Decision Procedure Research Articles

The Brazilian State Water Resources Councils: diagnosis of civil society participation through criteria of access to information, parity, and decision-making procedures

The aim of the article was to investigate whether the Brazilian State Water Resources Councils, including the Federal District, ensure civil society participation through access to information, parity, and decision-making procedures. In this context, verification criteria were selected, with maximum score of 1 point assigned based on their suitability for the Councils, which were classified in terms of participation adequacy. The states that were deemed suitable, in descending order, were: Paraíba (1st position), Mato Grosso do Sul, Minas Gerais, and São Paulo (2nd position), Amazonas, Pará, Paraná, Rio de Janeiro, Rio Grande do Sul, Rondônia, and Santa Catarina (3rd position), Bahia, Ceará, Federal District, Espírito Santo, and Mato Grosso (4th position), Rio Grande do Norte and Tocantins (5th position), and Alagoas (6th position). The states deemed unsuitable were: Acre and Pernambuco (7th position), Piauí and Roraima (8th position), Goiás and Sergipe (9th position), and Amapá and Maranhão (10th position). As for the criteria, the ones that scored the highest were: the presence of the website and Internal Regulations of the Councils, decision-making by the Council in plenary sessions, the presence of Meeting Minutes on the website, identification of the Technical Chambers of the Councils on these channels, adherence to the regular meeting schedule, identification of the members who make up the Councils, and parity among the three sectors that constitute them. It was concluded that the State Water Resources Councils need to ensure equal participation for civil society members in their discussions and deliberations as well as provide public access to the information generated.

INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH

Context. The situational approach is based on the real-time decision-making methods for solving current problem situation. An effective tool for implementing the concept of a situational approach is an experience-based technique that widely known as сasebased reasoning approach. Reasoning by precedents allows solving new (latest) problems using knowledge and accumulated experience of previously solved problems. Since cases (precedents) describing a scenario for solving a certain problem situation are stored in the case library, their search and retrieval directly determine the system response time. In these conditions, there is a need to find ways of solving an actual scientific and practical problem aimed at optimizing case searching and extracting processes. The object of the paper is the processes of searching and extracting of cases from the case library. Objective. The purpose of the article is to improve the process of cases searching in CBR approach by narrowing down the set of cases permissible for solving the current target situation, and excluding from further analysis such cases that do not correspond to the given set of parameters of the target situation. Method. The research methodology is based on the application of rough set theory methods to improve the decision-making procedure based on reasoning by precedents. The proposed two-stage procedure for narrowing the initial set of cases involves preliminary filtering of precedents whose parameter values belong to the given neighborhoods of the corresponding parameters of the target situation at the first stage, and additional narrowing of the obtained subset of cases by the methods of rough set theory at the second stage. The determination of the R-lower and R-upper approximations of a given target set of cases within the notation of rough set theory allows dividing (segmenting) the original set of cases available for solving the current problem stored in case library into three subgroups (segments). The search for prototype solutions can be performed among a selected subset of cases that can be accurately classified as belonging to a given target set; which with some degree of probability can be attributed to the given target set, or within the framework of the union of these two subsets. The third subset contains cases that definitely do not belong to the given target set and can be excluded from further consideration. Results. The problem of presentation and derivation of knowledge based on precedents has been considered. The procedure for searching for precedents in case library has been improved in order to reduce the system response time required to find the solution closest to the current problem situation by narrowing the initial set of cases. Conclusions. The case-based reasoning approach is received the further development by segmenting cases in terms of their belonging to a given target set of precedents uses methods of the rough set theory, then the search for cases is carried out within a given segment. The proposed approach, in contrast to the classic CBR framework, uses additional knowledge derived from obtained case segment; allows modeling the uncertainty regarding the belonging / non-belonging of a case to a given target set; removing from further consideration cases that do not correspond to a given target set.

