research-article

Casual Competition by Design: A Study of the All Random All Mid (ARAM) Mode in League of Legends

Authors:

Yubo KouAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue CSCW2

Article No.: 453, Pages 1 - 28

https://doi.org/10.1145/3686992

Published: 08 November 2024 Publication History

Abstract

Competitive gaming, a long-standing study context for CSCW, has recently faced criticism due to its design emphasis on competition and achievement, which is associated with adverse phenomena such as player toxicity and anxiety. Recognizing this limit, game designers have proactively made design attempts to ameliorate these unintended consequences of competitive gaming. A notable example is the All Random All Mid (ARAM) mode in League of Legends (LoL), designed to introduce casualness into competitive gaming. To understand how players experience both casualness and competitiveness, a seemingly contradictory pair, we conducted an interview study with ARAM players, finding that ARAM supports 'casual competition' through decentering competition, diversifying interpersonal dynamics, and filling gaps in player needs. We further discuss how game design and player agency co-constitute casual competition, reflect on key aspects of competitive gaming design such as diversity and fairness, and provide implications for competitive gaming design, which may help combat toxicity.

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https://dl.acm.org/doi/10.1145/3659614
Zhang QLiu KWang D(2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659598
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Index Terms

Casual Competition by Design: A Study of the All Random All Mid (ARAM) Mode in League of Legends
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 8, Issue CSCW2

CSCW

November 2024

5177 pages

EISSN:2573-0142

DOI:10.1145/3703902

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 08 November 2024

Published in PACMHCI Volume 8, Issue CSCW2

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Cited By

Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin A(2024)Machine Learning Systems are Bloated and VulnerableACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365506452:1(37-38)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3673660.3655064
Zhang QLan YGuo KWang D(2024)Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596148:2(1-29)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659614
Zhang QLiu KWang D(2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659598
Zhang HAlhanahnah MAhmed FFatih DLeitner PAli-Eldin AGaretto MMarin ACiucu FFanti GRighter R(2024)Machine Learning Systems are Bloated and VulnerableAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655064(37-38)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3652963.3655064

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