This study reveals how users’ play patterns can predict their next purchase after recommending the game. While prior research has recognized what convinces users to choose the games, what has been lacking is considering unique characteristics of video games allowing consumers to experience video games permanently until satiation. Thus, this study examines: (1) How does user play time influence next purchase timing after expressing opinions via the review systems? and (2) How does the relationship between play patterns and re-purchase timing patterns change by game genres? To answer these research questions, we collected data from Valve Corporation’s Steam. Using an R program to crawl the data, we gathered 244,360 reviews from 6,170 games between February 2018 and March 2018. With Cox proportional-hazards models, we found that user play patterns were dependening on their satisfaction with the games. We also found that if a user stops playing, even after recommending a game, they are less likely to buy a new game. Additionally, frustrated players turn to variety-seeking behavior, thus spending time playing other games, and are more likely to buy new games. In the sub-group analysis, we also found that users who recently played a Casual game have the highest probability of buying a new game. Video games are an interesting experience goods due to their unique characteristics; consumers can keep consuming products until they are satiated. However, to our best knowledge, there is a lack of study about satiation’s effect on game consumption. Many researches on video games have examined various topics with many research methodologies, such as descriptive statistics, experiments, survey, observational studies, or network analyses. However, unlike previous studies, we directly collected user play pattern and purchase data from Valve and examined how the behavior of purchasing a new game is related to user play pattern after the recommendation. This is a novel methodology of using data crawling and econometrics modeling to investigate game users’ purchase behavioral patterns with the observational data. So, we believe that our study can benefit marketing managers and game developers, providing critical information regarding target markets and play patterns of potential consumers.

这项研究揭示了用户的游戏方式如何在推荐游戏后预测他们的下一次购买。尽管先前的研究已经认识到说服用户选择游戏的原因，但缺乏的是考虑视频游戏的独特特征，从而使消费者可以永久性地体验视频游戏直到饱足。因此，本研究考察了：（1）用户的游戏时间在通过评论系统表达意见后如何影响下一个购买时间？（2）游戏类型和重新购买时机之间的关系如何随游戏类型而改变？为了回答这些研究问题，我们从Valve Corporation的Steam收集了数据。使用R程序抓取数据，我们在2018年2月至2018年3月之间收集了6,170个游戏的244,360条评论。使用Cox比例风险模型，我们发现用户的游戏方式取决于他们对游戏的满意程度。我们还发现，即使用户在推荐游戏后仍停止玩游戏，他们购买新游戏的可能性也较小。此外，沮丧的玩家会转向寻求多样性的行为，从而花时间玩其他游戏，并且更有可能购买新游戏。在小组分析中，我们还发现最近玩过休闲游戏的用户购买新游戏的可能性最高。电子游戏由于其独特的特性而成为一种有趣的体验产品。消费者可以继续消费直到满意为止。但是，就我们所知，缺乏关于饱足感对游戏消费影响的研究。关于视频游戏的许多研究已经通过多种研究方法研究了各种主题，例如描述性统计数据，实验，调查，观察性研究或网络分析。但是，与以前的研究不同，我们直接从Valve收集用户玩法和购买数据，并研究了推荐后购买新游戏的行为与用户玩法的关系。这是一种使用数据爬网和计量经济学模型来利用观察数据调查游戏用户购买行为模式的新颖方法。因此，我们认为我们的研究可以使营销经理和游戏开发商受益，提供有关目标市场和潜在消费者的游戏方式的重要信息。我们直接从Valve收集用户玩法和购买数据，并在推荐后检查了购买新游戏的行为与用户玩法的关系。这是一种使用数据爬网和计量经济学模型来利用观察数据调查游戏用户购买行为模式的新颖方法。因此，我们认为我们的研究可以使营销经理和游戏开发商受益，提供有关目标市场和潜在消费者的游戏方式的重要信息。我们直接从Valve收集用户玩法和购买数据，并在推荐后检查了购买新游戏的行为与用户玩法的关系。这是一种使用数据爬网和计量经济学模型来利用观察数据调查游戏用户购买行为模式的新颖方法。因此，我们认为我们的研究可以使营销经理和游戏开发商受益，提供有关目标市场和潜在消费者的游戏方式的重要信息。