Big data marketing during the period 2012–2019: a bibliometric review

The present study identifies the most significant trends in production of high impact scientific papers related to the Big Data Marketing variable during the period between the years 2012 and 2019 through a revision of the Scopus database, which manages to highlight the relevance of 113 indexed papers. For this purpose, the following descriptive bibliometric indicators are implemented: production volume, type of document, number of citations, and country of application. In the studied time period, the evidence suggests an annual growth in the production volume of papers related to the variable, but with a significant drop in 2017. The knowledge areas that showcases more researches about the Big Data Marketing variable are computer science, mathematics, decision-making, and engineering domain

Destination imagery diagnosis model ::the case of Switzerland

This research investigates destination imagery of Switzerland as a travel destination. This research first conducted survey and content analysis to identify 23 unique statements reflecting travel in Switzerland. Through an online survey, this research collected 399 responses from French and Italian respondents. Based on the comparisons of association strength and association valence of every statement to the aggregated association strength and association valence, this research developed the Destination Imagery Diagnosis model. The results show that, overall, French and Italian respondents have strong and positive associations to statements related to Switzerland’s nature and opportunities for outdoor activities. Furthermore, respondents rated “Healthy lifestyle” and “Welcoming and friendly” positively but the associations to Switzerland were weaker. This research also identified marketing opportunities specifically for French and Italian respondents. The Destination Imagery Diagnosis Model serves as a new tool to compare destination imageries between markets or keep track of changes of destination imagery

Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews

Developments in ICT and the massive growth in social media usage have increased the availability of data on travel behaviour. This brings an array of new possibilities to improve destination management through Data-driven decisions. This data, however, needs to be analysed and interpreted in order to be beneficial for destination management. Different kinds of methodologies and data have already been applied to analyse spatial behaviour of tourists between and within destinations. The novelty of our paper in this sense that we apply a relational approach by conducting a network analysis methodology on a readily available big data source: user generated content (UGC) from TripAdvisor. The collected data from the city of Antwerp, Belgium shows how locals, Belgians, Europeans and non-Europeans have distinct review patterns, but also shows recurring behavioural patterns. By comparing the relational constellation of the review network to the spatial distribution of central and peripheral attractions, hotels and restaurants, we discuss the added value of social network analysis on UGC for translating (big) data into applicable information and knowledge. The results show a dominant position of a limited number of clustered attractions in the historic city centre, and shows how geographical proximity and relational proximity are interrelated for international reviewers but less for domestic reviewers. This finding is translated into a set of recommendations for policy makers and destination managers trying to accomplish a better distribution of tourists over the entire destination

Law in Books and Law in Action: The Readability of Privacy Policies and the GDPR

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