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Service providers on highly competitive online outsourcing platforms employ various signals to entice buyers to make online purchases. One such signal—the solution exemplars—attracts attention through depictions of exemplary prior work completed by the service providers. Unfortunately, it is not known the extent to which solution exemplar characteristics affect sales performance nor is it clear how such signals perform in the presence of complementary signals such as service provider expertise. This paper explores these issues.

Extending signaling theory, the authors develop a model to explore the effects of solution exemplar characteristics (i.e. exemplar quantity, exemplar diversity and exemplar popularity) on sales performance under the moderating impact of service provider expertise. The authors test the model using proprietary data from ZBJ.com, a leading online outsourcing platform in China.

Exemplar quantity and exemplar popularity positively affect sales performance; exemplar diversity has no significant impact on sales performance and service provider expertise positively moderates the relationships between exemplar quantity, exemplar popularity and sales performance.

This work makes several significant contributions. First, the authors enrich the research on signals in online outsourcing by exploring the impact of solution exemplar characteristics on sales performance. Second, the authors analyze three solution exemplar characteristics: exemplar quantity, exemplar diversity and exemplar popularity. Third, this work shows that service provider expertise moderates the relationship between solution exemplar characteristics and sales performance. Important practical implications for both online outsourcing platforms and service providers are discussed.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

Online product ratings play an important role in the decision-making process of consumers, which are not only sources of information used by consumers to understand the function and quality of a product or service but also sources of information used to find desirable products. The purpose of this paper is to develop a decision-based method for supporting the purchase decisions of consumers based on not only the online product ratings but also the actual product attributes.

First, two types of utility values are designed to measure the preference of the consumer based on either online ratings or actual product attributes. Then, the traditional TOPSIS method is adopted to achieve a comprehensive value by integrating the two types of utility values so that all of the alternative products can be ranked. Further, a product selection support system prototype is designed and developed to support the purchase decisions of consumers.

To help consumers select desirable products efficiently, it is necessary to develop a product selection method based on the online ratings of alternative products and consumer expectations.

The research shows that the proposed method can not only support consumers’ purchase decisions based on a large number of online product ratings but also help manufacturers to find out consumers’ demands or requirements on products so as to facilitate the design of new products or the improvement of products. On the basis of the proposed method, the developed system prototype is helpful for consumers to select desirable products.

To support the purchase decisions of consumers, a new decision-based method for selecting desirable online products is proposed.

This paper shows how a shorter fecundity horizon for females (a biological constraint) leads to age and educational disparities between husbands and wives. Empirical support is based on data from a natural experiment commencing before and ending after China’s 1980 one-child law. The results indicate that fertility in China declined by about 1.2–1.4 births per woman as a result of China’s anti-natalist policies. Concomitantly spousal age and educational differences narrowed by approximately 0.5–1.0 and 1.0–1.6 years, respectively. These decreases in the typical husband’s age and educational advantages are important in explaining the division of labor in the home, often given as a cause for the gender wage gap. Indeed, as fertility declined, which has been the historical trend in most developed countries, husband-wife age and educational differences diminished leading to less division of labor in the home and a smaller gender wage disparity. Unlike other models of division of labor in the home which rely on innately endogenous factors, this paper’s theory is based on an exogenous biological constraint.

By using a nontraditional configuration approach, the purpose of this paper is to examine the effect of personality on entrepreneurial intention and success. Previous research has focused on why individuals become entrepreneurs and why some are more successful than others. However, most studies have investigated only single factors or primary personality traits. The current study investigates not only the strength of the personality-entrepreneurship link, but also clarifies the nature of the relationship.

Using two independent samples and an innovative regression-based pattern recognition procedure, the study investigates whether the nature of the personality-entrepreneurship relationship is driven by individuals’ absolute trait levels or by their idiosyncratic configuration of personality traits. The non-entrepreneur sample consisted of 225 business students in Eastern China, specializing in a variety of business subjects. The entrepreneur sample consisted of 120 business owners in a university entrepreneurial park in Eastern China.

Results support hypotheses that the two different types of entrepreneurship criteria are predicted by different personality profile effects. Entrepreneurial intentions are driven by individuals’ personality patterns (peaks and valleys in profiles). In contrast, entrepreneurial success is driven by personality levels (individuals’ relative standing on personality traits compared to other entrepreneurs).

The findings enrich the understanding of entrepreneurial personality. The more significant contribution of the present study was that it differentiated between personality profile pattern and level effects and investigated whether the nature of the personality-entrepreneurship relationship is driven by individuals’ absolute trait levels (i.e. how high or low they score compared to others), or by their idiosyncratic configuration of personality traits (i.e. their strengths and weaknesses).

The findings of this study may help entrepreneurs to figure out how to be successful running their own businesses, if they are not graced with a personality pattern that is not favorable to entrepreneurship. In addition, these findings can help entrepreneurship educators to understand how best to train entrepreneurs.

