E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

COVID-19 virus: the immune reaction to vaccines

The immune system is developed to preserve its hosts from an ever-expanding cluster of pathogenic microbes. The elimination of toxic substances, allergens, or any other harmful existences that come in, passing the mucosal surfaces, is as well the responsibility of this special system. Its ability to distinguish self (our bodies' functioning cells and tissues) from non-self is the key aspect to its ability to mobilize some reaction to an invasion initiated by the stranger substances listed above. To identify and kill unsafe microorganisms, the host applies both natural and versatile systems, our innate and adaptive immune systems. Vaccines are used to combat the current SARS-CoV-2 strain by utilizing immune system mechanisms, specifically the adaptive immune system. Vectored vaccines, protein vaccines, genetic vaccine, and monoclonal antibody for passive vaccination are among the vaccine platforms under consideration for SARS-CoV-2. Each vaccine has its own benefits and drawbacks. This paper is written to describe the three major forms of COVID-19 vaccines, as well as the unique mechanisms of elements of the immune system associated with the virus. © 2023 SPIE.

Breaking the Ice with Group Flow: A Collaborative VR Serious Game for Relationship Enhancement

It is usually hard for unfamiliar partners to rapidly 'break the ice' in the early stage of relationship establishment, which hinders the development of relationship and even affects the team productivity. To solve this problem, we proposed a collaborative serious game for icebreaking by combining immersive virtual reality (VR) with brain-computer interface based on the team flow framework. We designed a multiplayer collaboration task with the theme of fighting COVID-19 and proposed an approach to improve empathy between team members by sharing their real-time mental state in VR;in addition, we propose an EEG-based method for dynamic evaluation and enhancement of group flow experience to achieve better team collaboration. Then, we developed a prototype system and performed a user study. Results show that our method has good ease of use and can significantly reduce the psychological distance among team members. Especially for unfamiliar partners, both functions of mental state sharing and group flow regulation enhancement can significantly reduce the psychological distance. © 2023 IEEE.

The Reduction of SARS-CoV-2 RNA Concentration in the Presence of Sewer Biofilms

Wastewater surveillance has been widely used to track the prevalence of SARS-CoV-2 in communities. Although some studies have investigated the decay of SARS-CoV-2 RNA in wastewater, understanding about its fate during wastewater transport in real sewers is still limited. This study aims to assess the impact of sewer biofilms on the dynamics of SARS-CoV-2 RNA concentration in naturally contaminated real wastewater (raw influent wastewater without extra SARS-CoV-2 virus/gene seeding) using a simulated laboratory-scale sewer system. The results indicated that, with the sewer biofilms, a 90% concentration reduction of the SARS-CoV-2 RNA was observed within 2 h both in wastewater of gravity (GS, gravity-driven sewers) and rising main (RM, pressurized sewers) sewer reactors. In contrast, the 90% reduction time was 8–26 h in control reactors without biofilms. The concentration reduction of SARS-CoV-2 RNA in wastewater was significantly more in the presence of sewer biofilms. In addition, an accumulation of c.a. 260 and 110 genome copies/cm2 of the SARS-CoV-2 E gene was observed in the sewer biofilm samples from RM and GS reactors within 12 h, respectively. These results confirmed that the in-sewer concentration reduction of SARS-CoV-2 RNA in wastewater was likely caused by the partition to sewer biofilms. The need to investigate the in-sewer dynamic of SARS-CoV-2 RNA, such as the variation of RNA concentration in influent wastewater caused by biofilm attachment and detachment, was highlighted by the significantly enhanced reduction rate of SARS-CoV-2 RNA in wastewater of sewer biofilm reactors and the accumulation of SARS-CoV-2 RNA in sewer biofilms. Further research should be conducted to investigate the in-sewer transportation of SARS-CoV-2 and their RNA and evaluate the role of sewer biofilms in leading to underestimates of COVID-19 prevalence in communities.

Mixup-Inf-Net: A data augmentation algorithm for segmentation of new coronary pneumonia infections

In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.

Performance Analysis of Learner Behavior Through Online Learning Using Learning Management System

This paper discusses the performance analysis of learner behavior through online learning using Learning Management System (LMS). The analysis is performed based on the survey of lecturers and students activities. The parameters of survey consist of the problems discussion which arise in the online learning, the level of student absorption of lecture material, the level of student attendance, and the feedback on lecturer performance carried out by students. Problems that arise in the online learning include lecturers are not being able to control as much as 37%, network disturbances are as much as 22%, students having difficulty understanding lecture material are as much as 19% which are indicated by students with D score of 10%, C score of 60%, and B score of 30%. Meanwhile 17% of students use LMS and the remaining 5% have no problems with the online learning. On the other hand, students have difficulty obtaining connection for online learning of 45%, do not have a quota of 28%, and lazy of 17%. Lecturer performance feedback carried out by students based on competency parameters of pedagogic, personality, professionalism, and social shows very good score. © 2023 IEEE.

Large-Scale Analysis of New Employee Network Dynamics

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.

