From the projected to the transmitted image: The 2.0 construction of tourist destination image and identity in Catalonia
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This document summarizes a PhD thesis about analyzing the projected and perceived tourist destination images of Catalonia online. It includes: 1) The aims are to explore projected vs. perceived online images of Catalonia and understand how user-generated content influences image formation. 2) The methodology involves collecting data from official tourism websites and travel blogs/reviews, then performing content analysis to compare word frequencies and identify themes around geography, attractions, feelings, and cultural identity. 3) Preliminary results show the most frequent words differ between the official and user-generated sources, with mentions of specific places, people, and attractions more common in blogs/reviews than websites.
From the projected to the transmitted image: The 2.0 construction of tourist destination image and identity in Catalonia
- 1. ENTER 2015 PhD Workshop Slide Number 1 From the projected to the transmitted image: The 2.0 construction of tourist destination image and identity in Catalonia Estela Mariné-Roig DOCTORAL THESIS Supervised by Prof. Dr. Salvador Anton Clavé University Rovira i Virgili, Catalonia, Spain estela.marine@urv.cat http://www.urv.cat
- 2. ENTER 2015 PhD Workshop Slide Number 2 Content 1. Aims and hypotheses 2. Theoretical framework 3. Methodology 4. Selected results 5. Concluding remarks
- 3. ENTER 2015 PhD Workshop Slide Number 3 1. Aims Explore online projected and perceived images of a destination and assess their correspondence. Shed light on the role of online user-generated image in the destination image formation process by building an explanatory theoretical-conceptual model. General Specific Provide a useful methodology for studies concerning online image and identity. Give insight into the specific case of Catalonia and provide an understanding of the online image identity of a destination.
- 4. ENTER 2015 PhD Workshop Slide Number 4 1. Hypotheses H1. The perceived image that tourists hold once they have visited a destination does not correspond to the one constructed and projected by the destination (neither in terms of general represented place identity, nor in terms of cultural identity). H2. With the creation of user-generated content and its transmission through the social media, image perceived by tourists becomes transmitted image and the image formation circle is increasingly closed from tourist to tourist due to its great potential influence. The user-generated image online (such as in travel blogs and reviews) has a greater capacity or potential to influence than the official destination image (such as in official tourism websites).
- 5. ENTER 2015 PhD Workshop Slide Number 5 2. Background: Destination image Tourist image is the total sum of ideas, feelings, values, impressions, attributes and identities attached to a place, within both the perceived image by tourists and the representations projected by different actors, which are transmitted in certain contexts and through certain communication channels (Derived from Gartner, 1993; Gallarza et al., 2002; Kim & Richardson, 2003; Mercille, 2005; Almeida & Buzinde, 2007; Anton & González, 2008, among others).
- 6. ENTER 2015 PhD Workshop Slide Number 6 2. Background: Online image Intentionality and power struggles in image projection with Web 2.0 Users have a social and leisure motivation to post tourist contents online (Cox et al.,2008;Casaló, 2009; Clever et al. 2009; Bosangit & Mena, 2009). The power of the different agents on the Internet seems to become diluted empowerment of users in detriment of tourism organizations (Akehurst, 2008). Representative dissonance and image congruency issue Perceived tourist image becomes projected through user generated content sharing experience (González, 2010). Travel blogs and reviews Official tourism websites Online image Specially interesting to study perceived-transmitted image of tourists Objects for projected and perceived travel research. Representatives of the destinations.
