Your Favorite Gameplay Speaks Volumes about You: Predicting User Behavior and Hexad Type

by   Reza Hadi Mogavi, et al.

In recent years, the gamification research community has widely and frequently questioned the effectiveness of one-size-fits-all gamification schemes. In consequence, personalization seems to be an important part of any successful gamification design. Personalization can be improved by understanding user behavior and Hexad player/user type. This paper comes with an original research idea: It investigates whether users' game-related data (collected via various gamer-archetype surveys) can be used to predict their behavioral characteristics and Hexad user types in non-game (but gamified) contexts. The affinity that exists between the concepts of gamification and gaming provided us with the impetus for running this exploratory research. We conducted an initial survey study with 67 Stack Exchange users (as a case study). We discovered that users' gameplay information could reveal valuable and helpful information about their behavioral characteristics and Hexad user types in a non-gaming (but gamified) environment. The results of testing three gamer archetypes (i.e., Bartle, Big Five, and BrainHex) show that they can all help predict users' most dominant Stack Exchange behavioral characteristics and Hexad user type better than a random labeler's baseline. That said, of all the gamer archetypes analyzed in this paper, BrainHex performs the best. In the end, we introduce a research agenda for future work.


page 1

page 2

page 3

page 4


Do people's user types change over time? An exploratory study

In recent years, different studies have proposed and validated user mode...

A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data

Predicting user churn and taking personalized measures to retain users i...

Jump on the Bandwagon? – Characterizing Bandwagon Phenomenon in Online NBA Fan Communities

Understanding user dynamics in online communities has become an active r...

One Size Does Not Fit All: A Study of Badge Behavior in Stack Overflow

Badges are endemic to online interaction sites, from Question and Answer...

Open Player Modeling: Empowering Players through Data Transparency

Data is becoming an important central point for making design decisions ...

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

Recommender systems that learn from implicit feedback often use large vo...

Automating Gamification Personalization: To the User and Beyond

Personalized gamification explores knowledge about the users to tailor g...

Please sign up or login with your details

Forgot password? Click here to reset