Artificial intelligence has been widely applied to e-commerce and the online business service field. However, few studies have focused on studying the differences in the effects of types of customer service on customer purchase intentions. Based on service encounter theory and superposition theory, we designed two shopping experiments to capture customers’ thoughts and feelings, in order to explore the differences in the effects of three different types of online customer service (AI customer service, manual customer service, and human–machine collaboration customer service) on customer purchase intention, and analyses the superposition effect of human–machine collaboration customer service. The results show that the consumer’s perceived service quality positively influences the customer’s purchase intention, and plays a mediating role in the effect of different types of online customer service on customer purchase intention; the product type plays a moderating role in the relationship between online customer service and customer purchase intention, and human–machine collaboration customer service has a superposition effect. This study helped to deepen the understanding of AI developers and e-commerce platforms regarding the application of AI in online business service, and provides reference suggestions for the formulation of more perfect business service strategies.

Against the background of the new era, the rapid progress of information science and technology represented by big data, cloud computing and Internet of things promotes the intelligent transformation of tourism industry. Based on the technical support of big data, it integrates tourism resources to generate value creation and apply it in smart tourism, and promotes the optimization and innovation of tourism at the level of marketing, service and management. In the context of informatization, the implementation of smart tourism has become the only way for the development of the tourism industry (Dai, 2015). However, the application of big data in smart tourism also faces such problems as dense and miscellaneous information data, uneven level of information technology, lack of smart tourism big data professionals, and privacy data security (Yang, 2015). This paper discusses the development status and future direction of smart tourism in the context of big data, so as to boost the steady development of smart tourism relying on big data platform.

The crosslinked poly(methylmethacrylate) (PMMA) heat-sensitive nanocapsules were prepared by emulsion polymerization, in which Triton X-100 was used as an emulsifier and unsaturated hyperbranched poly(amide-ester) (UHBP) as a crosslinker. The effects of three determinative process parameters on the particle size distributions, glass transition temperatures(Tgs) and heat sensitive color-developing properties of nanocapsules were investigated in detail. As a result, the mean size of nanocapsules became smaller and their particle size distribution became narrower with the increase in emulsifying rate. The Tg of nanocapsules was 123.8°C with the emulsifier content being 0.6%. The color-developing absorbency was the highest with the crosslinker content being 6.0 %.

Object matching is a key technology for map conflation, data updating, and data quality assessment. This article proposed a new Voronoi diagram-based approach for matching multi-scale road networks (VAMRN). Using this method, we first created Voronoi diagrams of the road network using the strategy of discretizing road lines into points and adding dense points to special road intersection segments. Then, we used the Voronoi diagram of road segment to find matching candidates. Finally, we obtained matching results by judging the geometric similarity metrics we designed and a heuristic combination optimization strategy. The experimental results demonstrated that the VAMRN outperformed two existing methods in generality and matching quality. The F-measures of VAMRN were 18.4, 29.6, 3.8, and 7.6% higher than the buffer growing method, and 4.5, 2.8, 1.8, and 6.1% higher than the probabilistic relaxation method. And the time performance is improved by more than 90% over the probabilistic relaxation method.

A heat sensitive color-developing nanocapsule as leucocompound delivery carrier was prepared by emulsion polymerization. The nanocapsules were characterized by Malvern particle size analysis, scanning electron microscopy (SEM) and UV/visible spectrophotometer. The heat sensitive color-developing curves of absorbency at different temperature were investigated. The effects of core/wall mass ratio on the heat sensitive color-developing properties of nanocapsules were discussed in detail. The particle size analysis demonstrates that the particle sizes mostly concentrate between 124 nm and 312 nm. SEM result shows that the nanocapsules have smooth surface. The resultant nanocapsules have the highest heat sensitive color-developing absorbency with the the core/wall mass ratio being 1:8.