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Micro-holes are drilled and plated in flexible printed circuit boards (FPCs) for connecting circuits from different layers. More holes, with diameters smaller than 0.3 mm, are required to be drilled in smaller areas with flexible circuits’ miniaturization. The micro-hole quality of micro-drilling is one of the biggest issues of the flexible circuit manufacturers’ production. However, it is not easy to control the quality of micro-holes. The purpose of this study was to conduct research on the tool wear characteristics of FPC drilling process and its influence on micro-hole quality to improve the micro-hole quality of FPC.

The tool-wear characteristics of micro-drills after FPC drilling were observed. The influence of spindle speed, feed rate, number of drilled holes and entry board materials on tool-wear was analyzed. The hole qualities of FPC micro-drilling were measured and observed. The relationship between tool-wear and hole quality was analyzed.

The result showed that the tool-wear characteristics of FPC micro-drilling was similar to the tool-wear characteristics of rigid printed circuit board (RPC) micro-drilling. Abrasive wear occurred on both the main cutting edges and the chisel edges of micro-drills, even though there was no glass fiber reinforcing the cloth inside FPC. Resin adhesion was observed on the chisel edge. The influence of feed and number of drilled holes on tool-wear was significant. Tool-wear significantly influences the hole quality of FPC. Tool-wear will largely decrease the hole position accuracy of FPC micro-holes. Tool-wear will increase the thickness of PI nail heads and the height of exit burrs. Fracture was the main difference between tool wear of FPC and RPC micro-drilling. Resin adhesion of RPC was much more severe than FPC micro-drilling. Increasing the spindle speed properly may improve tool life and hole quality.

The technology and manufacturing of FPC has been little investigated. Research on micro-drilling FPC and research data is lacking so far. The micro-hole quality directly affects the reliability of FPC. Thus, improving the micro-hole quality of FPC is very important.

Drawing on social identification theory, this research aims to explore an important mechanism – patients’ perceived empathy from a hospital, which is defined as caring, individualized attention provided by the hospital – that explains how service quality influences patients’ loyalty to the hospital.

This study adopts a structural equation modelling framework to estimate the mediation relationship between service quality and patient loyalty using two studies based on hospital employees and patients.

In Study 1, hospital staff survey data showed that service quality enhanced patients’ perceived empathy from the hospital. In Study 2, the authors confirmed and extended the findings of Study 1 by using patient survey data that suggested that patients’ empathy mediated the relationship between service quality and patients’ loyalty to the hospital.

The results of the two studies suggested that service quality increased patients’ empathy, which in turn improved patients’ loyalty to a hospital. The results extend the customer loyalty literature by exploring the critical antecedents and mechanisms of customer loyalty in the healthcare context. The studies interpret healthcare phenomena from the service aspect of fulfilling patients’ unique needs and providing a good hospital service experience. Moreover, the authors offer an insightful approach to explaining the service quality–patient loyalty linkage in the healthcare industry. Important theoretical and managerial contributions and suggestions for future research are discussed.

The purpose of the present study is to build a research model to study how the use of different enterprise social media platforms affects employees' relationship capital, and the moderating role of innovation culture is also examined.

Structural equation modeling was performed to test the research model and hypotheses. Surveys were conducted in an electronic commerce company in China that uses different social media platforms, generating 301 valid responses for analysis.

First, private social media used for work-related purposes can contribute to employees' relationship capital, and public social media QQ used for work-related purposes can contribute to employees' communication quality. WeChat used for social-related purposes has a positive effect on employees' information exchange. Second, innovation culture acts as a positive moderator between work-related media use and employees' information exchange, while innovation culture acts as a negative moderator between social-related WeChat use and employees' information exchange. Third, innovation culture acts as a positive moderator between work-related QQ use and employees' trust, while innovation culture acts as a negative moderator between social-related QQ use and employees' trust.

First, this paper contributes to the information system (IS) social media literature by studying the effect of the use of different enterprise social media platforms used for different purposes on employees' relationship capital. Second, the authors contribute to relationship capital theory by clarifying that use of public and private social media platforms for social- and work-related purposes is an important driver of the formation of employees' relational capital. Third, the present study also contributes to enterprise social media literature by confirming that innovation culture acts as a different moderator between use of different enterprise social media platforms and employees' relationship capital.

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.

An increasing number of users join and become immersed in WeChat official accounts, but many users quit using these services as well. Nevertheless, most of the previous studies mainly focussed on the usage behavior. The purpose of this paper is to fill the gap by examining factors affecting users’ unfollow intentions for WeChat subscriptions in a Chinese context.

Structural equation modeling is used in our study. A field survey with 260 WeChat users is conducted to test the research model and hypotheses.