The adoption of both person-centered approach and process perspective of entrepreneurship allowed this study to make major contributions to entrepreneurial personality research.

The innovative rural land property right mortgage loan program (RLPRMLP) provides a new channel for farmers to secure borrowing from microfinance institutions. Farmers’ land property right could be used as collateral to deal with moral hazard and adverse selection issues. The purpose of this paper is to document the effects of the RLPRMLP on households’ income using a unique data set from 1,279 households’ survey in Western China during 2012–2014.

At the first stage, the authors evaluate the impacts of RLPRMLP on households’ income to get the benchmark results when the authors control household’s observed and unobserved characteristics. To address the potential endogeneity issue resulting from the self-selection of farmers into the rural financial market, the authors apply the treatment effect model to identify the csusal effects of the innovative loan approach on a household’s income.

The empirical results favor the belief that participating in the RLPRMLP helps the households improve their total income (at least by 20.2 percent) and income per capita. This income-improving channel is only through agricultural sector, rather than through non-agricultural sector which potentially helps to deal with the inequality issue within poor regions. The results are robust when the authors control households’ characteristics, including observed and unobserved, and solve the endogeneity issue. Participating in the RLPRMLP could encourage farmers to invest more in the agricultural sector and increase agricultural productivity, which is the main mechanism of the income-improving effect of the RLPRMLP.

The innovative mortgage loan program provides a new channel for farmers to get loan. Land property right reform is being currently applied in rural China. Testing the effectiveness of combining land property right and microfinance loan method is necessary for the government policy making and development of rural areas. The findings are striking. The income improvement mechanism mainly works through agricultural sector, potentially because of the reform of land property, contributing to the increase of marginal product of land, i.e., the increase of agricultural productivity. These could help the development of microfinance theory, and the innovative loan method could be applied to other developing countries.

The purpose of this paper is to improve the conductivity and processability of polyaniline (PANI).

The study opted for synthesis of the conductive PANI/polyvinyl alcohol (PVA) composite film, co-doped with 5-sulphosalicylic acid and sulphuric acid. Using an electrochemical method, a small amount of silver (Ag) was electrodeposited on the film. The PVA/PANI and PVA/PANI/Ag composite films were characterised by scanning electron microscope, X-ray diffraction and infrared. The composite deposition mechanism of the composite film was investigated by cyclic voltammetry for the first time.

The conductivity of the optimum PVA/PANI composite film reached 21.2 S · cm⁻¹.Then, a small amount of Ag was deposited on the PVA/PANI film, and the conductivity significantly increased by 1250 S · cm⁻¹. Through appropriate degree of stretching, the conductivity of the films was enhanced. The results indicate that uniform PVA/PANI fibres and dendritic Ag can combine to form complete three-dimensional conductive networks that exhibit better conductivity and mechanical properties. The cyclic voltammetry curves reveal that the dedoping potential of PANI was more negative than the reduction potential of Ag. Therefore, the procedure for the deposition of Ag on the PANI/PVA composite film cannot decrease the conductivity.

This paper for the first time described and revealed the effective and practical synthesis approach and composite mechanism to prepare multi-types metal-conductive polymer composites and improve the conductivity of a conductive polymer with a less expense and one-step electrochemical method.

This paper first explored galvanostatic oxidation to synthesise a PANI composite film to resolve the processability and conductivity of PANI by co-doped with mixed acids and deposited Ag on film. Furthermore, for the first time, the composite mechanism of metal and conductive polymer was studied.

The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

Shrink fit is an important method used in mechanical transmission, but the design of these connections is not sufficiently precise due to lack of knowledge regarding the effects of many parameters. The paper aims to discuss these issues.

This paper presents the torque capacity and contact stress analysis for a new shrink disc by numerical analysis and experimental methods. Torque capacity analysis aims to predict the effectiveness of mechanical transmission, and to check the structural safety. Stress-strain curves are measured using the universal testing machine, and Bilinear Kinematic Hardening rate-independent plasticity model characterises the elastic-plastic response of the materials.

The numerical result shows that there is no plastic deformation at the interference regions, and the maximum equivalent strain happens on the inner ring. The stress of outer ring is decreased with radius increasing, and shows a periodic variation along the circumference, but stress concentration happens at the threaded holes. Finally, the torque capacity of the shrink disc system is measured through a developed test machine, and the torque capacity difference between numerical and experimental results is about 3.3 per cent.

This paper presents a numerical and experimental carrying capacity analysis for a shrink disc. Plasticity model characterises the materials’ elastic-plastic response based testing. A test machine is designed to measure the torque capacity; the error is about 3.3 per cent.

AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity.

The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity.

The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm³ and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm³. The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains.

The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications.

To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.