Rank Reversal Phenomenon Research Articles

Intelligent characteristic objects method (INCOME): a data knowledge-based multi-criteria decision analysis

Multi-criteria decision analysis (MCDA) methods are vital in assessing decision variants under multiple conditions. However, involving domain experts in developing decision models can be challenging and costly, necessitating more scalable and independent solutions. This paper introduces the intelligent characteristic objects method (INCOME), which combines the k-Nearest Neighbor (kNN) algorithm and the COMET method to create a theoretical decision-maker for comparing characteristic objects (COs). INCOME overcomes limitations of classical MCDA methods, such as the TOPSIS approach, which struggles with complex functions and non-monotonic modeling. INCOME influences data-based knowledge to provide a robust framework for assessing decision options. The integration of the COMET method and kNN algorithm enables improved modeling of decision functions based on evaluated data, increasing the flexibility and independence of the INCOME approach. A case study assessing gas power plants based on four criteria is presented to validate the performance of the INCOME method. The results demonstrate high correlations with the reference model and slightly higher classical approaches like TOPSIS and TOPSIS-COMET. However, INCOME exhibits greater stability and flexibility by utilizing all available data instead of relying on limited expert knowledge. The proposed INCOME approach offers several advantages, including creating a continuous decision model, resistance to the Rank-Reversal phenomenon, and the potential for replacing domain experts with artificial experts. This study highlights the effectiveness of INCOME in Multi-Criteria Decision Analysis. It suggests future research directions, such as parameter selection and testing in different decision-making problems.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

ObjectiveThe objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. MethodsIt integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. ResultsThe findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. ConclusionsTo ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Analysis of third alternatives' impact on PROMETHEE II ranking

AbstractSo far, the rank reversal (RR) phenomenon has been heavily discussed in the multiple criteria analysis literature. In this work, instead of focusing on RR occurrences within PROMETHEE II, an analysis of the third alternative with respect to a given pair of alternatives is conducted. The aim is to provide a way to identify third alternatives able to cause RR between a specific pair. This opens the question of building alternatives causing RR occurrences. Such alternatives can then be used to assess the resistance a given ranking has to RR. In the end, two different approaches are used. The first considers the number of third alternatives to be added to generate RR for a given pair of alternatives. While, the second one, provides a new threshold for RR occurrences in PROMETHEE II.

The uncertainty related to the inexactitude of prioritization based on consistent pairwise comparisons.

When the in/consistency in Pairwise Comparisons (PCs) is taken into consideration as the subarea of the Multi Attribute Decision Making (MADM) scientific field, it has many repercussions in various types of research areas including different modelling scenarios e.g. reduction of inconsistency during PCs, deriving appropriate consistency thresholds for inconsistent Pairwise Comparison Matrices (PCMs), completing of incomplete PCMs, aggregating of individual PCMs in relation to Group Decision Making (GDM) aspects, and PCMs in/consistency relation to credibility of Priority Vectors (PV) derived from PCMs with the application of various Priorities Deriving Methods (PDMs). The examination objective in the latter area of research is the uncertainty related to the inexactitude of prioritization based on derived PVs. However, only few research studies examine this problem from the perspective of PCM applicability for credible designation of decision maker's (DM) priorities in the way that leads to minimization of the prioritization uncertainty related to possible, and sometimes very probable, ranking fluctuations. This problem constitutes the primary area of interest for this research paper as no research study was thus far identified that examines this problem from the perspective of consistent PCMs. Hence, a research gap was identified. Thus, the objective of this research paper is to fill in this scientific gap. The research findings have serious repercussions in relation to prioritization quality with the application of PCs methodology, mostly in relation to the interpretation and reliability evaluation of prioritization results. Firstly, the research study outcome changes the perspective of the rank reversal phenomenon, which shed new light on many research studies that have been presented in the subject's literature for many decades. Secondly, the research study results throw new light on the discussion concerning the fuzziness of AHP's results. Last but not least, the effect of the research opens the unique opportunity to evaluate the prioritization outcome obtained within the process of consistent PCs from the well-known perspective of statistical hypothesis testing i.e. the probability designation of the chance that accepted ranking results which were considered as correct due to low probability of change may be incorrect, hence they should be rejected, and the probability designation of the chance that rejected ranking results which were considered as incorrect due to high probability of change may be correct and should be accepted. The paramount finding of the research is the fact that consistent PCMs provide PVs, which elements cannot be considered as established, but only approximated within certain confidence intervals estimated with a certain level of probability. As problems related to heuristics can be analyzed only via a computer simulation process, because they cannot be mathematically determined, the problem examined in this research paper is examined via Monte Carlo simulations, appropriately coded and executed with the application of Wolfram's Mathematica Software. It is believed that this research findings should be very important and useful for all decision makers and researchers during their problems' examinations that relate to prioritization processes with the application of PCs methodology.

