ABSTRACT
Ridesharing services such as Uber and Lyft have been substantially affected by the ongoing COVID-19 pandemic. Drawing on social capital theory, the current research investigates how social trust relates to three types of trust in compliance with COVID-19 guidelines and consumers' ridesharing intentions. Analyzing data from two economically and culturally distinct countries, the results suggest that social trust positively affects trust in platform companies' compliance with COVID-19 guidelines (TPC), but not (or to a lesser extent) trust in drivers' (TDC) and other riders (TRC) compliance with COVID-19 guidelines in both the United States and Bangladesh. Importantly, TPC, TDC, and TRC are positively related with consumers' ridesharing intentions in the United States but not in Bangladesh. Furthermore, the analysis reveals two counterintuitive moderating effects of fear of COVID-19 and trust in God. The results provide important insights on factors affecting the ridesharing industry in the context of the COVID-19 pandemic, and they emphasize the importance of considering cultural context in understanding consumers' intentions to engage in the sharing economy. © 2022 Elsevier Ltd
ABSTRACT
Google Meet has been identified as one of the effective virtual meeting platforms that has the potential to deliver the learning materials to students during the COVID-19 pandemic. However, a scale evaluating its use for instructional activities has yet to be developed. Therefore, we developed the Google Meet use scale (GMU-S) and evaluated its characteristics among two samples with a total of 560 participants. The results indicated that the GMU-S has initial evidence of internal consistency reliability, construct, convergent, and discriminant validity. This study provides evidence that the developed scale is sound to evaluate the use of Google Meet in educational activities during and beyond the COVID-19 pandemic and other emergencies that might affect the education sector. Theoretically, this research is believed to be one of the pioneered studies that reported the development and initial testing of a new scale (GMU-S). Practically, the developed scale can be generalized to evaluate the use of other virtual meeting platforms (e.g., Skype, Zoom, Microsoft Teams, etc.). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The rapid growth of artificial intelligence (AI) has reached unprecedented levels across different fields. In this bibliometric analysis, we reviewed 1999 studies published between 2011 and 2021 on the role of AI applications in facilitating healthcare services. This review aims to shed light on the scientific achievements of AI in healthcare through examining the research focus of existing studies, major diseases, major AI tasks and applications, most productive authors and countries, and most common journals in the domain. The results showed that the extant literature has focused on four distinct clusters, including the theory and process behind machine learning, deep learning algorithms, experiments and results, and COVID-19 related issues. The results indicated that COVID-19, pneumonia, different cancer types, neurodegenerative diseases, and diabetes are the major diseases that received careful attention from AI applications. The results also indicated that image processing and diagnostic imaging were the most common tasks, while deep learning techniques were the most common applications of AI in healthcare. The taxonomy of the analyzed literature would be helpful for practitioners, researchers, and decision-makers working in healthcare sectors to advance the wheel of medical informatics. It can be argued that the door is still open for improving the role of AI in healthcare, whether in its theoretical (e.g., models and algorithms) or physical (e.g., surgical robots) form. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Purpose While there is an abundant amount of literature studies on mobile payment adoption, there is a scarce of knowledge concerning the sustainable use of mobile payment contactless technologies. As those technologies are mainly concerned with security and users' trust, the question of how security factors and trust can influence the sustainable use of those technologies within and beyond the COVID-19 pandemic is still unanswered. This research thus develops a theoretical model based on integrating the protection motivation theory (PMT) and the expectation-confirmation model (ECM), extended with perceived trust (PT) to explore the sustainable use of mobile payment contactless technologies. Design/methodology/approach The developed model is evaluated based on data collected through a web-based survey from 523 users who used contactless payment technologies. Unlike the existing literature, the collected data were analyzed using a hybrid structural equation modeling-artificial neural network (SEM-ANN) technique. Findings The data analysis results reinforced all the proposed relationships in the developed model. The sensitivity analysis results showed that PT has the largest impact on the sustainable use of mobile payment contactless technologies with 97.2% normalized importance, followed by self-efficacy (SE) (77%), satisfaction (72.1%), perceived vulnerability (PV) (48.9%), perceived usefulness (PU) (48.2%), perceived severity (PS) (40.7%), response efficacy (RE) (28.7%) and response costs (RCs) (24.1%). Originality/value The originality of this research lies behind the development of an integrated model based on PMT and ECM to understand the sustainable use of mobile payment contactless technologies. The study provides several managerial implications for decision-makers, policy-makers and service providers to ensure the sustainability of those contactless technologies within and beyond the COVID-19 pandemic.