Keynote presentation done by Rodolfo Baggio, during ENTER2015 PhD Workshop.

1. 1
ENTER 2015 PhD Workshop Slide Number 1
Tourism & networks
Rodolfo Baggio
Master in Economics and Tourism
Dondena Centre for Research on Social Dynamics
Bocconi University, Milan, Italy
rodolfo.baggio@unibocconi.it

2. 2
why
networks?

3. 3
Internet
International trade

4. 4
Food web
Diseases

5. 5
Proteins
Music

6. 6

7. 7

8. 8
City
Brain

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Phone calls
Soccer

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Air traffic
Tourists

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why
networks?
we live
in a
complex
world

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complex ?
[many] elements
nonlinear relationships
emergent properties
self‐organizing
no “master‐mind”
no blueprint
evolving
adaptive
resilient
a complex system :

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Behind a complex system
there is a network,
that defines the interactions
between the components
We cannot understand
complex systems unless
we map out and understand
the networks behind them
why
now?

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Leonhard Euler

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end XX century
Internet & the Web
Year 2000
30 million hosts
25 million WWW
400 million users

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The emergence of ‘cyberspace’
and the World Wide Web is like
the discovery of a new
continent
Jim Gray ‐ 1944‐2007(?)

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1998‐99
Network science:
not only an academic divertissement …

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Network science
how?

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matrix algebra
abstraction

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Measures
• Global metrics (system structure)
– average measures, global metrics
(density, diameter, etc.), functional form
of distributions
– clusters, communities, hierarchies etc.
• Local metrics (nodal characteristics)
– centrality: the relative importance of a
node within the graph
– connectivity characteristics of self and
neighbors
• Numerical simulations for dynamic
processes
– growth, information diffusion, opinion
formation, resilience …
Statistical physics
Graph theory
Statistics
Computer science
Social network theory
plus
Economics, Biology, Arts, Neuroscience, Demography,
History, Psychology, Engineering, Sociology,
Geography, Architecture, Politics, Ecology, Physics, …
a truly interdisciplinary effort

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Crossing the
qualitative/quantitative chasm
there are many unexpected outcomes
that cannot be explained by using
only one approach
e.g.

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e.g.
e.g.
be careful, especially when ‘‘sampling’’

23. 23
what about
tourism?
complex
phenomenon
Tourism
natural environment
for network analysis
complex systems
(tourism destinations)

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• Examined structural characteristics
– identify & characterize “important” stakeholders
– measure extent of and attitudes towards collaboration
– discover emergent communities
(beyond traditional distinctions by type/geography etc.)
– assess ICT usage
• Studied dynamic behaviors & optimization
– resilience towards external shocks
– diffusion of information & knowledge
– effectiveness of advertising & word‐of‐mouth
– destination’s visibility on WWW
– evolution models
What have we done?
• Three Italian destinations
– Elba, Gallura, Livigno
– Similar size ( 1000 firms)
– Similar tourism intensity
(500k tourists/year, strong seasonality)
– but different settings
• Collect data & build network
– core tourism operators + websites
– links btw firms & websites
Some examples

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Similar characteristics & topology
Livigno
Structure of a tourism destination
the standard view

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System
SELF-ORGANIZES
Community identification
(stochastic modularity algorithm)
(color = type)
Structure of a tourism destination
the system view
Question:
which management style?

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Collaboration, harmonization and coordination
of stakeholders are pivotal for effective &
competitive tourism development
Collaboration & efficiency:
can we balance the efficiency of the
system with that of individual actors?

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Influence
Message & social structure
Modelling opinion diffusion…
At top spread:
3 Rnd vs. 1 Rnd  +20%
3 Top vs. 3 Rnd  +67%
Start infection from
single, multiple nodes
or “top” nodes
Epidemiological model

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Advertising vs. Word‐of‐mouth
• Model effects via numerical simulations
– WOM: effective, “spontaneous”, strongly depends on social
structure, decays quickly
– ADV: expensive, more efforts to achieve same results as
WOM, (probably) more persistent
what about
information
technology?

30. 30
Tourism destination: digital business ecosystem
– dynamically interlinked real and virtual agents
– digital components are intelligent, active & adaptive
– system is in continuous evolution (perpetual beta)
Digital ecosystems
Tourism digital ecosystem
Strong structural coupling
Livigno

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Final remarks
• Tourism systems are adaptive
systems & need to be studied as such
• Methods & techniques to measure,
model and interpret behaviors &
phenomena are available
• Appealing from a theoretical
point of view
• Can become an interesting tool to
assist practical endeavors
and…
makes fun!
(remember Richard Feynman)
ENTER 2015 PhD Workshop Slide Number 62
References
www.iby.it/tourism