Abstract
Consumers seek out online user-generated content to inform their purchase decisions because they perceive content created by other consumers as more believable than marketing communications. This research provides a theory of consumer digital trust in which consumer trust in user-generated content requires a digital environment that minimizes consumer suspicion of misrepresented or missing content. The theory is supported with empirical evidence from a hierarchical meta-analysis of 128 effects from 19 online platforms over 19 years (2004–2022). Account verification features, which alleviate suspicions of misrepresented content creator identities, increase the effect of user-generated content on firm performance, but content-enhancing features, such as photo filters, that can prompt suspicion of misrepresented brand experiences, weaken this link. Content-removal features that can spark speculation of missing information in content creators’ historical content and platform moderation media, which creates questions about missing content in brand conversations, weaken the influence of some user-generated content.
Similar content being viewed by others
Notes
Percent change calculated based on a mean-split of effect sizes at the average volume of media coverage considering the average effect size according to high (i.e., above the mean) or low (i.e., below the mean) amounts of media coverage and type of user-generated content (restricted or unrestricted).
Sales rank has an inverse relationship with sales, such that we reverse the sign of the effect size unless the original study already did so (Babić-Rosario et al., 2016).
Following Babic-Rosario et al. (2016) and Rosenthal (1988), we use the following formula to calculate the partial correlation coefficient effect sizes: rxy = t/sqrt(t2 + df), where t is the t-value associated with the regression parameter, and df are the degrees of freedom of the reported regression model. We ran an additional model, controlling for whether an effect size reflects a full or partial correlation. Including this control variable does not influence the results.
We use the midpoint of a study’s data collection period rather than the year the study was published to account for the delay between when data are collected from platforms and when a study is published. In instances where the authors of the original study were not able to provide the exact dates of data collection, we substituted the publication year.
Variance inflation factors are < 5, and correlations are < .70, limiting multicollinearity concerns (Chang and Taylor 2016).
To calculate a pseudo R2 for the model, we compare a baseline version without moderators with a model that includes moderators to determine the amount of heterogeneity explained by the addition of the moderating variables.
With small samples (19 platforms) and limited variance in measures (binary measures for content-enhancing and content-removal features), the power to detect an effect is significantly impaired (38 possible unique values), so for directional hypotheses, less conservative significance values (p < .10; one-tailed p < .05) can provide valid tests of significant relationships (Sawyer and Ball 1981).
To calculate standardized effect sizes, we used the formula \(b(\frac{sd\left(x\right)}{sd\left(y\right)})\), where \(b\) is the estimated meta-regression coefficient, sd(x) is the standard deviation of the focal independent variable, and sd(y) is the standard deviation of the effect size.
References
Akpinar, E., & Berger, J. (2017). Valuable virality. Journal of Marketing Research, 54(2), 318–330.
Anderson, E. T., & Simester, D. I. (2014). Reviews without a purchase: Low ratings, loyal customers, and deception. Journal of Marketing Research, 51(3), 249–269.
Angelis, M., Bonezzi, A., Peluso, A., Rucker, D. D., & Costabile, M. (2012). On braggarts and gossips: A self-enhancement account of word-of-mouth generation and transmission. Journal of Marketing Research, 49(4), 551–563.
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95.
Arnowitz, L. (2018). Kevin Hart isn’t the only one: Other stars whose past tweets have come back to haunt them. Retrieved February 13, 2023 from https://www.usatoday.com/story/life/people/2018/12/07/kevin-hart-isnt-alone-stars-whose-past-tweets-caused-controversy/2236790002/
Audrezet, A., de Kerviler, G., & Moulard, J. G. (2020). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 117, 557–569.
Babić-Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297–318.
Baik, C. (2012). Twitter photos: Put a filter on it. Retrieved January 5, 2023 from https://blog.twitter.com/official/en_us/a/2012/twitter-photos-put-a-filter-on-it.html
Bart, Y., Shankar, V., Sultan, F., & Urban, G. L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing, 69(4), 133–152.
