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Mobile health service (MHS) can provide users with convenient health services and information to reduce their medical costs from aging or other health issues. Previous studies confirm the underdevelopment of the Chinese MHS. The purpose of this paper is to analyze the factors that affect the intention to use MHS.

This paper develops a research model that integrates personal health differences with theory of planned behavior (TPB) and protection motivation theory (PMT). This model is empirically tested using data from 494 valid questionnaires. Structural equation modeling is used to test the hypotheses.

Perceived vulnerability to disease, perceived severity of disease, response efficacy and self-efficacy positively affect attitude, thereby exerting a positive influence on the behavioral intention to use MHS. Subjective norms also influence users’ behavioral intention. Personal health status and personal health value have quasi-moderating effects on the relationship between attitude and behavioral intention.

This paper presents an early attempt to conceptualize and validate a research model of MHS acceptance by integrating TPB and PMT in a complementary manner. The integrated model provides a holistic view of people’s intention to use MHS by considering health threat beliefs, individual role (i.e. attitude and self-efficacy) and social influences (i.e. subjective norms). Furthermore, this research highlights the role of two individual health characteristics (i.e. personal health status and personal health value) in MHS adoption. These new findings are beneficial toward an in-depth understanding of technology adoption in the MHS context.

The purpose of this study is to examine the role of customer experience moderating the relationship between reputation (online consumer reviews) and price premium.

This paper collected half-year period transaction of Nokia 5230XM and Kingston SD card from Taobao.com, the largest e-commerce platform in China. This paper combined theoretical analysis and empirical analysis together. Two-stage regression and logistic regression analysis was applied in this empirical analysis. The sensitivity analyses (robustness check) were also conducted in this paper.

Customer experience negatively moderates reputation price premium; thus, the positive effect of the reputation system is weaker for the experienced customer than for the naïve customer. Customers with more experience are less likely to pay the price premium and rely on a reputation system.

The results help sellers to strategize in the online marketplace. Sellers that wish to compete in the e-market must understand the type of customers they are addressing and differentiate the way they treat customers based on the level of customer experience.

This research contributes to the reputation management and customer behavior literature by identifying the effects of customer experience on the relationship between the reputation system and price premium. The results address the conflicts found in previous studies by extending the explanation of the negative reputation price premium.

The magnetostriction of grain-oriented electrical silicon steel sheet is studied for the magnetic field direction along the rolling direction and deviating from it. The method of calculating the vibration of transformer is developed through COMSOL. The paper aims to discuss these issues.

Measurements of signals of magnetostriction and magnetic polarization, and calculation through software.

The angle between the magnetic field direction and the rolling direction does a great influence on magnetostriction strain.

The maximum λ _p-p of transversal magnetostriction is above 30 times more than the value when the angle is 0°. The transversal magnetostriction is a main reason of vibration increasing at the corner of transformer.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment.

This paper proposes a dynamic reliability and variance-based global sensitivity analysis (GSA) strategy with non-probabilistic convex model for pipes conveying fluid based on the first passage principle failure mechanism. To illustrate the influence of input uncertainty on output uncertainty of non-probability, the main index and the total index of variance-based GSA analysis are used. Furthermore, considering the efficiency of traditional simulation method, an active learning Kriging surrogate model is introduced to estimate the dynamic reliability and GSA indices of the structure system under random vibration.

The variance-based GSA analysis can measure the effect of input variables of convex model on the dynamic reliability, which provides useful reference and guidance for the design and optimization of pipes conveying fluid. For designers, the rankings and values of main and total indices have essential guiding role in engineering practice.

The effectiveness of the proposed method to calculate the dynamic reliability and sensitivity of pipes conveying fluid while ensuring the calculation accuracy and efficiency in the meantime.

A two-dimensional (2D) body measurement system was implemented to study the application of sportswear design in measurement garment development. A total of 50 participants were recruited. The basic demographic information and sportswear preference data were collected through a survey to understand consumer preferences and acceptance of the new designs. The body measurements were collected through both the selected 2D measurement system and a commercial three-dimensional scanning booth to evaluate measurement accuracy.

Finding the right size has been a long-existing problem for clothing consumers. Size problem is the most common reason for e-commerce returns and adds a high cost for retailers. One possible solution is to offer consumers an easy-to-use method that extracts accurate body measurements to be used for clothing size selection. The purpose of this research is to apply sportswear design elements on measurement garments to see if consumers’ interest in using the 2D measurement system can be increased without influencing the measurement accuracy.

The results showed that the added design features increased consumers’ interest in using 2D body measuring technology without significantly influencing measurement accuracy.

This research applied sportswear elements to convert a 2D measurement bodysuit to a fashionable clothing product. The solution resolved users’ privacy concerns and increased their acceptance and use of the technology. Other studies have not focused on using aesthetic features to improve the 2D measurement technology.

