Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS

Prioritizing of sectors for establishing a sustainable industrial symbiosis network with Pythagorean fuzzy AHP- Pythagorean fuzzy TOPSIS method: a case of industrial park in Ankara

Difficulty in accessing resources and increasing environmental concerns encourage industrial manufacturing enterprises to establish a symbiosis network. The identification of symbiotic relationships contributes to the more sustainable development of industrial activities. However, businesses operating in industrial parks are diversified by sector. In order to establish a sustainable symbiosis network in industrial parks, the symbiotic relations of each sector in industrial parks should be evaluated separately. Thus, the installation process of the symbiosis network will be easier and more sustainable. In this context, this study aims to prioritize the sector in which a symbiosis network will be established by presenting an innovative approach for the evaluation of symbiosis potentials. For this purpose, criteria for the implementation process affecting the establishment of the symbiosis network were determined. Multi-criteria decision-making methods were used to solve the problem. Considering the uncertainties in the process, fuzzy multi-criteria decision-making methods were used. As a result, a decision-making model has been proposed to determine the priority sector in order to establish a symbiosis network in industrial parks. According to the results obtained with the multi-criteria decision-making methods, the number of enterprises that will evaluate the waste, that is, the number of customers with waste, has been determined as the criterion with the highest level of importance. While evaluating the alternatives, the casting sector was chosen as a priority. This sector is followed by the petro and chemical sector as the second alternative.

A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development

This study explores how to enhance the decision-making processes in the phases of idea generation or alternative selection during the new product development (NPD) process. NPD is acknowledged as the central function of businesses in an increasingly competitive marketplace. In the current era, the highly uncertain and rapidly changing market environment makes the NPD extremely vague and complex. To be able to find a suitable solution to this complexity, the proposed research aims to categorize the decision points in the process of NPD regarding software development and identify the fuzziness elements affecting the process. The goal of a decision-making process is to prioritize several alternatives with respect to some required objectives and to select the best among them. Multi-Criteria Decision-Making (MCDM) may support reaching a consensus judgment with the collective assessment of Decision Makers (DMs). We introduce a novel evaluation approach for this problem. The proposed approach employs a MULTIMOORA (Multi-objective Optimization by Ratio Analysis plus the Full Multiplicative Form) MCDM technique under Pythagorean Fuzzy Sets (PFSs) objective world environment to cope with an ambiguous environment using Group Decision Making (GDM) setting to shape the decisions. The PFSs have demonstrated their advantages in dealing with vagueness and uncertainty over crisp, fuzzy, or intuitionistic fuzzy sets. Therefore, PFSs can represent the DMs' judgments and preferences in a better structure, ensuring enhanced decision-making in a group consensus. A case study on gaming software and app development is presented to validate the functionality of the proposed method. The results are compared and assessed with the help of a sensitivity analysis. This research makes a real contribution to the literature by proposing a novel evaluation method to rate and select NPD (gaming software and apps) to deal with the inexactness and vagueness associated with the criteria and alternatives.

Designing and Simulation Assessment of a Chair Attachment Air Blowing Methods to Enhance the Safety of Prolonged Sitting

Musculoskeletal disorders and the stagnation of sitting are among the side effects of excessive sitting in awkward sitting positions. In this study, a developed chair attachment cushion design with an optimal air blowing technique is proposed to eliminate the negative side effects of prolonged sitting. Instantaneously reducing the contact area between the chair and its occupant is the fundamental goal of the proposed design. The fuzzy multi-criteria decision-making approaches represented by FAHP and FTOPSIS were integrated to evaluate and select the optimal proposed design. An ergonomic and biomechanics assessment of the occupant's seating position while employing the novel safety cushion design was validated using simulation software (CATIA). Sensitivity analysis was also used to confirm the design's robustness. Results show that the manual blowing system using an accordion blower was the optimal design concept based on the selected evaluation criteria. In fact, the proposed design provides an acceptable RULA index value for the examined sitting postures and performed very safely in the biomechanics single action analysis.

