The purpose of this study is to make good use of the massive amount of online user comment data to explore and analyze the dimensions of customer-perceived value and the importance of each dimension, given the background of China’s huge e-commerce market. We compiled a web crawler program to collect online comment data from online reviews. The crawled data were pre-processed and content analysis were performed. A customer-perceived value dictionary was constructed based on the extraction of frequent terms, literature review, and expert opinions. We re-identified the dimensions of customer-perceived value to include four key dimensions and corresponding subdivisions. Both the rationality and operability of the dimension model of customer-perceived value were validated and applied. Thereafter, the importance of various dimensions and the impacts of customer-perceived value dimensions on customer loyalty were analyzed and discussed. The empirical research results reveal that all four dimensions of customer-perceived value play an important role in customer-perceived value and that the patterns and degrees of the role of each dimension are rather different. Further, only certain parts of the dimensions of customer-perceived value have an impact on customer loyalty, and the degree of the impact differs substantially.

Product–service systems (PSSs) have great potential for competitiveness and sustainability. Customers’ requirements cannot be directly used in the design of a PSS. Accurate identification of customer requirements, especially hidden requirements in the product life cycle, and transformation of customer requirements into specific engineering characteristics for PSS design are urgent problems. This study proposed a systematic and whole-process framework employing specific identification processes and methods, as well as a big data analysis. A set of refined and integrated methods were used to better identify customer requirements and to transform the customer requirements into specific engineering characteristics more accurately and efficiently. We also used customers’ online review data—a huge information resource to be explored—and big data technology to improve the requirement information identification process. A case study was implemented to verify our methodology. We obtained the engineering characteristics of a smartphone PSS matching the customer requirements as well as the exact importance rankings of customer requirements and engineering characteristics. The analysis results revealed that the proposed methodology allowed PSS designers to assess the PSS requirements more specifically and accurately by providing an intuitive evaluation of the role and importance of the requirements, engineering characteristics, and their mutual interactions that were hidden or indirect.

Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.