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Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.
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http://dx.doi.org/10.1109/TVCG.2023.3326911 | DOI Listing |
PLoS One
April 2025
Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaysia.
Live streaming e-commerce emphasizes the role of live streaming influencers and the dynamic interactions between viewers and live streaming influencers. Utilizing data collected from 400 questionnaires, this study delves into the mechanisms through which characteristics of live streaming influencers influence consumer purchase intentions, with a focus on consumer emotional trust as a mediating variable as well as consumer education level, age, perceived risk, and live-stream engagement as moderating factors. The findings indicate that the traits of live streaming influencers have a positive effect on consumers' intent to purchase.
View Article and Find Full Text PDFSci Rep
September 2024
Suzhou Winndoo Network Technology Co., Ltd., Suzhou, 215000, China.
Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply-demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
January 2024
Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback.
View Article and Find Full Text PDFFront Psychol
January 2023
Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang, China.
Introduction: The rise of live-stream selling has made the e-commerce platform attractive to many small and medium-sized retailers that are often faced with capital constraints. The choice between the e-commerce platform financing (EPF) and trade credit financing (TCF) for the capital-constrained e-retailers engaging in live-stream selling is particularly important problem.
Methods: This paper considers a supply chain made up of a manufacturer, an e-commerce platform that offers live-stream selling service to consumers and an online retailer with capital constraint.