Top

Electronic Commerce Research

Published in:

16-05-2019

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

Authors: Sang-Hyeak Yoon, Hee-Woong Kim

Published in: Electronic Commerce Research | Issue 3/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Factors affecting consumers’ mobile payment behavior: a meta-analysis

next article Towards a cooperative security system for mobile-health applications

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

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

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.

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

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

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/.

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/.

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

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.

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.CrossRef

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.

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.CrossRef

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.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

17.

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

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.CrossRef

19.

Yantis, S. (2000). Goal-directed and stimulus-driven determinants of attentional control. Attention and Performance, 18, 73–103.

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.

21.

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

22.

Berger, J., & Milkman, K. (2010). Social transmission, emotion, and the virality of online content. Wharton Research Paper, 106, 1–52.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

28.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

33.

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

34.

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

35.

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

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.CrossRef

37.

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

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.CrossRef

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.CrossRef

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.CrossRef

43.

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

44.

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

Title: What content and context factors lead to selection of a video clip? The heuristic route perspective
Authors: Sang-Hyeak Yoon
Hee-Woong Kim
Publication date: 16-05-2019
Publisher: Springer US
Published in: Electronic Commerce Research / Issue 3/2019
Print ISSN: 1389-5753
Electronic ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-019-09355-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 3/2019

An efficient anonymous remote attestation scheme for trusted computing based on improved CPK

A survey on anonymous voice over IP communication: attacks and defenses

Factors affecting consumers’ mobile payment behavior: a meta-analysis

End-to-middle-to-end solution for IMS media plane security

Editorial: special issue on advances in security and privacy for future mobile communications

The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation