Purpose This study aims to propose a group decision-making model designed to support the assessment of supplier-related risks. Design/methodology/approach The model uses the possibility distribution hesitant fuzzy linguistic term sets combined with the technique for order of preference by similarity to ideal solution (PDHFLTS-TOPSIS) to categorize suppliers within a segmentation matrix. While PDHFLTS is used to represent the judgments of decision makers (DMs), the TOPSIS algorithm is applied to generate the overall scores of the suppliers. A pilot application was conducted in a civil defense organization. Sensitivity analysis tests were performed to assess the impact of varying criteria weights on the results. Findings The criteria related to cost (C1), import instability (C2) and delivery time (C4) received greater weights. Suppliers A3 and A4 were classified as low risk, while A1 and A2 were categorized as intermediate risk. Practical implications Implementing the proposed model requires structured protocols for identifying risk criteria and training DMs in HFLTS. Integration into routine processes demands adapting digital systems to enable real-time processes. In addition, the use of consensus-based evaluations calls for a cultural shift toward collaborative decision making. Originality/value This is the first supplier risk assessment model that performs supplier segmentation while allowing DMs to use linguistic expressions to evaluate supplier risk levels under conditions of hesitation. It is also the first to incorporate possibility distributions associated with judgments for risk assessment and support the weighting of DM opinions based on the degree of concordance with the group, enhancing the reliability and representativeness of the final assessment.

Purpose This study aims to assess the orderliness of the humanitarian material donation system (HMDS) following the 2022 “9·5” Luding Earthquake in China, providing insights into improving disaster relief efficiency and promoting postdisaster recovery. Design/methodology/approach The orderliness of the HMDS is evaluated across three dimensions: donation input, donation output and donation environment. Correlation entropy and running entropy models are used to assess the system’s dynamics and to compare the performance of four donor types: government, enterprises, organizations and individuals. Findings The early emergency phase (the first 12 days after the earthquake) is characterized by disorder and repeated fluctuations, reflecting instability in donation activities. From Day 13 onward, the system transitions into a stable state, signaling the onset of recovery. The government plays a leading role in coordination, and enterprises serve as primary material suppliers, while organizations and individuals display higher uncertainty and spontaneity. Originality/value This study advances disaster management research by applying entropy-based models to dynamically assess the orderliness of HMDS. It extends theoretical understanding by linking entropy, orderliness and multiagent coordination and provides empirical evidence from a recent major earthquake. Practically, the findings highlight the differentiated roles of donor types and the importance of adaptive supply strategies in enhancing the efficiency and resilience of postdisaster relief systems.

Purpose Disasters pose a significant threat to healthcare systems, underscoring the need for evidence based operational frameworks to enhance the efficiency and effectiveness of pharmaceutical supply operations. Therefore, an operational framework for managing pharmaceutical supply was developed and validated through a multi-stage process, including scoping reviews, in-depth interviews and world cafe discussion methods. This study aims to further evaluate the effectiveness of the operational framework through a tabletop exercise. Design/methodology/approach A multi-hazard tabletop exercise was developed by the research team from March to July 2024 and conducted during a three-day workshop in August 2024. The exercise involved 25 participants from various operational levels, who were exposed to the scenarios first without the aid of the operational framework and then with the operational framework. Participants’ perceptions of disaster management activities, the framework’s effectiveness and the conduct and design of the exercise were gathered through a pre- and post-survey and hot wash assessment. Findings Participants’ understanding and capability in disaster management activities were significantly improved after being introduced to the operational framework (p < 0.001). Most participants strongly agreed and agreed that the operational framework was comprehensive (96%, 24/25), easy to understand (100%, 25/25), enhanced efficiency (96%, 24/25) and facilitated effective coordination (96%, 24/25) and communication (96%, 24/25). All participants also strongly agreed and agreed that the tabletop exercise was well-structured, realistic and met its objectives. Practical implications This study demonstrated that the operational framework enhances policymakers and practitioners understanding and capabilities in managing pharmaceutical supply operations in multi-hazard disaster scenarios. The operational framework provides valuable guidance for efficient decision-making among practitioners and policymakers, enhancing effectiveness, efficiency and resilience in pharmaceutical supply operations. Originality/value There is a notable gap in the literature concerning the practical application and evaluation of frameworks in managing pharmaceutical supply operations in disaster settings. While conceptual models exist, few studies have rigorously tested their effectiveness in real-world or simulated disaster contexts. This paper aims to address this gap by presenting a practical and comprehensive operational framework that has been rigorously tested across multiple hazard scenarios throughout all phases of the disaster management cycle and at multiple operational levels.

