TY - JOUR
T1 - A two-actor model for understanding user engagement with content creators
T2 - Applying social capital theory
AU - Hussain, Khalid
AU - Nusair, Khaldoon
AU - Junaid, Muhammad
AU - Aman, Waqas
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/7/1
Y1 - 2024/7/1
N2 - The emergence of video sharing platforms has given rise to the creation and consumption of tourism-related content. However, there is limited knowledge about the characteristics of content creators that enhance users' engagement with their content. The present study aims to fill this gap by examining creator characteristics and their impact on three tiers of user engagement. Tourism-related content, comprising 366 videos across six destinations, was extracted from YouTube using three social media analytic tools: VidIQ, TubeBuddy, and SocialBlade. The data were analyzed using PLS-SEM with SmartPLS 4.0. The findings reveal that channel subscribers positively influence user engagement at three levels – views, likes, and comments. However, a higher number of video uploads negatively impacts engagement. Furthermore, older videos tend to garner more views, but users' tendency to like the videos decreases over time. In addition, we extracted 23,993 comments and performed sentiment analysis on users’ comments using Python-based VADER social media sentiment analysis tool. The compound-based sentiment analysis reveals that 59.5 percent of users show positive sentiments toward tourism-related content on YouTube while only 9.3 comments were negative, and 31.2 percent of sentiments remain neutral. Temporal analysis shows the rising trend in qualitative user engagement from 2010 to 2023, highlighting a growing interest in consuming and interacting with tourism-related content. This study discusses its theoretical contributions and managerial implications for content creators, destination managers, and advertising agencies.
AB - The emergence of video sharing platforms has given rise to the creation and consumption of tourism-related content. However, there is limited knowledge about the characteristics of content creators that enhance users' engagement with their content. The present study aims to fill this gap by examining creator characteristics and their impact on three tiers of user engagement. Tourism-related content, comprising 366 videos across six destinations, was extracted from YouTube using three social media analytic tools: VidIQ, TubeBuddy, and SocialBlade. The data were analyzed using PLS-SEM with SmartPLS 4.0. The findings reveal that channel subscribers positively influence user engagement at three levels – views, likes, and comments. However, a higher number of video uploads negatively impacts engagement. Furthermore, older videos tend to garner more views, but users' tendency to like the videos decreases over time. In addition, we extracted 23,993 comments and performed sentiment analysis on users’ comments using Python-based VADER social media sentiment analysis tool. The compound-based sentiment analysis reveals that 59.5 percent of users show positive sentiments toward tourism-related content on YouTube while only 9.3 comments were negative, and 31.2 percent of sentiments remain neutral. Temporal analysis shows the rising trend in qualitative user engagement from 2010 to 2023, highlighting a growing interest in consuming and interacting with tourism-related content. This study discusses its theoretical contributions and managerial implications for content creators, destination managers, and advertising agencies.
KW - Content creation
KW - Creator characteristics
KW - Sentiment analysis
KW - Social capital theory
KW - Travel and tourism
KW - User engagement
UR - http://www.scopus.com/inward/record.url?scp=85189933606&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189933606&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5e0fe680-6574-372b-95ce-b3dfa19fec0b/
U2 - 10.1016/j.chb.2024.108237
DO - 10.1016/j.chb.2024.108237
M3 - Article
AN - SCOPUS:85189933606
SN - 0747-5632
VL - 156
JO - Computers in Human Behavior
JF - Computers in Human Behavior
M1 - 108237
ER -