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This study aims to develop the maqasid index (MI) for Islamic corporate social responsibility (CSR), namely, Dana Abadi Umat (DAU) (Ummah’s Endowment Fund) or MI-DAU in Indonesia.

Modeling and weighting are based on Delphi and analytic network process (ANP) methods, called Delphi–ANP additive weighting. The Delphi method was applied to design and validate the factors of the MI-DAU model, and the ANP method was applied to generate and validate weights for these factors. Finally, the MI-DAU is calculated, based on the planned budget and actual allocation of DAU returns, called the maslahah fund, using additive weighting.

Delphi and ANP show significant and robust results. The priority order and weights of maqasid Shariah are safeguard the faith (0.32), safeguard the intellect (0.219), safeguard the life (0.204), safeguard the wealth (0.171) and safeguard the lineage (0.104). Meanwhile, the priority order and weights of the main activities are education (0.190), Ummah’s economy (0.167), Hajj service (0.155), Da’wah (0.124), health care (0.118), social-religious (0.097), worship facilities (0.085) and disaster emergency response (0.065). Finally, the results of MI-DAU show a high index in 2019 and 2020 of 71.89 and 69.51, respectively, generated from allocation ratio of 90.63% and 85.98%, respectively.

Maqasid Shariah used in this study follows Al-Ghazali, where it could also follow maqasid Shariah of Abu Zahrah or Al-Najjar. Moreover, the MI-DAU score uses additive calculations, where it can also use Pentagon calculation.

The improved framework and method used to design MI-DAU in this study could be applied to design more scientific MI for other Islamic financial institutions.

The novelty of this study is in the improved method used to design the MI model, including its factors, using Delphi, and to assign weights of all factors using ANP, where both provide validation for more robust MI model.

The primary purpose of this study was to generate a composite sport fan equity index (SFEI) for use in estimating the asset value of an individual fan to a sport organization. The index was developed by applying a simple additive weighting (SAW) method.

A cross-sectional survey-based research was carried out to validate key components of sport fan equity (SFE) and formulate the SFEI on the basis of the NCAA Division I collegiate sport context. These objectives were satisfied through a twofold process involving first-order confirmatory factor analysis intended to assess the validity of SFE measurement scales and SAW designed to produce the composite SFEI.

The developed index indicated that the average SFE of focal sport fans of Division I collegiate sport was 54.8 and that the overall SFE of such contributors ranged from 20.4 (the lowest score) to 94.6 (the highest score). The SFEI serves as a single, summary score and an essential gauge for sport marketers to use when assessing profitable fans and tracking/comparing them over time.

This research contributes to the sport marketing literature through its application of SAW in an initial effort to produce an easily understandable index that determines the asset value of sport fans and serves as an imperative criterion for overall sport team valuation from the sport consumer side. Specifically, the SFEI can function as a standard numeric measure that enables sport marketers to identify fans from whom sport organizations can generate considerable profits, segment these devotees systematically, and tailor marketing strategies to each fan base.

The purpose of this research is to determine the competitive advantages of higher education institutions (HEIs) and create a new methodology to rank universities according to the competitive advantages.

The research determines the competitive advantages of HEIs by analysing expert opinions through a semi-structured interview form, matches codes and themes to performance indicators using Saldana's two-cycle coding methods, evaluates content validity through Lawshe and reveals the item weights of the ranking with analytical hierarchy process (AHP). Simple additive weighting (SAW) and Technique for Order of Preference by Similarity (TOPSIS) methods were used for ranking universities.

Seven dimensions stand out in regard to what should be considered while ranking HEIs: research and publication, education, management, infrastructure, financial resources, human resources and social and economic contribution. Under the 7 dimensions, 69 indicators were determined.

The research provides a scientific reference point where HEIs can compare themselves with other HEIs regarding where they are in the sector, especially in terms of competitive advantages.

Although there are many different ranking methods that rank universities in the national and international literature, almost all these methods are largely based on the outputs of the university such as the number of publications, the number of patents, the number of projects, etc. A framework which ranks universities by considering different aspects of the institution, such as management, human resources and financial resources, has not been developed yet. In this respect, this research aims to fill this gap in the literature.

Based on the existing literature in the field of green supply chain management (GSCM), the purpose of this paper is to find essential to conceptualize and develop an efficient appraisement platform for the purpose of benchmarking green alternative in supply chain network.

The authors explored multiple approaches, i.e. Višekriterijumsko kompromisno rangiranje (VIKOR), simple additive weighting (SAW) and grey relational analysis (GRA) by amalgamating fuzzy sets theory to select the most appropriate alternative for GSCM. The work is supported by triangular fuzzy number sets to choose the green alternative industry among available industries, while dealing with the uncertainty and vagueness in GSCM. A case study is exposed to identify strong and weak indices and to exhibit the feasibility of the proposed work.

