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21 – 30 of over 3000Yin Kedong, Shiwei Zhou and Tongtong Xu
To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The…
Abstract
Purpose
To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.
Design/methodology/approach
The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.
Findings
Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.
Originality/value
The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.
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Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote…
Abstract
Purpose
Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.
Design/methodology/approach
The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.
Findings
The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).
Research limitations/implications
As in other systematic literature review studies, the results are limited by the content of the selected papers.
Practical implications
The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.
Originality/value
This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.
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Rajiv Kumar Sharma, Dinesh Kumar and Pradeep Kumar
This paper aims to permit the system reliability analysts/managers/practitioners/engineers to analyze the system failure behavior using fuzzy methodology (FM)
Abstract
Purpose
This paper aims to permit the system reliability analysts/managers/practitioners/engineers to analyze the system failure behavior using fuzzy methodology (FM)
Design/methodology/approach
In order to deal with both qualitative and quantitative information related to system performance the authors have adopted failure mode effect analysis (FMEA) and Petrinets (PNs), the well‐known tools for reliability analysis, to build an integrated framework aimed at helping the reliability and maintenance managers in decision‐making.
Findings
Using the proposed framework an industrial case from the paper mill is examined. From the results it is observed that the limitations associated with the traditional procedure of risk ranking in FMEA are efficiently modeled using fuzzy decision‐making system (FDMS) based on FM. Also, the fuzzy synthesis of system failure and repair data helps to quantify the system behavior in a more realistic manner.
Originality/value
The simultaneous adoption of the proposed techniques to model, analyze and predict the uncertain behavior of an industrial system will not only help the reliability engineers/managers/practitioners to understand the behavioral dynamics of system but also to plan/adapt suitable maintenance practices to improve system reliability, availability and maintainability (RAM) aspects.
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Changiz Valmohammadi and Vahid Shahrashoob
Due to the important role of strategic human resources in fulfilling the main objectives of organizations on the one hand and the necessity of having suitable functional…
Abstract
Purpose
Due to the important role of strategic human resources in fulfilling the main objectives of organizations on the one hand and the necessity of having suitable functional strategies in place to operationalize the developmental programs on the other hand, this study aims to identify the factors and sub-factors of developmental programs and their priorities as well as the relationship and interactions of the identified criteria in human capital developmental programs through a hybrid fuzzy decision-making trial and evaluation laboratory –analytic network process approach. Also, the rank of functional strategies to achieve these human resource developmental programs is determined using fuzzy VIsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) technique.
Design/methodology/approach
Through an in-depth review of the relevant literature, the most important criteria and sub-criteria were determined. Then, a questionnaire was designed and distributed among 20 top managers and experts of the surveyed bank. Using geometric mean, the criteria were screened. In the next step, the second pairwise questionnaire was designed and distributed among eight experts, to determine the relations and interrelations among these factors their relevant sub-factors and prioritize them. Finally, using the third designed questionnaire and fuzzy, VIKOR (FVIKOR) technique the ranks of functional strategies were determined.
Findings
Analysis of the results showed that “future wellness and retirement” is the most influential factor and the “retention” factor is the most permeable factor. Also, human capital planning is the most important factor of this department’s developmental programs in achieving its strategic objectives. Factors “recruiting and hiring,” “retention,” “empowerment” and “future wellness and retirement” were ranked second to fifth, respectively. Finally, the application of the FVIKOR technique revealed that “enhancement and improvement of incentive systems” is the best functional strategy to achieve the developmental plans of the human capital department.
Research limitations/implications
One of the limitations of this study is the generalizability of the findings, which may be limited by the single case study method used.
Practical implications
This study presents a comprehensive and effective tool which could specifically help policymakers and top managers of the survey company and other managers of the banking sector in general, to use a quantitative approach toward identification and prioritizations of the determinants factors of the human capital developmental programs toward achieving functional strategic objectives to enhance the satisfaction of their internal customer as the most important asset of their organizations which might lead to the increased external customer satisfaction and, subsequently, increased competitive advantage.
Originality/value
To the best knowledge of the authors, this is one the first studies of its kind which attempts through a hybrid fuzzy analytical network process and fuzzy DEMATEL approach, presents a structural network model to examine the interrelationships among the human capital developmental programs and prioritizes them, also simultaneously rank the functional strategies toward achieving these programs using FVIKOR technique.
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The purpose of this paper is to analyze the reliability of the washing system in a paper plant in a more promising way under vague environment by reducing the accumulating…
Abstract
Purpose
The purpose of this paper is to analyze the reliability of the washing system in a paper plant in a more promising way under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process using the Tω (weakest t-norm) based fuzzy lambda-tau (TBFLT) technique.
Design/methodology/approach
This paper presents a unified approach for analyzing the fuzzy reliability of the washing system under vague environment. This approach applies the TBFLT technique which uses triangular fuzzy numbers for incorporating data uncertainty, fault tree and lambda-tau method for finding system failure rate and repair time mathematical expressions while simplified Tω-based arithmetic operations are applied for computing various reliability parameters of the system. The effectiveness of the TBFLT technique has been demonstrated by analyzing fuzzy reliability of the system using five different techniques including TBFLT. Moreover, this paper applies extended Tanaka’s (1983) approach to rank the critical components of the system.
Findings
The TBFLT technique has the advantage of low computation complexity in comparison to other techniques and effectively reduces the accumulating phenomenon of fuzziness. This main finding verifies the conclusion made by Chen (1994).
