Skip to main content
Log in

What content and context factors lead to selection of a video clip? The heuristic route perspective

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. For reasons of confidentiality, we will refer to this Korean video service by the pseudonym “S-Service”.

  2. S-Service has restricted auto play on mobile to prevent excessive cellular data charges. The user can change to setting for auto play on the preferences page.

  3. IBM Watson developers’ site. Retrieved July 23, 2017 from https://www.ibm.com/watson/kr-ko/what-is-watson.html.

  4. Note that the length of the videos in our research environment did not exceed 10 min.

References

  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/.

  3. Crompton, J. L., & Ankomah, P. K. (1993). Choice set propositions in destination decisions. Annals of Tourism Research, 20(3), 461–476.

    Article  Google Scholar 

  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.

  5. Ghose, A., Goldfarb, A., & Han, S. P. (2012). How is the mobile Internet different? Search costs and local activities. Information Systems Research, 24(3), 613–631.

    Article  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.

  7. De Vries, L., Gensler, S., & Leeflang, P. S. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83–91.

    Article  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.

  9. Groves, P. M., & Thompson, R. F. (1970). Habituation: A dual-process theory. Psychological Review, 77(5), 419.

    Article  Google Scholar 

  10. Fu, W. W., & Sim, C. C. (2011). Aggregate bandwagon effect on online videos’ viewership: Value uncertainty, popularity cues, and heuristics. Journal of the American Society for Information Science and Technology, 62(12), 2382–2395.

    Article  Google Scholar 

  11. Hilligoss, B., & Rieh, S. Y. (2008). Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context. Information Processing and Management, 44(4), 1467–1484.

    Article  Google Scholar 

  12. Fu, W. W. (2012). Selecting online videos from graphics, text, and view counts: The moderation of popularity bandwagons. Journal of Computer-Mediated Communication, 18(1), 46–61.

    Article  Google Scholar 

  13. Cheung, C. M., Xiao, B. S., & Liu, I. L. (2014). Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions. Decision Support Systems, 65, 50–58.

    Article  Google Scholar 

  14. Susarla, A., Oh, J.-H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23(1), 23–41.

    Article  Google Scholar 

  15. Hsieh, J.-K., Hsieh, Y.-C., & Tang, Y.-C. (2012). Exploring the disseminating behaviors of eWOM marketing: Persuasion in online video. Electronic Commerce Research, 12(2), 201–224.

    Article  Google Scholar 

  16. Pieters, R., & Wedel, M. (2004). Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing, 68(2), 36–50.

    Article  Google Scholar 

  17. Zhou, S., & Guo, B. (2017). The order effect on online review helpfulness: A social influence perspective. Decision Support Systems, 93, 77–87.

    Article  Google Scholar 

  18. Dewan, S., Ho, Y.-J., & Ramaprasad, J. (2017). Popularity or proximity: Characterizing the nature of social influence in an online music community. Information Systems Research, 28(1), 117–136.

    Article  Google Scholar 

  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 

  21. Ang, S. H., & Low, S. Y. (2000). Exploring the dimensions of Ad creativity. Psychology and Marketing, 17(10), 835–854.

    Article  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 

  23. Lane, R. D., Chua, P. M., & Dolan, R. J. (1999). Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures. Neuropsychologia, 37(9), 989–997.

    Article  Google Scholar 

  24. Schupp, H. T., Flaisch, T., Stockburger, J., & Junghöfer, M. (2006). Emotion and attention: Event-related brain potential studies. Progress in Brain Research, 156, 31–51.

    Article  Google Scholar 

  25. Wiens, S., Molapour, T., Overfeld, J., & Sand, A. (2012). High negative valence does not protect emotional event-related potentials from spatial inattention and perceptual load. Cognitive, Affective, and Behavioral Neuroscience, 12(1), 151–160.

    Article  Google Scholar 

  26. Kensinger, E. A., & Schacter, D. L. (2006). Processing emotional pictures and words: effects of valence and arousal. Cognitive, Affective, and Behavioral Neuroscience, 6(2), 110–126.

    Article  Google Scholar 

  27. Bayer, M., Sommer, W., & Schacht, A. (2010). Reading emotional words within sentences: The impact of arousal and valence on event-related potentials. International Journal of Psychophysiology, 78(3), 299–307.

    Article  Google Scholar 

  28. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.

    Article  Google Scholar 

  29. Barua, A., Ravindran, S., & Whinston, A. B. (1997). Efficient Selection of Suppliers over the Internet. Journal of Management Information Systems, 13(4), 117–137.