Nurses' Perception of Threats to Human Dignity in Caring for Patients with COVID-19: A Qualitative Study

PurposeIdentifying threats to patients' dignity is crucial for maintaining and promoting their dignity. Preserving patient dignity should be regarded as both a patient’s right and a moral obligation. This study was conducted to evaluate nurses' understanding of threats to human dignity in the care of patients with COVID-19. MethodsThis qualitative study was conducted using conventional content analysis in various clinical departments in Iran. Clinical nurses from different departments were selected through purposeful sampling. Data were collected via in-depth semi-structured interviews with 15 clinical nurses over two months in 2021 and analyzed using qualitative content analysis. ResultsDuring data analysis, two main categories and ten subcategories were identified: 1) Unethical behaviors (lack of respect for patient privacy, lack of patient involvement in decision-making procedures, ignoring the patient as an individual, aggressive behaviors, stigmatization, and failure to provide palliative and end-of-life care to patients and their families); 2) Organizational factors (inadequate financial support, lack of patient-care facilities, strict restrictions, and employing less experienced nurses in COVID wards). ConclusionThe present study showed that most participants expressed concerns about violations of patients' dignity and identified many threats to it. Based on the findings, it is suggested that other factors threatening patients' dignity be identified, the threats eliminated, and healthy treatment environments created to promote patients' dignity.

Legal Certainty in the Division of Joint Property after Divorce

This study aims to analyze the legal certainty in the distribution of joint property after divorce, with an emphasis on the challenges faced in the implementation of the law in Indonesia. The division of joint property is a crucial issue in the context of divorce which often causes disputes between former spouses, where the principles of justice and equality should take precedence. In this study, a normative juridical approach is used to examine laws and regulations, legal doctrines, and court decisions related to the division of common property, with a focus on Law Number 1 of 1974 concerning Marriage and the Civil Code. The results show that despite the legal provisions governing the division of common property, its implementation is often hampered by various factors, including differences in interpretation among judges, the existence of cultural values that influence decisions, and technical obstacles in asset valuation. Variations in court decisions create legal uncertainty that not only harms one party, but also impacts the welfare of children involved in divorce. Uncertainty about the rights and obligations of each party has the potential to cause prolonged conflicts and harm all parties. As part of the analysis, the study also highlights the social and economic impact of legal uncertainty in the distribution of assets, especially for the financially weaker parties. To improve fairness and certainty in the division of common property, this study recommends the need for more detailed and uniform guidelines in court decision-making procedures, as well as harmonization between national and local regulations. Thus, it is hoped that the process of distributing joint property after divorce can be carried out in a fairer, more efficient, and transparent manner, so that the interests of all parties, especially children, can be well protected.

Dynamic output feedback model predictive control for polytopic uncertain systems under decode and forward cooperative relay protocol

This paper addresses the robust model predictive control (RMPC) problem for a class of polytopic uncertain systems under the decode and forward cooperative relay protocol (DFCP). In order to combat signal fading from sensors to controllers and improve communication reliability, spatial diversity is achieved through cooperative terminal relay signals. At each transmission instant, the terminal only selects a better signal for transmission and a switched system is constructed. The aim of this paper is to design a set of desired dynamic output feedback controllers in the framework of RMPC such that the switched system with the underlying DFCP is exponentially stable. By taking the influence of the randomness of DFCP derived from a three nodes model composed of DF relay, a new objective function based on the mathematical expectation of the traditional quadratic function of MPC is established. Then, the orthogonal complement technique is applied to handle the inherent couplings of variables to formulate a solvable optimisation problem. Moreover, the sufficient conditions are provided to guarantee the recursive feasibility of the MPC strategy and the stability of the established closed-loop system in terms of the average dwell time (ADT) method. Finally, three experiments are given to illustrate the effectiveness of the proposed method.

Devising a project risk management method under scrum conditions based on cognitive approach

