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

TL;DR

This study explores whether game-related data can predict user behavior and Hexad types in non-gaming environments, highlighting BrainHex's superior predictive performance and emphasizing personalization in gamification design.

Contribution

It demonstrates that gameplay data can effectively predict behavioral traits and Hexad types in non-gaming contexts, with BrainHex showing the best results.

Findings

Gameplay data reveals behavioral characteristics and Hexad types.

BrainHex archetype predicts user types most accurately.

All three archetypes outperform baseline predictions.

Abstract

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.