ORCID Profile
0000-0001-8907-0213
Current Organisation
Murdoch University
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Publisher: Elsevier BV
Date: 12-2020
Publisher: Informa UK Limited
Date: 02-2023
Publisher: Elsevier BV
Date: 03-2021
Publisher: MDPI AG
Date: 28-12-2022
DOI: 10.3390/SU15010554
Abstract: The hospitality sector has been one of the worst-hit industries due to the onset of the COVID-19 pandemic, followed by nationwide lockdowns and curfews. Further, other factors, including the Russia–Ukraine war, commodity price rise, and recession, have acted as hurdles in the slow recovery process. Policy experts at different forums have advocated for proactive and robust measures by the government to reduce adverse impacts during these unprecedented times. To design such measures, determining the firm-specific factors that significantly impact their profitability is essential. In this context, this study tries to understand firm-specific factors that affect the hospitality sector’s performance in India. It also explores whether the firm-specific characteristics have changed over time due to changes in political regimes and differ between private and publicly listed companies. Using a s le of 440 public and private hospitality firms for 11 years (2010–2020) and after controlling for unobserved heterogeneity using firm fixed effects, we tested the relationship between firm characteristics and performance. The estimation results demonstrate that the net asset turnover, liquidity, foreign earnings intensity, and age have significant, positive impacts on profitability. In contrast, solvency and size have negatively impacted firm performance. Further, we found differences in the magnitudes of coefficients for private and publicly listed companies. The findings provide important implications for managers and regulators to stimulate new solutions to overcome the ongoing difficult period.
Publisher: MDPI AG
Date: 10-11-2022
DOI: 10.3390/SU142214850
Abstract: The ability of an organization to respond to a crisis with agility is vital for business leaders to maintain business continuity. Our paper examined how business owners responded to the challenges caused by the pandemic. Using online surveys for data collection, we investigated a critical agility issue of supply chain risks through understanding the interrelationship of various business capability factors. Partial least squares path modeling (PLS-PM) was applied to a s le of 220 participants who were owners of micro, small, and medium businesses in Western Australia. The findings showed that the businesses’ efficiency, financial strength, and flexibility in sourcing affected the businesses’ supply chain risks negatively. More support for labor productivity, asset utilization, waste elimination, financial reserves, portfolio ersification, and credit access needs to be introduced to enhance the resilience of the business supply chain. This paper is novel, as we used the data collected in Western Australia, where the SMEs were still affected by the global supply chain disruption but lacked protracted lockdowns, as had occurred nationally and globally during the COVID-19 period.
Publisher: Springer Science and Business Media LLC
Date: 03-06-2021
DOI: 10.1186/S40854-021-00259-9
Abstract: Inclusive finance is a core concept of finance that makes various financial products and services accessible and affordable to all in iduals and businesses, especially those excluded from the formal financial system. One of the leading forces affecting people's ability to access financial services in rural areas is financial literacy. This study investigated the impacts of financial knowledge on financial access through banking, microfinance, and fintech access using the Bangladesh rural population data. We employed three econometrics models: logistic regression, probit regression, and complementary log–log regression to examine whether financial literacy significantly affects removing the barriers that prevent people from participating and using financial services to improve their lives. The empirical findings showed that knowledge regarding various financial services factors had significant impacts on getting financial access. Some variables such as profession, income level, knowledge regarding depositing and withdrawing money, and knowledge regarding interest rate highly affected the overall access to finance. The study's results provide valuable recommendations for the policymaker to improve financial inclusion in the developing country context. A comprehensive and long-term education program should be delivered broadly to the rural population to make a big stride in financial inclusion, a key driver of poverty reduction and prosperity boosting.