Any project implemented using Scrum is characterized by the impact of risks, including negative changes in the environment and crisis circumstances. Therefore, the processes related to risk management, which is the object of this paper, become important. The problem solved in this study is to improve the efficiency of projects through the construction of a long-term strategy for reducing the level of risk and avoiding negative consequences for projects in the context of Scrum. The proposed method of risk management has been developed on the basis of the application of the synthesis of management of intelligent decision-making technologies and formalized methods. Difficult external circumstances are characterized by a high degree of uncertainty and do not always contribute to the successful implementation of the project. Therefore, this method of project risk management under Scrum conditions based on a cognitive approach is characterized by a combined combination of cognitive analysis, mathematical modeling, and expert methods. As part of the method, a model of project risk management under Scrum conditions has been built in the form of a fuzzy cognitive map, which could ensure determining the optimal strategic decision in dynamics, taking into account the effects of various factors. The result of applying this method is compliance with time limits, reduction of overspending of resources and losses in the project, as well as adaptation to rapidly changing circumstances and adequate response. The method of project risk management is characterized by solving the problem of formalizing management decision-making procedures and their information support, taking into account the availability of both quantitative and qualitative data. Within the framework of this method, a project risk management procedure under Scrum conditions has been proposed, which contributes to the systematization, monitoring, and control of risks under the conditions of complex, rapidly changing crisis circumstances

Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)

Abstract Background Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms. Purpose In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management. Methods To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data. Results The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%. Conclusions The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.

Unveiling the Ethical Dilemmas of Digital Piracy: A Comprehensive Exploration of Motivations, Attitudes, and Behaviors

Driven by several elements, including the unwillingness to pay for material, the spread of subscription services, and the profitability of illegal activity, digital piracy has grown to be a common problem in the contemporary world. Along with financial damages for businesses and content creators, this phenomenon has legal repercussions for individuals involved in piracy and cybersecurity issues. The effective fight against digital piracy depends on an awareness of the drivers and hazards connected with this illegal activity. With an eye towards a thorough knowledge of this intricate problem, in this study, the focus centers on the reasons, attitudes, and actions behind digital piracy. Through examining how people defend their behavior in particular situations, in this study, the aim is to clarify the ethical issues and decision-making procedures related to piracy—even among otherwise law-abiding people. Combining a variety of the relevant literature and ideas, including situation ethics theory, in this study, we seek to provide insights for legislators, business players, and academics eager to meet the difficulties presented by digital piracy. This study adds to the continuous discussion on lessening the detrimental effects of digital piracy on content creators, businesses, and cybersecurity by looking at the underlying reasons for piracy and supporting legal access to material. Along with a dearth of research on the origins and moral questions of digital piracy, there is a lot of contradictory material. By examining the factors behind this unlawful activity, clarifying the complex dynamics involved, and offering understanding of the causes of digital piracy, this study seeks to close a significant void in the knowledge. The findings of this study will enable individuals to develop strategies for handling this prevalent issue in the digital era. Developing successful interventions that can reduce this illegal conduct and safeguard the interests of content producers, businesses, and cybersecurity overall depends on an awareness of the reasons behind digital piracy. This study advances the knowledge of the difficulties presented by this global phenomenon by investigating the ethical issues and decision-making procedures related to digital piracy.

Better Hardness Results for the Minimum Spanning Tree Congestion Problem

AbstractIn the spanning tree congestion problem, given a connected graph G, the objective is to compute a spanning tree T in G that minimizes its maximum edge congestion, where the congestion of an edge e of T is the number of edges in G for which the unique path in T between their endpoints traverses e. The problem is known to be $$\mathbb{N}\mathbb{P}$$ N P -hard, but its approximability is still poorly understood, and it is not even known whether the optimum solution can be efficiently approximated with ratio o(n). In the decision version of this problem, denoted $${\varvec{K}-\textsf {STC}}$$ K - STC , we need to determine if G has a spanning tree with congestion at most K. It is known that $${\varvec{K}-\textsf {STC}}$$ K - STC is $$\mathbb{N}\mathbb{P}$$ N P -complete for $$K\ge 8$$ K ≥ 8 , and this implies a lower bound of 1.125 on the approximation ratio of minimizing congestion. On the other hand, $${\varvec{3}-\textsf {STC}}$$ 3 - STC can be solved in polynomial time, with the complexity status of this problem for $$K\in { \left\{ 4,5,6,7 \right\} }$$ K ∈ 4 , 5 , 6 , 7 remaining an open problem. We substantially improve the earlier hardness results by proving that $${\varvec{K}-\textsf {STC}}$$ K - STC is $$\mathbb{N}\mathbb{P}$$ N P -complete for $$K\ge 5$$ K ≥ 5 . This leaves only the case $$K=4$$ K = 4 open, and improves the lower bound on the approximation ratio to 1.2. Motivated by evidence that minimizing congestion is hard even for graphs of small constant radius, we also consider $${\varvec{K}-\textsf {STC}}$$ K - STC restricted to graphs of radius 2, and we prove that this variant is $$\mathbb{N}\mathbb{P}$$ N P -complete for all $$K\ge 6$$ K ≥ 6 .

