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This paper aims to study the initial trust of the owner to the contractor, establish the initial trust mechanism, explore the factors that affect the initial trust of the owner to the contractor and analyze its influence mechanism. Based on this, it is easy for the owners and contractors to take targeted measures to improve the initial trust, which is conducive to the sustainable development of the project.

On the basis of reading a large amount of literature, this paper constructs the occurrence mechanism of the owner's initial trust to the contractor from the five factors of trust propensity, trust belief, trustee’s characteristics, institution-based trust, trust motivation and from the perspective of the owner using the structural equation model for questionnaire survey and empirical analysis.

The results of this paper show that the institution-based trust, the trustee’s characteristics and the trust belief of the trustor clearly have a positive effect on trust motivation, and the trustee’s characteristics have the most significant effect on the trust motivation. The influence of trust propensity on trust motivation was not significant.

This paper studies the occurrence mechanism of the owner's initial trust in the contractor, discusses its influencing factors and analyzes the influence of these factors on the initial trust, which enriches the theoretical system of initial trust research. The results of this study can help owners and contractors to develop targeted measures to build good initial trust.

The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.

The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearings

Several control charts have been used and compared in this paper to identify the initial point of a defect. Overall, the adaptive Shewhart average level chart is a good choice since it overcomes the drawback of adaptive moving charts by working out the limits using all the bearings' data, with no such a need for a subjective threshold level. They are also not very sensitive to the small casual changes in the data.

The model developed can be served as part of a prognosis tool for maintenance decision making since once the earlier warning point has been identified, corrective maintenance actions may be taken. It has practical application areas in vibration based monitoring or any monitoring scheme where a trend in the monitored measurements may exist. The method proposed is easy to use and can be implemented in any condition based maintenance software packages.

The approach proposed in this paper is a new application of existing methods and of original contribution from a point of view of applicability. It adds value to the existing literature and is of value to practitioners.

This paper aims to propose and evaluate an intrusion sensitivity (IS)-based approach regarding the detection of pollution attacks in collaborative intrusion detection networks (CIDNs) based on the observation that each intrusion detection system may have different levels of sensitivity in detecting specific types of intrusions.

In this work, the authors first introduce their adopted CIDN framework and a newly designed aggregation component, which aims to collect feedback, aggregate alarms and identify important alarms. The authors then describe the details of trust computation and alarm aggregation.

The evaluation on the simulated pollution attacks indicates that the proposed approach is more effective in detecting malicious nodes and reducing the negative impact on alarm aggregation as compared to similar approaches.

More efforts can be made in improving the mapping of the satisfaction level, enhancing the allocation, evaluation and update of IS and evaluating the trust models in a large-scale network.

This work investigates the effect of the proposed IS-based approach in defending against pollution attacks. The results would be of interest for security specialists in deciding whether to implement such a mechanism for enhancing CIDNs.

The experimental results demonstrate that the proposed approach is more effective in decreasing the trust values of malicious nodes and reducing the impact of pollution attacks on the accuracy of alarm aggregation as compare to similar approaches.

Grounding the research in the stimulus-organism-resource (S-O-R) framework, this study aims to address the research gap of explaining and predicting the relationship between price discounts, interactivity and professionalism on college students’ purchasing intention in live-streaming shopping. It also attempts to understand if trust plays the role of mediator in the effect of these relationships.

This study collected data using a questionnaire protocol adapted and refined from the original scales in existing studies. The partial least squares structural equation modeling was used to analyze data collected from 258 college students in China. Other than assessing the path model’s explanatory power, this study examined the model’s predictive power toward predicting new cases using PLS predict.

Results indicated that all three predictors have a positive significant relationship with trust, while only price discounts demonstrate a significant relationship with purchase intention. Simultaneously, the mediation results provide support to the S-O-R framework demonstrating that external factors (professionalism, interactivity and price discounts) can arouse organism (trust), which in return, generate a behavioral outcome (purchase intention).

