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Travel interests of tourists during pandemics and outbreaks are reduced due to the prevalence of fear. It induces lifestyle changes, which may hinder efforts to recover the tourism value chain during post-COVID-19 lockdowns. Subscribing to domestic travel and domestic tourism is deemed to mitigate fear and gradually reopen the tourism industry. Although a crucial initiative, evaluating the perceived degree of exposure of tourists to COVID-19 in tourist sites operating under domestic tourism has not been fully explored in the emerging literature, which forms the main departure of this work.

The problem domain is addressed by adopting multiple criteria sorting method – the VIKORSORT. To demonstrate such application, with 221 survey participants, 35 tourist sites in a province in the central Philippines struggling to revive the tourism industry are evaluated under six attributes that characterize tourists' exposure to COVID-19. To assess its efficacy, the performance of the VIKORSORT is compared to other distance-based multiple criteria sorting methods (i.e. TOPSIS-Sort and CODAS-SORT).

Results show that proximity and volume of tourist arrivals are considered on top of the priority list of attributes. The use of VIKORSORT yields the assignment of 27 sites to the “moderate exposure” class, and eight under the “high exposure” class, with no tourist site assigned to the “low exposure” class. Sorting the tourist sites reveals some observations that tourists prefer sites (1) with open spaces, (2) with activities having limited group dynamics, (3) that are nature-based, and (4) with tourist arrivals that are not relatively high, with enough land area to practice social distancing. In addition, the assignments of the VIKORSORT with TOPSIS-Sort and CODAS-SORT are consistent at least 90% of the time, demonstrating its efficacy in addressing multiple criteria sorting problems.

This work provides an integrative approach in evaluating tourist sites in view of tourism recovery during pandemics. The findings offer crucial insights for the primary stakeholders (i.e. government, tourist operators, and tourists) in planning, resource allocation decisions, and policy formulation. Policy insights are offered, as well as avenues for future works.

Contemporary changes have occurred in country-level policies and tourists’ intentions in recent years. The role of maintaining a country’s image is trendy in crisis control but has not yet been discussed in domestic tourism research. Extending the Stimulus Organism Response model, this study aims to focus on “trustable WOM creation” in China. In addition, it aimed to discover how behavioral changes encourage domestic tourism intention (DTI).

This study explored the mediating role of DTI and the moderating role of maintenance of country image (MCI) for trustable word of mouth (WOM) creation. Using the snowball sampling technique, a structural equation modeling analysis (Smart PLS-4) was employed to analyze the data of 487 Chinese tourists.

Findings confirm that behavioral changes positively encourage domestic tourism and discourage international tourism, with significant negative moderation by MCI. MCI has an insignificant positive moderating effect between government-media trust and DTI. Furthermore, DTI positively and directly affects the creation of trustable WOM. In addition, it had a 20% mediation effect (VAF%) between behavioral changes and WOM creation, higher than the rejected mediation effect (12%), in the causal relationship between government-media trust and WOM creation.

WOM creation varies from different behavioral changes, but findings suggest that government-media trust and DTI influenced it significantly. Based on the study findings, the government and media can enhance domestic tourism by maintaining the country’s image. These findings both encourage and control the recovery of tourism.

This study provides a theoretical explanation for tourists' behavioral changes during the pandemic. Moreover, it shows that despite avoiding international tourism due to behavioral changes and government-media trust, MCI moderation with the mediation effect of DTI can create trustable WOM. To the best of the authors’ knowledge, this is the first study to theoretically promote tourism through DTI-induced psychology as a mediator and an organism affect prevailing among Chinese tourists.

Mega-infrastructure development plans pave the way for policies to upgrade the infrastructure, environmental management and different aspects of locals’ well-being. These developmental dynamics can positively affect rural tourism including heritage sites destinations. The quality of life of local people can be linked to this positive developmental change through long-term and sustainable economic revitalization projects. In terms of this process, developing large-scale infrastructure and incorporating tourism sustainability can improve the quality of life-related to different dimensions that are critical to the community's well-being. Therefore, researchers have attempted to address this issue.

Data were collected between September and October 2020. The study sample size was the residents of Zhabagly village, including Zhabagaly, Abaiyl and 115 Railway settlements. Moreover, the residents were older than 18 years. A systematic random sampling technique was utilised to reach the targeted sample size and the researchers received 243 responses from the locals. Structural equation modelling (SEM) was used for analysis.

The findings from the structural equation modelling suggest that sustainable tourism increases due to the positive effect of mega-infrastructure development and positively impacts the locals' quality of life. Notably, no direct effect of mega-infrastructure development on quality of life reveals the pivotal role of sustainable tourism. Therefore, during the COVID-19 period, the dimensions of sustainable tourism – economic, market, socio-cultural and environmental – played a role in securing the positive impact of mega-infrastructure development on the locals’ quality of life.

This research highlighted the fact that when infrastructure projects are implemented to their full potential, they will generate sustainable tourism activities, provide eco-adventure activities, relax, treat signatories and boost the economy of all stakeholders. The study used AMOS to test the hypotheses. Qualitative research methods, including interviews with citizens, government officials and tourism managers, require further study.

