While there is an increasing number of studies highlighting the power of videos in influencing audience attitudes and behavior, academic research in tourism is largely behind due to the methodological challenges of analyzing unstructured video data. This study adopts an automatic video analytics approach to examine the relationship between content features of pro-environmental videos and audience engagement in tourism. Artificial intelligence was used to extract video content features by detecting scenes and shots as well as labels (e.g., trees). Our findings suggest that there exists an inverted U-shape relationship between video informativeness and audience engagement. This study makes significant theoretical and methodological contributions to extant tourism literature by theoretically explaining and empirically testing how video content features influence audience engagement in pro-environmental video communications in tourism.

• Jingjie Zhu(Curtin University, Australia)
• Mingming Cheng(Curtin University, Australia)
• Ying Wang(Sichuan University, China)
• Jie Tan(Curtin University, Australia)