Search results

The purpose of this paper is to shed light on the effect of high‐level human capital investment, using tertiary education as the proxy, on the urban‐rural income gap in China.

Using a panel dataset covering 28 provinces of China over the period from 1988 to 2007, this paper employs Hansen's method and two‐step GMM‐SYS estimator to estimate the threshold regression model and the dynamic fixed‐effect panel model, respectively.

The urban‐rural income gap is found to be related to high‐level human capital investment in an inverted U‐shaped pattern with respect to economic development level. The estimated threshold turning point is around 20,000 RMB GDP per capita. This estimate is sufficiently robust to model specifications and variants of the dependent variable.

The authors forecast that high‐level human capital investment could play a role in bridging the urban‐rural income gap at the national level by 2014, when China's GDP per capita assumes an annual growth rate of 7.5 percent.

This, it is believed, is the first research to find an inverted U‐shaped pattern for high‐level human capital investment and urban‐rural income gap nexus in China.

The purpose of this paper is to gain an understanding of the current research on Internet-plus.

The authors collected scholarly publications from the scientific databases Web of Science, Core Collection, Inspec and Compendex (Ei Village 2) and performed statistical analysis of the retrieved data from five perspectives.

The research on Internet-plus has obtained increasing attention in China. The top three research fields were social science, education and management.

The study will help researchers understand the current trends on Internet-plus.

The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.

This paper begins its research with the statistics and analysis of plagiarism data and a review of expert interviews regarding online literature publishing. All of these data materials were collected from anti-plagiarism platforms, online literature websites, news report websites and judiciary office websites.

The paper provides empirical insights into why the plagiarism is so rampant in the publishing of online literature in China. It suggests that the current task of controlling network literature plagiarism is arguably created by the literary production platform, which leads to the problem of the validity of the “self-monitoring model.” In fact, controlling plagiarism must be emphasized by means of external monitoring, because strict supervision and various external punitive measurements for committing plagiarism can force literature-generating platforms to strengthen their own internal monitoring.

Online plagiarism occurs almost constantly, but it rarely results in court cases over copyright because of the lack of a robust copyright ecology in China. This paper considers large amounts of data and cases from self-publishing media platforms.

The paper includes implications for the development of plagiarism management in online literature publishing from the publishing Association, media and government.

This paper suggests to online literature users that plagiarism will be controlled when certain active measures against it are taken. The authors hope that this view will promote the development of original online literature.

This paper points out that China must strengthen supervision that comes from outside the online literature generate platforms to control the current rampant plagiarism that occurs on these platforms.

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

Twitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.

In the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.

The proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.

This paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

Recent legislation in Europe and North America encourages women’s participation in corporate boards based on the belief that gender-diversified boards contribute positively to firm performance and increased competitiveness. Contrary to the West, the women’s participation rate in business has been traditionally high in China. The purpose of this paper is to find out whether gender-diverse corporate boards of Chinese automotive firms perform better financially than gender-homogeneous boards.

By drawing on data from the Chinese Government and Bloomberg, the authors compare and analyze the differences in financial performance (return on equity, asset growth, sales growth) and risk behavior (debt risk, R&D expenditure) of Chinese automotive firms with and without women on their corporate board.

There is significant evidence that firms with women on the board perform better across all three categories, with the exception of return on equity, for which they found no significant differences among the analyzed firms.

While women’s participation in corporate boards in China is low, the results of this study suggest to policy makers and firms alike to implement measures that support gender-diversified boards in order to take advantage of their potential to increase corporate performance.

So far, the performance of corporate boards of countries with a traditionally high share of female participation in the workforce has rarely been analyzed. Research focusing on the Chinese automotive industry is new and underrepresented, although China is the largest automotive market worldwide and a key industry of the domestic economy. This investigation contributes to the literature stream on board diversity in as well as to industry-related studies. With the example of the Chinese automotive industry, it provides empirical evidence of better performance of firms with gender-diversified boards within the categories tested.

