Name
Abstract | ||
|---|---|---|
Online gaming has now become an extremely competitive business. As there are so many game titles released every month, gamers have become more difficult to please and fickle in their allegiances. Therefore, it would be beneficial if we could forecast how addictive a game is before publishing it on the market. With the capability of game addictiveness forecasting, developers will be able to continuously adjust the game design and publishers will be able to assess the potential market value of a game in its early development stages. In this paper, we propose to forecast a game's addictiveness based on players' emotional responses when they are first exploring the game. Based on the account activity traces of 11 commercial games, we develop a forecasting model that predicts a game's addictiveness index according to electromyographic measures of players' two facial muscles. We hope that with our methodology, the game industry could optimize the odds of successful investments and target more accurately the provision of a better entertaining experience. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/NetGames.2012.6404029 | NetGames |
Keywords | Field | DocType |
facial muscles,game addictiveness index,investments,facial emg,affective computing,forecasting online game addictiveness,game design adjustment,forecasting model,game exploration,players emotional responses,account activity trace,potential market value assessment,game title,psychology,online game addictiveness forecasting model,internet,potential market value,electromyography,electromyographic measures,game design,online gaming,game addictiveness forecasting,addictiveness index,commercial game,game industry,quality of experience,computer games,addiction | Game mechanics,Simulation,Computer science,Game design,Quality of experience,Affective computing,Game Developer,Game testing,Market value,The Internet | Conference |
ISSN | ISBN | Citations |
2156-8138 E-ISBN : 978-1-4673-4577-4 | 978-1-4673-4577-4 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-Kai Lou | 1 | 53 | 6.22 |
| Kuan-Ta Chen | 2 | 1896 | 136.86 |
| Hwai-Jung Hsu | 3 | 135 | 9.08 |
| Chin-Laung Lei | 4 | 1686 | 201.07 |