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We examine how corporate violations impact trade credit behaviors within the supply chains, focusing on both trade credit financing and provision through the lens of reputation damage and restoration. We distinguish trade credit financing under different supply chain concentrations, firm characteristics and analyst attention. Additionally, we examine how violating firms adjust trade credit provision to customers and suppliers of varying sizes.

We empirically test the hypotheses using fixed effects ordinary least squares with a sample of Chinese A-share listed companies from 2007 to 2022. Additionally, we conduct robustness tests, including the use of alternative estimation models, substitution of independent variables and the application of instrumental variables.

Firstly, trade credit financing declines post-violations. Secondly, companies raise trade credit provision to suppliers and customers to restore trust relationships and reputation. Thirdly, reputation acts as a mediator between violations and trade credit behaviors. Fourthly, negative effects are more pronounced in low supply chain concentration, more analysts and non-state-owned companies. Lastly, trade credit provision is increased for small suppliers and large customers to restore trust relationships and reputation.

We find corporate violations reduce trade credit financing while increasing trade credit provision, thereby increasing operating costs. Even in emerging economies with weak market institutions, firms should avoid violations to maintain trust and ensure operational stability.

We examine how violations influence credit management in supply chains, filling the research gap. Additionally, we supplement an understanding of reputation damage and restoration behaviors, contributing to the literature on how to restore reputation.

Leisure‐based tourism constitutes an important part in China’s domestic tourist market. This article reports the findings of a case study of leisure‐based tourist behavior conducted in Changchun, the capital city of Northeast China’s Jilin Province. The composition and demographic profile of the leisure‐based tourist market are presented. Leisure‐based tourists’ preference with regard to destination choice, leisure facilities used, and accommodation is analyzed. It is concluded that in‐depth understanding of leisure‐based tourist behavior will contribute significantly to the enhancement of tourist city images, modification of urban tourist attraction systems, effective exploitation of urban tourism resources and destination management and marketing.

The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk.

Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression.

The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk.

Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions.

To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time.

The purpose of this paper is to empirically analyze the transmission mechanism between benchmark interest rate of financial market, money market interest rate and capital market yields in order to reveal the dynamic evolution characters and core influential structure between different market interest rates.

Using Dirichlet-VAR (DVAR) model, this study analyze the relationship between markets rates according to the equilibrium model in money market and capital market.

Empirical results show that the interest rate transmission mechanism functions smoothly between interest rates of different levels. Interest rate of bills issued by the central bank can effectively reflect changes in monetary policy and guide the fluidity of market, playing the anchor role in interest rate pricing. There exists a closed loop feedback between interest rate of bills issued by the central bank, and money market interest rate, as well as between money market interest rate and bond market interest rate. The former is a loop by administrative means while the latter is the one mainly affected by market-oriented means. The response by money market and bond market toward the change of benchmark interest rate is unsymmetrical as money market is more sensitive to a loose monetary policy while bond market is more sensitive to a tight monetary policy. Stock market is strongly affected by uncertainty of benchmark interest rate.

DVAR model is the extension of research on instable data and multiple variable causality test, which expands the causality analysis between two variables to multiple variables causality impact analysis which contains non-stable and structurally instable economic data.

In order to pursue men's suit wear comfort, the basic data on an accurate men's suit comfort analysis with clothing pressure is required. Therefore, in such investigation, it is difficult to find out ideal persons as test subjects. In the measuring experiments, we used dummies designed for resuscitation practice to obtain the clothing pressure with both normal standing posture and movement patterns and compared these data with subjects' to give the relationship of clothing pressure between the dummy and subject. The correlation between the measurements with dummies and the subjects' are shown. It turns out that the dummy D1 and D6 are mainly as intended in pressure measurement with normal standing posture. But the dummy D2 and D3 are not. It turns out that the dummy D1 can imitate the human shoulder's movement patterns well, and the dummy D4 and D6 are mainly as intended. But the dummy D2 is not. It turns out that all the dummies have a certain limitation to be placed in clothing pressure measurements. It also shows that instead of subject to use dummy to investigate clothing pressure with both normal standing posture and movement pattern, not only dummy's features such as compression hardness, form and size, but measuring postures are also needed to take into consideration.