Becker, M., Wiegand, N., & Reinartz, W. J. (2019). Does it pay to be real? Understanding authenticity in TV advertising. Journal of Marketing, 83(1), 24–50.
Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477–500.
Berman, R., & Katona, Z. (2020). Curation algorithms and filter bubbles in social networks. Marketing Science, 39(2), 296–316.
Burstein, D. (2021). Customer-First Marketing: 3 case studies about how the NFL, Dawn Foods, and TeamUp used social media badges, quiz-based ads, and B2B ecommerce. Retrieved January 5, 2023 from https://www.marketingsherpa.com/article/case-study/customer-first-marketing-case-studies-social-media-badges-quiz-based-ads-B2B-ecommerce
Campbell, C., Sands, S., Montecchi, M., & Jensen Schau, H. (2022). That’s so Instagrammable! Understanding how environments generate indirect advertising by cueing consumer-generated content. Journal of Advertising, 51(4), 411–429.
Capra, D., Chung, A., & Swope A. (2013). Verified pages and profiles. Retrieved January 5, 2023 from https://newsroom.fb.com/news/2013/05/verified-pages-and-profiles/
Cascio Rizzo, G.L., Berger, J., De Angelis, M., & Pozharliev, R. (2023). How sensory language shapes influencer’s impact. Journal of Consumer Research. https://doi.org/10.1093/jcr/ucad017
Chang, W., & Taylor, S. A. (2016). The effectiveness of customer participation in new product development: A meta-analysis. Journal of Marketing, 80(1), 47–64.
Chen, L., Yan, Y., & Smith, A. N. (2023). What drives digital engagement with sponsored videos? An investigation of video influencers’ authenticity management strategies. Journal of the Academy of Marketing Science, 51(1), 198–221.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
Constine, J. (2012). Facebook for iOS gets photo filters and multi-shot sharing, beating Twitter to the punch. Retrieved January 5, 2023 from https://techcrunch.com/2012/11/05/facebook-multi-photo-uploads/
DeGruttola, M. (2021). Survey reveals that UGC can drive improved trust and loyalty for e-commerce brands. Retrieved September 24, 2022 from https://www.socialmediatoday.com/news/survey-reveals-that-ugc-can-drive-improved-trust-and-loyalty-for-ecommerce/606801/
Demopolous, A. (2023), Free the nipple: Facebook and Instagram told to overhaul ban on bare breasts. Retrieved June 28, 2023 from https://www.theguardian.com/technology/2023/jan/17/free-the-nipple-meta-facebook-instagram
Deve, A. (2017). New features for groups to build communities. Retrieved January 5, 2023 from https://about.fb.com/news/2017/10/new-features-for-groups-to-build-communities
De Vries, L., Sonja, G., & Leeflang, P. S. H. (2017). Effects of traditional advertising and social messages on brand-building metrics and customer acquisition. Journal of Marketing, 81(5), 1–15.
Eckhardt, G. M., Houston, M. B., Jiang, B., Lamberton, C., Rindfleisch, A., & Zervas, G. (2019). Marketing in the sharing economy. Journal of Marketing, 83(5), 5–27.
Fast Running (2017). Strava wants runners & athletes to share their real unfiltered photos. Retrieved January 5, 2023 from https://www.fastrunning.com/running-athletics-news/world/strava-wants-all-runners-athletes-to-share-their-real-unfiltered-photos/6948
Fernandes, D., Lynch, J. G., Jr., & Netemeyer, R. G. (2014). Financial literacy, financial education, and downstream financial behaviors. Management Science, 60(8), 1861–1883.
Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society, 9(2), 319–342.
Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.
Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31.
Garcia-Retamero, R., & Rieskamp, J. (2009). Do people treat missing information adaptively when making inferences? Quarterly Journal of Experimental Psychology, 62(10), 1991–2013.
Gelper, S., Peres, R., & Eliashberg, J. (2018). Talk bursts: The role of spikes in prerelease word-of-mouth dynamics. Journal of Marketing Research, 55(6), 801–817.
