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Human capital is acquired not only through formal education (e.g. general skills) but also through training at the workplace. Prior studies have ignored the role of government subsidies explicitly for on-the-job training, which may influence firm training decisions and firm innovation performance. Hence, the authors establish a comprehensive theoretical framework to consider these issues and fill these gaps.

Considering the Chinese manufacturing firms listed in the Shanghai and Shenzhen Stock Exchange from 2010 to 2017, the authors investigate the influence of training investment on innovation performance by illustrating the role of human capital updating in enhancing firm innovation. The authors also explore serval mechanisms on how training investment influences innovation performance.

The authors propose that training investment promotes firm innovation performance, whereas government training subsidies negatively moderate this relationship. The authors also reveal how technicists' involvement and corporate culture mediate the relationship between training investment and innovation performance.

This study provides policy implications for stimulating firm innovation by improving learning and absorption ability, strengthening cultural identity and implementing system norms. Effective policies should be adopted to provide subsidies for on-the-job training of enterprises, particularly for firms with technical executives and firms in diversified life-cycle.

This work contributes to the literature on the role of on-the-job training in promoting firm innovation and reveals the crowding-out effect of subsidies. This study also shows the heterogeneous effects of training investment on firm innovation.

The affordable loss (AL) heuristic, as one crucial sub-dimension of effectuation, delineates the maximum level of investment entrepreneurs are ready to lose in a worst-case scenario. Conflicting conceptualizations remain regarding whether entrepreneurs’ psychological traits matter for AL. Based on the narcissistic admiration and narcissistic rivalry perspective, this study investigates the relationship between chief executive officer (CEO) narcissism and AL behaviors.

Using data collected from the CEOs and paired vice presidents at 122 small and medium enterprises (SMEs) in mainland China, the authors intend to further explore the association between psychological traits, especially CEO narcissism and AL behaviors under environment and resource constraints (e.g. perceived uncertainty and slack resources).

The findings show that CEO admiration-based narcissism is positively related to AL behaviors in the firm. Furthermore, when firms hold more slack resources, narcissistic admiration has a stronger positive association with AL; while when the environment becomes more uncertain, narcissistic admiration has a weaker positive association with AL. In contrast, CEO rivalry-based narcissism is negatively related to AL behaviors in the firm. When the environment becomes more uncertain, narcissistic rivalry has a stronger negative association with AL.

This article contributes to trait-based effectuation research and suggests that individual psychological traits affect AL behaviors at the firm level, though the patterns of the relationship vary with both the type of narcissism and contexts.

The purpose of this paper is to investigate the cointegration and volatility spillover dynamics between the USA and South Asian stock markets, namely, India, Pakistan and Sri Lanka. The main objective of this study is to provide the knowledge about integration of financial market and volatility spillovers before, during and after global financial crisis to investors, fund managers and policy-makers.

The Johansen and Juselius cointegration test, Granger Causality test and bivaraite EGARCH model have been applied in this study to examine integration and volatility spillovers between selected stock markets.

The findings show that long-term integration between the USA market and South Asian emerging stock markets. It is found that USA stock market has causal relationship with emerging stock markets in short-term. The findings of EGARCH model reveal that asymmetric volatility spillover effects significant in all selected stock markets in pre, during and post-crisis periods. Furthermore, significant volatility spillover is found from stock markets of USA to all selected South Asian markets during and post-crisis periods. However, volatility spillovers from USA to India and Sri-Lanka markets are significant, while insignificant in case of Pakistani market in pre-crisis period. Overall, we find that returns and volatility spillover effects are higher in financial crisis period as compared to non-financial crisis period.

The findings of this paper have important implications for investors, portfolio managers and policy-makers. They can take potential benefits from international portfolio diversification by considering all these facts. The understanding and knowledge of across volatility transmission help them to maximize the gains from diversification and minimize the risk. Policy-makers can develop such strategies which protect the markets of these economies from future financial crisis.

Although in finance literature numerous studies have been conducted on integration between different stock markets, most of the studies investigated the integration and volatility spillovers between developed stock markets. However, many studies also analyzed the integration among emerging stock markets in literature review but it is hard to find studies in the context of South Asian stock markets on the effect of global financial crisis on stock markets. The main contribution of this study is to investigate the stock markets integration and volatility transmission between the USA and South Asia by considering the effect of recent 2007 US subprime financial crisis.