The infrastructural development on a mega-scale means building an upscaling tourism ecosystem. This ecosystem is marked by the availability of drinking water, waste and energy management facilities that support the elevation of living material, community, health, safety and emotional well-being. It reflects the policy-level implications for future Belt and Road initiatives (BRIs). The tourism industry's resilience during COVID-19 has practical lessons for other industries.

Large-scale infrastructure construction must create favourable conditions for the rapid development of tourism. The availability of clean water, waste and energy management facilities contributes to the food production, social cohesion, physical and mental health and general well-being of the ecosystem. This is one of the few studies that used sustainable tourism as a mediator between the impact of large infrastructure projects and their impact on the quality of life of locals during the COVID-19 pandemic. Aksu-Zhabagly, a World Heritage Site in Kazakhstan, was the site of this field study.

Supplier segmentation provides companies with suitable policies to control each segment, thereby saving time and resources. Sustainability has become a mandatory requirement in competitive business environments. This study aims to develop a clustering-based approach to sustainable supplier segmentation.

The characteristics of the suppliers and the aspects of the purchased items were considered simultaneously. The weights of the sub-criteria were determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four criteria. The proposed model is applied to a real case study to test the performance of the proposed approach.

The results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are selected for sustainable supplier segmentation and managing supplier relationships.

This study integrates sustainability considerations into the supplier segmentation problem using a hybrid approach. The proposed sustainable supplier segmentation is a practical tool that eliminates complexity and presents the possibility of convenient execution. The proposed method helps business owners to elevate their sustainable insights.

This paper deals with the combined management and design of a sustainable pharmaceutical supply chain network with considering recycling.

This paper first utilizes the analytical hierarchy process to select and rank green manufacturers. Second, the authors proposed a multi-objective nonlinear mathematical model to design a sustainable pharmaceutical supply chain network. The proposed model has been linearized and solved using the LP-metric method using GAMS software.

A real case study has been conducted in Iran. The results show that environmental and social issues can be improved while minimizing total costs.

Given the criticality and importance of drugs in human health and the importance of recycling in today's world, proper management and design of a sustainable drug supply chain are necessary. This study pays special attention to environmental issues by utilizing multi-criteria decision approaches and customer satisfaction.

The present study aims to provide a practical and robust assessment technique for assessing countries' investability in global supply chains to practitioners. Thus, the proposed approach can help decision-makers evaluate and select appropriate countries in the expansion process of the global supply chains and reduce risks concerning country (market) selection.

The present study proposes a novel decision-making approach, namely the REF-Sort technique. The proposed approach has many valuable contributions to the literature. First, it has an efficient basic algorithm and can be applied to solve highly complicated decision-making problems without requiring advanced mathematical knowledge. Besides, some characteristics differentiate REF-Sort apart from other techniques. REF-Sort employs the value or value range that reflects the most typical characteristic of the relevant class in assignment processes. The reference values in REF-Sort and center profiles are similar in this regard. On the other hand, class references can be defined as ranges in REF-Sort. Secondary values, called successors, can also be employed to assign a value to the appropriate class. REF-Sort can also determine the reference and successor values/ranges independently of the decision matrix. In addition, the proposed model is a maximally stable and consistent decision-making tool, as it is resistant to the rank reversal problem.

The current papers' findings indicate that countries have different features concerning investment. Hence, the current paper pointed out that only 22% of the 95 countries are investable, whereas 19% are risky. Thus, decision-makers should make detailed evaluations using robust, powerful, and practical decision-making tools to make more reasonable and logical decisions concerning country selection.

The current paper proposes a novel decision-making approach to evaluate. According to the authors' information, the proposed model has been applied to evaluate investable countries for the global supply chains for the first time.

The purpose of this paper is to monitor the environmental efficiency of suppliers in the presence of undesirable output and dual-role factors with static and dynamic aspects. Meanwhile, it also aims to explain the main reason for the low efficiency of suppliers.

The authors propose a modified data model considering undesirable output and dual-role factors. The study integrates the modified data envelopment analysis model into the distance function of the Malmquist–Luenberger index. Moreover, this study uses the global benchmark technology to formulate a two-stage model. To verify the validity of this model, a model application is conducted on an automotive spare components company in China.

The results identify the unique status of dual-role factors based on the global optimality of the model and then categorize inefficient suppliers in an individual evaluation cycle. In addition, each supplier is projected on a frontier curve after obtaining the improved data. Furthermore, through the status plot of M-L and its components, this paper concludes that efficiency scale change is the main reason for the gap in ecological performance between different suppliers.

The proposed model considers both undesirable output and dual-role factors; however, variables with different features, such as imprecise, fuzzy and qualitative characteristics, can be embedded into the presented two-stage model.

Evaluating green suppliers through multiple consecutive evaluation cycles will aid a company in effectively managing its key suppliers. Furthermore, the evaluation provides policy guidance for further improvement of suppliers.