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Teacher well-being has been a concern, but there has been a lack of research on how teacher leadership can contribute to teacher well-being in a high-accountability context and a hierarchical education system such as that of China, particularly through the meditating roles of trust in the leader and teacher efficacy. Therefore, the purpose of this study was to understand the relationship between teacher leadership and teacher well-being while exploring the mediating roles of trust in leaders and teacher efficacy in this relationship.

Using structural equation modeling (SEM) and bootstrap methods with valid answers from 1,144 teachers in 25 primary schools in 1 Chinese city, this study mainly answered three questions: Is there a significant relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of trust in leaders on the relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of teacher efficacy on the relationship between teacher leadership and teacher well-being?

This study reported a positive relationship between teacher leadership and teacher well-being. This study also found positive mediating roles for trust in leaders and teacher efficacy in the relationship between teacher leadership and teacher well-being in a high-accountability and hierarchical system like that of China.

This study provides an understanding of the transferability of teacher leadership theories across cultures and has practical significance for educational practice in high-accountability and hierarchical education contexts similar to that of China.

In recent years, private brands for e-commerce platforms have experienced rapid growth. However, whether these platforms developing private brands should share their demand information with others and how such information sharing affects the sales format selection of national brand manufacturers have puzzled firm managers in practice. This paper aims to investigate the information-sharing strategy for the e-commerce platform and its influence on the sales format selection in the presence of the private brand.

The authors use a game-theoretical model to examine the interaction between the information-sharing strategy and sales format selection in a supply chain consisting of a manufacturer and a platform that operates a private brand.

The equilibrium results show that when the commission rate is low, the manufacturer favors agency selling, and the platform shares demand information with the manufacturer; when the commission rate is high, the manufacturer prefers reselling, and the platform does not share the information. This preference is affected by information forecasting accuracy; as the information forecasting accuracy increases, the manufacturer prefers to adopt agency selling, and the platform tends to share the information. Interestingly, under agency selling, sharing information with the manufacturer can increase the platform’s profit from selling the private brand and achieve a win-win situation for them. Furthermore, we show that the manufacturer can inspire the platform to share the information with himself by adopting agency selling, whereas the platform sharing the information improves the probability that the manufacturer adopts agency selling. Moreover, the manufacturer may have a first-mover advantage. In particular, the manufacturer moving first increases the likelihood that the manufacturer chooses agency selling and the platform shares the information.

This paper contributes to sales format literature by exploring the effect of information sharing strategy on sales format selection in the presence of the private brand and can help manufacturers and platforms to make suitable decisions regarding information sharing and sales format selection.

This paper examines how the level of low-carbon awareness (LCA) affects the remanufacturing strategy in a supply chain with an original equipment manufacturer (OEM) and an independent remanufacturer (IR) competing with each other.

Game theory and operations optimization.

The studies analytically characterize the threshold levels of the LCA in response to which the OEM and the IR will change their remanufacturing strategies from no remanufacturing to partial remanufacturing and then to full remanufacturing. In addition, the studies reveal that as compared with the OEM, the IR has more flexibility in terms of the market entry to remanufacturing with the level of LCA increasing. With the extended studies, it is exhibited that the above findings are robust to a good extent.

It can provide decision support for remanufacturing enterprises.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Despite the many advantages this type of equipment offers, there are still some major drawbacks. Linear cutting machine (LCM) cannot accurately simulate the true rock-cutting process as 1. it does not account for the circular path along which tunnel boring machine (TBM) disk cutters cut the tunnel face, 2. it does not accurately model the position of a disk cutter on the cutterhead, 3. it cannot perfectly replicate the rotational speed of a TBM. To enhance the knowledge of these issues and in order to mimic the real rock-cutting process, a new lab testing equipment was developed by Hyundai Engineering and Construction.

A new testing machine called rotary cutting machine (RCM) is designed to simulate the excavation process of hard-rock TBMs and includes features such as TBM cutterhead, RPM simulation, constant normal force mode and constant penetration rate mode. Two sets of tests were conducted on Hwandeung granite using different disk cutter sizes to analyze the cutting forces in various excavation modes. The results are analyzed using statistical analysis and dimensional analysis. A new model is generated using dimensional analysis, and its results are compared against the results of actual cases.