    Article  Google Scholar 

  30. Zhang, W., Liu, C., Wang, Z., Li, G., Huang, Q., & Gao, W. (2014). Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. Multimedia Tools and Applications, 73(1), 547–571.

    Article  Google Scholar 

  31. Liebe, U., Hundeshagen, C., Beyer, H., & von Cramon-Taubadel, S. (2016). Context effects and the temporal stability of stated preferences. Social Science Research, 60, 135–147.

    Article  Google Scholar 

  32. Haugtvedt, C. P., & Wegener, D. T. (1994). Message order effects in persuasion: An attitude strength perspective. Journal of Consumer Research, 21(1), 205–218.

    Article  Google Scholar 

  33. Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24(1), 1–55.

    Article  Google Scholar 

  34. Leibenstein, H. (1950). Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. The Quarterly Journal of Economics, 64, 183–207.

    Article  Google Scholar 

  35. Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107, 797–817.

    Article  Google Scholar 

  36. Dewan, S., & Ramaprasad, J. (2012). Research note: Music blogging, online sampling, and the long tail. Information Systems Research, 23(3-part-2), 1056–1067.

    Article  Google Scholar 

  37. Walther, J. B., & Jang, J. (2012). Communication processes in participatory websites. Journal of Computer-Mediated Communication, 18(1), 2–15.

    Article  Google Scholar 

  38. Rout, J. K., Choo, K.-K. R., Dash, A. K., Bakshi, S., Jena, S. K., & Williams, K. L. (2018). A model for sentiment and emotion analysis of unstructured social media text. Electronic Commerce Research, 18(1), 181–199.

    Article  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.

  40. Das, E., Galekh, M., & Vonkeman, C. (2015). Is sexy better than funny? Disentangling the persuasive effects of pleasure and arousal across sex and humour appeals. International Journal of Advertising, 34(3), 406–420.

    Article  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.

  42. Miller, H. J. (1993). Consumer search and retail analysis. Journal of Retailing, 69(2), 160–192.

    Article  Google Scholar 

  43. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263–291.

    Article  Google Scholar 

  44. Xu, Y. C., & Kim, H.-W. (2008). Order effect and vendor inspection in online comparison shopping. Journal of Retailing, 84(4), 477–486.

    Article  Google Scholar 

  45. Zhou, R., Khemmarat, S., Gao, L., Wan, J., & Zhang, J. (2016). How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications, 75(10), 6035–6058.

    Article  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.

  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.

  48. Kurdi, B., Lozano, S., & Banaji, M. R. (2017). Introducing the open affective standardized image set (OASIS). Behavior Research Methods, 49(2), 457–470.

    Article  Google Scholar 

  49. Cao, D., Ji, R., Lin, D., & Li, S. (2016). Visual sentiment topic model based microblog image sentiment analysis. Multimedia Tools and Applications, 75(15), 8955–8968.

    Article  Google Scholar 

  50. An, A., & Kim, H.-W. (2015). Building a Korean Sentiment Lexicon Using Collective Intelligence. Journal of Intelligence and Information Systems, 21(2), 49–67.

    Article  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.

  52. Kim, S., & Kim, N. (2014). A Study on the effect of using sentiment lexicon in opinion classification. Journal of Intelligence and Information Systems, 20(1), 133–148.

    Article  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 

  54. Xu, X., & Lee, L. (2015). A spatial autoregressive model with a nonlinear transformation of the dependent variable. Journal of Econometrics, 186(1), 1–18.

    Article  Google Scholar 

  55. Rahman, M., Rodríguez-Serrano, M. Á., & Lambkin, M. (2017). Corporate social responsibility and marketing performance: The moderating role of advertising intensity. Journal of Advertising Research, 57(4), 368–378.

    Article  Google Scholar 

  56. Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 31, 395–421.

    Article  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.

  58. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170.

    Article  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 

  60. Liang, T.-P., Li, X., Yang, C.-T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236–260.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge professor Jeonghye Choi for providing insightful advice during review period.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hee-Woong Kim.

Appendix

Appendix

Appendix A Correlation matrix
Appendix B 2SLS result with instrument variable (Others views)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yoon, SH., Kim, HW. What content and context factors lead to selection of a video clip? The heuristic route perspective. Electron Commer Res 19, 603–627 (2019). https://doi.org/10.1007/s10660-019-09355-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-019-09355-6

Keywords

Navigation