Achieving customers’ repurchase intention through stimuli and site attachment

Image and text presentation forms in destination marketing: An eye-tracking analysis and a laboratory experiment

The COVID-19 pandemic has severely impacted the tourism and hospitality industries worldwide. Tourism destination marketing has been an heated focus in tourism and hospitality academia, it is widely believed that it can promote the revival of industries in the post-pandemic era. But there is a lack of research on different graphic presentation forms in tourism advertisements. To bridge the gap in the related literature, this study aims at studying the impact of the image and text presentation forms of the scenic spot’s name in tourism advertisements on tourists’ visit intention to the tourist destination city by combining the theory of constructivism in cognitive psychology, SOR model, and affective-cognitive model to conduct a 2 × 2 between-group experiment. The study found that when the text part contains the scenic spot’s name, the tourism advertisement has a significant impact on tourists’ perceived advertising effectiveness, destination affective image, and visit intention. The results of eye tracking analysis also showed that fixation points are primarily distributed in the text part. Furthermore, this study explored the chain mediating mechanism of perceived advertising effectiveness and destination affective image and discovered that the impact of the text presentation form on the visit intention can be realized through the mediating effect of perceived advertising effectiveness and destination affective image. This study puts forward some suggestions for the tourism advertising and destination marketing of scenic spots with high-familiarity of destination cities with low-familiarity and improving the image of tourist destination cities.

An improved deep forest model for prediction of e-commerce consumers' repurchase behavior

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model’s overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.