Accessing financial services is considered one of the main challenges facing communities during crises. This research studies the role of using FinTech applications to build resilience during the COVID-19 pandemic. The research empirically examines the factors affecting Jordanian citizens’ intention to use FinTech applications. The sample of the research comprised 500 potential FinTech service users in Jordan. Based on the research conceptual model, five hypotheses were developed and tested using structural equation modeling techniques (SEM-PLS). The research results indicate that perceived benefits and social norms significantly affect the intention to use FinTech applications. However, it has been found that perceived technology risks do not significantly affect the intention to use FinTech applications. Moreover, the results also indicate that customer trust is significantly mediating the relationship between perceived risks and intention to use FinTech applications. FinTech service providers should insure that their products are easy to use, fulfill needs and protect consumers’ data in order to ensure trust, hence positively influencing consumer adoption.

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Although the performance of Humanitarian Supply Chain (HSC) receives considerable attention in current literature, measuring HSC performance remains challenging. HSC performance depends largely on the ability to meet the needs of the sufferers which contradicts with current performance measures that focus on input metrics such as donations and expenditures rather than output metrics. In this paper, we address this gap in the literature by examining refugee service performance as perceived by refugees themselves. We examine the impact of information sharing and information quality on HSC service performance. We further draw on social capital theory to investigate how the dimensions of social capital influence information sharing and information quality. Data collected from 276 refugees in Zaatari camp in Jordan provide support for our proposed model. Our paper makes two contributions. First, we extend current literature on HSC performance by examining the impact of information sharing and the quality of the information shared on the beneficiaries’ perception of HSC performance. We therefore focus on output metrics rather than input metrics. Second, we apply a social capital theoretical lens to investigate how social ties and relations influence information sharing and information quality in HSC. We also offer theoretical and practical implications for academics and stakeholders in the field of HSC.

Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient diagnosis, low latency, and low energy consumption, etc., are advantages confirmed according to the achieved experimental results.