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This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

A substantial amount of research has examined the firm value impact of corporate social responsibility (CSR). Nevertheless, the findings have been inconsistent, prompting researchers to identify contingencies under which the impact varies. This study examines how information technology (IT)-enabled knowledge capabilities moderate the relationship between CSR and firm value.

We conducted the ordinary least squares (OLS) regression analysis on a sample of S&P 500 companies spanning from 2010 to 2017. We employed additional methods to test the robustness of the results, including the generalized method of moments (GMM) estimator and the two-stage least squares (2SLS) method.

The results show that IT-enabled absorptive capability (IT-AC) and IT-enabled social integration capability (IT-SIC) positively moderate the CSR–value relationship. Further, their moderating effects vary in distinct ways when environmental dynamism changes, hinting at the distinct underlying rationales behind the moderating roles of IT-AC and IT-SIC.

This study improves the understanding of the business value of CSR and IT. It has limitations in generalizability due to the use of secondary data.

This study provides practical guidelines to managers about how to strategically leverage IT resources for the creation of CSR value.

Encouraging businesses to enhance their CSR efforts and uphold sustainability extends beyond our immediate benefit and impacts future generations as well. However, due to an imbalance between costs and returns, companies often refrain from being wholeheartedly devoted to CSR. Our insights on guiding companies to derive more value from CSR can inspire their greater investment in CSR. Meanwhile, companies can obtain additional returns from deployed IT.

This study extends the IT business value literature by revealing how IT generates firm value in the context of CSR. It also adds critical insights into the mixed findings in previous research regarding the CSR–firm value link. The study’s findings offer useful guidance on the strategic deployment and utilization of IT resources to facilitate the creation of CSR value.

This study investigates whether and how place-based industrial relocation policy affects firm innovation.

By exploiting the establishment of China's National Industrial Relocation Demonstration Zones (NIRDZs) as a quasi-natural experiment in a difference-in-differences design, the authors examine the externalities of industrial policies that support sustainable development and growth from the perspectives of firms' patenting activities.

The study consistently finds that the NIRDZs policy significantly boosts local firm innovation, translating into a 60.46% increase in the patent applications of treated firms. The estimation results remain robust to a series of alternative specifications. Moreover, heterogeneity analysis suggests that the firms that benefited most were state-owned enterprises, firms with higher productivity, or firms in non-high-tech industries. Further, the authors find that the NIRDZs policy stimulates firm innovation mainly in the form of utility model patents, followed by designs and invention patents.

The results provide suggestions and implications for policymakers to improve the efficiency of state-led industrial policies and avoid “government failure” in policy implementation.

This study provides suggestions and implications for policymakers to improve the efficiency of state-led industrial policies and avoid “government failure” in the policy implementation.

This study fills the research gap by exploiting quasi-experiments to assess the effectiveness of state-led industrial policies for emerging economies. (2) The analysis sheds empirical light on how corporate innovation is motivated and financed by selective and functional industrial policies. (3) Theoretically, the results rationalize why state-led industrial relocation fuel innovation capabilities of localities from Marshall externalities and competition crowding-out effects.

This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.

The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.

The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.

This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

Agency theory postulates that research and development (R&D) investments are subject to managerial discretion and thus may not enhance firm value as expected. The inconclusive empirical findings in the literature is a testament of that. This paper aims to investigate the interplay between board gender diversity (i.e. women on boards) and value relevance of firms’ effort to innovate as indicated by firms’ R&D investments.

Through a sample of 1,626 US-listed firms from the period 2004 to 2019, the authors examine whether board gender diversity promotes or hampers value relevance of firms’ efforts to innovate. The authors use ordinary least squares as the baseline model and address potential endogeneity through instrumental variable two-stage least square, and selection bias through Heckman selection model. Finally, the authors use the financial crisis of 2008 as a natural experiment to investigate the effect of board gender diversity during the crisis period.

The results show that board gender diversity positively moderates the relation between R&D and firm value. In times of financial crisis, R&D does not destroy firm value in firms with gender diverse board. The results are robust to measurement error, endogeneity issue, particularly simultaneity and selection bias.

The findings in this study have several practical implications. Firms that invest heavily in R&D should be mindful of gender diversity in their board recruitment strategies to enhance innovation outputs and firm value. Current and potential investors (i.e. shareholders) should take into consideration board gender diversity in their investment decision-making processes as the results show that gender diverse boards promote more effective governance, which, in turn, leads to better alignment of R&D investments with shareholder value. Regulators aiming to improve corporate governance policies should encourage gender diversity on the boards. The results align with global initiatives such as the United Nations Sustainable Development Goals, particularly Goal 5 on gender equality. Policymakers may use the findings in this study to advocate for more gender diverse governance structures within corporations.

This study investigates the role gender diverse boards play in creating value from firms’ R&D activities.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

Despite the reputation of the metal-based porous media for their ability to augment heat transfer as widely witnessed in the literature and practically operating heat exchanging applications, the coexisting penalty of the increased pressure drop demanding increased pumping power poses a major concern that invites the need for an alternate solution to handle this unsought outcome. Therefore, this study aims at providing a better solution to the existing cost and benefit scenarios to benefit a plethora of engineering applications including energy transfer, energy storage and energy conversion.

This work highlights on the property of stacked woven wire mesh porous media such as their stacking types, porous conditions and thickness scenarios that can potentially result in distinct trade-off scenarios. A vertical channel is numerical modelled by using REV scaled modelling technique using Darcy-Forchheimer and local thermal non-equilibrium models to illustrate the possibilities of this variety of trade off scenarios between the desirable heat transfer and the unsought flow resistance.

This work illustrates the advantages of wire mesh-based porous medium and its distinct potential in controlling the existing trade-offs between the cost and benefit aspects. It is found that by varying the features of wire mesh porous media, the interplay between the conflictingly existing characteristics can be much easily handled specific to distinct requirements associated with variety of engineering applications.

The study emphasizes on a new solution or methodology to handle the penalty of pressure drop associated with metal-based porous media. Through this study, a novel approach to control the ultimately costing pumping power at the benefit of increased heat transfer is provided considering various requirements that could be associated with any thermal management systems. Various possibilities and potentials of wire mesh porous media are illustrated highlighting on their benefit of ease with which the mentioned goals can be achieved.

Public reviews on educational robots are of great importance for the design, development and management of the most advanced robots with an educational purpose. This study explores the public attitudes and emotions toward educational robots through online reviews on Weibo and Twitter by using text mining methods.

Our study applied topic modeling to reveal latent topics about educational robots through online reviews on Weibo and Twitter. The similarities and differences in preferences for educational robots among public on different platforms were analyzed. An enhanced sentiment classification model based on three-way decision was designed to evaluate the public emotions about educational robots.

For Weibo users, positive topics tend to the characteristics, functions and globalization of educational robots. In contrast, negative topics are professional quality, social crisis and emotion experience. For Twitter users, positive topics are education curricula, social interaction and education supporting. The negative topics are teaching ability, humanistic care and emotion experience. The proposed sentiment classification model combines the advantages of deep learning and traditional machine learning, which improves the classification performance with the help of the three-way decision. The experiments show that the performance of the proposed sentiment classification model is better than other six well-known models.

Different from previous studies about attitudes analysis of educational robots, our study enriched this research field in the perspective of data-driven. Our findings also provide reliable insights and tools for the design, development and management of educational robots, which is of great significance for facilitating artificial intelligence in education.