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Record W4388221057 · doi:10.1097/cxa.0000000000000183

Impulsivity and Video Game–related Motives as Predictors of Video Game Loot Box Use

2023· article· en· W4388221057 on OpenAlexaffvenue
Jan Slattery, Igor Yakovenko

Bibliographic record

VenueThe Canadian Journal of Addiction · 2023
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsConcordia UniversityDalhousie University
Fundersnot available
KeywordsImpulsivityVideo gamePurchasingPsychologyAvatarAddictionSensation seekingSocial psychologyAdvertisingDevelopmental psychologyComputer sciencePersonalityMarketingMultimediaPsychiatryBusiness

Abstract

fetched live from OpenAlex

ABSTRACT Objective: The purpose of this study was to investigate potential gambling and gaming-related traits and motivations as predictors of increased loot box purchasing to identify risk factors for engagement with this potentially addictive element of video games. Methods: This study investigated whether impulsivity, completionism (ie, a need to collect in-game items), perceived social pressure, and an overall desire to have one’s gaming avatar look a certain way contribute to increased monthly loot box spending. It was hypothesized that impulsivity would predict increased loot box spending over and above age and gender, and that the sequential addition of completionism, perceived social pressure, and desire to have one’s game avatar look a certain way, will predict increased monthly loot box spending over and above antecedent variables. Results: A total of 601 adult video game players recruited via an online survey panel were analyzed. A large proportion of participants spent $0 on loot boxes. Thus, a gamma hurdle model to investigate binary endorsement of loot box purchasing and changes in monthly loot box spending. All the hypothesized variables significantly predicted binary endorsement, but not changes in monthly loot box spending. Conclusion: The findings highlight the nuance of how players engage in predatory gambling mechanics of video games such as loot boxes. A risk factors related to addiction like impulsivity and factors aligned with video game-related motivations may predict the decision to purchase a loot box, but perhaps not continued spending. Objectif: L’objectif de cette étude était d'étudier les traits de caractère et les motivations liés aux jeux d’argent et de hasard en tant que prédicteurs de l’augmentation des achats de coffres de butin afin d’identifier les facteurs de risque d’engagement avec cet élément des jeux vidéo qui peut créer une dépendance. Méthodes: Cette étude a cherché à savoir si l’impulsivité, l’achèvement (c’est-à-dire le besoin de collecter des objets dans le jeu), la pression sociale perçue et le désir général d’avoir un avatar de jeu d’une certaine façon contribuent à l’augmentation des dépenses mensuelles pour les coffres de butin. L’hypothèse est qu’au-delà de l'âge et du sexe, l’impulsivité prédit l’augmentation des dépenses en coffres de butin et que l’ajout séquentiel de l’achèvement, de la pression sociale perçue et du désir de donner une certaine apparence à l’avatar du jeu permettrons de prédire l’augmentation des dépenses mensuelles en coffres de butin au-delà des variables antécédentes. Résultats: Un total de 601 joueurs de jeux vidéo adultes recrutés par le biais d’un panel d’enquête en ligne a été analysé. Une grande partie des participants ont dépensé 0 $ pour des coffres à butin. Ainsi, un modèle de hurdle gamma a permis d'étudier l’endossement binaire de l’achat de coffres de butin et l'évolution des dépenses mensuelles en coffres de butin. Toutes les variables hypothétiques ont permis de prédire de manière significative l’endossement binaire, mais pas l'évolution des dépenses mensuelles en coffres de butin. Conclusion: Les résultats soulignent les nuances sur la façon dont les joueurs s’engagent dans les mécanismes de jeu prédateurs des jeux vidéo tels que les coffres de butin. Un facteur de risque lié à la dépendance, comme l’impulsivité, et des facteurs alignés sur les motivations liées aux jeux vidéo peuvent prédire la décision d’acheter un coffre de butin, mais peut-être pas la poursuite des dépenses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.312
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes2
Has abstractyes

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