Q-ROF Fuzzy TOPSIS and VIKOR Methods for the Selection of Sustainable Private Health Insurance Policies

Round 1

Reviewer 1 Report

The study develops a hybrid model to aid people in evaluating various plans and selecting the most appropriate one to provide the best healthcare environment. For this purpose, the authors have combined the VIKOR and TOPSIS approaches with q-rung orthopair fuzzy information. The proposed approaches are not new. Only considering the combination of TOPSIS and VIKOR methods with q-rung orthopair fuzzy set is not sufficient for the acceptance of this article. After reading this article “Q-Rung Orthopair Fuzzy TOPSIS and VIKOR Methods for Selection of Sustainable Private Health Insurance Policy: A Case Study from Turkey”, I feel that the scientific contribution is low and the novelty is very weak. My comments are as follows:

·         There are several methods have been developed in the literature under q-rung orthopair fuzzy environment including TOPSIS, CRITIC-VIKOR, MULTIMOORA, CoCoSo, WISP, ARAS, WASPAS, GLDS etc. What is the motivation behind the proposed hybrid model as both the models determine only the rank of the alternatives? The reader cannot understand the research motivation of the study.

·         Why the proposed hybrid model is the most adequate for solving this Sustainable Private Health Insurance Policy Selection Problem in Turkey?

·         In the TOPSIS approach, the relative importance of the distances between each alternative and the positive ideal solution and the negative ideal solution is ignored. In the VIKOR method, the subordinate ranks which are derived from the “group utility” values and the “individual regret” values are not considered. While these two points are successfully considered in GLDS approach. What is the need for combined TOPSIS-VIKOR approach in comparison with GLDS approach?

·         The authors should provide more thorough information about the existing q-ROF information based studies and emphasize their weaknesses. Then discuss the advantages of your proposed method over existing methods? Explain in detail.

·         What is the criticism and gap analysis for academic literature that attempts to provide a solution?

§  The originality and the importance of this work are not clearly understandable. The authors should work on this and prove that why this work is valuable.  

·         The validity of the developed method is vague. More analysis and discussion for the obtained results should be provided to prove the efficiency.

·         A comparative study should be added to verify the advantages of the proposed method.

Please correct the English writing of the manuscript. 

Author Response

The study develops a hybrid model to aid people in evaluating various plans and selecting the most appropriate one to provide the best healthcare environment. For this purpose, the authors have combined the VIKOR and TOPSIS approaches with q-rung orthopair fuzzy information. The proposed approaches are not new. Only considering the combination of TOPSIS and VIKOR methods with q-rung orthopair fuzzy set is not sufficient for the acceptance of this article. After reading this article “Q-Rung Orthopair Fuzzy TOPSIS and VIKOR Methods for Selection of Sustainable Private Health Insurance Policy: A Case Study from Turkey”, I feel that the scientific contribution is low and the novelty is very weak. My comments are as follows:

• There are several methods have been developed in the literature under q-rung orthopair fuzzy environment including TOPSIS, CRITIC-VIKOR, MULTIMOORA, CoCoSo, WISP, ARAS, WASPAS, GLDS etc. What is the motivation behind the proposed hybrid model as both the models determine only the rank of the alternatives? The reader cannot understand the research motivation of the study.

Thank you for your comments. This research aims to propose a framework through integrated the TOPSIS and VIKOR methods under Q-Rung Orthopair Fuzzy environments and solve private health insurance selection. The innovative part of this research is existing in the concept of the proposed decision-making model. The proposed model improves the current multi criteria decision-making models by combining 2 different methods and generalized fuzzy logic concepts.

• Why the proposed hybrid model is the most adequate for solving this Sustainable Private Health Insurance Policy Selection Problem in Turkey?

Thank you for your comments. The proposed hybrid model aims to solve general multi criteria decision-making problems. Private Health Insurance Policy Selection Problem is accepted as a case scenario in this research.