Ghaffary, S. (2023). Elon’s blue check disaster is getting worse. Retrieved July 3, 2023 from https://www.vox.com/technology/2023/4/25/23697830/elon-musk-twitter-checkmark-removal-blue-kara-swisher-lebron-james-doja-cat
Gillespie, T. (2018). Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.
Grewal, L., & Stephen, A. T. (2019). In mobile we trust: The effects of mobile versus nonmobile reviews on consumer purchase intentions. Journal of Marketing Research, 56(5), 791–808.
Grinberg, I. (2012) Impact of online word-of-mouth on Amazon ranks in selected product categories. The State University of New Jersey - New Brunswick.
Haines, A. (2021). From ‘Instagram face’ to ‘Snapchat dysmorphia’: How beauty filters are changing the way we see ourselves. Retrieved September 25, 2023 from https://www.forbes.com/sites/annahaines/2021/04/27/from-instagram-face-to-snapchatdysmorphia-how-beauty-filters-are-changing-the-way-we-see-ourselves/?sh=6632e2664eff
Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2016). Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science, 45(3), 312–335.
Haskins, E. (2007). Between archive and participation: Public memory in a digital age. Rhetoric Society Quarterly, 37(4), 401–422.
He, S., Hollenbeck, B., & Proserpio, B. (2022). The market for fake reviews. Marketing Science, 41(5), 896–921.
Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375–394.
Herhausen, D., Ludwig, S., Grewal, D., Wulf, J., & Schoegel, M. (2019). Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing, 83(3), 1–21.
Hewett, K., Rand, W., Rust, R. T., & van Heerde, H. J. (2016). Brand buzz in the echoverse. Journal of Marketing, 80(3), 1–24.
Hoch, S. J., & Deighton, J. (1989). Managing what consumers learn from experience. Journal of Marketing, 53(2), 1–20.
Ho-Dac, N. N., Carson, S. J., & Moore, W. L. (2013). The effects of positive and negative online customer reviews: Do brand strength and category maturity matter? Journal of Marketing, 77(6), 37–53.
Hong, S., Jahng, M. R., Lee, N., & Wise, K. R. (2020). Do you filter who you are?: Excessive self-presentation, social cues, and user evaluations of Instagram selfies. Computers in Human Behavior, 104, 106–159.
Horton, D., & Wohl, R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19(3), 215–229.
Hsu, T., & Lutz, E. (2021). More tan 1,000 Companies Boycotted Facebook. Did it Work? Retrieved September 12, 2023 from https://www.nytimes.com/2020/08/01/business/media/facebook-boycott.html
Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11(2), 193.
Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78–96.
Kardes, F. R., Posavac, S. S., Cronley, M. L., & Herr, P. M. (2008). Consumer inference. In C. P. Haugtvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of consumer psychology (pp. 165–191). Psychology Press.
Kemp, D., & Ekins, E. (2021). Poll: 75% don’t trust social media to make fair content moderation decisions, 60% want more control over posts they see. Retrieved September 26, 2023 from https://www.cato.org/survey-reports/poll-75-dont-trust-social-media-make-fair-content-moderation-decisions-60-want-more
Kempf, D. S., & Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325–338.
Kozinets, R., Abrantes Ferreira, D., & Chimenti, P. (2021). How do platforms empower consumers? Insights from the affordances and constraints of Reclame Aqui. Journal of Consumer Research, 48(3), 428–455.
Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71–89.
Leaser, D. (2019). Do digital badges really provide value to businesses? Retrieved January 5, 2023 from https://www.ibm.com/blogs/ibm-training/do-digital-badges-really-provide-value-to-businesses/
Lee, K., Lee, B., & Oh, W. (2015). Thumbs up, sales up? The contingent effect of Facebook likes on sales performance in social commerce. Journal of Management Information Systems, 32(4), 109–143.
Leung, F. F., Gu, F. F., Li, Y., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer Marketing Effectiveness. Journal of Marketing, 86(6), 93–115.
