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The purpose of this study is to present a highly stretchable and flexible strain sensor with simple and low cost of fabrication process and excellent dynamic characteristics, which make it suitable for human motion monitoring under large strain and high frequency.

The strain sensor was fabricated using the rubber/latex polymer as elastic carrier and single-walled carbon nanotubes (SWCNTs)/carbon black (CB) as a synergistic conductive network. The rubber/latex polymer was pre-treated in naphtha and then soaked in SWCNTs/CB/silicon rubber composite solution. The strain sensing and other performance of the sensor were measured and human motion tracking applications were tried.

These strain sensors based on aforementioned materials display high stretchability (500 per cent), excellent flexibility, fast response (approximately 45 ms), low creep (3.1 per cent at 100 per cent strain), temperature and humidity independence, superior stability and reproducibility during approximately 5,000 stretch/release cycles. Furthermore, the authors used these composites as human motion sensors, effectively monitoring joint motion, indicating that the stretchable strain sensor based on the rubber/latex polymer and the synergetic effects of mixed SWCNTs and CB could have promising applications in flexible and wearable devices for human motion tracking.

This paper presents a low-cost and a new type of strain sensor with excellent performance that can open up new fields of applications in flexible, stretchable and wearable electronics, especially in human motion tracking applications where very large strain should be accommodated by the strain sensor.

The purpose of this paper is to develop a propeller performance measurement method for high-altitude platforms by analyzing of the propeller aerodynamic characteristics and application of a mobile testing system.

An experimental approach is adopted for this study. Considering the aerodynamic characteristics of the high-altitude propeller, the similitude of the scaled propeller model in the experiment is analyzed and determined. Then, the experimental method and procedure to obtain the propeller’s performance under different altitudes are presented, and the structure of hardware and software and the key techniques of the testing system are introduced in detail.

The applicability and effectiveness of the testing system is verified through comparison between experimental and numerical results. In addition, the performance of the 6.8-m propeller for a high-altitude airship is tested, which proves that the high-altitude propeller can meet the requirements of the propulsion system.

The testing methodology and the mobile testing system could be applied to aerodynamic performance evaluation of the high-altitude propellers under different altitudes.

This testing approach exhibits significant time and cost benefits over many other experimental methods to obtain the performance of the high-altitude propellers, which is important in the preliminary design of the propulsion system for high-altitude platforms.

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.

Online reviews have shown important information that affects consumers’ online shopping behavior. However, little research has examined how they may influence consumers’ online impulse buying behavior. The purpose of this paper is to bring theoretical and empirical connections between them.

The framework of this study was tested on three popular online group shopping websites in China (ju.taobao.com, dianping.com, and meituan.com). An online survey with 315 participants who had experience using these websites was recruited to verify the effects of consumers’ perceived value from reading online reviews on urge to buy impulsively and impulse buying behavior.

The empirical findings show that consumers’ perceived utilitarian and hedonic value from reading online reviews enhance their browsing behavior. Browsing positively affects consumers’ urge to buy impulsively and finally affects their impulse buying behavior. Further, this study finds that consumers with high impulsiveness focus more on hedonic value of online reviews, whereas consumers with low impulsiveness put more emphasis on utilitarian value. Browsing demonstrates a stronger effect on urge to buy impulsively for consumers with high impulsiveness.

This study is one of the early studies to investigate the relationship between social influence (e.g. influence of online reviews) and impulse buying. It draws upon the perspectives of browsing and consumer’s perceived value from the literature. This research also considers consumer differences regarding the level of impulsiveness.

This paper aims to investigate an approach for mental fatigue detection and estimation of assembly operators in the manual assembly process of complex products, with the purpose of founding the basis for adaptive transfer and demonstration of assembly process information (API), and eventually making the manual assembly process smarter and more human-friendly.

The proposed approach detects and estimates the mental state of assembly operators by electroencephalography (EEG) signal recording and analysis in an engine assembly experiment. When the subjects perform assembly tasks, their EEG signal is recorded by a portable EEG recording system called Emotiv EPOC+ headset. The feature set of the EEG signal is then extracted by calculating its power spectrum density (PSD), followed by data dimension reduction based on principal component analysis (PCA). The dimension-reduced data are classified by using support vector machines (SVMs), and hence, the mental state of assembly operators can be estimated during the assembly process.

The experimental result shows that the proposed approach is able to estimate the mental state of assembly operators within an acceptable accuracy range, and the PCA-based dimension reduction method performs very well by representing the high-dimensional EEG feature set with just a few principal components.

This paper provides theoretical and experimental basis for the API transfer and demonstration based on human cognition. It provides a new idea to seek balance between the improvement of production efficiency and the sustainable utilization of human resources.

Peer-to-peer (P2P) accommodation has become increasingly popular in recent years, and hotels are facing unprecedented impacts. Attracting new consumers and retaining existing ones are critical to the success of P2P accommodation and hotels. The purpose of this paper is to examine three categories of antecedents for hotels consumers’ switching intention: push (i.e. satiation), pull (i.e. perceived value) and mooring (i.e. optimal stimulation level) factors using push–pull–mooring (PPM) model.

