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The study investigates the inter-linkages between geopolitical risk (GPR) and food price (FP).

By employing the bootstrap full- and sub-sample rolling-window Granger causality tests.

The empirical results show that there is a time-varying bidirectional causality between GPR and FP. High GPR leads to a rise in FP, suggesting that geopolitical events usually may disrupt supply and demand conditions in food markets, and even trigger global food crises. However, the negative effect of GPR on FP does not support this view in certain periods. This is mainly because GPR is also related to the global economic situation and oil price, which together have impacts on the food market. These results cannot always be supported by the inter-temporal capital asset pricing model, which states that GPR affects FP in a positive manner. Conversely, there is a positive impact of FP on GPR, indicating that the food market is an effective tool that can reflect global geopolitical environment.

In the context of the Russia–Ukraine conflict, these analyses can assist investors and policymakers to understand the sensitivity of FP to GPR. Also, it will provide significant revelations for governments to attach importance to the role of food price information in predicting geopolitical events, thus contributing to a more stable international environment.

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.

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 examine the causal relationship between Chinese housing and stock markets. The authors discuss the three transmission mechanisms between the two markets: wealth effect, modern portfolio theory and credit-price effect. Moreover, the authors focus on the effects of inflation on the relationship between the two markets.

This paper uses wavelet analysis to test the housing and stock markets relationship both in the frequency domain and time domain.

The empirical results indicate that housing prices have a positive effect on stock prices, and these have the same effect on housing prices. Moreover, this positive effect means that stock prices have a wealth effect on housing prices and these have a credit-price effect on stock prices.

These results provide information to financial institutions and individual investors in China to assist them in constructing investment portfolios within these two asset markets.

The authors first use wavelet analysis to analyze Chinese housing and stock markets and to provide information both on the frequency domain and time domain. Moreover, the authors take the inflation factor as a control variable in the causal relationship between the housing and stock markets.

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.

The purpose of this paper is to investigate the causal link between foreign direct investment (FDI) and exports in China based on the knowledge capital model (KK model, Markusen, 2002).

The bootstrap Granger full-sample and sub-sample rolling window causality test is used to determine whether FDI can promote exports.

The full-sample causality test indicates no causal relationship from FDI to exports. However, considering structural changes of exports and FDI, the authors’ find that the full-sample test is not reliable. Instead, the authors use the rolling window causality test to revisit the dynamic causal relationship, and the results present significant effects from FDI on exports, mostly around periods in which the proportion of FDI from Hong Kong, Macao and Taiwan is increasing. Specifically, positive impacts of FDI on exports are stronger than the negative impacts in China.

The findings in this study suggest a significant time-varying nature of the correlation between FDI and exports. The promotion effect of FDI to exports is proved by the rolling window approach; it thus supports the KK model that divides FDI into lateral FDI and vertical FDI and proves that the constitution of FDI is critical to the relationship between FDI and exports.

China has been facing adjustment of its economic structure in recent years, and in this situation, increasing the proportion of FDI that can bring advanced production function is critical for the industrial structural adjustment.

This paper uses the bootstrap rolling window causality test to investigate the time-varying nature of the causality between FDI and exports, considering structural changes for the first time. The authors further deepen the previous research and draw a more realistic conclusion.

Recent studies in the accounting literature have investigated the economic consequences of R&D capitalization. Discretionary R&D capitalization for target beating can be characterized as a firm signaling private information on its future economic benefits or as opportunistic earnings management. R&D capitalization also has an impact on a firm’s marginal costs and product market competition. The purpose of this paper is to address how firms choose R&D levels for the purpose of meeting or beating their earnings targets and how this influences sequential product market competition.

The authors study this issue in a stylized game-theoretic model where R&D choices of a firm are not only strategically made but also used to convey proprietary information to its rival. The model provides a rationale for a firm distorting its R&D level to earn more profits and meet its earnings target.

The equilibrium result indicates that before the realization of common cost shock, a firm can influence the output of its accounting system (i.e. meeting an earnings target) through adjusting its R&D choices. This firm will overinvest in R&D, and this will give an opportunity to create some reserves to be used later to earn a higher profit and reach the earnings target.

This paper contributes to the research on real earnings management in terms of how R&D capitalization affects a firm’s R&D choices by influencing the output of its accounting system through adjusting its R&D choices and the strategic impact of those choices.

