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This study aims to help understand individual donors’ preferences over different designs of humanitarian–business partnerships in managing humanitarian operations and to help understand if donors’ preferences align with their actual donation behavior.

Choice-based conjoint analysis was used to understand donation preferences for partnership designs, and a donation experiment was performed using real money to understand the alignment of donors’ preferences with actual donation behavior.

The results show that partnering with the business sector can be a valuable asset for humanitarian organizations in attracting individual donors if these partnerships are managed well in terms of partnership strategy, partnership history and partnership report and disclosure. In particular, the study finds that the donation of services and products from businesses corporations to humanitarian organizations are preferable to individual donors, rather than cash. Furthermore, donors’ preferences are not necessarily aligned with actual donation behavior.

The results highlight the importance of presenting objective data on projects to individual donors. The results also show that donors value the provision of services and products by business corporations to humanitarian operations.

Partnerships between humanitarian organizations and business corporations are important for the success of humanitarian operations. However, little is known about which partnership designs are most preferable to individual donors and have the biggest chance of being supported financially.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

Obtaining building permit certificate is an essential component of construction endeavors, but it can be cumbersome sometimes. The process is frequently beset with obstacles, including bureaucratic impediments, red-tapism, prolonged authorization protocols and insufficient inter-agency collaboration which result in project timeline extension, cost escalation and applicant dissatisfaction. Therefore, this study aims to examine customer satisfaction with the assessment of building construction permit certificates in Lalitpur, Nepal.

Following the notion of evaluation model theory, this study adopts an explanatory research design to determine the causal relationship between latent and observed variables. People who have recently completed the construction of their building and those people whose construction work is pending make up the population for the study. A total of 198 samples were collected by following the convenience sampling method from Lalitpur, Nepal. The primary data are collected by using the structured questionnaire with the interview process where the data are statistically evaluated using descriptive and inferential analysis using the KOBO toolbox, SPSS and AMOS. The connection between variables was examined using structural equation modeling (SEM).

Results indicate that the negligence of the employees, the attitude of the employees, the need for additional costs and the hiring of the agent are the most significant obstacles encountered by customers during the process of getting construction permit. Regarding the whole assessment system, the general population expresses displeasure. SEM results indicate that environment and quality are significantly related to customer satisfaction.

This paper's novelty lies in its Nepal-specific inquiry into the relationship between building permit acquisition procedures and customer contentment. The study provides a distinctive viewpoint on this context by combining evaluation model theory and SEM. The localized approach emphasizes the importance of customized strategies to improve customer satisfaction, adding to the current literature on the subject. The study's use of SEM as a quantitative analysis tool enhances its methodological rigor. This interdisciplinary research offers valuable insights for academics, practitioners and policymakers in Nepal and contributes to the wider field of construction and customer satisfaction.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

As climate change increasingly affects rural food production, there is an urgent need to adopt agricultural adaptation strategies. Because the agricultural sector in Nepal is one of the most vulnerable to the effects of climate change, the adaptation strategies of household farmers in rural areas are crucial. This study aims to address the impacts of agricultural climate change adaptation strategies in Nepal. The research empirically analyzed climate hazards, adaptation strategies and local adaptation plans in Mangalsen Municipality, Achham District, Sudurpashchim Province, Nepal.

This study used a purposive sampling of household lists, categorized as resource-rich, resource-poor and intermediate households. The analysis used primary data from 110 household surveys conducted among six focus groups and 30 informants were selected for interviews through purposive random sampling.

Climate change significantly impacts rainfall patterns and temperature, decreasing agriculture productivity and increasing household vulnerability. To overcome these negative impacts, it is crucial to implement measures such as efficient management of farms and livestock. A comprehensive analysis of Nepalese farmers' adaptation strategies to climate change has been conducted, revealing important insights into their coping mechanisms. By examining the correlation between farmers' strategies and the role of the local government, practical policies can be developed for farmers at the local level.

This study represents a significant breakthrough in the authors' understanding of this issue within the context of Nepal. It has been conclusively demonstrated that securing land tenure or land security and adopting appropriate agricultural methods, such as agroforestry, can be instrumental in enabling Nepalese households to cope with the effects of climate change effectively.

