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Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.

Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.

The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.

This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

For the basic problems on platform innovation, such as platform innovation connotation and characteristics, the driving mechanism and the influence mechanism are less been studied. This study aims to explore how to achieve platform innovation in traditional service enterprises.

Based on the theory of enterprise network and binary learning, respectively, this paper discusses the behavior of binary learning based on network structure and network impact on efficiency and innovative platform innovation, and analyzed the realization of the platform innovation path.

The research draws the following conclusions: the network structure-based exploitative learning can promote the efficiency platform innovation, while the network behavior-based exploratory learning can promote the novelty platform innovation. The interaction between network structure and network behavior embedded in traditional services is more conducive to exploratory learning so as to promote novelty platform innovation, and the platform innovation of traditional service enterprises is a process from efficiency-oriented to novelty-oriented. The innovation effect generated by exploratory learning based on network behavior is much higher than that generated by exploitative learning based on network structure. The theoretical contributions of this study are as follows: first, this study compares the similarities and differences between service innovation of platform-oriented enterprises and platform innovation of service enterprises. On this basis, it clearly defines the concept of platform innovation and divides it into two categories: efficiency platform innovation and novelty platform innovation. Second, it reveals the two paths for traditional service enterprises to realize platform innovation, and the interaction between these two paths are also explored, which promotes the scenario-based and dynamic study of platform innovation in traditional service enterprise. The conclusion of this study provides theoretical reference for traditional service enterprises to carry out platform innovation.

Theoretical contribution of this paper lies in: first, the concept of platform innovation is clearly defined. Current research about platform innovation is mainly around the innovation of platform enterprise and the platform innovation of traditional enterprise, but there is no document that makes clear distinction; some literature even equates innovation of platform enterprise with platform innovation of traditional enterprise. In this paper, through a detailed literature review and analysis, clearly define the concept of platform innovation and divided into efficiency platform innovation and novel platform innovation, which has made theoretical contribution to the depth of the research. Second, expand the platform innovation research of traditional service industry. In recent years, the platform innovation research of traditional enterprise has become a hot spot, but they focus on the attention of the platform transformation of traditional manufacturing industry, such as Haier; the traditional service industries seem to be “empty,” but, in fact, the traditional service industry platform innovation is of great significance and more worth looking forward to. In this paper, the longitudinal case studies can promote academic concerns focus on the traditional service industry, and also provides the theory instruction practice. Third, it promotes the platform innovation research of traditional enterprise and dynamic analysis. Based on the theory of enterprise network and binary learning, respectively, it discusses the behavior of binary learning based on network structure and network impact on efficiency and innovative platform innovation, and analyzed the realization of the platform innovation path. On the one hand, it enriches the research type of platform innovation; on the other hand, the dynamic evolution mechanism of platform innovation research can make up for the deficiency of the existing literature.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

This study aims to develop a comprehensive framework for assessing servicescape of cruise tourism and provides practical suggestions to improve the perception of Chinese tourists toward cruise servicescape.

A multistage mixed-method design was used in the sequence of in-depth interviews (n = 18), expert panel (n = 5), on-site survey (n = 317) and online survey (n = 300). Grounded theory, exploratory factor analysis and confirmatory factor analysis were used to assess cruise tourism servicescape.

The cruise tourism servicescape construct was identified with six dimensions: facilities and décor, natural scenery, onshore excursions, onboard entertainment, social interactions and dining services. These dimensions were in the order of importance, as perceived by Chinese tourists.

Cruise lines operating large ships can be more attractive to Chinese consumers than luxury cruise lines operating smaller vessels. Cruise operators can enhance perceived servicescape by integrating natural and built environments, such as air, sea and on-shore tours. Services provided by foreign crew members may serve as a strong selling point for Chinese tourists. Consumer-to-consumer activities may be introduced into the Chinese market. Cruise operators may also provide quality meal service in terms of variety, quality and flexibility.

Considering that minimal research has been conducted on cruise servicescape scale development, this study serves as the first empirical research effort in this regard. The findings also identify the specific needs of Chinese tourists, which is a fast-growing market in the cruise industry.

With the appearance of additional review functionality on e-commerce platforms emotional changes in composite reviews have become more diverse. How consumers process the emotional changes in composite reviews is an important concern for companies. This study investigates the impact of explores how changes in the emotional valence and emotional intensity of composite reviews on consumers' information adoption.

Based on emotion as social information theory, this study constructs a double mediation model of how the change in emotional valence of composite reviews affects consumers' adoption intention and examines the moderating effect of the dynamic change of emotional intensity. One field and three online experiments were conducted to test the proposed hypotheses.

