Search results

In recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.

The authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.

The findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.

The methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

The proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

The purpose of this study is to analyze and forecast the financial sustainability and resilience of commercial banks of Bangladesh in response to the negative effects of COVID-19 pandemic.

Eighteen publicly listed commercial banks of Dhaka Stock Exchange (DSE) have been taken as a sample for this study. To measure the riskiness of banks' credit portfolio, nine industries of DSE have been considered to determine probable loss of revenue arising from the COVID-19 pandemic shock. Moreover, two commonly used multiple-criteria-decision-making (MCDM) tools namely TOPSIS method and HELLWIG method have been used for analyzing the data.

Based on the performance scores under TOPSIS and HELLWIG method, banks are categorized into three groups (six banks each) namely top resilient, moderate resilient and low resilient. It is found that EBL and DBBL are the most resilient banks, and ONEBANK is the worst resilient bank in Bangladesh in managing the COVID-19 pandemic shock.

This study concludes that banks with low capital adequacy, low liquidity ratio, low performance and higher NPLs are more vulnerable to the shocks caused by the COVID-19 pandemic. The management of commercial banks should emphasize on maintaining higher capital base and reducing default loans.

Resilience of the Bangladeshi banking sector under any adverse economic event has been examined by only using stress testing approach. This study is empirical evidence where both TOPSIS and HELLWIG MCDM methods have been used to make the result conclusive.

The usage rate of mobile financial services (MFS) has shown an uptick since the emergence of the COVID-19 pandemic in Bangladesh. This study aims to reveal the underpinning reasons for such MFS surge and its continuance by integrating health belief model (HBM) and expectation confirmation model (ECM).

The study analyzes 529 MFS users' responses during the second wave of the COVID-19 outbreak in Bangladesh using the partial least square method.

Satisfaction is more predictive than perceived usefulness in explaining continuance usage intention. Expectation confirmation also indirectly affects continuance intention. Among the HBM constructs, the indirect effect of perceived severity on continuance intention via perceived usefulness and satisfaction is significant. Besides, the impact of self-efficacy on continuance intention is also significant. Moreover, perceived credibility significantly affects satisfaction and indirectly affected continuance usage intention via satisfaction.

The study projects boosting customers' satisfaction is critical for the successful retention of existing MFS customers. MFS service providers should emphasize the factors that amplify satisfaction. They must evaluate preadoption factors so that customers can have positive confirmation. Especially, the service providers, the policymakers and the regulators should take an active role in improving the users' self-efficacy and the system's credibility. Undertaking the MFS literacy program, installing hotline service to provide emergency help will boost users' confidence in using the system.

The study is a unique contribution in the context of Bangladesh. To the best of the authors’ knowledge, no previous MFS studies in Bangladesh explored MFS continuance usage intention during COVID-19 and beyond. Besides, the inclusion of “perceived credibility” in the framework will supplement the earlier studies conducted on this aspect.