Search results

Enterprises' multichannel operations provide various avenues for customer interaction; however, existing literature investigating customer-to-customer interaction (CCI) mainly focuses on a single channel. The purpose of this paper is to investigate the spillover effect of CCI and potential underlying mediating mechanisms in different information channels.

Three between-subjects experiments with 946 participants were employed to empirically validate the proposed hypotheses in the context of an experiential product and a material product.

Results suggest the clear spillover effect of CCI, indicating that positive CCI improves focal customers' satisfaction and purchase intention, whereas negative CCI reduces focal customers' satisfaction and purchase intention. Moreover, CCI's spillover effect varies based on the CCI channel. Offline CCI has a stronger positive spillover effect than online CCI. Contrarily, online CCI has a stronger negative spillover effect than offline CCI. Customer experience and trust are demonstrated to have mediating roles in this process.

This study is the first to comprehensively understand and compare the CCI spillover effect of the two information channels. The findings add to the existing knowledge of information processing in the psychological mechanisms influencing the belief in addition to providing insights for companies engaged in multichannel operations management across different channels.

The purpose of this study is to clarify the mechanism of ambient and transient temperature effects on piezoelectric pressure sensors, and to propose corresponding compensation measures. The temperature of the explosion field has a significant influence on the piezoelectric sensor used to measure the shock wave pressure. For accurate shock wave pressure measurement, based on the actual piezoelectric pressure sensors used in the explosion field, the effects of ambient and transient temperatures on the sensor should be studied.

The compensation method of the ambient temperature is discussed according to the sensor size and material. The theoretical analysis method of the transient temperature is proposed. For the transient temperature conduction problem of the sensor, the finite element simulation method of structure-temperature coupling is used to solve the temperature distribution of the sensor and the change in the contact force on the quartz crystal surface under the step and triangle temperatures. The simulation results are highly consistent with the theory.

Based on the analysis results, a transient temperature control method is proposed, in which 0.5 mm thick lubricating silicone grease is applied to the sensor diaphragm, and 0.2 mm thick fiberglass cloth is wrapped around the sensor side. Simulation experiments are carried out to verify the feasibility of the control method, and the results show that the control method effectively suppresses the output of the thermal parasitic.

The above thermal protection methods can effectively improve the measurement accuracy of shock wave pressure and provide technical support for the evaluation of the power of explosion damage.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

This study is conducted to determine the factors that affect profitability in Chinese listed companies (by using financial ratios). Four independent variables liquidity, intangible assets, working capital and company leverage were empirically tested for their relationships with profitability besides two control variables which are firm size and company efficiency.

This study used secondary data extracted manually from the annual reports of non-financial Chinese listed companies on the Shanghai stock exchange (http://www.szse.cn/); the data set covers 100 companies during the period of 2017–2019, and a random selection method was used in order to achieve credibility and fairness as much as possible.

The findings show firm size, working capital and intangible assets have positive and significant relationships with profitability [return on assets (ROA) and earnings per share (EPS)]. Positive working capital is important to lower the cost of capital and improve companies' profitability. Intangible assets are also an essential source to improve profitability due to their low costs. In addition, the findings display a negative and strong relationship between liquidity and profitability, meaning that companies suffer low profit due to inefficient use of liquid items. Interestingly, leverage, which is measured by debt ratio and leverage ratio, shows mixed results; debt ratio shows a positive and strong association with ROA but not with EPS; while leverage ratio displays a strong but negative association with ROA but not with EPS. These results confirm the inverted U-shape relationship between leverage and profitability, which depends on the balance between benefit and cost of debt.

Profitability is also important for employees and society where business organization provides sustainability and stability for both of them. Employees can then significantly contribute to achieve higher firm's profitability by efficiently using firm's resources.

This study differs than previous studies in number of aspects: First, this study focuses on financial ratios to explain profitability in Chinese companies. This study provides empirical results about the factors connected to profitability and help stakeholders to make their right decisions. Second, it examines the impact of four independent factors and two control variables that some of them are new in Chinese context such as intangible assets. Third previous studies focus on financial industry such as banks; however, this study focuses on non-financial industry.

It is imperative to achieve sustainable growth in farmers' earnings to sustain poverty alleviation efforts and achieve rural revitalization goals. The authors investigated the nature of the non-linear relationship between farmers' e-commerce participation and income growth, analyzed the rationale behind this correlation and examined the moderating effect of digital finance on this relationship.

The authors conducted an empirical investigation using rural household data from the China Household Finance Survey and the regional digital finance index compiled by Peking University. The authors employed a fixed-effect model and a moderating effect model to identify the non-linear influences of e-commerce participation on farmers' income and to analyze the positive synergies of digital finance. The authors used identification and estimation techniques to mitigate the endogeneity problem, specifically employing heteroscedasticity-based instruments.

There is an inverted U-shaped relationship between e-commerce participation and farmers' income. Digital finance reduces the declining trend in the marginal effects of e-commerce and increases marginal values. Furthermore, the synergistic effect can promote the quality and efficiency of business activities by easing credit constraints, reducing risk aversion and stimulating innovative activities, which in turn can lead to sustained revenue growth.

Few studies have focused on the non-linear relationship between e-commerce and farmers' income. This implies that achieving sustained income growth using e-commerce alone is difficult. The synergy between e-commerce and digital finance is a feasible path for achieving this goal.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.

Firstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.

The proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.

This study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.