Airbnb was chosen as the research context. An online survey was conducted to examine the proposed research model and hypotheses. A total of 292 valid data were collected from Airbnb users through a survey.

The findings show that the three categories of factors have positive and significant effects on switching intention. Additionally, the mooring factor has a significant moderating effect on the relationship between pull factors and switching intention. Furthermore, the mooring factor affects both pull and push factors.

First, this is one of the early studies to pay attention to switching intention from hotels to P2P accommodation. Second, to provide a comprehensive understanding of consumers’ switching intention, the authors use PPM model to establish the research framework. This research improves the understanding of consumer’s switching intention by identifying the push and pull factors based on the differences between hotels and P2P accommodation in accordance with optimal stimulation level theory and consumer value theory.

The purpose of this paper is to find out whether the election of star analysts leads to the conflict of interests between analysts\institutional investors and individual investors. And then, further investigate how the election results to influence the individual investors’ decision making.

Given the fact that earnings forecasts and stock ratings are the most important foundations for the investor’s investment decision, the authors investigate the relationship among the earnings forecasts, abnormal returns and the election of star analyst. This paper further analyzes the impact factors on investors’ decision. The data used in this paper for star analysts’ information, analysts’ forecast and recommendations, as well as stock performances-related data are from 2005 to 2012.

This paper finds that mass media cannot select analysts with high forecast accuracy, and then misleads investors. It demonstrates that the analysts with poorer forecast ability and more optimistic stock recommendations are more prone to be entitled as star analysts by mass media, and these titled star analysts tend to show a poorer performance. Therefore, the star analyst worsens investors’ cognition on analysts forecast ability and then misleads investors’ decision making.

Media plays a critical role in corporate governance, information collection and diffusion and reducing the information asymmetry, however, it is good to know the role of media in financial markets from a broader perspective. Because media may also bring negative factors to the financial markets such as misguiding the investors and intensify the conflict of interests between analyst and individual investors.

This paper supports a new perspective of the role of mass media in financial market, which is different from existing studies.

Intangible resources (IRs) play an important role in enterprise innovation; previous studies find inconsistent results (positive and negative). The authors develop and test a framework to analyze IRs to see whether and how to impact firm innovation performance to reconcile the conflicting results.

This study empirically examined the curvilinear effect of IRs and innovation performance (IP) based on data from the Annual Census of Chinese Industrial Enterprises. The moderating effect of institutional development (ID) and state ownership (SO) in the relationship between firms' IRs and IP was also examined.

It was found that there is a U-shaped relationship between IRs and IP. Moreover, the institutional development weakens the U-shaped relationship.

The U-shaped relationship explains the inconsistent results in previous studies. It offers some important implications for managers and policymakers, who must understand the role of IRs.

The purpose of this paper is to analyze the operational decisions of a manufacturer who produces multiple products and the government's selection of cap-and-trade and carbon tax regulations.

This paper explores the production decisions of a multi-product manufacturer under cap-and-trade and carbon tax regulations in a cap-dependent carbon trading price setting and compares carbon emission, the manufacturer's profits and social welfare under the two regulations. Game theory and extreme value theory are used to analyze our models.

First, the authors find that the optimal profit of the manufacturer (the optimal cap) increases and then decreases with the cap (the unit carbon emission of product). Second, if the environmental damage coefficient is moderate, the optimal cap of unit environmental damage coefficient is independent of the product carbon emission or other related product parameters. Ultimately, cap-and-trade regulation always generates more carbon emission than carbon tax regulation. And cap-and-trade regulation (carbon tax regulation) can generate more social welfare if the environmental damage coefficient is low (high), and the social welfare under the two regulations is equal to each other, or otherwise.

This paper contributes the prior literature by considering the inverse relationship of the allocated cap and the carbon trading price and discusses the social welfare under cap-and-trade and carbon tax regulations. Some important and new results are found, which can guide the government's implementation of the two regulations.

Considering the cross-market network externality, this paper aims to explore the platform’s pricing decisions and its optimal profit under the given government investment, and then investigate the investment decision to improve social responsibility, which is measured by the social welfare.

When exploring the optimal pricing decisions under the given government investment, extreme value theory and sensitive analysis are used. When investigating the investment level, game theory and optimization method are used. Numerical examples are conducted to further illustrate the results.

First, after considering the government investment, whether the buyers and the sellers are charged depends on the investment level and the difference of the cross-market network externality (CNC) of the sellers and the buyers. Second, the optimal price on the sellers is decreasing (increasing) in the CNC of the buyers (sellers). The optimal price on the buyers is significantly affected by the investment level. Finally, the government investment is win-win for both the platform and the government, and Chinese Government should invest on the sellers heavily.

This study specifies the role of the government investment on the sellers in determining the platform’s pricing decisions and the improvement of the social responsibility, which is measured by social welfare.