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This study aims to investigate the predictive role of team personality composition in facilitating shared leadership through team member exchange (TMX), while also to examine the moderating effect of organizational culture.

The authors conducted a two-stage online survey and selected the customer service teams, claims teams and financial teams of 26 Chinese insurance companies as the research samples. The authors finally obtained validated questionnaires from 107 teams with 457 members. The hypothesized relationships were tested using SPSS 25.0 and Mplus.

The results indicate that both team relationship-oriented and task-oriented personality composition have significant positive effects on shared leadership with team-member exchange serving as a full mediator for both paths. As a boundary condition, organizational culture (i.e. including internal integration values and external adaptation values) has a moderating effect on the influence of TMX on shared leadership.

The study investigates the predictive role of team personality composition on shared leadership, which complements the empirical studies of shared leadership antecedents in the literature. Drawing on social exchange perspective, the authors find out that TMX serves as a mediator between team personality composition and shared leadership. The authors also identify the moderating effect of organizational culture on the emergence of shared leadership. The research emphasizes the contextual boundary condition in this process.

As an increasingly important variable in the career field, career sustainability has received particular attention, yet few empirical studies have been conducted to examine its antecedents. The authors propose a moderated mediation model based on the goal-setting theory and the wise proactivity perspective for exploring when and how self-goal setting can influence career sustainability.

The authors use a time-lagged design and collect three waves of data from 1,260 teachers in basic education schools in China. The authors test the proposed hypotheses with SPSS 26.0 and Mplus 8.3.

The results show that self-goal setting positively relates to career sustainability and that career crafting plays a mediating role in this relationship. This relationship is strengthened when perceived organizational goal clarity is high.

The authors extend the application scenarios of the goal-setting theory to the field of career research and find out that self-goal setting is also a self-initiated and wise antecedent of career sustainability. From a wise proactivity perspective, the authors examine the mediating mechanism of career crafting to make positive career outcomes. Furthermore, the authors consider the impact of perceived organizational goal clarity as a boundary condition and broaden the understanding of “when to wise proactivity” from the goal-setting theory.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

A study of teachers in Chinese schools examined the antecedents of career sustainability with a focus more on proactive behavior from employees than internal traits, or the company’s organizational goals. Their research showed that self-goal setting has a positive influence on career sustainability and also that career crafting mediates the relationship. The effect is stronger when perceived organizational clarity is high.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of this study is to present the results of an experiment that examines the effects of client management’s increased disclosure of related party transactions (RPTs) on auditors’ judgments of financial reports that contain RPTs.

This study used a 2 × 2 between-subjects experiment to investigate auditors’ judgments in response to questionable RPTs in a Chinese context.

The results show that the auditor participants assessed a lower likelihood that the client’s financial statements were intentionally misstated and that they were less likely to request additional evidence when the client management chose to disclose more, as opposed to less, detailed RPT information in their disclosure. Moreover, there was a significant interaction between disclosure level and client incentive to manipulate earnings on the likelihood of the auditor requesting additional evidence.

This study should be of interest to regulatory agencies that have expressed concerns over auditing practices related to RPTs.

The findings from this study help to provide a more in-depth understanding of disclosure literature by investigating voluntary RPT disclosure and the moderation role of clients’ incentives to manipulate earnings.

This study aims to address the problem of the divergence of traditional inertial navigation system (INS)/celestial navigation system (CNS)-integrated navigation for ballistic missiles. The authors introduce Doppler navigation system (DNS) and X-ray pulsar navigation (XNAV) to the traditional INS/CNS-integrated navigation system and then propose an INS/CNS/DNS/XNAV deep integrated navigation system.

DNS and XNAV can provide velocity and position information, respectively. In addition to providing velocity information directly, DNS suppresses the impact of the Doppler effect on pulsar time of arrival (TOA). A pulsar TOA with drift bias is observed during the short navigation process. To solve this problem, the pulsar TOA drift bias model is established. And the parameters of the navigation filter are optimised based on this model.

The experimental results show that the INS/CNS/DNS/XNAV deep integrated navigation can suppress the drift of the accelerometer to a certain extent to improve the precision of position and velocity determination. In addition, this integrated navigation method can reduce the required accuracy of inertial navigation, thereby reducing the cost of missile manufacturing and realising low-cost and high-precision navigation.

The velocity information provided by the DNS can suppress the pulsar TOA drift, thereby improving the positioning accuracy of the XNAV. This reflects the “deep” integration of these two navigation methods.

The chapter attempts to evaluate the utility of applying multi-level governance outside of the EU, and also outside of the group of democratic states, to states that have defied the third wave of democratization and that are characterized by a so-called new authoritarianism. The case is the People’s Republic of China, and the focus falls on policy-making and implementation in the field of hydropower with special attention to the issue area of environmental protection.

