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Modeling Attractiveness as a Quality Measurement in Gamified Software (Preprint)

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BACKGROUND Video games have attracted noticeable attention, especially in recent years. In 2018, the game industry achieved a larger market than video and music combined for the first time ever. As a result, a group of experts decided to take advantage of the attraction of video games in other computer software. Their efforts formed a concept called “Gamification”. Although an increasing number of studies have been conducted on video games, a few of them have investigated the attractiveness of this type of game. Due to a lack of research on this area, game makers and gamifiers are confused about what attraction to invest in. In this study, we have introduced a model for ranking attractiveness in video games. We created an online game with four attractions: visual attraction, audio attraction, interactivity attraction, and puzzle attraction. Users of different ages were invited to play the game, and we measured the total time each user spent on the game, and we compared the groups by one-way ANOVA test. The results revealed a significant difference between different attractions, and finally, we could order these four attractions based on the means plot of the groups. OBJECTIVE Due to a lack of research on this area, game makers and gamifiers are confused about what attraction to invest in. In this study, we have introduced a model for ranking attractiveness in video games. METHODS We created an online game with four attractions: visual attraction, audio attraction, interactivity attraction, and puzzle attraction. Users of different ages were invited to play the game, and we measured the total time each user spent on the game, and we compared the groups by one-way ANOVA test. RESULTS The results revealed a significant difference between different attractions, and finally, we could order these four attractions based on the means plot of the groups. CONCLUSIONS Finally, drawing Means Plot, we were able to sort attractions based on their impact on user satisfaction and introduce a model called PGAI, which determines the priority of attractions from the users' point of view.
Title: Modeling Attractiveness as a Quality Measurement in Gamified Software (Preprint)
Description:
BACKGROUND Video games have attracted noticeable attention, especially in recent years.
In 2018, the game industry achieved a larger market than video and music combined for the first time ever.
As a result, a group of experts decided to take advantage of the attraction of video games in other computer software.
Their efforts formed a concept called “Gamification”.
Although an increasing number of studies have been conducted on video games, a few of them have investigated the attractiveness of this type of game.
Due to a lack of research on this area, game makers and gamifiers are confused about what attraction to invest in.
In this study, we have introduced a model for ranking attractiveness in video games.
We created an online game with four attractions: visual attraction, audio attraction, interactivity attraction, and puzzle attraction.
Users of different ages were invited to play the game, and we measured the total time each user spent on the game, and we compared the groups by one-way ANOVA test.
The results revealed a significant difference between different attractions, and finally, we could order these four attractions based on the means plot of the groups.
OBJECTIVE Due to a lack of research on this area, game makers and gamifiers are confused about what attraction to invest in.
In this study, we have introduced a model for ranking attractiveness in video games.
METHODS We created an online game with four attractions: visual attraction, audio attraction, interactivity attraction, and puzzle attraction.
Users of different ages were invited to play the game, and we measured the total time each user spent on the game, and we compared the groups by one-way ANOVA test.
RESULTS The results revealed a significant difference between different attractions, and finally, we could order these four attractions based on the means plot of the groups.
CONCLUSIONS Finally, drawing Means Plot, we were able to sort attractions based on their impact on user satisfaction and introduce a model called PGAI, which determines the priority of attractions from the users' point of view.

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