Work-from-home intention during the COVID-19 pandemic: a perspective integrating inclusive leadership and protection motivation theory

Purpose: The present study integrates inclusive leadership and protection motivation theory to propose a new model predicting employees' intention to work from home during an emergency situation such as the COVID-19 pandemic. Design/methodology/approach: A questionnaire was developed to collect data from 939 Taiwanese and Vietnamese office employees using a non-probability convenience sampling method. A total of 887 valid questionnaires were used for further analysis. The data were analysed following a two-stage structural equation modelling using SPSS 22 and AMOS 20 software. The validity and reliability of the instrument were tested and ensured. Findings: The results revealed that inclusive leadership and factors related to protection motivation theory– including perceived severity and perceived vulnerability – have positive direct and indirect effects on employees' work-from-home intentions through the mediating role of employees' work-from-home-related attitudes. Protection motivation theory factors were found to have a stronger effect on employees' work-from-home intention than inclusive leadership. Differences in the relationship between perceived vulnerability, perceived severity and employees' intentions towards working from home were also discovered among participants from the two studied countries. Practical implications: The integration of inclusive leadership and protection motivation theory brings into light what will drive employees' intention to work from home during an emergency situation. The present study has several theoretical and practical implications for scholars, governments, managers and policymakers that can help them improve management policies for working from home in the future. Originality/value: Based on integrating inclusive leadership and protection motivation theory to explore employees' intention to work from home during an emergency situation, the present study demonstrated that inclusive leadership and protection motivation theory should be considered for studies on working from home in a pandemic setting. © 2023, Emerald Publishing Limited.

Birds of a Feather Flock Together: How Set Bias Helps to Deanonymize You via Revealed Intersection Sizes

Secure two-party protocols that compute intersection-related statistics have attracted much attention from the industry. These protocols enable two organizations to jointly compute a function (e.g., count and sum) over the intersection of their sets without explicitly revealing this intersection. However, most of such protocols will reveal the intersection size of the two sets in the end. In this work, we are interested in how well an attacker can leverage the revealed intersection sizes to infer some elements' membership of one organization's set. Even disclosing an element's membership of one organization's set to the other organization may violate privacy regulations (e.g., GDPR) since such an element is usually used to identify a person between two organizations. We are the first to study this set membership leakage in intersection-size-revealing protocols. We propose two attacks, namely, baseline attack and feature-aware attack, to evaluate this leakage in realistic scenarios. In particular, our feature-aware attack exploits the realistic set bias that elements with specific features are more likely to be the members of one organization's set. The results show that our two attacks can infer 2.0 similar to 72.7 set members on average in three realistic scenarios. If the set bias is not weak, the feature-aware attack will outperform the baseline one. For example, in COVID-19 contact tracing, the feature-aware attack can find 25.9 tokens of infected patients in 135 protocol invocations, 1.5 x more than the baseline attack. We discuss how such results may cause negative real-world impacts and propose possible defenses against our attacks.

2-((1H-indol-3-yl)thio)-N-phenyl-acetamides: SARS-CoV-2 RNA-dependent RNA polymerase inhibitors

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of Coronavirus Disease 2019 (COVID-19) pandemic. Despite intensive and global efforts to discover and develop novel antiviral therapies, only Remdesivir has been approved as a treatment for COVID-19. Therefore, effective antiviral therapeutics are still urgently needed to combat and halt the pandemic. Viral RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 demonstrates high potential as a reliable target for the development of antivirals. We previously developed a cell-based assay to assess the efficiency of compounds that target SARS-CoV-2 RdRp, as well as their tolerance to viral exoribonuclease-mediated proof-reading. In our previous study, we discovered that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides specifically targets the RdRp of both respiratory syncytial virus (RSV) and influenza A virus. Thus, we hypothesize that 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides may also have the ability to inhibit SARS-CoV-2 replication by targeting its RdRp activity. In this research, we test a compound library containing 103 of 2-((1H-indol-3-yl)thio)-N-phenyl-acetamides against SARS-CoV-2 RdRp, using our cell-based assay. Among these compounds, the top five candidates strongly inhibit SARS-CoV-2 RdRp activity while exhibiting low cytotoxicity and resistance to viral exoribonuclease. Compound 6-72-2a is the most promising candidate with the lowest EC50 value of 1.41 mu M and highest selectivity index (CC50/EC50) (above 70.92). Furthermore, our data suggests that 4-46b and 6-72-2a also inhibit the replication of HCoV-OC43 and HCoV-NL63 virus in a dose-dependent manner. Compounds 4-46b and 6-72-2a exhibit EC50 values of 1.13 mu M and 0.94 mu M, respectively, on HCoV-OC43 viral replication. However, higher concentrations of these compounds are needed to effectively block HCoV-NL63 replication. Together, our findings successfully identified 4-46b and 6-72-2a as promising inhibitors against SARS-CoV-2 RdRp.

On the Use of MPC Techniques to Decide Intervention Policies against COVID-19

This paper aims at demonstrating how and that model predictive control (MPC) strategies can be used to determine optimal intervention policies against the COVID-19 pandemic. Especially for the time after a first wave of infection and before a vaccine can be safely distributed to a sufficient extent, the intervention experience from the first outbreak can be utilized to guide the policy decision in this period. The MPC problem in this paper takes the pandemic in different regions of a country and its neighboring countries into account, while policies such as wearing masks or social distancing are selected as inputs to be optimized. This optimized policy balances the risk of a second outbreak and socio-economic costs, while considering that the measure should not be too severe to be rejected by the population. Effectiveness of this policy compared to standard intervention policies is compared through numerical simulations. Copyright (C) 2021 The Authors.