This study aims to develop a novel and robust simulation-based integration framework to identify optimal locations for wave energy projects in Australia. The process of multi-criteria evaluating and selecting locations based on feasible conditions, published numerical data, and linguistic judgments of experts. With this aim in mind, this paper proposes the first combination of Data Envelopment Analysis (DEA) approach, Fuzzy Best-Worst Method (Fuzzy BWM), and Simulation-based Fuzzy Multi-Criteria Interactive Decision-Making method (Fuzzy TODIM). Firstly, the DEA model helps to filter out potentially and highly effective locations from feasible locations. Afterward, Fuzzy BWM is a worthy and effective alternative to Analytic Hierarchy Process (AHP) in weighting criteria. Finally, the simulation-based Fuzzy TODIM method evaluates potential locations, taking into account both the consideration of the criteria interaction and the decision maker’s aversion to loss. Based on the simulation results, the study finds the thresholds of the loss attenuation coefficient that change the ranking of potential locations. Last but not least, to validate the ranking result, the comparison shows that the proposed method’s results have a high degree of similarity with actual wave energy projects in Australia. Accordingly, governments, investors, and institutions can sustainably optimize the efficiency of wave energy projects by applying the proposed model. Moreover, the integrated framework can provide robust solutions to other multiple criteria decision-making problems.

As a consequence of increased awareness of environmental preservation and the associated rigorous regulations, the adoption of sustainable practices has become a crucial element for corporate organizations in regard to their supply chains. In the chemical industry, which is characterized by high risks, high pollution, and high efficiency, these characteristics can help businesses analyze their long-term development and sustainability. The goal of this research is to analyze and choose possible suppliers based on their sustainability performance in the chemical sector. A methodology based on multi-criteria decision making (MCDM) is proposed for this evaluation, using spherical fuzzy analytical hierarchy process (SF-AHP) and combined compromise solution (CoCoSo) methods, in which the novel spherical fuzzy sets theory is employed to present the ambiguous linguistic preferences of experts. In the first stage, an evaluation criteria system is identified through literature review and experts’ opinions. The SF-AHP is used to determine the criteria weights, while the CoCoSo method is utilized to select the right sustainable supplier. A case study in the chemical industry in Vietnam is presented to demonstrate the effectiveness of the proposed approach. From the SF-AHP findings, “equipment system and technology capability”, “flexibility and reliability”, “logistics cost”, “green materials and technologies”, and “on-time delivery” were ranked as the five most important criteria. From the CoCoSo analysis, Vietnam National Chemical Group (CHE-05) was found to be the best supplier. A sensitivity study and a comparison analysis of methods were also conducted to verify the robustness of the proposed model, and the priority rankings of the best suppliers were very similar. To the best of our knowledge, this is the first study that has proposed SF-AHP and CoCoSo to prioritize SSS evaluation criteria and determine the best alternatives. The suggested method and findings can be used to make well-informed decisions that help businesses to achieve supply chain sustainability, capture opportunities, and maintain competitiveness through reconfiguring resources. The method could be useful for case studies in other countries and for other sustainability problems.

Sustainable development has become a global catchphrase in the recent development age. This leads to the growth of various methodologies in evaluating environmental efficiency, such as the Data Envelopment Analysis (DEA) method. The purpose of this study is to propose an extended DEA model, i.e., the undesirable output model, in measuring the relative eco-efficiency scores across nations. The study collected the data of inputs, namely bad outputs and good outputs of the top 20 Asian economies in the period of 2005–2019, and then estimated the environmental efficiency of each country and classified them. The results have shown that there are four nations having higher average environmental efficiency than others. Japan is a good example of sustainable development that simultaneously balances economic development and environmental protection. The study has also discussed possible solutions for improvement to the group of nations with low environmental efficiency. Contributing to applying a novelty extended DEA model, this work recommends a more precise model, taking the weight of outputs into account for further studies.

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

Purpose: The purpose of this paper is to investigate how emotional experience factors affect the obligation-related commitment. The emotional experience factors consist of positive factor, job satisfaction and negative factor, and emotional exhaustion from the study of Wong and Tay (2010); both facets have been studied with affective commitment but yet lack of study about normative commitment. Affective and normative commitments are both components of organizational commitment; while affective commitment refers to the intention of keeping the position because of the desire but not by any other attachment, whereas normative commitment is defined as staying for the reasons related to obligation. Also, by applying withdrawal construct of validation of job-hopping behavior, a connection was constructed between normative commitment and the two separated motives of job-hopping behavior, advance motive and escape motive (Lakeet al., 2017).Design/methodology/approach: To achieve such objectives, quantitative research is implicated using the primary data collected from online and offline surveys from employees who are currently working in many information technology (IT)-related companies in Ho Chi Minh City. As for analyzing tactic and method, SmartPLS software is applied to ensure the reliability and validity of the factors as well as confirming the significant and developing the hypotheses from the proposed construct.Findings: The findings reveal the significant affect from workplace escape motive that leads to the behavior of job hop, but not from the career enhancement. The result also revealed the effect of indebted obligation when most of the employees maintain the low level of normative commitment and the proper increase in emotional exhaustion and job satisfaction can help to improve the normative commitment of such employees.Originality/value: This study makes its contribution and recommendations to human resource management in IT industry.

As the population ages and families become less able to offer care, the need for long-term care among older people increases. Evaluation of daycare centers, which provide localized long-term care services, is essential for the growth and direction of these institutions. Nevertheless, the present evaluation indexes do not adequately emphasize the significance of each index item or the actual effectiveness of an organization’s operations and management. To solve this issue, the purpose of this research was to develop an evaluation model for the operation and administration of daycare centers. Experts were consulted to collect pertinent criteria, which were further assessed using the Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) techniques. The results indicated that organizational operation management was the top priority, with administrative operation management and service quality management having the largest impact on productivity. Among the 10 daycare services tested, Institution 3 was judged to have the highest score. These findings shed light on the operational management effectiveness of daycare centers and give a novel evaluation methodology for gauging the efficacy of nursing management.