A modified EDAS model for comparison of mobile wallet service providers in India

The present paper has two-fold purposes. First, the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users' views in the Indian context. To this end, we propose a new grey correlation-based Picture Fuzzy-Evaluation based on Distance from Average Solution (GCPF-EDAS) framework for the comparative analysis. We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-à-vis service quality dimensions for criteria selection. For comparative ranking, we conduct our analysis under uncertain environments using picture fuzzy numbers. We find that user-friendliness, a wide variety of use, and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users. It is seen that PhonePe (A3), Google Pay (A2), Amazon Pay (A4) and PayTM (A1) hold top positions. For validation of the result, we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution. We observe a significant consistency. We then carry out rank reversal test for GCPF-EDAS model. We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon. To examine the stability in the result for further validation, we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights. We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.

A new multi-criteria decision making approach based on wins in league to avoid rank reversal: A case study on prioritizing environmental deterioration strategies in arid urban areas

In the present study, a novel approach based on multi-criteria analysis was proposed to prioritize PM mitigation strategies from economic, environmental and social aspects. Multi-criteria decision making (MCDM) methods are suitable tools for prioritizing strategies on various topics. However, it has been observed that adding/removing strategies has led to a change in preference which is called rank reversal. The present study was aimed with three objectives: (i) evaluating the rank reversal phenomenon in MCDM methods frequently used in environmental topics, (ii) introducing a novel approach based on wins in league (WIL) in order to reduce the rank reversal in MCDM methods and (iii) a new application of this method in prioritizing PM mitigation strategies in arid urban areas. Qualitative results showed that in studies in the field of environment, the rank reversal has occurred frequently in classical MCDM methods but its occurrence was prevented in the proposed method. Quantitative evaluation also showed that the probability of rank reversal occurrence in Technique For Order Performance By Similarity To Ideal Solution (TOPSIS), Additive Ratio ASsessment (ARAS), Weighted Aggregated Sum Product Assessment (WASPAS), Evaluation Based On Distance From Average Solution (EDAS), Combinative Distance-Based Assessment (CODAS) and Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) methods was 36.4%, 32.8%, 21.2%, 31.6%, 36.1% and 29.9%, respectively. However, the probability of rank reversal occurrence in the proposed approach was almost 90% less than classical MCDM methods. Evaluation of PM mitigation strategies in arid urban areas has also shown that urban green belt plan is the most appropriate strategy.

A robust method for avoiding rank reversal in the TOPSIS

The ability to obtain robust ranking results is an important reference factor when choosing a method for multiple criteria decision-making (MCDM). However, the rank reversal phenomenon of MCDM makes its credibility greatly compromised, as represented by the technique for order preference by similarity to ideal solution (TOPSIS). Although some research papers have presented interesting ideas and solutions to minimize/void rank reversal in relation to the TOPSIS, there are still some gaps that need further research, such as do not utterly eliminate the rank reversal, need more additional decision information, cannot theoretically guarantees the stability of ranking. Therefore, an improved TOPSIS method, named IE-TOPSIS, is developed based on information expansion and virtual ideal points. An algorithm for the extreme value extension of criteria indicators is initially constructed, based on the existing decision-making information, which can effectively avoid the influence of the data standardization process on decision-making ranking. And then, the concept of a virtual ideal point is proposed, which can prevent the rank reversal caused by the change of ideal points in varying decision-making environments. At the same time, this paper proves that the IE-TOPSIS satisfies ranking stable according to the given definition of ranking robust, which theoretically guarantees that the rank reversal phenomenon does not exist. In the case where the IE-TOPSIS is established, a new ranking index is proposed that can better distinguish the evaluation objects and solve the problem that the classic TOPSIS ranking index cannot completely rank the decision objects. Finally, in the verification of examples, the ranking results show that the improved TOPSIS has strong consistent with the theoretical analysis, and is robust to handle rank reversal phenomenon in all the scenarios in comparison with other studies available in the literature.