A study on the assessment of risk management performance in maritime start-ups: evidence from China

Due to the COVID-19 pandemic, the international shipping market has been highly volatile, posing a serious threat to the survival and development of many maritime start-ups. With the development of the digital economy, digital transformation is affecting the evolution and upgrading of many traditional enterprises, including maritime enterprises. In the post-COVID-19 era, start-up small and medium-sized enterprises will need to consider the importance of enterprise risk management to achieve transformation and upgrading. The purpose of this study is to provide guidance for the establishment and upgrading of risk management systems for start-ups based on the identification of risk management strategies of maritime enterprises and the evaluation of their performance. The fuzzy analytic hierarchy process and importance-performance analysis methods were used to rank the operational risk, financial risk, market risk, innovation risk, and disaster risk according to sub-items and screen out the risk management schemes for priority improvements. Through empirical research, it was found that the financial risk and market risk response schemes have the lowest performance and need to be prioritised for improvement. This study argues that start-ups can appropriately challenge their risk management strategies to meet potential risk management needs based on their own circumstances.

Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Telemedicine in the Era of Covid-19: Teleconsultation Architecture Platform

Teleconsultation is a type of medical practice similar to face-to-face consultations, and it allows a health professional to give a consultation remotely through information and communication technologies. In the context of the management of the coronavirus epidemic, the use of teleconsultation practices can facilitate healthcare access and limit the risk of avoidable propagation in medical cabinets. This paper presents the monitoring of international teleconsultation referrals in the era of Covid-19 to facilitate and prevent the suspension of access to care, the most common architecture for teleconsultation, communication technologies and protocols, vital body signals, video transmission, and the conduct of teleconsultation. The aim is to develop a teleconsultation platform to diagnose the patient in real time, transmit data from the remote location to the doctor, and provide a teleconsultation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Scaling law of COVID-19 cases and city size and its evolution over time

Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.

MR.Brick: Designing A Remote Mixed-reality Educational Game System for Promoting Children's Social & Collaborative Skills

Children are one of the groups most influenced by COVID-19-related social distancing, and a lack of contact with peers can limit their opportunities to develop social and collaborative skills. However, remote socialization and collaboration as an alternative approach is still a great challenge for children. This paper presents MR.Brick, a Mixed Reality (MR) educational game system that helps children adapt to remote collaboration. A controlled experimental study involving 24 children aged six to ten was conducted to compare MR.Brick with the traditional video game by measuring their social and collaborative skills and analyzing their multi-modal playing behaviours. The results showed that MR.Brick was more conducive to children's remote collaboration experience than the traditional video game. Given the lack of training systems designed for children to collaborate remotely, this study may inspire interaction design and educational research in related fields. © 2023 ACM.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Scalability of digital psychological innovations for refugees: A comparative analysis in Egypt, Germany, and Sweden

E-mental health interventions may offer innovative means to increase access to psychological support and improve the mental health of refugees. However, there is limited knowledge about how these innovations can be scaled up and integrated sustainably into routine services. This study examined the scalability of a digital psychological intervention called Step-by-Step (SbS) for refugees in Egypt, Germany, and Sweden. We conducted semi-structured interviews (n = 88) with Syrian refugees, and experts in SbS or mental health among refugees in the three countries. Data collection and analysis were guided by a system innovation perspective. Interviewees identified three contextual factors that influenced scalability of SbS in each country: increasing use of e-health, the COVID-19 pandemic, and political instability. Nine factors lay at the interface between the innovation and potential delivery systems, and these were categorised by culture (ways of thinking), structure (ways of organising), and practice (ways of doing). Factors related to culture included: perceived need and acceptability of the innovation. Acceptability was influenced by mental health stigma and awareness, digital trust, perceived novelty of self-help interventions, and attitudes towards non-specialist (e-helper) support. Factors related to structure included financing, regulations, accessibility, competencies of e-helpers, and quality control. Factors related to practice were barriers in the initial and continued engagement of end-users. Many actors with a potential stake in the integration of SbS across the three countries were identified, with nineteen stakeholders deemed most powerful. Several context-specific integration scenarios were developed, which need to be tested. We conclude that integrating novel e-mental health interventions for refugees into routine services will be a complex task due to the many interrelated factors and actors involved. Multi-stakeholder collaboration, including the involvement of end-users, will be essential.

