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This paper aims to investigate the links between accounting values in Chinese listed companies’ balance sheets and the exposure of their fraudulent activities.

Every balance sheet account is proposed to be a potential vehicle to manipulate financial statements.

Other receivables, inventories, prepaid expenses, employee benefits payables and long-term payables are important indicators of fraudulent financial statements. These results confirm that asset account manipulation is frequently carried out and cast doubt on earlier conclusions by researchers that inflation of liabilities is the most common source of financial statement manipulation.

Previous practices of solely scaling balance sheet values by assets are revealed to produce spurious relationships, while scaling by both assets and sales effectively detects fraudulent financial statements and provides a useful fraud prediction tool for Chinese auditors, regulators and investors.

To provide a clear picture of the current status of mechanical drilling of printed circuit boards (PCBs).

A review paper detailing the developments of micro‐drill bit and PCB mechanical drilling techniques.

Mechanical drilling will still dominate the PCB hole processing methods. A design method on the basis of theoretical analysis, numerical simulation and experimental verifications is proved as an applicable way to improve the drill bit design efficiency. Newly developed tungsten carbide, novel coating techniques and high‐performance steel‐shank micro‐drill bits are expected. Solutions of micro‐drill bits for high‐density interconnection, IC substrate flexible PCBs, halogen and lead‐free assembly compatible PCBs, as well as 2 mm shank diameter drill bit are worthy of being concerned.

The paper highlights the state‐of‐the‐art techniques of micro‐drill bit manufacturing and novel developed micro‐drill bit. The development direction of micro‐drill bit in the future is concluded.

The purpose of this paper is to investigate the risk factors for A‐shares listed on both Shenzhen and Shanghai Stock Exchange in China using variables from Akgun and Gibson.

The paper applies cross‐sectional regression on the orthogonal components by rearranging these risk variables into several principal components.

The results produced strong evidence that size and book‐to‐market (BM) ratio could be well explained by these alternative risk variables. Additionally, the alternative variables are better at explaining returns in terms of adjusted R‐squares.

The practical implication of the study is that investors can improve both their pricing of the investment risk and their management of the risk factors with the alternatives identified in the study.

The paper provides evidence in explaining the size and BM effects in China's stock markets.

Firm growth in industry clusters is a complex issue. On the one hand, industrial clusters can promote firm growth. On the other hand, they can restrict the growth of a firm in some aspects. Their various effects have to be analyzed in detail. The purpose of this paper is to examine these effects and the law of enterprise growth in electronic information industry clusters of China.

This paper makes use of the panel data of the Chinese manufacturing industry with the intention of testing Gibrat's law. It carries out an empirical analysis on the influence of Chinese electronic information industry clusters on firm growth.

The result of the present research indicates that industry clusters definitely have a positive impact on firm growth, profit and longevity. However, in regard to the firms' data of China 2006 to 2007, the electronic information industry clusters have negative effects on scale of business growth of small and medium‐sized companies but not big companies. Moreover, the innovation of companies inside a cluster could not catch up with that of companies outside the cluster.

For the electronic information enterprises, growth rate is positively correlated with the enterprise age. Gibrat's law is tenable, that is, firm growth mainly depends on firm age. In Chinese electronic information industry clusters, R&D has only a weak influence on enterprise growth. In contrast, the economic soundness of the region where the electronic information industry clusters are located is more beneficial to the growth of enterprises in the cluster.

This study aims to provide an overview of the development of corporate governance and ownership structure literature and offers a synopsis of the top contributors, influential articles, journals and potential research prospects on this subject.

This study used bibliometric analysis to review the literature. In all, 1,368 articles published between 1992 and 2022 in Scopus-indexed journals were considered.

This review reveals the top leading authors, institutions, countries and sources in the ownership structure research. Using bibliographic coupling, this study fetches four significant clusters. The theme of the first cluster revolved around cash holding. The second and third groups revealed how distinct characteristics of ownership impact the performance of the firm and disclosure decisions, respectively. The last and fourth cluster deals with risk-taking activities in financial institutions. Furthermore, this study suggests a road map in each cluster for future research.

