Malicious Selling Strategies in Livestream E-Commerce: A Case Study of Alibaba’s Taobao and ByteDance’s TikTok

Qunfang WU, Yisi SANG, Dakuo WANG, Zhicong LU*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

25 Citations (Scopus)

Abstract

Due to the limitations imposed by the COVID-19 pandemic, customers have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, various streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms. We conclude by discussing the policy and design implications of countering malicious selling. © 2022 Association for Computing Machinery.
Original languageEnglish
Article number35
JournalACM Transactions on Computer-Human Interaction
Volume30
Issue number3
Online published22 Dec 2022
DOIs
Publication statusPublished - 10 Jun 2023

Bibliographical note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Funding

This research was supported in part by the Research Grants Council of Hong Kong and Tencent under grant 9229095.

Research Keywords

  • dark pattern
  • livestream e-commerce
  • deceptive design
  • Douyin
  • livestream shopping
  • Taobao
  • interview
  • malicious selling strategy
  • TikTok
  • taxonomy
  • deception

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