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Understanding customer engagement and social media activities in tourism: A latent profile analysis and cross-validation

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  • So, Kevin Kam Fung
  • Wei, Wei
  • Martin, Drew

Abstract

Customer engagement has gained noteworthy attention among academics and practitioners over the last decade. Two studies employ latent profile analysis (LPA), a person-centered approach, to uncover discrete customer subgroups with similar profiles based on multiple customer engagement dimensions and relate these LPA groups to brand loyalty. Study 1 identifies four latent classes of airline and hotel customers. Customer engagement dimension scores inform four class profiles: enthusiastic, reserved, unpassionate, and disengaged. Results show a significant association between the latent profiles and customer participation in customer engagement activities. Interestingly, the results indicate that highly engaged customers do not necessarily participate in cyberspace-related customer engagement activities; instead, they participate more often in traditional, offline customer engagement activities. Study 2 validates these findings in a tourism destination context. The consistent findings across the two studies challenge the customer engagement literature’s general assumption linking customer engagement and consumer participation in social media.

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  • So, Kevin Kam Fung & Wei, Wei & Martin, Drew, 2021. "Understanding customer engagement and social media activities in tourism: A latent profile analysis and cross-validation," Journal of Business Research, Elsevier, vol. 129(C), pages 474-483.
    • Handle: RePEc:eee:jbrese:v:129:y:2021:i:c:p:474-483
    DOI: 10.1016/j.jbusres.2020.05.054
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    References listed on IDEAS

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    2. Philp, Matthew & Jacobson, Jenna & Pancer, Ethan, 2022. "Predicting social media engagement with computer vision: An examination of food marketing on Instagram," Journal of Business Research, Elsevier, vol. 149(C), pages 736-747.

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