MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexaboutno aff
Leon Y. Xiao, Laura L. Henderson, Philip Newall

Notice bibliographique

RevueAddiction · 2022
Typeletter
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesIT-Universitetet i København
Mots-clésVideo gameKingdomPsychologyComputer scienceMultimediaGeologyPaleontology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,188
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,071
Tête enseignante GPT0,357
Écart entre enseignants0,287 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations41
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAddictionMême sujetGambling Behavior and TreatmentsTravaux en français237 207