- Apply for Authority
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS
ISSN : 1738-3110
Context Centrality in Distributions of Advertising Messages and Online Consumer Behavior
- Downloaded
- Viewed
Abstract
Purpose: As moment-based marketing messages (i.e., messages related to current moments or event), companies put significant investments to distribute TV advertising related to external moments in a retail environment. While the literature offers strong support for the value of distributions of context-based messaging to advertisers, less attention has been given to how to design those messages to effectively communicate across channels. This research adds a new dimension of analysis to the study of advertising context and its cross-channel effects on online consumer behavior. Research Design, Data and Methodology: A system-of-equations Tobit regression model was adopted using data collected from an advertising agency that consists of 1,223 TV ads aired during the Rio Olympics and NCAA, tagging from consumers, and a text analysis. Results: First, TV ads with high centrality of context lead to lower online search behavior and higher online social actions. Second, how brands can design messages more effectively was explored by using product information as a moderator that could improve the impact of context-based TV advertisements. Conclusions: Given that expenses in traditional channels are still one of the biggest channel management decisions, it is critical to understand how consumer engagement varies by design of context-based TV advertising.
- keywords
- Advertising Distribution, Online Consumer Behavior, Context-Based Messaging, Cross-Media Behavior, Cross-Channel Management
Reference
Berger, J. (2014). Word of mouth and interpersonal communication:A review and directions for future research. Journal of Consumer Psychology, 24(4), 586-607.
Bharadwaj, N., Ballings, M., & Naik, P. A. (2020). Cross-media consumption: Insights from super bowl advertising. Journal of Interactive Marketing, 50(1), 17-31.
Borah, A., Banerjee, S., Lin, Y. T., Jain, A., & Eisingerich, A. B. (2020). Improvised marketing interventions in social media. Journal of Marketing, 84(2), 69-91.
Chaiken, S., & Eagly, A. H. (1983). Communication modality as a determinant of persuasion: The role of communicator salience. Journal of Personality and Social Psychology, 45(2), 241.
Coulter, K. S. (1998). The effects of affective responses to media context on advertising evaluations. Journal of Advertising, 27(4), 41-51.
Dahlén, M. (2005). The medium as a contextual cue: Effects of creative media choice. Journal of Advertising, 34(3), 89-98.
Dahlén, M., & Edenius, M. (2007). When is advertising advertising? Comparing responses to non-traditional and traditional advertising media. Journal of Current Issues & Research in Advertising, 29(1), 33-42.
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.
Du, R. Y., Xu, L., & Wilbur, K. C. (2017). Should TV advertisers maximize immediate online response. SSRN Electronic Journal.
Du, R. Y., Xu, L., & Wilbur, K. C. (2019). Immediate responses of online brand search and price search to TV ads. Journal of Marketing, 83(4), 81-100.
Fossen, B. L., & Schweidel, D. A. (2017). Television advertising and online word-of-mouth: An empirical investigation of social TV activity. Marketing Science, 36(1), 105-123.
Godley, C. (2012). THR’s social media poll: How facebook and twitter impact the entertainment industry. The Hollywood Reporter, 21.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?. Journal of Interactive Marketing, 18(1), 38-52.
Joo, M., Wilbur, K. C., Cowgill, B., & Zhu, Y. (2014). Television advertising and online search. Management Science, 60(1), 56-73.
Keller, K. L. (1987). Memory factors in advertising: The effect of advertising retrieval cues on brand evaluations. Journal of Consumer Research, 14(3), 316-333.
Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, 50(5), 561-576.
Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105-5131.
Lee, A. Y., & Labroo, A. A. (2004). The effect of conceptual and perceptual fluency on brand evaluation. Journal of Marketing Research, 41(2), 151-165.
Lesscher, L., Lobschat, L., & Verhoef, P. C. (2021). Do offline and online go hand in hand? Cross-Channel and synergy effects of direct mailing and display advertising. International Journal of Research in Marketing, 38(3), 678-697.
Lewis, R. A., & Reiley, D. H. (2013, June). Down-to-the-minute effects of super bowl advertising on online search behavior. In Proceedings of the fourteenth ACM conference on Electronic commerce (pp. 639-656).
Liaukonyte, J., Teixeira, T., & Wilbur, K. C. (2015). Television advertising and online shopping. Marketing Science, 34(3), 311-330.
Loo, P. T. (2020). Exploring airline Companies’ engagement with their passengers through social network: An investigation from their Facebook pages. Tourism Management Perspectives, 34, 100657.
MacInnis, D. J., Moorman, C., & Jaworski, B. J. (1991). Enhancing and measuring consumers' motivation, opportunity, and ability to process brand information from ads. The Journal of Marketing, 55(4), 32-53.
Onishi, H., & Manchanda, P. (2012). Marketing activity, blogging and sales. International Journal of Research in Marketing, 29(3), 221-234.
Pletikosa Cvijikj, I., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843-861.
Punj, G. N., & Staelin, R. (1983). A model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9(4), 366-380.
Renjith, R. (2017). The effect of information overload in digital media news content. Communication and Media Studies, 6(1), 73-85.
Schmidt, J. B., & Spreng, R. A. (1996). A proposed model of external consumer information search. Journal of the Academy of Marketing Science, 24(3), 246-256.
Segev, S., Wang, W., & Fernandes, J. (2014). The effects of ad–context congruency on responses to advertising in blogs:Exploring the role of issue involvement. International Journal of Advertising, 33(1), 17-36.
Soldow, G. F., & Principe, V. (1981). Response to commercials as a function of program context. Journal of Advertising Research, 21(2), 59-65.
Stephen, A. T., Sciandra, M., & Inman, J. (2015). Is it what you say or how you say it? How content characteristics affect consumer engagement with brands on Facebook. How Content Characteristics Affect Consumer Engagement with Brands on Facebook (October 1, 2015). Saïd Business School WP, 19.
Tirunillai, S., & Tellis, G. J. (2017). Does offline TV advertising affect online chatter? Quasi-experimental analysis using synthetic control. Marketing Science, 36(6), 862-878.
Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546-562.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
Yang, M., Ren, Y., & Adomavicius, G. (2019). Understanding usergenerated content and customer engagement on Facebook business pages. Information Systems Research, 30(3), 839-855.
Zhang, K., & Katona, Z. (2012). Contextual advertising. Marketing Science, 31(6), 980-994.
Zigmond, D., & Stipp, H. (2010). Assessing a new advertising effect: Measurement of the impact of television commercials on internet search queries. Journal of Advertising Research, 50(2), 162-168.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations