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This research examines the dynamic interrelationship between economic policy uncertainty (EPU) and the inflows of foreign direct investment (IFDI) in China.

This research used the Granger causality and sub-sample time-varying rolling window causality method.

The empirical results reveal that EPU tends to have a negative impact on the IFDI in most periods that have been taken into consideration. However, there has been a positive relationship observed between the periods of the US subprime crisis. That is to say that the uncertainty of the Chinese economic policy does not always impede the IFDI. These results are supported by the general equilibrium model, which states that there are certain influences that come into play when moving from EPU to IFDI. On the other hand, the IFDI exert a positive influence on EPU during times of economic crisis and trade war, which indicates that the uncertainty in the economy may increase due to the sudden soar of foreign investment.

During tense global trade situations and complicated economic scenarios, the results suggest the Chinese government should dedicate itself to expanding its initiatives to open up and improve the domestic business environment in order to increase the foreign investors' confidence and prevent the decline in the IFDI. In addition to this, it also suggests that multinational companies pay attention to the policy environment of the host country, especially when they decide to invest there.

This study aims to examine the unemployment hysteresis effects from the perspective of the heterogeneity of genders within Asian countries.

The authors use the annual unemployment rate dataset of 12 Asian countries ranging from 1991–2020. Traditional unit root tests are initially employed to investigate the unemployment hysteresis effect. Considering the structural break and cross-section dependence problems, the sequential panel selection method (SPSM) and the Kapetanios–Snell–Shin (KSS) panel unit root test with Fourier functions have proven to be more applicable.

The empirical results indicate that the unemployment rate is stationary in most Asian regions for both females and males, which confirms the mean reversion process of the natural unemployment hypothesis. This suggests that these countries' unemployment rates are flexible to quickly revert to its long-run equilibrium determined by the labor markets. However, only the female unemployment rate in Pakistan and Nepal and adult female unemployment rates in these two economies present non-stationary series. In line with the unemployment hysteresis effect, it means shocks will leave a permanent impact on their labor market.

On the one hand, in most of the Asian countries, it can be inferred that the trade-off between inflation and unemployment is temporary because the natural unemployment hypothesis holds. Therefore, policymakers may consider using monetary policy as a tool to control inflation and stimulate growth during a recession. Such policy measures should not have a long-run impact on unemployment or cause a permanent shift in the natural unemployment rate. On the other hand, the government should implement active labor protective programs such as education or training schemes, job search assistance programs and maternity protection, especially for female adults, to reduce the negative shocks in the economic downturn, which is beneficial for them away from being long-term unemployed. It is also necessary to improve the labor unions to reduce the discrimination between female and male labors.

This paper innovatively concentrates on the heterogeneity performances between genders about the unemployment hysteresis effect within Asian countries. Furthermore, taking into account the age-specific characteristics, the youth and adult unemployment rates have been investigated. Additionally, the approximation of bootstrap distribution and the advanced panel KSS unit root test with a Fourier function are employed. Thereby, targeted policies for the government can be applied to reduce the discrimination and negative shocks on female adults in the labor market.

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

This research analyzes borrowers' credit utilization through prepayment behavior in peer-to-peer (P2P) lending. The authors investigate factors influencing the decision to prepay and assess the role of P2P lending as an alternative source of consumer credits.

The authors use individual loan-level data from the LendingClub, one of the largest P2P platforms in the USA. The authors use a Logit model and a sample selection model estimated by the two-stage Heckman method. The empirical analysis considers borrower-specific and loan-specific characteristics as well as macroeconomic factors.

The authors present a number of significant findings that can enhance understanding consumers' financing decisions. The authors offer evidence that borrowers are able to take advantage of cheaper loans offered by P2P lending to better manage credit card balance and consolidate debt. The authors find that borrowers tend to prepay P2P loans quickly when the aggregate cost of borrowing is low, suggesting that P2P lending offers an efficient alternative to obtain credit. This is particularly true for creditworthy borrowers that are able to take advantage of competing sources of finance. The authors' results provide evidence that P2P lending can improve consumers' optimal credit utilization.

P2P lending has grown exponentially and has become a significant credit supplier to consumers and small businesses. While the existing literature mostly focuses on default risks, prepayment has received much less attention. This research fills in the gap and investigates borrowers' prepayment behavior in P2P loans and the role of P2P lending as an alternative source of consumer credits.

The purpose of this paper is to investigate the non-linear threshold effects between trade openness and female labours to participate in the labour markets.

The authors consider data for nine Asian countries from 1990 to 2016 period and perform the panel threshold regression method.

Empirical results indicate that the threshold value is occurred. With the increase of trade openess, the female labour force participation rate shows a trend of rising first and then declining. Furthermore, exports also have an asymmetric threshold effect on female labour force participation, which is partly in accordance with the discrimination model (Becker, 1957). On the other hand, imports dependency will hinder female labour force participation regardless of a threshold effect. The authors obtain similar results when the authors consider the female employment rate as substitution.

