Evidence map›Paper›PMID 42081484›Full record

ArticlePloS one2026

Esports in-game consumption across generations: Integrating motivated reasoning theory and the theory of planned behavior.

Kun Chang, Jun-Phil Uhm, Hyun-Woo Lee

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kun ChangDepartment of Sport and Entertainment Management, University of South Carolina, Columbia, South Carolina, United States of America.ORCID https://orcid.org/0000-0003-0322-1354
Jun-Phil UhmDepartment of Kinesiology, Inha University, Incheon, Republic of Korea.ORCID https://orcid.org/0000-0002-7323-1200
Hyun-Woo LeeDepartment of Kinesiology & Sport Management, Texas A&M University, College Station, Texas, United States of America.ORCID https://orcid.org/0000-0002-1022-0264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The revenue from virtual non-functional products has become a significant revenue source for the video game industry. Despite growing interest, understanding the decision-making processes behind virtual non-functional products consumption in free-to-play games remains limited. This study contributes to existing literature by testing a multi-relational model (motivation-cognition-intention-behavior). We aim to examine how gamers' self-presentation motivation is associated with their attitude, subjective norm, and perceived behavioral control, and how these factors relate to their purchase behavior. Data were collected via an online survey distributed on Reddit to esports gamers with prior purchase experience (N = 239). All constructs were measured using established multi-item Likert scales. Structural equation modeling and multi-group analysis were conducted using Mplus 8.8. Results showed that gamers' self-presentation motivation in the game is positively associated with gamers' attitude (β = .246, p < .001) and subjective norm (β = .169, p < .05). Attitude (β = .398, p < .001), subjective norm (β = .155, p < .05), and perceived behavioral control (β = .224, p < .001) were positively associated with purchase intention, which in turn predicted purchase behavior (β = .600, p < .001). Further, we identified generational differences on the path of motivation-cognition-intention-behavior. The current study moves beyond a single-theory explanation by proposing a more holistic perspective of how gamers' innate self-presentation motivations are associated with virtual non-functional products consumption. The findings revealed novel insights into generational differences in the decision-making processes, guiding tailored marketing strategies to boost virtual non-functional products consumption across segments.

Indexed as

Consumer BehaviorMotivationVideo GamesAdultAttitudeDecision MakingFemaleHumansIntentionMaleSurveys and QuestionnairesTheory of Planned BehaviorYoung Adult

Identifiers

PMID42081484
PMCPMC13138612

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.