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 2024
Publisher: MDPI AG
Date: 19-07-2023
Abstract: At present, with the rise of information technology revolution, such as mobile internet, cloud computing, big data, machine learning, artificial intelligence, and the Internet of Things, the banking industry is ushering in new opportunities and encountering severe challenges. This inspired us to develop the following research concepts to study how data innovation impacts banking. We used qualitative research methods (systematic and bibliometric reviews) to examine research articles obtained from the Web of Science and SCOPUS databases to achieve our research goals. The findings show that data innovation creates opportunities for a well-developed banking supply chain, effective risk management and financial fraud detection, banking customer analytics, and bank decision-making. Also, data-driven banking faces some challenges, such as the availability of more data increasing the complexity of service management and creating fierce competition, the lack of professional data analysts, and data costs. This study also finds that banking security is one of the most important issues thus, banks need to respond to external and internal cyberattacks and manage vulnerabilities.
Publisher: Springer International Publishing
Date: 2021
Publisher: Frontiers Media SA
Date: 15-06-2022
Abstract: The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz ., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor—current health expenditure as a percentage of GDP, USA IS predictor—spread, and Macro-factor GDP—annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S& P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, erse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.
Publisher: LLC CPC Business Perspectives
Date: 23-09-2020
DOI: 10.21511/IMFI.17(3).2020.17
Abstract: This study examines the predictive power of implied volatility smirk to forecast foreign exchange (FX) return. The volatility smirk contains critical information, especially when the market experiences negative news. The Australian dollar, Canadian dollar, Swiss franc, Euro, and British pound options traded in the opening, midday and closing periods of the trading day are selected to estimate the currency smirk. Research results reveal that the currency smirk outperforms in forecasting FX returns. In addition, the steeper slope in the middle of the trading day suggests that the predictive power of currency smirk in the midday period is higher compared to the opening and closing periods. However, currency smirks’ predictability lasts for a short period, as the FX market is highly adept at incorporating the vital information embedded in the currency smirk. These findings imply that the currency smirk is distinctive for forecasting very short-term FX fluctuations, and the day- or overnight FX traders can use its uniqueness to profit from quick price swings in the 24-hour global FX market.
Publisher: MDPI AG
Date: 02-11-2022
Abstract: Technological innovation has changed the financial market significantly with the increasing application of high-frequency data in research and practice. This study examines the performance of intraday implied volatility (IV) in estimating currency options prices. Options quotations at a different trading time, such as the opening period, midday period and closing period of a trading day with one-month, two months’ and three months’ maturity, are employed to compute intraday IV for pricing currency options. We use the Mincer–Zarnowitz regression test to analyse the volatility forecast power of IV for three different forecast horizons (within a week, one week and one month). Intraday IV’s capability in estimating currency options price is measured by the mean squared error, mean absolute error and mean absolute percentage error measure. The empirical findings show that intraday IV is the key to accurately forecasting volatility and estimating currency options prices precisely. Moreover, IV at the closing period of the beginning of the week contains crucial information for options price estimation. Furthermore, the shorter maturity intraday IV is suitable for pricing options for a shorter horizon. In comparison, the intraday IV based on the longer maturity options subsumes appropriate information to price options with higher accuracy for the longer horizon. Our paper proposes a new approach to accurately pricing currency options using high-frequency data.
Publisher: MDPI AG
Date: 05-08-2021
DOI: 10.3390/SU13168766
Abstract: The sharing economy has acquired a lot of media attention in recent years, and it has had a significant impact on the transport sector. This paper investigates the existing impact and potential of various forms of shared mobility, concentrating on the case study of Wanneroo, Western Australia. We adopted bibliometric analysis and visualization tools based on nearly 700 papers collected from the Scopus database to identify research clusters on shared mobility. Based on the clusters identified, we undertook a further content analysis to clarify the factors affecting the potential of different shared mobility modes. A specially designed questionnaire was applied for Wanneroo’s residents to explore their use of shared mobility, their future behaviour intentions, and their perspectives on the advantages and challenges of adoption. The empirical findings indicate that the majority of respondents who had used shared mobility options in the last 12 months belong to the low-mean-age group. The younger age group of participants also showed positive views on shared mobility and would consider using it in the future. Household size in terms of number of children did not make any impact on shared mobility options. Preference for shared mobility services is not related to income level. Bike sharing was less commonly used than the other forms of shared mobility.
No related grants have been discovered for Le Thi Ngoc Quynh.