- 7. ENTER 2015 PhD Workshop Slide Number 7 Catalonia First order world tourism destination. Second top tourist region in EU-27. 2013: 15.6 million foreign tourists. Barcelona is among the top European tourist capitals. 9 regional tourist brands. 3. Methodology: Case study
- 8. ENTER 2015 PhD Workshop Slide Number 8 Process for obtaining a suitable database for analysis: 1. Data sources 2. Data collection 3. Data download 4. Data arrangement 5. Data cleaning 6. Data debug 7. Data language detection 8. Data mining 9. Data dissemination Travel blogs and reviews Official tourism websites 3. Methodology: Database
- 9. ENTER 2015 PhD Workshop Slide Number 9 3. Methodology: Database Process for obtaining a suitable database for analysis: 1. Data sources 2. Data collection 3. Data download 4. Data arrangement 5. Data cleaning 6. Data debug 7. Data language detection 8. Data mining 9. Data dissemination
- 10. ENTER 2015 PhD Workshop Slide Number 10 Process for obtaining a suitable database for analysis: 1. Data sources 2. Data collection 3. Data download 4. Data arrangement 5. Data cleaning 6. Data debug 7. Data language detection 8. Data mining 9. Data dissemination Correct noun Misspellings Barcelona Bathelona, Barcellona, Barthelonaaaa, Bar-th-elona, Bar-tha-lona, Bar-the-lona ... Casa Batlló Batllo House; Casa Batillo, Batilló, Batlla, Batllao, Batllò, Bátllo, Batlo, Battllo, Battló ... Antoni Gaudí Antonio Gaudi; Gaudì, Gaüdi, Gaudie, Gaudii, Goudi, Goudí, Guadi, Gualdi, Gudi ... Barri Gòtic Barri Gotico; Bari Gotic; Ghotic Barrio, District, Quarter; Gotic area, neighborhood ... Parc Güell Parc Guël, Güel, Guéll, Guelle; Park Gueil, Guel, Güelle, Guelli; Parque Guelle, Güelle ... Montjuïc Monjuic, Montjeuic, Montjic, Montjouïc, Montjuîc, Montjuich, Montjuiic, Montjuik ... Visibility Usage Size 3. Methodology: Database
- 11. ENTER 2015 PhD Workshop Slide Number 11 Pyrenees Val d’Aran Terres de Lleida Catalunya Central Costa Brava Costa Barcelona Barcelona Costa Daurada Terres de l’Ebre Whole Catalonia Domain (particular acronym) Brand (particular acronym) HTML en PDF en PDF en + other BarcelonaTurisme.com (BT) Barcelona (Barna) 258 12 3 CostaBrava.org (CB) Costa Brava (cBrav) 1,012 8 - CostaDaurada.info (CD) Costa Daurada (cDaur) 51 6 7 GenCat.cat (GC) Catalonia (unClass) 10 21 - GenCat.cat/turistex_nou (GC) Catalonia (unClass) 48 29 1 LleidaTur.com (LT) Terres de Lleida (tLlei) 173 12 2 Spain.info (SP) Catalonia (unClass) 981 5 - TerresDelEbre.org (TE) Terres de l’Ebre (tEbre) 82 7 1 TurismeDeCatalunya.com (TC) Catalonia (unClass) 250 1 - TurismePropBarcelona.cat (PB) Costa Barcelona (cBarc) Catalunya Central (CatCe) 55 - 27 VisitPirineus.com (VP) Pirineus (Pyren) 28 2 - VisitValdAran.com (VV) Val d’Aran (vAran) 73 - 6 3. Methodology: Database Official Tourism Websites
- 12. ENTER 2015 PhD Workshop Slide Number 12 Domain Barcelona Other towns Unclassified Empty First blog GetJealous.com (GJ) 0 0 1,164 * 371 2001-08-27 MyTripJournal.com (MT) 536 72 0 - 2001-07-25 RealTravel.com (RT) 409 69 0 - 1984-07-31 TravBuddy.com (TY) 832 60 0 ** 11 1985-05-20 TravelBlog.org (TB) 2,348 280 106 - 1997-03-07 TravellersPoint.com (TS) 0 0 596 - 1986-05-09 TravelPod.com (TP) 998 481 0 - 1984-12-27 TripAdvisor.com (TA) 67,882 34,519 43 *** 112,698 2002-10-17 VirtualTourist.com (VT) 10,289 2,192 285 *** 515 1999-12-08 Travel blogs and reviews per tourism brands Web au ca de es fr ie il It nl+be nz uk us RT 36 32 4 8 3 2 0 2 4 5 23 103 TY 26 36 8 26 5 10 4 7 62 5 46 209 TP 149 100 14 38 11 10 2 10 4 23 70 315 TS 54 21 2 6 1 2 1 1 2 11 25 61 TA 191 275 39 217 53 272 59 43 96 29 1,587 2,049 VT 32 29 27 142 19 22 15 32 87 8 234 286 Travel blog and review database Country of origin Language of posts 3. Methodology: Database Travel blogs & Reviews
