Analysing cluster evolution using repeated cross-sectional ordinal data
Authors: Disegna, M., D'Urso, P. and Massari, R.
Journal: Tourism Management
Volume: 69
Pages: 524-536
eISSN: 0261-5177
DOI: 10.1016/j.tourman.2018.06.028
Abstract:This study contributes to the existing literature on tourism market segmentation by providing a new matching-clustering procedure that allows patterns of behaviours to be identified using repeated cross-sectional surveys. By extracting equivalent samples over time, the matching method allows inter-temporal cluster analyses to be performed so that a deeper insight into a phenomenon can be obtained beyond the traditional aggregate level of understanding. The paper provides a step-by-step description of the matching-clustering procedure that can be easily replicated, both within and outside the tourism field, when repeated cross-sectional data are available. From a practical and managerial perspective, the proposed procedure helps destination managers and municipalities to describe and verify the efficacy of policy and strategies adopted over years without the necessity to rely on longitudinal surveys, which are often difficult to conduct.
https://eprints.bournemouth.ac.uk/30919/
Source: Scopus
Analysing cluster evolution using repeated cross-sectional ordinal data
Authors: Disegna, M., D'Urso, P. and Massari, R.
Journal: Tourism management
Volume: 68
Pages: 524-536
Publisher: Elsevier
ISSN: 0261-5177
DOI: 10.1016/j.tourman.2018.06.028
Abstract:This study contributes to the existing literature on tourism market segmentation by providing a new matching-clustering procedure that allows patterns of behaviours to be identified using repeated cross-sectional surveys. By extracting equivalent samples over time, the matching method allows inter-temporal cluster analyses to be performed so that a deeper insight into a phenomenon can be obtained beyond the traditional aggregate level of understanding. The paper provides a step-by-step description of the matching-clustering procedure that can be easily replicated, both within and outside the tourism field, when repeated cross-sectional data are available. From a practical and managerial perspective, the proposed procedure helps destination managers and municipal- ities to describe and verify the efficacy of policy and strategies adopted over years without the necessity to rely on longitudinal surveys, which are often difficult to conduct.
https://eprints.bournemouth.ac.uk/30919/
Source: Manual
Analysing cluster evolution using repeated cross-sectional ordinal data
Authors: Disegna, M., D'Urso, P. and Massari, R.
Journal: Tourism Management
Volume: 69
Issue: December
Pages: 524-536
ISSN: 0261-5177
Abstract:This study contributes to the existing literature on tourism market segmentation by providing a new matching-clustering procedure that allows patterns of behaviours to be identified using repeated cross-sectional surveys. By extracting equivalent samples over time, the matching method allows inter-temporal cluster analyses to be performed so that a deeper insight into a phenomenon can be obtained beyond the traditional aggregate level of understanding. The paper provides a step-by-step description of the matching-clustering procedure that can be easily replicated, both within and outside the tourism field, when repeated cross-sectional data are available. From a practical and managerial perspective, the proposed procedure helps destination managers and municipal- ities to describe and verify the efficacy of policy and strategies adopted over years without the necessity to rely on longitudinal surveys, which are often difficult to conduct.
https://eprints.bournemouth.ac.uk/30919/
Source: BURO EPrints