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Gamification Archetypes Validation
Identify an archetype that is different from conventional gamification models and validate it using online survey tools in anticipation of its implementation on a distributed energy resource agreement platform
- Project funded by the Department of Energy
Let's get started with the research journey
Research Topic
Gamification Archetypes Validation for Energy Applications
Reegine Lim
Rimvydas Baltaduonis
Mayank Malik
David P. Chassin
What is the research direction?
Evaluate potential new consumer archetypes specifically for energy applications
#regressionanalysis
#surveypro
#transactiveenergy
#litreview
#gamification
#correlations
#prolific
#surveyresearch
#consumerarchetypes
#dataevaluation
What inspired the research?
Wanting to uncover the users’ concerns, expectations, and limitations, and capture important observations that add value to the product lifecycle management.
Bartle's taxonomy of player types
(1996)
Achiever
Explorer
Socializer
Killer
Dominant archetype in gaming setting
What about the non-gamers
Nick Yee pointed out that Bartle’s four player types may exhibit high correlations with each other, indicating potential ambiguity in the results (Yee, 2006).
Added new motivations - Escapism and Customization under the category of Immersion
How can we build a survey model that evaluates the user archetypes under
non-game settings?
What efforts did I made to achieve the goal?
1
Achiever
Explorer
Socializer
Killer
Influencer
because it better fits the gamification concept in non-game domain
2
Binary Questions
Designed universally comprehensive & relatable 8 behavioral questions, each with 4 options, where each option corresponds to a different archetype to help determine the participant's type
3
Included demographic questions to learn if an individual's personality type can be predicted from self-reported data about themselves
Age Group
Location
Home Ownership
Tech Savviness
4
Included one "Preferred Activity Choice" - an action-based question towards the end of the survey to determine if an individual's personality type can predict their choices
Susceptibility
Risk Aversion
Influencer
5
Distributed the same survey to the same pool of participants 2 months later to identify if an individual's personality type corresponds to an archetype that is stable over time
🔍 Internal Validation: To assess if the data collected Preferred Activity Question and More About Yourself can predict the type identified through Behavioral Questions
6
External Validation: Aim to assess the relevance of the questions and options for the 4 proposed archetypes: Achiever, Explorer, Socializer, and Influencer. As suggested by the [Delphi method], achieving a median threshold of 75% agreement among validators indicates high relevance between the archetypes and the designed options for the questions
I ran the survey with 100 external validators (experts) and 81.25% of the questions achieved 75% or higher agreement among validators 🎉
Let's get into the data allocation and data analysis
Each response provides a score of +1 or -1 in two dimensions, which are then averaged to determine the individual locus
Using these axes, quadrants are formed, categorizing participants into one of four types: Achiever (++), Influencer (-+), Socializer (–), and Explorer (+-)
7
Both simple average as well as the weighted average are computed based on the validator's weights assigned to each answer type to determine the locus point of a participant’s answers
Without Validator's Weight
With Validator's Weight
Without Validator's Weight
With Validator's Weight
Survey results show that most participants (500) are between Achiever and Explorer 📃
Survey-Reevaluation that runs 2 months later captured the shift in re-evaluation sample locus does not appear to be significant, with the locus remaining almost the same between achievers and explorers
8
Regression results capture tendencies of the personality types to predict the choices given specific sex and age group categories
The regression analysis supports the hypothesis that personality type can predict corresponding actions, with the strongest matches for archetype-aligned choices. However, the socializer archetype showed weak results, likely due to its low representation in the participant pool
Age is a significant explanatory variable for some archetyped choices with the older age groups standing out the most
Other individual specific data collected — such as susceptibility, risk aversion, home ownership, tech savviness and sex — ❌ cannot explain the choices under the defined archetypes
1-GenZ, 2-Millennial, 3-GenX-Young, 4-GenX-Old, 5-Baby Boomer, 6-Silent
What are the conclusions we draw from this research?
Age is a significant explanatory variable for some archetyped choices with the older age groups standing out the most
Archetype stability is not fully consistent at the individual level but it is remarkably consistent in the aggregate
In contrast to gaming environments where people typically fall into the 'socializer' category, our research found that in non-game settings, most individuals are classified as 'achievers' and 'explorers'
Thanks for reading the research till this point! If you are interested to learn more -- click here to read full paper
How do I want to further improve this research?
Recruited participants may lean towards exploration and achievement due to the need to first join the Prolific survey pool, indicating early platform engagement and active survey participation. Those motivated by financial gain remain attentive to survey notifications and quickly participate in qualifying surveys.
These factors may bias the results towards characteristics of exploration and achievement, reflecting a preference for system-focused behaviors.
The researchers are considering adapting the incentivized research tools such as those used in experimental economics to motivate participants to be more attentive to the assigned tasks or surveys and complete them more thoroughly.
The next phase will focus on refining the methodology for conducting incentivized surveys or experiments targeting the energy sector.
Additionally, the researchers are also interested in incorporating latent (anti-social) behavior into this model to understand how participants engage in general electricity usage when incentives are involved.
More? Supervised an intern this summer to develop a survey application
About this project:
The intern was assigned to develop a custom survey application to enhance self-reporting precision through immediate personality feedback in behavioral surveying
Participants get to view their personality type in percentage breakdown
✨ Proud mentor moment! ✨
Introduce the most talented intern - Amanda Li 👑⭐️
