ENTER2017 eTourism Conference presentation

1. Researching Individual
Satisfaction with Group Decisions
in Tourism: Experimental Evidence
A. Delic (TU Wien)
J. Neidhardt (TU Wien)
L. Rook (TU Delft)
H. Werthner (TU Wien)
M. Zanker (Free University of Bozen-Bolzano)
Rome, 25.01.2017

2. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & summary
 Limitations & future work

3. Motivation (1)
 Overall goal - Build a group recommender system that
recommends destinations and considers:
1. Travel personality types of each individual
2. Personality traits of individuals
3. Group dynamics in such a setting
4.Destination preferences (ratings / rankings of destinations)
 What determines a successful recommender system or any software
development?
• One of the key metrices: satisfaction of the users
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4. Motivation (2)
 Understanding group behavior in the context of e-Tourism
1.How groups reach decisions (group decision process)?
2.How individuals’ personality types influence individuals’ behavior
in group settings?
3.How individuals’ group dynamics influence group decisions?
4.What are the determinants of individuals’ satisfaction
with group decision?
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5. Motivation (3)
What are the determinants of individuals’ satisfaction with
group decision in the e-Tourism context?
 Current research: The difference between the individual’s initial
destinations preferences and group choice as the main indicator
 Our hypothesis adds:
1. Individual’s personality traits (Big Five Factors)
2. Thomas-Kilmann Conflict Resolution Style
3. Travel personality types
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6. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & Summary
 Limitations & future work

7. Background (1)
Big Five Factors (McCrae & Costa, 1987)
 One of the most widely used personality theories
 Personality broken to five dimensions
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Openness - the extent to which
one is inclined towards new and
unusual experiences
Conscientiousness - the extent to
which one is precise, careful and
reliable
Extraversion - the extent to which
one is outgoing, cheerful, and warm
Agreeableness - the extent to
which one is altruistic, caring, and
emotionally supportive
Neuroticism - the extent
to which one is distressed

8. Background (2)
Thomas-Kilmann Conflict Mode (Thomas & Kilmann, 2010)
 In group decision-making setting conflicts might arise
 Thomas & Kilmann defined behavior categories in a conflict
 Four conflict resolution styles defined:
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Competing (low cooperation &
high assertion)
Collaborating (high
cooperation & high assertion)
Avoiding (low cooperation &
low assertion)
Accommodating &
compromising (high
cooperation & low assertion)

9. Background (3)
Travel personality types (Neidhardt et al., 2014)
 Seven travel personality types as a combination of:
• Short term behavioral patterns - 17 Tourist Roles (Gibson &
Yiannakis, 2002)
• Long term personality descriptors - Big Five Factors
• Sun & Chill-Out, Knowledge & Travel, Independence & History, Culture &
Indulgence, Social & Sport, Action & Fun, Nature & Recreation
 Evidence of association between 17 Tourist Roles and Big Five
Factors (Delic et al., 2016)
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10. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & Summary
 Limitations & future work

11. Method
 Study initiation within International Federation for Information
Technologies in Travel and Tourism (IFITT)
 200 participants in 55 groups (two, three, four and five members)
deciding on a joint travel destination
 Study procedure organized in three phases (Delic et al., 2016a)
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12. Measures (1)
1. High and low satisfied participants with the group choice
 In (post)questionnaire - individual’s satisfaction with the final group
choice (five-point Likert scale)
 High satisfied participant: individual satisfaction >= median
satisfaction of the sample
 Low satisfied participant: individual satisfaction < median
satisfaction of the sample
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# of High Satisfied # of Low Satisfied SUM
124 76 200

13. Measures (2)
2. Winners and losers
 Comparison of the participant’s private ranking of the ten travel
destinations and group ranking
 Kendall tau distance: distance function for ranking lists where more
similar rankings have smaller distance
 Winner: Distance < median Kendall tau distance
 Loser: Distance >= median Kendall tau distance
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# of Winners # of Losers SUM
99 101 200