This study is the first few studies that focus on college students’ behavioral responses in an online shopping environment. At the same time, this is the first study supplement the explanatory perspective with a predictive focus, which is of particular importance in making sound recommendations on managerial decision-making.

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.

In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.

The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.

The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.

To solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.

Finally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.

By using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.

This study aims to uncover the black box of the influence mechanism between external stakeholder drivers and green public procurement practice, and meanwhile to explore the moderating role of administrative level in this process. Green public procurement (GPP) has been widely implemented. Existing literature has found that external stakeholder drivers can affect public sectors' GPP practice, however, the definition of its connotation is still unclear, and how external stakeholders affect GPP practice has remained a black box.

After defining the major external stakeholders, this study develops a multiple mediation theoretical model using survey data from 142 Chinese local public sectors. It aims to uncover the black box of the influence mechanism between external stakeholder drivers and GPP practice and meanwhile explore the moderating effect of administrative levels in this process.

The results show that external stakeholder drivers have a positive relationship with GPP practices. The knowledge of GPP implementation policies and the knowledge of GPP benefits can both mediate this relationship. This study also finds that the administrative level of public sectors can positively moderate the mediating effect produced by the knowledge of GPP implementation policies and negatively moderate the mediation effect produced by the knowledge of GPP benefits.

Local governments need to better encourage public sectors to implement GPP. Managers of public sectors need to pay attention to organizational learning to acquire relevant knowledge on GPP.

This study makes a theoretical contribution to a better understanding of the influence mechanism for GPP practice. This study also provides comparisons of GPP implementation policies between China and European Union.

Managing tacit knowledge effectively and efficiently is a huge challenge for organizations. Based on the social exchange and self-determination theories, this study aims to explore the role of social interactions in motivating employees' willingness to share tacit knowledge (WSTK).

The study used a survey approach and collected data from 228 employees in service and manufacturing organizations.

Interactional justice and respectful engagement are positively related to WSTK. The perceived cost of tacit knowledge sharing (CostTKS) partially mediates the relationship between interactional justice and WSTK. Respectful engagement moderates the negative relationship between interactional justice and the perceived CostTKS.

The study advances the understanding of the role of social interaction in facilitating employee WSTK by integrating the direct and intermediate relationships involving the effect of supervisor's interactional justice and peers' respectful engagement and employee perceived CostTKS on WSTK.

The findings have important practical implications for organizations as these suggest how organizations can help tacit knowledge holders experience less negative and more supportive behaviors when they engage in voluntary TKS.

This study examines the effect of both vertical and horizontal work-related interactions on perceived CostTKS and sequentially on WSTK, thereby extending existing literature.

The purpose of this paper is to analyze the demands of the core stakeholders and how these stakeholders drive the information disclosure behaviors of the enterprise and local government.

Content analysis was conducted. The authors collected and analyzed information disclosure laws and regulations regarding environmental emergencies in China, as well as related media reports and official accident investigation report about the oil pipeline leakage and explosion accident in City Q. The authors divided the whole process of the accident into four stages, i.e., the prodromal stage, acute stage, chronic stage, and resolution stage, and then analyzed the different demands of stakeholders and the different information disclosure behaviors of the enterprise and local government during these four stages.

During the environmental emergency, the enterprise and local government exhibited information disclosure behaviors for their own benefits. There was severe information asymmetry between the enterprise and local government. Local government acted more positively in terms of information disclosure than the enterprise due to the demands of stakeholders. There were significant differences between the driving effects of different stakeholders. The effects of central government and local communities were the strongest, followed by news media and environmental organizations, whereas general public had the weakest impact. In addition, the effects of stakeholders on the information disclosure varied throughout different stages.

This paper considered a Chinese typical case study, thereby providing details of information disclosure behaviors of the enterprise and local government during an environmental emergency, and making comparative analysis on the driving effects on information disclosure by different stakeholders.

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.