It is requisite by the managers of many firms to identify the strong and weak indices relating their firms. Thus, the authors presented an approach for measuring and appraising the performance of the selected green alternative by determining the strong and weak indices. The presented work illustrates the performance measurement model that identifies comprehensive GSCM practices of the firms. The presented work incorporates green supply chain activities to support environmental sustainability throughout the supply chain.

GSCM is necessary to the firms, as it considers impact onto the environment due to their supply chain activities. The authors build decision support system to facilitate the managers of various firms for modeling green practices in their decision making. The authors attempt to devise a conceptual framework linked with knowledge-based theory.

The authors conceptualized VIKOR, SAW and GRA methodology to rank and benchmark the green performance of distinguish alternative industries among available industries. Additionally, the performance measurement model for the selected significant green alternative is presented for determining the strong and weak indices.

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.

Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns.

Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices.

Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective.

The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs.

A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

This paper aims to provide a framework for evaluating the impact of implementing customer relationship management (CRM) based on the balanced scorecard (BSC). The outcomes illustrate the gaps between the present conditions of CRM implementation in a specific organization, which leads to some strategic remedies. These remedies are going to be ranked to achieve the best solution for enhancing the quality of CRM in the organization.

This study investigates the weights of measures presented in the CRM‐BSC by distributing the questionnaires among 44 experts in the beverage industry of Iran. It also benefits from judgment‐purposive in non‐probability sampling method for collecting data. The results are analyzed through a fuzzy approach. The strategic remedies for the drawbacks of the organization that were obtained from the CRM‐BSC are also proposed by the experts. These remedies are again evaluated by questionnaires and some selective tools of multi‐criteria decision‐making approach namely: simple additive weighting and technique for ordering preference by similarity to ideal solution.

Through the evaluation process, six significant gaps related to the CRM performance of the organization are agreed upon. For each of these gaps, the strategic remedies are proposed by the experts. The outcomes of ranking these remedies imply that customer feedbacks, updating managerial knowledge and employee belongingness should be the main objectives of the manufacturer for improvement.

This study provides a better understanding of a more effective CRM system for different kinds of organizations by first, clarifying the customer‐related performance gaps of the target organization and second, by presenting strategic solutions for the detected areas. The framework could be also beneficial in other fields of industry, but the relevancy of the measures should be considered.

The CRM‐BSC framework is customized to the Iranian industrial environment. The structure of the measures in the scorecard is proposed for the first time.

This paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.

Self-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.

The proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.

The choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.

To prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.

To the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

Delivering premium services and quality products are critical strategies for success in manufacturing. Continuous improvement (CI), as an underlying foundation for quality management, is an ongoing effort allowing manufacturing companies to see beyond the present to create a bright future. We propose a novel integrated fuzzy framework for analyzing the barriers to the implementation of CI in manufacturing companies.

We use the fuzzy failure mode and effect analysis (FMEA) and a fuzzy Shannon's entropy to identify and weigh the most significant barriers. We then use fuzzy multi-objective optimization based on ratio analysis (MOORA), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy simple additive weighting (SAW) methods for prioritizing and ranking the barriers with each method. Finally, we aggregate these results with Copeland's method and extract the main CI implementation barriers in manufacturing.

We show “low cooperation and integration of the team in CI activities” is the most important barrier in CI implementation. Other important barriers are “limited management support in CI activities,” “low employee involvement in CI activities,” “weak communication system in the organization,” and “lack of knowledge in the organization to implement CI projects.”

We initially identify the barriers to the implementation of CI through rigorous literature review and then apply a unique integrated fuzzy approach to identify the most important barriers based on the opinions of industry experts and academics.

Contractor selection is carried out in order to choose a competent and capable contractor to do the work. To help in this selection, baselines are established to ensure that the contractors have the required skills, resources, and abilities to execute the project. Contractor selection is a multiple criteria decision making wherein several criteria are required to be evaluated simultaneously. This paper aims to propose a decision‐making model.

The proposed model utilizes superiority and inferiority ranking (SIR) method and it provides six preference structures in order to compare the performance of alternatives' criteria. As such, it can represent discrete or continuous criteria. The preference structures utilize indifference and preference thresholds to capture the characteristics of functions that represent the specified criteria. The model provides two aggregation procedures (simple additive weighting and technique for order preference by similarity to the ideal solution) to generate superiority and inferiority flows.

The proposed model is generic and can be used as a tool to evaluate alternatives in several applications such as value engineering, optimum organization structure, and constructability analysis. It enables its users to define the criteria that are deemed important for evaluation.

The proposed multiple criteria decision making (SIR method) is novel to construction. This ranking method can be utilized as a successful tool in contractor selection problem.