In the TOPSIS approach, the relative importance of the distances between each alternative and the positive ideal solution and the negative ideal solution is ignored. In the VIKOR method, the subordinate ranks which are derived from the “group utility” values and the “individual regret” values are not considered. While these two points are successfully considered in GLDS approach. What is the need for combined TOPSIS-VIKOR approach in comparison with GLDS approach?

Thank you for your suggestions. This research aims to evaluate the private health insurance plans through a combined TOPSIS-VIKOR approach under the q-rung fuzzy environment. Your suggestion is valuable, and we appreciate your suggestion as a future work direction. As a new approach to the problem, the gained and lost dominance score (GLDS) could be another MCDM method to literature.

• The authors should provide more thorough information about the existing q-ROF information-based studies and emphasize their weaknesses. Then discuss the advantages of your proposed method over existing methods. Explain in detail.

Thank you. More information about q-ROF information-based studies and emphasizing their weaknesses are addressed in the literature review and methodology sections as follows.

On literature review:

“The membership degree of an element can be represented using a set of q-rung functions thanks to a fuzzy set extension known as q-rung fuzzy sets. Various fields, including decision-making, pattern recognition, image processing, and control systems, have used Q-rung fuzzy sets (Garg, 2022)⁠⁠⁠⁠⁠⁠. For instance, q-rung fuzzy sets were utilized in the creation of a fuzzy controller for a mobile robot with two wheels (Luqman et al., 2017)⁠⁠⁠⁠⁠⁠. They have also been employed to categorize medical images (Khan et al., 2022).”

On methodology section:

“The summary of the q-rung information-based studies' weaknesses: There are parameters such as risk parameters whose values vary for various practical applications; consequently, their optimization is required. To effectively provide preferences and comprehend inferences, specialists must be trained in the preferred style.”

 

• What is the criticism and gap analysis for academic literature that attempts to provide a solution?

Thank you. In the literature, there is no research regarding the selection of private health insurance policies. This research aims to solve this problem. This objective is stated in the introduction and literature review sections.

• The originality and the importance of this work are not clearly understandable. The authors should work on this and prove that why this work is valuable.

Thank you for your valuable comments. The originality of the research exists in the concept of the proposed decision-making model which TOPSIS and VIKOR methods under Q-Rung Orthopair Fuzzy environments and solve private health insurance selection. This research is one of the first attempts to solve private health insurance selection problems.

• A comparative study should be added to verify the advantages of the proposed method.

Thank you. Comparative studies according to q-values are presented in Figure 4 and Figure 5 and explanations are given in the Methodology Section's last paragraph.

“Figure 4 and Figure 5 indicate that A5 is a dependable option regardless of the decision-makers preference for various q-values. It is essential to note that both the Q-ROF TOPSIS and Q-ROF VIKOR methods are effective in addressing multi-criteria decision-making problems, but their sensitivity to changes in q-values differs. The Q-ROF TOPSIS method is more stable and less sensitive to q-value changes than the Q-ROF VIKOR method, which is more dynamic and sensitive to q-value changes. The choice between these two methods ultimately depends on the decision maker's particular requirements and the nature of the decision problem at hand. Regardless of the chosen method, it is evident that A5 is a viable alternative that should be thoroughly considered by any decision-maker seeking to make an informed and effective selection.”

References

Garg, H. (Ed.). (2022). q-Rung orthopair fuzzy sets: theory and applications. Springer Nature.

Luqman, A., Akram, M., & N. Al-Kenani, A. (2019). q-Rung orthopair fuzzy hypergraphs with applications. Mathematics, 7(3), 260.

Khan, M. J., Kumam, P., Shutaywi, M., & Kumam, W. (2021). Improved Knowledge Measures for q-Rung Orthopair Fuzzy Sets. Int. J. Comput. Intell. Syst., 14(1), 1700-1713.

