The Impact of Social Media Content Characteristics on Consumer Brand Search Behavior:A Quasi-Experimental Study Using Difference-in-Differences

DOI: https://doi.org/10.62517/jse.202611406

Author(s)

Peilin Tian

Affiliation(s)

University of Sydney, NSW 2006, Australia

Abstract

Consumer decision journeys are shifting from 'ad-exposure to purchase' toward 'content-encounter to brand-search to purchase.' Brand search—consumers' proactive querying of a brand name on search engines following social media content exposure—has become a critical bridging behavior between content marketing and purchase conversion, yet remains systematically overlooked in the digital marketing literature. Grounded in Stimulus–Organism–Response (S–O–R) theory , this study examines how three social media content characteristics—emotion valence, message framing, and interactivity—influence brand search behavior through the mediating mechanism of curiosity, with brand familiarity and product involvement as moderators. Using a quasi-experimental design with Difference-in-Differences (DID) and 12,480 brand × time panel observations, we deploy a multi-layered identification strategy encompassing platform-level exogenous shocks, PSM-DID, and staggered DID estimation .Results reveal that interactivity is the strongest driver of brand search (DID coefficient: b = 6.82, p < 0.001), followed by positive emotion valence (b = 4.15, p < 0.01) and gain-framed content (b = 2.47, p < 0.05). Curiosity mediates approximately 48% of the total content-to-search effect, with the strongest indirect path via interactivity. Brand familiarity negatively moderates (b = -2.13, p < 0.01) and product involvement positively moderates (b = 2.89, p < 0.01) the relationship. A comprehensive battery of robustness checks confirms the stability of these findings. This study establishes brand search as a distinct outcome variable in digital marketing, integrates three content characteristics into a unified causal framework, and demonstrates rigorous causal identification strategies in a domain historically dominated by correlational evidence.

Keywords

Social Media Content Characteristics; Brand Search Behavior; Difference-In-Differences; Quasi-Experiment; S–O–R Theory; Curiosity; Interactivity

References

[1] Barry, T. E. (1987). The development of the hierarchy of effects: An historical perspective. Current Issues and Research in Advertising, 10(2), 251–295. [2] Google Consumer Insights. (2024). The role of search in the consumer decision journey. Google/Ipsos, Internal Report. [3] 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. [4] Ashley, C., & Tuten, T. (2015). Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & Marketing, 32(1), 15–27. [5] Stephen, A. T. (2016). The role of digital and social media marketing in consumer behavior. Current Opinion in Psychology, 10, 17–21. [6] Mehrabian, A., & Russell, J. A. (1974). An Approach to Environmental Psychology. Cambridge, MA: MIT Press. [7] Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1993). Emotional contagion. Current Directions in Psychological Science, 2(3), 96–99. [8] Beatty, S. E., & Smith, S. M. (1987). External search effort: An investigation across several product categories. Journal of Consumer Research, 14(1), 83–95. [9] Punj, G. N., & Staelin, R. (1983). A model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9(4), 366–380. [10] Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers' motives to engage in electronic word-of-mouth communication through social media platforms. Journal of Marketing Management, 29(5–6), 562–583. [11] Huang, L., Tan, C. H., Ke, W., & Wei, K. K. (2017). Comprehension and assessment of product reviews: A review-product congruity proposition. Journal of Management Information Systems, 30(3), 311–343. [12] Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. [13] Tellis, G. J., MacInnis, D. J., Tirunillai, S., & Zhang, Y. (2019). What drives virality (sharing) of online digital content? Journal of Marketing, 83(2), 1–20. [14] Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. [15] Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. [16] Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121(1), 3–19. [17] O'Keefe, D. J., & Jensen, J. D. (2007). The relative persuasiveness of gain-framed and loss-framed messages for encouraging disease prevention behaviors. Journal of Health Communication, 12(7), 623–644. [18] Rafaeli, S. (1988). Interactivity: From new media to communication. In R. P. Hawkins, J. M. Wiemann, & S. Pingree (Eds.), Advancing Communication Science (pp. 110–134). Sage. [19] Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37(4), 509–523. [20] Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75–98. [21] Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34–57. [22] Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184. [23] Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? Information & Management, 51(8), 1017–1030. [24] Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. [25] Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. [26] Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275. [27] Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. [28] Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277. [29] de Chaisemartin, C., & D'Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996. [30] Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. [31] Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. [32] Rubin, D. B. (2001). Using propensity scores to help design observational studies. Health Services and Outcomes Research Methodology, 2(3), 169–188. [33] Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341–352.