14. Measures (3)
3. The Big Five Factors
 20 five-point Likert scale questions from the International Personality Item
Pool in (pre)questionnaire
4. Thomas-Kilmann Conflict Mode Instrument
 Derived from the Big Five Factors (Wood & Bell, 2008)
5. Travel personality types
 Represented as seven factors (Neidhardt et al., 2014)
 Derived from 17 Tourist Roles & Big Five Factors
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# of
Collaborating
# of
Avoiding
# of
Accommodating
# of
Competing
SUM
45 72 48 35 200

15. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & Summary
 Limitations & future work

16. Results (1)
1. The closer the individual’s preferences to group preferences were
the higher individual’s satisfaction was (corr. coefficient: -.35, p-value
< .001)
2. Differences between high and low satisfied participants (t-tests)
 High satisfied participants were more reliable, agreeable and less
neurotic (Big Five Factors)
 High satisfied participants scored higher on Social & Sport and
Action & Fun (Travel types)
 High satisfied participants were more collaborative (Thomas-
Kilmann Conflict Mode Instrument)
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17. Results (2)
 Not all losers were dissatisfied - indicated also by moderate correlation
(-.35, p-value<.001) between individual’s satisfaction and similarity of
their individual preferences to group preferences
3. Differences between high and low satisfied losers (t-tests)
 High satisfied losers were more open to new experiences, more
social, outgoing and agreeable and less neurotic (Big Five Factors)
 High satisfied losers scored high on Social & Sport (Travel types)
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18. Results (3)
 Research suggests that conflict resolution style influences the group
decision-making outcome also
4. Relation between Thomas-Kilmann Conflict Resolution Mode and four
group decision-making outcomes (contingency table)
 Cooperative participants often became High satisfied Winners and
often were satisfied even when they lost
 Avoiders (passive participants) were highly satisfied when they were
among winners but they fell particularly hard into low satisfaction
when they lost
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19. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & Summary
 Limitations & future work

20. Implications
Overall goal: Building more effective e-tourism group recommender
systems
 Most current research use simple aggregation functions
• Aggregation of individual preferences at the group level
• Arrow’s theorem - no single best aggregation strategy
 Using the results of this analysis enables:
• Customizing aggregation to contextual conditions of groups
• Treating group members differently based on their personality types (e.g.,
weighting group members)
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Homo economicus, the basic concept of consumerism, marketing and
of course recommender systems, does not stand in a group setting
and the e-tourism context that we analysed.

21. Summary & Conclusion
 Difference between individuals’ initial destinations preferences
and group choice not the only indicator of users’ satisfaction
 Personality traits, travel personality types and group
dynamics are important aspects to be considered
 Study results inline with theory of behavioral science
 e.g., Agreeable people – “easy going” in group settings
 e.g., Neurotic people – usually less satisfied in any setting
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22. Agenda
 Motivation
 Background
 Method & measures
 Results
 Implications & Summary
 Limitations & future work

23. Future work & Limitations
Future work
 Presented study is the analysis at the individual level
 Next step is to look at the group level (multilevel analysis)
 Incorporating social relationships of individuals
 Combine the findings of different levels
Limitations
 Questionable conflicting preferences
 No actual travel experience
 High influence between group members (certain analysis models do
not support this assumption)
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24. Thank you & Questions
Amra Delic
Faculty of Informatics
TU Wien
Vienna, Austria
amra.delic@tuwien.ac.at
Laurens Rook
Delft University of Technology
Delft, Netherlands
l.rook@tudelft.nl
Julia Neidhardt
Faculty of Informatics
TU Wien
Vienna, Austria
julia.neidhardt@tuwien.ac.at
Hannes Werthner
Faculty of Informatics
TU Wien
Vienna, Austria
werthner@ec.tuwien.ac.at
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Markus Zanker
Free University of Bozen-Bolzano
Bolzano, Italy
mzanker@unibz.it

25. Statistics
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