Player-centric Game design: Adding UX Laddering to the Method Toolbox for Player Experience...
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Player-Centric Game Design:
Adding UX Laddering to the Method Toolbox for Player
Experience MeasurementA poker case study
Bieke ZamanCUO, KULeuven – iMinds
http://www.linkedin.com/in/biekezamanPresentation at Measuring Behaviour Conference
2012
Measuring player experiences
Informing game designUser eXperience
Laddering
Overview methods
Physiological data
Initial Experience
Playtest
Critical Facet Playtest
Playtestinge.g. RITE
metrics
Deep Gameplay
Quantitat
ive
Physiological data
Initial Experience
Playtest
Critical Facet Playtest
Playtestinge.g. RITE
Benchmark
Deep Gameplay
Qualitative
Physiological data
LADDERING
Critical Facet Playtest
Playtestinge.g. RITE
Benchmark
Mixed-method
When to use which method?
PIII-approach
When to use Laddering?
Marketing final product
http://www.flickr.com/photos/pensiero/100754831/UX
Laddering
Origins
Means-End Chain Theory
How do specific features of a product relate to personal values?
People choose a product because it contains attributes
that are instrumental to achieving the desired consequences
and fulfilling values
People choose a product because it contains
attributes (the means)
that are instrumental to achieving the desired consequences and fulfilling values (the ends)
Means-End Chain Theory inspiredGame eXperience Model
Insight into1. Player2. Game system3. Game context
Laddering?
One particular method for interviewing and data treatment within Means-End Theory
Origins: Popular in consumer research
Current use: broader research domainsrelevance for user profiling, revealing personal benefits of product use, supporting the redesign process, supporting marketing campaigns, product benchmarking, ...
What is UX Laddering?
UX Laddering refers to BOTH
the Lenient Laddering interview AND the data analysis approach
Example
Real participants!
Product Choice Situation
1Product Interaction
2 Preference Ranking
3 Lenient Laddering
4 Data analysisQualitative & Quantitative
5 OutputHierarchical Value Map
keyboard Cuddly toy interaction game
Arrow keys
Game speed
Real moves
Real example
What are the motivations to play online poker (i.c. Poker Stars & FB Zynga)?
What are the differences between amateur, semi-pro and a professional player, if there are any?
How does the design of the online poker website influence the game play experiences and website preferences?
?
n=18 6 amateur 6 semi-pro 6 pro
18-28 year olds17 men, 1 womanBelgium, higher education
1
Preference Ranking
I: “You’ve been playing both online poker games. If you had the choice, which one would you prefer?”
R: “Pokerstars”
Interview 6 – semi-professional2
Which attributes top of mind? Direct elicitation
I: “You usually play poker on Facebook, euhm, now that I asked you to play poker on PokerStars, which one would you prefer?”
R: “Yes, now I actually prefer PokerStars because I find it clearer and more user-friendly than Facebook poker.”
Interview 15 - amateur
Lenient LadderingProbing why these attributes are important
• I: “Why do you play 6 tables at a time?”• R: “Eh, it is just a matter of being able to play
more hands an hour so that you can earn more. It is a matter of playing so many tables so that you think you can always play your best game.”
• I: “It is maybe a stupid question but why do you want to play better or be more focused?”
• R: (laughing) “Well euh, yes, I want to earn more money.” 3
Interview duration: 6 minutes – 47 minutes
Qualitative Data analysisTranscribing the interviewsCoding & categorizingSecond coder ICR (n=6/ntotal=18, k=.934) 4
Concrete Attributes:–Extra features (time bank, search
function, multi table, filters, hand history…)–Stand alone software–Real money–…
CA
Abstract Attributes: –User friendly–Serious game play–Compatibility–Large user base–Legal–…
AA
Functional Consequences:–Being more focused–Play quicker–Playing more hands an hour–Profit maximalization–Earn more money–….
FC
Psycho-Social Beliefs:–Challenge–Trust–Playing amongst friends–Fun–Better life–…
PSB
Quantitative Data analysisScore Matrix
Ladderux.com Avg. ladders/resp= 7.8Avg. elements/ladder=3.7
Quantitative Data analysisImplication Matrix
Ladderux.com
5HVM – Amateur
HVM – Semi-pro
HVM – Professional player
Challenges
Duration and effort of data gathering and analysis– Interviewing, transcribing, coding…
Research aim –Can it successfully feed the design?
Products studied–Not always existing, hence fewer
ladders, no values?
Questions?
Bieke Zaman
Kristof Geurden master student, poker study
KU Leuven, Belgium
Vero Vanden AbeeleLadderux.com
Thanks!