The chapter draws on the notion of scales and indigenous Chinese governance concepts and brings these into a conversation with the concept of multi-level governance. Case studies on hydropower decision-making in China contribute empirical data in order to investigate the utility of multi-level governance in the Chinese governance context.

The chapter argues that if multi-level governance is to have utility in other cultural contexts it needs to move away from a consideration of pre-given scales as locus of authority and consider indigenous governance concepts and notions of scale, and it crucially needs to map power relationships in the making and implementation of policies in order to reach analytical depth.

The case of China shows that authoritarian regimes can be analysed in terms of multiple levels as authoritarianism no longer automatically implies strict top-down entities. Instead, autocracies can be highly fragmented and subject to complex decision-making processes that can arise during processes of administrative reform. This can lead to vibrant and reflexive systems of governance that exhibit adaptive skills necessary to ensure regime survival amidst a continuously diversifying society and changing external circumstances. As a consequence, a research programme looking at the new authoritarianism from a multi-level governance perspective has the capacity to uncover and describe new forms of governance, by bringing the concept into a conversation with indigenous governance concepts.

In China, informal networks between the energy bureaucracy and hydropower developers determine the hydropower decision-making process. This is particularly detrimental at a time when the Chinese government emphasizes the importance of the rule of law and social stability. Informal networks in which key government agencies are involved actively thwart the attempt of creating reliable institutions and more transparent and accountable processes of decision-making within the authoritarian governance framework.

The findings show the dominance of informal networks versus the formal decision-making process. This sidelines the environmental bureaucracy and fails to fully realize the importance of public input into the decision-making process as one potential element of institutionalized conflict resolution.

The chapter builds on existing multi-level governance approaches and fuses them with notions of scales and indigenous Chinese governance concepts in order to enable the applicability of the concept of multi-level governance outside of its area of origin. This advances the explanatory depth and theoretical reach of multi-level governance.

The purpose of this paper is to analyze the structure and changes of China’s land system. To achieve this aim, the paper is divided into four parts. The first part gives a brief introduction to the structural characteristics of the Chinese land institutional arrangements; the second part analyzes the reform process of the land system in the past 40 years and its path of change; the third part engages the discussion about the historic contribution made by the land institutional change to rapid economic growth and structural changes; and the final part is conclusion and some policy implications.

After 40 years of reforms and opening up, China has not only created a growth miracle unparalleled for any major country in human history, but also transformed itself from a rural to an urban society. Behind this great transformation is a systemic reform in land institutions. Rural land institutions went from collectively owned to household responsibility system, thereby protecting farmers’ land rights. This process resulted in long-term sustainable growth in China’s agriculture, a massive rural-urban migration and a historical agricultural transformation. The conversion of agricultural land to non-agricultural uses and the introduction of market mechanisms made land a policy tool in driving high economic growth, industrialization and urbanization.

Research shows that the role of land and its relationship with the economy will inevitably change as China’s economy enters a new stage of medium-to-high speed growth. With economic restructuring, low-cost industrial land will be less effective. Urbanization is also shifting from rapid expansion to endogenous growth so that returns on land capitalization will decrease and risks will increase. Therefore, China must abandon land-dependent growth model through deepening land reforms and adapt a new pattern of economic development.

This paper gives a brief introduction to the structural characteristics of the Chinese land institutional arrangements, analyzes the reform process of the land system in the past 40 years and its path of change, and evaluates the historic contribution made by the land institutional change to rapid economic growth and structural changes.

Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper.

Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively.

Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved.

This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

The purpose of the study is to investigate and analyze the dynamics of the government-enterprise grain joint storage mechanism, particularly, focusing on profit-driven speculative behaviors exhibited by enterprises within this context. The study aims to understand the various factors influencing the behavior of stakeholders involved in grain storage, including government storage departments, agent storage enterprises and quality inspection agencies.

The study employs a tripartite evolutionary game model to investigate profit-driven behaviors in government-enterprise grain joint storage. It analyzes strategies of government departments, storage enterprises and quality inspection agencies, considering factors like supervision costs and speculative risks. Simulation analysis examines tripartite payoffs, initial probabilities and the impact of digital governance levels to enhance emergency grain storage effectiveness.

The study finds that leveraging digital governance tools in government-enterprise grain joint storage mechanisms can mitigate risks, enhance efficiency and ensure the security of grain storage. It highlights the significant impact of supervision costs, speculative risks and digital supervision levels on stakeholder strategies, offering guidance to improve the effectiveness of emergency grain storage systems.

The originality of this study lies in its integration of digital governance tools into the analysis of the government-enterprise grain joint storage mechanism, addressing profit-driven speculative behaviors. Through a tripartite evolutionary game model, it explores stakeholder strategies, emphasizing the impact of digital supervision levels on outcomes and offering insights crucial for enhancing emergency grain storage effectiveness.

Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments.

This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set.

This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively.

This research work is original.