Amanda Chu - Academia.edu (original) (raw)

Papers by Amanda Chu

International Journal of Environmental Research and Public Health

This study sought to investigate the role of consumers’ emotional, cognitive, and financial conce... more This study sought to investigate the role of consumers’ emotional, cognitive, and financial concerns in the development of food waste reduction, reuse, and recycling behavior among restaurant patrons. Food waste in restaurants is a major problem for the food service industry, and it is a growing source of concern in developing countries, where eating out is becoming increasingly popular. A large portion of restaurant food waste in these markets originates from the plates of customers, highlighting the importance of consumer behavior changes in reducing waste. The current study has used a quantitative approach to analyze the impact of anticipated negative emotion of guilt, awareness of consequences, habit, and financial concern on food waste reduction behaviors, i.e., reduce, reuse, and recycle. The study collected 492 responses and data is analyzed for hypotheses testing through Partial Least Square-Structural Equation Modelling. The findings showed that anticipated negative emotion...

Journal of Risk and Financial Management

Crude oil draws attention in recent research as its demand may indicate world economic growth tre... more Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis, including a preparedness risk score and a severity risk score, Granger-cause changes in oil future prices. In our empirical analysis, we observe 49% to 60% of days in 2020 to 2021 during which the pandemic scores significantly affected oil futures. We also find an asymmetric lead–lag relationship, indicating that there is a tendency for oil futures to move significantly when the pandemic is less severe but not when it is more severe. This study adopts preparedness risk score and severity risk score as proxy variables to measure the impact of the COVID-19 pandemic risk on oil market. The asymmetric lead–lag behavior between pandemic risk a...

The Lancet Regional Health - Europe, 2022

People who have been infected with SARS-CoV-2 may suffer persistent symptoms after infection such... more People who have been infected with SARS-CoV-2 may suffer persistent symptoms after infection such as fatigue, dyspnea dysgeusia/anosmia, asthenia, brain fog and insomnia. These persistent symptoms can last for weeks or months, and their negative impacts can be as worrying as the risk of contracting SARS-CoV-2 infection itself.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb ... more Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Econometrics and Statistics, 2022

PLOS ONE, 2022

During the 2019 novel coronavirus disease (COVID-19) pandemic, many employees have switched to wo... more During the 2019 novel coronavirus disease (COVID-19) pandemic, many employees have switched to working from home. Despite the findings of previous research that working from home can improve productivity, the scale, nature, and purpose of those studies are not the same as in the current situation with the COVID-19 pandemic. We studied the effects that three stress relievers of the work-from-home environment–company support, supervisor’s trust in the subordinate, and work-life balance–had on employees’ psychological well-being (stress and happiness), which in turn influenced productivity and engagement in non-work-related activities during working hours. In order to collect honest responses on sensitive questions or negative forms of behavior including stress and non-work-related activities, we adopted the randomized response technique in the survey design to minimize response bias. We collected a total of 500 valid responses and analyzed the results with structural equation modellin...

Journal of Travel Medicine, 2020

Informatics for Health and Social Care, 2021

This study examined the association between caregivers' burdens and their individual characte... more This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

Education Sciences, 2021

The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to ... more The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of pe...

Communications in Statistics: Case Studies, Data Analysis and Applications, 2021

Abstract This article proposes a novel multivariate generalized autoregressive conditionally hete... more Abstract This article proposes a novel multivariate generalized autoregressive conditionally heteroscedastic (GARCH) model that incorporates the modified Cholesky decomposition for a covariance matrix in order to reduce the number of covariance parameters and increase the interpretation power of the model. The modified Cholesky decomposition for covariance matrix reduces the number of covariance parameters to , where p is the dimension of the stocks in the data, and enables us to obtain a regression equation. To account for the nonlinearity in the GARCH model, the parameters in our model are modeled using long short-term memory. The proposed model is compared with DCC model with respect to portfolio optimization and the distances between the actual covariance matrices and predicted covariance matrices. It is found that although the distances may or may not be reduced by our proposed model in different cases presented in this article, our proposed model outperforms the DCC model in terms of mean portfolio returns.

JMIR Public Health and Surveillance, 2021

International Journal of Environmental Research and Public Health, 2021

In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus dis... more In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.

Finance Research Letters, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Sustainability, 2021

The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instru... more The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instructors in an unprecedented way. The majority of educational establishments were forced to take their courses online within a very short period of time, and both instructors and students had to learn to navigate the digital array of courses without much training. Our study examined factors that affect students’ attitude toward online teaching and learning during the COVID-19 pandemic. It is different from other online learning studies where online courses are mostly a method of choice, with suitable support from institutions and expectation from instructors and students, rather than a contingency. Under this specific environment, we utilized an online survey to collect students’ feedback from eleven universities across Hong Kong. Using partial least squares for analysis on the 400 valid samples we received, we found that peer interactions and course design have the most salient impact on s...

Stat, 2021

The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has... more The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

Sustainability, 2020

This article examines the occurrences of four types of unethical employee information security be... more This article examines the occurrences of four types of unethical employee information security behavior—misbehavior in networks/applications, dangerous Web use, omissive security behavior, and poor access control—and their relationships with employees’ information security management efforts to maintain sustainable information systems in the workplace. In terms of theoretical contributions, this article identifies and develops reliable and valid instruments to measure different types of unethical employee information security behavior. In addition, it investigates factors affecting different types of such behavior and how such behavior can be used to predict employees’ willingness to report information security incidents. In terms of managerial contributions, the article suggests that information security awareness programs and perceived punishment have differential effects on the four types of unethical behavior and that certain types of unethical information security behavior exer...

ABSTRACTThe spread of coronavirus disease 2019 (COVID-19) has caused more than 24 million confirm... more ABSTRACTThe spread of coronavirus disease 2019 (COVID-19) has caused more than 24 million confirmed infected cases and more than 800,000 people died as of 28 August 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of ‘co-movement’ of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pande...

Journal of Econometrics, 2020

This paper aims to explore a modified method of high-dimensional dynamic variancecovariance matri... more This paper aims to explore a modified method of high-dimensional dynamic variancecovariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of highdimensional return variables using the co-movement concept of returns with respect to risk factors. A novelty of the proposed methodology is to allow mapping matrices, which govern the co-movement of returns, to be time-varying. We also consider the flexible modeling of risk factors by a copula multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model. Through the proposed risk factor mapping model, the number of parameters and the time complexity are functions of a small number of risk factors instead of the number of stocks in the portfolio, making our proposed methodology highly scalable. We adopt Bayesian methods to estimate unknown parameters and various risk measures in the proposed model. The proposed risk mapping method and financial applications are demonstrated by an empirical study of the Hong Kong stock market. The assessment of the effectiveness of the mapping via risk measure estimation is also discussed.

Emerging Markets Review, 2020

We investigate growth determinants for Mongolia as a small emerging economy considering China as ... more We investigate growth determinants for Mongolia as a small emerging economy considering China as its large neighbor. Our causality analysis during January 1992 to August 2017 reveals significant linear and nonlinear relationships in growth explanation. China's GDP and coal prices, together with some of their linear and nonlinear lagged components, predict Mongolia's GDP, where a one percent increase in China's GDP relates to an increase in Mongolia of 1.5 percent. Current exchange rates and the nonlinear components of lagged levels of consumer prices also explain growth. Our results underline the role of macroeconomic drivers of growth in emerging economies.