- 13. ENTER 2015 PhD Workshop Slide Number 13 Indexed pagesVisibility Presence in the social media Link-based ranks Usage Geographical distribution of users Visit-based ranks Domain (particular acronym) Google GetJealous.com (GJ) 716,000 MyTripJournal.com (MT) 921,000 RealTravel.com (RT) 2,630,000 TravBuddy.com (TY) 282,000 TravelBlog.org (TB) 8,860,000 TravellersPoint.com (TS) 1,380,000 TravelPod.com (TP) 11,400,000 TripAdvisor.com (TA) 116,000,000 VirtualTourist.com (VT) 7,910,000 Domain (particular acronym) Google BarcelonaTurisme.com (BT) 27,400 CostaBrava.org (CB) 116,000 CostaDaurada.info (CD) 439 GenCat.cat (GC) 8,210,000 LleidaTur.com (LT) 23,000 Spain.info (SP) 1,510,000 TerresDelEbre.org (TE) 33,400 TurismePropBarcelona.cat (PB) 109,000 VisitPirineus.com (VP) 58,000 VisitValdAran.com (VV) 115,000 Official Tourism Websites Travel Blog & Review Websites Travel Blog and Review websites have a greater visibility than Official Tourism websites Travel blogs and reviews audience Travel Blog and Review websites have a wider usage than Official Tourism websites Official Tourism Websites Mainly from Spain 3. Methodology: Database Data dissemination
- 14. ENTER 2015 PhD Workshop Slide Number 14 CONCEPT DECISION Receptacle Text Approach Quantitative approach massive analysis Interpretation Thematic approach Categories of analysis Geography: region and sub-regions Attraction factors Feelings and dichotomies Cultural identity references Analysis units Keywords within categories Measuring system Frequency counts Software Site Content Analyzer Other software: Java utility to process strings 3. Methodology:Content analysis
- 15. ENTER 2015 PhD Workshop Slide Number 15 Word groups (total databases) Matrix with content categories (file-per-file) Statistics Additional measures Descriptive statistics P-correlation 1st level Most frequent words (50 meaningful words) Specific preliminary study of outstanding elements (Gaudí) User-generated image per brands Spatial indexes 2nd level 3rd level Group of categories Territory1 Territory 2 Territory 3 Territory 4 … Category 1 SW Density ‰ … … … … Subcategory 1a … … … … … Subcategory 1b … … … … … Category 2 … … … … … Subcategory 2a … … … … … Subcategory 2b … … … … … Subcategory 2c … … … … … … … … … … … Location quotient: .. . . x x x x LQ i j ij ij Localization coefficient: h j j i ij i x x x x LC 1 .. . .2 1 Specialization coefficient: n i i j ij j x x x x SC 1 .. . .2 1 Diversification coefficient: n i ij n i ij j xn x DC 1 2 1 2 )( 1 n: Number of sectors .ix : Total value in sector i ijx : Value in sector i in region j h: Number of regions jx. : Total value in region j ..x : Total value (in all sectors and regions) Confirmatory cluster analysis Zoom into a subject through “regular language” Group or category Count Site-Wide Density Average Weight Word_a1 ... ... ... Word_a2 ... ... ... Word_a3 ... ... ... Word_a4 ... ... ... GROUP A ... ... ... Word_b1 ... ... ... Word_b2 ... ... ... GROUP B ... ... ... CATEGORY 1 CATEGORY 2 CATEGORY 3 CATEGORY 4 T-BLOG 1 XXX XXX XXX XXX T-BLOG 2 XXX XXX XXX XXX T-BLOG 3 XXX XXX XXX XXX T-BLOG 4 XXX XXX XXX XXX … … … … … 3. Methodology:Measures