A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment

Healthcare tends to be one of the most complicated sectors, and hospitals exist at the core of healthcare activities. One of the most significant elements in hospitals is service quality level. Moreover, the dependency between factors, dynamic features, as well as objective and subjective uncertainties involved endure challenges to modern decision-making problems. Thus, in this paper, a decision-making approach is developed for hospital service quality assessment, using a Bayesian copula network based on a fuzzy rough set within neighborhood operators as a basis of that to deal with dynamic features as well as objective uncertainties. In the copula Bayesian network model, the Bayesian Network is utilized to illustrate the interrelationships between different factors graphically, while Copula is engaged in obtaining the joint probability distribution. Fuzzy rough set theory within neighborhood operators is employed for the subjective treatment of evidence from decision makers. The efficiency and practicality of the designed method are validated by an analysis of real hospital service quality in Iran. A novel framework for ranking a group of alternatives with consideration of different criteria is proposed by the combination of the Copula Bayesian Network and the extended fuzzy rough set technique. The subjective uncertainty of decision makers' opinions is dealt with in a novel extension of fuzzy Rough set theory. The results highlighted that the proposed method has merits in reducing uncertainty and assessing the dependency between factors of complicated decision-making problems.

Selection of ergonomic risk assessment method with pythagorean fuzzy sets: practice in emergency medical services

It is difficult to evaluate ergonomic risk factors in occupations with unpredictable tasks, random demands, and variable settings such as emergency medical services (EMS). This study deals with the problem of selecting an ergonomic risk-evaluation method with Pythagorean Fuzzy Sets (PFSs) based Pythagorean Fuzzy AHP (PF-AHP) and Pythagorean Fuzzy WASPAS (PF-WASPAS) methodology. The method selection criteria were obtained by consulting five different anonymous experts on the candidate criteria obtained from the literature review. The final four main criteria and ten sub-criteria were then decided. After the determination of the decision criteria, five experts were asked to evaluate the criteria and to express their opinions on criteria-alternative scoring by means of a questionnaire for method selection. A two-step method is suggested for the selection of the ergonomic risk-evaluation method. In the first step, PF-AHP is utilized in order to identify the weight of criteria used in the method selection. In the second step, the PF-WASPAS method is proposed in order to OWAS, RULA, and REBA methods. The accuracy and validity of the suggested hybrid model is tested with real data in İstanbul Ambulance Service stations. A sensitivity analysis is carried out to test the reliability of the model. Moreover a comparative analysis is carried out with AHP and Fuzzy AHP methods to identify criteria weights. Study results show that REBA is the most appropriate ergonomic risk-evaluation method in EMS.

A new approach for spherical fuzzy TOPSIS and spherical fuzzy VIKOR applied to the evaluation of hydrogen storage systems

This study proposes a new perspective of the TOPSIS and VIKOR methods using the recently introduced spherical fuzzy sets (SFSs) to handle the vagueness in subjective data and the uncertainties in objective data simultaneously. When implementing these techniques using SFSs, two main problems might arise that can lead to incorrect results. Firstly, the reference points might change with the utilized score function. Secondly, the distance between reference points might not be the largest, as known, among the available ratings. To overcome these deficiencies and increase the robustness of these two methods, they are implemented without utilizing any reference points to minimize the effect of defuzzification and without measuring the distance to eliminate the effect of distance formulas. In the proposed methods, when using an SFS to express the performance of an alternative for a criterion, this SFS per se can be viewed as a measure of proximity to the aspired level. On the other hand, the conjugate of the SFS can be viewed as a measure of proximity to the ineffectual level. Two practical applications are presented to demonstrate the proposed techniques. The first example handles a warehouse location selection problem. The second example evaluates hydrogen storage systems for automobiles with different types of data (crisp, linguistic variables, type 1 fuzzy sets). These data are transformed to SFSs to provide a more comprehensive analysis. A comparative study is conducted with earlier versions of TOPSIS and VIKOR to explicate the adequacy of the proposed methods and the consistency of the results.

Application of fuzzy multicriteria decision-making model in selecting pandemic hospital site

One of the most important challenges for decision-makers and investors is location selection, which may be assessed using multicriteria decision-making (MCDM) methodologies. Problems with picking a location include deciding between alternative locations, analyzing alternatives, and identifying the best location for a hospital. Because they analyze options with multiple perspectives in terms of numerous competing criteria, MCDM approaches are useful instruments for solving decision-making challenges. The fuzzy set theory (FST), which represents uncertainty in human beliefs, may be effectively used with MCDM approaches to produce more sensitive, tangible, and accurate findings in this context. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on a combination of factors. In the first stage, the Fuzzy Analytical Hierarchy Process (FAHP) is used to estimate the relative criteria classification through the evaluation process. In the second stage, the fuzzy technique of order preference using similarities to the perfect solution (FTOPSIS) is applied to rank the possible alternative sites. The findings from this study indicate that integrate FAHP and FTOPSIS is the most often used FMCDM approach in Aleppo for selecting the best location for a new hospital.