Enhancing Robo-Advisors: A Study of Personalized Financial Planning Through AI-Driven Insights

The emergence of robo-advisors has revolutionized the financial advisory field, presenting automated investment suggestions tailored to individual investors' distinct goals and risk appetites. This research paper aims to explore the dynamic evolution of robo-advisory services, delving into the ways artificial intelligence (AI) and data-derived insights can be harnessed to provide an even more personalized and efficient financial planning encounter. Through an exploration of advanced algorithms, machine learning methodologies, and user-centered design concepts, this investigation strives to uncover the techniques robo-advisors use to refine their decision-making procedures, accommodating the distinct financial ambitions of each investor. By means of practical analyses, illustrative case studies, and a discerning assessment of industry tendencies, this research illuminates the obstacles and prospects inherent in attaining genuine personalization within the domain of automated financial guidance.

What’s Impossible about Algorithmic Fairness?

The now well-known impossibility results of algorithmic fairness demonstrate that an error-prone predictive model cannot simultaneously satisfy two plausible conditions for group fairness apart from exceptional circumstances where groups exhibit equal base rates. The results sparked, and continue to shape, lively debates surrounding algorithmic fairness conditions and the very possibility of building fair predictive models. This article, first, highlights three underlying points of disagreement in these debates, which have led to diverging assessments of the feasibility of fairness in prediction-based decision-making. Second, the article explores whether and in what sense fairness as defined by the conjunction of the implicated fairness conditions is (un)attainable. Drawing on philosophical literature on the concept of feasibility and the role of feasibility in normative theory, I outline a cautiously optimistic argument for the diachronic feasibility of fairness. In line with recent works on the topic, I argue that fairness can be made possible through collective efforts to eliminate inequalities that feed into local decision-making procedures.

A Practical Decision Procedure for Quantifier-Free, Decidable Languages Extended with Restricted Quantifiers

A Practical Decision Procedure for Quantifier-Free, Decidable Languages Extended with Restricted Quantifiers

Community Engagement in Local Governance

Community engagement in local governance is crucial for effective and inclusive decision-making. Lack of Awareness and Information has been found that limited knowledge can have its community members may not fully understand the governance processes or their rights and responsibilities. Information gaps have been found that the insufficient information about local issues and decision-making processes can hinder meaningful participation. Low Participation Rates have been found that apathy and disinterest is that some community members may be disengaged due to a lack of trust in the system or a perception that their participation will not make a difference. Barriers to access has been found that the physical, economic, or social barriers may prevent certain groups from participating, such as the elderly, disabled, or economically disadvantaged. During the initial phase (2008-2013) of the Local Governance and Community Development Program (LGCDP) in Nepal, social mobilization emerged as one of its most effective components. Approximately 1 million citizens actively influenced local government decisions, leading many local bodies to develop annual plans that more accurately addressed the needs of citizens, particularly marginalized and disadvantaged groups. However, there remain several lessons to be learned in addressing the challenges of implementation. It is crucial to harmonize local social mobilization efforts and enhance the quality and support of social mobilization initiatives across Nepal. Addressing the challenges of community engagement in local governance involves a multifaceted approach that includes enhancing communication, building capacity, fostering inclusivity, and ensuring accountability. Public education campaigns have been found to Conduct outreach programs to educate community members about their rights, governance processes, and how they can participate. Workshops and training have been identified to offer workshops and training sessions to increase understanding of local governance and decision-making procedures. Diverse engagement channels have to utilize multiple platforms for engagement, including town hall meetings, online forums, surveys, and community events, to reach a broader audience. Inclusive scheduling has to schedule meetings and events at times that are convenient for different community groups, including evenings and weekends.

