Everyone Can Be a Star: Quantifying Grassroots Online Sellers’ Live Streaming Effects on Product Sales

Chen, Cheng; Hu, Yuheng; Lu, Yingda; Hong, Yili

doi:10.24251/hicss.2019.544

Cited by 32 publications

(20 citation statements)

References 29 publications

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“…Let y(t) ≥ 0 denote the streamer's effort in selling, including the commodity introduction, feature demonstration, live show, and real-time communication with the viewers (Chen et al, 2019). Note that both the efforts exerted by the platform and the streamer are limited.…”

Section: Model Developmentmentioning

confidence: 99%

“…Additionally, both the viewers' trust in the streamers (Wongkitrungrueng & Assarut, 2018) and the match between streamers and products play a decisive role in the final sales of products (Park & Lin, 2020). Chen et al (2019) employ a difference-indifferences model to find that the adoption of live streaming solutions can significantly increase the sales of products. Nevertheless, the above literature is all based on empirical studies, and the analyses are all single objects.…”

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confidence: 99%

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Optimal decisions and coordination of live streaming selling under revenue‐sharing contracts

Liu

2021

Manage Decis Econ

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Live streaming selling is setting off new waves worldwide, especially in China. Considering the effects of number of viewers and average reference price, this paper investigates the optimal decisions of platform's recommending effort and streamer's selling effort by differential game theory. We find that if the platform sets a higher revenue-sharing rate, its effort to recommend streamer will decrease, which could reduce the platform's and streamer's revenues. Thus, we propose a subsidy mechanism that incentivizes the streamer to increase investments in platform's recommendations, while enabling the platform and the streamer to achieve a win-win situation by setting reasonable revenue-sharing rates. | INTRODUCTIONLive streaming selling has become one of the preferred channels for major vendors to increase the sales of products and clear inventory.Supported by the technology of the live streaming platforms, the streamers, namely, broadcasting in the chat room, have a group of loyal followers with the skills of eating, singing, dancing, making up, entertaining, and so on (Rodriguez-Gil et al., 2018). Vendors focus on the high loyalty and accuracy of fans in a specific category of streamers and then target sales based on the relevant attributes of the products (Choi & Rifon, 2012;McCormick, 2016). Especially in China, Taobao Live, Tiktok, Kuaishou, and other platforms all have already set off a blowout fever of live streaming selling (X. C. Liu, 2016). In 2018, the amount of sales of products on Taobao Live has exceeded 100 billion yuan, with a year-on-year growth rate of nearly 400%. 1 Taobao Live has developed into a venerable industry benchmark in the field of live streaming e-commerce. More and more platforms, streamers, and vendors are scrambling to follow suit to join the wave of live streaming selling.

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Section: Model Developmentmentioning

confidence: 99%

mentioning

confidence: 99%

Optimal decisions and coordination of live streaming selling under revenue‐sharing contracts

Liu

2021

Manage Decis Econ

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“…The fit of rescue missions and IoT technology can increase the adoption of IoT technology and improve the performance of rescue missions [24]. In the field of livestreaming, livestreaming technology promotes interaction between users, creates a pleasant marketing environment for sellers and shoppers, and increases the sales of experiential products [54].…”

Section: Task-technology Fit Technology Adoption and Performancementioning

confidence: 99%

An Empirical Study on How Livestreaming Can Contribute to the Sustainability of Green Agri-Food Entrepreneurial Firms

Wang

Fan

2021

Sustainability

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During the COVID-19 pandemic, digital technology has been used in all aspects of the agricultural field. How to seize the opportunity to achieve the production-marketing connection is increasingly becoming a top concern for green agri-food enterprises. Based on the theory of a task–technology fit, this study analyzes the fitness between livestreaming e-commerce and green agri-food. More specifically, the task characteristics cover the seasonality, locality, and eco-friendliness of green agri-food, and the technology characteristics cover the responsiveness, interactivity, and entertainment of livestreaming e-commerce. Using data of a sample of 574 green agri-food entrepreneurial firms collected through a web-based questionnaire, we preform structural equation modeling (SEM) analysis and find that the locality and eco-friendliness of green agri-food, the responsiveness, interactivity, and entertainment of livestreaming e-commerce have a positive effect on the fit of green agri-food livestreaming e-commerce. Moreover, the fit of green agri-food livestreaming has a positive effect on firm performance and the intention to adopt livestreaming e-commerce. This study also demonstrates that perceived corporate social responsibility has a moderating effect on the relationship between the fit of livestreaming of green agri-food and the intention to adopt livestreaming e-commerce. This study extends prior research on the task–technology fit into livestreaming e-commerce companies and provides insights into our understanding of successful adoption of livestreaming e-commerce.

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“…This new form of sales can greatly shorten the decision-making time of consumers and provoke the sales volume [1,10]. For example, according to a report from Alibaba's Taobao Live platform, there are several numbers worthy of attention in last year's 11.11 Global Shopping Festival: (1) the total GMV driven by live streaming achieved $6 Billion USD, (2) approximately 300 million Taobao users watched live streams, and (3) 33 live streaming channels achieved over $15 million USD in sales and nearly 500 live streaming channels reached $1.5 million USD [4].…”

Section: Introductionmentioning

confidence: 99%

“…For example, according to a report from Alibaba's Taobao Live platform, there are several numbers worthy of attention in last year's 11.11 Global Shopping Festival: (1) the total GMV driven by live streaming achieved $6 Billion USD, (2) approximately 300 million Taobao users watched live streams, and (3) 33 live streaming channels achieved over $15 million USD in sales and nearly 500 live streaming channels reached $1.5 million USD [4]. Some quantitative research results show that adopting live streaming in sales can achieve a 21.8% increase in online sales volume [1].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

Jiang

Chen

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with their audiences, and hence greatly improve the performance of selling products. Despite of the successful applications in industries, the live stream Ecommerce has not been well studied in the data science community. To fill this gap, we investigate this brand-new scenario and collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from conventional E-commerce activities, the streamers play a pivotal role in the LSEC events. Hence, the key is to make full use of rich interaction information among streamers, users, and products. We first conduct data analysis on the tripartite interaction data and quantify the streamer's influence on users' purchase behavior. Based on the analysis results, we model the tripartite information as a heterogeneous graph, which can be decomposed to multiple bipartite graphs in order to better capture the influence. We propose a novel Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the node representations of each bipartite graph, and further design a multi-task learning approach to improve product recommendation. Extensive experiments on two real-world datasets with different scales show that our method can significantly outperform various baseline approaches.

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Everyone Can Be a Star: Quantifying Grassroots Online Sellers’ Live Streaming Effects on Product Sales

Cited by 32 publications

References 29 publications

Optimal decisions and coordination of live streaming selling under revenue‐sharing contracts

Optimal decisions and coordination of live streaming selling under revenue‐sharing contracts

An Empirical Study on How Livestreaming Can Contribute to the Sustainability of Green Agri-Food Entrepreneurial Firms

Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

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