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The purpose of this paper is to investigate the influence of religious entrepreneurs on bank loans and further examine the moderating effect of entrepreneurial gender.

In 2010, the Chinese national survey reported the different religious beliefs of private entrepreneurs. Using this set of survey data, the authors obtain a sample of 4,330 Chinese family firms and employ the Tobit regression approach to examine the relationship between the amount of bank loans and the religious background of entrepreneurs. In addition, the authors use the propensity score matching approach to address the endogeneity issue.

Based on the data from the 2010 national survey, the authors document that the amount of bank loans is significantly higher for Chinese family firms with religious entrepreneurs than for their counterparts. This finding suggests that religious individuals are inclined to be more ethical and honest and Chinese family firms with religious entrepreneurs transfer soft information to banks, and eventually lenders favor religious entrepreneurs with more bank loans. Moreover, the authors reveal that the amount of bank loans is significantly larger for firms with female entrepreneurs than for those without female entrepreneurs. In addition, entrepreneurial gender attenuates the positive relationship between religious entrepreneurs and bank loans.

This study is one of few studies to examine the influence of an entrepreneur’s religious belief on bank credit decisions and adds to previous studies about religious influence on corporate behavior by revealing a positive association between religious entrepreneurs and bank loans. Moreover, this study validates that female entrepreneurs exert positive effects on the amount of bank loans and attenuate the positive influence of religious entrepreneurs on bank loans.

The purpose of this paper is to study a fractional grey model FAGM(1,1,t^α) based on the GM(1,1,t^α) model and the fractional accumulated generating operation, and then predict the national health expenditure, the government health expenditure and the out-of-pocket health expenditure of China.

The presented univariate grey model is systematically studied by using the grey modelling technique, the fractional accumulated generating operation and the trapezoid approximation formula of definite integral. The optimal system parameters r and α are evaluated by the particle swarm optimisation algorithm.

The expressions of the time response function and the restored values of this model are derived. The GM(1,1), NGM(1,1,k,c) and GM(1,1,t^α) models are particular cases of the FAGM(1,1,t^α) model with deterministic r and α. Compared with other forecasting models, the results of the FAGM(1,1,t^α) model have higher precision.

The superiority of the new model has high potential to be used in the medicine and health fields and others. Results can provide a guideline for government decision making.

The univariate fractional grey model FAGM (1,1,t^α) successfully studies the China’s health expenditure.

The purpose of this paper is to analyze theory of inventive problem-solving (TRIZ) in terms of knowledge, skill, workload and affect to understand its effectiveness in enabling designers to achieve their optimized mental performance.

TASKS framework, which aims to capture the causal relations among Task workload, affect, skills, knowledge and mental stress, is adopted as our methodology. The framework supports the analysis of how a methodology influence designer’s affect, skills, knowledge and workload. TRIZ-related publications are assessed using the TASKS framework to identify the barriers and enablers in TRIZ-supported design.

TRIZ has limitations on its logic and tools. Nevertheless, it could create a beneficial impact on mental performance of designers.

This paper provides a theory-driven TRIZ usability analysis based on the materials in the literature following the TASKS framework. The impact of TRIZ, as an enabler or a barrier, has been analyzed in accomplishing a design task.

Understanding the customers’ habits of getting cosmetic products information is of great importance to enhance the effectiveness of advertising and marketing strategy of cosmetics. In China, as females are the major target customers of cosmetics, this paper aims to analyze the information behaviors of obtaining cosmetics information of different women groups, including the preferences of information channels and the corresponding means of getting information.

This research is based on a questionnaire survey. A total of 419 effective questionnaires were collected from females who had bought cosmetics in China. Descriptive statistics and chi-square analysis were used to analyze the data.

According to the result of the questionnaire survey, women preferred to get information through social platforms and physical stores. And the primary reason of the choice was that they believed the information from the channel they chose was reliable. The survey also revealed that age, educational level and occupation would significantly affect their preferences of information channels.

There was no previous research which analyzed Chinese women consumers’ information-seeking behaviors about cosmetic products information as well as the influence factors of the behaviors.

In order to study the static and dynamic comfort of tight sportswear in winter, the subjective comfort was aimed to be evaluated by collecting sensory data such as humidity feeling, cold feeling and other perceptions. In this paper, the experiment was divided into standing, squatting, jumping, jogging, walking and so on.