Specifically, increased trade openness may contribute positively or negatively towards overall female labour force participation rate (FLFPR), attributed to the relative importance of these opposing effects. Thus, when the cost reduction effect, resulting from intensified competitive pressure and comparative advantages would enhance the participation rate, the technology channel operates in the opposite direction. Therefore, from the perspective of female employment, trade openness is not the more the better.

This study innovatively discusses the non-linear correlation between trade openness and FLFPR and distinguishes the different contributions from exports and imports. The advanced threshold regression model assumes the existence of threshold value from trade to female employment. Thereby, targeted policies for the government should be applied to promote active female in the labour market.

The purpose of this paper is to identify bank deserts in the USA in 2009 and 2015, separately for inner city, suburban, and rural areas. It also identifies correlations between bank deserts, population characteristics, market competition, and payday lending restrictions, both cross-sectionally and over time.

FDIC data on bank office locations are used to identify bank deserts, defined as the 5 percent of census tracts with the greatest distance from the centroid to the nearest office. Those data are matched to both American Community Survey data to identify population characteristics, to a list of states with payday lending prohibitions, and to levels of market competition. An alternative measure of bank deserts corrects for population density. Geography is analyzed, mean characteristics compared, and random effects regressions capture static and dynamic correlates.

Population density explains approximately half of bank distance variance. Bank deserts appear more often in southern and western states, and expanded significantly in inner cities while contracting in rural areas. Regression results suggest that African Americans were overall and increasingly likely to live in bank deserts and Native Americans were overall more likely to live in rural bank deserts. Rural poverty is linked to bank deserts, and the effects of competition are complex.

The space for policy intervention exists in African American inner cities and Native American rural communities.

The relative measure of bank deserts is novel, as are dynamic estimates and random effects analysis of correlates.

High speed axial flow pumps are widely used in aircraft fuel systems. Conventional axial flow pumps often generate radial secondary flows at partial-load conditions which influence the flow structure and form a “saddle-shaped” region in the Q-H curve that can destabilize the operation. Thus, the “saddle-shaped” Q-H region must be eliminated. The paper aims to discuss these issues.

The swept stacking method is often used for radial flow control in turbo-machinery impeller blade design. Hence, this study uses the swept stacking method to design a high speed axial flow pump. The detached eddy simulation method and experiments are used to compare the performance of a swept blade impeller in a high speed axial fuel pump with the original straight blade impeller. Both the pump performance and internal flow characteristics are studied.

The results show separation vortices in the impeller with the straight blade design at partial-load conditions that are driven by the rotating centrifugal force to gather near the shroud. The swept geometry provides an extra force which is opposite to the rotating centrifugal force that creates a new radial equilibrium which turns the flow back towards the middle of the blade which eliminates the vortices and the “saddle-shaped” Q-H region. The swept blade impeller also improves the critical cavitation performance. Analysis of the pressure pulsations shows that the swept blade design does not affect the stability.

This study is the initial application of swept blades for axial flow liquid pumps. The results show how the swept stacking changes the radial equilibrium of the high density, high viscosity flow and the effects on the mass transfer and pressure pulsations. The swept blade effectively improves the operating stability of high speed fuel pumps.

The purpose of this paper is to fill the knowledge gap in the microscopic origin of high corrosion resistance in the passivated 316 L stainless steel.

Here, the pitting corrosion potential of the passivated 316 L stainless steel is measured, as well as the non-passivated one. Using the aberration-corrected scanning transmission electron microscopy, the microstructure of the passive film is unambiguously revealed. Combining the electron energy loss spectroscopy with the X-ray photoelectron spectroscopy, the depth profiling analysis is conducted and the variations in composition from the very surface of the passive film to the internal steel are clarified.

By optimizing the passivation treatment process, the authors significantly increase the pitting corrosion potential of the passivated 316 L stainless steel by 300 mV, compared with the non-passivated one. The passive film features a unique amorphous multilayer structure. On the basis of the depth profiling analysis, the origin of the high corrosion resistance achieved is unraveled, in which the redistribution of elements in the multilayer passive film, especially the enrichment of Cr in the topmost layer and Ni at the film-metal interface, prevent the oxidization of the inner iron of the steel.

This study advances understanding of the nature of the passive film from a microscopic view, which can be helpful for the further improvement of the corrosion resistance performance.

This study introduces a model for the multilayer structure of passive films that reveals the reconstitution of the passive films after the opportune passivation treatments. Due to the redistribution of elements caused by passivation, the enrichment of Cr in the outer layer and Ni near the film-metal interface leads to enhance corrosion resistance performance.