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This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

Most prior studies treated human resource management (HRM) strength as a whole, while neglecting the dynamic interactions between distinct components (consensus, consistency and distinctiveness). The authors lack a deep understanding of how different components operate together to influence burnout. To address these gaps, this study aims to adopt signaling theory to investigate the interactions among different components and their impacts on employee burnout.

The authors collected time-lagged data from 231 full-time employees in manufacturing firms in Suzhou, China. The authors used the PROCESS Model 6 and hierarchical multiple regression to analyze the data.

This study found that HRM system consensus and consistency mitigate employee burnout, whereas HRM distinctiveness is not significantly related to burnout. Furthermore, the authors revealed that HRM system consistency (rather than distinctiveness) mediated the relationship between consensus and burnout. Moreover, the authors found the sequential mediating effects of HRM system distinctiveness and consistency on the association between consensus and burnout.

Considering that employees’ well-being problems may be debilitating and overwhelming during the COVID-19 pandemic, it is particularly ethical and timely for managers to direct attention to the role of HRM system strength in addressing employee burnout.

This study advances the HRM system literature by teasing out the interactions between the three pivotal components of HRM strength. Our study is among the first to empirically investigate the internal relationships between the meta-features of the HRM system and employee burnout. In doing so, the authors develop a more nuanced understanding of the collective nature of a strong HRM system that conveys a shared message about HRM to promote well-being.

The literature on information technology outsourcing (ITO) prioritises monetary considerations and overlooks human aspects. This qualitative study adopts a contextualised approach to address a research gap in understanding agency workers’ intent to stay in the ITO sector.

In contrast to previous studies that focus on intra-organisational factors and use quantitative designs, this study takes a qualitative approach. It analyses data from 85 in-depth interviews with agency workers in the Chinese ITO supply chain and project managers of supplier and client firms.

The study constructs an integrated framework covering 15 factors at three levels and shows how they interact to influence Chinese agency workers’ intent to stay in the ITO supply chain. Variations in outsourcing management styles and practices among U.S., Japanese and Chinese client firms are presented to enrich the understanding of outsourcing dynamics in a global context.

This study contributes to the ITO literature by providing new insights into the retention of highly skilled agency workers and deepening the contextual understanding of this issue, throwing light on the human aspects often overshadowed by monetary considerations.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.

Selective laser melting (SLM) is a promising additive manufacturing technology in the field of complex parts’ fabrication. High temperature gradient and residual stress are vital problems for the development of SLM technology. The purpose of this paper is to investigate the influence of substrate characteristics on the residual stress of SLMed Inconel 718 (IN718).

The SLMed IN718 samples were fabricated on the substrates with different characteristics, including pre-compression stress, materials and pre-heating. The residual stress at the center of the top surface was measured and compared through Vickers micro-indentation.

The results indicate that the residual stress reduces when the substrate contains pre-compression stress before the SLM process starts. Both substrate thermal expansion coefficient and thermal conductivity affect the residual stress. In addition to reducing the difference of thermal expansion coefficient between the substrate and the deposited material, the substrate with low thermal conductivity can also decrease the residual stress. Substrate pre-heating at 150°C reduces nearly 42.6 per cent residual stress because of the reduction of the temperature gradient.

The influence of substrate characteristics on the residual stress has been studied. The investigation results can help to control the residual stress generated in SLM processing.

Reducing employees’ knowledge hiding is vital for enterprise managers. The development of enterprise social media brings opportunities for them to manage employees’ knowledge-hiding behaviors. However, whether the use of enterprise social media inhibits or promotes knowledge hiding is still unclear. The purpose of this study is to explore how enterprise social media usage affect employees' knowledge hiding.

Based on the intrinsic and extrinsic motivation model, this paper proposes a research model to investigate the relationship between enterprise social media usage and knowledge hiding, using a structural equation modeling analysis of 288 employees’ data.

The results show that work-related public social media usage has an inhibiting effect on employees’ knowledge hiding, whereas the effect of work-related private social media usage on employees’ knowledge hiding is not significant; socially related public social media and private social media usage has a promoting effect on employees’ knowledge hiding; and job engagement acts in a positive moderating role between socially related private and public social media usage and evasive hiding.