Consumers were more likely to adopt positive–negative composite reviews than negative–positive composite reviews. Compared to negative–positive composite reviews, positive–negative composite reviews led to higher perceived empathy and lower motivational suspicion, which, in turn, led to higher information adoption. Moreover, dynamic changes in emotional intensity played a moderating role in this effect. Interestingly, the amount of attribute difference changed the differences in perceived empathy and motivated consumer suspicion generated by the composite review when considering the reviewer’s attribute difference description.

The findings have important theoretical contributions that deepen business and consumer understanding of the impact of composite reviews and have practical implications for improving the management of composite reviews by businesses.

After the COVID-19 outbreak began, travel demand dropped sharply and the potential impact of COVID-19 on sharing accommodations appears to be significant. Thus, it would be meaningful to investigate how travelers have changed their perceptions of staying at sharing accommodations in the wake of the coronavirus pandemic. The purpose of this research was to compare consumers' perceived risks of using sharing accommodations, such as Airbnb, before and during the coronavirus pandemic.

Paired sample t-tests were applied, using two surveys collected in 2017 (pre-pandemic) and 2020 (peri-pandemic). The effects of stress levels from COVID-19 and previous experience with sharing lodging services on risk perception changes were also examined.

Consumers showed higher social, physical, performance and convenience risk perceptions during the pandemic. Not surprisingly, those respondents who were more conscious of the pandemic in terms of concern and anxiety had higher changes in their risk perceptions. In addition, changes in risk perception differed by consumers' usage experience.

The results of this study add to the body of knowledge about consumers' risk perceptions of the sharing economy, particularly in connection with a huge disruption such as the COVID-19 pandemic.

This paper aims to study microwave pad dyeing process for wool fabric. Influences of various dyeing process conditions including galactomannan dosage, urea dosage, sodium bisulphite dosage, pH value, microwave irradiation power, treating time and cold batching time before microwave fixation on K/S values were analysed. The colour yield, fixation and levelness were compared between microwave fixation and cold batching fixation.

Colour yield (K/S values) was calculated using a Datacolor SF650 colour measuring and matching instrument (10° standard observer, CIE D65 light source Measuring; Datacolor, USA) and was used to determine the depth of the shade of dyed wool fabrics. Levelness of dyeing was evaluated also using the Datacolor SF650 colour measuring and matching instrument by measuring average deviation (S), range (P) of the maximum and the minimum for lightness (L), chroma (C) and hue (h), and balanced colour difference (ΔE) at 20 specified uniform locations on the wool fabrics. The colour difference was calculated as per the equation ΔE=(ΔL2+Δa2+Δb2)1/2 as appearing in the Experimental section. Fixation was determined using a Datacolor SF650 colour measuring and matching instrument by measuring ratio the of K/S for wool fabrics that were rinsed, washed, neutralised and then dried, and wool fabrics that were dried after fixation without washing. The pH of the padding solution was evaluated using a PHSJ-4A PH meter (Datacolor, USA). SEM analysis was done on JEOL JSM-5600LV machine (JEOL Ltd, Japan).

This study is based on application of microwave technology in the processing of silk.

It was found in laboratory experiments that uniform dyeing and deeper colour can be achieved throughout the microwave pad dyeing process for wool by using galactomannan. The novel process could reduce the dyeing time and the energy consumption of the traditional cold pad-batch dyeing process for wool fabric.

Squirrel cage induction motors suffer from several faults such as rotor broken bar. One of the powerful methods to detect induction motor faults is the line current signature analysis. This paper aims to present a novel algorithm based on continuous wavelet transform (CWT) to diagnose a rotor broken bar fault.

The proposed CWT has high flexibility in monitoring any frequency of interest in a waveform. Based on this transform, stator current frequency spectrum is analyzed to diagnose the rotor broken bar fault. The algorithm distinguishes the healthy motor from the faulted one based on a proper index. The method can be used in steady-state running time of induction motor and under different loading conditions. Experimental results are presented to show the validity of the proposed approach.

The proposed index considerably increases at the broken bars conditions compared to the healthy conditions. It can clearly diagnose the faulty conditions. The experimental results are found to be in good agreement with the theoretical and simulated results. The proposed method can reduce the noise and spectral leakage effects.

The main contribution of the paper are as follows: using CWT for detection of broken bar faults; introducing a proper index for diagnosing broken bars; and introducing a supplementary index to reduce the noise and spectral leakage effects.