 

Author Response File: Author Response.docx

Reviewer 2 Report

See the attachment.

Comments for author File: Comments.pdf

The language should be further improved with the help of a native.

Author Response

The paper is somewhat interesting, but needs further improvement.

1) The abstract should focus on the main contribution of the paper. Please discuss less about the

background.

Thank you for your comments. This research aims to propose a framework through integrated the TOPSIS and VIKOR methods under Q-Rung Orthopair Fuzzy environments and solve private health insurance selection. The innovative part of this research is existing in the concept of the proposed decision-making model. The proposed model improves the current multi criteria decision-making models by combining 2 different methods and generalized fuzzy logic concepts. In this purpose, it is stated in the abstract as follows:

“This research is one of the first attempts to solve private health policy selection under imprecise information by applying Q-ROF TOPSIS and Q-ROF VIKOR method.”

2) The motivation should be clarified. Why Q-Rung Orthopair Fuzzy sets, why TOPSIS, why VIKOR?

Thank you very much for your suggestion. The reason why Q-ROF fuzzy is selected is explained in the Methodology section as follows:

“Q-Rung Fuzzy logic is a form of fuzzy logic that seeks to overcome some of the limitations of conventional fuzzy logic systems. Q-Rung Fuzzy logic is founded on the concept of q-rung orthopair fuzzy sets, a generalization of fuzzy sets. Q-Rung fuzzy logic has many benefits over other fuzzy logic systems; such as it handles uncertainty and imprecision better than conventional fuzzy logic systems. It also handles non-monotonic reasoning better than traditional fuzzy logic systems. In addition, Q-Rung Fuzzy logic models complex systems more effectively than conventional fuzzy logic systems. The extension of TOPSIS and VIKOR methods with Q-Rung Fuzzy logic helps to improve decision-making processes. “

3) Table 2 seems meaningless, which can be removed or redesigned.

Thank you. The purpose of TABLE 2 is to show the current research on Q-Rung Fuzzy logic in MCDM applications. More information is added in the literature review section.

4) Q-Rung Orthopair Fuzzy TOPSIS and VIKOR are not proposed by the authors. So what are the main contributions of the paper? I think the authors should provide more details of the background of the case study.

Thank you. The innovative part of this research is existing in the concept of the proposed decision-making model. The proposed model enhances existing multi-criteria decision-making models by combining two distinct methods and fuzzy logic concepts in a generalized form. The hybrid model proposed seeks to solve general multi-criteria decision-making issues. In this study, the Private Health Insurance Policy Selection Problem is adopted as a case scenario. In this context, the contributions of the paper are stated in the Abstract, literature review, and case study section.

5) Some comparative analysis can be added to justify this study.

Thank you. Comparative studies according to q-values are presented in Figure 4 and Figure 5 and explanations are given in the Methodology Section's last paragraph.

“Figure 4 and Figure 5 indicate that A5 is a dependable option regardless of the decision-makers preference for various q-values. It is essential to note that both the Q-ROF TOPSIS and Q-ROF VIKOR methods are effective in addressing multi-criteria decision-making problems, but their sensitivity to changes in q-values differs. The Q-ROF TOPSIS method is more stable and less sensitive to q-value changes than the Q-ROF VIKOR method, which is more dynamic and sensitive to q-value changes. The choice between these two methods ultimately depends on the decision maker's particular requirements and the nature of the decision problem at hand. Regardless of the chosen method, it is evident that A5 is a viable alternative that should be thoroughly considered by any decision-maker seeking to make an informed and effective selection.”

6) A preliminary section is needed to introduce some basic knowledge about Q-Rung Orthopair

Fuzzy set. Some further discussions about how the criteria are selected should be discussed in the

paper.

