This document summarizes research on the relationship between personality and participation in peer-to-peer (P2P) travel accommodation services like Airbnb. The researchers administered a Big Five personality inventory to 600 Airbnb users and 826 non-users. Using confirmatory factor analysis and multiple-group analysis, they found that Airbnb users scored higher on conscientiousness, extraversion, agreeableness, and openness compared to non-users. This suggests personality traits like sociability and openness to new experiences predict participation in the sharing economy for travel accommodations.

1. ENTER 2017 Research Track Slide Number 1
Personality impacts on the
participation in peer-to-peer (P2P)
travel accommodation services
Pezenka, Ilona1
Weismayer, Christian2
Lalicic, Lidija2
1
FHWien der WKW, Austria
ilona.pezenka@fh-wien.ac.at, http://www.fh-wien.ac.at
2
MODUL University Vienna, Austria
christian.weismayer@modul.ac.at, http://www.modul.ac.at

2. ENTER 2017 Research Track Slide Number 2
Outline
1. Introduction
2. Literature Review
2.1. Airbnb as part of the sharing economy
2.2. Personality as a predictor of travel behaviour
1. Methodology
2. Results
4.1. Descriptive statistics
4.2. Personality dimensions and differences
1. Discussion and Conclusion

3. ENTER 2017 Research Track Slide Number 3
Introduction
•Airbnb – commission-based online market platform for room
sharers and travelers
•Founded 2008
•Accommodations in more than 34,000 cities in 191 countries
•2016: 60 million guests and over 2 million worldwide listings
(Airbnb, 2016)
•Almost 100 million bookings beyond 2016 with a 40-50%
growth in accommodation offered by year (Huston, 2015)
=> Serious threat for the traditional hospitality industry

4. ENTER 2017 Research Track Slide Number 4
•…collaborative consumption/peer economy/collaborative
economy (Uber, Zipcar…)
•Despite the growing importance little is known about the
people participating in it (Guttentag, 2015)
•People were studied in the car sharing context (e.g., Zhou & Kockelman, 2011)
but hardly in the tourism context
•Airbnb success factors:
• Idealistic motives, authenticity of the accommodation experience,
economic benefits (Oskam & Boswijk, 2016)
• Economic benefits, social interaction, sustainability (Tussyadiah, 2015)
• Experiential appeal (Guttentag, 2015)
Airbnb as part of the sharing economy

5. ENTER 2017 Research Track Slide Number 5
•Satisfaction/likelihood of using Airbnb is influenced by the
hosts‘ trustworthiness (photos & reviews) (Ert et al., 2016) and distrust
(Olson, 2013), financial motivation (guest & host) (Guttentag, 2015; Hamari et al., 2015; Möhlmann
2015; Tussyadiah 2015), familiarity (Möhlmann, 2015)
•Highly educated frequent travellers who are more open to new
offerings - innovativeness (Tussyadiah, 2015)
•Demographics are not significant predictors (Tussyadiah & Pesonen, 2015; Yoo & Gretzel,
2011; Jani et al., 2011)
•Other suggestions: lifestyle and values (Yoo & Gretzel, 2011; Jani et al., 2011; Jackson et al.,
2001), personality (Jani et al., 2011)
Airbnb as part of the sharing economy

6. ENTER 2017 Research Track Slide Number 6
•Only few studies examined the impact of personality in the tourism
domain (Allport, 1937)
•Tourism-specific personality scale: Destinations are appealing for
specific types of people (Plog, 1974)
=> Tourists‘ personality determines their travel patterns&preferences
•Psychocentric (not venturesome) and allocentric (venturers) (Plog, 1974),
activation – extroversion(Nickerson and Ellis, 1991), combination resulting in 4
tourist personality types (explorer, adventurer, guided, groupie) (Jackson
et al., 2001), tourist typologies (Inbakaran, 2006) were compared to extroversion (Eysenck &
Eysenck, 1970) and openness of the Big Five Factors (McCrae and Costa, 1999)
=> both scales can capture tourist personality (Jackson and Inbakaran, 2006)
Personality as a predictor of travel behaviour

7. ENTER 2017 Research Track Slide Number 7
•Relationship between Big Five and eco-friendly tourist
behaviour (Kvasova, 2015)
•Relationship between RIASEC personality types (Holland, 1985)
personality types) and tourism behaviour (Frew et al., 1999)
•Utility of Big Five in explaining online travel information search
and tourists‘ online purchases (Jani et al., 2011)
•Relationship of personality and online travel information, in
terms of consumer-generated media(Yoo and Gretzel, 2011)
Tourism related studies

