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The purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.

In the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.

A case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.

Theoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.

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.

Using two theoretical lenses – social identity theory and generation cohort theory – the present study analyzes the influence of sport motivations (i.e., patriotism, drama and excitement of the game, nostalgic associations, interest in star players and social influence) on the intentions to watch the International Cricket Council (ICC) Twenty-20 (T20) World Cup of three different generation cohorts (i.e., Generations X, Y and Z).

Data were collected from N = 499 cricket lovers from Pakistan based on a non-probability sampling technique. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and multi-group modeling techniques were used as methods.

SEM results show that cricket fans' intentions to watch the T20 World Cup are positively influenced by patriotism, drama and excitement of the game, and social influence. The results of multi-group modeling reveal significant differences between Generation X-ers, Y-ers and Z-ers regarding the effect of sport motivations on their intentions to watch the ICC T20 World Cup. Specifically, our findings show that for X-ers, interest in star players and nostalgic associations are the main motivations behind watching the T20 World Cup, whereas drama and excitement appeared to be an important predictor for Y-ers, and patriotism and social influence are more likely to increase Z-ers' intentions to watch the T20 World Cup.

This study is the first of its kind to report the motivations of Generations X, Y and Z to watch the T20 World Cup.

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.