Green electricity generation assessment using the CODAS-COMET method

• We investigate Multi-Criteria Decision Analysis (MCDA) methods application in the renewable energy sources (RES) evaluation domain. • We propose a new MCDA method called CODAS-COMET. • We assess Green Electricity Generation in Poland using the method developed. The purpose of this paper is to present the new hybrid multi-criteria decision analysis (MCDA) method CODAS-COMET for evaluating sustainable electricity generation considering renewable energy sources (RES). The introduced approach combines the advantages of two known MCDA methods: Characteristic Object METhod (COMET) and COmbinative Distance-based ASsessment (CODAS). Due to the advantages of the COMET method, the proposed approach enables the identification of the whole domain model, is resistant to the rank reversal phenomenon, is easy to apply, and allows to obtain an objective and reliable recommendation based on the gathered data. Meanwhile, combining the COMET method with CODAS eliminates the limitations of COMET by automatizing and accelerating the characteristic object comparison procedure. The presented method’s practical application includes evaluating Poland’s sixteen voivodeships, considering the sustainable use of RES in the electricity generation domain. An additional contribution of this research is to investigate the impact on results of distance metrics other than the default distance metric used by the method that is the focus of this paper and demonstrate the design and practical application of a research framework for selecting the most appropriate metrics. A comparative analysis of the CODAS-COMET method with the TOPSIS-COMET reference approach was performed to confirm the results’ reliability. The results showed a high correlation among all final rankings and confirmed the robustness of the CODAS-COMET approach to changing metrics in the algorithm. Furthermore, the results proved that our approach is a suitable and valuable tool for sustainability assessment in renewable energy domains and other complex issues requiring consideration of multiple criteria.

Materials selection method using improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values

Technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi attribute decision making (MADM) method and it has been widely used in materials selection. However, the main drawback of the traditional TOPSIS is that it has a rank reversal phenomenon. To overcome this drawback, we propose an improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values by modifying normalization formula and ideal solutions. Moreover, to study the impacts of changing attribute weights on relative closeness values of alternatives, we propose a sensitivity analysis method to attribute weights on the relative closeness values of the alternatives. We applied the proposed method to select best absorbent layer material for thin film solar cells (TFSCs). As a result, copper indium gallium diselinide was selected as the best one and the next cadmium telluride from among five materials. When the alternative is added to or removed from the set of original alternatives, the elements of the normalized decision-matrix, PIS, NIS and the relative closeness values don’t change at all, they are always coincide with the corresponding elements of the original ones. The relative closeness values are absolute values irrelevant to the composition of the alternatives in the improved TOPSIS, while the relative closeness values are relative values relevant to the composition of the alternatives in the traditional TOPSIS. Therefore, the proposed TOPSIS overcomes the rank reversal phenomenon, perfectly. It could be actively applied to practical problems for materials selection.

Decision framework with integrated methods for group decision-making under probabilistic hesitant fuzzy context and unknown weights

A hesitant fuzzy set is a flexible generalization of a fuzzy set that permits agents to furnish multiple views and the occurrence probability of each element is either the same or unknown. However, in our day-to-day problems, such an assumption is always narrow. The researchers state a concept of probabilistic hesitant fuzzy set (PHFS) to handle this. Based on the previous studies on PHFS, specific gaps can be identified, such as (i) agents' weights are not methodically determined, (ii) approaches for criteria weights do not consider criteria interrelationship and the importance of agents, (iii) preferences are aggregated without considering the agents' discrimination factors, risk appetite, and interdependencies, (iv) Broad/moderate rank values with reduced rank reversal phenomenon during prioritization is not taken. To overcome these drawbacks, in this article, we presented a new decision-making approach in which an attitude-based Shannon entropy and regret/rejoice approach is utilized to calculate the criteria and agents’ weights, respectively. Further, a variance-based Muirhead mean operator is proposed by considering the interdependencies and variations to aggregate the different preferences represented in PHFS. Finally, an approach based on the WASPAS (“Weighted Arithmetic Sum Product Assessment”) method is presented to rank the different objects. The proposed framework is demonstrated with a numerical example and compares their results with the several existing studies' results to reveal the framework's superiority.

