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This paper deals with the 1‐Way Fuzzy ANOVA and compares it with other classical techniques. Difficulties in obtaining general theoretic results force one to use simulation techniques. A variety of situations is considered in order to validate the conclusions.

The paper aims to provide insight into the opportunities and impact of political risks in China and selected Asian countries on opportunities in infrastructure projects under public‐private partnership (PPP) schemes.

The impact of political risks on PPPs was investigated through an international survey among senior staff of international lenders, investors, insurers, and legal and financial advisors from the public and private sector. The surveyed political risk categories base on the Multilateral Investment Guarantee Agency Convention and literature review. They comprise six categories: currency inconvertibility and transfer restriction, expropriation, breach of contract, political violence, legal, regulatory and bureaucratic risks, and non‐governmental action risks. The survey evaluation uses fuzzy sets and non‐parametric statistics.

The findings comprise rankings of political risk factors within China and Asian countries as well as rankings of these countries with respect to the risk categories and rankings of future PPP opportunities over time. Also, survey comments are discussed.

The survey response rate is relatively low with 29 respondents. This is attributed to the specialized nature of the survey questions and topic area. It also indicates that political risks are little understood and paid attention to, though significant in impact. Owing to the chosen evaluation methods for small sample sizes, the results are robust and show high correlations with market data.

The results provide insight into the impact of political risks and the perceived magnitude of each risk category. It supports decision makers prioritizing and analyzing country risks.

The value and originality is in the use of a fuzzy‐coded survey scale to quantify perceptions on single political risk factors across China and Asian countries with focus on PPP infrastructure projects. The paper demonstrated that robust results from small sample sizes can be derived with the employed methods.

Negative perceptions of political risk can prevent capital from being committed to support cross‐border investment. Information about risks that impact infra‐structure projects is often vague, imprecise, subjective, or ambiguous. Political risks in developing countries also often lack meaningful historical and numerical data. A novel fuzzy set approach for quantifying qualitative information on risks (QQIR) in structured finance transactions that bridges the gap between qualitative and quantitative risk assessment methods has been developed. The QQIR Method is validated empirically through the results of an international survey to determine the impact of perceived political risk on Asian infrastructure projects. The impact is measured by the effect on financial project criteria. The impact was assessed across 14 Asian countries and 14 infrastructure sectors. The survey findings are validated by triangulation of three data sets and employing non‐parametric statistics. The validation shows that in 77.5% of all observations the QQIR Method produces mean results that are within 0.85 standard deviations of the absolute values, without elimination of any seemingly unusual or unreasonable responses or data. The validation also shows that with increasing perceived risks, the costs of equity investment, debt finance, and insurance also increase. The QQIR Method is thus a valid tool to quantify perceptions on risks. In this case it has been applied to political risks, but the Method is generic and may be applied to any problem set in which perceptions can be structured and assessed with opinions.

The current study aims to identify and classify the financial resilience measurement indices using the intuitive fuzzy 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.

The study results indicate a higher significance level of redundancy and visibility in financial resilience.

The present study is the pioneer study to assess, identify and classify the contributing indices to financial resilience.

The purpose of this paper is to examine combinations of contract clauses in order to ascertain which combinations correlate to high operational performance (OP).

Two hypotheses were formulated from contracting theory and tested on data collected from 45 projects. Fuzzy set qualitative comparative analysis was used and validated with multiple regression and simulation.

The hypotheses were tested to determine whether combinations of classical, relational, and/or associational contract clauses correlate to high OP. The results show that whereas high OP correlates to combinations of relational and associational contract clauses, classical and relational clauses should not be combined.

Directions are proposed to guide future research in order to produce a more nuanced testing of contractual complementarity.

The managerial implications of the findings include a more thorough understanding of the use of contract clauses and of which clauses managers should combine to achieve high OP.

This study contributes to the theory of contractual incompleteness and complementarity, specifically in the context of project contracting. The analysis produced two theoretical implications: first, that better performing contracts are created when combining relational and associational contract clauses; and second, that in projects, relational and classical contract clauses are not complementary with regards to realizing high OP.

This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator.

In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller.

Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness.

A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.

We present configurational theorising as a novel approach to developing middle-range theory in two steps: (1) we illustrate configurational theorising as a new form of supply chain inquiry by connecting its philosophical assumptions with a methodological execution, and (2) we generate new insights underpinning a middle-range theory for supply chain resilience.

We synthesise information from a range of sources and invoke ‘critical realism” to suggest a five-phase configurational theorising roadmap to develop middle-range theory. We demonstrate this roadmap to explain supply chain resilience by analysing qualitative data from 22 organisations within the Australian food supply chain.

Coopetition and supply chain collaboration are necessary causal conditions, but they need to combine with either supply chain agility or multi-sourcing strategy to build supply chain resilience. Asymmetrical analyses showed that the simultaneous absence of supply chain collaboration, supply chain agility and multi-sourcing results in low supply chain resilience, but coopetition was indifferent to low supply chain resilience. Similarly, high supply chain resilience is possible with the non-presence of supply chain agility and multi-sourcing.

The configurational middle-range theorising roadmap presented and empirically tested in this paper constitutes a substantial advancement to both theory and the methodological domain.

This is the first attempt at developing a middle-range theory for supply chains by explicitly drawing on configurational theorising.

The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection.

The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP.

The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus.

Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better.

The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise.

This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

The purpose of this paper is to examine the relationship between cultural dimensions and the roots of work-family balance issues in a developing non-Western cultural context. Drawing upon Hofstede’s cultural dimensions, the study suggests national cultural dimensions can shape the roots of work-family balance.

A survey was distributed among employees of 12 companies operating in the health industry. The survey collected both quantitative and qualitative data through two separate approaches: seven-point Likert scales and open-ended questions. The data were analyzed via thematic exploratory analysis and fuzzy-set qualitative comparative analysis.

The quantitative data confirms the extant literature in relation to the negative effects of moonlighting, economic hardship and the absence of a comprehensive work-family balance strategy on employee work-family balance. The qualitative data proposes a new variable, weak social interaction, which becomes the most important roots of work-family imbalance at both work and family domain.

The present study suggests certain cultural dimensions such as high power distance hinders social interactions in the work and family domains, thereby increasing levels of work conflict, family conflict, and stress. Findings suggest organizations, especially multinationals, need to be cognizant of the role of cultural dimensions on human resource management practices related to work-family balance.

Very little literature addresses the impact of Hofstede’s cultural dimensions on work-family balance issues. The present study has contributed to the existing body of literature by introducing a new variable (weak social interaction) as an antecedent of work-family imbalance. Further, the study is the first in Iran that has collected qualitative data to investigate work-family balance issues.