By the same authors

From the same journal

How the Business Model of Customisable Card Games Influences Player Engagement

Research output: Contribution to journalArticlepeer-review

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How the Business Model of Customisable Card Games Influences Player Engagement. / Hodge, Victoria J.; Sephton, Nicholas John; Devlin, Sam Michael; Cowling, Peter Ivan; Goumagias, Nikolaos; Shao, Jianhua; Purvis, Kieran; Cabras, Ignazio; Fernandes, Kiran; Li, Feng.

In: IEEE Transactions on Games, 12.03.2018.

Research output: Contribution to journalArticlepeer-review

Harvard

Hodge, VJ, Sephton, NJ, Devlin, SM, Cowling, PI, Goumagias, N, Shao, J, Purvis, K, Cabras, I, Fernandes, K & Li, F 2018, 'How the Business Model of Customisable Card Games Influences Player Engagement', IEEE Transactions on Games. https://doi.org/10.1109/TG.2018.2803843

APA

Hodge, V. J., Sephton, N. J., Devlin, S. M., Cowling, P. I., Goumagias, N., Shao, J., Purvis, K., Cabras, I., Fernandes, K., & Li, F. (2018). How the Business Model of Customisable Card Games Influences Player Engagement. IEEE Transactions on Games. https://doi.org/10.1109/TG.2018.2803843

Vancouver

Hodge VJ, Sephton NJ, Devlin SM, Cowling PI, Goumagias N, Shao J et al. How the Business Model of Customisable Card Games Influences Player Engagement. IEEE Transactions on Games. 2018 Mar 12. https://doi.org/10.1109/TG.2018.2803843

Author

Hodge, Victoria J. ; Sephton, Nicholas John ; Devlin, Sam Michael ; Cowling, Peter Ivan ; Goumagias, Nikolaos ; Shao, Jianhua ; Purvis, Kieran ; Cabras, Ignazio ; Fernandes, Kiran ; Li, Feng. / How the Business Model of Customisable Card Games Influences Player Engagement. In: IEEE Transactions on Games. 2018.

Bibtex - Download

@article{82199907c20e4fc696f6fa6478ac65af,
title = "How the Business Model of Customisable Card Games Influences Player Engagement",
abstract = "In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of cross-game analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimising a game's updates strategy is key as it affects player engagement and retention which directly influence businesses' revenues and profitability in the $95 billion global games market.",
author = "Hodge, {Victoria J.} and Sephton, {Nicholas John} and Devlin, {Sam Michael} and Cowling, {Peter Ivan} and Nikolaos Goumagias and Jianhua Shao and Kieran Purvis and Ignazio Cabras and Kiran Fernandes and Feng Li",
note = "{\textcopyright} 2018, The Author(s). ",
year = "2018",
month = mar,
day = "12",
doi = "10.1109/TG.2018.2803843",
language = "English",
journal = " IEEE Transactions on Games",
issn = "2475-1510",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - How the Business Model of Customisable Card Games Influences Player Engagement

AU - Hodge, Victoria J.

AU - Sephton, Nicholas John

AU - Devlin, Sam Michael

AU - Cowling, Peter Ivan

AU - Goumagias, Nikolaos

AU - Shao, Jianhua

AU - Purvis, Kieran

AU - Cabras, Ignazio

AU - Fernandes, Kiran

AU - Li, Feng

N1 - © 2018, The Author(s).

PY - 2018/3/12

Y1 - 2018/3/12

N2 - In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of cross-game analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimising a game's updates strategy is key as it affects player engagement and retention which directly influence businesses' revenues and profitability in the $95 billion global games market.

AB - In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of cross-game analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimising a game's updates strategy is key as it affects player engagement and retention which directly influence businesses' revenues and profitability in the $95 billion global games market.

U2 - 10.1109/TG.2018.2803843

DO - 10.1109/TG.2018.2803843

M3 - Article

JO - IEEE Transactions on Games

JF - IEEE Transactions on Games

SN - 2475-1510

ER -