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Extreme events undermine progress toward social sustainability and require an immediate response from the private sector. The authors propose a two-dimensional framework (response time and the scope of activities to the various stakeholders) to benchmark firms’ effectiveness in mitigating the threats to social sustainability post-disaster. This framework allows establishing a taxonomy of corporate emergency responses. Within such a framework, the authors group 218 corporate actions from 111 companies operating in Spain during the COVID-19 pandemic into four corporate relief strategies: symbolic, selective, reactive, and supportive. The authors show how companies can use this framework as a guide to foster social sustainability in times of shock in an efficient way. Also, stakeholders can use this framework as a reference to distinguish corporate initiatives aiming to propel community resilience from corporate “covidwashing.”

The advantages of lasers over broad‐band light sources for spectroscopic measurements have long been recognized. Laser techniques offer the ability to measure the concentrations of trace species in a gas stream in “real time” with sensitivity and molecular selectivity not possible with broad band spectroscopic techniques. Until recently, the use of laser‐based instruments has been largely limited to laboratory or one‐of‐a‐kind devices. However, the increased availability of solid‐state tunable diode lasers, recent advancements in fiber optic coupling and advanced signal processing have made it possible to develop instruments that offer customers enabling technology at an affordable price. Spectrum Diagnostix has developed a family of tunable diode laser‐based instruments used in a variety of applications including: fugitive release detection of HF and H₂S in the refining and petrochemical industries, stack monitoring, and using extractive sample probe, in situ process control. SpectraScan instruments obtain their high sensitivity and chemical selectivity utilizing a technique known as wavelength modulated spectroscopy. Described simply, a near infra‐red diode laser’s wavelength is scanned rapidly and repeatedly through a molecular absorption line. A photodetector senses the instantaneous fraction of emitted laser power that is transmitted through the chemical bearing gas. Measurement of the relative amplitudes of offline to online transmission yields a precise value of the quantity of chemical along the laser beam’s path. The amplitude modulated signals are then detected using established radio receiver and signal processing techniques. The SpectraScan monitors are designed for permanent installation in harsh industrial and petrochemical environments and are approved for Class I, Division 2 hazardous area use. Describes the SpectraScan instruments, their field applications, and reviews operating data compiled from open path measurements of HF in refineries.

The present effects of transnational corporations (TNCs) on social, health, and environmental aspects of local societies have a long history. The pre-conditions for the insertion of the types of economic initiatives now seen in the Global South, and driven by TNCs, were set through histories of colonialism and development schemes. These initiatives disrupted local economies and modified environments, delivering profound effects on livelihoods. These effects were experienced as structural violence, and have produced social suffering through the decades.

In this paper, we compare two African cases across time; the conjunction of development initiatives and structural adjustment in the Zambezi Valley, Zimbabwe in the early 1990s and industrial plantation forestry in present-day Uganda. Each case presents a specific constellation of political and economic forces that has produced prejudicial effects on local populations in their time period of application and are, essentially, different versions of structural violence that produce social suffering. While each case depicts a specific type of violent encounter manifest at a particular historical moment, these are comparable in the domains of environmental impacts, disruptions to societies, co-opting of local economies, disordering of systems of meaning and social reproduction, and nefarious effects on well-being. We analyze the conjunction of these effects through a theoretical lens of structural violence and social suffering. Our analysis draws particular attention to the role of TNCs in driving this structural violence and its effects.

The purpose of this paper is to construct a typology of a disaster that informs humanitarian-relief supply chain (HRSC) design across the stages of disaster relief.

In addition to an interdisciplinary review of pertinent literature, this paper utilises a typology construction method to propose theoretically and methodologically sound dimensions of disasters.

Whilst semantic arguments surrounding the concept of a “disaster” are ongoing, the authors propose three typologies based upon six dimensions that serve as interdependent variables informing resultant HRSC design considerations. These are speed of onset, time horizon, spatial considerations, affected population needs, perceived probability of occurrence and perceived magnitude of consequence. These combinational and independent relationships of the variables offer insight into key HRSC design-making considerations.

The study improves conceptual knowledge of disasters, distilling the concept to only the dimensions applicable to HRSC design, omitting other applications. The typologies provide empirical cell types based on extant literature, but do not apply the models towards new or future phenomena.

This paper provides HRSC practitioners with normative guidance through a more targeted approach to disaster relief, with a focus on the impacted system and resulting interactions’ correspondence to HRSC design.

This paper provides three typological models of disasters uniquely constructed for HRSC design across the various stages of disaster relief.

The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction.

After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC.

The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance.

The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated.

Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities.

Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs.

The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

This study advocates the importance of taking an evolutionary perspective in the strategic configuration of closed-loop supply chains (CLSC) in the transition to a circular economy. Building on the supply chain management and industrial dynamics research domains, an evolutionary analytical framework was developed and applied in the empirical context of the ongoing industrial transition to e-mobility.

This study is designed as an in-depth exploratory case study to capture the multi-layer dynamic complexities and their interplay in CSLC development. The empirical investigation was based on two-year interactions between the authors and various departments in a leading European heavy vehicle manufacturer. The proposed evolutionary analytical framework was used for investigating the dynamics of four CLSC configurations through ten possible trajectories.

The findings demonstrate that the evolution of each CLSC configuration comes with multiple challenges and requirements and point out the necessity for the co-development of technologies, product design and production, and infrastructure through long-term relationships among key supply chain actors. However, this evolutionary journey is associated with multiple dilemmas caused by uncertainties in the market and technology developments. All these factors were properly captured and critically analyzed, along with their interactions, thanks to the constructs included in the proposed evolutionary analytical framework.

