并不是所有的点击都是一样的:检测与数字内容的互动

IF 0.6 Q4 BUSINESS
Yayu Zhou, B. Calder, E. Malthouse, Y. K. Hessary
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引用次数: 14

摘要

摘要记录每个用户在媒体网站上每次点击的点击流数据已成为评估数字平台的货币,以最大限度地提高广告和/或订阅收入。然而,人们越来越认识到,仅仅点击量是不够的。我们提出了一种基于基本参与理论的新的系统方法来解决这个问题。从理论上讲,参与被解释为与更高层次的个人目标或社会价值观相关的用户体验。我们表明,可以使用形成参与度测量量表的调查项目来定性描述这种经历,事实上,这些参与度量表解释了支付意愿的结果变量。此外,这些经验可以转化为代理分解的点击流变量。我们分析了三个新闻网站的数据,发现这些分解后的点击流变量比原始的、未分解的点击流数据更好地预测了网站的付费意愿。因此,我们的方法框架提供了一种使用点击流数据来检测数字内容参与度的新方法,这种方法为提高参与度和最终结果(如内容付费意愿)提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Not all clicks are equal: detecting engagement with digital content
ABSTRACT Clickstream data recording each click that each individual user makes on a media website has become the currency for evaluating digital platforms in order to maximise advertising and/or subscription revenue. There is a growing recognition, however, that the mere volume of clicks is not adequate for this purpose. We propose a new systematic approach to this problem based on an underlying theory of engagement. Engagement is construed theoretically as user experiences that connect to higher-order personal goals or social values. We show that such experiences can be described qualitatively using survey items that form engagement measurement scales and that these engagement scales, in fact, explain a willingness-to-pay outcome variable. Moreover, these experiences can be translated into surrogate decomposed clickstream variables. We analyse data from three news websites and show that these decomposed clickstream variables predict willingness-to-pay for the sites better than raw, undecomposed clickstream data. Our methodological framework thus provides a new way of using clickstream data to detect engagement with digital content, a method that provides a basis for improving engagement and ultimately outcomes such as the willingness to pay for content.
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来源期刊
CiteScore
4.00
自引率
7.70%
发文量
10
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