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Analysing cluster evolution using repeated cross-sectional ordinal data.

Disegna, M., D'Urso, P. and Massari, R., 2018. Analysing cluster evolution using repeated cross-sectional ordinal data. Tourism Management, 69 (December), 524-536.

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2018TM_DisegnaDursoMassari_proof.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.tourman.2018.06.028


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.

Item Type:Article
Additional Information:This article has not been published yet and is embargoed. Additionally, there is a 24-month embargo after the official publication date, before the article can be made available in full on BURO.
Uncontrolled Keywords:Repeated cross-sectional data; Matching; Evolution; Ordinal data; Fuzzy data; Fuzzy clustering;
Group:Bournemouth University Business School
ID Code:30919
Deposited By: Symplectic RT2
Deposited On:28 Jun 2018 11:17
Last Modified:14 Mar 2022 14:11


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