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The main objective of this paper is to analyze how leadership unity (LU) within universities affects the innovativeness of faculty, with a focus on the potential moderating role of strategic sensitivity (SS).

The conceptual model of this research shows that SS and LU of the university impact the faculty's innovativeness. Meantime, the moderating effect of SS is assessed. A 49-item questionnaire was administered to 350 respondents who were managers and faculties of the university. The hierarchical regression technique was used for analyzing data and testing hypotheses.

The findings support both a curvilinear relationship based on a concave upward pattern and a linear relationship between LU in the university and the innovativeness of faculty. In addition, the university's SS positively influences the faculty's innovativeness. SS negatively moderates the curvilinear relationship between LU and faculty's innovativeness, i.e. the U-shaped effect exists only when the level of SS is high.

The results of this study shed new light on the relationships between LU and SS with innovativeness in the higher education landscape. It underlines the importance of SS as a moderator in the relationship between LU and innovativeness. This study was conducted in a developing country under sanctions with an Eastern culture, Iran. Thus, it is recommended that the conceptual framework of this study be tested in different countries with cultural diversity to generalize its findings.

Administrators of universities need to recognize that creating unity and cohesion among managers of various levels of the university is crucial. They should also be aware that responses to external changes can lead to new opportunities for the university. Embracing transformation within the organizational strategies of the university will have a significant influence on competition, politics, and internal operations.

This research contributes to the academic discussions on the importance of LU and SS and also the moderation effect of SS in driving and promoting innovativeness in among faculties by providing empirical evidence. The results present valuable insights for scholars, practitioners and policymakers seeking to understand innovativeness among faculties in the higher education setting.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

Based on upper echelons theory and social contagion theory, we investigate how environmental leadership affects GIC via green human resource management (GHRM) and examine the moderating role of environmental climate.

We conduct hierarchical regression and use the bootstrap method to analyze the two-waved data from 317 Chinese manufacturers in order to verify the hypotheses.

The results indicate that GHRM mediates the impacts of environmental leadership on green human capital, structural capital and relational capital. In addition, environmental climate strengthens the positive impact of environmental leadership on GHRM.

Our study enriches the literature on GIC by uncovering the “black box” between environmental leadership and GIC, providing a logical framework opposite to mainstream GIC research, and expanding the boundary condition for GIC accumulation. This study provides more logical paths for enterprises and governments to increase the accumulation of GIC and promote green intellectual economy development.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

The role of inventors' creativity is crucial for technological innovation within enterprises. The mobility of inventors among different enterprises is a primary source for companies to acquire external knowledge. The mechanism of “learning-by-hiring” is widely recognized by companies. Therefore, it is important to determine how to allocate network resources to enhance the creativity of inventors when companies hire mobile inventors.

The study suggests an analytical framework that analyzes alterations in tie strength and structural holes resulting from the network embeddedness of mobile inventors as well as the effect of the interaction between these two variables on changes in inventor’s creativity after the mobility. In addition, this paper examines the moderating impact of cognitive richness of mobile inventors and cognitive distance between mobile inventors and new employers on the correlation between network embeddedness and creativity.

This study found that: (1) The increase of tie strength has a significant boost in creativity. (2) Increasing structural holes can significantly improve the creativity of mobile inventors. (3) When both the tie strength and the structural holes increase, the creativity of the mobile inventors significantly increases. (4) It is important to note that when there is a greater cognitive distance, stronger tie strength promotes the creativity of mobile inventors. Additionally, cognitive richness plays a significant role in moderating the relationship between changes in structural holes and the creativity of mobile inventors.

These findings provide theoretical guidance for firms to effectively manage mobile inventors and optimize collaborative networks within organizations.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

This study introduces a new type of platform recommendation about mutual funds and draws on the signaling theory to conduct a quasi-experimental design to investigate how the platform recommendation influences investors’ investment decisions. Moreover, the authors examine the combined effect of star ratings and the platform recommendation on fund flow and test the investment value of recommended funds.

This study implements a quasi-experimental design based on 1,295 mutual funds traded on Alipay’s online platform to test the hypotheses.

The empirical results show that the recommended funds received higher fund flows from investors when the platform recommendation was established. Moreover, a substitution effect between tag recommendation and star ratings on fund flow was identified. We also uncovered that investing in platform-recommended funds can yield significant and higher fund returns for investors than those without platform recommendations.

Our findings shed new insights into the role of platform recommendations in helping fund investors make investment decisions and contribute to the business of online mutual fund transactions by investigating the effect of platform recommendations on fund flow and performance.

This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field.

Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements.

The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles.

This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

Wage employees enter self-employment either directly or in a staged manner and may subsequently undertake multiple stints at self-employment. Extant research on the relationship between entry modes and the persistence and outcomes of self-employment is inconclusive. This study investigates the relationship between wage employees’ initial mode of entry into self-employment and the duration of the subsequent first two stints of self-employment.

This study used a matched longitudinal sample of 9,550 employees who became majority owners of incorporated firms from 2005 to 2016.

The findings demonstrate that the initial mode of entry into self-employment matters for the first two stints at self-employment. Staged entry into self-employment was associated with a shorter first stint and became insignificant for the second stint. Staged entry into self-employment was positively related to the odds of becoming self-employed for the second time in the same firm.

Using a comprehensive and reliable dataset, the paper shifts focus from the aggregated onward journey of novice entrepreneurs (survival as the outcome) to the duration of their self-employment stints. By doing so, the paper offers insights into the process of becoming self-employed and the patterns associated with success/failure in entrepreneurship associated with self-employment duration.