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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 study is to examine the influence of moral intensity on the ethical decision-making process of professional accountants based on a combined importance-performance map analysis (cIPMA).

Using partial least squares structural equation modeling (PLS-SEM) on data from 309 accountants, the study examined the relationships between four moral intensity dimensions: magnitude of consequences, social consensus, probability of effect and concentration of effect, and the ethical decision-making process. The study also methodologically complemented by a combined importance-performance map analysis and necessary condition analysis.

The findings reveal that moral recognition and moral judgment are necessary conditions for predicting accountants’ moral intent. However, in terms of importance and performance, moral recognition plays a less significant role compared to moral judgment. Furthermore, the influence of moral intensity dimensions is pronounced on moral recognition, while their influence on moral judgment and moral intent is more context dependent. This is also exhibited in the combined importance-performance map analysis results.

The study highlights the importance of considering specific ethical dilemmas and the differential influence of moral intensity when developing strategies to strengthen ethical decision-making in the accounting profession. Future research is encouraged to explore how cultural backgrounds and diverse settings influence accountants’ ethical decision-making with tailored measurement tools for a more comprehensive understanding.

To the best of the authors’ knowledge, the study is among the first to complement the results of PLS-SEM with importance-performance map analysis and necessary condition analysis to examine the relationship between moral intensity and ethical decision-making of accountants.

This paper reviews the recent collapse of two cryptocurrency enterprises, FTX and Celsius. These two cases of institutional bankruptcy have generated criminal charges and other civil complaints, mainly alleging fraud against the CEOs of the companies. This paper aims to analyse the fraud leading to these bankruptcies, drawing on key concepts from the research literature on economic crime to provide explanations for what happened.

This paper uses a case study approach to the question of how large financial institutions can go off the rails. Two theoretical perspectives are applied to the cases of the FTX and Celsius collapses. These are the “normalisation of deviance” theory and the “cult of personality”.

In these two case studies, there is an interaction between the “normalisation of deviance” on the institutional level and the “cult of personality” at the level of individual leadership. The CEOs of the two companies promoted themselves as eccentric but successful examples of the visionary tech finance genius. This fostered the normalisation of deviance within their organisations. Employees, investors and regulators allowed criminal and highly financially risky practices to become normalised as they were caught up in the attractive story of the trailblazing entrepreneur making millions in the new cryptoeconomy.

This paper makes a contribution both to the case study literature on economic crime and to the development of general theory in economic criminology.

Inspired by a food bank distribution operation, this paper aims to study synchronized vehicle routing for equitable and effective food allocation. The primary goal is to improve operational efficiency while ensuring equitable and effective food distribution among the partner agencies.

This study introduces a multiobjective Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of effectively distributing food, particularly for food banks serving vulnerable populations in low-income urban and rural areas. The optimization approach described in this paper places a significant emphasis on social and economic considerations by fairly allocating food to food bank partner agencies while minimizing routing distance and waste. To assess the performance of the approach, this paper evaluates three distinct models, focusing on key performance measures such as effectiveness, equity and efficiency. The paper conducts a comprehensive numerical analysis using randomly generated data to gain insights into the trade-offs that arise and provide valuable managerial insights for food bank managers.

The results of the analysis highlight the models that perform better in terms of equity and effectiveness. Additionally, the results show that restocking the vehicles through the concept of synchronization improves the overall quantity of food allocation to partner agencies, thereby increasing accessibility.

This paper contributes significantly to the literature on optimization approaches in the field of humanitarian logistics.

This study provides food bank managers with three different models, each with a multifaceted nature of trade-offs, to better address the complex challenges of food insecurity.

This paper contributes significantly to social responsibility by enhancing the operational efficiency of food banks, ultimately improving their ability to serve communities in need.

To the best of the authors’ knowledge, this paper is the first to propose and analyze this new variant of vehicle routing problems in nonprofit settings.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

This study aims to provide a framework aligning corporate social responsibility (CSR) initiatives with sustainable development goals (SDGs) 2030, applying the triple bottom line (TBL) approach. The research examines and evaluates the reach of Maharatna Central Public Sector Enterprises’ (CPSE) CSR spending towards sustainability and maps them with SDGs focusing on economic, social and environmental aspects. In addition, state-wise spending for CSR of all eligible Indian companies has been discussed.