The q-rung orthopair fuzzy sets (q-ROFs), which are the generic form of IFS and PFS, were first developed by Yager (2017) and Pınar A et al(2021) after PFS. The total of the membership degree and non-membership degree in q-ROFs is constrained to one. The following describes a qth rung orthopair fuzzy subset of X:

 

where  is membership degree and  is non-membership degree of  to A and their sum is as follows:

                                                                                                       

 The hesitation degree is as follows:

 

7) Actually, the selection problem of sustainable private health insurance policy is a group decision making problem. I suggest the authors consider using group decision making methods to deal with this problem as their future studies. Please discuss this point by referring to Threshold based value driven method to support consensus reaching in multi-criteria group sorting problems: A minimum adjustment perspective; Consensus reaching in multi-criteria social network group decision making:  A stochastic multicriteria acceptability analysis-based method; of group decision making in shipping industry 4.0: Bibliometric analysis, trends and future directions.

Thank you. We appreciate your grateful recommendation and great research suggestions.

In the literature review section,

 

“The problem of private health insurance policies selection problem could be analyzed by group decision-making methods such as (Li and Zhang, 2023; Li et al., 2023).”

 

This will be a great research topic in the future research. It is stated in the future studies paragraph in the last paragraph of the Conclusion section.

 

“Also, the selection problem of private health insurance policies is a group decision-making problem. As a future research direction, the problem of private health insurance policies selection problem could be analyzed by group decision-making methods.”

 

8) The language should be further improved with the help of a native

Thank you, the entire manuscript is reviewed by a professional proofreader.

 

References

Li, Z., & Zhang, Z. (2023). Threshold-Based Value-Driven Method to Support Consensus Reaching in Multicriteria Group Sorting Problems: A Minimum Adjustment Perspective. IEEE Transactions on Computational Social Systems.

Li, P., Xu, Z., Zhang, Z., Li, Z., & Wei, C. (2023). Consensus reaching in multi-criteria social network group decision making: A stochastic multicriteria acceptability analysis-based method. Information Fusion, 101825.

 

Author Response File: Author Response.docx

Reviewer 3 Report

Dear authors,

Please find the report in the attached file.

Best,

Comments for author File: Comments.pdf

The English language needs some improvements. Especially in conclusion section.

Author Response

Journal: Sustainability

Manuscript ID: sustainability-2407504

Review article: Q-Rung Orthopair Fuzzy TOPSIS and VIKOR methods for selection of sustainable private health insurance policy: A case study from Turkey

The paper presents an interesting multicriteria group analysis to assess by expert decision-makers via Q-Rung Orthopair Fuzzy (Q-ROF) Sets Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Q-ROF VIKOR methods.

Only minor comments:

- The title is very long.

Thank you. The title is changed as follows:

“Q-ROF fuzzy TOPSIS and VIKOR methods for the selection of sustainable private health insurance policies”

- Line 93, change the font size of the word “aggregation”.

Dear reviewer, thank you very much for your careful and detailed review. The manuscript is reviewed carefully a couple of times to eliminate any grammatical errors and typos.

- It is necessary to write the first letter in some words in the middle sentences by a small letter. See line 95, change the sentence “Fully Multiplicative Form” by “fully multiplicative Form”. See line 96 too. There are more. Fix them.

Thank you. Fully Multiplicative Form is part of the abbreviation which is Fermatean Fuzzy Set-based Fully Multiplicative Form (MULTIMOORA). That is why the upper case is used. However, the entire manuscript is reviewed carefully a couple of times to eliminate any grammatical errors and typos.

- In your case study, why did you use 10 criteria using Q-ROF TOPSIS and Q-ROF VIKOR methods?

In the literature, these criteria are the most used criteria to evaluate insurance plans which are stated in Table 1.

- In your case study, why did you select five insurance companies operating in Turkey? Why did you operate in Turkey?

In this study, 5 big national insurance companies in Turkey are considered as an alternative. That is the reason why five insurance companies were selected.

- In line 293, change the sentence “Normalized Decision Matrix” by “Normalized decision matrix”. Also, for the lines 301 and 306.