Identifying Root Causes of Important Service Failures across Medical Examination Processes with Integration of 4M1E, ITLV, GRA, DEMATEL and FMEA

Medical examination plays an essential role in most medical treatment processes, and thus, the quality of service relevant to medical examination has great impact on patient satisfaction. The targeted hospital has long been faced with the problem that patient satisfaction of its medical examination department is below average. An assessment model, integrating 4M1E, ITLV, GRA, DEMATEL and FMEA, was developed in this study to identify the root causes of important service failures across medical examination processes, where (1) a cause-and-effect diagram was enhanced with 4M1E, identifying the list of failure modes relevant to service quality over the medical examination process with the 4M1E analysis framework, (2) FMEA experts were enabled to report their assessment results in their preferred ways by using the ITLV scheme, (3) causes of failure to failure modes with was figured out with DEMATEL, and (4) the evaluation results were improved by integrating GRA. Experimental results obtained by the proposed approach are compared with several benchmarks, and it was observed that (1) the results obtained by the proposed model are more suitable when FMEA experts prefer using different assessment languages versus other approaches; (2) the proposed model can figure out the key root causes according to their impact on overall failure modes.

The quality of Syrian healthcare services during COVID-19: A HEALTHQUAL approach

Syria is a developing country that face enormous healthcare challenges that aggravated with the outbreak of COVID-19. In the study, we evaluate the perceived healthcare service quality based on hospital type, public and private, using five HEALTHQUAL dimensions. We find that service quality in Syrian private hospitals is perceived better that in public hospitals. However, neither type of hospitals scores exceptionally high in any of the examined HEALTHQUAL dimensions. On the contrary, both hospitals score extremely low in the Improvement dimension. We argue that crowdedness environment, medical staff availability and their low salaries, pricing policies as well as the health insurance system, are to blame for such low perceived quality.

Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic

Healthcare is considered one basic necessity to sustaining life; thereby, assessing the character of a healthy and resilient supply chain can help a nation develop ideas to combat the healthcare crisis. COVID-19 has led to a long-term strain on the healthcare supply chain (HCSC) and has resulted in a lack of basic healthcare necessities. It has become apparent that supply chain disruptions and increased usage has led to a lack of medical supplies needed to provide the proper care to patients. Multicriteria decision-making (MCDM) will help to indicate what characteristics contribute to resilient healthcare supply chains. To assess the characteristic of a resilient supply chain, significant healthcare supply chains will help indicate significant characteristics. A case study on the medical supplies’ supply chains is presented. A rank reversal proximity index MCDM method ranks criteria to assist with decision making. The proximity index will reduce the chances of the rank reversal phenomenon that results in incorrect rankings from occurring. Results show that redundancy, collaboration, and robustness are key indicators of a resilient supply chain while, supply chain design, communication capabilities, and supply chain risk management become comparatively less important during the COVID-19 pandemic. Furthermore, an unsupervised machine learning tecnique named "cluster analysis" is conducted to group the resilience indicators of the respective supply chain. Through this study, the best way to combat disruptions in the healthcare supply chain due to large-scale pandemics is to share information quickly, reduce reliance on the design of the supply chain, and track the usage of necessary medical supplies. Alternatively, we validated our study by comparing a Preference Selection Index (PSI) to the proposed method.

Fuzzy Multi Criteria Decision Making Model for Agritourism Location Selection: A Case Study in Vietnam

Agritourism is an increasing new trend in the global tourism industry. Vietnam has a long tradition of agricultural production combined with diverse natural resources stretching from the north to the south, bringing advantages in the development of agritourism. The study aims to choose the most appropriate agritourism location in Vietnam for long-term investment. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on eco-nomic, social, and environmental factors. In the first stage, the fuzzy analytic hierarchy process (FAHP) is used to estimate the relative criteria rating through the evaluation process. In the second stage, the fuzzy technique for order preference using similarities to ideal solution (FTOPSIS) is applied to rank the potential alternative locations. Finally, the best alternative to tourist site investment is Can Tho (A8), which maximizes resources and enhances the local benefits. Future research can also be used to support similar site-selection processes in other regions or could be applied to other types of tourism.

