With the rapid development of high quality industries, it is particularly important to study the sustainable competitiveness of manufacturing and its driving factors. The aim of this paper is to build the whole competitiveness index to analyze the recent development trends of manufacturing in G20 participating countries from 2008 to 2018. Meanwhile, based on the diamond theory, this paper adopted a panel regression model to conduct an empirical analysis on various factors that affect the sustainable competitiveness of manufacturing. These results showed the following: (1) Transport services have the most significant effect on manufacturing in developing countries. (2) Intellectual property only has a positive and significant effect on manufacturing in developed countries. (3) Information technology plays a significant role in all countries, but it is more effective in developed countries. Finally, this paper puts forward some suggestions for the sustainable development of manufacturing.

With the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability. First, we decompose failure data series into some subdata series components with strong regularity by using C‐C algorithm and Cao algorithm. On this basis, we use the maximum Lyapunov index to identify chaotic characteristics of failure data series. And then, we establish BP neural network prediction model by using reconstructing failure data to predict SMISs failure behaviors. Finally, we use two groups of failure data series to verify the effectiveness of chaotic BP neural network model, and the experiment results verify that chaotic BP neural network model has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR). LEAD PARAGRAPH SMISs are open and complex systems, human error and external environment cause the uncertainty of reliability. The threat of the external environment mainly comes from malicious attacks and the threat of the human error mainly comes from the wrong operation of the operator. Human error and external environment often cause frequent failures of software and hardware of the system or physical failures of devices. As an open complex system, the reliability operation of SMISs is very important for manufacturing enterprises. However, in the daily use of SMISs, the most common failures are time failure caused by human error and external environment. Therefore, it is very important to study the time failure of SMISs. Main points of this paper: (1) SMISs are complex and open systems, so we establish an interdependent network based on the characteristics of SMISs, and use the cascade effect of the complex network to point out that when SMISs fail, the system will easily fall into failure. (2) The phase space reconstruction method is used to restore the real data characteristics of failure data. (3) By using the reconstructed data and the neural network, the failure behavior can be predicted accurately. Compared with other popular prediction methods, it is found that general machine learning methods cannot predict data with chaotic characteristics. The research results of this paper find that when SMISs fail, the failure behavior can easily lead SMISs into chaos through the propagation of interdependent network. Therefore, when future scholars conduct fault analysis on SMISs, they should consider the chaos of the data, otherwise the systems fault analysis and diagnosis cannot be carried out accurately.

More and more e-commerce companies realize the importance of analyzing the online reviews of their products. It is believed that online review has a significant impact on the shaping product brand and sales promotion. In this paper, we proposed a polymerization topic sentiment model (PTSM) to conduct textual analysis for online reviews. We applied this model to extract and filter the sentiment information from online reviews. Through integrating this model with machine learning methods, the results showed that the prediction accuracy had improved. Also, the experimental results showed that filtering sentiment topics hidden in the reviews are more important in influencing sales prediction, and the PTSM is more precise than other methods. The findings of this paper contribute to the knowledge that filtering the sentiment topics of online reviews could improve the prediction accuracy. Also, it could be applied by e-commerce practitioners as a new technique to conduct analyses of online reviews.

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With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

This study aims to analyze the development trend of the manufacturing industry of the Guangdong–Hong Kong–Macao Greater Bay Area (from 2008 to 2018) by constructing an evaluation system. On the basis of push–pull–mooring theory, we analyze these factors by using an entropy and cluster model. The results show the following: (1) Technological development had an obvious spatial distribution pattern of core regional radiation, while others did not. (2) Economic development was based on the city’s existing industrial development system, while environmental development depended on governmental policies. (3) Compared with the environmental factor, the development trends of the economic and technological factors were more similar. Lastly, we provide four strategies for the development of the manufacturing industry in different cities.

This study aims to find a robust method to improve the accuracy of online sales prediction. Based on the groundings of existing literature, the authors proposed a Dependency SCOR-topic Sentiment (DSTS) model to analyze the online textual reviews and predict sales performance. The authors took the online sales data of tea as empirical evidence to test the proposed model by integrating the auto-regressive review information model into the DSTS model. The findings include: 1) the effect of the distribution of SCOR-topic from reviews on sales prediction; 2) the effect of review text sentiment on sales prediction increases as the specific topic probability dominates; and 3) the effect of review text sentiment on sales prediction increases as the rest topic probability evenly distributes. These findings demonstrate that the DSTS model is more precise than alternative methods in online sales prediction. This study not only contributes to the literature by pointing out how the distribution of sentiment topic impacts on sales prediction but also has practical implications for the e-commerce practitioners to manage the inventory better and advertise by this prediction method. INDEX TERMS Sentiment analysis, SCOR-topic distribution, sales prediction.

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.