Empirical analysis of customer behaviors in Chinese e-commerce

With the burgeoning e-Business websites, E-Commerce in China has been developing rapidly in recent years. From the analysis of Chinese E-Commerce market, it is possible to discover customer purchasing patterns or behavior characteristics, which are indispensable knowledge for the expansion of Chinese E-Commerce market. This paper presents an empirical analysis on the sale transactions from the 360buy website based on the analysis of time interval distributions in perspectives of customers. Results reveal that in most situations the time intervals approximately obey the power-law distribution over two orders of magnitudes. Additionally, time interval on customer’s successive purchase can reflect how loyal a customer is to a specific product category. Moreover, we also find an interesting phenomenon about human behaviors that could be related to psychology of customers. In general, customers’ requirements in different product categories are similar. The investigation into individual behaviors may help researchers understand how customers’ group behaviors generated

Service Failure and Consumers’ Satisfaction with the Healthcare Industry: Moderating Role of Recommendation

This study explores the effects of service failure on different service attributes related to patients’ satisfaction (i.e., therapeutic effect and service attitude). We consider patients’ recommendation-seeking behavior and examine the moderating effects of recommendation before medical consultation and its differences between the online and offline word-of-mouth (WOM) recommendations. We collected over 3,000,000 reviews from a leading Chinese online health community to facilitate the empirical analysis. We use two ordinal logit models as bases and, find that service failure exerts a negative effect on patients’ both therapeutic effect satisfaction and service atti-tude satisfaction. Moreover, the effect of service fail-ure will be attenuated if patients seek recommenda-tions on doctors before consulting them. Moreover, the moderating effects of online WOM recommenda-tions is demonstrated to be lower than those of the offline ones. Our findings provide important perspectives for the literature and managerial suggestions for stakeholders

Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation

Recent advancements in diffusion models and large language models (LLMs) have significantly propelled the field of AIGC. Text-to-Audio (TTA), a burgeoning AIGC application designed to generate audio from natural language prompts, is attracting increasing attention. However, existing TTA studies often struggle with generation quality and text-audio alignment, especially for complex textual inputs. Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment. Our objective and subjective evaluations demonstrate that Auffusion surpasses previous TTA approaches using limited data and computational resource. Furthermore, previous studies in T2I recognizes the significant impact of encoder choice on cross-modal alignment, like fine-grained details and object bindings, while similar evaluation is lacking in prior TTA works. Through comprehensive ablation studies and innovative cross-attention map visualizations, we provide insightful assessments of text-audio alignment in TTA. Our findings reveal Auffusion's superior capability in generating audios that accurately match textual descriptions, which further demonstrated in several related tasks, such as audio style transfer, inpainting and other manipulations. Our implementation and demos are available at https://auffusion.github.io.Comment: Demo and implementation at https://auffusion.github.i