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Identifying and forecasting the reverse salient in video game consoles: A performance gap ratio comparative analysis

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  • Daim, Tugrul
  • Justice, Jay
  • Hogaboam, Liliya
  • Mäkinen, Saku J.
  • Dedehayir, Ozgur

Abstract

This study uses the reverse salient methodology to contrast subsystems in video game consoles in order to discover, characterize, and forecast the most significant technology gap. We build on the current methodologies (Performance Gap and Time Gap) for measuring the magnitude of Reverse Salience, by showing the effectiveness of Performance Gap Ratio (PGR). The three subject subsystems in this analysis are the CPU Score, GPU core frequency, and video memory bandwidth. CPU Score is a metric developed for this project, which is the product of the core frequency, number of parallel cores, and instruction size. We measure the Performance Gap of each subsystem against concurrently available PC hardware on the market. Using PGR, we normalize the evolution of these technologies for comparative analysis. The results indicate that while CPU performance has historically been the Reverse Salient, video memory bandwidth has taken over as the quickest growing technology gap in the current generation. Finally, we create a technology forecasting model that shows how much the video RAM bandwidth gap will grow through 2019 should the current trend continue. This analysis can assist console developers in assigning resources to the next generation of platforms, which will ultimately result in longer hardware life cycles.

Suggested Citation

  • Daim, Tugrul & Justice, Jay & Hogaboam, Liliya & Mäkinen, Saku J. & Dedehayir, Ozgur, 2014. "Identifying and forecasting the reverse salient in video game consoles: A performance gap ratio comparative analysis," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 177-189.
    • Handle: RePEc:eee:tefoso:v:82:y:2014:i:c:p:177-189
    DOI: 10.1016/j.techfore.2013.06.007
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    Cited by:

    1. Huotari, Pontus & Järvi, Kati & Kortelainen, Samuli & Huhtamäki, Jukka, 2017. "Winner does not take all: Selective attention and local bias in platform-based markets," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 313-326.
    2. Lantano, Francesco & Petruzzelli, Antonio Messeni & Panniello, Umberto, 2022. "Business model innovation in video-game consoles to face the threats of mobile gaming: Evidence from the case of Sony PlayStation," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Miremadi, Iman & Khoshbash, Mostafa & Saeedian, MohammadMahdi, 2023. "Fostering generativity in platform ecosystems: How open innovation and complexity interact to influence platform adoption," Research Policy, Elsevier, vol. 52(6).
    4. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada M. & Caby, Jérôme, 2023. "The influence of Twitch and sustainability on the stock returns of video game companies: Before and after COVID-19," Journal of Business Research, Elsevier, vol. 157(C).
    5. Tsai, Pei-Hsuan & Chen, Chih-Jou, 2021. "Entertainment in retailing: Challenges and opportunities in the TV game console industry," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    6. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2018. "Knowledge Push Curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers' competitiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 150-160.

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