8. ENTER 2017 Research Track Slide Number 8
Measurement construct
• Original questionnaire of the Big Five (NEO-FFI) (Costa & McCrae, 1992):
60 items
• BFI-S developed for the German Socio-Economic Panel (Gerlitz
& Schupp, 2005): 15 items
• Three items/factor (5) (Little et al., 1999): neuroticism, extraversion,
openness to experience, agreeableness, and
conscientiousness
• 5-point Likert agreement scale(‘1–disagree strongly’, ‘2–disagree a little’, ‘3–neither agree nor
disagree’, ‘4–agree a little’, to ‘5–agree strongly’)
• Online questionnaire (May 2016)
• Convenience sampling through social media platforms

9. ENTER 2017 Research Track Slide Number 9
Methodology
• 1st
step: Exploratory factor analysis in Mplus (Muthén & Muthén, 1998)
=> EFA-SEM (or ESEM) with Geomin rotation (oblique)
• 2nd
step: Confirmatory factor analysis (CFA)
=> differences between Airbnb users and non-users
• On the manifest level: Mann-Whitney U-tests
• On the latent level factor means and variance
comparisons just allowed if measurement invariance
assumption fulfilled!!! (e.g. Vandenberg & Lance, 2000; Milfont & Fischer, 2010)

10. ENTER 2017 Research Track Slide Number 10
Measurement invariance
1. Configural invariance: factorial structure (number of
factors, same pattern of fixed and free parameters)
=> same understanding of factors across groups
2. Metric invariance: factor loadings equal
=> factor variance comparison across groups allowed
3. Scalar invariance: equal item intercepts across groups
=> factor means comparison across groups allowed
4. Factor variance invariance
5. Factor covariance invariance
6. Factor mean invariance
7. Error variance invariance

11. ENTER 2017 Research Track Slide Number 11
Measurement invariance
• Seldom fulfilled => Partial invariance (configural invariance is
a must, partial invariance of the other restrictions accepted)
(Vandenberg and Lance, 2000 see this critical but accept them for a minority of indicators)
• Likelihood ratio (LR) comparisons:
1st
step: configural (baseline) vs. metric invariance model
2nd
step: metric invariance model vs. scalar invariance model
=> Modifications if LR tests significant
• Problem: „does not guarantee that the simplest most
interpretable model with the fewest noninvariant
parameters is reached“ (Asparouhov & Muthén, 2013)
• New fully data-driven optimization routine:
Multiple-group factor analysis alignment (Asparouhov & Muthén, 2013, 2014;
Muthén & Asparouhov, 2014)

12. ENTER 2017 Research Track Slide Number 12
Results
- Descriptive Statistics
• 600 Airbnb users (2.76 times booked via Airbnb), 826
people never used Airbnb
• 821 Europeans, 507 Asians, 98 from other continents
• No significant differences regarding education.
• In both groups: >40% high school diploma, nearly 25%
employed, >60% are students. Average age: ~25 years,
average income: little bit more than € 1,000.
Airbnb users Airbnb nonusers
Gender Male/female 35.0%/65% 44.1%/55.9%
Marital status
Single/married 60.7%/8.0% 67.2%/10.2%
Partnership/divorced 30.7%/.7% 22.3%/.4%
With whom do you primarily travel?
Family/alone 30.5%/8.3% 41.3%/9.6%
Friends/Partner 44.5%/16.7% 36.7%/12.5%
How often do you travel annually? 4.93 4.36
Typical price per night of the places that you book? 94.80 119.27