The purpose of the study is to investigate the stressors faced by migrant entrepreneur-managers of small and medium-sized enterprises (SMEs) during the COVID-19 pandemic, as well as their resilience strategies for reviving their businesses.

This study employs a qualitative research design based on grounded theory. Semi-structured interview questionnaire was used for one-to-one interviews with 20 migrant entrepreneur-managers, representing ten different business sectors during the peak of the COVID-19 pandemic of 2020 and 2021. Interviews were transcribed, coded into open code, axial code and selective code to identify the major themes, and analysis was done into three levels to explore the stressors and initial strategies implemented to cope with the crisis. Trustworthiness of the findings was ensured by credibility, transferability, dependability and conformability, and reflexivity.

This study explored three types of stressors: finance-related stressors, supplies-related stressors and human resources-related stressors in migrant SME entrepreneur-managers during the COVID-19 pandemic. The study revealed the use of comprehensive supply chain strategies followed by migrant SME entrepreneur-managers to be resilient enough to cope with a crisis situation like the COVID-19 pandemic.

This study covers an under-researched area of research related to stressors and resilience strategies in migrant SME entrepreneur-managers during the pandemic situation. A large body of prior research contributes to employees' stress and coping behaviors, while this paper focuses on stressors in migrant entrepreneur-managers in the special context of pandemics and their strategies to be resilient during a crisis situation. Thus, the findings of this study contribute to SME entrepreneur-managers, policy makers and academicians so that a large number of migrant entrepreneurs can develop resilient strategies for crisis situations. Furthermore, this study contributes to the supply chain resilience literature and resource dependency theory.

This study aims to evaluate the perception of the local experts and end users on the drivers, barriers and strategies to the use of alternative building technologies (ABTs), with a focus on sandbag building technologies (SBTs) in the provision of sustainable housing in South Africa towards improving the public's understanding of SBTs.

This research adopted a qualitative approach that used focus group meetings as the primary data collection method for this study. This study's focus group participants comprised ABT experts and end users of ABT houses in South Africa who were selected using a convenient sampling technique. The data were recorded, transcribed verbatim and analysed using NVivo 11 software.

This study found that the perceived drivers to using ABTs such as SBT comprise sustainability, affordability, job creation potentials, fire-resistant and earthquake resistance. This study revealed strategies for the SBTs, including awareness, building sandbag prototypes across cities and training.

This study's findings have practical implications for the practice and praxis of ABT implementation and uptake in South Africa. This study provides a framework for broadening the worldwide understanding of use and uptake of SBTs to provide sustainable and affordable housing.

This study adds significantly to the limited body of knowledge on ABTs, focusing on sandbag houses. Consequently, the findings provide policymakers with information on the expert and end-user perspectives on the barriers and strategies to using ABTs.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

Urban communities can be faced with many destructive events that can disrupt the daily functioning of activities and livelihood of people living in the communities. In this regard, during the last couple of years, many governments have put a lot of efforts into building resilient urban communities. Essentially, a resilient urban community has the capacity to anticipate future disasters, prepare for and recover timely from adverse effects of disasters and unexpected circumstances. Considering this, it is therefore important for the need to continuously review the existing urban community resilience indicators, in order to identify emerging ones to enable comprehensive evaluation of urban communities in the future against unexpected events. This study therefore aims to conduct a systematic review to develop and critically analyse the emerging and leading urban community resilience indicators.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRSIMA) protocol, 53 journal articles were selected using Scopus. The selected papers were subjected to thorough content analysis.

From the review, 45 urban community resilience indicators were identified. These indicators were grouped into eight broad categories namely, Socio-demographic, Economic, Institutional Resilience, Infrastructure and Housing Resilience, Collaboration, Community Capital, Risk Data Accumulation and Geographical and Spatial characteristics of community. Further, the results indicated that the U.S had the highest number of publications, followed by Australia, China, New Zealand and Taiwan. In fact, very few studies emanated from developing economies.

The outputs of this study will inform policymakers, practitioners and researchers on the new and emerging indicators that should be considered when evaluating the resilience level of urban communities. The findings will also serve as a theoretical foundation for further detailed empirical investigation.

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.