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Convergence of Internet, Broadcasting and Communication

Meta’s Metaverse Platform Design in the Pre-launch and Ignition Life Stage

초록

영어

We look at the initial stage of Meta (previous Facebook)’s new metaverse platform and investigate its platform design in pre-launch and ignition life stage. From the Rocket Model (RM)’s theoretical logic, the results reveal that Meta firstly focuses on investing in key content developers by acquiring virtual reality (VR), video, music content firms and offering production support platform of the augmented reality (AR) content, ‘Spark AR’ last three years (2019~2021) for attracting high-potential developers and users. In terms of three matching criteria, Meta develops an Artificial Intelligence (AI) powered translation software, partners with Microsoft (MS) for cloud computing and AI, and develops an AI platform for realistic avatar, MyoSuite. In ‘connect’ function, Meta curates the game concept submitted by game developers, welcomes other game and SNS based metaverse apps, and expands Horizon Worlds (HW) on VR devices to PCs and mobile devices. In ‘transact’ function, Meta offers ‘HW Creator Funding’ program for metaverse, launches the first commercialized Meta Avatar Store on Meta’s conventional SNS and Messaging apps by inviting all fashion creators to design and sell clothing in this store. Mata also launches an initial test of non-fungible token (NFT) display on Instagram and expands it to Facebook in the US. Lastly, regarding optimization, especially in the face of recent data privacy issues that have adversely affected corporate key performance indicators (KPIs), Meta assures not to collect any new data and to make its privacy policy easier to understand and update its terms of service more user friendly.

목차

Abstract
1. Introduction
2. Literature Review and Theoretical Background
2.1 Previous Literature Review
2.2 Theoretical Background
3. Research Design
4. Results
4.1 Attraction: Key participants and Tools
4.2 Match: Relevant, Timely, and Filtered
4.3 Connect: Direct or Indirect
4.4 Transact: Centralized or Decentralized
4.5 Optimize: KPIs and Data Management
5. Conclusion
References

저자정보

  • Song, Minzheong Assistant Professor, Department of Media Communication & Advertising, Hansei University, Korea

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