Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences.

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.

Digital finance and M&As: An empirical study and mechanism analysis.

With the rapid growth and wide application of digital technology, enterprises have entered the digital era with both opportunities and challenges existing. Mergers and acquisitions are one of the most efficient ways to integrate resources and achieve profit growth, giving enterprises advantages in competing in the new mode of economic growth. Based on this, this research tries to explore whether the development of digital finance will contribute to the emergence of M&As activities through combining M&As data of the Chinese stock market with the digital finance inclusion index between 2012 and 2020. The results show that the development of digital finance largely influences M&As activities through lower acquirers' financial constraints. We further replace digital finance with three sub-indexes including coverage breadth, usage depth, and digitalization level to explore the impact of different dimensions of digital finance on M&As. Results show that coverage breadth plays a more important role. In addition, heterogeneity tests reveal that the relationship between the development of digital finance and M&As activities varies significantly. The influences of digital finance on private and western and central enterprises are more significant compared with state-owned and eastern enterprises. According to the study, since the development of digital finance can be an efficient way to ease financial constraints and boost M&As activities, the government should promote the development of digital finance while companies strive to make the most use of it.

Evaluation of the coordinated development between Chinese cultural industry and scientific & technological innovation.

Based on the coupling and interaction relationship between China's cultural industry (CI) and scientific & technological innovation (STI), this study constructed an index system for their coordinated development. The weight of each indicator was determined by using the entropy value method (EVM), and the coupling coordination degree (CCD) model was used to calculate CCD and coordination degree of China's CI and STI from 2012 to 2020. On this basis, the key factors in the coupling effect were analyzed using grey correlation degree (GCD). The results demonstrate that: (1) there is a high-level coupling relationship between China's CI and technological innovation; (2) the level of coupling coordination between the two is generally on the rise, experiencing a development process from serious maladjustment to high coordination; (3) Industry resources, policy support and the cost of cultural undertakings are the endogenous factors restricting the development of CI, and the environment and output of STI are the key factors restricting the coupling and coordinated development of Chinese CI and STI.

Exploring the effect of industrial agglomeration on income inequality in China.

Income inequality is a good indicator reflecting the quality of people's livelihood. There are many studies on the determinants of income inequality. However, few studies have been conducted on the impacts of industrial agglomeration on income inequality and their spatial correlation. The goal of this paper is to investigate the impact of China's industrial agglomeration on income inequality from a spatial perspective. Using data on China's 31 provinces from 2003 to 2020 and the spatial panel Durbin model, our results show that industrial agglomeration and income inequality present an inverted "U-shape" relationship, proving that they are the non-linear change. As the degree of industrial agglomeration increases, income inequality will rise, after it reaches a certain value, income inequality will drop. Therefore, Chinese government and enterprises had better pay attention to the spatial distribution of industrial agglomeration, thereby reducing China's regional income inequality.

Absence of ultimate controller and investment efficiency: Evidence from China.

In modern enterprises with a separation of powers, the ultimate controller can effectively influence the implementation of corporate strategy and operational management efficiency, as well as improve corporate governance by monitoring and limiting the management entrenchment effect within enterprises. Based on the information pertaining to ultimate controllers disclosed by enterprises in their annual reports, this study empirically tested whether the absence of the ultimate controller impacts investment efficiency using the data of Chinese A-share listed companies from 2007 to 2020. It was found that the investment efficiency of enterprises without ultimate controllers is relatively lower than those with ultimate controllers. This is reflected in the insufficient investment of enterprises without an ultimate controller. Moreover, the effect is more significant when the financial environment, internal governance environment, and external governance environment of firms are worse. The mechanism analysis demonstrated that the absence of an ultimate controller causes a more severe insider agency problem and a significantly higher degree of financing constraints, which leads to underinvestment and reduces investment efficiency of firms. The economic consequence test also found that the inefficient investment caused by the absence of ultimate controllers would damage the future value of enterprises, but would increase managers' compensation. Overall, this study suggests that ultimate controllers are an important part of a firm's internal governance, especially for monitoring management behavior and resolving agency conflicts.

Exploring the impact mechanism of executives' environmental attention on corporate green transformation: evidence from the textual analysis of Chinese companies' management discussion and analysis.

