Predicting travel intentions using self-disclosure, trust and intimacy: the case of Tinder users during COVID-19

Kyrie Eleison Munoz

doi:10.1108/jtf-09-2021-0232

Abstract

Purpose This paper determines how travel intentions can be predicted using self-disclosure behaviour, trust and intimacy. This case study focuses on Tinder users who utilised the application's Passport feature which allowed them to travel virtually and interact with other users around the globe amid global travel restrictions. Design/methodology/approach This quantitative research conveniently sampled 294 Tinder users who used the Passport feature during COVID-19 pandemic lockdowns. Data were analysed using PLS-SEM. Findings This study revealed that self-disclosure had a significant influence towards future travel intentions. Findings show that the more users self-disclose, the more their intent to travel increase. Trust and intimacy also had significant relationship on travel intentions while intimacy had a mediating effect between self-disclosure and travel intentions. Practical implications Tourism-oriented establishments and destination marketers should consider Tinder users as a market segment of future tourists. These users have developed travel intentions through in-app interactions and thus comprise an untapped market of potential tourists seeking for meet-ups and niche experiences in a post-pandemic era. Originality/value This study provides novelty in showing the predictive relationship of self-disclosure, trust and intimacy towards travel intentions. A model consisting of these constructs in the context of online interactions was also empirically tested and found adequate to predict travel intentions.

Highlights

Tinder is a geosocial dating application which utilises the Internet and global positional system to scout fellow users in proximal distance to afford individuals the convenience to facilitate online encounters and offline meet-ups (Choi et al, 2016)
This study draws its theoretical contribution on a proposed model which was the first to empirically test the predictive relationship between self-disclosure and travel intentions which has not been explored in previous literature
Using the case of Tinder users during the COVID-19 pandemic, the adequacy of the predictive relevance of the said model was determined with self-disclosure having a large effect size on travel intention

Summary

Introduction

Tinder is a geosocial dating application which utilises the Internet and global positional system to scout fellow users in proximal distance to afford individuals the convenience to facilitate online encounters and offline meet-ups (Choi et al, 2016). The Passport feature allows users to change their locations and interact with other users from anywhere across the globe right at the comfort of their homes. This recorded over 10M daily users navigating through the application with almost 6.7M subscribing and paying for premium features that produced a revenue of USD$ 1.4B during the pandemic alone (Best of Apps, 2021; Tinder, 2020). Even prior to the pandemic, Tinder users have already resorted to the Passport feature to plan future travel by virtually meeting users from foreign countries in advance and establishing relationships which can help create niche interests and genuine tourism experiences from local users on their actual travel (Condie et al, 2018; Leurs and Hardy, 2019). Amid the Received 29 September 2021 Revised 21 November 2021 23 January 2022 Accepted 25 January 2022

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