Adherence to dietary guide for elderly adults and health risks of older adults in ethnic minority areas in China: a cross-sectional study

BACKGROUND

The impact of dietary guidelines on health in ethnic minority regions needs to be further explored because of multiple sociocultural factors. Therefore, this study was conducted to analyze the association between adherence to dietary guidelines and health risks in an elderly population in an ethnic minority region.

METHODS

A cross-sectional survey was conducted among 836 older adults in ethnic minority areas. They were asked to describe their daily dietary intake levels through a semi-quantitative food frequency questionnaire. The closeness coefficient for each study subject was calculated by using the technique for order preference by similarity to an ideal solution (TOPSIS), which measures the adherence to Dietary Guide for Elderly Adults (DGEA). Regression models were used to analyze the association between adherence and health risks.

RESULTS

The daily food of the elderly in this area comprised cereals and vegetables. They had low intake of milk, dairy products, and water and high intake of salt. The closeness coefficient for the total population was 0.51, and the adherence of this population to dietary guidelines for the elderly was low. In both the crude model and the models adjusted for covariates, the closeness coefficient was not significantly associated with clinical indicators and health outcomes (p > 0.05).

CONCLUSIONS

No association was found between adherence to large sample-based dietary guidelines and clinical indicators or health outcomes in ethnic minority populations. The applicability of dietary guidelines to ethnic minority areas and whether they yield the expected health benefits require further study.

An integrated approach for service quality evaluation of online health communities based on q-rung orthopair fuzzy linguistic aggregation operators

Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach.

AN ONLINE REVIEWS INFORMATION FUSION METHOD AND ITS APPLICATION TO PUBLIC PROPERTY SERVICE QUALITY EVALUATION

With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets

COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1-A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives.

A New Entropy Measurement for the Analysis of Uncertain Data in MCDA Problems Using Intuitionistic Fuzzy Sets and COPRAS Method

In this paper, we propose a new intuitionistic entropy measurement for multi-criteria decision-making (MCDM) problems. The entropy of an intuitionistic fuzzy set (IFS) measures uncertainty related to the data modelling as IFS. The entropy of fuzzy sets is widely used in decision support methods, where dealing with uncertain data grows in importance. The Complex Proportional Assessment (COPRAS) method identifies the preferences and ranking of decisional variants. It also allows for a more comprehensive analysis of complex decision-making problems, where many opposite criteria are observed. This approach allows us to minimize cost and maximize profit in the finally chosen decision (alternative). This paper presents a new entropy measurement for fuzzy intuitionistic sets and an application example using the IFS COPRAS method. The new entropy method was used in the decision-making process to calculate the objective weights. In addition, other entropy methods determining objective weights were also compared with the proposed approach. The presented results allow us to conclude that the new entropy measure can be applied to decision problems in uncertain data environments since the proposed entropy measure is stable and unambiguous.

Attribute significance based on inclusion degree for incomplete information systems and their applications to MADM problems

Attribute significance is very important in multiple-attribute decision-making (MADM) problems. In a MADM problem, the significance of attributes is often different. In order to overcome the shortcoming that attribute significance is usually given artificially. The purpose of this paper is to give attribute significance computation formulas based on inclusion degree. We note that in the real-world application, there is a lot of incomplete information due to the error of data measurement, the limitation of data understanding and data acquisition, etc. Firstly, we give a general description and the definition of incomplete information systems. We then establish the tolerance relation for incomplete linguistic information system, with the tolerance classes and inclusion degree, significance of attribute is proposed and the corresponding computation formula is obtained. Subsequently, for incomplete fuzzy information system and incomplete interval-valued fuzzy information system, the dominance relation and interval dominance relation is established, respectively. And the dominance class and interval dominance class of an element are got as well. With the help of inclusion degree, the computation formulas of attribute significance for incomplete fuzzy information system and incomplete interval-valued fuzzy information system are also obtained. At the same time, results show that the reduction of attribute set can be obtained by computing the significance of attributes in these incomplete information systems. Finally, as the applications of attribute significance, the attribute significance is viewed as attribute weights to solve MADM problems and the corresponding TOPSIS methods for three incomplete information systems are proposed. The numerical examples are also employed to illustrate the feasibility and effectiveness of the proposed approaches.

Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul

Environment and social life are open to hazards, because of the distribution, diffusion, and conversion processes of chemicals contained in hazardous materials. These chemicals are very dangerous. Various precautions should be taken into consideration during the displacement of hazardous materials. Therefore, it is important to identify and minimize the risks in the transportation of hazardous material. This work investigates to identify the critical risk factors and their weights for hazardous material transportation operations. The literature is reviewed, critical risk factors for hazardous material transportation are defined, and data from different experts is collected. A two-level hierarchical structure is established to evaluate risk factors. Then, the experts' evaluations of main and sub-risk factors are consolidated using the modified Delphi method. Weights of main and sub-risk factors are obtained using the Pythagorean fuzzy analytic hierarchy process method. To show the robustness of the proposed decision-making methodology, a sensitivity analysis is conducted.

Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach

Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify "competitive advantage" as the most important factor among Fintech-based determinants. Moreover, "payment and money transferring systems" are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank' receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

An Investigation of Human Errors in Medication Adverse Event Improvement Priority Using a Hybrid Approach

The aim of this study was to analyze and provide an in-depth improvement priority for medication adverse events. Thus, the Human Factor Analysis and Classification System with subfactors was used in this study to analyze the adverse events. Subsequently, the improvement priority for the subfactors was determined using the hybrid approach in terms of the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution. In Of the 157 medical adverse events selected from the Taiwan Patient-safety Reporting system, 25 cases were identified as medication adverse events. The Human Factor Analysis and Classification System and root cause analysis were used to analyze the error factors and subfactors that existed in the medication adverse events. Following the analysis, the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution were used to determine the improvement priority for subfactors. The results showed that the decision errors, crew resource management, inadequate supervision, and organizational climate contained more types of subfactors than other error factors in each category. In the current study, 16 improvement priorities were identified. According to the results, the improvement priorities can assist medical staff, researchers, and decisionmakers in improving medication process deficiencies efficiently.

Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Since content audiences, including libraries and information centres, are increasingly geared to digital environments and virtual networks, the production and delivery of high-quality digital content are becoming continuously important. So far, several components have been introduced by researchers for evaluating the quality of digital content generation. However, due to the uncertainty of the importance rate and value of each of these components, it has not yet been possible to use them effectively to evaluate the content produced. This study aimed to rank the components of content generation to allow accurate evaluation of them for users as well as content providers and distributors including libraries and marketers. The ranked content can motivate digital content producers and distributors to better evaluate the quality of digital content, better attract customers and make more effective decisions about the quality of digital content use based on their specific goals. Initially, 42 of the most important components were identified from the literature. Then, the next steps were taken to rank these components, and based on three rounds of Delphi interviews, the experts’ views on the importance rate of each of the components were obtained, analysed and ranked. Since in this ranking, the importance of a wide range of components should be highlighted towards each other, the fuzzy TOPSIS technique was emphasised for analysing the views of 16 experts in the field of content generation in Iran. This ranking indicated that components such as ‘findable and access’, ‘non-disturbing and helpful’, ‘clear’ and ‘remarkable’ are the main pillars of content generation and are of the utmost importance. The results can be used as an effective tool to improve the quality of content. Moreover, it increases audience engagement in digital environments and social networks, and encourages them to make more use of the digital content of libraries.

An integrated model for evaluation of maternal health care in China

In recent years, in the context of China’s continuous medical and health reforms, the health status of Chinese women and children has been significantly improved through the continuous efforts of staff at all levels of maternal and child health care institutions. Many indicators in maternal health care have improved significantly, but the speed and magnitude of changes have varied. The purpose of this study is to evaluate the dynamic changes in China’s maternal health status from 2004 to 2018, in order to determine whether China’s medical and health reform measures in recent years have improved maternal health. A total of 6 evaluation indicators from the data of China Health Statistics Yearbook 2019 were selected. Then, based on the multi-criteria decision-making (MCDA) methodology, the entropy weighted technique for order preference by similarity to an ideal solution (TOPSIS), entropy weighted rank-sum ratio (RSR) method and the fuzzy comprehensive evaluation were employed in this study. In addition, sensitivity analysis was engaged to validate the stability and accuracy of the achieved results. The study results shows the ranking values of various methods were not exactly the same, but the overall trend was consistent. Overall, the maternal health care in China improved from 2004 to 2018 year by year, of which the top four were ranked from 2015 to 2018, and relatively poor from 2004 to 2006. This means that the policies and measures implemented in China’s medical and health reform in the past few decades have effectively promoted China’s maternal health care, and this will also provide a theoretical basis for future decisions to promote maternal health care.

Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach

Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify "competitive advantage" as the most important factor among Fintech-based determinants. Moreover, "payment and money transferring systems" are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank' receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0

Supply chain operations reference (SCOR) is a combined benchmarking, business process reengineering, and best practices, and it also references a model that is intended to be an industry standard. SCOR model is one of the best models to describe supply chain activities in operations management for research and practice alike. There are radical changes in the structure of supply chains as well as developing technology in today’s information age. The purpose of this paper is to extend the SCOR model with new metrics related to Industry 4.0 and digitalization to understand and evaluate the performance of supply chains. New metrics added to the SCOR model and a novel SCOR 4.0 model is proposed. The novel performance evaluation model is structured as a three-level hierarchical structure to evaluate the supply chain. This problem is handled as a multi-criteria decision-making problem. This study uses the hybrid Best worst method and Pythagorean fuzzy AHP method to determine the weights of metrics. SCOR model is adapted to performance evaluation of the supply chain in the globalizing world. The most important metrics on the supply chain performances are determined and classified. Level 1 metrics are evaluated by Best worst method and their inner levels are evaluated by the Pythagorean fuzzy AHP method and the importance weights of each level 2 and level 3 metrics are obtained. A real application for the oil supply chain is presented to show the applicability of the proposed model. It is aimed to show the SCOR 4.0 model can be used by both public and private sectors to improve their supply chain strategies in globalizing world.

Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals

Considering the unexpected emergence of natural and man-made disasters over the world and Turkey, the importance of preparedness of hospitals, which are the first reference points for people to get healthcare services, becomes clear. Determining the level of disaster preparedness of hospitals is an important and necessary issue. This is because identifying hospitals with low level of preparedness is crucial for disaster preparedness planning. In this study, a hybrid fuzzy decision making model was proposed to evaluate the disaster preparedness of hospitals. This model was developed using fuzzy analytic hierarchy process (FAHP)-fuzzy decision making trial and evaluation laboratory (FDEMATEL)-technique for order preference by similarity to ideal solutions (TOPSIS) techniques and aimed to determine a ranking for hospital disaster preparedness. FAHP is used to determine weights of six main criteria (including hospital buildings, equipment, communication, transportation, personnel, flexibility) and a total of thirty-six sub-criteria regarding disaster preparedness. At the same time, FDEMATEL is applied to uncover the interdependence between criteria and sub-criteria. Finally, TOPSIS is used to obtain ranking of hospitals. To provide inputs for TOPSIS implementation, some key performance indicators are established and related data is gathered by the aid of experts from the assessed hospitals. A case study considering 4 hospitals from the Turkish healthcare sector was used to demonstrate the proposed approach. The results evidenced that Personnel is the most important factor (global weight = 0.280) when evaluating the hospital preparedness while Flexibility has the greatest prominence (c + r = 23.09).

A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul

The petrol station location selection problem is taken into consideration in this study. In order to identify the main and sub-criteria for evaluation, the literature is reviewed and five experts from different companies are interviewed. After that, thirteen different alternative locations are for specified to evaluation. Then, the novel spherical fuzzy AHP-integrated spherical WASPAS methodology is structured in a fuzzy environment, and the petrol station location selection problem is evaluated with this methodology. In this study, a real application is presented for Istanbul to show the applicability of the proposed methodology. Subsequently, a sensitivity analysis is performed to explain and analyze the proposed methodology results. Finally, the results are presented and discussed with future directions.

