This study constructs a coupling model and establishes an index system for the coupled and coordinate development between cross-border e-commerce supply chain system and economic system. The index system adopts supply chain, foreign trade, e-commerce, e-commerce logistics, economic development, social consumption, talent environment, and technological innovation as sequential covariates. The calculation shows that the first three batches of 35 cross-border e-commerce comprehensive pilot areas (CBECCPA in brief) in China, in 2017, are classified into three degrees of the coupled and coordinated development: superior, good, and moderate. The study provides useful recommendations for promoting cross-border e-commerce supply chain, such as to pursue high-quality balanced development, to cultivate professional composite talents, to encourage innovation and entrepreneurship, and to create a favorable economic environment.

Digitalization is critical to the growth of cultural industry today. However, existing research has not explained the mechanism by which the external environment affects the digitalization of cultural enterprises. Based on structural equation modeling, we tested the model empirically using data from 295 cultural enterprises in Beijing. The results show that two external environmental variables, the market pressure and the government’s supportive policies, have a positive impact on the adoption of digitalization in cultural enterprises through technical and organizational factors. The research conclusion explains how the external environment influences digitalization in cultural enterprises. Technological factors of digitalization capability and digitalization compatibility and organizational factors of perceived benefits play a mediating role between the external environment and the digitalization of cultural enterprises. Based on these conclusions, we finally put forward a series of countermeasures to enhance the digitalization of cultural enterprises.

With the concept of sustainable development, enterprises are facing severe challenges in ecological protection and economic development. Approaches to improving effectiveness of the coordinated development strategy must continue to evolve to address uncertainty and hazards that may be encountered in the future. We propose a coordinated development strategy model based on the combination of soft fuzzy and rough set theory and construct its prediction model. For the multistrategy dataset in the paper, parameter for each kind shall be selected through converting the multistrategy data into two prediction datasets. An algorithm transformed by SFRC shall be subject to weighted average for each parameter. Furthermore, we use training methods and soft fuzzy rough sets’ learning algorithm to calculate, and the evaluating indicator rough set is constructed with a three-tier model structure. After the final rough set training is completed, test results show that the rough set model which has a higher rating accuracy builds a better completed business performance evaluation. By comparing the prediction effect, both SVM algorithm and multistrategy prediction model for the soft fuzzy rough set in the paper can realize effective prediction for the enterprise’s coordinated development strategy. Moreover, the prediction result obtained at the time of adopting boundary to get the expected value is superior to that of giving one fixed threshold. It shows that the prediction performance of the algorithm in the paper is more excellent and represents the advantage of the algorithm prediction performance at the time of adopting boundary to get the expected value. The model provides support methods to assist enterprise management in making more efficient and scientific decisions for enterprise’s coordinated development.

Recently, professionals have highlighted the need for students to have information technology and data analytic skills to be successful in the profession. To meet this demand, educators attempt to integrate technology into curricula. However, the satisfaction of students is of greater importance to evaluating curriculum quality than teaching. This study explores the perceptions that second-year undergraduate students (n = 51) enrolled in a Chinese University held about the teaching contents and teaching approaches of intelligent curriculum. Based on the data sample of the students’ summary text for curriculum learning, this study adopts TFIDF analysis, topic modeling, text sentiment analysis, and other text mining technologies so as to have a profound analysis on the students’ satisfaction. We find that: (1) the students have a higher satisfaction on the teaching contents involved in the financial sharing center compared to RPA financial robot; (2) students have a better adjustment to case analysis and flipped classroom compared to simulation training and classroom lecturing. Our findings and discussion should be of interest to leaders and teachers of business program seeking to integrate technology. We believe that this study’s results provide opportunities to have a further improvement of the teaching contents and optimization of teaching design to effectively improve the curriculum quality in order to achieve enhancement of students’ satisfaction.