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Effective post-disaster communication is essential for destination marketing organizations to encourage visitors following natural disasters. This research aimed to analyze the impact of two typical types of post-disaster communication messages – solidarity messages and testimonial messages – on post-disaster visiting intention. This research proposes effective communication strategies for post-disaster destination marketing.

For the case of Jiuzhai Valley, China, which experienced market recovery after two years of rebuilding following a 2017 earthquake, this study designed three contextual experiments based on post-disaster communication scenarios on the Internet and social media to test the causal chain between recovery message types and post-disaster visiting intentions. The data were analyzed using analysis of variance, independent samples t-tests, and the bootstrap method.

The results indicate that both solidarity messages and testimonial messages evoked higher visiting intention than the no-message group. However, solidarity messages (vs testimonial messages) were more effective when shared on the destination management organization's official account than when they were shared on an influencer's account, with their effects being mediated by the symbolic – as opposed to the hedonic – value of tourist behavior.

Destination management organizations should actively carry out marketing communication through the Internet and social media for areas that have experienced disasters. Crisis communicators should consider inviting popular social media influencers to visit the destination and share their experiences to enhance market confidence, while also paying more attention to the operation of official social media accounts.

This study sheds light on the use of the Internet and social media as tools for post-disaster marketing. By expanding on post-disaster communication theory, this study fills a research gap regarding the effectiveness of tourism marketing strategies after a crisis.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

The purpose of this paper is to examine the role of human resources (HR) leaders in disaster preparedness and response efforts of organizations. The study used Bronfenbrenner's Ecological Systems Theory as anchor and was conducted in the Philippines – a country that regularly experiences disruptions due to disasters.

The study utilized a phenomenological approach in gathering and analyzing data from semi-structured interviews with 16 HR leaders. They hold either an executive or managerial position and belong to organizations situated in areas that have experienced disasters within the past two years. The areas are likewise prone to further experience of disasters such as flooding, earthquake and volcanic eruption as identified by international disaster watch organizations.

Results surfaced three superordinate themes that reflect the role of HR in disaster situations – (1) contributor to the business continuity plan formulation and implementation; (2) in-charge of building a disaster-resilient culture; and (3) primary unit that takes care of employee wellbeing and welfare during and post-disaster.

The study identified factors that can help HR in fulfilling its role of surfacing and addressing employee needs amidst disasters.

The study expands literature on strategic human resource management by describing the positioning of HR in disaster preparedness and response efforts of organizations and illustrates how HR puts focus on the human side of organizational crises management.

The most important need after natural disasters is the sheltering. However, most of the existing temporary shelters do not meet all requirements for long-term use and not provide adequate flexibility within the space. This paper aims to develop a transitional postdisaster shelter transforming from a closed shape to an expanded form in response to changing functional and spatial needs of disaster victims. The study also proposes alternative unit combinations for various functions, and settlement layouts to create a comfortable living environment for occupants.

The research methodology is based on theoretical and design frameworks which requires inductive and deductive approaches. Forming the background of the study, the theoretical framework consists of four parts which are literature review on temporary shelters presenting state-of-the-art; determination of design guidelines and strategies based on shelter standards; identification of technical requirements; and analysis of existing temporary shelters. Having three parts, the design framework includes design of transformable transitional shelter based on three-dimensional modeling, creation of different unit combinations to be used for various purposes and development of settlement layouts as case studies.

The analysis conducted in this study demonstrates that most of the existing temporary shelters have limited geometric configurations and major problems in terms of their performance, transportation and storage. On the other hand, the transformable shelter proposed by the authors can provide form and spatial flexibilities thanks to its expansion properties, occupy less space for transportation, easily be transported to any desired location in its compact state and be customized according to user needs. Several units can be combined either to serve larger families or to be used for different functions.

This paper contributes to the literature as presenting not only a theoretical framework on temporary shelters but also a design framework on transformable shelter design for the ones who are willing to develop similar transformable shelters based on the determined guidelines, strategies and requirements.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Organizations working in high-hazard environments contribute significantly to modern society and the economy, not only for the valuable resources they hold but also for the indispensable products and services they provide, such as power generation, transportation and defense weapons. Therefore, the main purpose of this study is to develop a framework that outlines future research on systems safety and provides a better understanding of how organizations can effectively manage hazard events.

In this research, we developed the high hazard theory (HHT) and a theoretical framework based on the grounded theory method (GTM) and the integration of three established theoretical perspectives: normal accident theory (NAT), high reliability theory (HRT) and resilience engineering (RE) theory.

We focused on the temporal aspect of accidents to create a timeline showing the progression of hazard events and the factors contributing to safety and hazards in organizations. Given the limitations of the previous theories in providing a coherent explanation of hazard event escalation in high-hazard organizations (HHOs), we argue that the highlighted theories can be more complementary than contradictory regarding their standpoints on disasters and accident prevention.

A proper appreciation of the hazard nature of organizations can help reduce their susceptibility to failure, prevent outages and breakdowns of systems, identify areas for improvement and develop strategies to enhance performance.

By developing HHT, we contribute to systems safety research by developing a new, refined theory and enrich the theoretical debate. We also expand the understanding of scholars and practitioners about the characteristics of organizations working in high-hazard environments.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.