Exploring the Effect of In-Game Purchases on Mobile Game Use with Smartphone Trace Data

Microtransactions have become an integral part of the digital game industry. This has spurred researchers to explore the effects of this monetization strategy on players’ game enjoyment and intention to continue using the game. Hitherto, these relationships were exclusively investigated using cross-sectional survey designs. However, self-report measures tend to be only mildly correlated with actual media consumption. Moreover, cross-sectional designs do not allow for a detailed investigation into the temporal dimension of these associations. To address these issues, the current study leverages smartphone trace data to explore the longitudinal effect of in-game purchase behavior on continual mobile game use. In total, approximately 100,000 hours of mobile game activity among 6,340 subjects were analyzed. A Cox regression with time-dependent covariates was performed to examine whether performing in-game purchases affects the risk of players removing the game app from their repertoire. Results show that making an in-game purchase decreases this risk initially, prolonging the survival time of the mobile gaming app. However, this effect significantly changes over time. After the first three weeks, a reversal effect is found where previous in-game purchase behavior negatively affects the further survival of the game. Thus, mobile games without previous monetary investment are more prone to long-term continual game use if they survive the first initial weeks. Methodological and theoretical implications are discussed. As such, the current study adds to those studies that use computational methods within a traditional inferential framework to aid theory-driven inquiries

(What) can journalism studies learn from supervised machine learning?

In recent years, scholars have explored the applicability of supervised machine learning (SML) within journalism studies. While such computational methods could be of added value to the field, the rationale for employing these supervised models harbors some assumptions that deserve further inspection. This paper seeks to specify under which conditions SML could be useful for journalism scholars and where the field stands in exploiting its potential benefits. We start with an introduction to SML and give an overview of its applications within journalism studies. Next, we identify challenges for the field in its adoption of such techniques. These include overstating the time and financial savings caused by automatic coding, neglecting proper sampling methods, the danger of algorithmic determinism and the limited generalizability of predictive modeling across different domains, contexts and time periods. At the same time, we distinguish several opportunities. These include sharing classifiers, standardizing coding schemes and adopting general purpose techniques. Most importantly, in order for SML to contribute to the epistemological advancements in the field, SML could be used to explain how long-standing theories in journalism are changing. In turn, this might help us to disentangle the inner workings of our contemporary complex news ecosystem