Prioritization of Medical Errors in Patient Safety Management: Framework Using Interval-Valued Intuitionistic Fuzzy Sets

Medical errors negatively affect patients, healthcare professionals, and healthcare establishments. Therefore, all healthcare service members should be attentive to medical errors. Research has revealed that most medical errors are caused by the system, rather than individuals. In this context, guaranteeing patient safety and preventing medical faults appear to be basic elements of quality in healthcare services. Healthcare institutions can create internal regulations and follow-up plans for patient safety. While this is beneficial for the dissemination of patient safety culture, it poses difficulties in terms of auditing. On the other hand, the lack of a standard patient safety management system, has led to great variation in the quality of the provided service among hospitals. Therefore, this study aims to create an index system to create a standard system for patient safety by classifying medical errors. Due to the complex nature of healthcare and its processes, interval-valued intuitionistic fuzzy logic is used in the proposed index system. Medical errors are prioritized, based on the index scores that are generated by the proposed model. Because of this systematic study, not only can the awareness of patient safety perception be increased in health institutions, but their present situation can also be displayed, on the basis of each indicator. It is expected that the outcomes of this study will motivate institutions to identify and prioritize their future improvements in the patient safety context.

A Fuzzy Analytic Hierarchy Process-Based Analysis of the Dynamic Sustainable Management Index in Leisure Agriculture

Agricultural leisure is one of the development trends in modern society. However, in leisure agriculture management, managers must consider the dual characteristics of agricultural production technology and the leisure service industry. Therefore, managers often face various selection pressures during management decision-making. This study examined the following five evaluation aspects: organization and operation, leisure agricultural resources, environmental maintenance management, public facility maintenance management, and operational performance. Thereafter, according to the five evaluation aspects, 21 subprojects were further divided at the second level on the basis of different attributes. Fuzzy analytic hierarchy process (FAHP) was adopted to calculate the relative weights of various indicators, and the indicators were dynamically sorted according to their importance. Moreover, the study summarized the dynamic management indicators of leisure agriculture and contributed to research on leisure farming. The results show that managers’ approach toward leisure agriculture management can be divided into three types: active, moderate, and conservative. The indicators in which the active managers attach importance when managing leisure agricultural enterprises are clearly different from those valued by moderate and conservative managers. Finding managers who are suitable for leisure agriculture management is of great significance to leisure agriculture enterprises. Appointing managers with appropriate attitudes who can continuously improve the efficiency of enterprises and create a competitive advantage can bring sustainable business value to these enterprises.

Passenger Satisfaction Evaluation of Public Transportation Using Pythagorean Fuzzy MULTIMOORA Method under Large Group Environment

Passenger satisfaction is an important factor that affects the choice of travel modes for municipalities, especially in big cities. This evaluation is an important task for managers when they are considering improving the competitiveness of the public transportation system. However, passenger satisfaction evaluation is difficult as the information provided by passengers is often vague, imprecise, and uncertain. This paper aims to propose a new method, using Pythagorean fuzzy sets and multi-objective optimization by a ratio analysis plus full multiplicative form method (MULTIMOORA), to evaluate the passenger satisfaction level of the public transportation system under large group environment. The former is employed to represent the satisfaction assessments of rail transit network provided by passengers. The latter is extended and used to determine the passenger satisfaction levels of rail transit lines. In addition, a combination weighting method is suggested to compute the relative weights of evaluation criteria. A case study of the rail transit network in Shanghai is provided to demonstrate the effectiveness of the proposed passenger satisfaction evaluation method. The result shows that the new method proposed in this study can not only model passengers’ satisfaction evaluation information with more uncertainties, but also determine more reasonable and credible satisfaction levels of rail transit lines.

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

With the rapid outbreak of COVID-19, most people are facing antivirus mask shortages. Therefore, it is necessary to reasonably select antivirus masks and optimize the use of them for everyone. However, the uncertainty of the effects of COVID-19 and limits of human cognition add to the difficulty for decision makers to perfectly realize the purpose. To maximize the utility of the antivirus mask, we proposed a decision support algorithm based on the novel concept of the spherical normal fuzzy (SpNoF) set. In it, firstly, we analyzed the new score and accuracy function, improved operational rules, and their properties. Then, in line with these operations, we developed the SpNoF Bonferroni mean operator and the weighted Bonferroni mean operator, some properties of which are also examined. Furthermore, we established a multi-criteria decision-making method, based on the proposed operators, with SpNoF information. Finally, a numerical example on antivirus mask selection over the COVID-19 pandemic was given to verify the practicability of the proposed method, which the sensitive and comparative analysis was based on and was conducted to demonstrate the availability and superiority of our method.