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International construction joint ventures (ICJVs) implementation is plagued with several barriers, full understanding of which is still lacking due to a lack of an in-depth exploration of them, particularly in developing countries. To fill this knowledge gap, this study aims to investigate the critical barriers to the success of ICJVs hosted in developing countries by examining the Ghanaian case.

This study builds on a previous study that identified 37 barriers factors to ICJVs success via a systematic literature review. Through expert interviews, 34 potential barriers were identified, and a two-round survey was conducted with 84 ICJVs practitioners in Ghana. The data collected was analyzed using the combination of a multidimensional fuzzy logic method and confirmatory factor analysis.

Results showed that 22 barriers were critical. The top five most critical barriers were “lack of preparedness to accept company philosophy,” “competing objectives,” “opportunistic behavior of parties,” “conflicts” and “lack of management control.” Furthermore, the results uncovered and confirmed five significant underlying components for the 22 critical barriers, namely, organizational-related, cultural-related, knowledge-related, individual-related and logistics-related barriers.

The findings could be useful to ICJVs practitioners and policymakers in developing suitable strategies for the successful implementation of ICJVs. Further, foreign firms aiming to execute and promote ICJVs in Ghana could have prior knowledge of the critical barriers and prepare for them.

This study empirically analyzed the individual levels of barriers criticalities in ICJVs context and from a specific-country perspective – the developing country of Ghana – rather than in the context of construction joint ventures and from a cross-country perspective in extant studies.

This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research involves seven medical device companies located in the Tehran Province, Iran.

To identify and prioritize companies based on CSFs of RL, the study proposes a three-phase decision-making framework that integrates the Delphi method, the best-worst method (BWM) and the Additive Ratio Assessment (ARAS) method with Z-numbers. The weights required for this method are obtained by a variant of the BWM based on Z-numbers, denoted as Z-numbers Best-Worst Method, or ZBWM. Since decision-makers face an uncertain environment, Z-numbers, which are a kind of fuzzy numbers, are applied.

First, after customizing CSFs by the Delphi method and obtaining 15 CSFs of RL, these are ranked by the hybrid BWM-ARAS method with Z-numbers. Results reveal which company appears to perform best with respect to their RL implementations. Based on this result, healthcare device companies should choose the highest priority company based on the selected RL CSFs and results from using the BWM-ARAS method with Z-numbers.

The contribution of this paper is using a hybrid ARAS-BWM method based on Z-numbers. Each of these methods has some merits compared to other similar methods. The combination of these methods contributes a new approach for prioritizing companies based on RL CSFs with high accuracy and reliability.

To design a novel hybrid approach to illustrate a reciprocal alignment to integrate future products and technologies. This mixed qualitative-quantitative method aims to optimize the final product portfolio and production technologies alignment in the food industry.

A list of products and technologies is extracted and evaluated by experts employing Market Attractiveness and Ease of Implementations Matrix (MA-EI) for products and attractiveness and technological Capability Matrix (A-C) for technologies. Weights of high-scored alternatives are attained applying the Z-number extension of Best Worst Method (ZBWM). After the product-technology matrix is formed and the alignment scores of each pair are determined by experts. Subsequently, final scores are computed, and a framework is proposed by electing high-ranked products and technology of each cluster to form the aligned product and technology portfolios of a food and hygiene industry company.

By employing an uncertain multicriteria decision-making approach besides product and technology matrices in a food industry corporation, among 40 technology and product, 13 products by 6 technologies are proposed. Thus, only six technology are necessary to manufacture the highly important and effective products.

The combination of product and technology analysis matrixes with an uncertain decision-making approach is considered as a novel approach in this research. Moreover, the distinctness between the present study and other researches is the concurrent unified aspect of product portfolio and technology optimization and its implementation in the planning discussion, especially in the food industry.

This paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making (MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple decision-making involving a large set of countrywide customer complaints in the Iranian automotive sector.

The countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of seven determinant complaint criteria were identified in brainstorming sessions with three groups (six each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently, the complaints were clustered into five categories with respective customer locations (province), car type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy complex proportional assessment of alternatives (FCOPRAS) method.

The results indicated that the majority of complaints (1,027) from the various regions of the country belonged to one specific model of car made by a particular producer. The analyses revealed that only a few complaints were related to product quality, with the majority related to service and financial processes including delays in automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a registration ( licence) and failure to supply the agreed product. The proposed method is an efficient way to solve large-scale multidimensional problems and provide a robust and reliable set of results.

The proposed method makes it much easier for management to deal with complaints by significantly reducing their number. The highest-ranked complaints from customers of the car industry in Iran are those related to delivery time, price alternations, customer service support and quality issues. Surveying the list of complaints shows that paying attention to the four most voiced complaints can reduce them more than 54%. Management can make appropriate strategies to improve the production quality as well as business processes, thus producing a significant number of customer complaints.

This paper proposes a comprehensive approach to critically analyse the VoCs by combining qualitative and decision-making approaches including K-mean, FCOPRAS, fuzzy Delphi and FBWM. This is the first paper that analyses the VoCs in the automotive sector in a developing country’s context involving large-scale decision-making problem-solving.

Risk response decisions (RRDs) are vital for project risk mitigation. Although past research has focused on RRDs for independent single projects, it has scarcely explored how to make RRDs for single projects in project portfolios (SPPPs). Consequently, this study aims to bridge the gap in extant literature by developing an integrated approach to select risk response strategies (RRSs) for SPPPs considering objective adjustments and project interdependencies (PIs).