The results show the following interesting key findings: first, the effect of actual cost on users’ unfollow intentions is larger than the effect of opportunity cost; second, users’ unfollow intentions will decrease with the increase of users’ perceptions of information usefulness; third, the results of the control variables showed that only landing frequency has a negative effect on users’ unfollow intentions; and fourth, users’ demographic differences are also examined in regard to how they may affect users’ unfollow intentions.

First, this paper studies factors influencing users’ unfollow intentions for WeChat subscriptions from a social exchange theory perspective; the authors considered both extraneous factors and users’ internal perception factors potentially affecting users’ unfollow intentions, which has rarely been researched. Furthermore, the authors examined significant differences among users’ demographic characteristics in affecting users’ unfollow intentions. The results of the study provide a more comprehensive understanding of the influencing factors of users’ unfollow intentions.

The rise of social media has gained increasing attention in recent years; however, few studies have focused on social media users’ specific behavior and subjective well-being. To fill this research gap, the purpose of this paper is to develop an integrated model to investigate factors that affect social media user’s share intention and the relationship between user’s share intention and subjective well-being.

Structural equation model is used in this study. A field survey with 398 WeChat users is conducted to test the research model and hypotheses.

The empirical results show that: utilitarian value, hedonic value, user satisfaction and information source credibility are important factors affecting users’ share intention; users’ share intention positively affects user’s subjective well-being; moderating effects show that relative significance positively moderates the relationship between utilitarian value and users’ share intention; and users’ demographic characteristics differences actually exist in users’ share intention.

First, the authors clear that factors affect social media users’ share intention from the perspective of customer-perceived value. The results deepen our understanding about the factors that affect WeChat users’ share intention. Second, the authors focus on the effect of users’ specific behavior on users’ subjective well-being and found that users’ share intention is one of the important aspects that affect user’s subjective well-being. More importantly, the authors tested users’ characteristic differences in social media users’ share intention, which have previously received limited attention.

While prior research provides interesting insights into the effect of social media use in enterprises, there is limited research on how use of different social media platforms affects employee job satisfaction and work efficiency. This study developed a research model to investigate how public and private social media platforms used for different motivations affect employee job satisfaction and work efficiency.

Online surveys were conducted in China, generating 453 valid responses for analysis. Structural equation modeling is performed to test the research model and hypotheses.

The results suggest that (1) public social media used for both work- and social-related motivations positively affects employee job satisfaction, while private social media only used for social-related motivations can contribute to employee job satisfaction. (2) Public and private social media used for work-related motivations can contribute to employee work efficiency, while social-related motivations for use of public and private social media and employee work efficiency are not significant. (3) In the process of social media usage influencing employee job satisfaction and work efficiency, employees of different genders show significant differences.

First, this paper contributes to information systems social media research by examining the joint effects of different motivations for public and private social media usage on employee job satisfaction and work efficiency in organizations. Second, it contributes to uses and gratification theory by clarifying the relationship between different motivations for enterprise social media use and its needs.

The modern manufacturing industry has put forward higher requirements for the assembly accuracy of components with the development of the industrial technology. For precision assembly, the traditional assembly process study based on tolerance has had difficulty in meeting these requirements. Hence, the distribution of the form errors must be considered. The registration between the two mating surfaces with form errors determines the parts’ assembly position, and is the basis for the prediction and control of the assembly accuracy. This study aims to provide a new surfaces registration method which takes form errors into consideration.

This study presents a new registration approach based on the minimum potential energy. A unique set of contact points on mating surfaces that meet the actual conditions can be obtained and the spatial position of the assembled part is calculated.

The experimental results show that the calculated values are in good agreement with the experimental values. Furthermore, the root mean square error is within 2%, which proves the validity and accuracy of the approach.

This paper provides an effective and new method for precision assembly which takes form errors into consideration. The method can give the optimal solution of the contact points, which is more consistent with the actual assembly situation and provides a basis for predicting assembly accuracy.

Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.

To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.

Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.

This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

New information technologies such as IoT and big data analytics have reshaped the development of smart green products. These products exhibit two important features that are not seen in traditional products: environmental friendliness and data network effect. Based on these unique features, the authors investigate a firm's optimal selling strategy of smart green products from both the profitability and environmental perspectives.

The authors establish stylized models to consider the optimality of three selling strategies: (1) traditional strategy – only offering traditional products, (2) green strategy – only offering smart green products, and (3) hybrid strategy – offering both traditional and smart green products.

The authors’ analysis shows that in the absence of data network effect, there will always be a conflict between profit maximization and environmental protection. However, a strategy that benefits both the firm and the environment exists when data network effect is present. Interestingly, hybrid and traditional strategies can be win-win strategies, but the green strategy cannot. Also surprisingly, the green strategy may harm the environment more as smart products become greener.

This study examines the economic and environmental implications of selling smart green products, and contributes to existing literature on sustainable operations and green product design by incorporating the impact of both consumer environmental awareness and data network effect. The authors’ findings shed light on how to coordinate the profitability and environmental impact of selling smart green products in the era of big data and IoT.