Ownership structure plays a significant role in corporate governance by affecting manager incentives and determining the extent of monitoring. Previous studies have contributed to this field while focusing on the board of directors. However, no study synthesises the literature on ownership structure within corporate governance, which is the core element of the corporate governance system. Hence, this study gives a comprehensive overview and determines the latest and prominent research in ownership structure within corporate governance through bibliometric analysis.

The time series analysis is an essential methodology which comprises the tools for analyzing the time series data to identify the meaningful characteristics for making future ad-judgments. The purpose of this paper is to propose a Taylor series approximation and unbiased GM(1,1) based new hybrid statistical approach (HTS_UGM(1,1)) for forecasting time series data under the poor, incomplete and uncertain information systems in a short period of time manner.

The gray forecasting is a dynamical methodology which can be classified into different categories based on their respective functions. The new proposed methodology is made up of three different methodologies including the first-order unbiased GM(1,1), Markov chain and Taylor approximation. In addition to that, two different traditional gray operational mechanisms include GM(1,1) and unbiased GM(1,1) used as the comparisons. The main objective of this study is to forecast gold price demands in a short-term manner based on the data which were taken from the Central Bank of Sri Lanka from October 2017 to December 2017.

The error analysis results suggested that the new proposed HTS_UGM(1,1) is highly accurate (less than 10 percent) with lowest RMSE error values in a one head as well as weakly forecasting’s than separate gray forecasting methodologies.

The findings suggested that the new proposed hybrid approach is more suitable and effective way for forecasting time series indices than separate time series forecasting methodologies in a short-term manner.

This study aims to examine psychological mechanisms underlying tacit knowledge-sharing behaviours. The personality trait of conscientiousness is tested in relation to knowledge sharing, and the effect of eagerness and subjective norm on the intention to share is measured in the context of local and multinational knowledge-intensive enterprises in Croatia.

The quantitative study was conducted on a sample of 288 employees of small and medium-sized companies working on knowledge-intensive tasks. The purposive sampling technique and a survey strategy were used in the study. Organizational affiliation, as it was presumed that these individuals possess a higher degree of tacit knowledge. The data collection was conducted in October 2019. Respondents worked in science and technology companies in Croatia on assignments involving information technology, electronics, petrochemicals, medicine and biochemistry. Statistical product and service solutions analysis of a moment structures software was used to perform confirmatory factor analysis and structural equation modelling.

The findings suggest that the personality trait of conscientiousness has a positive impact on tacit knowledge sharing behaviour. An attitude of eagerness and subjective norm were also confirmed as predictors of tacit knowledge sharing behaviour. Furthermore, conscientiousness influences the eagerness to share knowledge. A significant association between subjective norm and conscientiousness was also established. Finally, the mediating effects were identified, indicating that subjective norm and eagerness mediate the relationship between conscientiousness and tacit knowledge sharing.

Explaining the relationship between personality and attitude in the context of knowledge sharing will result in a better understanding of factors that should be nurtured within individuals. Accordingly, distinct management initiatives are to be developed to suit these factors. Furthermore, to intensify the knowledge exchange when working on knowledge-intensive tasks of significant economic value, organizations tailor a more particularistic application to suit the individual in the domain of leadership, staffing decisions, work organization and incentive systems.

This study provides an in-depth analysis and theoretical understanding of factors salient for knowledge-sharing behaviour. The authors provide an overview of how knowledge sharing evolves during social interaction through intensive problem-solving sessions and teamwork. The authors render the explanation on how the personality trait of conscientiousness, conjoint with the attitude of eagerness to share know-how in the expert surrounding, is conducive to the generation of tacit knowledge sharing. Underpinning this study are employees’ psychological motives and internal drives to communicate individual cognitive capital outweighing the potential negative consequences, such as losing the competitive advantage over the colleagues.

Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.

The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.

Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.

The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.

The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.

The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.

The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.

The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.

The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.