Two-dimensional (2D) measurement technology has become more popular than before, thanks to the widespread availability of smartphones and smart devices. However, most existing 2D body measurement systems have background constraints and may raise privacy concerns. The purpose of this research was to test the idea of designing a 2D measurement system that works with a color-coded measurement garment for background removal and privacy protection. Clothing consumers can use the proposed system for daily apparel shopping purposes.

A 2D body measurement system was designed and tested. The system adopted a close-fitted color-coded measurement garment and used neural network models to detect the color-code in the garment area and remove backgrounds. In total, 78 participants were recruited, and the collected data were split into training and testing sets. The training dataset was used to train the neural network and statistical prediction models for the 2D system. The testing dataset was used to compare the performance of the 2D system with a commercial three-dimensional (3D) body scanner.

The results showed that the color-coded measurement garment worked well with the neural network models to process the images for measurement extraction. The 2D measurement system worked better at close-fitted areas than loose-fitted areas.

This research combined a color-coded measurement garment with neural network models to solve the privacy and background challenges of the 2D body measurement system. Other researchers have never studied this approach.

The purpose of this paper is to identify the studies that have had the greatest impact on entrepreneurial orientation (EO) research and to analyze the changes that have taken place in the intellectual structure of this key topic in the entrepreneurship field.

This paper adopts the methods of citation analysis, co-citation analysis, social network analysis and factor analysis to analyze 2,005 studies on the theme of EO in the 12 years from 2008 to 2019.

The research identified the representative literature and journals in the field of EO, presented the development process and knowledge cluster by stages in the form of intuitive network mapping and summarized the main research fields and the evolutionary trend of EO during the period 2008–2019.

This study provides a more comprehensive and objective knowledge structure evolution context for EO researchers, which is conducive to the mature development of the entrepreneurship-oriented field.

It is helpful for enterprise managers to have a deeper understanding of the connotations of EO and to make more scientific entrepreneurial decisions, so as to improve their ability in enterprise innovation and entrepreneurship.

This study visualizes the evolution process of EO knowledge structure, fills the gaps of recent knowledge structure analysis literature in this field and provides theoretical support for the findings of other studies.

The research effort on entrepreneurship education has been mainly for the higher education settings and on the individual level of analysis. On the contrary, this research urges scholars to expedite attention to the secondary education settings, especially in the emerging economies in Asia and Africa. This paper aims to reveal the existing landscape of literature development on the topic and promote ecological approaches of constructing entrepreneurship education programs in schools. It advocates the “incubator” role of schools for students and the necessity of establishing socially embedded entrepreneurship education as the playground for future entrepreneurs.

This study followed the systematic literature review as its research design. It obtained 1,555 publications from six academic databases and 60 more publications from expert consulting and backward snowballing technique. Data screening resulted in a total of 101 relevant publications with the upper secondary education as their research context. The qualitative integrative synthesis method was then applied to integrate research evidence to the five circles of systems according to Urie Bronfenbrenner’s ecological systems theory.

This study contributes to the entrepreneurship education and youth career development literature, especially in the developing countries. Results discovered that entrepreneurship education programs, when interacting with ecological systems, resulted in training success. The most frequently studied systems were microsystems; here, there was a dominant focus on program-level reporting and analyzing. There was less focus on other systems such as mesosystems, exosystems or macrosystems. Moreover, only one study was associated with chronosystems, suggesting a significant research gap regarding the longitudinal studies. However, this review validated the different approaches to delivering entrepreneurship education in emerging and developed economies.

One limitation of this research lies in the methodology. The inclusion criteria limited the studies to the context of upper secondary education and excluded those of secondary education in general. The sampling method limited the power of this research to analyze and discuss policy-level studies because policies most likely embrace the whole secondary education level as its target. Another limitation is associated with the lack of experimental studies in assessing the comparative advantages of following the ecological approach when constructing entrepreneurship education. It, therefore, remains an undiscussed matter within this study regarding whether following the ecological approach means empirically a better educational choice or not.

This study discusses the implications for policymakers, especially in emerging economies, and suggests that awareness, attention and funding are needed to empower youth entrepreneurship education from an ecological systems view.

To the best of the authors’ knowledge, this research is one of few studies that use the ecological systems theory in the context of entrepreneurship education with the purpose of focusing on environment-level analysis instead of individual-level analysis. Through the systematic literature review, this study proposes an ecological approach to comprehend, guide, evaluate and improve the design and implementation of entrepreneurship education programs in schools based on well-articulated research evidence. The research can inform both researchers and educators by offering a holistic perspective to observe and evaluate entrepreneurship education programs and their levels of social connectedness.