Live streaming commerce, which has led to the emergence of new forms of social commerce called live streaming, has already improved the performance of many social commerce streamers. Live streaming commerce is an emerging business model that has blended the functions of e-commerce and live webcasting. The total sales volume will increase when the streamers and the customers communicate in the real-time interaction platform. In live streaming commerce, customer engagement behavior has been beneficial for realizing the value of live streaming commerce, which scholars have studied and valued. However, internal and external factors often affect customer engagement behavior, and most of the existing research focuses on only one aspect. This article will analyze internal and external influencing factors (internal influencing factors are based on self-efficacy theory, and external influencing factors are based on value-based adoption models) and their interactions to get a broad conclusion. To understand customer engagement behavior in the live streaming commerce with comprehensive research, the article constructs a customer engagement model in the live streaming commerce environment and uses structural equations for empirical testing. In applying self-efficacy theory, it is committed to distinguishing the functional logic between general self-efficacy and special self-efficacy and understanding the mechanism of special self-efficacy, namely, the influence mechanism of live streaming self-efficacy on perceived value and customer engagement in specific situations. The research results show that general self-efficacy positively influences perceived usefulness, perceived entertainment, and live streaming commerce self-efficacy, while perceived usefulness, perceived entertainment, and live streaming commerce self-efficacy positively affect perceived value, live streaming commerce self-efficacy, and self-efficacy. Perceived value has a positive and significant impact on customer engagement behavior. This research explains the influencing factors and mechanism of customer engagement in the live streaming commerce environment from the internal and external perspectives, enriches the theoretical research on customer engagement, and provides practical guidance for customer engagement behavior in the live streaming commerce environment.
The text mining of online reviews is currently a popular research direction of e-commerce and is considered the next blue ocean. Online reviews can dig out consumer preferences and provide theoretical guidance for the improvement of product features. However, current research mostly focuses on sentiment analysis methods and rarely involves feature extraction and large-scale data recognition. This paper uses word segmentation technology to create a new feature extraction method. With long-short term memory(LSTM) neural network and latent dirichlet allocation(LDA) topic model, we proposes a product feature improvement model (Consumer online reviews-Extract short text-Sentiment analysis-Cluster feature, CESC). The model can derive the product features and attitudes that consumers prefer based on consumer online reviews, and use it to improve product features. According to the experimental results of three electronic products sold on the e-commerce platform, the model can effectively dig out consumer preferences for online reviews. Enterprises can improve the quality of products and services, better meet the needs of consumers, promote consumers’ consumption, and achieve the enterprises’ goals and values.
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