Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls

Haris Krijestorac, R. Garg, V. Mahajan
{"title":"Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls","authors":"Haris Krijestorac, R. Garg, V. Mahajan","doi":"10.2139/ssrn.3011533","DOIUrl":null,"url":null,"abstract":"To inform product release and distribution strategies, research has analyzed cross-market spillovers in new product adoption. However, models that examine these effects for digital and viral media are still evolving. Given resistance to advertising, firms often seek to promote their own viral content to boost brand awareness. However, a key shortcoming of virality is its ephemeral nature. To gain insight into sustaining virality, we develop a quasi-experimental approach that estimates the backward spillover onto a focal platform by introducing a piece of content onto a new platform. We posit that introducing content to the audience of a new platform can generate word of mouth, which may affect its consumption within an earlier platform. We estimate these spillovers using data on 381 viral videos on 26 platforms (e.g., YouTube, Vimeo) and observe how consumption of videos on an initial “lead” platform is affected by their subsequent introduction onto “lag” platforms. This spillover is estimated as follows: for each multiplatform video, we compare its view growth after being introduced onto a new platform to that of a synthetic control based on similar single-platform videos. Analysis of 275 such spillover scenarios reveals that introducing a video onto a lag platform roughly doubles its subsequent view growth in the lead platform. This positive cross-platform spillover is persistent, bursty, and strongest in the first 42 days. We find that spillover is boosted when the video is consumed more in the lag platform, when the consumption rate peaks earlier in the lag platform, and when the lag platform targets a foreign market. Our findings suggest that firms can sustain the popularity of their viral content by introducing it onto additional platforms (e.g., Vimeo) after posting it on a focal platform (e.g., YouTube). As a result of their posting on the latter platforms, firms can expect subsequent view growth on the focal platform to roughly double. The aforementioned benefits persists for up to five lag platforms. Platforms should also consider that a positive cross-platform spillover may help platforms reinforce each other’s usage, rather than cannibalize each other.","PeriodicalId":414091,"journal":{"name":"Innovation & Management Science eJournal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Innovation & Management Science eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3011533","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

Abstract

To inform product release and distribution strategies, research has analyzed cross-market spillovers in new product adoption. However, models that examine these effects for digital and viral media are still evolving. Given resistance to advertising, firms often seek to promote their own viral content to boost brand awareness. However, a key shortcoming of virality is its ephemeral nature. To gain insight into sustaining virality, we develop a quasi-experimental approach that estimates the backward spillover onto a focal platform by introducing a piece of content onto a new platform. We posit that introducing content to the audience of a new platform can generate word of mouth, which may affect its consumption within an earlier platform. We estimate these spillovers using data on 381 viral videos on 26 platforms (e.g., YouTube, Vimeo) and observe how consumption of videos on an initial “lead” platform is affected by their subsequent introduction onto “lag” platforms. This spillover is estimated as follows: for each multiplatform video, we compare its view growth after being introduced onto a new platform to that of a synthetic control based on similar single-platform videos. Analysis of 275 such spillover scenarios reveals that introducing a video onto a lag platform roughly doubles its subsequent view growth in the lead platform. This positive cross-platform spillover is persistent, bursty, and strongest in the first 42 days. We find that spillover is boosted when the video is consumed more in the lag platform, when the consumption rate peaks earlier in the lag platform, and when the lag platform targets a foreign market. Our findings suggest that firms can sustain the popularity of their viral content by introducing it onto additional platforms (e.g., Vimeo) after posting it on a focal platform (e.g., YouTube). As a result of their posting on the latter platforms, firms can expect subsequent view growth on the focal platform to roughly double. The aforementioned benefits persists for up to five lag platforms. Platforms should also consider that a positive cross-platform spillover may help platforms reinforce each other’s usage, rather than cannibalize each other.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
病毒内容消费的跨平台溢出效应:使用合成控制的准实验分析
为了为产品发布和分销策略提供信息,研究分析了新产品采用中的跨市场溢出效应。然而,检验数字和病毒媒体的这些影响的模型仍在发展中。由于对广告的抵制,公司经常寻求推广自己的病毒式内容来提高品牌知名度。然而,病毒式传播的一个主要缺点是它的短暂性。为了深入了解如何维持病毒式传播,我们开发了一种准实验方法,通过在新平台上引入一段内容来估计焦点平台的后向溢出效应。我们认为,向新平台的受众介绍内容可以产生口碑,这可能会影响其在早期平台中的消费。我们使用26个平台(如YouTube、Vimeo)上381个病毒视频的数据来估计这些溢出效应,并观察最初“领先”平台上的视频消费如何受到随后被引入“滞后”平台的影响。这种溢出效应估计如下:对于每个多平台视频,我们将其引入新平台后的观看增长与基于类似单平台视频的合成控制进行比较。对275个此类溢出场景的分析表明,将视频引入滞后平台,其随后在领先平台的观看量增长大约翻倍。这种积极的跨平台溢出效应在头42天是持续的、突然的、最强的。我们发现,当视频在滞后平台上的消费更多、在滞后平台上的消费率达到峰值的时间更早以及滞后平台面向国外市场时,溢出效应会增强。我们的研究结果表明,公司可以通过在重点平台(如YouTube)上发布病毒式传播内容后,将其引入其他平台(如Vimeo)来维持其受欢迎程度。由于他们在后一个平台上发布,公司可以预期焦点平台上的后续浏览量增长大约翻倍。上述优势最多可在5个滞后平台上持续存在。各平台还应考虑到,积极的跨平台溢出效应可能有助于各平台加强彼此的使用,而不是相互蚕食。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Estimating the Costs of Standardization: Evidence from the Movie Industry Does Gender Affect Innovation? Evidence from Female Chief Technology Officers To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment Innovation Contests With Risk-Averse Participants Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1