Abstract
PurposeWith the usage of social media and Web 2. 0, YouTube channels currently play a pivotal role in supporting a traveler’s destination visit. Travelers create their YouTube channels and share their past experiences in the form of videos, which helps other potential travelers to support their destination visit. The purpose of this paper is to understand how travelers adopt information through YouTube channels and how it influences the traveler’s intention to visit a destination.Design/methodology/approachA research model was constructed and empirically tested by using a sample of 486 respondents who watch YouTube channels before visiting a destination. Further, the hypotheses of this study were validated with the help of structural equation modeling using partial least squares. The respondents in this study were from Delhi.FindingsThis paper found comprehensiveness, relevance, timeliness, source expertise and attitude as the most significant predictors of a traveler’s destination visit intention through YouTube channel adoption. Further, source trustworthiness and accuracy were not found to be statistically significant.Research limitations/ImplicationsThe findings of this paper were based on data taken from the local respondents in Delhi. Further, it analyzed the influence of only seven dimensions on destination visit intention, which could have excluded some important factors that influence tourists’ destination visit intention.Practical implicationsThis paper has provided implications for YouTube vloggers and tourists. The result proves that while making decisions regarding destination visit, tourists thinks critically and scrutinize the content of YouTube channels prior to deciding a destination. So, vloggers should provide relevant, comprehensive and accurate destination information through their videos to tourists.OriginalityTo the best of authors’ knowledge, this paper is the first in the Indian context to examine the adoption of YouTube channels before visiting destinations through information adoption models with additional constructs.
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