Does algorithmic filtering lead to filter bubbles in online tourist information searches?

When tourists search information online, personalization algorithms tend to contextually filter the vast amount of information and provide them with a subset of information to increase relevance and avoid overload. However, limited attention is paid to the dark side of these algorithms. An influential critique of personalization algorithms is the filter bubble effect, a hypothesis that people are isolated in their own information bubble based on their prior online activities, resulting in narrowed perspectives and fewer discovery of new experiences. An important question, therefore, is whether algorithmic filtering leads to filter bubbles. We empirically explore this question in an online tourist information search with the three-dimensional ‘cascade’ tourist decision-making model in a two-step experiment. We train two virtual agents with polarized YouTube videos and manipulate them to conduct travel information searches from both off-site and on-site geolocations in Google Search. The first three pages of search results are collected and analyzed with two mathematical metrics and follow-up content analysis. The results do not show significant differences between the two virtual agents with polarized prior training. However, when search geolocations change from off-site to on-site, 39–69% of the search results vary. Additionally, this difference varies between search terms. In summary, our data show that while algorithmic filtering is robust in retrieving relevant search results, it does not necessarily show evidence of filter bubbles. This study provides theoretical and methodological implications to guide future research on filter bubbles and contextual personalization in online tourist information searches. Marketing implications are discussed.