Personalizing the theme park: Psychometric profiling and physiological monitoring Stefan Rennick Egglestone University of Nottingham

1. The theme park as a target for UMAP 2. Proof-of-concept profiling study in a theme park 3. Questions for future research Overview

More than 100 million visits per year Little published research Few operators (but all large scale) Substantial investment in innovation Why the theme park?

Challenges

Challenge one: The theme park recommender system Challenge two: Personalised rides Challenges A common approach involves building a user profile!

Information collected before the visit –Psychometric personality profiling Information collected during the visit –Physiological monitoring Research into profile design

Personality profiling overview Sensation Seeking Scale 40 questions Thrill seeking:8/10 Experience seeking: 7/10 Disinhibition:8/10 Boredom susceptibility:7/10 Big 5 38 questions Openness to experience:9/10 Conscientiousness: 6/10 Extraversion:9/10 Agreeableness:5/10 Neuroticism:5/10 Questionnaire on entry or during on-line ticket purchase?

Heart-rate Skin conductance Breathing rate Physiological monitoring Affective computing: Analysis of physiological data reveals emotional response

What does physiological monitoring reveal about ride experience? Research questions Can personality profiles predict experiences on rides?

1. Negotiate access to local theme park 2. Choose single ride 3. Recruit cohort of participants 4. Profile: Personality tests on entry to theme park 5. Profile: Heart-rate response recorded on single ride 6. Profile: Participant quantifies their experience on the ride 7. Analysis: Relationships between profile and experience Approach

Alton Towers

Arousal: How much do you feel alert, with your body pumped up and buzzing, ready for action? (1,9) Valence: How positive or negative do you feel? (-4,+4) The circumplex model

Self-report data ArousalValence During drop

What does heart-rate reveal?

Correlations between personality dimensions and self- reports at various places on the ride (r~0.3, all at p=0.001) –Thrill seeking –Extraversion –Openness to Experience Two different ways of using these dimensions to cluster participants into groups who report similar experience –Thrill seeking –Extraversion and Openness to Experience Results – personality profiling

Potential use of heart-rate as a measure of whether visitors are excited or relaxed Evidence exists for the efficacy of personality profiling in predicting experience Conclusions profile design

Extend analysis to multiple rides Consider other attractions in theme park How to make recommendations for groups? Different physiological measures Different psychometric measures Considering patterns of queuing? Further work – theme park

When is personality modelling applicable in profiling? Trade-off between accuracy of model and time taken to fill out questionnaire High-value applications? –E.g internet dating Issues of multiple identity / personality Cross-cultural issues Personality profiling