This chapter examines the changing state–university–student relationships in post/socialist China since the late 1980s. We begin with an introduction to four salient themes in scholarship on Chinese post/socialism that are highly relevant to higher education: globalization, gradualism, civic society, and a critique of holism. These themes help us explain interrelated educational trends that affect the state–university–student relationship: the globalization, “massification,” and stratification of higher education; the redefined role of the state in university governance and management; higher education marketization and privatization; and the quest for meaning and (e)quality in and through higher education. Our general argument is that during the “socialist” period the main relationship central to higher learning was between the state and students. Universities were agents of the state; from a legal point of view, indeed, universities did not have an independent status from the state. In the “post-socialist” era the university–student relationship has become more significant. We examine this reconfiguration through two case studies, one on the development of college student grievance and rights consciousness, and the other on reforms in higher education student services administration. When looked at from the point of view of the state, we see that appropriation and implementation of policies and regulations shaping student rights and services are in partial contradiction with state policies to accelerate economic growth and bolster party authority. From the point of view of universities, we see institutions grappling with how to deliver on forward-looking structures and actions while navigating between the state's policy mandates and growing expectations and demands of its student and business stakeholders. From the point of view of students, we see how constrained agency, uncertainty, and the power of the credential motivates social praxis. At all levels of the state–institution–student relationship actors are employing a kind of pragmatic improvisation (one of the salient features of post/socialism) captured by the well-known Chinese proverb “groping for stones to cross the river.” This saying is an apt metaphor for the tentative searching by state, institution, and individual for a safe foothold in the post/socialist world.

This paper aims to investigate, in China stock market, whether the reputation loss of a firm caused by financial restatements will lead to significant economic consequences such as financial distress and how a firm should respond to such a crisis.

This paper uses Chinese A-share listed firms from 2004 to 2013 as research samples to test research hypotheses using regression analyses.

This paper finds a significant relationship between restatements and financial distress, and such a relationship will be affected by both the type and the magnitude of restatements. More importantly, we find joint effects of restatements and state ownership on financial distress, which provides a unique contribution to the extant literature in restatement, financial distress and crisis management using Chinese stock markets data. It shows that ownership structure, affecting the firm reputation and crisis responses strategies, plays a significant role in consequences of restatements, and it is more important for state-owned enterprises (SOEs) to undertake an appropriate crisis response strategy to reduce the negative impact of restatements.

The results suggest that the damage to a firm’s reputation caused by restatements is affected by restatement type and state ownership. To reduce the negative consequences and avoid financial distress, firms should consider both the restatement type and their firm characteristics when deciding different actions to respond to restatements. In particular, SOEs should act in a more timely manner and take reputation-rebuilding actions such as taking the responsibility and making apologies and taking prompt remedial actions after restatements to regain the public trust and avoid more serious economic consequences. The Chinese government should strengthen their supervisions of SOEs and put more effort to help SOEs reduce administrative procedures, and to improve the efficiency of the implementation of recovery plans after restatements to reinstate firm credibility.

First, this paper is among the first to link financial restatement, including the type and magnitude of restatements, with financial distress, and the authors find a significant relationship between restatement type and financial distress in China stock markets. Second, this paper is the first to examine whether there is a joint effect of state ownership and restatements on financial distress. Third, this study examines how the magnitude and pervasiveness of restatements influence financial distress and find that both result in an increase of financial distress. Finally, this paper is among the first to connect crisis management and accounting literature to explain how a reputation loss caused by financial restatement may damage a firm’s value and subsequent performance, and based on which to suggest crisis-responses strategies.

The main purpose is to minimize the total inventory cost of chain, whereas the stochastic constraints are satisfied. In other words, the goal is to find optimum agreed stockpiles and period length for products to minimize the total inventory cost of the chain while the stochastic constraints are fulfilled.

This paper designs and optimizes an integrated inventory model in a four-echelon supply chain that contains a supplier, a producer, a wholesaler and multiple retailers. All four levels agree with each other to make an integrated inventory system. Products in this model have a multi-stage production process, and the model is bounded by multiple stochastic constraints. The problem model is nonlinear and large. So, the interior point method as an effective algorithm is used for solving the recent convex nonlinear model. Two numerical examples are solved to demonstrate the application of this methodology and to evaluate the performance of the proposed approach.

The findings showed the model is applicable for real-world supply chain problems in the cases that echelons are going to do executive external integration. Also, the Interior Point algorithm has a satisfactory performance and a high efficiency in terms of optimum solution for solving nonlinear and large models.

The authors designed and optimized the inventory cost in a four-level integrated supply chain in stochastic conditions. The new decision variables, number of chain levels, multi-products, stochastic constraints and multi-stage products in four-level integrated supply chain are other novelties of this paper. The authors provided an efficient algorithm for solving a large-scale and nonlinear model in this research, too.