opinion and consensus dynamics in tourism digital ecosystems
TRANSCRIPT
ENTER 2014 Research Track Slide Number 1
Opinion and consensus dynamics in tourism digital ecosystems
Rodolfo BaggioBocconi University, Italy
Giacomo Del ChiappaUniversity of Sassari and CRENoS, Italy
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Background (1)
The Web is not simply a technological manifestation but a reflection of social structures and processes
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Background (2)
Tourism destination : digital business ecosystem– dynamically interlinked real and virtual agents – digital components are intelligent, active and adaptive
organisms– system is in continuous evolution (perpetual beta)
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Tourism digital ecosystem
Effici
ency
System structure affects functions
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Background (3)
• Collaboration, harmonization and coordination of stakeholders’ views pivotal for effective & competitive tourism development
• Enforced through consensus building
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Objectives
• Reconfirm, on more solid bases, structural interdependence of real & virtual components in a tourism digital ecosystem
• Investigate how digital ecosystem topology affects opinion sharing & consensus development among stakeholders
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Materials• Three Italian destinations
– Elba, Gallura, Livigno– Similar size ( 1000 firms)– Similar tourism intensity
(500k tourists/year, strong seasonality)
• Collected data & built network– core tourism operators +
websites– links btw firms & websites
• also weighted
Livigno
Elba
Gallura
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Materials
Cum
ulati
ve d
eg. d
istr
ib.
Similar characteristics& topology
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Dynamic processes
• Information diffusion– epidemiological models on network substrate; – main parameter: infectivity τ – infection process possible when τ > τC (critical threshold)
• Synchronization– models consensus formation– physical model by Kuramoto: system elements are coupled
oscillators, each with intrinsic frequency & characteristic phase– main parameter: coupling K – whole system synchronises when K > KC (critical coupling)
(i.e. all oscillators have same phase -> opinions are aligned)• NB: critical values depend on system configuration
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Nets, matrices, eigenvalues & eigenvectors
• For a square (nn) matrix M, it is possible to find a scalar λ and a vector xn10 satisfying Mx = λx.
• λ, x are called eigenvalues & eigenvectors of M; – a real symmetric nn matrix M has n real eigenvalues– the set of distinct eigenvalues is called the spectrum of M
• Eigenvalues and eigenvectors “summarize” network topology– eigenvalues: global information,
eigenvectors: local (nodal) information
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Methods
• Spectral analysis, i.e. analysis of the eigenvalues and eigenvector of the adjacency & Laplacian matrices of the 3 networks– useful, and often computationally more efficient, way to
assess network main parameters
Adjacency matrix:
Laplacian matrix:
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Methods
Use 2 results from graph spectral theory:• Fiedler vector: eigenvector associated with second
smallest Laplacian eigenvalue 2 renders algebraic connectivity of the network– large gaps in plot separation between “communities”
• Spectral radius: largest eigenvalue of adjacency matrix λN
– SIS epidemic diffusion in undirected graph: critical threshold τC = 1/λN
– Synchronization: critical coupling KC 1/λN
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Results: topologyFiedler vector
Artificial network w. 2well separated modules
No trivial separation possible
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Results: diffusion & synchronization
• The values for whole ecosystems < those of single components (minimum is for weighted networks)
NB: weights assigned to links considering probable cost of links (RR=1, VR=2, VV=3)
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Concluding remarks
• Reconfirm that no trivial structural separation is possible between real and virtual components in a tourism system
• Combination of real and virtual elements in a single integrated system provides a more efficient substrate for the spreading of ideas or the reaching of a consensus on some issue