Thank you for your suggestions. The required changes are made.

- In the Conclusions and Future Studies, the grammatical tenses are wrong. See line 374. Also, the aims should not be included in Conclusion.

Thank you for your constructive suggestions. The required changes are made.

 

Author Response File: Author Response.docx

Reviewer 4 Report

This study explores the evaluation of private health insurance plans using Q level fuzzy set and decision support tools. The goal is to provide a comprehensive perspective on the assessment process and assist clients in making informed decisions. The study compares several distance measures and finds that they have little impact on the results.

The methodology described in this manuscript presents the Q-ROF TOPSIS and Q-ROF VIKOR methods for selecting private health insurance. Overall, the methodology provides a structured approach for decision-makers to evaluate and rank private health insurance options based on multiple criteria. The Q-ROF TOPSIS and Q-ROF VIKOR methods offer a systematic framework for decision-making under conditions of uncertainty and imprecision. Despite this, I would like to point out that there is a lack of detail in some cases. For example, the authors never mention what the Q-level they often mention means.

Overall, the article offers insights into the historical context of insurance and its significance in contemporary society, particularly in the context of private health insurance. The proposed methodology looks promising in addressing the challenges faced by individuals in selecting insurance plans.

 

Minor comments:

Justify the formulas to be more readable

Author Response

This study explores the evaluation of private health insurance plans using Q level fuzzy set and decision support tools. The goal is to provide a comprehensive perspective on the assessment process and assist clients in making informed decisions. The study compares several distance measures and finds that they have little impact on the results.

The methodology described in this manuscript presents the Q-ROF TOPSIS and Q-ROF VIKOR methods for selecting private health insurance. Overall, the methodology provides a structured approach for decision-makers to evaluate and rank private health insurance options based on multiple criteria. The Q-ROF TOPSIS and Q-ROF VIKOR methods offer a systematic framework for decision-making under conditions of uncertainty and imprecision. Despite this, I would like to point out that there is a lack of detail in some cases. For example, the authors never mention what the Q-level they often mention means.

Overall, the article offers insights into the historical context of insurance and its significance in contemporary society, particularly in the context of private health insurance. The proposed methodology looks promising in addressing the challenges faced by individuals in selecting insurance plans.

Minor comments:

Justify the formulas to be more readable,

Thank you for your valuable comments. The entire manuscript is reviewed carefully and equations/formulas are reviewed.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The revised version has improved well, but it still requires some major modifications. My comments are as follows:

·         My previous comment “The authors should provide more thorough information about the existing q-ROF information based studies and emphasize their weaknesses. Then discuss the advantages of your proposed method over existing methods? Explain in detail” has not completely addressed by the authors. Only writing this statement ‘The membership degree of an element can be represented using a set of q-rung functions thanks to a fuzzy set extension known as q-rung fuzzy sets. Various fields, including decision-making, pattern recognition, image processing, and control systems, have used Q-rung fuzzy sets (Garg, 2022)⁠⁠⁠⁠⁠⁠. For instance, q-rung fuzzy sets were utilized in the creation of a fuzzy controller for a mobile robot with two wheels (Luqman et al., 2017)⁠⁠⁠⁠⁠⁠. They have also been employed to categorize medical images (Khan et al., 2022)’ is not sufficient to prove the advantages of your proposed work over the exsting ones.

·         Although the advantages of the developed approach are highlighted by comparing TOPSIS and VIKOR methods but I think more analysis should be added in terms of stability and reliability by comparisons with some of the existing methods.

·         The authors are saying that “This research is one of the first attempts to solve private health insurance selection problems.” Then the authors should describe the process of data collection. In addition, source of the criteria should be mentioned in the case study section.

·         I am still unconvinced that how the decision-makers were incorporated in the study and what task they were asked to perform during the assessment of insurance policies. For what purpose were they ranking the insurance policies?