Purpose This study investigates the factors influencing free riding behavior, as well as cooperation and voluntary contribution decisions, in the context of public goods. This study aims to contribute to sustainable resource management debates by offering empirical insights into fostering voluntary cooperation and emphasizing the importance of individual and collective action in overcoming public goods challenges. Design/methodology/approach This study examines the free rider problem in the context of public goods, using the blood donation system in northeastern Turkey as a case study. The analysis consists of two phases. Binary logistic models were used to examine the probability of individuals being free riders and multinomial logistic regression models were used to explain the reasons for being free riders. Findings The findings reveal that free rider behavior is influenced by factors such as income level, gender, community trust and participation in collective action. Individuals with higher incomes and women are more likely to engage in free-rider behavior. Community trust emerged as a key determinant, with higher levels of trust reducing the likelihood of free riding. Additionally, factors such as incentives or coercion, participation in collective action, willingness to donate organs posthumously, confidence in obtaining blood if needed and prior blood donation experience help explain free rider behavior. Originality/value This study addresses a significant gap in the literature by integrating socioeconomic and demographic, psychological and behavioral factors to analyze free riding behavior, whereas traditional models often offer a one-dimensional perspective.

Purpose This paper aims to explore the role of information sharing, swift trust and collaboration as critical success factors for distributing food parcels in South Africa during the COVID-19 pandemic. Design/methodology/approach Generic cross-sectional qualitative research was conducted through semi-structured interviews among South African humanitarian relief organisations (HROs). Interviews focused on exploring the roles of information sharing, swift trust and collaboration between HROs and other stakeholders when distributing food parcels during the COVID-19 pandemic. Findings The researchers found that HROs identified the disaster victims and determined their needs through a needs assessment. HROs planned their responses respectively. The planning process involved information sharing with other humanitarian supply chain (HSC) stakeholders, whose responses met expectations. This led to swift trust development and stakeholder commitment in the HSC, fostering collaboration that positively impacted food distribution to beneficiaries. In some cases, information sharing did not result in an expected behaviour. In these cases, the nature of information sharing changed, and relationships with the involved HSC stakeholders were not built on trust. Practical implications HROs can improve future disaster preparedness by understanding the role of information sharing, swift trust and collaboration in HSCs and how these factors apply to their relationships with stakeholders. They can build on relationships where trust has been developed, respond better to current disasters and enhance preparedness for future disasters. HROs can also be better prepared to manage relationships with stakeholders where trust is lacking. Originality/value Information sharing, swift trust and collaboration are critical success factors in disaster management. Based on current research, there is very little evidence of the correlation between these factors from an HSC perspective, especially during a disaster like the COVID-19 pandemic. This research addresses this research gap from the context of an emerging country and builds on the commitment-trust theory and existing frameworks that incorporate these factors. The research findings can be used to improve information sharing, trust and collaboration between humanitarian stakeholders to prepare for future disasters.

Purpose Local humanitarian supply chains (HSCs) have experienced increasing social and ecological pressures over the past two decades. Enhancing their social-ecological resilience (SER) has thus become increasingly important. Surprisingly, the existing supply chain management literature does not provide unified theoretical explanations or practical guidelines for the SER construct. This study aims to fill this gap. Design/methodology/approach The authors investigate the antecedents of SER in local HSCs employing a qualitative empirical study of cataract camps in Africa, using semi-structured in-depth interviews with relevant experts and subsequent qualitative data analysis. Findings The findings highlight that while conventional resilience typically depends on the robustness and flexibility of associated HSCs, their SER is primarily determined by their actors’ engagement with the local conditions shaped by the regional communities, regulations, and environments. Originality/value The study offers a novel theoretical understanding and practical application of the SER construct in an HSC context, shedding light on regional challenges and opportunities. HSC managers and policymakers can build on them to shape the SER profile of their local HSCs.