Re-humanisation, Recognising the person behind the patient

Submission content Introduction: At the end of a particularly hectic night shift on the intensive care unit (ICU) I found myself sitting in the relatives' room with the mother and aunt of a young patient, listening to their stories of her hopes and aspirations as she grew up. She had been diagnosed with lymphoma aged 14 and received a bone marrow transplant from her younger sister. Fighting through treatment cycles interposed with school studies, she eventually achieved remission and a portfolio of A-levels. Acceptance into university marked the start of a new era, away from her cancer label, where she studied forensic science and took up netball. Halfway through her first year she relapsed. Main body: When I met this bright, ambitious 20-year-old, none of this history was conveyed. She had been admitted to ICU overnight and rapidly intubated for type-1 respiratory failure. The notes contained a clinical list of her various diagnoses and treatments, with dates but no sense of the context. Rules regarding visitation meant her family were not allowed onto the unit, with next-of-kin updates carried out by designated non-ICU consultants to reduce pressures on ICU staff. No photos or personal items surrounded her bedside, nothing to signify a life outside of hospital. She remained in a medically-induced coma from admission onwards, while various organ systems faltered and failed in turn. Sitting in that relatives' room I had the uncomfortable realisation that I barely saw this girl as a person. Having looked after her for some weeks, I could list the positive microbiology samples and antibiotic choices, the trends in noradrenaline requirements and ventilatory settings. I had recognised the appropriate point in her clinical decline to call the family in before it was too late, without recognising anything about the person they knew and loved. She died hours later, with her mother singing 'Somewhere Over the Rainbow' at her bedside. Poignant as this was, the concept of this patient as more than her unfortunate diagnosis and level of organ failure had not entered my consciousness. Perhaps a coping mechanism, but dehumanisation none-the-less. Conclusion(s): Striking a balance between emotional investment and detachment is of course vital when working in a clinical environment like the ICU, where trauma is commonplace and worst-case-scenarios have a habit of playing out. At the start of my medical career, I assumed I would need to consciously take a step back, that I would struggle to switch off from the emotional aspects of Medicine. However, forgetting the person behind the patient became all too easy during the peaks of Covid-19, where relatives were barred and communication out-sourced. While this level of detachment may be understandable and necessary to an extent, the potential for this attitude to contribute to the already dehumanising experience of ICU patients should not be ignored. I always thought I was more interested in people and their stories than I was in medical science;this experience reminded me of that, and of the richness you lose out on when those stories are forgotten.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

Farmers' willingness to stabilize the land contract relationship facing the upcoming expiry of land contracts

On the verge of the expiry of land contracts, it is theoretically and practically important to explore the willingness and motivations of farmers to stabilize the land contract relationship, with regards to protecting their land contract rights, addressing potential contradictions during the land contract extension, and maintaining the stability of contracted land. Using China Land Economic Survey Data in 2020, this paper explores the impact of differences in areas per capita of household contracted land on farmers' willingness to stabilize land contract relationship. The findings show that most farmers support the stability of land contract relationship;the smaller areas per capita of contracted land are occupied by households than the average in the village, the weaker of the farmers' willingness to stabilize the land contract relationship. The difference between the areas per capita of contracted land ownership of a household and the average in the village has a greater impact on the willingness to stabilize land contract relationship for middle-and low-income farmers, while the development of land transfer market does not increased the willingness. Affected by the COVID-19 pandemic, the land plays a more important role of employment security, which reduces farmers' willingness to stabilize the land contract relationship. Furthermore, the promotion of socialized agricultural service has also mitigated the willingness of farmers o stabilize the land contract relationship.

Have students' perceptions of online education changed with the easing of COVID-19 restrictions?

This research examines student preference toward online and on-ground (i.e., face-to-face) course delivery methods in higher education as a result of the easing of COVID-19 pandemic restrictions. Over 130 undergraduate and graduate students enrolled in Computer and Information Systems courses at a university located in the northeastern United States were surveyed from April 2021 to May 2022. The study found that with the easing of COVID-19 restrictions in Spring 2022, students significantly preferred on-ground over online courses in comparison to their preferences when COVID-19 restrictions were still high in 2021. None of the potential influencing factors contributing to the changed preference, including students' perceptions of online course effectiveness, self-skills supporting online learning (e.g., work independently without supervision, prioritization and time management), and the usefulness of classroom interaction in learning, were found to have significant differences from the time when COVID-19 restrictions were high to the present easing of them. © 2023 Issues in Information Systems. All rights reserved.

Consumers' perceived value and use intention of cashless payment in the physical distancing context: evidence from an Asian emerging market

PurposeCOVID-19 and its measures such as physical distancing have shifted consumer payment behaviors toward cashless payment. Physical distancing is likely to remain a norm for some time to come and will be relevant in any future pandemics. This study aims to examine the impact of consumers' perceived value of cashless payment on their use intention in the physical distancing context, with the mediating role of psychological safety and the moderating role of trust propensity.Design/methodology/approachThis study used a survey method to obtain data from 690 consumers in an Asian emerging market, i.e. Vietnam. The data were analyzed using different statistical methods, including structural equation modeling.FindingsResults show that perceived value of cashless payment positively affects use intention, and this effect is mediated by psychological safety. Furthermore, trust propensity has a positive moderating effect on the link between perceived value and psychological safety.Practical implicationsThis study's findings provide implications for retailers and other stakeholders in implementing and promoting cashless payment systems, especially in the physical distancing context.Originality/valueThis study is among the first attempt to explain the relationships between consumers' perceptions, feelings of psychological safety and use intention toward cashless payment in the physical distancing context. The study's findings may also be relevant to any future pandemics.