The study used secondary data related to CSR spending and disclosure from the annual reports and sustainability reports accessible on the official websites of CPSE, Global Reporting Initiative standards, CSR Guidelines of Department of Public Enterprises and Securities Exchange Board of India, Government of India’s National Guidelines on Responsible Business Conduct (NGRBC) (2018) research papers, financial dailies and websites. The study includes the CPSEs awarded with the status of Maharatna companies under the Guidelines of Maharatna Scheme for CPSEs.

The top CSR initiatives focused on by Maharatna companies were related to poverty, hunger, sanitation and well-being, promotion of education and contribution to the Prime Minister’s National Relief Fund. These initiatives aligned with the top SDGs related to life on land, education and health care, which proved responsible business leadership (RBL) through TBL. The alignment indicates that India is moving towards sustainable development achievements systematically.

The practical consequences can be understood through the CSR spending of Maharatna Public Sector Undertakings towards economic, social and environmental aspects. The spending demonstrates their commitment, which other public and private sector organizations can adopt.

The Government of India’s NGRBC’s guidelines towards inclusive growth and equitable development, addressing environmental concerns, and being responsive to all its stakeholders is a thorough indication of driving the business towards being more responsible. This research has developed a framework aligning CSR and SDG through the TBL approach, which other developing countries can adopt as a model.

There is dearth of research among public sector company’s contribution towards attaining SDGs and demonstrating RBL. This research fulfils this gap. Mapping CSR activities to SDG’s also has not been clearly carried out in previous research, which is a contribution of this study.

The Elementary and Secondary School Emergency Relief (ESSER) requires districts to deliver “evidence-based interventions” to students impacted by the pandemic. The policy has created a unique opportunity for researchers and practitioners to engage with evidence to learn how recovery interventions work and under what conditions.

This study is part of a research-practice partnership (RPP) between Guilford County Schools, AIR-CALDER, Harvard University and NWEA to understand the impacts and implementation of ESSER-funded recovery programs. We use a case analysis approach and frameworks of evidence-use and RPPs to explain how researchers and Guilford leaders engage with evidence to improve and evaluate programs.

The RPP used evidence to inform Guilford leaders’ recovery approaches and strengthened researchers’ evaluations of programs. Conditions that enabled evidence engagement included the RPP’s goals, research activities and collaborative conditions such as boundary spanning activities, team meetings, relationships and trust. We also observed factors that hindered evidence engagement, including the RPP’s nascent stage, structure and breadth of goals, rapid policy timelines and other organizational conditions in Guilford.

Given the complexities of pandemic recovery, RPPs can help researchers evaluate programs in their local context, and present evidence in ways that are actionable to guide decision-making. District leaders can play a valuable role in co-designing research studies attuned to local priorities and context and facilitating research participation among internal stakeholders. However, newly formed RPPs with broad goals for impact will need more time and resources to build an improvement infrastructure for sustaining pandemic recovery.

To help alleviate the immense suffering caused by humanitarian crises worldwide, organisations are forming relationships for effective coordination and resource sharing. However, organisations can struggle to build trust because of the uncertain context, varying institutional mandates and socio-cultural differences. Thus, this paper aims to better understand how humanitarian groups can leverage formal mechanisms to produce greater trust.

This paper adopts a logical–positivist research paradigm to formulate and test its hypotheses. This paper answered this study’s research question using structural equation modelling from survey data of 180 humanitarian managers.

In inter-organisational humanitarian relationships, formal mechanisms indirectly foster trust through two mediators: distributive justice and information sharing.

This research presents the perspective of only one partner in inter-organisational relationships. Moreover, the operationalisations of formal mechanisms and trust were not comprehensive (i.e. only contracts and integrity-based trust, respectively).

To the best of the author’s knowledge, this research is a first attempt to empirically link the widely discussed idea of formal mechanisms, distributive justice, information sharing and trust in inter-organisational humanitarian relationships. Further, this research is the first attempt to present and empirically validate a theoretical model that addresses how formal mechanisms foster trust in inter-organisational relationships.