2026-04-13T01:18:51Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/676022024-12-05T22:11:40Zcom_2072_3622col_2072_479130

00925njm 22002777a 4500 dc Mariné Roig, Estela author Ferrer Rosell, Berta author 2019-11-25T12:36:14Z 2021-03-31T22:21:17Z 2018 2019-11-25T12:36:14Z Tourist destination image (TDI) is considered crucial when planning a trip. The aim of this paper is to propose a methodology to analyse and measure the (in)congruity or gap between the two sides of the TDI (supplyside projected vs. demand-side perceived) based on the difference between proportions of appearance. This method is applied to an outstanding Mediterranean destination, Catalonia, based on three different information sources: induced (Catalan Tourist Board dossier), autonomous (Lonely Planet travel guide), and organic (UGC: user-generated content). UGC consists of a random sample of 80,000 online travel reviews written in English by tourists who visited Catalonia during 2015. Our findings emphasize discrepancies in three aspects of the TDI, namely spatial, cognitive and affective image. The measurement of the gap between these TDI components shows that organic images (perceived) are significantly different from autonomous and induced images (projected), and that, the last two resemble one another much more. Acknowledgements Both authors acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness [Grant id.: TURCOLAB ECO2017-88984-R]. Second author acknowledges the support of the Catalan Autonomous Government [Grant id.: 2014SGR551] funding the consolidated research group “Compositional and Spatial Data Analysis (COSDA)” and the FEDER / Spanish Economy and Competitiveness Ministry [Grant id.: MTM2015-65016-C2-1-R] funding the project “COmpositional Data Analysis and RElated meThOdS (CODA-RETOS)”. http://hdl.handle.net/10459.1/67602 Destination image Multiscalar destination Image gaps Image components Compositional distance Measuring the gap between projected and perceived destination images of Catalonia using compositional analysis