- 16. ENTER 2015 PhD Workshop Slide Number 16 OFFICIAL TOURISM WEBSITES TRAVEL BLOGS AND REVIEWS Rank Word Count Site-wide Density Average Weight Rank Word Count Site-wide Density Average Weight 1 Barcelona 8105 0.69% 9.31 1 Barcelona 100248 3.36% 40.02 2 Catalonia 5628 0.48% 11.59 2 Sagrada Família 18,859 0.62% 38.65 3 route 4904 0.42% 11.05 3 city 17372 0.58% 9.78 4 area 3665 0.31% 4.37 4 Gaudí 16777 0.56% 17.80 5 tourist 3629 0.31% 11.65 5 great 13222 0.44% 10.85 6 museum 3516 0.30% 12.48 6 Parc Güell 12482 0.40% 40.27 7 Girona 3344 0.28% 15.96 7 people 11706 0.39% 4.30 8 town 3071 0.26% 4.57 8 night 10941 0.36% 7.20 9 Catalan 3055 0.26% 2.74 9 way 10800 0.36% 6.62 10 centre 2950 0.25% 4.90 10 tour 10673 0.35% 9.61 11 century 2924 0.25% 2.08 11 Spain 10540 0.35% 19.72 12 turisme 2893 0.25% 14.91 12 good 10231 0.34% 5.56 13 church 2793 0.24% 5.82 13 place 9941 0.33% 5.98 14 city 2742 0.23% 3.60 14 Spanish 8802 0.29% 9.06 15 museu 2702 0.23% 7.75 15 bus 8729 0.29% 10.66 3,171 OTWs pages 27,104 T-B&R entries 1st level Top words Mentions of brands and municipalities (Site-wide density) 4. Selected results
- 17. ENTER 2015 PhD Workshop Slide Number 17 Attraction factors Official tourism websites Travel blogs and reviews Count Density Weight Count Density Weight 1. Food and Wine 13,715 14.18‰ 7.30 38,167 13.67‰ 7.23 1.1 Food and Drinks 10,554 10.92‰ 7.45 33,162 11.88‰ 6.80 1.2 Wine 3,161 3.27‰ 6.85 5,005 1.79‰ 10.11 2. Intangible heritage 2,226 2.30‰ 6.29 6,303 2.26‰ 14.98 3. Leisure and Recreation 11,408 11.80‰ 10.25 37,133 13.30‰ 15.26 3.1 Nightlife & partying 2,424 2.51‰ 6.04 18,667 6.69‰ 12.19 3.2 Theme Parks 550 0.57‰ 10.36 4,366 1.56‰ 34.37 3.3 Wellness 2,126 2.20‰ 6.87 3,851 1.38‰ 15.54 3.4 Theatre/spectacle/cinema 5,528 5.72‰ 14.11 9,258 3.32‰ 12.76 3.5 Leisure remaining words 780 0.81‰ 5.06 991 0.35‰ 11.24 4. Nature and active tourism 27,402 28.34‰ 6.37 15,847 5.68‰ 11.34 4.1 Nature elements and places 16,945 17.53‰ 6.28 10,133 3.63‰ 12.08 4.2 Nature sports/ active tourism 3,069 3.17‰ 7.78 2,673 0.95‰ 11.60 4.3 Rural landscape 1,916 1.98‰ 6.92 580 0.21‰ 6.55 4.4 Nature remaining words 5,472 5.66‰ 5.58 2,461 0.88‰ 9.63 5. Sports 6,082 6.29‰ 7.54 15,264 5.47‰ 17.82 5.1 FC Barcelona 118 0.12‰ 9.41 4,362 1.56‰ 27.51 5.2 Sporting events/watching 999 1.03‰ 4.06 9,523 3.41‰ 13.52 5.3 Other sports 4,965 5.14‰ 8.44 1,125 0.40‰ 15.18 6. Sun, Sea, Sand + nautical 18,667 19.31‰ 13.77 32,406 11.61‰ 12.41 7. Tangible Heritage 69,133 71.50‰ 7.69 175,669 62.92‰ 22.33 7.1 Buildings and architecture 10,177 10.53‰ 3.58 29,412 10.54‰ 13.75 7.2 Gaudí 2,025 2.09‰ 8.33 54,648 19.57‰ 32.19 7.3 Artists 3,920 4.05‰ 7.26 9,190 3.29‰ 23.72 7.4 Art, design, art styles 13,436 13.90‰ 5.40 17,523 6.28‰ 13.04 7.5 Museums 7,095 7.34‰ 12.26 12,264 4.39‰ 25.65 7.6 Archaeological sites 2,453 2.54‰ 9.76 3,167 1.13‰ 13.41 7.7 History/industrial 6,568 6.79‰ 8.27 5,037 1.80‰ 7.19 7.8 Military/castles 5,140 5.32‰ 8.57 9,202 3.30‰ 19.66 7.9 Monuments/sculptures 1,768 1.83‰ 9.78 8,108 2.90‰ 19.32 7.10 Religious heritage 11,345 11.73‰ 7.05 24,497 8.77‰ 22.75 7.11 Culture remaining words 5,206 5.38‰ 13.60 2,621 0.94‰ 9.14 8. Urban environment 13,523 13.99‰ 6.90 74,675 26.75‰ 17.34 8.1 Shopping 1,628 1.68‰ 4.86 12,230 4.38‰ 8.15 8.2 Barcelona attractions 2,812 2.91‰ 7.60 30,685 10.99‰ 23.66 8.3 Other city attractions 316 0.33‰ 3.01 612 0.22‰ 22.58 8.4 Urban tourism/general sites 8,767 9.07‰ 7.20 31,148 11.16‰ 14.62 3,124 OTWs pages 25,357 T-B&R entries 2nd level 4. Selected results Attraction factors
- 18. ENTER 2015 PhD Workshop Slide Number 18 3,124 OTWs pages 25,357 T-B&R entries 2nd level Official tourism websites Travel blogs and reviews 1. Old/Old-fashioned 1. Beautiful 2. Beautiful 2. Fun/Interesting 3. Fun/Interesting 3. Old/Old-fashioned 4. New/Fashionable 4. Love 5. Authentic 5. New/Fashionable 6. Quiet 6. Authentic 7. Lively 7. Full 8. Pleasant 8. Relax 9. Love 9. Cheap 10. Relax 10. Noisy Attraction factor Official tourism websites Travel blogs and reviews Count Density Weight Count Density Weight 1 Good feelings 11,144 11.53‰ 3.05 105,519 37.80‰ 8.52 2 Bad feelings 807 0.83‰ 2.59 21,591 7.73‰ 5.14 3 Love 573 0.59‰ 3.09 6,973 2.50‰ 7.15 4 Hate 7 0.01‰ 0.00 484 0.17‰ 4.98 5 Beautiful 3,754 3.88‰ 2.94 16,799 6.02‰ 9.93 6 Ugly 6 0.01‰ 0.00 341 0.12‰ 5.25 7 Pleasant 