This paper aims to propose image stitching by reduction of full line and taking line image as registration image to solve the problem of automatic optic inspection in PCB detection. In addition, surf registration was introduced for image stitching to improve the accuracy and speed of stitching.

First, image stitching proceeded by method of full line reduction and taking line image as registration image; second, surf registration was introduced based on the traditional PCB image stitching algorithm. Scale space of the image pyramid was adopted for confirming relative future points between stitching image. The registration means of nearest neighbourhood and next neatest neighborhood was selected for feature matching and fused in region of interest to fulfil image stitching.

The improved stitching algorithm with small data size of image, high speed and noncumulative transitive error eliminated displacement deviation and solved the stitching gap caused by uneven illumination, to greatly improve the accuracy and speed of stitching.

The research of this paper can only used for appearance detection and cannot be used for solder joint inspection with circuit detection or invisible solder joint detection; it can identify and mark PCB component defects but cannot classify automatically, thus artificial confirmation and processing is needed.

Based on the traditional image stitching means, this paper proposed full line reduction for image stitching, which reduces processing of data and speeds up image stitching; in addition, surf registration was introduced into the study of PCB stitching algorithm, which greatly improves the accuracy and speed of stitching and solves stitching gap formed by opposite variation trend of image local edge caused by uneven illumination.

Guided by the proposed opportunity and resource integrative entrepreneurial growth model, the purpose of this paper is to explore how different opportunity type firms grow integrally and what the laws for different opportunity integration growth modes are.

A multiple case study on six firms in information and communication technologies industry in China is conducted.

Three types of entrepreneurial opportunities exist. These are identification type opportunity, discovery type opportunity, and creation type opportunity. Entrepreneurship among the three types of entrepreneurial opportunities operates under different laws. For each type of entrepreneurial opportunity, firms need to balance the two growth modes, namely, the opportunity identification and assessment/resource allocation mode and the opportunity utilization and resource identification/acquisition mode, in order to achieve a leveraging effect.

Vertical comparison is missing.

Firms need to balance the two growth modes, namely, opportunity identification and assessment and resource allocation mode and opportunity utilization and resource identification and acquisition mode, to achieve leverage effect.

This paper integrates system theory with entrepreneurship research and proposes the opportunity and resource integrative entrepreneurial growth model. This model is helpful, both in aiding firms to follow the laws for entrepreneurial opportunities and to identify entrepreneurial opportunities. The business model plays a vital role in entrepreneurship. Firms taking advantage of the Internet of Things are more likely to gain a competitive advantage and to achieve success in their entrepreneurial activities.

The purpose of this paper is to explore the role of CEO transformational leadership in promoting ambidexterity of top managers. This paper posits that connecting CEO transformational leadership with the CEO-top manager interface offers a better explanation of heterogeneity in top manager ambidextrous behavior.

This study is based on a questionnaire survey of 388 senior managers in 80 top teams nested in 80 small- to medium-sized Taiwan manufacturing firms.

The findings indicate that transformational CEOs shape the CEO-top manager interface, characterized by senior team behavioral integration, decentralization of responsibilities, long-term compensation, and individual manager risk propensity, and in turn promote ambidexterity at the individual top manager level.

Hence, the authors contribute to the existing understanding that transformational CEOs may not only have a beneficial influence on firm-level ambidexterity, but also may be particularly effective in enabling individual-level top managers to simultaneously explore and exploit.

Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM).

Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations.

The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations.

This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.

The purpose of this paper is to provide an effective members-adding method for truss topology optimization in plastic design.

With the help of the distribution of principal stress trajectories, obtained by finite element analysis of the design domain, ineffective zones for force transmission paths can be found, namely, areas whose nodes may have ersatz nodal displacements. Members connected by these nodes are eliminated and the reduced ground structure is used for optimization. Adding members in short to long order and limiting the number of members properly with the most strained ones added, large-scale truss problems in one load case and multiple-load cases are optimized.

Inefficient members (i.e. bars that fulfil the adding criterion but make no contribution to the optimal structure) added to the ground structure in each iterative step are reduced. Fewer members are used for optimization than before; therefore, faster solution convergence and less computation time are achieved with the optimized result unchanged.

The proposed members-adding method in the paper can alleviate the phenomenon of ersatz nodal displacements, enhance computational efficiency and save calculating resources effectively.