The purpose of this study is to present a bibliometric and network analysis that uses the Scopus and Dimension databases to provide new insights into the progression toward the study of sustainable economic development.

This analysis has been drawn on 665 papers published between 2015 and 2023. Bibliometric analysis characterizes a research topic by identifying leading nations, the most significant authors and expressive publications. Network analysis revealed keyword evolution over time, co-citation patterns and study grouping. Content analysis was used to identify major topic in the discipline, with a focus on their interrelationships. Each publication in the data set is briefly described, along with its methodological approach.

The results of this study show that green finance plays a major role in long-term economic growth, having a significant influence on the preservation of environmental quality, economic efficacy and a more comprehensive economic system. Financial technology also accelerates the transition to a carbon-neutral economy by enhancing the beneficial effects of green finance on aspects of the economic system and environmental conservation.

The investigation is based only on Scopus and Dimensions-indexed journal articles. However, additional studies should incorporate publications from other reputable databases, such as Web of Science, PubMed and Science Direct, for the bibliometric analysis, so that the findings of the model analysis become more reliable and valid with examination of more documents. The visualization of similarity viewer was used for data analysis in the study, there is a scope for using other tools such as Biblioshiney and CitNet Explorer.

To support long-term economic growth, authorities should encourage Fintech companies to actively participate in various green finance initiatives and environmental conservation businesses. Financial managers should facilitate the integration of technology and green finance for financial services. It is important to encourage institutional and individual investors alike to look into more environmentally friendly ways to invest and save money. Policymakers should provide a platform for global awareness and government agencies should enhance their recommendations to state governments to increase the efficacy of green finance.

This study contributes to the literature by investigating the relationship between Fintech and green financing. This study holds significance for financial intermediaries, industrialists, investors and policymakers by providing insights into the integration of Fintech with green finance for sustainable development. These findings affirm the pivotal role of Fintech and green finance in fostering sustainable economic development. The novelty of the topic and the variety of publications in which it has been published demonstrate that sustainable economic development has piqued the interest of a wide range of areas.

The purpose of this paper is to propose a mathematical model that can quantitatively and vividly measure the current capability of enterprises by evaluating their existing knowledge inventory.

Knowledge management starts from assessing the current position of enterprises, visioning future intention, and then specifying knowledge objectives and formulating knowledge strategies. Along with this process, a critical component that must be realized in order to assist management in determining knowledge objective and strategies is the assessment of existing knowledge inventory.

The paper finds that enterprises can better position themselves by deciding realistic objectives and formulating achievable strategies.

The force that drives human history from agricultural age to industrial age, information age, and finally to the knowledge age was innovation, and the crucial element determining the level of products and services innovation lies in people. In other words, all innovations come from human resources, and knowledge enables human beings to be creative. Especially, in this global era with stiff competition, enterprises should create, manage and preserve knowledge better than their competitors in order to maintain competitive advantage, and therefore remain ever‐lasting existence.

Previous investigations mainly employed qualitative methods, such as questionnaire survey and in‐depth interviews, to explore knowledge management performance of enterprises.

The paper shows that by categorizing the knowledge inventory in terms of knowledge depth and width, enterprises would be able to formulate suitable knowledge strategies of accumulation, widening, deepening, or strengthening and specify proper learning strategies to bridge the knowledge gap to cope with the competition.

The purpose of this paper is to find the optimal values of process parameters in injection molding when both warpage and shrinkage are minimized.

In finding the optimal values, advantages of finite element software, Moldflow, and dual response surface method (dual RSM) combined with the nonlinear programming technique by Lingo are exploited. Considering the nine process parameters, injection time, injection pressure, packing pressure, packing time, cooling time, coolant temperature, mold‐open time, melting temperature and mold surface temperature, a series of mold analyses are performed to exploit the warpage and shrinkage data. In the analyses, warpage is considered the primary response, whereas shrinkage is the secondary response.

The results indicate that dual RSM combined with the nonlinear programming technique can outperform the Taguchi's optimization method. The optimal process values are also confirmed by re‐running experiments on Moldflow. Additionally, an auxiliary dual RSM model is proposed to search for a better result based on the given findings by dual RSM at the cost of running extra experiments. Based on dual RSM, a multiple objective optimization for the whole plastic product is finally suggested to integrate the dual RSM models that are developed for the individual nodes or edges.

This paper proposes a new method to find the optimal process for plastic injection molding.