Abstract
The relationships between destination image and tourist satisfaction and loyalty have been studied extensively through surveys. This study aims to measure these constructs through big data analytics by going one step further in a line of research undertaken 8 years ago. The data source is content generated by travelers and shared on social media regarding the 10 districts of the city of Barcelona (Catalonia): more than 750,000 online travel reviews (OTRs) hosted on the Airbnb platform. This study also explores a relationship demonstrated by numerous researchers through surveys: the impact of destination image on tourist loyalty through satisfaction. However, the results are not satisfactory due to the great weight of the lodging price variable that unbalances the relationship. For example, the first district in the ranking of cognitive image categories is also the first in the ranking of average scores and of positive feelings and moods. However, the last two districts in the ranking of cognitive categories are the first in the rankings of satisfaction, positive recommendations, and cheaper prices. Additionally, the findings show that the location of the accommodation significantly determines the theme of the OTR narrative. Moreover, the results confirm previous studies on the exaggerated positivity of peer-to-peer accommodation scores: only 0.92% of 15,625 rated properties had negative overall scores.
Highlights
During the 2010s, user-generated content (UGC) increased notably, as did its use as a data source for researchers [1] and an information source for prospective customers [2].UGC is usually disseminated through electronic word-of-mouth communication, as users and consumers share their comments and ratings on social media
Given that social media content generated by visitors coexists with content generated by destination stakeholders, the data source used in this study consists solely of traveler-generated content (TGC), understood to be narratives, opinions, and ratings shared on social media and based on visitors’ experiences in travelling, sightseeing, entertaining, shopping, lodging, and dining at tourist destinations [5]
online travel reviews (OTRs) are characterized by a large diversity of languages, which requires the use of big data analytics [10] and natural language processing (NLP)
Summary
During the 2010s, user-generated content (UGC) increased notably, as did its use as a data source for researchers [1] and an information source for prospective customers [2]. UGC is usually disseminated through electronic word-of-mouth communication (eWoM), as users and consumers share their comments and ratings on social media. Given that social media content generated by visitors coexists with content generated by destination stakeholders, the data source used in this study consists solely of traveler-generated content (TGC), understood to be narratives, opinions, and ratings shared on social media and based on visitors’ experiences in travelling, sightseeing, entertaining, shopping, lodging, and dining at tourist destinations [5]. Most researchers use online travel reviews (OTRs) hosted on travel-related platforms as sources of TGC [9]. OTRs are characterized by a large diversity of languages, which requires the use of big data analytics [10] and natural language processing (NLP)
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