Search results

Since East Asian Confucian societies are relational societies that view harmonious relationships with others as important, the issue of consumer ethics has received considerable attention in the quest to improve the effectiveness of business transactions. This is especially true of China, a large developing country with many business investment opportunities. The purpose of this paper is to examine the relationships between consumer ethical beliefs and the personality traits of consumer relationship proneness, religiousness, attitude toward business and love of money, which are seldom explored in the literature.

The sample is collected by systematic sampling in China and a sample of 434 individuals is surveyed. This study performs confirmatory factor analysis to examine the reliability and validity and then uses the evaluation of the structural equation model exhibited a good fit between the model and the observed data to test the hypotheses via LISREL 8.7.

Findings show that individuals with stronger relationship proneness and high religiousness tend to more strongly consider many questionable consumer practices as unacceptable. Attitude toward business and love of money partially impact consumer unethical beliefs.

While there cultural differences between East Asian and western societies, consumer relationship proneness and the three other constructs featuring Chinese characteristics should be considered a vital personality in Chinese society. The results of this study offer findings which are not fully consistent with the findings of studies conducted in other countries and can improve friendly relationship marketing practices with Chinese consumers. More research is necessary to understand consumer ethical behavior in the varied cultures of Asia.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Facial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of society. Facial expression data can reflect people's mental state. In health care, the analysis and processing of facial expression data can promote the improvement of people's health. This paper introduces several important public facial expression databases and describes the process of facial expression recognition. The standard facial expression database FER2013 and CK+ were used as the main training samples. At the same time, the facial expression image data of 16 Chinese children were collected as supplementary samples. With the help of VGG19 and Resnet18 algorithm models of deep convolution neural network, this paper studies and develops an information system for the diagnosis of autism by facial expression data.

The facial expression data of the training samples are based on the standard expression database FER2013 and CK+. FER2013 and CK+ databases are a common facial expression data set, which is suitable for the research of facial expression recognition. On the basis of FER2013 and CK+ facial expression database, this paper uses the machine learning model support vector machine (SVM) and deep convolution neural network model CNN, VGG19 and Resnet18 to complete the facial expression recognition.

In this study, ten normal children and ten autistic patients were recruited to test the accuracy of the information system and the diagnostic effect of autism. After testing, the accuracy rate of facial expression recognition is 81.4 percent. This information system can easily identify autistic children. The feasibility of recognizing autism through facial expression is verified.

The CK+ facial expression database contains some adult facial expression images. In order to improve the accuracy of facial expression recognition for children, more facial expression data of children will be collected as training samples. Therefore, the recognition rate of the information system will be further improved.

This research uses facial expression data and the latest artificial intelligence technology, which is advanced in technology. The diagnostic accuracy of autism is higher than that of traditional systems, so this study is innovative. Research topics come from the actual needs of doctors, and the contents and methods of research have been discussed with doctors many times. The system can diagnose autism as early as possible, promote the early treatment and rehabilitation of patients, and then reduce the economic and mental burden of patients. Therefore, this information system has good social benefits and application value.

The purpose of this paper is to evaluate the effect of logistics collaboration, meetings, relationship history, and supplier selection on the logistics performance of shippers, carriers, and logistics services providers. Rather than focusing on collaboration and performance, the research provides a wide analysis of how logistics collaboration and performance interact with other organizational practices.

To investigate the interaction among the constructs, the authors proposed a structural equation model to understand the influence of meetings, relationship history, supplier selection, and collaboration on logistics performance. The data were obtained through a survey of 199 managers of Brazilian companies in the retail sector.

Supplier selection has the strongest effect on logistics collaboration, and relationship history has the strongest effect on logistics performance. Rather than meetings and operational features, the elements of interpersonal skills, organizational culture, and communication appear to be the most important contributors to logistics performance achievements; relationship history leads to better performance.

This study contributes to our understanding of how and with whom to collaborate by highlighting the relationships among supplier selection, relationship history, meetings, and logistics collaboration and logistics performance.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

Financial reporting is the cornerstone of financial management. In 1999, the Governmental Accounting Standards Board (GASB) ratified unanimously GASB Statement No. 34. In this paper, using the Georgia Comprehensive Annual Financial Report (CAFR) and interviews with practitioners, we examine the implication of GASB 34 for the linkage between reporting and accountability. The research findings show that Statement 34 introduces significant impacts on reporting. However, its implications for accountability and better financial management are yet to be as significant as hoped.

The purpose of this paper is to study the influence of cooperative and competitive personalities on tacit knowledge sharing (TKS) by exploring the mediating role of organizational identification (OI) and the moderating role of perceived organizational support (POS) among Chinese employees.