Innovative Marketing Strategies in the Competitive Economic Environment in Sustainable Development Goals (SDGs): A Case Study of Top Glove Manufacturers in Malaysia

Objective: The aim is to identify the key marketing strategies implemented by the manufacturers to achieve growth and sustainable development goals (SDGs) market leadership by glove-producing companies in Malaysia in the highly competitive global marketplace. Theoretical Framework: This paper contributes to the existing literature on the marketing strategies practiced in the manufacturing industries, mainly focusing on the glove manufacturing industry in Malaysia, which ranks as the global supplier of hand gloves in the industrial and medical categories. Taken in total, this work provides a refined appreciation of marketing and innovation’s relationship within a particular sector, and it does so by examining how those Malaysian glove makers who contest global markets leverage creativity in their marketing strategies in response to supply chain interruptions, approving bodies, and shifts in international buying trends. Methodology: Following a case study approach, this study focuses on large glove manufacturers in Malaysia; Top Glove is a crucial player. Hence, secondary sources will be adopted during the data collection stage. Perceptual data is also gathered from workers of key executives, marketing managers, and other industry professionals, which gives a picture of these business entities' marketing campaigns and strategic decision-making procedures. Industry journals, company reports, and market study papers validate secondary data. Results and Discussion: This study reveals that policymakers, manufacturing businesses, and marketing scholars can learn about the necessity of niche-based policies and friendly legal environments to boost innovation and competitiveness within manufacturing industries. It provides recommendations on creating a favorable climate for employing innovative marketing strategies and the possible enlargement of home-based businesses internationally. Originality/Value: Based on the findings and insights gathered from the research on innovative marketing strategies in the competitive global economic environment, particularly focusing on the case study of top glove manufacturers in Malaysia, several recommendations can be proposed to guide businesses in enhancing their marketing approaches and organizational performance

Anti-factor is FPT Parameterized by Treewidth and List Size (but Counting is Hard)

AbstractIn the general AntiFactor problem, a graph G and, for every vertex v of G, a set $$X_v\subseteq {\mathbb {N}}$$ X v ⊆ N of forbidden degrees is given. The task is to find a set S of edges such that the degree of v in S is not in the set $$X_v$$ X v . Standard techniques (dynamic programming plus fast convolution) can be used to show that if M is the largest forbidden degree, then the problem can be solved in time $$(M+2)^{{{\text {tw}}}}\cdot n^{{\mathcal {O}}(1)}$$ ( M + 2 ) tw · n O ( 1 ) if a tree decomposition of width $${{\text {tw}}}$$ tw is given. However, significantly faster algorithms are possible if the sets $$X_v$$ X v are sparse: our main algorithmic result shows that if every vertex has at most $$x$$ x forbidden degrees (we call this special case AntiFactorx), then the problem can be solved in time $$(x+1)^{{\mathcal {O}}({{\text {tw}}})}\cdot n^{{\mathcal {O}}(1)}$$ ( x + 1 ) O ( tw ) · n O ( 1 ) . That is, AntiFactorx is fixed-parameter tractable parameterized by treewidth $${{\text {tw}}}$$ tw and the maximum number $$x$$ x of excluded degrees. Our algorithm uses the technique of representative sets, which can be generalized to the optimization version, but (as expected) not to the counting version of the problem. In fact, we show that #AntiFactor1 is already #$$[1]$$ [ 1 ] -hard parameterized by the width of the given decomposition. Moreover, we show that, unlike for the decision version, the standard dynamic programming algorithm is essentially optimal for the counting version. Formally, for a fixed nonempty set $$X$$ X , we denote by $$X$$ X -AntiFactor the special case where every vertex v has the same set $$X_v=X$$ X v = X of forbidden degrees. We show the following lower bound for every fixed set $$X$$ X : if there is an $$\epsilon >0$$ ϵ > 0 such that #$$X$$ X -AntiFactor can be solved in time $$(\max X+2-\epsilon )^{{{\text {tw}}}}\cdot n^{{\mathcal {O}}(1)}$$ ( max X + 2 - ϵ ) tw · n O ( 1 ) given a tree decomposition of width $${{\text {tw}}}$$ tw , then the counting strong exponential-time hypothesis (#SETH) fails.