Swimmer Assessment Model (SWAM): Expert System Supporting Sport Potential Measurement

A swimmer’s potential can be exploited to the full if an individual approach is applied to training early enough. In the initial stage of a competitor’s career, it is hard to say whether the person will reach the world level. It is influenced by factors related to physical and mental characteristics, swimming technique, or the current skills level of the athlete. Some of them cannot be improved by swimmers; however, a few of these factors can be affected and upgraded by working on them. The data from 30 swimmers were collected in the research. Then, adopting the COMET method, the values of attributes characteristic for the athletes were entered into the proposed model to determine the predispositions of each of them. The applied method proved to be effective in assessing the parameters of competitors, and at the same time, it is a modern approach free of the rank reversal phenomenon. In addition, the model obtained allows for the study of input data to check how the change of a given criterion will affect the overall rating. The model developed is forward-looking. The results are satisfactory but can be improved to analyze somatic traits better and achieve more accurate predictions.

Selection of optimum plant layout using AHP-TOPSIS and WASPAS approaches coupled with Entropy method

Layout design and selection often have notable effects on the performance of the manufacturing industry. This research investigates the Multi-Criteria Decision Making (MCDM) approach to find out the optimum plant layout design. The proposed methodology is demonstrated through the real-life setting for the gearbox manufacturing industry. Manual and computerized layout generation approach is used efficiently and accordingly, six layout designs are generated. The approach takes into account qualitative as well as quantitative performance criteria for the selection of layout design. Analytical Hierarchy Process (AHP) is applied to obtain the weight of qualitative measures. Ranking of alternatives is obtained through the application of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum-Product Assessment (WASPAS) both integrated with the Entropy method. Empirical findings indicate that the rank acquired using the TOPSIS method is perfectly parallel to those acquired through the WASPAS method, which confirms the applicability and potential of these methods. Also, the effect of the parameter λ in WASPAS method on performance score is stable. At the same time, this paper analyses the rank reversal phenomenon and proves that the ranking proposed by TOPSIS satisfies ranking stability.

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.

Comparative Analysis of Solar Panels with Determination of Local Significance Levels of Criteria Using the MCDM Methods Resistant to the Rank Reversal Phenomenon

This paper aims to present an innovative approach based on two newly developed Multi-Criteria Decision-Making (MCDM) methods: COMET combined with TOPSIS and SPOTIS, which could be the basis for a decision support system (DSS) in the problem of selecting solar panels. Solar energy is one of the most promising and environmentally friendly energy sources because of the enormous potential of directly converting available solar radiation everywhere into electricity. Furthermore, ever-lower prices for photovoltaic systems make solar electricity more competitive with power from conventional energy sources, increasing interest in solar panels among companies and households. This fact generates the need for a user-friendly, objective, fully automated DSS to support the multi-criteria selection of solar panels. Both MCDM methods chosen for this purpose are rank-reversal-free and precise. First, the objective entropy weighting method was applied for determining criteria weights. Final rankings were compared by two ranking correlation coefficients: symmetrical rw and asymmetrical WS. Then the sensitivity analysis providing local weights of alternatives for decision criteria was performed. The obtained results prove the adequacy and practical usefulness of the presented approach in solving the problem of solar panels selection.

