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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.

The purpose of this study is to understand the research on numeracy coach in the Canadian context.

This study was a systematic review of studies about numeracy coaching in Canada, which described the trend of knowledge production on this topic. The study made use of qualitative meta-analysis. According to Timulak (2009, p. 591), qualitative meta-analysis is “an attempt to conduct a rigorous secondary qualitative analysis of primary qualitative findings. Its purpose is to provide a more comprehensive description of a phenomenon and an assessment of the influence of the investigation on findings.” By using meta-analysis, this article analyzed the general pattern of knowledge production, the nature of the publications, the focal topics, the research methods and the citation impact of numeracy coaching studies in Canada. The literature analyzed in this review included all the available sources we were able to find with regard to Canadian numeracy coaches. The sources included peer-reviewed journal articles, online magazines, program reports and information from non-profit organizations, as well as doctoral theses.

Research about numeracy coaches in Canadian contexts remains in an initial stage, and there is, as yet, no accepted definition of a Canadian numeracy coach. Scholars in this area have been fond of empirical studies and qualitative research methods. This research has shown that numeracy coaches, who can be regarded as directors, evaluators and consultants in schools, also experience difficulties such as a lack of background knowledge in math. Scholars have advised several ways to improve the quality of coaching activities, including teacher collaboration and coaching models. The citation impact of the current Canadian literature about numeracy coaches has been quite uneven; only a few articles have been cited by other researchers. This article concludes by summarizing the results of this meta-analysis, its limitations and suggestions for future research.

This is one of important studies to explore numeracy coach research in Canadian context. Theoretical and practical significance is explored.

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.

Developed countries control pandemics using smart decisions and processes based on medical standards and modern technologies. Studies on risk-reduction and humantechnology interaction are scarce. This study developed a model to examine the relationship between citizens, pandemic-related technology and official safety practices.

This study investigated the mediating role of new health regulations and moderating role of safety incentives due to COVID-19 case reduction in pandemic severity control. This study included 407 operations managers, nursing staff conducting pandemic testing and reporting, doctors and security personnel in China. An artificial neural network (ANN) was used to check nonlinear regressions and model predictability.

The results demonstrated the impact of the introduction of new technology protocols on the implementation of new health regulations and aided pandemic severity control. The safety incentive of case reductions moderated the relationship between new health regulations and pandemic severity control. New health regulations mediated the relationship between the introduction of new technology protocols and pandemic severity control.

Further research should be conducted on pandemic severity in diversely populated cities, particularly those that require safety measures and controls. Future studies should focus on cloud computing for nurses, busy campuses and communal living spaces.

Authorities should involve citizens in pandemic-related technical advances to reduce local viral transmission and infection. New health regulations improved people's interactions with new technological protocols and understanding of pandemic severity. Pandemic management authorities should work with medical and security employees.

This study is the first to demonstrate that a safety framework with technology-oriented techniques could reduce future pandemics using managerial initiatives.

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.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

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.