What content and context factors lead to selection of a video clip? The heuristic route perspective
- 240 Downloads
The popularity of watching video clips on mobile devices is rapidly increasing. The providers of such video services have developed mobile capabilities and have worked to increase their video selections. This study investigates the effect of the factors of preview content (the thumbnail and the title) and context (the popularity cue and the serial position) on video selection in a mobile context by adopting dual process theory and the model of attention capture and transfer. We performed a logit transformation on the dependent variable, and then applied generalized least squares (GLS) regression to analyze 206,221 logs and 323 thumbnails and titles of a video service. Image and text- mining techniques were used to ascertain the level of valence and response to content. This study has four main findings: (1) low valence but high arousal of a thumbnail has a positive effect on video selection; (2) high valence and arousal by a title has a positive effect on video selection; (3) the upper serial position of a video clip and a high popularity cue have a positive effect on the video selection; and (4) the length and recency of a video have a positive effect on the video selection. The results of this study suggest practical implications to help the programming and marketing strategy of the video service as well.
KeywordsMobile context Video clip Sentiment analysis Heuristic route Order effect Bandwagon effect Machine learning Text mining
The authors would like to acknowledge professor Jeonghye Choi for providing insightful advice during review period.
- 1.IAB Research. (2015). One in four U.S. adults watches original digital video, According to IAB Research. IAB. Retrieved July 23, 2017 from https://www.iab.com/insights/one-in-four-u-s-adults-watches-original-digital-video-according-to-iab-research/.
- 2.Statista. (2018). U.S. YouTube video ad advertising revenues 2018| Statistic. Statista. Retrieved August 28, 2018 from https://www.statista.com/statistics/248859/youtube-us-net-video-advertising-revenues/.
- 4.Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., et al. (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on recommender systems (pp. 293–296). ACM.Google Scholar
- 6.Weaver, K. A., Yang, H., Zhai, S., & Pierce, J. (2011). Understanding information preview in mobile email processing. In Proceedings of the 13th international conference on human computer interaction with mobile devices and services (pp. 303–312). ACM.Google Scholar
- 8.Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., & Lee, K. -C. (2016). Content Complexity, Similarity, and Consistency in Social Media: A Deep Learning Approach. SSRN Electronic Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2830377.
- 19.Yantis, S. (2000). Goal-directed and stimulus-driven determinants of attentional control. Attention and Performance, 18, 73–103.Google Scholar
- 20.Yantis, S., & Jonides, J. (1990). Abrupt visual onsets and selective attention: Voluntary versus automatic allocation. Journal of Experimental Psychology: Human Perception and Performance, 16(1), 121.Google Scholar
- 22.Berger, J., & Milkman, K. (2010). Social transmission, emotion, and the virality of online content. Wharton Research Paper, 106, 1–52.Google Scholar
- 39.Jenkins, B. (2011). Consumer sharing of viral video advertisements: A look into message and creative strategy typologies and emotional content. A Capstone Project.Google Scholar
- 41.Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical report C-1, the center for research in psychophysiology, Florida: University of Florida.Google Scholar
- 46.Liu, H., Jou, B., Chen, T., Topkara, M., Pappas, N., Redi, M., & Chang, S. -F. (2016). Complura: Exploring and leveraging a large-scale multilingual visual sentiment ontology. In Proceedings of the 2016 ACM on international conference on multimedia retrieval (pp. 417–420). ACM.Google Scholar
- 47.Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S. -F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proceedings of the 21st ACM international conference on Multimedia (pp. 223–232). ACM.Google Scholar
- 51.Kim, S., Kwon, S., & Kim, J. (2015). Building sentiment dictionary and polarity classification of blog review by using elastic net. Communications of the Korean Institute of Information Scientists and Engineers, 2015(12), 639–641.Google Scholar
- 53.Pfeffer, J., & Davis-Blake, A. (1986). Administrative succession and organizational performance: How administrator experience mediates the succession effect. Academy of Management Journal, 29(1), 72–83.Google Scholar
- 57.Santos, M. A. D., Lobos, C., Muñoz, N., Romero, D., & Sanhueza, R. (2017). The influence of image valence on the attention paid to charity advertising. Journal of Nonprofit and Public Sector Marketing, 0(0), 1–18.Google Scholar
- 59.Zhang, Z., Li, X., & Chen, Y. (2012). Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews. ACM Transactions on Management Information Systems (TMIS), 3(1), 5.Google Scholar