Conducting a network survey of ordinary employees from Chinese listed companies (2019) as the research objects, the authors collect 298 valid samples for research. The authors apply confirmatory factor analysis to test the reliability and validity of the constructs, structural equation modeling to verify the direct effect and the PROCESS macro to test the mediating and moderating effects.

The results show that there is a positive relationship between cooperative personality (COP) and TKS, and there is an inverted U-shaped relationship between competitive personality (CMP) and TKS. OI plays a mediating role between COP and TKS, while POS plays a negative moderating role between COP and TKS.

This paper only takes Chinese employees as the research sample, and future research can make a cross-cultural comparison between the impacts of cooperative and competitive personalities on employees’ behaviors.

The results of this study suggest that enterprises should actively cultivate the COP of employees, and managers should refrain from intervening in the behaviors of employees with COP. At the same time, for employees with CMP, managers should guide them to control their competitive tendency at a reasonable level. The conclusions of this paper also suggest that managers should pay attention to the cultivation of employees’ OI.

This study plugs the gap in research on the impacts of cooperative and competitive personalities on TKS. It makes a contribution to the research development of COP and CMP and their influence mechanisms on employees’ behaviors. In addition, the negative moderating effect of POS on COP–TKS link verifies the correctness of Y theory. Studying the relationships among personality traits (cooperative and competitive personalities), cognition (OI) and behaviors (TKS), this paper makes a contribution to such a research stream.

The purpose of this paper is to promote a wider understanding of the importance of distinguishing between presenteeism behavior and its motivation and between the avoidance and approach dimensions of motivation, and to rectify the neglect of presenteeism’s antecedents (in particular, situational ones). It develops a theoretical model that explains how situational antecedents affect presenteeism – conventionally defined as attending work while ill.

An ordinary least-squares regression-based path analysis is employed to analyze the findings of a sample of 277 employees in service organizations in southwestern China.

Findings demonstrate that the situational factor, leader–member exchange (LMX), is positively related to the approach dimension of presenteeism motivation and that of workload moderates the positive link between presenteeism motivation and behavior, such that employees who experience higher workload more frequently display presenteeism behavior.

Findings suggest that managers should be prudent when developing relationships with their subordinates and consider the ways in which they may most effectively encourage employees to support their organization.

This is the first study to consider LMX and workload as situational antecedents of presenteeism motivation and behavior.

Pipeline robots are often used in pipeline non-destructive testing. Given the need for long-range in-pipe inspections, this study aims to develop a wireless in-pipe inspection robot for image acquisition.

In this paper, an in-pipe robot with a new mechanical system is proposed. This system combines a three-arm load-bearing structure with spring sleeves and a half-umbrella diametric change structure, which can ensure the stability of the camera when acquiring images while maintaining the robot’s flexibility. In addition, data were transmitted wirelessly via a system that uses a 433 MHz ultra-high frequency and wireless local-area network–based image transmission system. Software and practical tests were conducted to verify the robot’s design. A preliminary examination of the robot’s cruising range was also conducted.

The feasibility of the robot was demonstrated using CATIA V5 and MSC ADAMS software. The simulation results showed that the centre of mass of the robot remained in a stable position and that it could function in a simulated pipeline network. In the practical test, the prototype functioned stably, correctly executed remote instructions and transmitted in near real-time its location, battery voltage and the captured images. Additionally, the tests demonstrated that the robot could successfully pass through the bends in a 200-mm-wide pipe at any angle between 0° and 90°. In actual wireless network conditions, the electrical system functioned for 44.7 consecutive minutes.

A wheeled wireless robot adopts a new mechanical system. For inspections of plastic pipelines, the robot can adapt to pipes with diameters of 150–210 mm and has the potential for practical applications.

Generation Z (Gen Z) is entering the hotel workforce and will soon become the dominant group. This has called for a better understanding of this workforce’s attitudes and perceptions towards working in the hotel industry. This study aims to examine the effect of organizational socialization on the retention of Chinese Gen Z employees, the mediating role of person–environment fit (P-E fit) and the moderating effect of career commitment.

Time-lagged data were collected from 426 Gen Z new employees from 20 upscale hotels at two different times (2 weeks and 12 weeks after the employees entered the hotel). Confirmatory factor analysis, structural equation modeling, bootstrapping analysis and moderated hierarchical regression analyses were used for data analysis.

Organizational socialization positively affects employee retention via person–environment fit. Moreover, career commitment positively moderates the relationship between person–environment fit and employee retention.

Hotels must view organizational socialization as a long-term investment in Gen Z talent management by offering effective training through diverse methods, creating a collaborative environment and helping them develop career plans to enhance their career commitment.

This study unpacks the four dimensions of organizational socialization and investigates their differential effects on Gen Z employees’ retention through P-E fit. The moderating role of career commitment is also examined. This study contributes to the growing body of hospitality human resources management research on this new generation of workforce in China.