The proposed evolutionary framework is applicable for examination of SC transformation in the context of market and technology development, and is particularly relevant for transitioning from linear SC to CLSC. The framework offers a single actor perspective, as it does not directly tackle dynamics and effects of actions taken by SC actors.

The developed framework can support SC managers in identifying, framing, and comparing alternative strategies for CLSC configuration in the transition process.

This study proposes the framework for understanding and guiding the evolutionary process of CLSC development. Its uniqueness lies in the integration of concepts from innovation and evolutionary theories coming from industrial dynamics and SCM literature streams.

The purpose of this paper is to examine the impact of cloud computing (CC) use on collaboration and its ultimate impact on the agility of humanitarian supply chains. Further, this paper aims to analyze the moderating role of inter-organizational trust in the relationship between CC use and collaboration.

This paper provides an empirical assessment of CC use based on an interview analysis of 19 individuals from humanitarian organizations. A survey questionnaire is later used with 107 participants from US relief organizations. Partial least squares test is used to examine the relationships depicted in the conceptual model.

The results provide an account of how CC is used in a humanitarian context. Further, the results indicate that CC use has a positive and significant impact on collaboration between humanitarian organizations and their suppliers. Collaboration is found to be significantly positively associated with agility in humanitarian organizations.

No study, to the best of the authors’ knowledge, has empirically assessed the impact of CC use on humanitarian supply chain collaboration. This will be the first study to empirically analyze the relationships between CC use, inter-organizational trust, collaboration and agility in a humanitarian context.

This study provides a theoretically and empirically validated model depicting the relationships between CC use, collaboration, agility and inter-organizational trust in humanitarian supply chains. Humanitarian organizations can use these findings to optimize agility.

This study contributes to supply chain management research, particularly humanitarian supply chain management knowledge, by empirically examining the usefulness of CC use on collaboration and agility in the supply chain.

Finding efficient disaster recovery center location-allocation-routing (DRCLAR) network schemes play a vital role in the disaster recovery logistics network (DRLN) design. The purpose of this paper is to propose and demonstrate how to design efficient DRCLAR network schemes under the risk of facility disruptions as a part of the disaster relief activities.

A goal programming (GP) model is formulated to consider four performance measures simultaneously for the DRCLAR design. The cross-evaluation based-super efficiency data envelopment analysis (DEA) approach is applied to better evaluate the DRCLAR network schemes generated by solving the GP model so that more efficient network schemes can be identified.

The proposed approach identifies more efficient DRCLAR network schemes consistently among various network schemes generated by GP. We find that combining these two methods compensates for each method's weaknesses and enhances the discriminating power of the DEA method for effectively identifying and ranking the network schemes.

This study presents how to generate balanced DRCLAR network schemes and how to evaluate various network schemes for identifying efficient ones. The proposed procedure of developing and evaluating them could be extended for designing some disaster recovery/relief supply chain systems with conflicting performance measures.

The hotel and tourism industry forms a crucial economic sector for all the economies around the world. However, it suffered the worst hit during the COVID-19 outbreak. Considering the hotel and tourism sector's critical situation, this manuscript aims to emphasise the importance of resilience in the hotel and tourism supply chain (HTSC) and explores the crucial barriers that tend to disturb the inculcation of stability in the hotel and tourism sector. The present research analyses the factors influencing the hotel and tourism sector's resilience and also takes into consideration the various critical success factors (CSFs) needed to build a resilient HTSC.

A two-phase research approach has been proposed and used in this study. In the first phase, eight CSFs and sixteen factors influencing the hotel and tourism sector's resilience during the COVID-19 pandemic were identified. The basis of the identification of the CSFs and factors was literature and inputs received from experts. In the second phase, the grey-Entropy-EDAS, a qualitative and quantitative analysis, was used to analyse the identified CSFs and factors to determine the priority of concern.

In this research, the most imperative facet influencing the hotel and tourism sector's resilience has been identified, and the findings will assist hotel and tourism sector in managing and mitigating the repercussions of the COVID-19 pandemic. The analysis of the results indicates that out of all the critical success factors, supply chain visibility is the most crucial aspect in building HTSC's resilience, whereas economic catastrophe is the most influential factor. Sensitivity analysis is also conducted to examine the priority ranking stability.

The results of this study can be used by the hotel supply chain managers and policymakers to plan for various challenges faced by them as they try to implement resilience-based strategies in their supply chain.

This research is unique as it analyses the general factors hindering the pathway of resilience in the hotel and tourism supply chain. This is also the first kind of study that has used grey-Entropy to analyse the critical success factors and grey-EDAS for analysing the impact of various factors influencing the hotel and tourism sector's resilience.

The purpose of this paper is to present a case study describing a collaboration with Last Mile Health, a non-governmental organization, to develop a framework to inform its community healthcare networks in remote Liberia.

The authors detail the process of using the unique problem setting and available data to inform modeling and solution approaches.

The authors show how the characteristics of the Liberian setting can be used to develop a two-tier modeling framework. Given the operating constraints and remote setting the authors are able to model the problem as a special case of the location-routing problem that is computationally simple to solve. The results of the models applied to three districts of Liberia are discussed, as well as the collaborative process of the multidisciplinary team.

Importantly, the authors describe how the problem setting can enable the development of a properly scoped model that is implementable in practice. Thus the authors provide a case study that bridges the gap between theory and practice.