摘要：This post will introduce the introduction of "Free Value-added Competition in Intensive Reading Journal Papers: Digital Competitio
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“喆学（147）：精读期刊论文
《Competing on freemium: Digital Competition with Network Effects》
引言”
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"Zhexue (147): Intensive reading of journal articles
《Competing on Freemium: Digital Competition with Network Effects》
Introduction"
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本期推文将从思维导图、精读内容、知识补充三个方面介绍精读期刊论文《Competing on Freemium: Digital Competition with Network Effects》的引言。
This post will introduce the introduction of "Free Value-added Competition in Intensive Reading Journal Papers: Digital Competition with Network Effects" from three aspects: mind map, intensive reading content, and knowledge supplement.
一、思维导图（Mind Maps）
二、精读内容（Intensive reading content）
文章讨论了网络效应对数字产品市场的影响，指出这通常导致单一公司主导市场。许多公司通过提供免费和付费版本的“免费增值”策略来建立用户基础。然而，关于这种策略在网络效应背景下对市场结果的影响，尤其是对市场领导者和追随者的不同影响，缺乏实证证据。本文研究了直接网络效应如何影响同一市场中领导者和追随者的收入，并与仅付费策略进行对比。
This article discusses the impact of network effects on digital product markets, noting that this often leads to a single company dominating the market. Many companies build their user base through a “freemium” strategy that offers both free and paid versions. However, there is a lack of empirical evidence on the impact of this strategy on market outcomes in the context of network effects, particularly the differential impact on market leaders and followers. This article examines how direct network effects affect the revenues of leaders and followers in the same market, and contrasts this with a paid-only strategy.
接着文章讨论网络效应对市场结果的影响，特别是在数字产品领域。许多公司采用“免费增值”策略，即提供免费和付费版本的产品，以增加用户基础并利用网络效应。这种策略的可行性取决于免费版本带来的用户增长是否能超过潜在的付费版本销售损失。大多数研究集中在单一公司，而本文则研究了直接网络效应如何影响同一市场中的市场领导者和追随者的收入，并将免费增值策略与仅付费策略进行对比。研究表明，网络效应可能以比“赢家通吃”逻辑更复杂的方式影响市场领导者和追随者的收入。
The paper then discusses the impact of network effects on market outcomes, particularly in the digital product space. Many companies adopt a “freemium” strategy, offering both free and paid versions of their products, to grow their user base and exploit network effects. The viability of this strategy depends on whether the user growth from the free version outweighs the potential loss of paid version sales. While most studies focus on single companies, this paper examines how direct network effects affect the revenues of market leaders and followers in the same market, contrasting freemium strategies with paid-only strategies. The research suggests that network effects may affect the revenues of market leaders and followers in ways that are more complex than “winner takes all” logic.
研究首先考虑了仅使用付费策略的环境，然后扩展到免费增值策略的情况。主要理论预测是，更强的网络效应在仅付费产品中会导致市场领导者相对于追随者的收入增加幅度较小，而在免费增值策略中，这种收入差距会更大。实证设计集中在苹果应用商店中引入Game Center政策后，如何增强单个产品（同侧或需求侧网络效应）的网络效应。Game Center的推出使开发者能够引入多人和互动功能，增加了产品的网络效应。该政策作为一次小型应用商店更新的一部分推出，并未与其他功能结合，但被本文研究的顶级应用广泛采用。
The study first considers the context of a paid-only strategy and then extends to the case of a freemium strategy. The main theoretical prediction is that stronger network effects in paid-only products lead to smaller increases in revenue for market leaders relative to followers, while this revenue gap is larger in a freemium strategy. The empirical design focuses on how the introduction of the Game Center policy in Apple's App Store enhances the network effects of a single product (same-side or demand-side network effects). The introduction of Game Center enabled developers to introduce multiplayer and interactive features, increasing the network effects of the product. The policy was introduced as part of a small App Store update and was not combined with other features, but was widely adopted by the top apps studied in this paper.
本文通过差分法研究了苹果应用商店在引入游戏中心后，市场领导者和追随者的表现。研究比较了游戏类别中网络效应增强的情况与未受影响的非游戏类别。结果显示，在仅使用付费策略的情况下，网络效应的引入对市场领导者和追随者的收入没有显著影响。然而，在使用免费增值策略时，网络效应使市场领导者的收入相对于追随者增加了55%，这表明领导者收入增加，追随者收入减少。研究结果在多种测试和规格下均稳健，包括不同的处理和对照组定义、观察单位以及收入变量的替代构建。
This paper uses a difference-in-difference approach to study the performance of market leaders and followers after the introduction of Game Center in the Apple App Store. The study compares the case of enhanced network effects in the game category with the unaffected non-game categories. The results show that the introduction of network effects has no significant impact on the revenues of market leaders and followers when using a paid-only strategy. However, when using a freemium strategy, network effects increase the revenues of market leaders by 55% relative to followers, indicating that leaders have increased revenues and followers have decreased revenues. The findings are robust to multiple tests and specifications, including different treatment and control group definitions, observation units, and alternative constructions of the revenue variable.
