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Analyzing social choice and group ranking of online games for product mix innovation

Author

Listed:
  • Wei-Feng Tung

    (Fu-Jen Catholic University)

  • Yu-Jei Lan

    (Fu-Jen Catholic University)

Abstract

With the increasing populations of the smartphones and mobile devices these years, the majority of people have played the mobile or online games on their smartphones. As such users have rapidly increased, this research is to propose an innovative service that can automatically recommend the users the appropriate online games as their optional references. In order for market competition, the most online games’ enterprises need to develop and sell the newer online game products or versions continuously for the customers who like to play online games. Now that the types and markets of online games have had the more diverse along with the sustainable development of online game products over time. This research is to propose an integrative analysis from a large amount of data -users’ preference sequences. Firstly, the research utilizes a sequence recommendation technology that can be used to analyze their preference sequences of online game’s types based on other similar users’ sequence preference data. In other words, these recommended online game’s types that can be generated according to the relationships between the query user’s preferences and users themselves. All sequences from numerous data are accumulated to be the inferences of the query users’ preferences. Secondly, the research also utilizes data mining methodology to explore the possibilities of different product mix of game types from numerous user data. For the main methodology, this research uses a ‘partial user ranking algorithm’ to analyze group ranking of online games based on similar users. The purpose is to explore how the players can be recommended based on the other users’ priorities, as well as the product mix of game types which can be the potential business trends come to markets. Furthermore, this study contributes to a research implication for online game industry, and the results also could be the business references of game combinations and product design.

Suggested Citation

  • Wei-Feng Tung & Yu-Jei Lan, 2017. "Analyzing social choice and group ranking of online games for product mix innovation," Information Systems Frontiers, Springer, vol. 19(6), pages 1301-1309, December.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9769-8
    DOI: 10.1007/s10796-017-9769-8
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    References listed on IDEAS

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    1. Chen, Yen-Liang & Cheng, Li-Chen, 2009. "Mining maximum consensus sequences from group ranking data," European Journal of Operational Research, Elsevier, vol. 198(1), pages 241-251, October.
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    Cited by:

    1. Shih-Chia Huang & Suzanne McIntosh & Stanislav Sobolevsky & Patrick C. K. Hung, 2017. "Big Data Analytics and Business Intelligence in Industry," Information Systems Frontiers, Springer, vol. 19(6), pages 1229-1232, December.
    2. Irfan Kanat & T. S. Raghu & Ajay Vinzé, 2020. "Heads or Tails? Network Effects on Game Purchase Behavior in The Long Tail Market," Information Systems Frontiers, Springer, vol. 22(4), pages 803-814, August.

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