Discovery of behavioral patterns in online social commerce practice

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-10-27 DOI:10.1002/widm.1433
Xiaoyun Jia, Ruili Wang, James H. Liu, Chuntao Jiang
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引用次数: 6

Abstract

Discovery of behavioral patterns in online social commerce practice becomes important in this digital era. In this article, we propose a systematic approach to behavioral pattern discovery, and apply it in an emerging online social commerce venue: live streaming. We investigate behavioral patterns in gifting encouragement in live streaming to understand online social commerce practice. Our proposed approach is based on multiple triangulation, including data source triangulation (i.e., streamers, viewers, and actual behavior) and data collection method triangulation (i.e., interviews, focus groups, and observations). Through multiple triangulation, four behavioral patterns of gifting encouragement are discovered: (i) requesting a certain gift for providing a particular service, (ii) creating a raffle, (iii) eliciting competition between individuals, and (iv) eliciting competition between groups. This research reveals the special behavioral patterns in live streaming, and thus increases our knowledge of social commerce practices. This research provides a systematic approach to discover online behavioral patterns, and provides practical implications in live streaming platforms, especially in marketing and platform design.
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发现在线社交商务实践中的行为模式
在这个数字时代,发现在线社交商务实践中的行为模式变得非常重要。在本文中,我们提出了一种系统的行为模式发现方法,并将其应用于新兴的在线社交商务场所:直播。我们调查了在直播中给予鼓励的行为模式,以了解在线社交商务实践。我们提出的方法是基于多重三角剖分,包括数据源三角剖分(即,主播,观众和实际行为)和数据收集方法三角剖分(即,访谈,焦点小组和观察)。通过多重三角测量,发现了四种鼓励赠与的行为模式:(i)为提供特定的服务而要求某种礼物,(ii)创造抽奖,(iii)引发个人之间的竞争,(iv)引发群体之间的竞争。本研究揭示了直播中特殊的行为模式,从而增加了我们对社交商务实践的认识。本研究提供了一种系统的方法来发现在线行为模式,并为直播平台提供了实际意义,特别是在营销和平台设计方面。
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来源期刊
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
期刊介绍: The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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