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An Empirical Analysis of Consumption Patterns for Mobile Apps and Web: A Multiple Discrete-Continuous Extreme Value Approach

机译:移动应用和网络消费模式的实证分析:多个离散连续的极值方法

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Using a unique panel data set detailing individual-level mobile app consumption, this study develops a utility theory-based structural model for multiple discrete/continuous choices in app use. We identify the dynamics and inter-dependencies between mobile apps and jointly quantify consumers' app choice and satiation simultaneously. The results suggest that mobile users' baseline utility is the highest for communication apps, while the lowest for personal financing apps. In addition, users' satiation level is the highest for the personal financing apps and the lowest for the game apps. However, a substantial heterogeneity in baseline utility and satiation is observed across diverse users. Furthermore, both positive and negative correlations exist in the baseline utility and satiation levels of mobile web and app categories. Consequently, the proposed frameworks could open new perspectives for handling large-scale, micro-level data, serving as important resources for big data analytics in general and mobile app analytics in particular.
机译:使用唯一的面板数据集详细介绍单独的移动应用程序消费,这项研究开发了一种基于实用理论的结构模型,可用于应用程序使用中的多个离散/连续选择。我们确定移动应用程序之间的动态和依赖性,并同时共同地量化消费者的应用选择和饱结。结果表明,移动用户的基准实用程序是通信应用最高的,而个人融资应用最低。此外,用户的饱诉水平是个人融资应用的最高版本,最低的游戏应用程序最低。然而,在不同的用户中观察到基线效用和饱和度和饱和度的大量异质性。此外,基线公用事业和应用程序类别的基线实用程序和饱和水平存在正相关和负相关性。因此,所提出的框架可以打开用于处理大规模的微级数据的新视角,该数据用于一般和移动应用分析中的大数据分析的重要资源。

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