The allocation of executives' environmental attention (EEA) is of great significance in promoting the green upgrading of industrial structures and achieving corporate green transformation. Based on upper echelon theory and the attention-based view, we use panel data of Chinese manufacturing companies from 2015 to 2020 to construct a two-way fixed effects model to explore the impact mechanism of EEA on corporate green transformation performance (CGTP). Baseline regression shows that EEA significantly improves CGTP. The reliability of findings is verified by reducing time windows, replacing the independent variable, expanding the data source, and adding missing variables. In the heterogeneity analysis, the positive effect of EEA on CGTP is significant for eastern companies and does not differ in the property rights grouping. After propensity score matching, environmental attribute grouping shows that the positive effect of EEA on CGTP is more significant for non-heavy polluters. Extended research shows that government subsidies have a positive moderating effect, while female executives play only a symbolic role. Moreover, green innovation activities have positive partial mediating effects. Green innovation is the best way to address environmental pollution and achieve corporate green transformation. Our research provides implications for decision-makers to allocate their attention, and thereby achieve green development appropriately.

Understanding the nexus between environmental, social, and governance (ESG) and financial performance: evidence from Chinese-listed companies.

Before discussing how to balance and decide on environmental, social, and corporate governance (ESG) and traditional revenue enhancement projects, it is crucial to clarify the relationship between corporate financial performance (CFP) and ESG. However, little attention has been paid to the nexus of ESG and CFP. This paper attempts firstly to investigate the bidirectional causality of ESG and CFP, followed by the micro-foundations, and finally, the moderating effect of intrinsic factors. A GMM-PVAR method was used to examine the research hypotheses, which can effectively deal with endogenous problems that have been ignored by traditional literature. The findings of this research demonstrate that CFP promoted ESG growth, but ESG did not boost CFP. This asymmetric causality was because CFP had a supportive effect on the environment and society pillars, while the social pillar cannot promote CFP, and the environment pillar negatively affects CFP. The relationship between ESG and CFP was moderated by total quality management, environmental sensitivity, and the pay gap. Furtherly, a panel threshold model was constructed to access the threshold effects of ESG on CFP, showing an inverted U-shape. Based on these findings, the theoretical implications, managerial prescriptions, and limitations are also discussed.

Mixed-ownership reform and strategic choice of Chinese state-owned enterprises.

The strategic choice of state-owned enterprises (SOEs) is crucial to the sustainable development of China's economy. This paper explores the impact of mixed-ownership reform on the strategic choice of SOEs from the shareholder power and the board power. We find that the greater the diversity of mixed shareholders, the depth of mixed equity, the control of mixed equity, and the excess control of mixed equity, the higher the degree of mixed-ownership reform, and the more likely it is to promote SOEs to choose the prospector strategy. The mechanism test states that the impact of mixed-ownership reform on enterprise strategy is achieved through the balance effect between non-state-owned shareholders and state-owned controlling shareholders with the same power, and the synergy effect between different powers of non-state shareholders. Further research indicates that the mixed-ownership reform has a stronger driving effect on the prospector strategy in SOEs under strict external supervision, competitive industries, and local areas. This study clarifies the governance logic of non-state-owned shareholders on the strategic positioning of SOEs by dual control rights, and it provides empirical evidence for the formulation of enterprises' market-oriented strategic objectives.

Priority evaluation factors for blockchain application services in public sectors.

Blockchain is rapidly becoming established as the core technology of the Fourth Industrial Revolution. By combining blockchain to improve processes in existing industries, innovative new services will emerge, but services not effectively applied by blockchain will also develop. This study investigated the factors to be considered when applying the characteristics of blockchain technology to business. We developed a framework of blockchain service utility evaluation indexes using the analytic hierarchy process method. The Delphi method is used to identify highly effective blockchain application service cases by applying the evaluation framework to actual use cases in the public sector. By proposing a framework of utility evaluation factors for blockchain application services, this study provides a systematic foundation for blockchain business review. We address the question of "why blockchain should be applied to this service" by providing a more comprehensive approach than existing research, such as a fragmentary decision tree. Blockchains are expected to become more active along with the full-scale digital transformation of industries, and thus, we must examine the ways to broadly use blockchain as a base technology in a form applicable to the diverse industries and societies constituting the digital economy. Accordingly, this study presents an evaluation solution for promoting efficient policies and developing successful blockchain application services.

Carbon emission fluctuations of Chinese inter-regional interaction: a network multi-hub diffusion perspective.

