1,721,010 research outputs found
Is participation in the tourism market an opportunity for everyone? Some evidence from Italy
No full textThis paper investigates whether there are differences in tourism consumption behaviour among families by analysing the main determinants of tourism participation at national and international levels. In particular, it explores whether tourism is becoming part of the lifestyle of Italians or whether it is still a luxury good only for the privileged. A Heckman model was used on micro-data on Italian family expenditure over the period 1997-2007, and an income elasticity analysis for different personal and household characteristics was carried out. The results show that participation in the tourism market is strongly affected by the personal characteristics of individuals and that tourism consumption is an income-sensitive good. The analysis reveals that tourism is generally a luxury good. Income elasticity analysis shows both similarities as well as differences in Italian tourism consumption patterns at national and international levels. The authors find that consumption behaviour in tourism is affected not only by economic constraints but also by cultural and territorial factors
Model selection for mixture hidden Markov models: an application to clickstream data
Full text linkIn a clickstream analysis setting, Mixture Hidden Markov Models (MHMMs) can be used to examine categorical sequences assuming they evolve according to a mixture of latent Markov processes, each related to a different subpopulation. These models involve identifying both the number of subpopulations and hidden states. This study proposes a model selection criterion based on an integrated completed likelihood approach that accounts for the two latent classes in the model.We implemented a Monte Carlo simulation study to compare selection criteria performance. In scenarios characterised by categorical short length sequences, our proposed measure outperforms the most commonly used model selection criteria in identifying components and states. The paper presents a case study on clickstream data collected from the website of a company operating in the hospitality industry and modelled by an MHMM selected by the proposed score
Firm Demography in the Accommodation Industry. Evidence from Italian Insular Regions
No full textThe purpose of this study is to analyse the spatial pattern and the post-entry performance of tourism businesses in the Italian insular regions of Sardinia and Sicily. Using geo-referenced micro-data for the period 2011-2014, we explore the spatial distribution of ac-commodation firms in the two insular regions, and then compare the coastal and inland areas of the two regions. We observe a higher dispersion of firms in Sardinia than in Sicily; the latter, however having a higher concentration of firms in coastal and urban areas. We do not find significant differences in survival probability across the two insular regions but we do detect significant differences be-tween coastal and inland areas in Sicily
The effect of agglomeration economies and geography on the survival of accommodation businesses in Sicily
No full textThe study explores the geographical pattern of the accommodation industry in the Italian insular region of
Sicily, focusing on the determinants of the risk of market exit. We adopt a standard framework of business
survival analysis where agglomeration economies play an important role. We then extend the analysis by
considering the role of geography to explore whether the risk of market exit depends on nearness to
desirable amenities. The geography is here measured by the distance from the coast and the altitude of
the place where the firm is located. When we look at the entire population of accommodation firms that
started between 2010 and 2014, we find evidence that the risk of failure increases for those which are
over 2 km from the coast
The Diversification of Sicilian Farms: A Way to Sustainable Rural Development
Full text linkRural areas still suffer from a lack of sustainable development, and the diversification of farms may be a step in the right direction. The paper provides a detailed picture of the diversification of Sicilian farms into tourism services. Specifically, we propose a simple indicator of localization intensity of agritourism farms and explore their spatial distribution at municipality level. Our study highlights that Sicilian farms rarely diversify into tourism services, despite being situated in attractive areas. That said, some significant spatial clusters of municipalities where agritourism farms are highly concentrated do emerge from the study
Clickstream Data Analysis: A Clustering Approach Based on Mixture Hidden Markov Models
Full text linkNowadays, the availability of devices such as laptops and cell phones enables one to
browse the web at any time and place. As a consequence, a company needs to have a
website so as to maintain or increase customer loyalty and reach potential new customers.
Besides, acting as a virtual point-of-sale, the company portal allows it to obtain insights on
potential customers through clickstream data, web generated data that track users accesses
and activities in websites. However, these data are not easy to handle as they are complex,
unstructured and limited by lack of clear information about user intentions and goals.
Clickstream data analysis is a suitable tool for managing the complexity of these datasets,
obtaining a cleaned and processed sequential dataframe ready to identify and analyse
patterns.
Analysing clickstream data is important for companies as it enables them to under stand differences in web user behaviour while they explore websites, how they move
from one page to another and what they select in order to define business strategies tar geting specific types of potential costumers. To obtain this level of insight it is pivotal to
understand how to exploit hidden information related to clickstream data.
This work presents the cleaning and pre-processing procedures for clickstream data
which are needed to get a structured sequential dataset and analyses these sequences by
the application of Mixture of discrete time Hidden Markov Models (MHMMs), a statisti cal tool suitable for clickstream data analysis and profile identification that has not been
widely used in this context. Specifically, hidden Markov process accounts for a time varying latent variable to handle uncertainty and groups together observed states based
on unknown similarity and entails identifying both the number of mixture components re lating to the subpopulations as well as the number of latent states for each latent Markov
chain.
However, the application of MHMMs requires the identification of both the number
of components and states. Information Criteria (IC) are generally used for model selection in mixture hidden Markov models and, although their performance has been widely
studied for mixture models and hidden Markov models, they have received little attention
in the MHMM context. The most widely used criterion is BIC even if its performance for
these models depends on factors such as the number of components and sequence length.
Another class of model selection criteria is the Classification Criteria (CC). They were
defined specifically for clustering purposes and rely on an entropy measure to account for
separability between groups. These criteria are clearly the best option for our purpose, but
their application as model selection tools for MHMMs requires the definition of a suitable
entropy measure.
In the light of these considerations, this work proposes a classification criterion based
on an integrated classification likelihood approach for MHMMs that accounts for the two
latent classes in the model: the subpopulations and the hidden states. This criterion is
a modified ICL BIC, a classification criterion that was originally defined in the mixture
model context and used in hidden Markov models. ICL BIC is a suitable score to identify
the number of classes (components or states) and, thus, to extend it to MHMMs we de fined a joint entropy accounting for both a component-related entropy and a state-related
conditional entropy.
The thesis presents a Monte Carlo simulation study to compare selection criteria per formance, the results of which point out the limitations of the most commonly used infor mation criteria and demonstrate that the proposed criterion outperforms them in identify ing components and states, especially in short length sequences which are quite common
in website accesses. The proposed selection criterion was applied to real clickstream data
collected from the website of a Sicilian company operating in the hospitality sector. Data
was modelled by an MHMM identifying clusters related to the browsing behaviour of
web users which provided essential indications for developing new business strategies.
This thesis is structured as follows: after an introduction on the main topics in Chapter
1, we present the clickstream data and their cleaning and pre-processing steps in Chapter
2; Chapter 3 illustrates the structure and estimation algorithms of mixture hidden Markov
models; Chapter 4 presents a review of model selection criteria and the definition of the
proposed ICL BIC for MHMMs; the real clickstream data analysis follows in Chapter 5
Almatourism - Journal of Tourism, Culture and Territorial Development. Issue: Advances in Tourism Analysis Vol 5, No 9 (2014)
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A Spatial Origin-Destination Analysis of International Tourism Demand. The Case of Italian Provinces
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