·         Were the decision-makers incorporated in both deciding upon the 10 criteria as well as ranking them? Is this standard practice? How did the decision-maker’ knowledge come into play to define the significance values of the considered criteria?

·         Managerial implications should be added. It can help the policymakers in assessing the health insurance policies under uncertain environment.

 

Author Response

The revised version has improved well, but it still requires some major modifications. My comments are as follows:

• My previous comment “The authors should provide more thorough information about the existing q-ROF information-based studies and emphasize their weaknesses. Then discuss the advantages of your proposed method over existing methods. Explain in detail” has not completely addressed by the authors. Only writing this statement ‘The membership degree of an element can be represented using a set of q-rung functions thanks to a fuzzy set extension known as q-rung fuzzy sets. Various fields, including decision-making, pattern recognition, image processing, and control systems, have used Q-rung fuzzy sets (Garg, 2022)⁠⁠⁠⁠⁠⁠. For instance, q-rung fuzzy sets were utilized in the creation of a fuzzy controller for a mobile robot with two wheels (Luqman et al., 2017)⁠⁠⁠⁠⁠⁠. They have also been employed to categorize medical images (Khan et al., 2022)’ is not sufficient to prove the advantages of your proposed work over the exsting ones.

 

Thank you. We appreciate your comment. More information regarding to the the advantages of your proposed work over the current research in TODIM and VIKOR methods.

 

     In this paper, TODIM and VIKOR methods are extended with q-rung orthopair fuzzy sets to capture vague information precisely. The proposed approach is applied to a case study in the field of private health insurance policy, where decision-making is often complicated by the presence of multiple criteria and conflicting objectives. The results show that the q-rung orthopair fuzzy TODIM and VIKOR methods are effective tools for handling imprecise information in decision-making processes. Moreover, the use of q-rung orthopair fuzzy sets allows decision-makers to consider both positive and negative aspects of the evaluated alternatives, leading to more comprehensive and accurate evaluations. Overall, this study highlights the potential of q-rung orthopair fuzzy sets as a valuable tool for decision-making in complex and uncertain environments. Further research could explore their application in other fields and compare their performance with other existing methods.

 

• Although the advantages of the developed approach are highlighted by comparing TOPSIS and VIKOR methods but I think more analysis should be added in terms of stability and reliability by comparisons with some of the existing methods.

Thank you. For this purpose, To emphasize the consistency of the results and to show the stability and reliability of the q-value, the changes on the model were followed by resolving at different q = (2 - 10) levels in both methods are given in Figures 4 and 5.

• The authors are saying that “This research is one of the first attempts to solve private health insurance selection problems.” Then the authors should describe the process of data collection. In addition, source of the criteria should be mentioned in the case study section.

Thank you. The data in the study is based on the literature review and expert opinions. The sources of criteria are shown in Table 4.

No	Criteria	Research	Description
C1	Premium Eligibility	Sehhat et al. (2015) Yücenur and Demirel (2012)	The level of compliance between policy coverage and the premium to be paid.
C2	Company Brand Strength and Value	Azizi et al. (2013) Saeedpoor et al. (2015) Puelz (1991)	The degree of trust the company has created in customers.
C3	Contracted Hospital Chain	Azizi et al. (2013) Sehhat et al. (2015) Puelz (1991)	The hospital chains where the services covered by the policy can be obtained differ depending on the service quality.
C4	Number Of Inspections	Azizi et al. (2013) Saeedpoor et al. (2015)	The number of examinations that are conducted annually in outpatient treatment.
C5	Efficiency In Emergencies	Sehhat et al. (2015) Yücenur and Demirel (2012) Puelz (1991)	The success of fast transportation to the patient in any emergency and transfer to the nearest health institution.
C6	Age Limit Acceptance	Azizi et al. (2013) Mikhailov and Almulhim (2015) Yücenur and Demirel (2012)	Insurance companies have determined an age limit because they consider a certain age above risky within the scope of regulation of private health policies.
C7	Renewal Guarantee	Sehhat et al. (2015) Saeedpoor et al. (2015) Mikhailov and Almulhim (2015)	When the health policy is started to be used, expenses that spread over the years may occur in the continuation of the treatment. Companies that want to avoid the high costs this may entail may refrain from renewing the policy. The renewal guarantee processes vary according to the number of policy renewals and the usage status of the insured.
C8	Renewal Premium Eligibility	Azizi et al. (2013) Saeedpoor et al. (2015) Yücenur and Demirel (2012)	Some companies apply additional fees for renewal policies in the following years due to the frequency of use of the policy by the insured or high expense items.
C9	Private Physician Coverage	Azizi et al. (2013) Mikhailov and Almulhim (2015) Puelz (1991)	Whether it covers private doctor examination fees other than contracted health institutions.
C10	Validity Abroad	Sehhat et al. (2015) Yücenur and Demirel (2012) Puelz (1991)	The geographical scope of the insurance is whether it is valid in countries other than the Republic of Turkey.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