The "double-carbon" policy is a new opportunity for the transformation of China's production sector. With steady economic growth, each province has proposed specific policies aimed at cleaner production. However, the interactions between regions and the complex linkages between industries have hindered the implementation of the "double-carbon" policy. In order to address this issue, we introduced a complex network framework with multiple industries at a national level. The framework aimed to clarify whether there is fluctuation diffusion in China's multi-province multi-industry carbon emission system, to identify key industries and regions, and to answer the question of "who" is the most effective in governance. The results showed that the fluctuations of industrial carbon emissions had a cross-regional diffusion effect in China indeed. The diffusion capacity of industry fluctuation depends on whether the industry is located at a "hub" position in the network. Hub industries with strong capacity can spread the carbon emission fluctuation of themselves and upstream or downstream industries to the whole country through regional interactions. This characteristic of the hub industry should be taken into account in governance to maximize the effectiveness of emission reduction. Shandong and Inner Mongolia, as important provinces for the production of intermediate products and energy chemicals in China, had a greater role to play in global carbon supply push from their hub industries than in the demand pull. The pulling capacity of Beijing-Tianjin-Hebei region to the national carbon demand side was greater than that of Yangtze River Delta and Pearl River Delta. These findings might have implications for environmental and economic policymaking, particularly with regard to cross-provincial coordinated systemic solutions and policy anchors for synergy with industries.

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm.

Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzy k-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.

Factors predicting companies' crisis in the engineering industry from the point of view of financial analysis.

A key factor for business management is the assessment of the financial situation of companies. Nowadays, it is essential to monitor the liquidity crisis, which is closely linked to corporate crises. The aim of the paper is to analyse a selected sector of the economy from the perspective of the corporate crisis and to identify the factors of crisis. More than 2000 engineering companies in Slovakia were analysed during the period from 2015 to 2019 with the aim of analysing financial results, especially in the area of financial forecast for the future. In the analysis, statistical testing of the significance of relationships using the Spearman correlation coefficient, the significance of differences by the power of t-test, regression and clustering were used. A significant part of the paper is the analysis of selected indicators of the company's crisis-Altman's Z score and the IN05 index. The results indicate that engineering companies in Slovakia are achieving good results and their financial situation is improving within the years between 2015-2019. The results can also be used as a starting point for research concerning the impact of COVID-19 in this area. In the context of corporate crisis management, engineering companies behave in the same way but it is necessary to monitor individual factors that can detect a corporate crisis. Possible measures would thus lead to the stabilization of financial results and long-term sustainable positive prospects for companies in the future.

The coordinated development of manufacturing industry and logistics industry in the Yangtze River Economic Belt: Empirical study by stages based on Haken Model.

It has great significance for improving the logistics service ability of the Yangtze River economic belt, optimizing the industrial structure of manufacturing industry, and realizing the integrated development of the Yangtze River economic belt to explore the collaborative evolution of logistics industry and manufacturing industry in the Yangtze River economic belt, and identify the leading position of the collaborative development of the two industries, so as to. Based on the Haken Model, this paper summarizes the coevolution law of logistics industry and manufacturing industry in the Yangtze River economic belt through two-stage empirical analysis, and identifies the order parameters of the co-development of logistics industry and manufacturing industry. The results show that the overall degree of coordination between the logistics industry and the manufacturing industry in the Yangtze River economic belt is high. And the order parameter has been changed from manufacturing industry in 2003-2009 to logistics industry in 2010-2017. The gap between regions has been reduced, and the western region has the advantage of post development.

Skill-driven recommendations for job transition pathways.

Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

RD internationalization, domestic technology alliance, and innovation in emerging market.

Although R&D internationalization plays an important role in enterprises' globalization, few studies explore the mechanism of R&D internationalization and emerging market companies' innovation, or the relationship between R&D internationalization, domestic technology alliances and absorptive capacity. How does the R&D internationalization of emerging market enterprises affect the innovation of those enterprises? Under fierce market competition, do absorption capacity and domestic technology alliances have a significant impact on enterprise innovation? From the perspective of the knowledge-based view, this paper studies 185 enterprises undergoing R&D internationalization in China from 2012 to 2017, using high-dimensional Poisson fixed effects model, we use instrumental (HDFE IV) estimation to explain the impact of R&D internationalization on the innovation of the parent company and the mechanism behind it. The study finds that R&D internationalization positively promotes the parent company's innovation, and domestic technology alliances and absorptive capacity play a partial mediator role in R&D internationalization. In the face of fierce market competition, domestic technical alliances play a significant role in promoting enterprise innovation, while absorptive capacity plays a negative role in promoting enterprise innovation with the moderating effect of market competition.

Development of a hybrid framework for inventory leanness in Technical Services Organizations.

Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization's annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan's leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study's finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study's proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.