Purpose The study aims to create a realistic model for emergency logistics, specifically focusing on relief distribution during disasters, by incorporting the prioritization of nodes. It addresses the vehicle routing problem (VRP) and resource allocation problem (RAP) to optimize routes and resource delivery. By integrating these two problems, the study seeks to enhance the efficiency and effectiveness of emergency response operations, ensuring timely and equitable distribution of resources to affected areas. This model is particularly relevant for disaster management agencies looking to improve their logistical strategies and minimize the impact of disasters on communities. Design/methodology/approach The study uses a two-phase multi-objective model. In the first phase, it minimizes routing costs associated with the VRP. In the second phase, it focuses on minimizing penalty costs for total unsatisfied demand, addressing the RAP. The model uses the CPLEX solver and introduces the modified decomposition and assignment heuristic (MDAH) and genetic algorithm (GA) for handling large-scale scenarios. This approach ensures both computational efficiency and solution quality, making it suitable for practical applications in emergency logistics. Findings The model effectively addresses both the VRP and RAP, optimizing routes and resource allocation. However among both the solution algorithms MDAH is found to solve the model faster for large-scale problems, whereas solution of the model from GA provides better solution quality. The practical application of the model to the 2019 Alappuzha flood data demonstrates its utility in real-world disaster scenarios, showcasing its potential to enhance emergency response operations and resource distribution during disasters. Research limitations/implications The study is limited to single-depot scenarios and does not consider demand uncertainty. Future research could explore multiple depots, uncertain demands and integrate additional problems such as the location allocation problem and the casualty allocation problem. These extensions would enhance the model’s applicability and robustness, providing more comprehensive solutions for emergency logistics. Addressing these limitations would further improve the efficiency and effectiveness of disaster response operations, ensuring better preparedness and resource management. Practical implications The model provides valuable insights for emergency management agencies, aiding in decision-making processes for relief distribution. By minimizing routing and penalty costs, the model ensures timely and efficient delivery of resources, improving overall resource utilization during disasters. This practical application can significantly enhance the effectiveness of emergency response operations, reducing the impact of disasters on affected communities and ensuring that resources are distributed equitably and efficiently. Social implications By optimizing relief distribution, the model helps reduce fatalities and property damage during disasters. It ensures equitable resource distribution, addressing the needs of affected communities effectively. This contributes to the overall resilience of communities, helping them recover more quickly from disasters. The model’s focus on efficient and timely resource allocation can significantly improve the quality of life for disaster-affected populations, providing them with the necessary support to rebuild and recover. Originality/value The study presents a novel approach by integrating the VRP and RAP in a two-phase model for emergency logistics. This innovative approach offers practical solutions for large-scale disaster scenarios, enhancing the efficiency of relief operations. The model’s ability to address both routing and resource allocation challenges simultaneously sets it apart from existing models, providing a valuable tool for disaster management agencies looking to improve their logistical strategies and response capabilities.

Purpose This study aims to investigates how human skills (HS) and big data and predictive analytics (BDPA) jointly influence humanitarian supply chain performance (HSCP), focusing on Chinese non-governmental organizations (NGOs). It also examines whether resilience (RS) moderates these relationships under stress-intensive conditions. Design/methodology/approach The study adopts a quantitative research approach, collecting data from 411 respondents across Chinese NGOs involved in humanitarian supply chains (HSC). Structural equation modeling (SEM) is used to validate the hypotheses, examining the direct and indirect effects of HS, big data and predictive analytics on supply chain performance. Findings The results demonstrate that HS has no direct effect on HSCP; instead, its impact is fully mediated by BDPA, which serves as a dynamic capability linking human capital to operational outcomes. The moderating role of RS was not statistically supported. These findings emphasize the sequential interplay between behavioral and digital capabilities and suggest that HS alone may be insufficient without technological integration. Research limitations/implications This study relies on cross-sectional data from a single country context, which may limit generalizability. Future research should consider cross-national and longitudinal designs to explore capability development over time and in diverse institutional environments. Practical implications The findings underscore the need for humanitarian organizations to design integrated training programs that enhance interpersonal competencies, such as communication, teamwork and decision-making, and build proficiency in data analytics and visualization tools. By equipping staff with hybrid skillsets, NGOs can more effectively deploy BDPA capabilities, which this study identifies as critical for improving supply chain agility and performance under crisis. Social implications This research highlights the strategic value of aligning human expertise with digital technologies in humanitarian contexts. Promoting a human-technology interface enables more transparent, data-driven and responsive disaster relief operations, ultimately improving aid delivery and service quality for vulnerable and underserved populations. Originality/value To the best of the authors’ knowledge, this study is among the first to empirically link HS and BDPA in the humanitarian context, offering a unified framework for understanding how soft and digital capabilities interact. It extends dynamic capability theory to non-profit and data-scarce environments and highlights BDPA’s central role in operationalizing human resource potential in crisis response.

Purpose Evaluating aid is crucial to ensure that assistance has reached the intended beneficiaries without exacerbating their vulnerabilities or causing further harm. This study aims to systematically review the academic literature and humanitarian reports to identify indicators that address the complex socio-cultural and environmental intricacies of regions receiving aid and actors involved in delivering operations. Such localized indicators are necessary for rigorous assessment of the impacts of aid. Design/methodology/approach The study uses a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis to collect and review 74 peer-reviewed papers and field reports. Findings Current frameworks suggest a universal understanding of aid effectiveness. Literature does not offer a systemic approach to incorporate local voices in humanitarian evaluation, specifically with respect to protection, inclusiveness and dignity (PID). Future research should address the following gaps: the need for the recognition of localized indicators in practice, the need for context-specific evaluation frameworks, the need for the involvement of target people and the need for meaningful and universally accepted indicators for PID. Research limitations/implications The study’s scope is limited to selected high-ranked journals and a single source for field reports. Future research could include a broader range of academic sources, additional field report repositories and other document types such as books, conference papers and PhD theses to provide a more comprehensive review. Originality/value The paper outlines new perspectives on how humanitarian aid should be analyzed and evaluated so that it can be tailored to the target context.