632 0.65‰ 3.40 2,389 0.86‰ 5.44 8 Unpleasant 8 0.01‰ 0.00 310 0.11‰ 5.55 9 Friendly 99 0.10‰ 2.22 1,434 0.51‰ 4.15 10 Unfriendly 2 0.00‰ 0.00 34 0.01‰ 8.82 11 Fun/interesting 2,966 3.07‰ 4.10 15,885 5.69‰ 7.58 12 Boring 33 0.03‰ 0.00 1,077 0.39‰ 3.51 13 Lively 663 0.69‰ 5.58 1,675 0.60‰ 6.57 14 Gloomy 89 0.09‰ 11.24 469 0.17‰ 2.75 15 Noisy 162 0.17‰ 0.93 2,467 0.88‰ 4.01 16 Quiet 812 0.84‰ 1.65 1,835 0.66‰ 5.39 17 Full 236 0.24‰ 0.30 4,637 1.66‰ 5.12 18 Empty 31 0.03‰ 0.00 626 0.22‰ 4.92 19 Orderly 241 0.25‰ 0.12 1,178 0.42‰ 3.07 20 Chaotic 101 0.10‰ 7.23 805 0.29‰ 6.86 21 Clean 164 0.17‰ 0.92 1,161 0.42‰ 5.00 22 Dirty 84 0.09‰ 0.24 1,060 0.38‰ 8.93 23 Relax 508 0.53‰ 1.30 3,943 1.41‰ 4.41 24 Distress 55 0.06‰ 0.36 343 0.12‰ 2.19 25 Authentic 1,619 1.67‰ 2.84 5,703 2.04‰ 6.55 26 Inauthentic 15 0.02‰ 0.67 647 0.23‰ 8.51 27 New/Fashionable 2,296 2.37‰ 1.74 5,771 2.07‰ 7.79 28 Old/old-fashioned 4,016 4.15‰ 5.48 8,059 2.89‰ 8.57 29 Cheap 35 0.04‰ 0.00 2,485 0.89‰ 4.40 30 Expensive 80 0.08‰ 5.62 2,043 0.73‰ 6.74 31 Modest/poor 480 0.50‰ 3.73 1,569 0.56‰ 4.47 32 Luxurious/wealthy 418 0.43‰ 1.89 759 0.27‰ 6.19 33 Safe 414 0.43‰ 6.55 779 0.28‰ 3.90 34 Unsafe 75 0.08‰ 5.87 2,150 0.77‰ 7.38 Top attributes Feelings & dichotomies 4. Selected results
- 19. ENTER 2015 PhD Workshop Slide Number 19 3,124 OTWs pages 25,357 T-B&R entries 2nd level OfficialTourism Websites Travel blogs and reviews Attraction factor Official tourism websites Travel blogs and reviews Count Density Weight Count Density Weight Festa Major 124 0.13‰ 20.32 355 0.13‰ 24.03 Rural/Farming festivities 45 0.05‰ 10.89 26 0.01‰ 31.15 Aplec 44 0.05‰ 14.77 0 0.00‰ 0.00 Patum 57 0.06‰ 0.35 2 0.00‰ 70.00 Fira del teatre 4 0.00‰ 10.00 0 0.00‰ 0.00 Tarraco viva 4 0.00‰ 0.00 2 0.00‰ 0.00 Religious traditions 293 0.30‰ 7.41 780 0.28‰ 17.30 Sant Joan 30 0.03‰ 11.00 34 0.01‰ 21.18 Sant Jordi 181 0.19‰ 11.21 178 0.06‰ 24.27 Castanyada 13 0.01‰ 10.00 4 0.00‰ 40.00 Saint Valentine 1 0.00‰ 0.00 12 0.00‰ 0.00 Halloween 2 0.00‰ 0.00 126 0.05‰ 11.51 Carnival 305 0.32‰ 8.00 1,209 0.43‰ 10.33 Feria de abril 0 0.00‰ 0.00 8 0.00‰ 25.00 Castellers 167 0.17‰ 0.90 445 0.16‰ 21.12 Correbou 4 0.00‰ 0.00 1 0.00‰ 0.00 Giants and Dwarfs 90 0.09‰ 1.33 223 0.08‰ 17.31 Correfoc 37 0.04‰ 1.89 139 0.05‰ 17.77 Fireworks 71 0.07‰ 2.54 466 0.17‰ 5.47 Siesta 1 0.00‰ 0.00 650 0.23‰ 5.52 Bullfighting 19 0.02‰ 2.11 918 0.33‰ 16.66 Sardana 78 0.08‰ 10.38 244 0.09‰ 28.28 Catalan dances 9 0.01‰ 0.00 2 0.00‰ 0.00 Accordionists 6 0.01‰ 0.00 0 0.00‰ 0.00 Flamenco 60 0.06‰ 3.66 1,637 0.59‰ 27.29 Spanish Guitar 20 0.02‰ 8.00 418 0.15‰ 4.95 Ballet 34 0.04‰ 0.00 31 0.01‰ 5.48 Belly dancing 0 0.00‰ 0.00 1 0.00‰ 0.00 Salsa 6 0.01‰ 1.67 247 0.09‰ 12.02 Cultural and geographical identity reference Cultural identity elements Catalan Spanish Mediterranean European Global Cultural Identity 4. Selected results
- 20. ENTER 2015 PhD Workshop Slide Number 20 3,124 OTWs pages 25,357 T-B&R entries 2nd level Official Tourism Websites Travel Blogs and Reviews Barna 1. New/fashionable (0.818) 2. Culture remaining words (0.784) 3. Cheap (0.756) 4. Catalan (0.747) 5. Urban environment (0.712) 6. History/industrial (0.696) 7. Authentic (0.696) 8. Leisure and recreational activities (0.695) 9. Leisure remaining words (0.690) 10. shopping (0.673) 11. orderly (0.670) 12. Barcelona attractions (0.666) 1. Tangible Heritage (0.368) 2. Urban Environment (0.347) 3. Gaudí (0.316) 4. Spanish (0.315) 5. Buildings and architecture (0.303) 6. Good feelings (0.300) 7. Barcelona attractions (0.286) 8. Urban tourism/general sites (0.280) 9. Bad feelings (0.253) 10. Food and Drinks (0.242) 11. Food and Wine (0.226) 12. Art, design, art styles (0.226) Official Tourism Websites Travel Blogs and Reviews cDaur 1. Other city