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Why do People Play P2E (Play-to-Earn) Games?: Focusing on Outcome Expectation and Social Influence (P2E(Play-to-Earn) 게임 지속이용의도에 대한 연구)

  • Jang, Moonkyoung
    • Knowledge Management Research
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    • v.23 no.3
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    • pp.23-44
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    • 2022

  • With the development of blockchain technology, play-to-earn (P2E) games, one of the decentralized applications (dApps), are receiving great social attention. P2E games are positively evaluated as areas with high growth potential based on blockchain technology, and at the same time, they are negatively evaluated as speculative as people can cash P2E game items in the form of cryptocurrency. In this situation, the purpose of this study is to investigate factors affecting the intention to use P2E games. Along with the discussion of hedonic system adoption, we consider the factors with perceived enjoyment, economic incentive, and social influence. In order to verify our research model, data were collected from 350 adults with P2E game experience or recognition, and a structural equation model was carried out. The analysis results find that perceived enjoyment and subjective norm have a significant positive effect on the intention to use P2E games, and economic incentive does not have a significant effect. In addition, peer influence and external influence have a significant positive effect on subjective norm. Drawing on these findings, we present several academic and practical implications for future research.

  • https://doi.org/10.15813/kmr.2022.23.3.002 인용 PDF KSCI

Mathematical explanation on the POD applications for wind pressure fields with or without mean value components

  • Zhang, Jun-Feng;Ge, Yao-Jun;Zhao, Lin;Chen, Huai
    • Wind and Structures
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    • v.23 no.4
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    • pp.367-383
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    • 2016

  • The influence mechanism of mean value components, noted as $P_0$, on POD applications for complete random fields $P_C(t)$ and fluctuating random fields $P_F(t)$ are illustrated mathematically. The critical philosophy of the illustration is introduction of a new matrix, defined as the correlation function matrix of $P_0$, which connect the correlation function matrix of $P_C(t)$ and $P_F(t)$, and their POD results. Then, POD analyses for several different wind pressure fields were presented comparatively as validation. It's inevitable mathematically that the first eigenmode of $P_C(t)$ resembles the distribution of $P_0$ and the first eigenvalue of $P_C(t)$ is close to the energy of $P_0$, due to similarity of the correlation function matrixs of $P_C(t)$ and $P_0$. However, the viewpoint is not rigorous mathematically that the first mode represents the mean pressure and the following modes represent the fluctuating pressure when $P_C(t)$ are employed in POD application. When $P_C(t)$ are employed, POD results of all modes would be distorted by the mean value components, and it's impossible to identify $P_0$ and $P_F(t)$ separately. Consequently, characteristics of the fluctuating component, which is always the primary concern in wind pressure field analysis, can only be precisely identified with $P_0$ excluded in POD.

  • https://doi.org/10.12989/was.2016.23.4.367 인용 KSCI