Lomas, N. (2023). Europe Wants Platforms to Label AI-Generated Content to Fight Disinformation. Retrieved September 12, 2023 from https://techcrunch.com/2023/06/06/eu-disinformation-code-generative-ai-labels/?guccounter=1
MacInnis, D. J., & Jaworski, B. J. (1989). Information processing from advertisements: Toward an integrative framework. Journal of Marketing, 53(4), 1–23.
Marshall, C. (2022). Twitter’s new wave of blue checks is sowing chaos. Retrieved February 6, 2023 from https://www.polygon.com/23450086/twitter-verified-accounts-fake-news-nintendo-valve-rockstar
Mazzella, F., Sundararajan, A., D’Espous, V., & Möhlmann, M. (2016). How digital trust powers the sharing economy. IESE Insight, 30, 24–30.
Mengersen, K., Jennions, M.D., & Schmid, C.H. (2013). Statistical models for the meta-analysis of nonindependent data. In J. Koricheva, J. Gurevitch, and K. Mengerson (Eds.), Handbook of Meta-Analysis in Ecology and Evolution (pp. 255–283). Princeton University Press.
Millward, W. (2022). IBM Channel Chief: We’re Making Partner Engagement ‘As Easy As Possible.’ Retrieved January 5, 2023 from https://www.crn.com/news/channel-programs/ibm-channel-chief-we-re-making-partner-engagement-as-easy-as-possible
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38.
Myers-West, S. (2018). Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms. New Media & Society, 20(11), 4366–4383.
Naylor, R. W., Lamberton, C. P., & Norton, D. A. (2011). Seeing ourselves in others: Reviewer ambiguity, egocentric anchoring, and persuasion. Journal of Marketing Research, 48(3), 617–631.
Olanoff, D. (2015). What in the hell is a #TacoEmojiEngine? Retrieved January 5, 2023 from https://techcrunch.com/2015/11/10/what-in-the-hell-is-a-tacoemojiengine/
Page, X., Ghaiumy Anaraky, R., Knijnenburg, B. P., & Wisniewski, P. J. (2019). Pragmatic tool vs. relational hindrance: Exploring why some social media users avoid privacy features, Proceedings of the ACM on Human-Computer Interaction, 1–23.
Pitman, J. (2022). Local consumer review survey 2022. Retrieved February 1, 2023 from https://www.brightlocal.com/research/local-consumer-review-survey/
Porten-Chee, P., & Eilders, C. (2020). The effects of likes on public opinion perception and personal opinion. Communications, 45(2), 223–239.
Rader, E., & Gray, R. (2015). Understanding user beliefs about algorithmic curation in the Facebook news feed. Proceedings of the 33rd annual ACM Conference on Human Factors in Computing Systems. Seoul, Korea.
Ramirez, E., Gau, R., Hadjimarcou, J., & Xu, Z. (2018). User-generated content as word-of-mouth. Journal of Marketing Theory and Practice, 26(1–2), 90–98.
Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2021). Mass-scale emotionality reveals human behaviour and marketplace success. Nature Human Behaviour, 5(10), 1323–1329.
Rogers, L. (2023). Did TripAdvisor Really Get Caught Deleting Negative Reviews? Retrieved July 2, 2023 from https://www.insidehook.com/daily_brief/travel/tripadvisor-deleting-negative-reviews
Rosenthal, R. (1988). Applied social research methods series meta-analytic procedures for social research. Sage Publications.
Santini, F., Ladeira, W. J., Pinto, D. C., Herter, M. M., Sampaio, C. H., & Babin, B. J. (2020). Customer engagement in social media: A framework and meta-analysis. Journal of the Academy of Marketing Science, 48(6), 1211–1228.
Sawyer, A. G., & Ball, A. D. (1981). Statistical power and effect size in marketing research. Journal of Marketing Research, 18(3), 275–290.