Through particle swarm optimization-cuckoo search model, the sensory factors that affect the overall comfort were optimized, and it was found that there were great differences in the overall comfort factors under different motions. Then, analytic hierarchy process was used to sort the optimized sensory indicators in each experimental stage, and the influence degree of sensory indicators was studied. Finally, by the long short-term memory (LSTM) model, taking comfort senses of standing, squatting, jumping and jogging as input parameters, and regarding comfort senses of walking, lifting legs and resting as output parameters, the prediction model was founded.

The results showed that there were certain differences between the prediction value and the real subjective evaluation value, but most of the predicted values were consistent with the real values on the sensory level, and the overall prediction level was good, which meant that the LSTM model had more accurate prediction ability for subjective evaluation and could be extended to other sports.

The research results could provide scientific methods for the design of tight-fitting sportswear in winter.

The purpose of this paper is to investigate the responsibility that should be taken by different pork supply chain participants to ensure pork quality and safety, with the aim of providing some guidance for strengthening the supervision of pork quality and safety.

The pig farmer survey and the pork consumer survey were conducted in Funing County, Jiangsu Province, using the best-worst scaling (BWS) and a mixed logit model.

The results showed that the designation of responsibility for ensuring pork quality and safety was of, in descending order, feed producers and suppliers, backyard farmers and farms of designated size, pork processing workshops and companies of and above designated size, slaughterhouses, supermarkets, farmer’s markets, pig transporters, and consumers. Both pig farmers and pork consumers believed that those involved in the initial pork supply chain should take greater responsibility for pork quality and safety.

Allocation of responsibilities across the entire pork industry chain was investigated from the perspective of pig farmers and pork consumers using the BWS and a mixed logit model. The results of this study might explain the unique problems that occur in pork supply chain management in large developing countries like China.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

The purpose of this paper is to optimize the degradation test for products subject to multiple types of inherent stresses and external random shocks. The mechanism that shows how the variables to be optimized influence the considered multiple objectives is also aimed to be explored by using the grey incidence analysis (GIA) model.

The Gamma process is employed to model the influences of different types of stresses and external random shocks. The GIA model is introduced to transfer multiple considered objectives as a comprehensive degree of grey incidence. The particle swarm optimization is integrated to search the globally optimal value of the characteristic variables to be optimized.

The acceleration of tested stresses and external random shocks both make the engineering systems become more vulnerable to the inherent degradation. And, the Kriging model can provide guidance of searching the optimal values of test characteristic variables and mitigate the computation burden. The grey incidence model can make the optimization focused and improve the optimality of objective values.

The proposed method can effectively overcome the drawbacks brought by the limitation of test data and can specify the dependence strength between the inherent degradation and external random shock. The computation cost and accuracy of optimization can be simultaneously ensured by the proposed model.

With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (ε_ij), relative degree of grey incidence (r_ij) or synthetic degree of grey incidence (ρ_ij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.

To achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).

It turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.

Limitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.

In this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.

The purpose of this study is to decompose the effects of country-of-origin labeling (COOL) into multiple dimensions—macrolevel image, related to the country image, and microlevel image, related to dairy industry/product attributes—and investigate how each dimension affects Chinese consumers' evaluation of imported milk.

This study adopted the Becker–DeGroot–Marschak (BDM) auction mechanism to elicit consumers' willingness to pay (WTP) for milk from different countries (New Zealand, Australia, Germany, France and China). The experiment was conducted with 348 shoppers at supermarkets in three major cities of China (Hangzhou, Wuhan and Shijiazhuang). The study subject was ultrahigh-temperature processing (UHT) milk (200 mL Tetra Pak aseptic brick package).

The results show that Chinese consumers are willing to pay a premium for UHT milk from New Zealand, Australia, Germany and France compared to domestic milk, and the premiums are 59.4, 58.9, 57.9, and 52.9% respectively. Both microlevel and macrolevel images exert a substantial influence on consumers' WTP, and the microlevel image has a greater impact on consumers' evaluation of milk than the macrolevel image. Particularly, the macropolitical, microtechnology/quality and microdesign/package dimensions have a positive influence on WTP for milk.

This study contributes to the existing literature in introducing the country-of-origin image (COI) construct with different dimensions to get in-depth knowledge about the country-of-origin (COO) effect in food or agricultural economics.