• I am still unconvinced that how the decision-makers were incorporated in the study and what task they were asked to perform during the assessment of insurance policies. For what purpose were they ranking the insurance policies?

Thank you. The purpose of this study is to provide a decision-making approach based on the expert opinion as stated in introduction section as follow “The proposed methodology’s main objectives are to 1) investigate the evaluation process of selecting a private health insurance plan and 2) apply Q-level fuzzy set to decision support tools.”  Also, the purpose of this study is stated in Abstract and Conclusion Sections as follows:

In Abstract:

In this study, the problem of choosing private health insurance was handled by considering a case study of evaluations of five alternative insurance companies made by expert decision-makers in line with the determined criteria.

In Conclusion:

Due to the uncertainties affecting the policy selection, Q-ROF-based clusters were used in this study to make the clearest decision. 5 alternative insurance companies were evaluated using the Q-ROF TOPSIS and Q-ROF VIKOR methods through comparison surveys made to three decision-makers working in the sector.

• Were the decision-makers incorporated in both deciding upon the 10 criteria as well as ranking them? Is this standard practice? How did the decision-maker’ knowledge come into play to define the significance values of the considered criteria?

Dear respectful reviewer, decision-maker are not in the deciding process of the criteria. The decision-makers evaluate the 5 alternatives insurance policies according to the 10 criteria which are derived from the literature. This is common usage of MCDM applications in literature. Each decision-maker has different effects on decision-making processes. The nature of these effects defined in the page 12 as follows:

“Decision-makers are chosen from the private health insurance customers. The expertise of the decision-makers has been determined based on the length of time they have been private health insurance customers, which is defined as follows:”

 

• Managerial implications should be added. It can help the policymakers in assessing the health insurance policies under uncertain environment.

 

Q-RoF TODIM and VIKOR are a powerful tool used to assess, rank, and select private health insurance plans based on cost, coverage, network access, and customer service. It incorporates uncertainty and vagueness, and can have managerial implications. Fuzzy MCDM is a decision-making tool that can aid in strategic planning, risk management, customer satisfaction, operational efficiency, competitive advantage, policy development, and resource allocation. It can help managers understand which features of health insurance plans are most valued by potential customers, assess the risk and return of different health insurance plans, meet the needs and expectations of their customers, optimize operations, gain competitive advantage, select better insurance plans and services, evaluate and compare different options, and allocate resources. However, it has its limitations, such as the quality and completeness of the data it is fed, the weighting of different criteria, and the expertise of the people interpreting the results. It should be used as a complementary tool, in conjunction with other decision-making methods and domain knowledge.

 

Author Response File: Author Response.docx

Reviewer 2 Report

The paper has been improved and can be considered for publication.

The quality of english is OK.

Author Response

Dear Reviwer

Thanks

 

Round 3

Reviewer 1 Report

Authors have made an effort to answer all the comments