Schaffer, N. (2022). 11 ways to use Twitter polls for marketing with examples. Retrieved January 5, 2023 from https://nealschaffer.com/11-ways-to-use-twitter-polls-for-marketing-with-examples/
Schau, H. J., & Gilly, M. C. (2003). We are what we post? Self-presentation in personal web space. Journal of Consumer Research, 30(3), 385–404.
Shapiro, S. P. (1987). The social control of impersonal trust. American Journal of Sociology, 93(3), 623–658.
Silver, N. C., & Dunlap, W. P. (1987). Averaging correlation coefficients: Should Fisher’s Z transformation be used? Journal of Applied Psychology, 72(1), 146–148.
Simeon, A. (2021). CVS Just Made A Huge Step To Make Its Beauty Aisle More Transparent. Retrieved January 5, 2023 from https://www.refinery29.com/en-us/2021/05/10484201/cvs-beauty-mark-campaign
Smith, R. K., vanDellen, M. R., & Ton, L. A. (2021). Makeup who you are: Self-expression enhances the perceived authenticity and public promotion of beauty work. Journal of Consumer Research, 48(1), 102–122.
Stephen, A. T., & Galak, J. (2012). The effects of traditional and social earned media on sales: A study of a microlending marketplace. Journal of Marketing Research, 49(5), 624–639.
Stewart, E. (2021). Detecting fake news: Two problems for content moderation. Philosophy & Technology, 34(4), 923–940.
Stone, B. (2006). Six more Twitter updates! Retrieved January 5, 2023 from https://blog.twitter.com/official/en_us/a/2006/six-more-twitter-updates.html
Stone, B. (2009). Not playing ball. Retrieved January 5, 2023 from https://blog.twitter.com/official/en_us/a/2009/not-playing-ball.html
Sundararajan, A. (2019). Commentary: The twilight of brand and consumerism? Digital trust, cultural meaning, and the quest for connection in the sharing economy. Journal of Marketing, 83(5), 32–35.
Tang, T., Fang, E., & Wang, F. (2014). Is neutral really neutral? The effects of neutral user-generated content on product sales. Journal of Marketing, 78(4), 41-58.
Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198–215.
Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.
Tucker, C. E. (2014). Social Networks, Personalized Advertising, and Privacy Controls. Journal of Marketing Research, 51(5), 546–562.
Van Laer, T., Escalas, J. E., Ludwig, S., & Van Den Hende, E. A. (2019). What happens in Vegas stays on Tripadvisor? A theory and technique to understand narrativity in consumer reviews. Journal of Consumer Research, 46(2), 267–285.
Villasenor, J. (2019). Artificial intelligence, deepfakes, and the uncertain future of truth. Retrieved June 29, 2023 from https://www.brookings.edu/articles/artificial-intelligence-deepfakes-and-the-uncertain-future-of-truth/
Virtual Humans (2023). Miquela Sousa. Retrieved September 12, 2023 from https://www.virtualhumans.org/human/miquela-sousa
Williams, D. R., Rodriguez, J. E., & Bürkner, P. C. (2021). Putting variation into variance: modeling between-study heterogeneity in meta-analysis. PsyArXiv, 1–17. https://doi.org/10.2307/2332010
World Wide Web Foundation (2018). “The invisible curation of content: Facebook’s news feed and our information diets,” World Wide Web Foundation.
Yazdani, E., Gopinath, S., & Carson, S. (2018). Preaching to the choir: The chasm between top-ranked reviewers, mainstream customers, and product sales. Marketing Science, 37(5), 838–851.
You, Y., Vadakkepatt, G. G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19–39.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors express no conflicts of interest regarding this manuscript.
Additional information
Bob Leone served as Guest Editor for this article.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hochstein, R.E., Harmeling, C.M. & Perko, T. Toward a theory of consumer digital trust: Meta-analytic evidence of its role in the effectiveness of user-generated content. J. of the Acad. Mark. Sci. (2023). https://doi.org/10.1007/s11747-023-00982-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11747-023-00982-y