attractions (0.546) 2. Good feelings (0.523) 3. Sun, Sea, Sand (0.518) 4. Theme Parks (0.516) 5. Archaeological sites (0.506) 6. Old/old-fashioned (0.503) 7. Leisure remaining words (0.501) 8. Catalan (0.501) 9. Leisure and recreational activities (0.497) 10. Intangible Heritage (0.490) 11. History/industrial (0.488) 12. Tangible heritage (0.473) 1. Archaeological sites (0.284) 2. Other city attractions (0.138) 3. Tarraco viva (0.138) 4. Theme Parks (0.110) 5. Old/old-fashioned (0.100) 6. Mediterranean (0.090) 7. Sun, Sea, Sand (0.084) 8. tllei (0.077) 9. Intangible Heritage (0.074) 10. Art, design, art styles (0.073) 11. Leisure and recreational activities (0.071) 12. History/industrial (0.069) Barcelona Costa Daurada 4. Selected results Correlations brands/categories
- 21. ENTER 2015 PhD Workshop Slide Number 21 Attraction factor Barna Most Freq. Words Comments 7. Tangible Heritage 46.88% - Sagrada Família - Gaudí - Parc Güell - Church - museum The main tourist attraction factor of the Barcelona brand is its tangible heritage. It can be observed that the Barcelona brand identity and image are mainly associated with Gaudí and architecture. Gaudí, his masterpieces and the related subject of architecture account for more than one half of the tangible heritage mentioned about Barcelona. 8. Urban environment 20.28% - ramblas - metro - rambla - market Within the “urban environment” category the most common element mentioned, as could be expected, is 8.2 “Barcelona attractions”. 1. Food and Wine 9.11% - tapas - wine - chocolate - beer - paella Barcelona has a good number of restaurants and places to drink. 1.2 “Wine” only represents 11.26% and the rest belongs to 1.1. “Food and drinks”. 3. Leisure and Recreation 8.44% - bar/s - club - recovered - Tibidabo These two categories (1. “Food and wine” and 3. “Leisure and recreation”) are considerably present because Barcelona, as a big city, has a great variety of restaurants and food offer and also a great leisure offer (concerts, theatre, nightlife, festivals, etc.). 6. Sun, Sea and Sand 6.76% - beach - port - sea - sun After that comes the identification of Barcelona with attraction factor 6. “sun, sea and sand” (6,76%), which is comprehensible as Barcelona is a coastal Mediterranean port city. 5. Sports 3.88% - Nou Camp - stadium - barca - Olympic - game Then we can see that the category of sports in Barcelona is the highest of all brands (3.88%) 4. Nature and active tourism 3.19% - mountain/s - tree/s - nature This factor is low because of the eminently urban nature of the space of the brand. 2. Intangible Heritage 1.45% - flamenco - parade - siesta - La Mercè/ Merce This last category is surprisingly low as Barcelona holds multiple folkloric, traditional and popular mass celebrations. However, it seems bloggers do not identify the intangible heritage with the city and its surroundings very strongly, and the little they do, they associate it with flamenco, parades or siesta which are not traditions of Catalan origin. The only genuine celebration of Barcelona mentioned is La Mercè. 23,435 T-B&R entries 3rd level Barcelona Indexed Card 4. Selected results
- 22. ENTER 2015 PhD Workshop Slide Number 22 23,387 T-B&R entries 3rd level TOURISM BRAND (j) Barna CatCe cBarc cBrav cDaur Pyren TOURISM ATTRACTION FACTORS (i) Location quotient by brand Sectorial Localization coefficient 1. Food and Wine 1.009 0.602 1.991 0.698 0.905 0.930 0.027 2. Intangible Heritage 0.978 0.505 2.592 0.545 1.674 0.747 0.053 3. Leisure & recreation 0.931 0.536 1.174 0.953 2.964 0.518 0.070 4. Nature & active tourism 0.850 5.406 0.959 1.628 1.379 8.428 0.132 5. Sports 1.088 0.486 0.755 0.283 0.373 0.450 0.076 6. Sun, Sea & Sand 0.871 0.362 3.307 1.913 1.784 0.334 0.123 7. Tangible Heritage 1.017 1.077 0.513 1.083 0.685 0.718 0.021 8. Urban Environment 1.054 0.752 0.618 0.642 0.547 0.879 0.047 Coefficient of Specialization 0.022 0.201 0.308 0.133 0.263 0.279 Coefficient of Diversification 0.559 0.603 0.318 0.586 0.412 0.497 4. Selected results Spatial indexes