An integrated quality function deployment (QFD) method was used throughout this study. More so, a balanced score card (BSC) and stratified-Z-numbers-full consistency method (SZFUCOM) was applied to identify SPPP success criteria (SP3SC) to determine their weights. In addition, a spherical fuzzy set-design structure matrix (SFDSM) was used to quantify the correlation between the risks and the relationship between the risks and the predecessor projects. Consequently, the relationships between the risks and SP3SC and RRSs were described by the spherical fuzzy set (SFS) and Z-numbers, respectively. Besides, the results are weaved into QFD to transform SP3SC into risks and then into RRSs, while a linear optimization model is used to obtain the optimal RRSs. Lastly, a construction project portfolio (PP) was used to test the veracity of the results to prove their validity.

The approach to RRDs for single projects is observed to be different from that of SPPPs. In addition, this study finds that project portfolio objective adjustments (PPOAs) and PIs have significant impacts on RRDs given that they influence the risk priorities of independent single projects and SPPPs. Moreover, the application of an integrated QFD effectively synthesized the results from the findings of this study, as well as enabled companies to determine robust RRSs. Finally, the consistency results of the SZFUCOM were better than those of the triangular fuzzy number-full consistency method.

The study innovatively explores the method of RRDs for SPPP, which has been ignored by past research. SP3SC highly compatible with PP success is determined. Z-numbers are first used to evaluate the effect of RRSs to enhance the robustness of RRDs. The study proposes a method of RRDs comprehensively considering PPOAs and PIs, which provides robust methodological guidance for SPPP managers to control risks.

This study identifies and ranks the appropriate critical success decision criteria (CSDC) for the bulk order distribution (sharing) amongst multiple manufacturers (suppliers) working as an extended enterprise (EE).

The study deploys a qualitative approach to generate the appropriate decision criteria. The balanced scorecard and Pareto's chart (using Minitab^® version 18) were used for gathering and analysing the pertinent criteria.

The process of evaluating and selecting the right manufacturers is essential. Manufacturer (supplier) selection is no longer decided solely based on cost/price criterion; currently, the quality and delivery criteria prevail. Additional incorporated criteria include price/cost, technical capability, production facilities and capacity, customer satisfaction and impression, geographical location, management and organisation, financial position, environmental concern, performance history, repair service, information technology and communication systems, procedural compliance, labour relation record, reputation, flexibility or diversification, attitude, operating controls, business desire, packaging ability, past business records, trust and loyalty, training aids, complaint handling service, warranties and claim policies, reciprocal arrangements, research and development and innovation, modern slavery concern, sustainable capability, collaborative/partnership and responsiveness. The study proposed a conceptual framework of an EE alongside how manufacturers working as a single virtual entity can consider the supply chain operations reference (SCOR^®) model.

The identified CSDC are suitable for order allocation to domestic manufacturers. The deployed approaches could be extended to the mixed and quantitative approaches for increasing the generalisability.

The study establishes the pertinent CSDC that are important to execute equitable order distribution to manufacturers in an EE framework.

Recognizing the factors affecting employees’ job motivation is one of the necessities that can improve people’s performance and increase their effectiveness. This study aims to determine the factors affecting job motivation and to examine effective strategies to increase motivation through identifying internal and external factors.

In this descriptive study, the statistical population was the employees of the largest petrochemical company in Iran. The questionnaire was randomly distributed to the organization’s employees and managers based on Herzberg’s motivation-hygiene theory. To analyze the obtained data, first, the best and the worst factors were identified using SPSS software and then were ranked using best–worst method (BWM).

The results demonstrated that the highest rank among the motivational factors of employees is related to working environment conditions and the lowest rank is related to career advancement and development indicator. In the second stage, the best strategies for motivational factors were determined using the fuzzy goal programming method. The findings showed that 12 out of the 17 proposed solutions have the highest motivation among employees, the implementation of which can increase employee productivity in the petrochemical company under study.

Further to the best of the authors’ knowledge, job motivation factors in the petrochemical industry have never been examined and ranked by using the BWM method so far. Also, the goal programming approach has never been applied to determine strategies for increasing job motivation and ultimately productivity.

Hospital activities restore health and save lives; yet they produce infectious waste that might harm people and the environment if not handled carefully. The sudden increase in bio-medical waste (BMW) generation was caused due to the outbreak of Coronavirus disease (COVID-19) at the beginning of year 2020. There is a huge potential of businesses from BMW generated in the developing economies. The document destruction process would prove to be beneficial in determining the strategies and policies for the business from BMW. Therefore, the purpose of this study was to identify and evaluate the factors that influence the business prospects from BMW in developing economies.

The study focuses on identifying 18 factors based on an extensive literature review, questionnaire, theoretical foundations and interviews with experts in the context of developing economies. These factors are categorized into four groups namely “Culture, education and microeconomics”, “Infrastructure, social provision, and technology”, “Policy and institution” and “Integrated waste management system”. Consequently, these factors are prioritized to assist the decision-maker in comprehending the most critical aspects of accomplishing the best business practices. A framework based on the Best–Worst Method (BWM) is applied to evaluate and determine the significance of each identified factor.

The major contribution of this paper is to identify key factors that are responsible for successful implementation of the business practices from the generated BMW. The study also explains their nature, significance and inter-relationship among them using BWM. Current study applied a distinctive modeling approach to examine the contextual relationship among the key factors and to rank them as per their importance. The findings of the study suggest that “Setup for collection and segregation is the best alternative for business from BMW produced”.

The paper also presents strategies, which can be adopted as a guiding step by the decision-makers to formulate policies for the effective business practice from BMW. This research is expected to stimulate future applications of the BWM to facilitate long-term decision-making.