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The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion.

The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model.

This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades.

The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

This study aims to explore how earnings management techniques are affected by corporate financial debt risk (FDR), internal control (IC) effectiveness and CEO education.

The study uses a sample from listed firms in China from 2010 to 2017, comprising different industries, including agriculture, forestry, livestock farming and fishing; mining; manufacturing; electric power, gas and water production and supply; construction; transport and storage; information technology; the real estate industry; social services; and communication and cultural. The regression analysis is used to test the hypotheses. The two-stage least squares technique is used to check for endogeneity issues.

The study finds that firms are less likely to manage real earnings when they have more robust IC and FDR. Likewise, companies with weak ICs are more likely to manipulate real earnings. Besides, the study finds an influence of CEO education on the relationship between IC, FDR and real earnings management (REM). These results can be applied to the sectors in the sample covered by the research, and the authors do not overlook the energy industry sector for the importance of its role in the economy.

There are some limitations for the researcher when performing any research, and this study is no exception. Researchers are urged to take these circumstances into consideration when generalizing or comparing the results because the methods used to calculate the measurement variables in each study may differ somewhat from those used in other research. In addition, expanding the current research design to incorporate additional nations may be an area of interest for future research and could aid in evaluating the effects of nation-specific elements (such as inflation, culture, legal systems and political considerations) on the usefulness of IC and decreasing FDR. Second, the current study focuses on the impact of IC and FDR on REM; this paper does not dissect the “black box” of IC and consider how each element affects earnings management. Future research may need to focus specifically on how effective IC would affect earnings management and precisely what IC mechanisms would discourage the management of earnings.

Helping companies listed in China to make decisions and improve investors’ vision of the results of real companies’ businesses, as well as helping management to avoid falling into debt risk and the consequent effects and manipulation of earnings.

By highlighting the significance of IC and debt risk in enhancing information quality in China, the results contribute to the body of work examining the relationship between IC, FDR and REM. In addition, this study uses a CEO’s education to moderate this link.

Performed over a five-year time horizon, this paper aims to analyze the progression rates of technological innovation across 15 sub-provincial Chinese cities. The authors quantify and rate innovation performance, then rank the cities based on a purpose-built index designed to gauge the rate of technological progress.

Using the inferior constraint method, and a variety of national sources of data, the authors construct an innovation index based in part on new product sales revenue, proportion of college students, research and development expenditure of industrial enterprises in relation to gross industrial output value, contract deals in technical markets per capita, hazard-free treatment rate of waste, enterprises with technical development agencies accounts for industrial enterprises, number of high-tech enterprises and invention patent ownership per million population.

The findings provide a methodology for indexing cities, with 15 Chinese provincial cities as examples. Among the top five cities with the highest technological innovation index were Shenzhen, Nanjing, Guangzhou, Hangzhou and Wuhan. In the bottom were Shenyang, Changchun, Dalian, Xi’an and Harbin.

This study applied a new model of innovation at the city level for China. Application to other industries (real estate, manufacturing, etc.) and countries will extend boundaries of this model and show its wider applicability.

Companies can use this research and methodology when seeking new investments in high tech and innovative products. Locations offering more hospitable environments should be prioritized ceteris paribus.

One weakness of much of the international business and competitiveness literature is that it often views the country as the primary unit of analysis. In this way, nuanced views of the institutional environments within countries are often overlooked. This paper proposes a measure of regional rates of innovativeness across China.

Compared with the fierce price competition in 1998, the well‐order price competition is witnessed in Chinese iron and steel industry now and the pricing behaviors of steel firms also follow the certain rules. Based on the methods of collecting the secondary data and interviewing, this paper examines the pricing behaviors of firms to explain the how Chinese steel firms make their pricing decisions and maintain the well‐order competitive relationship among them. The authors found out that (1) most Chinese steel companies adopt a kind of strategic perspective in their pricing decision making, in which understanding of the market trend and the close attention to their competitors are both important; (2) there obviously exists price leader and followers in Chinese iron and steel industry, and the relationship between price leader and followers is relatively stable and the factor behind this phenomenon is the existence of a kind of informal platform of communication among competitors, government and trade associations.

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

One increasingly popular financing option for entrepreneurial ventures is to attract corporate venture capital (CVC) investments. Prior research tends to take a CVC-centric perspective assessing the benefits and contingencies for incumbent firms or corporate investors to engage with entrepreneurial ventures. Few studies have taken the opposite perspective of investigating factors that entrepreneurial ventures need to take into account when engaging with CVC investments. As such, this study aims to investigate pre- and post-IPO entrepreneurial venture performance that partners with CVC providers or corporate investors, as well as to assess organizational and environmental contingencies.

This study draws on a sample of 631 entrepreneurial ventures from the CSMAR database ranging from 2009 to 2019, along with CVC financing data from the CVSource database and financial data in entrepreneurial ventures’ annual reports from the Juchao Network. This study applies multiple linear regression modelling and fixed effect panel data analyses to test the proposed hypotheses.

The results show that CVC investment contributes to entrepreneurial ventures’ financial performance, both pre- and post-IPO. However, while research and development (R&D) intensity and geographic proximity strengthen the positive relationship between CVC investment and entrepreneurial ventures’ performance pre-IPO, R&D intensity has a negative moderating effect on the relationship between CVC investment and entrepreneurial ventures’ performance post-IPO.

First, in emerging economies, adopting a CVC financing strategy is an important strategic choice for entrepreneurial ventures that have a great demand for external capital, resources and technology support. Second, leveraging the relationship between external financing and internal R&D investment is essential for them to maintain their core competitiveness and sustainable growth. Moreover, entrepreneurial ventures should deal with the coopetitive relationship with incumbent companies and manage their dependency on other market participants in the external environment.

This study focuses on the performance implications for entrepreneurial ventures engaging with CVC investments pre- and post-IPO. First, this study broadens and expands prior research on the mechanism of the relationship between CVC and entrepreneurial ventures’ financial performance. Second, the research conducts a comparative study of the moderating effects of different timings. Third, this study applies learning theory to the field of CVC in emerging economies.

This paper analyses competitive balance in the “big five” women's football leagues in Asia longitudinally between 2010 and 2019.

Competitive balance has been measured using recognised measures of concentration, HICB and NHICB, alongside recognised measures of dominance, i.e., identification of top teams. A time-trend analysis has been employed to identify trends of CB in the respective leagues followed by ANOVA and relevant post-hoc tests to identify difference in concentration measures. A multiple linear regression analysis has been conducted to identify the impact of external economic factors on CB.

Some significant differences were detected in the levels of concentration between leagues. There was also some variation in terms of some leagues being dominated by a fewer number of teams. However, these two measures of competitive balance (concentration and dominance) were not necessarily correlated with each other. The paper also tries to find the optimum number of teams to maintain CB in the women's football leagues in Asia, but an exact figure could not be found.

Some significant differences were detected in the levels of concentration between leagues. There was also some variation in terms of some leagues being dominated by a fewer number of teams. However, these two measures of competitive balance (concentration and dominance) were not necessarily correlated with each other. External economic factors were found to have negative impact on CB.

The paper is an original research and aims to add to the growing body of CB research in world through analysis of competitive balance (ACB).