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

ABSTRACT

This 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 tourism
• implicit self-theory
• semi-supervised Latent dirichlet allocation (LDA)
• machine learning approach
• tweets
• solitude

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The Complexity Advantage - Markets, Information & Complexity Area of Excellence, 415808, Neoma Business School.