Originality/value
The author has suggested a simple and more applicable technique for analyzing the fuzzy reliability of any complex process industrial system under vague environment. The effectiveness of the technique has been demonstrated by computing various reliability parameters of the washing system of a paper plant.
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Christine Domegan, Patricia McHugh, Brian Joseph Biroscak, Carol Bryant and Tanja Calis
The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and…
Abstract
Purpose
The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and definition in social marketing.
Design/methodology/approach
The paper is an illustrative case study approach, highlighting three real-world exemplars of causal modelling for wicked problem definition.
Findings
The findings show how the traditional linear research methods of social marketing are not sensitive enough to the dynamics and complexities of wicked problems. A shift to non-linear causal modelling techniques and methods, using interaction as the unit of analysis, provides insight and understanding into the chains of causal dependencies underlying social marketing problems.
Research limitations/implications
This research extends the application of systems thinking in social marketing through the illustration of three non-linear causal modelling techniques, namely, collective intelligence, fuzzy cognitive mapping and system dynamics modelling. Each technique has the capacity to visualise structural and behavioural properties of complex systems and identify the central interactions driving behaviour.
Practical implications
Non-linear causal modelling methods provide a robust platform for practical manifestations of collaborative-based strategic projects in social marketing, when used with participatory research, suitable for micro, meso, macro or systems wide interventions.
Originality/value
The paper identifies non-linear causality as central to wicked problem scoping identification, documentation and analysis in social marketing. This paper advances multi-causal knowledge in the social marketing paradigm by using fuzzy, collective and interpretative methods as a bridge between linear and non-linear causality in wicked problem research.
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Hajar Regragui, Naoufal Sefiani, Hamid Azzouzi and Naoufel Cheikhrouhou
Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance…
Abstract
Purpose
Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals.
Design/methodology/approach
Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution.
Findings
A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance.
Practical implications
The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being.
Originality/value
This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.
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Javad Zahedi, Mahdi Salehi and Mahdi Moradi
The current study aims to identify and classify the financial resilience measurement indices using the intuitive fuzzy approach.
Abstract
Purpose
The current study aims to identify and classify the financial resilience measurement indices using the intuitive fuzzy approach.
Design/methodology/approach
The present study aims to identify and classify firms' indices of financial resilience measurement using the Fuzzy–Delphi combined method and the intuitive fuzzy DEMATEL technique with interval values. For the study and the literature review, 29 financial resilience indices were identified, and 12 were finalised after screening and localisation. Next, the selected indices were classified into two groups of influencing and being influenced, and the significant range of each one was determined. Finally, the executive and research suggestions were presented based on the obtained results.
Findings
The study results indicate a higher significance level of redundancy and visibility in financial resilience.
Originality/value
The present study is the pioneer study to assess, identify and classify the contributing indices to financial resilience.
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Keywords
Amit Kumar Yadav and Dinesh Kumar
The already-strained vaccine supply chain (VSC) of the expanded program for immunization (EPI) require a more robust and structured distribution network for pandemic/outbreak…
Abstract
Purpose
The already-strained vaccine supply chain (VSC) of the expanded program for immunization (EPI) require a more robust and structured distribution network for pandemic/outbreak vaccination due to huge volume demand and time constraint. In this paper, a lean-agile-green (LAG) practices approach is proposed to improve the operational, economic and environmental efficiency of the VSC.
Design/methodology/approach
A fuzzy decision framework of importance performance analysis (IPA)–analytical hierarchy process (AHP)–technique for order for preference by similarity in ideal solution (TOPSIS) has been presented in this paper to prioritize the LAG practices on the basis of the influence on performance indicators. Sensitivity analysis is carried out to check the robustness of the presented model.
Findings
The derived result indicates that sustainable packaging, coordination among supply chain stakeholders and cold chain technology improvement are among the top practices affecting most of the performance parameters of VSC. The sensitivity analysis reveals that the priority of practices is highly dependent on the weightage of performance indicators.
Practical implications
This study's finding will help policymakers reframe strategies for sustainable VSC (SVSC) by including new management practices that can handle regular immunization programs as well as emergency mass vaccination.
Originality/value
To the best of the authors' knowledge, this is the first study that proposes the LAG framework for SVSC. The IPA–Fuzzy AHP (FAHP)–Fuzyy TOPSIS (FTOPSIS) is also a novel combination in decision-making.
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Komal, S.P. Sharma and Dinesh Kumar
The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically utilizing vague, imprecise, and uncertain data…
Abstract
Purpose
The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically utilizing vague, imprecise, and uncertain data. The press unit of a paper mill situated in a northern part of India, producing 200 tons of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behavior has also been done.
Design/methodology/approach
In the proposed approach, two important tools namely traditional Lambda‐Tau technique and genetic algorithm have been hybridized to build genetic algorithms‐based Lambda‐Tau (GABLT) technique to analyze the behavior of complex repairable industrial systems stochastically up to a desired degree of accuracy. This technique has been demonstrated by computing six well‐known reliability indices used for behavior analysis of the considered system in more promising way.
Findings
The behavior analysis results computed by GABLT technique have reduced region of prediction in comparison of existing Lambda‐Tau technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. The paper suggested an approach to improve the system's performance.
Originality/value
The paper suggests a hybridized technique for analyzing the stochastic behavior of an industrial subsystem by computing six well‐known reliability indices in the form of fuzzy membership function.
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