This study explores whether and how Internet usage improves the subjective health of middle-aged and older adults by analyzing the mediating role of social engagement and heterogeneity of different living arrangements.

Based on data from the China Health and Retirement Longitudinal Study, the ordinary least squares (OLS) method is adopted to explore the relationship between Internet usage and the subjective health of middle-aged and older adults. Propensity score matching method (PSM) is used to alleviate self-selection bias in the samples. The bootstrap method is adopted to test the mediating role of social engagement, and generalized structural equation modeling (GSEM) is employed to resolve endogeneity. A permutation test is adopted to examine the heterogeneous effects of Internet usage on different living arrangements.

Internet access can help relieve depression among middle-aged and older adults and enhance their self-rated health, leading to perceived changes in health status. However, Internet usage is not directly associated with health satisfaction among middle-aged and older adults. Nevertheless, Internet usage can enhance middle-aged and older adults' subjective health by facilitating social engagement and significantly influences middle-aged and older adults living with their children.

This study reveals the underlying role of Internet usage among older adults and provides insights for governments and families to help middle-aged and older adults actively adapt to a digital society and improve their health.

The purpose of this paper is to investigate the impact of product description and involvement on purchase intention in a cross-border e-commerce (CBEC) setting from a psychological perspective.

This study proposes a research model of purchase intention in CBEC based on the involvement theory and commitment-involvement theory. The research model was tested using the covariance-based structural equation modeling technique. Data were collected from consumers on a popular CBEC platform in China.

A high-quality product description has no significant positive effect on purchase intention, but it has significant positive effects on product cognitive involvement, product affective involvement, platform enduring involvement and platform situational involvement. In addition, product affective involvement, platform enduring involvement and platform situational involvement all have significant positive effect on purchase intention, but this effect is not significant in the relationship between product cognitive involvement and purchase intention.

This study calls for sellers to optimize product descriptions on CBEC platforms in order to attract more buyers and generate more profits.

This study integrates two theories of involvement into the research model in the CBEC context. Based on this model, the authors analyzed how product description affects purchase intention under the joint influence of two involvement factors.

The purpose of this paper is to explore the impact of product information on impulse purchases in a cross-border electronic commerce (CBEC) setting from the perspective of cue stimulation.

This study proposes a research model of impulse purchases in CBEC based on the cue utilization theory and Stimulus-Organism-Response (S-O-R) model. The research model was tested using covariance-based structural equation modelling. Data were collected from the consumers of a popular CBEC platform in China.

A high-quality product description has a significant positive effect on concentration but not on curiosity and autotelic experience. A high-quality product display has a significant positive effect on concentration, curiosity and autotelic experience. High-quality product content has a significant positive effect on curiosity and autotelic experience but not on concentration. Curiosity and autotelic experience both have a significant positive effect on impulse purchases; however, concentration has no such effect on an impulse purchase. Curiosity and autotelic experience have a full mediation effect between product display and impulse purchases and between product content and impulse purchases, respectively.

This study integrates the S-O-R model and cue utilization theory to construct a theoretical model of product information-flow experience-impulse purchases. According to the model, we can understand how product information influences consumers' impulse purchases in CBEC.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

To realise the shared development of the digital economy, people need to transcend the capital logic and advocate the logic of cooperative development, i.e. “co-construction, benefit-sharing and co-governance”. This study aims to discuss the aforementioned statement.

Platform economy is a new economic form produced by the transformation of the social production patterns in the era of digital capitalism. In the neo-imperialist stage, a new stage of capitalist development, capital logic promotes the global expansion of the platform economy and influences its development process, organisational form, contradictions and dilemmas and internal transcendence. Having the spatiotemporal chain of capital circulation repaired, the globalisation of the platform economy is reshaping how the means of production are combined with labour, affecting the local changes in the general relations of production and “international relations of production”.

In the accumulation of digital capitalism, the social contradictions and fundamental contradictions in the capitalist world have been further intensified, making exploitation, income distribution gap, monopoly and other problems increasingly severe. The imbalance and inequality in the global development of the digital economy are increasingly prominent.

Regarding the global governance of the digital economy, China, as a major responsible country, will strive to encourage all countries to co-build a community with a shared future in cyberspace. In the new international development pattern of digital economy globalisation, China must take effective measures to actively safeguard its national security and development interests to meet specific challenges.

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.