First, this paper contributes to knowledge-hiding literature by revealing the relationship between enterprise social media usage and knowledge hiding. Second, this study contributes to motivation theory by clarifying how the use of enterprise social media with different motivations affects knowledge hiding. Third, this paper also contributes to knowledge-hiding literature by revealing a boundary condition, namely, job engagement.

This paper aims to analyze the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints in electronic packaging.

Because of the infeasibility of analyzing the morphology transformation intuitively, a novel equivalent method is used. The morphology transformation on the Cu₃Sn grains, during the formation of full Cu₃Sn solder joints, is regarded as equivalent to the morphology transformation on the Cu₃Sn grains derived from the Cu/Sn structures with different Sn thickness.

During soldering, the Cu₃Sn grains first grew in the fine equiaxial shape in a ripening process until the critical size. Under the critical size, the Cu₃Sn grains were changed from the equiaxial shape to the columnar shape. Moreover, the columnar Cu₃Sn grains could be divided into different clusters with different growth directions. With the proceeding of soldering, the columnar Cu₃Sn grains continued to grow in a feather of the width growing at a greater extent than the length. With the growth of the columnar Cu₃Sn grains, adjacent Cu₃Sn grains, within each cluster, merged with each other. Next, the merged columnar Cu₃Sn grains, within each cluster, continued to merge with each other. Finally, the columnar Cu₃Sn grains, within each cluster, merged into one coarse columnar Cu₃Sn grain with the formation of full Cu₃Sn solder joints. The detailed mechanism, for the very interesting morphology transformation, has been proposed.

Few researchers focused on the morphology transformation of interfacial phases during the formation of full intermetallic compounds joints. To bridge the research gap, the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints has been studied for the first time.

The study’s purpose is to investigate the constraints and facilitators influencing skiing participation in Beijing. This research includes three segments based on the frequency of skiing participation (i.e. non-, low-frequency-, and high-frequency skiers). By doing so, the study offers an enhanced understanding of the Chinese skiing market and unveils insights assisting industry professionals to effectively address their customers' diverse needs and expectations.

An online survey was developed based on prior research and consisted of four sections: (1) skiing participation; (2) constraints; (3) facilitators; (4) demographics. Items in the constraint and facilitator scale were measured using a 7-point Likert scale. A total of 409 participants completed the survey. The participants included 137 non-skiers, 134 low-frequency skiers, and 138 high-frequency skiers.

Through an exploratory factor analysis, three constructs emerged: general constraints, facilitators and learning constraints. As expected, facilitators were a positive predictor of skiing participation. Importantly, the emergent construct of learning constraints was a negative predictor of skiing and yet, the construct of general constraints was insignificant. Furthermore, the three segments differ significantly in household status, income, and education level.

These results support previous research noting the relevance in skiing participation of the dimensions: facilitators and learning constraints. The findings point to the need for ski resorts in Beijing to offer instructional sessions for beginners so they may become familiar with skiing fundamentals and enhance their confidence, particularly among nonskiers and low-frequency skiers.

Social commerce is a subpart of electronic commerce (e-commerce), where social media is forced to support user contributions. The purpose of this study is to measure the impact of customers' interpersonal interactions in social commerce on customer relationship management (CRM) performance, based on the flow, commitment-trust and stimulus–organism–response (SOR) theories.

On the basis of the SOR framework, the authors developed a study model to determine the impact on CRM performance of customers' interpersonal interactions in social commerce. The primary data of the study were collected from 640 users of social commerce through a web questionnaire during the COVID-19 (coronavirus disease 2019) pandemic situation, and the authors tested the study model using the approach of covariance-based structural equation modeling (SEM).

Results of the current study reveal that customers' interpersonal interactions in social commerce optimistically influence their perceived flow. Moreover, perceived flow absolutely controls users' trust and CRM performance. In turn, collective users' trust positively influences users' commitment and CRM performance. Finally, collective users' commitment absolutely influences the performance of CRM.

The authors provide a valuable contribution to the theoretical field of online marketing and CRM. Besides, the findings of this study are relevant for marketers to know the issues for increasing customer trust, commitment and performance of CRM.

The current study develops a model based on the flow, commitment-trust and stimulus–organism–response (SOR) theories. The authors' research is the first to estimate the effect of customers' interpersonal interactions in social commerce on CRM performance.

The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).

We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.

Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.

This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.