The effectiveness of the new RCM test was demonstrated in its ability to apply various modes of excavation. Initial analysis of chip size revealed that the thickness of the chips is largely dependent on the cutter spacing. Tests with varying RPM showed that an increase in RPM results in an increase in the normal force and rolling force. The cutting coefficient (CC) demonstrated a linear correlation with penetration. The optimal specific energy is achieved at an S/p ratio of around 15. However, a slightly lower S/p ratio can also be used in the design if the cutter specifications permit. A dimensional analysis was utilized to develop a new RCM model based on the results from approximately 1200 tests. The model's applicability was demonstrated through a comparison of TBM penetration data from 26 tunnel projects globally. Results indicated that the predicted penetration rates by the RCM test model were in good agreement with actual rates for the majority of cases. However, further investigation is necessary for softer rock types, which will be conducted in the future using concrete blocks.

The originality of the research lies in the development of Hyundai Engineering and Construction’s advanced full-scale laboratory rotary cutting machine (RCM), which accurately replicates the excavation process of hard-rock tunnel boring machines (TBMs). The study provides valuable insights into cutting forces, chip size, specific energy, RPM and excavation modes, enhancing understanding and decision-making in hard-rock excavation processes. The research also presents a new RCM model validated against TBM penetration data, demonstrating its practical applicability and predictive accuracy.

This paper identifies the development of and gaps in knowledge in various management disciplines, including international marketing in relation to political ties in emerging markets, based on a systematic review of the related literature. The paper develops a synthesized integrative framework and provides a research agenda and pathways for future research.

The study adopts the systematic literature review protocol to investigate the ways in which political ties have been examined in the management literature in various disciplines, such as international business, marketing, entrepreneurship, strategy, innovation, and organization. In total, 114 articles published in peer-reviewed journals from 2000 to 2022 were analyzed.

The authors believe that studying the impact of political ties on firm outcomes is timely and important as interest in this area of research is growing rapidly. The review reveals that the diverse conceptual and methodological approaches adopted in different management disciplines have resulted in inconclusive and mixed findings on the relationship between political ties and performance.

This is one of the few systematic literature reviews of political ties and firm performance in emerging markets. The authors clarify some of the ambiguities around the subject and offer a path forward for developing current understanding and insights. The study also highlights the major perspectives in management and clarify the similarities and differences in the conceptualization of political ties. In addition, the authors develop an integrative framework of the political ties–performance link in emerging markets.

This study aims to explore how subsidiary capabilities collectively configure for performance. Additionally, it seeks to examine whether these configurations of capabilities can provide equifinal solutions through developing a comprehensive research framework that focuses on subsidiaries in China.

With a data set collected through a questionnaire from 172 Korean multinational enterprises (MNEs) in China, this study used a fuzzy-set qualitative comparative analysis to detect the capability conditions and configurations. These configurations represent combinations of various subsidiary capabilities linked to high performance.

This study identified several complex pathways with distinct configurations for high subsidiary performance. The findings demonstrate the importance of configurations over individual conditions. Thus, the results highlight that the effectiveness of diverse capabilities, which are widely believed to singularly contribute to the high performance of MNE subsidiaries, depends on how each combines with other capabilities. Overall, the findings provide a richer and fine-grained understanding of the role and relative importance of various forms of MNE subsidiary capabilities and how the joint effect of these subsidiaries contributes to high performance.

This study suggests that MNE managers should comprehensively understand how subsidiary capabilities are configured to produce subsidiary performance outcomes. This specifically illustrates the importance of understanding the mutually conflicting yet collectively exhaustive results of multi-selective solutions and aims to align with China’s industrial and regional heterogeneity.

By examining the role of MNE subsidiary capability configurations, which may collectively influence the subsidiary’s performance, this study contributes to the literature. It elucidates how MNE subsidiaries may achieve superior performance by developing and possessing various capabilities tailored to the local context.

This study investigates the impact of bank CEOs’ inside debt on shareholder benefits in the context of bank mergers and acquisitions (M&A) before the 2008–2009 financial crisis.

Employing an event-study methodology, this analysis delves into market reactions to bank M&A announcements during 2006–2007, encompassing 105 M&As by 79 public commercial banks. This era witnessed heightened risk-taking behavior on the verge of the financial crisis. We explore the relation between relative inside debt and market abnormal returns at M&A announcements and the association between relative inside debt and cash payment preferences in M&As.

