal the distinctive characteristics of the three longitudinal studies, providing insights into how the time dimension is explored within them. We highlight key criteria essential for consideration in longitudinal qualitative research, particularly regarding participants and methodology. In conclusion, we advocate for an expanded reflection within the realm of longitudinal qualitative methodology, encompassing aspects such as design choices, approaches to data analysis, integration of technology in information processing, and strategies for maintaining participant engagement. [ABSTRACT FROM AUTHOR]

Like common recommender systems, group recommendation usually suffers from popularity bias where popular items are more likely to be suggested and exposed to users over long-tailed ones. The skewed data distribution caused discrimination against a great amount of unpopular items, which will be further intensified during the group decision-making process. Despite previous studies devoted to addressing popularity bias issue in recommendations, rarely have other works concentrated on such problem in group recommender systems. In this paper, we identify the negative impact of item popularity in a causality manner and propose a Popularity Reweighting Framework for Group Recommendation (PopGR). Specifically, a popularity-aware weighting function is adopted to mitigate the bias problem by incorporating the popularity level of items along with their intrinsic characteristics into group modeling. Experiments conducted on two real world benchmark datasets justify the effectiveness of our model to alleviate bias while maintaining reasonable ranking accuracy. [ABSTRACT FROM AUTHOR]

When deciding where to visit next while traveling in a group, people have to make a trade-off in an interactive group recommender system between (a) disclosing their personal information to explain and support their arguments about what places to visit or to avoid (e.g., this place is too expensive for my budget) and (b) protecting their privacy by not disclosing too much. Arguably, this trade-off crucially depends on who the other group members are and how cooperative one aims to be in making the decision. This paper studies how an individual's personality, trust in group, and general privacy concern as well as their preference scenario and the task design serve as antecedents to their trade-off between disclosure benefit and privacy risk when disclosing their personal information (e.g., their current location, financial information, etc.) in a group recommendation explanation. We aim to design a model which helps us understand the relationship between risk and benefit and their moderating factors on final information disclosure in the group. To create realistic scenarios of group decision making where users can control the amount of information disclosed, we developed TouryBot. This chat-bot agent generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain [more specifically, the initial POI options were selected from the category of "Food" in Amsterdam (see Sect. 3.2 for the details)]. To understand the dynamics between the factors mentioned above and information disclosure, we conducted an online, between-subjects user experiment that involved 278 participants who were exposed to either a competitive task (i.e., instructed to convince the group to visit or skip a recommended place) or a cooperative task (i.e., instructed to reach a decision in the group). Results show that participants' personality and whether their preferences align with the majority affect their general privacy concern perception. This, in turn, affects their trust in the group, which affects their perception of privacy risk and disclosure benefit when disclosing personal information in the group, which ultimately influences the amount of personal information they disclose. A surprising finding was that the effect of privacy risk on information disclosure is different for different types of tasks: privacy risk significantly impacts information disclosure when the task of finding a suitable destination is framed competitively but not when it is framed cooperatively. These findings contribute to a better understanding of the moderating factors of information disclosure in group decision making and shed new light on the role of task design on information disclosure. We conclude with design recommendations for developing explanations in group decision-making systems. Further, we propose a theory of user modeling that shows what factors need to be considered when generating such group explanations automatically. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty in information, fuzzy theory has become a crucial approach for solving Multiple Attribute Group Decision Making (MAGDM) problems. This paper proposes two methods for addressing MAGDM problems with interval-valued Fermatean fuzzy information. Firstly, a series of Hamacher operators on the interval-valued Fermatean fuzzy numbers (IVFFNs) are derived, based on the fundamental operational norms of IVFFNs and the definitions of Hamacher operators. Secondly, two decision-making methods are proposed in this paper, based on the newly introduced operators, and they are effectively applied to address the enterprise recruitment problem. Finally, we investigate the influence of parameters on the final decision and observe that variations in parameters have small impact on the outcome. This feature simplifies the decision-making process by eliminating the need for extensive parameter tuning. Furthermore, a comparative analysis is conducted between the proposed methods and the research of other scholars, demonstrating the effectiveness of our approach and validating its reliability. [ABSTRACT FROM AUTHOR]

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decisionmaking efficiency. [ABSTRACT FROM AUTHOR]

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

The interval-valued intuitionistic fuzzy sets (IVIFSs), based on the intuitionistic fuzzy sets (IFSs), combine the classical decision method and its research and application is attracting attention. After a comparative analysis, it becomes clear that multiple classical methods with IVIFSs' information have been applied to many practical issues. In this paper, we extended the classical EDAS method based on the Cumulative Prospect Theory (CPT) considering the decision experts (DEs)' psychological factors under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, an original EDAS method, based on the CPT under IVIFSs (IVIF-CPT-EDAS) method, is created for multiple-attribute group decision making (MAGDM) issues. Meanwhile, the information entropy method is used to evaluate the attribute weight. Finally, a numerical example for Green Technology Venture Capital (GTVC) project selection is given, some comparisons are used to illustrate the advantages of the IVIF-CPT-EDAS method and a sensitivity analysis is applied to prove the effectiveness and stability of this new method. [ABSTRACT FROM AUTHOR]