- 23. ENTER 2015 PhD Workshop Slide Number 23 Tourists mention very popular prominent elements (must-sees), and concentrate on very specific territories (the region’s capital) while official tourism websites promote elements of all types and all brand regions. Bloggers and reviewers use more attribute-based words and feelings, while official tourism websites show a more “holistic” image of the destinations, more centered on general descriptions. Cultural identity images transmitted by tourists are highly based on stereotypes, fragmented and partial. Great geographical imbalance or disproportion and specialization of image transmission of the different sub-regional brand territories. Significant dissonances exist in the destination image (Bandyopahyay & Morais, 2005) of the region and sub-regions studied at the levels of attribute-based identity, cultural identity and spatial distribution of image. Great dissonance in front-nodes between user and destination generated images at regional and sub-regional levels. Multiple representations of the same destination and struggles exist (Andsager & Drzewicka, 2002) in the online environment (Choi et al., 2007; Krizman & Belullo, 2007). The perceived image that tourists hold once they have visited a destination does not correspond to the one constructed and projected by the destinationH1 5. Conclusions
- 24. ENTER 2015 PhD Workshop Slide Number 24 With the creation of user-generated content and its transmission through the social media, image perceived by tourists becomes transmitted image and the image formation circle is increasingly closed from tourist to tourist due to its great potential influence. User-generated image online has a greater potential to influence than official destination image. H2 Greater visibility and usage of travel blogs and reviews vs. official tourism websites. The image perceived by tourists becomes instantly transmitted to other tourists online in web 2.0 spaces. The potential to influence of user-generated contents and the social media is growing every day due to the increasing creation of contents, their acceptance, trustworthiness, the capacity of meeting tourists’ needs and interests, and the many advantages they present for users (Cox et al., 2008). The circle of image formation is increasingly taking place in a solely user milieu and the importance of the different sources throughout the tourist process changes. Local organizations’ loss of control over user-generated images. Now, pre-trip promotion is no longer dominated by outsiders (tour operators) (Dann, 1996) but by users. Need for a new paradigm when perceived image becomes transmitted image 5. Conclusions
- 25. ENTER 2015 PhD Workshop Slide Number 25 5. Conclusions Objective method to select the most relevant data sources for the case study and establishment of a selection criterion according to research goals. It includes the analysis of websites’ image dissemination to assess the capacity the information sources targeted have to disseminate the information they convey. Massive analysis of data: All travel blogs and reviews about our case study on the websites fulfilling the criterion. key: Creation of a database Download web pages and entries to the PC, arrange them into a structure of folders and files. Data cleaning and debugging, language detection, data mining. Quantitative content analysis performed on online texts, based on word counts or frequencies and word grouping