Evidence suggests that M&A announcements from banks where acquiring CEOs hold a substantial inside debt experience favorable stock market reaction, particularly for smaller banks. Additionally, banks with elevated CEO inside debt tend to favor cash as a payment mode for M&As.

One limitation of this study is the short period of data availability. The data used in this study covers only 2006 and 2007, the periods marked by notable risk-taking activities on the verge of the financial crisis. Although this period is perfectly suitable for our investigation, given the prevalence of conflicts between equity and debt holders, it is essential to acknowledge that our findings may not capture changes or trends over time. Nevertheless, the results offer valuable insights into the factors that influence the behavior of the studied population. Future research could employ a longitudinal design to address this limitation and gain a more comprehensive understanding of the dynamics over extended periods.

Our study has significant implications for businesses and policymakers as it provides insights into the factors contributing to financial crises and how compensation mechanisms can be used to moderate bank risk-taking. We propose that CEO inside debt compensation presents a plausible mechanism that boards of directors can incorporate into bank executive compensation contracts. By doing so, they can promote value-enhancing investments and moderate excessive risk-taking, thereby safeguarding the financial stability of individual banks and overall financial system.

Our study sheds light on the beneficial role of bank CEO inside debt for shareholders, contributing empirical backing to the conflict resolution viewpoint in the discourse on wealth appropriation. From a regulatory stance, our findings advocate for the inclusion of bank CEO inside debt in executive remuneration agreements. Such a strategy can empower boards of directors to mitigate undue risk and enhance shareholder value in M&As, safeguarding both individual bank and broader financial system stability.

Under the widely used testing methods for fabric bending behavior, only one result for one direction can be obtained by using one fabric which is low efficient. To obtain fabric bending anisotropy, it is necessary to conduct a great many testing experiments. The purpose of this paper is to investigate a novel, efficient and visual method that can measure fabric bending anisotropy.

Fabrics are first cut into special shapes with eight strips including four directions, 0°(warp direction), 90°(weft direction), 45 and 135°(true bias), then are put onto the self-designed instrument. After that a camera is used to take picture from the right above the prism. New parameters, projection area, projection length, projection length, falling height and falling index (S, L, H and I in short) are obtained. Furthermore, standard deviation of them (SDS, SDL, SDH and SDI in short) are extracted for the characterization of bending anisotropy.

Results show that the new method has good feasibility and S, L and I can be used to express fabric bending property well. Of all the four new parameters, SDL has the highest correlation with SD of bending length, SDS the second and SDH the third. That is, SDL can characterize bending anisotropy best. Taken convenience of data acquisition and correlation into consideration, bending length L is the best parameter. Average L and SDL in four directions can be combined to express the comprehensive bending behavior of fabrics.

The new method can measure and characterize both the fabric bending property and bending anisotropy. Besides its high efficiency, it can display fabric bending or bending anisotropy visually and directly.

The purpose of this study is to define and investigate the governance requirements of supply chain finance (SCF).

A qualitative analysis of 849 news articles published in UK newspapers (2000–2022) using the Gioia method.

SCF governance relies on developing capacities for reflexive scrutiny at two stages: (1) prior to entering into an SCF relationship and (2) during its operation. Based on the notion of SCF as a complex adaptive system, we theorise SCF governance requirements as a dual-layered semipermeable boundary. The semipermeability of the two layers allows for a dynamic exchange between the SCF system and its environment. The first layer is the capacity to selectively enable or control the entry and access of certain actors and practices into the SCF system. The second layer is a capacity for ongoing scrutiny of the SCF operation and its development. Further, we identify five aspects of governance to be enabled, i.e. enhancing adaptability, building confidence, improving efficiency, advancing technology and promoting transparency; and four aspects to be controlled, i.e. preventing abuse of power, curbing fraud risk, constraining operational risk and restricting risky extensions to SCF practices.

Our dynamic framework can guide supply chain (SC) members in making decisions about whether to participate, or continue to operate, in an SCF relationship. Moreover, the findings have implications for policymakers and authorities who oversee entry/access and the involvement of SCF providers, particularly, fintech firms.

The study contributes to both the SC and governance literature by providing a systematic analysis of what SCF governance has to accomplish. Our novel contribution lies in its analysis of SCF governance based on a complex adaptive system approach, which expands the existing literature where SCF is described in rather static terms. More specifically, it suggests a need for a dynamic duality of SCF governance through the semipermeable boundary that selectively enables and controls certain SCF actors and practices.