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Record W4210686431 · doi:10.1111/add.15829

Loot boxes are more prevalent in United Kingdom video games than previously considered: updating Zendle <i>et al</i> . (2020)

2022· letter· en· W4210686431 on OpenAlexaboutno aff
Leon Y. Xiao, Laura L. Henderson, Philip Newall

Bibliographic record

VenueAddiction · 2022
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersIT-Universitetet i København
KeywordsVideo gameKingdomPsychologyComputer scienceMultimediaGeologyPaleontology

Abstract

fetched live from OpenAlex

Paid ‘loot boxes’ are products in computer games that consumers can purchase to obtain randomised rewards [1]. Loot boxes are structurally similar to gambling [2, 3], and loot box expenditure is correlated with problem gambling severity [4, 5]. Zendle et al. [6] influentially reported that loot boxes are prevalently implemented in United Kingdom (UK) games in February 2019: specifically, inter alia, that 59.0% of the 100 highest-grossing iPhone games contained loot boxes. Xiao et al. [7] conducted a replication and found that 77.0% of a comparable sample contained loot boxes in June 2021. Overall, 76.9% of the 52 games only appearing in Xiao contained loot boxes, whereas 63.5% did in Zendle. This suggests that games with loot boxes became more popular over the intervening time period. However, there were 11 disagreements (22.9%) among the 48 overlapping titles, each of which involved only Xiao identifying loot boxes, whereas Zendle did not. Table 1 summarises each disagreement, and timestamped screenshots of loot boxes uniquely identified by Xiao are available at: https://doi.org/10.17605/OSF.IO/CX5RV. Known to have implemented loot boxes prior to Zendle's data collection through the egg incubator system, as documented by changes to the game's Fandom Wiki page dated 2016 [9] (The loot box identified by Xiao was relatively unique and similar to the implementation in Counter-Strike: Global Offensive (CS:GO) and may have been missed by Zendle: players were able to pay real-world money for incubators to act as ‘keys’ to unlock Pokémon eggs, which contained randomised content and were the ‘loot boxes’; the eggs themselves were not directly purchasable and were obtained through gameplay. However, there is an upper limit as to how many eggs a player is allowed to have simultaneously: without purchasing incubators using real-world money, the player will quickly reach that limit and lose the opportunity to obtain more eggs and, by implication, more randomised content.) Seemingly implemented randomised mechanics through the Materials Chest system prior to Zendle's data collection, as documented by YouTube videos dated, e.g. 2017 (timestamps: 0:56 and 1:05 show that Materials Chests contain randomised content and 17:02 shows that they can be purchased with real-world money) [10] (The loot box identified by Xiao could only be purchased as part of a bigger bundle of many products and therefore was relatively hidden and may have been missed by Zendle.) Game 1 disclosed starting to sell loot boxes between the two studies [8]. Xiao also discovered relatively obscure loot boxes in four games that Zendle did not: games 2 and 3 were known to implement loot boxes during Zendle's data collection period [9, 10]; game 4 likely also did as revealed by contemporaneous evidence [11]; and game 5 potentially contains loot boxes implemented by third-parties through user-generated content. A methodological difference may have allowed for more accurate identification by Xiao: Zendle reviewed online videos recorded by other players and, if unable to decide, then through personal gameplay, whereas Xiao determined through gameplay and, if unable to decide, then through online resources. Studying video games through personal gameplay, whenever possible, is likely preferable. Further, recovering older versions of the software to verify is now likely impossible, which is why future research studying video games should account for their easily changeable nature by following open science principles [12] (e.g. through providing screenshots). Additionally, five games were simulated casino games [13], and a sixth game allowed players to virtually operate physical claw machines (which are an older quasi-gambling product available to children) [14]. These games bore near identical names alluding to gambling at both data collection points, so their primary content likely did not change. Zendle did not recognise certain simulated casino games in which players can spend real-world money to buy more stakes to continue participating in simulated gambling as loot boxes, although such randomised mechanics requiring payment to engage do fall within the definition of ‘loot boxes’ from a ludology perspective [15], as they similarly use gambling-like mechanisms, and are, therefore, relevant to policymaking concerned with addressing potential harms [16]. Paid loot boxes are now more commonly implemented in the highest-grossing UK iPhone games (which are reflective of other Western markets) than reported by Zendle. This is because of multiple reasons: games with loot boxes becoming more popular; some popular games subsequently introducing loot boxes; methodological factors around loot box identification; and semantic ambiguities around what constitutes a ‘loot box.’ Policymakers [17-19] and researchers should proceed on that updated basis. Thanks to Dr. David Zendle for making the underlying data to Zendle et al. [6] publicly available for reanalysis at: https://doi.org/10.17605/OSF.IO/XNW2T. L.Y.X. is supported by a PhD Fellowship funded by the IT University of Copenhagen (IT-Universitetet i København), which is publicly funded by the Kingdom of Denmark. L.Y.X. was employed by LiveMe, a subsidiary of Cheetah Mobile (NYSE:CMCM) as an in-house counsel intern from July to August 2019 in Beijing, People's Republic of China. L.Y.X. was not involved with the monetisation of video games by Cheetah Mobile or its subsidiaries. P.W.S.N. is a member of the Advisory Board for Safer Gambling—an advisory group of the Gambling Commission in Great Britain, and was a special advisor to the House of Lords Select Committee Enquiry on the Social and Economic Impact of the Gambling Industry. In the last 3 years, P.W.S.N. has received research funding from Clean Up Gambling and has contributed to research projects funded by GambleAware, Gambling Research Australia, NSW Responsible Gambling Fund and the Victorian Responsible Gambling Foundation. P.W.S.N has received travel and accommodation funding from the Spanish Federation of Rehabilitated Gamblers and received open access fee grant income from Gambling Research Exchange Ontario. Leon Y. Xiao: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; visualization. Laura Henderson: Investigation; validation. Philip Newall: Conceptualization; methodology; project administration; supervision.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.357
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2022
Admission routes1
Has abstractyes

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