Solo tourism: exploration and conceptualization – a semi-supervised machine learning approach

ABSTRACTThis study aims at understanding and conceptualizing solo tourism. By using a semi-supervised machine learning approach and scrutinizing 27,208 solo tourist tweets. Based on implicit self-theory we capture solo consumers' self-development and self-discovery. We provide an all-inclusive slow tourism conceptualization and show that the solo tourism framework is a three-stage journey of self-discovery. This study not only provides tourism scholars and providers with an evidence-based solo tourism conceptualization, but also with a marketing, psychological, and operation tool to manage this segment.KEYWORDS: Solo tourismimplicit self-theorysemi-supervised Latent dirichlet allocation (LDA)machine learning approachtweetssolitude Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe Complexity Advantage - Markets, Information & Complexity Area of Excellence, 415808, Neoma Business School.