本文通过实证研究探讨了免费增值策略在网络效应背景下对市场领导者和追随者的影响，提供了三个主要贡献。首先，文章扩展了现有文献，展示了免费增值策略如何可能塑造并放大网络效应对市场结果的影响，特别是对赢家通吃动态的影响。其次，文章是首批实证研究免费增值策略与网络效应互动的研究之一，考虑了免费增值定价策略的竞争结果。研究结果显示，在免费增值策略下，网络效应对市场领导者和追随者的影响截然不同，领导者收入增加而追随者收入减少。最后，文章揭示了市场领导者和追随者之间的不对称性，以及两者之间的竞争互动如何直接影响市场结果，这在现有研究中尚未被充分考虑。
This paper empirically explores the effects of freemium strategies on market leaders and followers in the context of network effects, providing three main contributions. First, we extend the existing literature by showing how freemium strategies may shape and amplify the effects of network effects on market outcomes, particularly on winner-take-all dynamics. Second, this paper is one of the first studies to empirically investigate the interaction between freemium strategies and network effects, considering the competitive outcomes of freemium pricing strategies. Our results show that under freemium strategies, network effects have distinct effects on market leaders and followers, with leaders experiencing increased revenue and followers experiencing reduced revenue. Finally, we reveal the asymmetry between market leaders and followers and how the competitive interaction between them directly affects market outcomes, which has not been fully considered in existing research.
三、知识补充（Knowledge supplement）
差分法（Difference-in-Differences，简称DiD）是一种统计和计量经济学中用于评估政策或干预效果的方法。它通过比较处理组（受到政策或干预影响的群体）和对照组（未受到政策或干预影响的群体）在干预前后的变化差异，来估计干预的因果效应。
Difference-in-Differences (DiD) is a statistical and econometric method used to evaluate the effects of policies or interventions. It estimates the causal effect of an intervention by comparing the difference in changes between the treatment group (the group affected by the policy or intervention) and the control group (the group not affected by the policy or intervention) before and after the intervention.
差分法的基本步骤如下：
The basic steps of the difference-in-difference method are as follows:
1. 选择处理组和对照组：处理组是受到政策或干预影响的群体，而对照组是没有受到影响的群体。理想情况下，这两组在干预前应该是相似的。
1. Select the treatment and control groups: The treatment group is the group affected by the policy or intervention, and the control group is the group not affected. Ideally, the two groups should be similar before the intervention.
2. 收集干预前后的数据：需要收集干预实施前后两组的数据，以便比较变化。
2. Collect data before and after the intervention: Data from both groups before and after the intervention need to be collected in order to compare the changes.
3. 计算差异：首先计算处理组在干预前后的变化（即干预后的结果减去干预前的结果），然后计算对照组在干预前后的变化。
3. Calculate the difference: First calculate the change in the treatment group before and after the intervention (i.e., the result after the intervention minus the result before the intervention), and then calculate the change in the control group before and after the intervention.
4. 估计干预效应：通过比较处理组和对照组的变化差异，来估计干预的效应。具体来说，干预效应等于处理组的变化减去对照组的变化。
4. Estimate the intervention effect: Estimate the effect of the intervention by comparing the difference in changes between the treatment group and the control group. Specifically, the intervention effect is equal to the change in the treatment group minus the change in the control group.
差分法的优点在于它能够控制那些不随时间变化的未观测异质性和共同趋势，从而更准确地估计干预的因果效应。然而，这种方法的有效性依赖于一个关键假设，即如果没有干预，处理组和对照组的结果变化趋势将是相同的（平行趋势假设）。如果这一假设不成立，差分法估计的结果可能会有偏误。
The advantage of the difference method is that it can control for unobserved heterogeneity and common trends that do not change over time, thereby more accurately estimating the causal effect of the intervention. However, the effectiveness of this method relies on a key assumption that the trend of the outcome changes in the treatment group and the control group would be the same if there was no intervention (parallel trend assumption). If this assumption does not hold, the results estimated by the difference method may be biased.
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翻译：谷歌翻译
参考资料：谷歌、Chat GPT
参考文献：Kevin J. Boudreau, Lars Bo Jeppesen, Milan Miric. Competing on freemium: Digital competition with network effects [J]. Strategic Management Journal, 2021, 43(7): 1374-1401.
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