Okvir kombinovane procene zasnovane na udaljenosti (CODAS) koji koristi logaritamsku normalizaciju za višekriterijumsko odlučivanje

The purpose of this paper is to present an extended Combinative Distance based Assessment (CODAS) framework using logarithmic normalization (LN) scheme. LN is useful in the situations where criteria values differ significantly. This framework is used to carry out a comparative performance based ranking of the popular smartphones in India. The result obtained from this extended version of CODAS method (CODAS-LN) shows consistency with that generated by using some other established multi-criteria decision making (MCDM) approaches. The sensitivity analysis shows considerable stability in the result. Further, it is observed that CODAS-LN is free from rank reversal phenomenon and follows the transitivity property. Findings of the case study suggest that the smartphones with higher computational capability and features rank in top brackets.

Challenges in reliable solar panel selection using MCDA methods

The alternative sources of energy have significantly improved quality and are more prevalent in recent years. Solar panels as one of these energy sources continue to be chosen for commercial and private usage. One of the more complex decisions is to choose components to installed in the solar system. All of the public available solar panels have eight important characteristics related to electricity efficiency and dimensions. However, there are many possible alternatives, so it is hard to say which one will be the most proper choice. MCDA methods can make this complex decision problem.This work presents an assessment of solar panels obtained by two popular MCDA methods, i.e., the COMET and TOPSIS techniques. Both methods are distance-based MCDA methods which are using characteristic points idea. However, the COMET method is the rank reversal phenomenon free method and return more accurate results. Next, we present a comparison to find the most rational solar panel from the defined set of alternatives considered their criteria. For this purpose, the weights vector is determined by using three methods. Therefore, results are determined based not only on the used MCDA method but also on the applied weights of the criteria.

An example on the use and limitations of MCDA: The case of fuzzy VIKOR

VIKOR is one of the well known Multiple Criteria Decision Aid methods, used frequently and considered to produce robust results. However, it seems that it is not immune to the rank reversal phenomenon. The latter is a well studied phenomenon that occurs sometimes in many such methods and is the cause of often severe criticism. This short paper provides such an example (and a counter-example) in the case of a fuzzy version of VIKOR.

A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS

Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.

Intuitionistic Fuzzy Sets in Multi-Criteria Group Decision Making Problems Using the Characteristic Objects Method

Over the past few decades, several researchers and professionals have focused on the development and application of multi-criteria group decision making (MCGDM) methods under a fuzzy environment in different areas and disciplines. This complex research area has become one of the more popular topics, and it seems that this trend will be increasing. In this paper, we propose a new MCGDM approach combining intuitionistic fuzzy sets (IFSs) and the Characteristic Object Method (COMET) for solving the group decision making (GDM) problems. The COMET method is resistant to the rank reversal phenomenon, and at the same time it remains relatively simple and intuitive in practical problems. This method can be used for both symmetric and asymmetric information. The Triangular Intuitionistic Fuzzy Numbers (TIFNs) have been used to handle uncertain data. This concept can ensure the preference information about an alternative under specific criteria more comprehensively and allows for easy modelling of symmetrical or asymmetrical linguistic values. Each expert provides the membership and non-membership degree values of intuitionistic fuzzy numbers (IFNs). So this approach deals with a different kind of uncertainty than with hesitant fuzzy sets (HFSs). The proposed combination of COMET and IFSs required an adaptation of the matrix of expert judgment (MEJ) and allowed to capture the behaviour aspects of the decision makers (DMs). Therefore, we get more reliable solutions while solving MCGDM problems. Finally, the proposed method is presented in a simple academic example.

Performance Appraisal of Knowledge Workers Using Augmented Additive Ratio Assessment (A-ARAS) Method: A Case Study

Evaluating knowledge workers performance has always been one of the main concerns of knowledge-based organizations. The purpose of this article is to present two new versions of the additive ratio assessment (ARAS) method called Augmented ARAS (A-ARAS1 and A-ARAS2) that improve the rank reversal phenomenon of ARAS using some iterative methods to evaluate the optimal performance of knowledge workers. The study population consisted of the Academic Center for Education, Culture, and Research in Mashhad, Iran, which has been contributing to national development through the expansion of knowledge and applied research since 1995. The performance appraisal index was weighted using the best-worst method and then analyzed by the ARAS, A-ARAS1, and A-ARAS2. The results indicated that the A-ARAS1 and A-ARAS2 models outperformed the previous version of ARAS in terms of the performance appraisal of knowledge workers and that the ARAS model needed to be revised to improve performance appraisal.