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International construction joint ventures (ICJVs) will fully realize their potential for success and effectively monitor performance when an adequate and suitable performance benchmark is established. However, existing studies fall short of adequately providing a mutually acceptable benchmark for assessing the performance of ICJVs. This study aims to develop an adequate and suitable performance measurement framework for ICJVs.

A twofold structured questionnaire survey, supplemented by semi-structured interviews, was used to collect data from the practitioners of ICJVs hosted in the developing country of Ghana. The data were analyzed by using descriptive statistics, confirmatory factor analysis (CFA) and a hybrid-fuzzy logic approach.

A list of 30 performance indicators (PIs), defined by project performance, perceived satisfaction, company/partner performance, socio-environmental performance and performance of ICJV management, was validated and proved to be significant. Only 22 out of the 30 PIs, focusing on project efficiency, societal improvement and organizational goals are realized by the ICJV practitioners. Further, suitable determinants and viable quantitative ranges for measuring each PI are established to prevent different interpretations of the meanings of PIs and objectively express the level of success in quantitative terms. The results call for further investigation of the convergence between the practice of and research into some PIs (e.g. socio-environmental performance) and a range of different performance levels (PLs) in a more scientific manner.

This study not only advances the knowledge base and practice of performance measurement in ICJVs but could also assist stakeholders and decision-makers to assess, compare and monitor the performance of different ICJV projects on common grounds objectively.

This study not only comprehensively assessed PIs – what to measure – but also systematically determined suitable determinants – how to measure – for each PI.

The puprose of this paper is to analyse the stochastic behavior of an industrial system using a novel hybridized technique NGABLT. The forming unit of a paper mill situated in north India producing approximately 200 tons of paper per day has been considered for analysis. The authors have made efforts to incorporate vague, ambiguous, imprecise and conflicting information quantified by fuzzy numbers.

Field data for repairable industrial systems are in the form of failures and repair rates are vague, ambiguous, qualitative and imprecise in nature. Using the data, system stochastic behavior in terms of six well‐known reliability indices is analysed considering some desired degree of accuracy. A practical case of forming unit in a paper mill is considered to compute the reliability indices by using NGABLT technique. Sensitive of system behavior is analysed through surface plots by taking different combinations of reliability indices. The findings have been supplied to the nearby industry for future course of action in maintenance.

The behavior analysis results computed by NGABLT technique have reduced region of prediction in comparison of existing Lambda‐Tau technique region i.e. uncertainties involved in the analysis are reduced. It may be a more useful tool to assess the current system condition and to improve the system performance.

The authors have suggested a hybridized technique for analyzing the stochastic behavior of the repairable industrial system by computing its reliability indices.

This paper aims to identify, analyze and evaluate the major enablers for the sustainable public distribution system (PDS) supply chain in India in lessening food insecurity by distributing essentials food grains at a subsidized rate.

The major enablers for the sustainable PDS supply chain were explored by conducting the literature survey and discussion with academic and warehouse experts. Then, the fuzzy-DEMATEL (decision-making trial and evaluation laboratory) technique was applied to develop a causal model that analyses the interaction among the identified enablers.

This study recognizes fifteen enablers through literature survey and experts' opinions. The present work concludes that “proper identification of the PDS beneficiaries” and “willingness and commitment of the top management and policymaker” are the two major enablers for the sustainable PDS supply chain.

This work would be helpful for profoundly understanding the major enablers, and how they are affecting the entire PDS supply chain. The study would be beneficial for the general people and the entire society straightforwardly by providing suggestions for food security.

Identifying and analyzing the major enablers for the sustainable PDS supply chain helps to visualize the problem more effectively and efficiently. Besides, the causal model explains a comprehensive perspective on the identified enablers.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.

Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.

This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.

This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.

This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

Completing construction projects within the planned schedule has widely been considered as one of the major project success factors. This study investigates the use of technologies to address delays in construction projects and aims to address three research questions (1) to identify the adopted technologies and proposed solutions in the literature, (2) to explore the reasons why the delays cannot be prevented despite disruptive technologies and (3) to determine the major strategies to prevent delays in construction projects.

In total, 208 research articles that used innovative technologies, methods, or tools to avoid delays in construction projects were investigated by conducting a comprehensive literature review. An elaborative content analysis was performed to cover the implemented technologies and their transformation, highlighted research fields in relation to selected technologies, focused delay causes and corresponding delay mitigation strategies and emphasized project types with specific delay causes. According to the analysis results, a typological framework with appropriate technological means was proposed.

The findings revealed that several tools such as planning, imaging, geo-spatial data collection, machine learning and optimization have widely been adopted to address specific delay causes. It was also observed that strategies to address various delay causes throughout the life cycle of construction projects have been overlooked in the literature. The findings of the present research underpin the trends and technological advances to address significant delay causes.

Despite the technological advancements in the digitalization era of Industry 4.0, many construction projects still suffer from poor schedule performance. However, the reason of this is questionable and has not been investigated thoroughly.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

In a globalised environment, market volatility makes risk management an essential component of the supply chain. Similar to conventional supply chains, a Halal supply chain (HSC) is also affected by several factors making it vulnerable to risks. Therefore, the purpose of this study is to identify and analyse the elements of Halal supply chain management (HSCM) and their significant risk dimensions.

In total, 72 risk elements of HSCM are identified through a review of contemporary scientific literature along with news items and official websites related to risk management of conventional supply chain management, HSC and sustainable supply chain. Further, 42 risk elements are finalised using fuzzy Delphi and then these risk elements are categorised into 7 dimensions. The interrelationships among the risk dimensions as well as risk elements are developed using fuzzy DEMATEL.

Results suggest that production, planning, logistic & outsourcing and information technology-related risk are prominent risk dimensions. The causal relationships among the significant risk dimensions and elements related to the HSCM may help managers and policy planners.

This study faces a challenge due to inadequate availability of the literature related to risk management in the area of HSCM. Further, this study has used inputs from experts, which can be biased.

To the best of the author's knowledge, it is the first comprehensive study towards investigating the interrelationships among the risks in the context of the HSCM.

The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.