13. ENTER 2017 Research Track Slide Number 13
Latent trait Indicator (I see myself as someone who…) Extraversion Neuroticism Conscientiousness Openness Agreeableness
Extraversion (E)
…is communicative, talkative (E1). .868* (21.850) .032* (2.427) .146* (3.409) .016 (1.484) .016 (.919)
…is outgoing, sociable (E2). .634* (17.302) -.012 (-.855) .013 (1.018) .060* (2.071) .260* (7.419)
…is reserved (E_3). -.402* (-10.876) .230* (7.253) .134* (2.994) .275* (6.049) .011 (.505)
Neuroticism (N)
…worries a lot (N1). -.046 (-1.903) .748* (37.206) .073* (2.323) .018 (.932) .048 (1.676)
…gets nervous easily (N2). -.035 (-1.547) .802* (42.379) .002 (.157) -.038 (-1.471) .011 (.608)
…is relaxed, handles stress well (N_3). -.072* (-2.652) -.470* (-18.784) .107* (2.772) .327* (7.124) .129* (3.071)
Conscientiousness (C)
…does a thorough job (C1). .058* (1.992) .059* (2.421) .651* (17.854) .023 (.957) -.011 (-.443)
…tends to be lazy (C_2). .009 (.397) .258* (8.304) -.438* (-12.617) .279* (6.349) .010 (.553)
…does things effectively and efficiently (C3). .019 (.877) -.012 (-.938) .707* (16.757) .038 (1.433) .122* (2.940)
Openness (O)
…is original, comes up with new ideas (O1). .109* (3.057) -.067* (-2.884) .111* (3.146) .672* (16.811) -.066 (-1.810)
…values artistic experiences (O2). .035 (1.135) .067* (2.807) -.018 (-.697) .511* (12.731) .140* (2.945)
…has an active imagination (O3). .015 (.694) -.050* (-2.742) -.026 (-1.121) .715* (17.758) .121* (2.430)
Agreeableness (A)
…is sometimes somewhat rude to others (A_1). .019 (1.182) .211* (5.855) -.021 (-1.075) .404* (8.167) -.528* (-11.669)
…has a forgiving nature (A2). .054 (1.596) .135* (4.838) -.013 (-.448) .094* (2.414) .443* (12.016)
…is considerate and kind to others (A3). .053 (1.607) .031* (2.118) .012 (.514) .014 (1.391) .859* (19.955)
Big-Five structure
EFA-SEM Geomin rotated factor loadings (z-statistics)

14. ENTER 2017 Research Track Slide Number 14
Multi-group
confirmatory factor analysis (MGCFA)
Significant differences:
Chi-square df p-value
Configural vs. metric 21.430 10 .0183
Metric vs. scalar 50.205 20 .0002

15. ENTER 2017 Research Track Slide Number 15
Multi-group factor analysis
alignment procedure
Approximate measurement invariance for all item intercepts and
factor loadings
=> Scalar invariance: Factor means and variances can be compared
Factor mean: Airbnb users significantly higher on
conscientiousness, extroversion, agreeableness and openness.
Neuroticism: no difference.
Factor variance: Airbnb users more homogeneous except for
neuroticism.
Factor Airbnb C E A O N
mean/variance
User .000/1.000 .000/1.000 .000/1.000 .000/1.000 .050/1.000
Nonuser -.277/1.217 -.277/1.086 -.309/1.314 -.241/1.250 .000/.948

16. ENTER 2017 Research Track Slide Number 16
Discussion/Conclusion
• Higher openness score of Airbnb users:
• openness to experience represents one’s predisposition to appreciate
unusual ideas/to be imaginative (John & Srivastava, 1999)
• positive relationship between openness to experiences and social media
use – novel nature of technologies (Correa et al., 2010)
• Higher extraversion score of Airbnb users:
• authenticity of accommodation experience(Oskam & Boswijk, 2016)
• get in touch with locals (e.g., Guttentag, 2015; Oskam & Boswijk, 2016; Tussyadiah & Pesonen, 2015)
• Higher agreeableness score of Airbnb users:
• good natured, co-operative, high degree of trust(John & Srivastava, 1999)
• trust is a significant barrier to collaborative consumption (e.g., Ert et al., 2016; Olson, 2013)
• No difference in neuroticism score:
• higher level - more likely to engage in social media activities (Correa et al., 2010)
• higher level - more likely to search for online travel information (Jani et al., 2011)

17. ENTER 2017 Research Track Slide Number 17
Discussion/Conclusion
• P2P travel becomes more and more popular and will expand its
market share at the expense of low-budget hotels(Oskam & Boswijk, 2016)
=> traditional hotels should adapt their services according to
psychographic characteristics
(introverted and prefer solitary activities => private guides, customised packages; Airbnb users are
suspicious of strangers and novel things => emphasise the safety aspect; authenticity
important for Airbnb users => focus on food and events typical for the destination)
• Big Five is an adequate instrument for comparing Airbnb users and
non-users in terms of personality traits
• Alignment procedure overcomes model adaptation steps
• Future studies: relationships between factors and covariates in
multiple-group confirmatory factor analysis with covariates (MIMIC
– Multiple Indicators Multiple Causes)
• Limitations: convenience sample, average age ~25 years

18. ENTER 2017 Research Track Slide Number 18
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20. ENTER 2017 Research Track Slide Number 20
Personality impacts on the
participation in peer-to-peer (P2P)
travel accommodation services
Pezenka, Ilona1
Weismayer, Christian2
Lalicic, Lidija2
1
FHWien der WKW, Austria
ilona.pezenka@fh-wien.ac.at, http://www.fh-wien.ac.at
2
MODUL University Vienna, Austria
christian.weismayer@modul.ac.at, http://www.modul.ac.at