You Don’t Know Jack about Bandwidth

If you are an ISP and your customers hate you, take heart: This is now a solvable problem.

List coloring based algorithm for the Futoshiki puzzle

Given a graph G=(V, E) and a list of available colors L(v) for each vertex v\in V, where L(v) \subseteq {1, 2, ..., k}, List k-Coloring refers to the problem of assigning colors to the vertices of $G$ so that each vertex receives a color from its own list and no two neighboring vertices receive the same color. The decision version of the problem, List k-Coloring, is NP-complete even for bipartite graphs. As an application of list coloring problem we are interested in the Futoshiki Problem. Futoshiki is an NP-complete Latin Square Completion Type Puzzle. Considering Futoshiki puzzle as a constraint satisfaction problem, we first give a list coloring based algorithm for it which is efficient for small boards of fixed size. To thoroughly investigate the efficiency of our algorithm in comparison with a proposed backtracking-based algorithm, we conducted a substantial number of computational experiments at different difficulty levels, considering varying numbers of inequality constraints and given values. Our results from the extensive range of experiments indicate that the list coloring-based algorithm is much more efficient.

Decision trees for determining the fate of laboratory animals.

Facilities involved in laboratory animal research often face ethical challenges such as: what should I do with the animals that are no longer suitable for experimental purposes? One of the common answers to this question is to kill them. And while numerous scientifically justifiable reasons exist for killing laboratory animals, we must not overlook our ethical responsibility towards these sentient beings. Animal facility managers and scientists frequently find themselves in a moral dilemma, torn between furthering their research and addressing the well-being of experimental animals required for their studies. We elaborated a concept consisting of six decision trees and recommendations for making informed decisions about the need to kill laboratory animals in research facilities, considering legal and ethical considerations. The concept is based on the German regulatory perspective. However, the measures and decisions for animal welfare can be implemented in all laboratory animal facilities. These recommendations suggest several courses of action, including implementing consistent breeding plans, exploring alternative uses, reassigning surplus animals and their organs, and establishing appropriate housing capacity limits that ensure species-appropriate care. We encourage scientists and animal facility managers to develop and implement decision-making frameworks and procedures tailored to their specific facilities, in the hope that this work will promote a thoughtful and responsible approach to the complex challenges associated with the killing of laboratory animals, advancing scientific progress and the humane treatment of these animals.

Interval-valued picture fuzzy decision-making framework with partitioned maclaurin symmetric mean aggregation operators

Interval-valued picture fuzzy (IVPF) set is an extension of the picture fuzzy set theory used to represent uncertainty and vagueness in the processes of decision-making. This study focuses on exploring the interrelationships among multiple IVPFSs and criteria partitions. We investigate the IVPF partitioned Maclaurin symmetric mean operator and the weighted IVPF partitioned Maclaurin symmetric mean operator and discuss their respective properties. Subsequently, we identify certain special cases of these operators based on IVPF sets. Furthermore, we deploy a multi-criteria decision-making procedure utilizing the suggested IVPF partition operators. Through a numerical example, we demonstrate the practicality and validity of the presented approach. Finally, a thorough comparison with existing approaches is conducted to elucidate the superiority of the proposed method.

Creating airlines’ innovation: linking employees’ perspective of service quality, intellectual capital and innovation performance

ABSTRACT This study investigates various dimensions, including service quality, absorptive capacity, innovation performance, and intellectual capital (specifically organisational capital, social capital, and human capital), within the context of the airline industry. A survey questionnaire was administered to airline employees during the COVID-19 pandemic. The findings indicate that service quality has the potential to generate favourable innovation outcomes, such as increased absorptive capacity, thereby further enhancing employee innovation performance. Service quality is also found to have a simultaneous positive impact on human capital, leading to increased innovation performance and absorptive capacity. Therefore, organisations should prioritise the development of absorptive capacity to enhance both human capital and the accumulation of intellectual capital. Furthermore, both organisational capital and human capital play a vital role in these intricate strategy decision procedures. These factors play a crucial role in strengthening the connections between service quality and innovation performance.