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Proceedings of the ACM on Human-Computer Interaction
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
DBLP
Article . 2024
Data sources: DBLP
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Folk Models of Loot Boxes in Video Games

Authors: Jinhe Wen; Zhongyang Zhang; Tuan M. Tran; Lianrui Mu; Tauhidur Rahman; Haojian Jin;

Folk Models of Loot Boxes in Video Games

Abstract

Regulations require video games to provide transparency regarding loot box odds to keep players informed, leading many games to disclose probabilities in various ways; yet, the extent of players' comprehension of loot box mechanics remains unclear. We performed a content analysis on 80 online posts to understand players' perceptions of loot box odds in two popular video games (Genshin Impact and Honkai: Star Rail). We then conducted semi-structured interviews with 24 players to explore the causes of these folk models across more games. Utilizing a bottom-up open coding approach, we created a taxonomy of folk models players have about loot boxes. We found that participants generally possessed inaccurate mental models of how loot boxes work, and they wanted game companies to enhance loot box transparency in three areas of probability disclosures: granularity, longitude, and scope.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
hybrid