into categories, proved to be a useful and appropriate method of analysis to shed light on the projected and perceived images of a destination. Computerized content analysis through Site Content Analyzer and other software are suitable to deal with quantitative data and large sets of analysis. It enables to look deeper into certain complex aspects, such as cultural identity and the spatial distribution of tourist image through the adaptation of spatial indexes. Methodological framework could be used for other studies whose target were different destinations and different types of online media and tourist websites, and periods of time. Utility and benefits of the methodology
- 26. ENTER 2015 PhD Workshop Slide Number 26 Thanks for your attention! Estela Mariné-Roig University Rovira i Virgili, Catalonia, Spain estela.marine@urv.cat http://www.urv.cat
Editor's Notes
- #3: The structure of the presentation is as follows: first I will present the aims and hypothesis
- #6: The theoretical framework departs from and develops the concept of destination image as a complex construct and the image formation process. The definition proposed is that he
- #7: However, today destination image formation cannot be understood without the Internet and web 2.0. So these aspects are developed. What is new about the image transmitted through web 2.0 is that the intentionality and power struggles change, users don’t have an economic intentionality to post images online but a leisure or personal one, so that these images become highly trustworthy and influential. In this context, then more and more interest is being drawn to the different images existing online provided by users in web 2.0, and how different they are from DMO (Destination Management Organization)-produced images.
- #8: Now moving on to the methodology, the first important aspect is that we analysed a specific case study.
- #9: This methodology deals with a massive quantitative content analysis of official tourism web pages and of travel blogs and reviews. The first step to do that is to obtain a suitable database for analysis. The process to obtain it is as follows: First the specific data sources, both travel blog and reviews and official tourism websites must be chosen. We decided to compare both official tourism websites and travel blogs and reviews images.
- #12: From this process the first database obtained of files is the following: distributed in the different official tourism websites of the region.
- #13: Then the database ofbtained from travel blogs and reviews how blogs and re iews are distributed per tourism brands. Then data mining enabled to see the country of origin of tourists and finally the language of posts was also identified (the main language is English and this language was used for most of the analysis).
- #14: Then about the potential of data dissemination both sources have I will show a couple of interesting results. Vibility was measured through indexed pages, presence in the social media and link based ranks. In the case of indexed pages we see that travel blog and review websites have a much greated visibility online than official tourism websites and therefore have a greatedr potential to disseminate their image. Then about usage we analysed if the audience origin and what we saw is that the audience of travel blogs and review is much wider than that of official tourism websites which is mostly only Spanish.
- #15: Content analysis was the technique suitable for analysis.
- #21: I calculated correlations between the different brands and the categories. As you can see some of the brands are correlated mostly to the same elements in both official websites and travel blogs and reviews but in the case of Barcelona.